diff --git a/.github/scripts/check_diff.py b/.github/scripts/check_diff.py index fab2a1e565103..6e53c3d1b95ab 100644 --- a/.github/scripts/check_diff.py +++ b/.github/scripts/check_diff.py @@ -16,6 +16,10 @@ "libs/experimental", ] +# for 0.3rc, we are ignoring core dependents +# in order to be able to get CI to pass for individual PRs. +IGNORE_CORE_DEPENDENTS = True + # ignored partners are removed from dependents # but still run if directly edited IGNORED_PARTNERS = [ @@ -104,7 +108,7 @@ def _get_configs_for_single_dir(job: str, dir_: str) -> List[Dict[str, str]]: {"working-directory": dir_, "python-version": f"3.{v}"} for v in range(8, 13) ] - min_python = "3.8" + min_python = "3.9" max_python = "3.12" # custom logic for specific directories @@ -184,6 +188,9 @@ def _get_configs_for_multi_dirs( # for extended testing found = False for dir_ in LANGCHAIN_DIRS: + if dir_ == "libs/core" and IGNORE_CORE_DEPENDENTS: + dirs_to_run["extended-test"].add(dir_) + continue if file.startswith(dir_): found = True if found: diff --git a/.github/workflows/_dependencies.yml b/.github/workflows/_dependencies.yml deleted file mode 100644 index b96fe04ed0b28..0000000000000 --- a/.github/workflows/_dependencies.yml +++ /dev/null @@ -1,114 +0,0 @@ -name: dependencies - -on: - workflow_call: - inputs: - working-directory: - required: true - type: string - description: "From which folder this pipeline executes" - langchain-location: - required: false - type: string - description: "Relative path to the langchain library folder" - python-version: - required: true - type: string - description: "Python version to use" - -env: - POETRY_VERSION: "1.7.1" - -jobs: - build: - defaults: - run: - working-directory: ${{ inputs.working-directory }} - runs-on: ubuntu-latest - name: dependency checks ${{ inputs.python-version }} - steps: - - uses: actions/checkout@v4 - - - name: Set up Python ${{ inputs.python-version }} + Poetry ${{ env.POETRY_VERSION }} - uses: "./.github/actions/poetry_setup" - with: - python-version: ${{ inputs.python-version }} - poetry-version: ${{ env.POETRY_VERSION }} - working-directory: ${{ inputs.working-directory }} - cache-key: pydantic-cross-compat - - - name: Install dependencies - shell: bash - run: poetry install - - - name: Check imports with base dependencies - shell: bash - run: poetry run make check_imports - - - name: Install test dependencies - shell: bash - run: poetry install --with test - - - name: Install langchain editable - working-directory: ${{ inputs.working-directory }} - if: ${{ inputs.langchain-location }} - env: - LANGCHAIN_LOCATION: ${{ inputs.langchain-location }} - run: | - poetry run pip install -e "$LANGCHAIN_LOCATION" - - - name: Install the opposite major version of pydantic - # If normal tests use pydantic v1, here we'll use v2, and vice versa. - shell: bash - # airbyte currently doesn't support pydantic v2 - if: ${{ !startsWith(inputs.working-directory, 'libs/partners/airbyte') }} - run: | - # Determine the major part of pydantic version - REGULAR_VERSION=$(poetry run python -c "import pydantic; print(pydantic.__version__)" | cut -d. -f1) - - if [[ "$REGULAR_VERSION" == "1" ]]; then - PYDANTIC_DEP=">=2.1,<3" - TEST_WITH_VERSION="2" - elif [[ "$REGULAR_VERSION" == "2" ]]; then - PYDANTIC_DEP="<2" - TEST_WITH_VERSION="1" - else - echo "Unexpected pydantic major version '$REGULAR_VERSION', cannot determine which version to use for cross-compatibility test." - exit 1 - fi - - # Install via `pip` instead of `poetry add` to avoid changing lockfile, - # which would prevent caching from working: the cache would get saved - # to a different key than where it gets loaded from. - poetry run pip install "pydantic${PYDANTIC_DEP}" - - # Ensure that the correct pydantic is installed now. - echo "Checking pydantic version... Expecting ${TEST_WITH_VERSION}" - - # Determine the major part of pydantic version - CURRENT_VERSION=$(poetry run python -c "import pydantic; print(pydantic.__version__)" | cut -d. -f1) - - # Check that the major part of pydantic version is as expected, if not - # raise an error - if [[ "$CURRENT_VERSION" != "$TEST_WITH_VERSION" ]]; then - echo "Error: expected pydantic version ${CURRENT_VERSION} to have been installed, but found: ${TEST_WITH_VERSION}" - exit 1 - fi - echo "Found pydantic version ${CURRENT_VERSION}, as expected" - - name: Run pydantic compatibility tests - # airbyte currently doesn't support pydantic v2 - if: ${{ !startsWith(inputs.working-directory, 'libs/partners/airbyte') }} - shell: bash - run: make test - - - name: Ensure the tests did not create any additional files - shell: bash - run: | - set -eu - - STATUS="$(git status)" - echo "$STATUS" - - # grep will exit non-zero if the target message isn't found, - # and `set -e` above will cause the step to fail. - echo "$STATUS" | grep 'nothing to commit, working tree clean' diff --git a/.github/workflows/check_diffs.yml b/.github/workflows/check_diffs.yml index 0149f5ec11a4c..ad7aebb7310ef 100644 --- a/.github/workflows/check_diffs.yml +++ b/.github/workflows/check_diffs.yml @@ -89,19 +89,6 @@ jobs: python-version: ${{ matrix.job-configs.python-version }} secrets: inherit - dependencies: - name: cd ${{ matrix.job-configs.working-directory }} - needs: [ build ] - if: ${{ needs.build.outputs.dependencies != '[]' }} - strategy: - matrix: - job-configs: ${{ fromJson(needs.build.outputs.dependencies) }} - uses: ./.github/workflows/_dependencies.yml - with: - working-directory: ${{ matrix.job-configs.working-directory }} - python-version: ${{ matrix.job-configs.python-version }} - secrets: inherit - extended-tests: name: "cd ${{ matrix.job-configs.working-directory }} / make extended_tests #${{ matrix.job-configs.python-version }}" needs: [ build ] @@ -149,7 +136,7 @@ jobs: echo "$STATUS" | grep 'nothing to commit, working tree clean' ci_success: name: "CI Success" - needs: [build, lint, test, compile-integration-tests, dependencies, extended-tests, test-doc-imports] + needs: [build, lint, test, compile-integration-tests, extended-tests, test-doc-imports] if: | always() runs-on: ubuntu-latest diff --git a/.github/workflows/scheduled_test.yml b/.github/workflows/scheduled_test.yml index 61e1b3d25a6ae..31500362e9de6 100644 --- a/.github/workflows/scheduled_test.yml +++ b/.github/workflows/scheduled_test.yml @@ -17,7 +17,7 @@ jobs: fail-fast: false matrix: python-version: - - "3.8" + - "3.9" - "3.11" working-directory: - "libs/partners/openai" diff --git a/libs/cli/langchain_cli/integration_template/Makefile b/libs/cli/langchain_cli/integration_template/Makefile index 81124d8c32abf..b30039ace1fe0 100644 --- a/libs/cli/langchain_cli/integration_template/Makefile +++ b/libs/cli/langchain_cli/integration_template/Makefile @@ -12,6 +12,9 @@ integration_test integration_tests: TEST_FILE = tests/integration_tests/ test tests: poetry run pytest --disable-socket --allow-unix-socket $(TEST_FILE) +test_watch: + poetry run ptw --snapshot-update --now . -- -vv $(TEST_FILE) + # integration tests are run without the --disable-socket flag to allow network calls integration_test integration_tests: poetry run pytest $(TEST_FILE) diff --git a/libs/cli/langchain_cli/integration_template/pyproject.toml b/libs/cli/langchain_cli/integration_template/pyproject.toml index b8af44e74d15b..4d129d5dbdebd 100644 --- a/libs/cli/langchain_cli/integration_template/pyproject.toml +++ b/libs/cli/langchain_cli/integration_template/pyproject.toml @@ -23,6 +23,7 @@ pytest = "^7.4.3" pytest-asyncio = "^0.23.2" pytest-socket = "^0.7.0" langchain-core = { path = "../../core", develop = true } +pytest-watcher = "^0.3.4" [tool.poetry.group.codespell] optional = true diff --git a/libs/community/Makefile b/libs/community/Makefile index 1eff4253ca1a7..2e3430ff236ca 100644 --- a/libs/community/Makefile +++ b/libs/community/Makefile @@ -22,7 +22,7 @@ integration_tests: poetry run pytest $(TEST_FILE) test_watch: - poetry run ptw --disable-socket --allow-unix-socket --snapshot-update --now . -- -vv -x tests/unit_tests + poetry run ptw --disable-socket --allow-unix-socket --snapshot-update --now . -- -vv tests/unit_tests check_imports: $(shell find langchain_community -name '*.py') poetry run python ./scripts/check_imports.py $^ @@ -45,7 +45,6 @@ lint_tests: PYTHON_FILES=tests lint_tests: MYPY_CACHE=.mypy_cache_test lint lint_diff lint_package lint_tests: - ./scripts/check_pydantic.sh . ./scripts/lint_imports.sh . ./scripts/check_pickle.sh . [ "$(PYTHON_FILES)" = "" ] || poetry run ruff check $(PYTHON_FILES) diff --git a/libs/community/langchain_community/adapters/openai.py b/libs/community/langchain_community/adapters/openai.py index 8ec943a498239..8db939d5c9705 100644 --- a/libs/community/langchain_community/adapters/openai.py +++ b/libs/community/langchain_community/adapters/openai.py @@ -25,7 +25,7 @@ SystemMessage, ToolMessage, ) -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel from typing_extensions import Literal diff --git a/libs/community/langchain_community/agent_toolkits/ainetwork/toolkit.py b/libs/community/langchain_community/agent_toolkits/ainetwork/toolkit.py index 64d021bbc0ca8..9900ae73b7220 100644 --- a/libs/community/langchain_community/agent_toolkits/ainetwork/toolkit.py +++ b/libs/community/langchain_community/agent_toolkits/ainetwork/toolkit.py @@ -1,10 +1,10 @@ from __future__ import annotations -from typing import TYPE_CHECKING, List, Literal, Optional +from typing import TYPE_CHECKING, Any, List, Literal, Optional -from langchain_core.pydantic_v1 import root_validator from langchain_core.tools import BaseTool from langchain_core.tools.base import BaseToolkit +from pydantic import ConfigDict, model_validator from langchain_community.tools.ainetwork.app import AINAppOps from langchain_community.tools.ainetwork.owner import AINOwnerOps @@ -36,8 +36,9 @@ class AINetworkToolkit(BaseToolkit): network: Optional[Literal["mainnet", "testnet"]] = "testnet" interface: Optional[Ain] = None - @root_validator(pre=True) - def set_interface(cls, values: dict) -> dict: + @model_validator(mode="before") + @classmethod + def set_interface(cls, values: dict) -> Any: """Set the interface if not provided. If the interface is not provided, attempt to authenticate with the @@ -53,9 +54,10 @@ def set_interface(cls, values: dict) -> dict: values["interface"] = authenticate(network=values.get("network", "testnet")) return values - class Config: - arbitrary_types_allowed = True - validate_all = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + validate_all=True, + ) def get_tools(self) -> List[BaseTool]: """Get the tools in the toolkit.""" diff --git a/libs/community/langchain_community/agent_toolkits/amadeus/toolkit.py b/libs/community/langchain_community/agent_toolkits/amadeus/toolkit.py index 9e6c159c75d8d..87f8165322974 100644 --- a/libs/community/langchain_community/agent_toolkits/amadeus/toolkit.py +++ b/libs/community/langchain_community/agent_toolkits/amadeus/toolkit.py @@ -3,9 +3,9 @@ from typing import TYPE_CHECKING, List, Optional from langchain_core.language_models import BaseLanguageModel -from langchain_core.pydantic_v1 import Field from langchain_core.tools import BaseTool from langchain_core.tools.base import BaseToolkit +from pydantic import ConfigDict, Field from langchain_community.tools.amadeus.closest_airport import AmadeusClosestAirport from langchain_community.tools.amadeus.flight_search import AmadeusFlightSearch @@ -26,8 +26,9 @@ class AmadeusToolkit(BaseToolkit): client: Client = Field(default_factory=authenticate) llm: Optional[BaseLanguageModel] = Field(default=None) - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) def get_tools(self) -> List[BaseTool]: """Get the tools in the toolkit.""" diff --git a/libs/community/langchain_community/agent_toolkits/cassandra_database/toolkit.py b/libs/community/langchain_community/agent_toolkits/cassandra_database/toolkit.py index 11930f1c37458..2e017e994798e 100644 --- a/libs/community/langchain_community/agent_toolkits/cassandra_database/toolkit.py +++ b/libs/community/langchain_community/agent_toolkits/cassandra_database/toolkit.py @@ -2,9 +2,9 @@ from typing import List -from langchain_core.pydantic_v1 import Field from langchain_core.tools import BaseTool from langchain_core.tools.base import BaseToolkit +from pydantic import ConfigDict, Field from langchain_community.tools.cassandra_database.tool import ( GetSchemaCassandraDatabaseTool, @@ -24,8 +24,9 @@ class CassandraDatabaseToolkit(BaseToolkit): db: CassandraDatabase = Field(exclude=True) - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) def get_tools(self) -> List[BaseTool]: """Get the tools in the toolkit.""" diff --git a/libs/community/langchain_community/agent_toolkits/connery/toolkit.py b/libs/community/langchain_community/agent_toolkits/connery/toolkit.py index cf86f00f8fd81..05b15d18c269b 100644 --- a/libs/community/langchain_community/agent_toolkits/connery/toolkit.py +++ b/libs/community/langchain_community/agent_toolkits/connery/toolkit.py @@ -1,8 +1,8 @@ -from typing import List +from typing import Any, List -from langchain_core.pydantic_v1 import root_validator from langchain_core.tools import BaseTool from langchain_core.tools.base import BaseToolkit +from pydantic import model_validator from langchain_community.tools.connery import ConneryService @@ -23,8 +23,9 @@ def get_tools(self) -> List[BaseTool]: """ return self.tools - @root_validator(pre=True) - def validate_attributes(cls, values: dict) -> dict: + @model_validator(mode="before") + @classmethod + def validate_attributes(cls, values: dict) -> Any: """ Validate the attributes of the ConneryToolkit class. diff --git a/libs/community/langchain_community/agent_toolkits/file_management/toolkit.py b/libs/community/langchain_community/agent_toolkits/file_management/toolkit.py index 435817d638f3b..90b1f618ec1fb 100644 --- a/libs/community/langchain_community/agent_toolkits/file_management/toolkit.py +++ b/libs/community/langchain_community/agent_toolkits/file_management/toolkit.py @@ -1,10 +1,10 @@ from __future__ import annotations -from typing import Dict, List, Optional, Type +from typing import Any, Dict, List, Optional, Type -from langchain_core.pydantic_v1 import root_validator from langchain_core.tools import BaseTool, BaseToolkit from langchain_core.utils.pydantic import get_fields +from pydantic import model_validator from langchain_community.tools.file_management.copy import CopyFileTool from langchain_community.tools.file_management.delete import DeleteFileTool @@ -63,8 +63,9 @@ class FileManagementToolkit(BaseToolkit): selected_tools: Optional[List[str]] = None """If provided, only provide the selected tools. Defaults to all.""" - @root_validator(pre=True) - def validate_tools(cls, values: dict) -> dict: + @model_validator(mode="before") + @classmethod + def validate_tools(cls, values: dict) -> Any: selected_tools = values.get("selected_tools") or [] for tool_name in selected_tools: if tool_name not in _FILE_TOOLS_MAP: diff --git a/libs/community/langchain_community/agent_toolkits/financial_datasets/toolkit.py b/libs/community/langchain_community/agent_toolkits/financial_datasets/toolkit.py index 89708212e2577..0fd509d0017cb 100644 --- a/libs/community/langchain_community/agent_toolkits/financial_datasets/toolkit.py +++ b/libs/community/langchain_community/agent_toolkits/financial_datasets/toolkit.py @@ -2,9 +2,9 @@ from typing import List -from langchain_core.pydantic_v1 import Field from langchain_core.tools import BaseTool from langchain_core.tools.base import BaseToolkit +from pydantic import ConfigDict, Field from langchain_community.tools.financial_datasets.balance_sheets import BalanceSheets from langchain_community.tools.financial_datasets.cash_flow_statements import ( @@ -31,8 +31,9 @@ def __init__(self, api_wrapper: FinancialDatasetsAPIWrapper): super().__init__() self.api_wrapper = api_wrapper - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) def get_tools(self) -> List[BaseTool]: """Get the tools in the toolkit.""" diff --git a/libs/community/langchain_community/agent_toolkits/github/toolkit.py b/libs/community/langchain_community/agent_toolkits/github/toolkit.py index adeb0bd72519f..963c3727aa888 100644 --- a/libs/community/langchain_community/agent_toolkits/github/toolkit.py +++ b/libs/community/langchain_community/agent_toolkits/github/toolkit.py @@ -2,9 +2,9 @@ from typing import Dict, List -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.tools import BaseTool from langchain_core.tools.base import BaseToolkit +from pydantic import BaseModel, Field from langchain_community.tools.github.prompt import ( COMMENT_ON_ISSUE_PROMPT, diff --git a/libs/community/langchain_community/agent_toolkits/gmail/toolkit.py b/libs/community/langchain_community/agent_toolkits/gmail/toolkit.py index 66b3c2d252a5f..815bf89c2a84c 100644 --- a/libs/community/langchain_community/agent_toolkits/gmail/toolkit.py +++ b/libs/community/langchain_community/agent_toolkits/gmail/toolkit.py @@ -2,9 +2,9 @@ from typing import TYPE_CHECKING, List -from langchain_core.pydantic_v1 import Field from langchain_core.tools import BaseTool from langchain_core.tools.base import BaseToolkit +from pydantic import ConfigDict, Field from langchain_community.tools.gmail.create_draft import GmailCreateDraft from langchain_community.tools.gmail.get_message import GmailGetMessage @@ -117,8 +117,9 @@ class GmailToolkit(BaseToolkit): api_resource: Resource = Field(default_factory=build_resource_service) - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) def get_tools(self) -> List[BaseTool]: """Get the tools in the toolkit.""" diff --git a/libs/community/langchain_community/agent_toolkits/multion/toolkit.py b/libs/community/langchain_community/agent_toolkits/multion/toolkit.py index 133232e4561a8..5a67cb13f1121 100644 --- a/libs/community/langchain_community/agent_toolkits/multion/toolkit.py +++ b/libs/community/langchain_community/agent_toolkits/multion/toolkit.py @@ -6,6 +6,7 @@ from langchain_core.tools import BaseTool from langchain_core.tools.base import BaseToolkit +from pydantic import ConfigDict from langchain_community.tools.multion.close_session import MultionCloseSession from langchain_community.tools.multion.create_session import MultionCreateSession @@ -25,8 +26,9 @@ class MultionToolkit(BaseToolkit): See https://python.langchain.com/docs/security for more information. """ - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) def get_tools(self) -> List[BaseTool]: """Get the tools in the toolkit.""" diff --git a/libs/community/langchain_community/agent_toolkits/nla/toolkit.py b/libs/community/langchain_community/agent_toolkits/nla/toolkit.py index b7216956fba3f..88fb6e87fbf6b 100644 --- a/libs/community/langchain_community/agent_toolkits/nla/toolkit.py +++ b/libs/community/langchain_community/agent_toolkits/nla/toolkit.py @@ -3,9 +3,9 @@ from typing import Any, List, Optional, Sequence from langchain_core.language_models import BaseLanguageModel -from langchain_core.pydantic_v1 import Field from langchain_core.tools import BaseTool from langchain_core.tools.base import BaseToolkit +from pydantic import Field from langchain_community.agent_toolkits.nla.tool import NLATool from langchain_community.tools.openapi.utils.openapi_utils import OpenAPISpec diff --git a/libs/community/langchain_community/agent_toolkits/office365/toolkit.py b/libs/community/langchain_community/agent_toolkits/office365/toolkit.py index 3ee1e9f135a09..4bd826591d6ba 100644 --- a/libs/community/langchain_community/agent_toolkits/office365/toolkit.py +++ b/libs/community/langchain_community/agent_toolkits/office365/toolkit.py @@ -2,9 +2,9 @@ from typing import TYPE_CHECKING, List -from langchain_core.pydantic_v1 import Field from langchain_core.tools import BaseTool from langchain_core.tools.base import BaseToolkit +from pydantic import ConfigDict, Field from langchain_community.tools.office365.create_draft_message import ( O365CreateDraftMessage, @@ -40,8 +40,9 @@ class O365Toolkit(BaseToolkit): account: Account = Field(default_factory=authenticate) - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) def get_tools(self) -> List[BaseTool]: """Get the tools in the toolkit.""" diff --git a/libs/community/langchain_community/agent_toolkits/openapi/planner.py b/libs/community/langchain_community/agent_toolkits/openapi/planner.py index fc96e3284e77b..fbd3fc36b43aa 100644 --- a/libs/community/langchain_community/agent_toolkits/openapi/planner.py +++ b/libs/community/langchain_community/agent_toolkits/openapi/planner.py @@ -9,8 +9,8 @@ from langchain_core.callbacks import BaseCallbackManager from langchain_core.language_models import BaseLanguageModel from langchain_core.prompts import BasePromptTemplate, PromptTemplate -from langchain_core.pydantic_v1 import Field from langchain_core.tools import BaseTool, Tool +from pydantic import Field from langchain_community.agent_toolkits.openapi.planner_prompt import ( API_CONTROLLER_PROMPT, @@ -69,7 +69,7 @@ class RequestsGetToolWithParsing(BaseRequestsTool, BaseTool): name: str = "requests_get" """Tool name.""" - description = REQUESTS_GET_TOOL_DESCRIPTION + description: str = REQUESTS_GET_TOOL_DESCRIPTION """Tool description.""" response_length: int = MAX_RESPONSE_LENGTH """Maximum length of the response to be returned.""" @@ -103,7 +103,7 @@ class RequestsPostToolWithParsing(BaseRequestsTool, BaseTool): name: str = "requests_post" """Tool name.""" - description = REQUESTS_POST_TOOL_DESCRIPTION + description: str = REQUESTS_POST_TOOL_DESCRIPTION """Tool description.""" response_length: int = MAX_RESPONSE_LENGTH """Maximum length of the response to be returned.""" @@ -134,7 +134,7 @@ class RequestsPatchToolWithParsing(BaseRequestsTool, BaseTool): name: str = "requests_patch" """Tool name.""" - description = REQUESTS_PATCH_TOOL_DESCRIPTION + description: str = REQUESTS_PATCH_TOOL_DESCRIPTION """Tool description.""" response_length: int = MAX_RESPONSE_LENGTH """Maximum length of the response to be returned.""" @@ -167,7 +167,7 @@ class RequestsPutToolWithParsing(BaseRequestsTool, BaseTool): name: str = "requests_put" """Tool name.""" - description = REQUESTS_PUT_TOOL_DESCRIPTION + description: str = REQUESTS_PUT_TOOL_DESCRIPTION """Tool description.""" response_length: int = MAX_RESPONSE_LENGTH """Maximum length of the response to be returned.""" @@ -198,7 +198,7 @@ class RequestsDeleteToolWithParsing(BaseRequestsTool, BaseTool): name: str = "requests_delete" """The name of the tool.""" - description = REQUESTS_DELETE_TOOL_DESCRIPTION + description: str = REQUESTS_DELETE_TOOL_DESCRIPTION """The description of the tool.""" response_length: Optional[int] = MAX_RESPONSE_LENGTH diff --git a/libs/community/langchain_community/agent_toolkits/playwright/toolkit.py b/libs/community/langchain_community/agent_toolkits/playwright/toolkit.py index 3cb5d430220b4..6d69383e469cb 100644 --- a/libs/community/langchain_community/agent_toolkits/playwright/toolkit.py +++ b/libs/community/langchain_community/agent_toolkits/playwright/toolkit.py @@ -2,10 +2,10 @@ from __future__ import annotations -from typing import TYPE_CHECKING, List, Optional, Type, cast +from typing import TYPE_CHECKING, Any, List, Optional, Type, cast -from langchain_core.pydantic_v1 import root_validator from langchain_core.tools import BaseTool, BaseToolkit +from pydantic import ConfigDict, model_validator from langchain_community.tools.playwright.base import ( BaseBrowserTool, @@ -68,12 +68,14 @@ class PlayWrightBrowserToolkit(BaseToolkit): sync_browser: Optional["SyncBrowser"] = None async_browser: Optional["AsyncBrowser"] = None - class Config: - arbitrary_types_allowed = True - extra = "forbid" + model_config = ConfigDict( + arbitrary_types_allowed=True, + extra="forbid", + ) - @root_validator(pre=True) - def validate_imports_and_browser_provided(cls, values: dict) -> dict: + @model_validator(mode="before") + @classmethod + def validate_imports_and_browser_provided(cls, values: dict) -> Any: """Check that the arguments are valid.""" lazy_import_playwright_browsers() if values.get("async_browser") is None and values.get("sync_browser") is None: diff --git a/libs/community/langchain_community/agent_toolkits/powerbi/toolkit.py b/libs/community/langchain_community/agent_toolkits/powerbi/toolkit.py index 2fe081bd4c0f7..424d967abbf1e 100644 --- a/libs/community/langchain_community/agent_toolkits/powerbi/toolkit.py +++ b/libs/community/langchain_community/agent_toolkits/powerbi/toolkit.py @@ -13,9 +13,9 @@ HumanMessagePromptTemplate, SystemMessagePromptTemplate, ) -from langchain_core.pydantic_v1 import Field from langchain_core.tools import BaseTool from langchain_core.tools.base import BaseToolkit +from pydantic import ConfigDict, Field from langchain_community.tools.powerbi.prompt import ( QUESTION_TO_QUERY_BASE, @@ -63,8 +63,9 @@ class PowerBIToolkit(BaseToolkit): output_token_limit: Optional[int] = None tiktoken_model_name: Optional[str] = None - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) def get_tools(self) -> List[BaseTool]: """Get the tools in the toolkit.""" diff --git a/libs/community/langchain_community/agent_toolkits/slack/toolkit.py b/libs/community/langchain_community/agent_toolkits/slack/toolkit.py index abec2a8e676a4..fd61311326d70 100644 --- a/libs/community/langchain_community/agent_toolkits/slack/toolkit.py +++ b/libs/community/langchain_community/agent_toolkits/slack/toolkit.py @@ -2,9 +2,9 @@ from typing import TYPE_CHECKING, List -from langchain_core.pydantic_v1 import Field from langchain_core.tools import BaseTool from langchain_core.tools.base import BaseToolkit +from pydantic import ConfigDict, Field from langchain_community.tools.slack.get_channel import SlackGetChannel from langchain_community.tools.slack.get_message import SlackGetMessage @@ -91,8 +91,9 @@ class SlackToolkit(BaseToolkit): client: WebClient = Field(default_factory=login) - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) def get_tools(self) -> List[BaseTool]: """Get the tools in the toolkit.""" diff --git a/libs/community/langchain_community/agent_toolkits/spark_sql/toolkit.py b/libs/community/langchain_community/agent_toolkits/spark_sql/toolkit.py index 85c08f20678bf..a339307452e08 100644 --- a/libs/community/langchain_community/agent_toolkits/spark_sql/toolkit.py +++ b/libs/community/langchain_community/agent_toolkits/spark_sql/toolkit.py @@ -3,9 +3,9 @@ from typing import List from langchain_core.language_models import BaseLanguageModel -from langchain_core.pydantic_v1 import Field from langchain_core.tools import BaseTool from langchain_core.tools.base import BaseToolkit +from pydantic import ConfigDict, Field from langchain_community.tools.spark_sql.tool import ( InfoSparkSQLTool, @@ -27,8 +27,9 @@ class SparkSQLToolkit(BaseToolkit): db: SparkSQL = Field(exclude=True) llm: BaseLanguageModel = Field(exclude=True) - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) def get_tools(self) -> List[BaseTool]: """Get the tools in the toolkit.""" diff --git a/libs/community/langchain_community/agent_toolkits/sql/toolkit.py b/libs/community/langchain_community/agent_toolkits/sql/toolkit.py index b0ea6428f7491..6920349cd3f29 100644 --- a/libs/community/langchain_community/agent_toolkits/sql/toolkit.py +++ b/libs/community/langchain_community/agent_toolkits/sql/toolkit.py @@ -3,9 +3,9 @@ from typing import List from langchain_core.language_models import BaseLanguageModel -from langchain_core.pydantic_v1 import Field from langchain_core.tools import BaseTool from langchain_core.tools.base import BaseToolkit +from pydantic import ConfigDict, Field from langchain_community.tools.sql_database.tool import ( InfoSQLDatabaseTool, @@ -83,8 +83,9 @@ def dialect(self) -> str: """Return string representation of SQL dialect to use.""" return self.db.dialect - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) def get_tools(self) -> List[BaseTool]: """Get the tools in the toolkit.""" diff --git a/libs/community/langchain_community/agents/openai_assistant/base.py b/libs/community/langchain_community/agents/openai_assistant/base.py index 07bd8566099b8..136917eaceccf 100644 --- a/libs/community/langchain_community/agents/openai_assistant/base.py +++ b/libs/community/langchain_community/agents/openai_assistant/base.py @@ -15,10 +15,11 @@ from langchain_core._api import beta from langchain_core.callbacks import CallbackManager from langchain_core.load import dumpd -from langchain_core.pydantic_v1 import BaseModel, Field, root_validator from langchain_core.runnables import RunnableConfig, ensure_config from langchain_core.tools import BaseTool from langchain_core.utils.function_calling import convert_to_openai_tool +from pydantic import BaseModel, Field, model_validator +from typing_extensions import Self if TYPE_CHECKING: import openai @@ -209,14 +210,14 @@ def execute_agent(agent, tools, input): as_agent: bool = False """Use as a LangChain agent, compatible with the AgentExecutor.""" - @root_validator(pre=False, skip_on_failure=True) - def validate_async_client(cls, values: dict) -> dict: - if values["async_client"] is None: + @model_validator(mode="after") + def validate_async_client(self) -> Self: + if self.async_client is None: import openai - api_key = values["client"].api_key - values["async_client"] = openai.AsyncOpenAI(api_key=api_key) - return values + api_key = self.client.api_key + self.async_client = openai.AsyncOpenAI(api_key=api_key) + return self @classmethod def create_assistant( diff --git a/libs/community/langchain_community/chains/ernie_functions/base.py b/libs/community/langchain_community/chains/ernie_functions/base.py index 3db70397b9f76..51e479c58fd02 100644 --- a/libs/community/langchain_community/chains/ernie_functions/base.py +++ b/libs/community/langchain_community/chains/ernie_functions/base.py @@ -22,9 +22,9 @@ BaseOutputParser, ) from langchain_core.prompts import BasePromptTemplate -from langchain_core.pydantic_v1 import BaseModel from langchain_core.runnables import Runnable from langchain_core.utils.pydantic import is_basemodel_subclass +from pydantic import BaseModel from langchain_community.output_parsers.ernie_functions import ( JsonOutputFunctionsParser, diff --git a/libs/community/langchain_community/chains/graph_qa/arangodb.py b/libs/community/langchain_community/chains/graph_qa/arangodb.py index 9add2ffc719ab..0af3e49b62646 100644 --- a/libs/community/langchain_community/chains/graph_qa/arangodb.py +++ b/libs/community/langchain_community/chains/graph_qa/arangodb.py @@ -10,7 +10,7 @@ from langchain_core.callbacks import CallbackManagerForChainRun from langchain_core.language_models import BaseLanguageModel from langchain_core.prompts import BasePromptTemplate -from langchain_core.pydantic_v1 import Field +from pydantic import Field from langchain_community.chains.graph_qa.prompts import ( AQL_FIX_PROMPT, diff --git a/libs/community/langchain_community/chains/graph_qa/base.py b/libs/community/langchain_community/chains/graph_qa/base.py index 79ba406033bef..8f13712acd664 100644 --- a/libs/community/langchain_community/chains/graph_qa/base.py +++ b/libs/community/langchain_community/chains/graph_qa/base.py @@ -9,7 +9,7 @@ from langchain_core.callbacks.manager import CallbackManagerForChainRun from langchain_core.language_models import BaseLanguageModel from langchain_core.prompts import BasePromptTemplate -from langchain_core.pydantic_v1 import Field +from pydantic import Field from langchain_community.chains.graph_qa.prompts import ( ENTITY_EXTRACTION_PROMPT, diff --git a/libs/community/langchain_community/chains/graph_qa/cypher.py b/libs/community/langchain_community/chains/graph_qa/cypher.py index 2d6263cf8b058..a3175603786f6 100644 --- a/libs/community/langchain_community/chains/graph_qa/cypher.py +++ b/libs/community/langchain_community/chains/graph_qa/cypher.py @@ -22,8 +22,8 @@ HumanMessagePromptTemplate, MessagesPlaceholder, ) -from langchain_core.pydantic_v1 import Field from langchain_core.runnables import Runnable +from pydantic import Field from langchain_community.chains.graph_qa.cypher_utils import ( CypherQueryCorrector, diff --git a/libs/community/langchain_community/chains/graph_qa/falkordb.py b/libs/community/langchain_community/chains/graph_qa/falkordb.py index 9f8ac20e56eb3..360c4ead90903 100644 --- a/libs/community/langchain_community/chains/graph_qa/falkordb.py +++ b/libs/community/langchain_community/chains/graph_qa/falkordb.py @@ -10,7 +10,7 @@ from langchain_core.callbacks import CallbackManagerForChainRun from langchain_core.language_models import BaseLanguageModel from langchain_core.prompts import BasePromptTemplate -from langchain_core.pydantic_v1 import Field +from pydantic import Field from langchain_community.chains.graph_qa.prompts import ( CYPHER_GENERATION_PROMPT, diff --git a/libs/community/langchain_community/chains/graph_qa/gremlin.py b/libs/community/langchain_community/chains/graph_qa/gremlin.py index e208e78b353c6..d323a19462268 100644 --- a/libs/community/langchain_community/chains/graph_qa/gremlin.py +++ b/libs/community/langchain_community/chains/graph_qa/gremlin.py @@ -10,7 +10,7 @@ from langchain_core.language_models import BaseLanguageModel from langchain_core.prompts import BasePromptTemplate from langchain_core.prompts.prompt import PromptTemplate -from langchain_core.pydantic_v1 import Field +from pydantic import Field from langchain_community.chains.graph_qa.prompts import ( CYPHER_QA_PROMPT, diff --git a/libs/community/langchain_community/chains/graph_qa/hugegraph.py b/libs/community/langchain_community/chains/graph_qa/hugegraph.py index 8608fce6310c7..f88d8d456b4c1 100644 --- a/libs/community/langchain_community/chains/graph_qa/hugegraph.py +++ b/libs/community/langchain_community/chains/graph_qa/hugegraph.py @@ -9,7 +9,7 @@ from langchain_core.callbacks import CallbackManagerForChainRun from langchain_core.language_models import BaseLanguageModel from langchain_core.prompts import BasePromptTemplate -from langchain_core.pydantic_v1 import Field +from pydantic import Field from langchain_community.chains.graph_qa.prompts import ( CYPHER_QA_PROMPT, diff --git a/libs/community/langchain_community/chains/graph_qa/kuzu.py b/libs/community/langchain_community/chains/graph_qa/kuzu.py index 950885bec60d5..b79d1e69a86dc 100644 --- a/libs/community/langchain_community/chains/graph_qa/kuzu.py +++ b/libs/community/langchain_community/chains/graph_qa/kuzu.py @@ -10,7 +10,7 @@ from langchain_core.callbacks import CallbackManagerForChainRun from langchain_core.language_models import BaseLanguageModel from langchain_core.prompts import BasePromptTemplate -from langchain_core.pydantic_v1 import Field +from pydantic import Field from langchain_community.chains.graph_qa.prompts import ( CYPHER_QA_PROMPT, diff --git a/libs/community/langchain_community/chains/graph_qa/nebulagraph.py b/libs/community/langchain_community/chains/graph_qa/nebulagraph.py index 3e1067b8a4442..f0e305a7edd93 100644 --- a/libs/community/langchain_community/chains/graph_qa/nebulagraph.py +++ b/libs/community/langchain_community/chains/graph_qa/nebulagraph.py @@ -9,7 +9,7 @@ from langchain_core.callbacks import CallbackManagerForChainRun from langchain_core.language_models import BaseLanguageModel from langchain_core.prompts import BasePromptTemplate -from langchain_core.pydantic_v1 import Field +from pydantic import Field from langchain_community.chains.graph_qa.prompts import ( CYPHER_QA_PROMPT, diff --git a/libs/community/langchain_community/chains/graph_qa/neptune_cypher.py b/libs/community/langchain_community/chains/graph_qa/neptune_cypher.py index 5b786f9f57670..01de2641f6bb6 100644 --- a/libs/community/langchain_community/chains/graph_qa/neptune_cypher.py +++ b/libs/community/langchain_community/chains/graph_qa/neptune_cypher.py @@ -9,7 +9,7 @@ from langchain_core.callbacks import CallbackManagerForChainRun from langchain_core.language_models import BaseLanguageModel from langchain_core.prompts.base import BasePromptTemplate -from langchain_core.pydantic_v1 import Field +from pydantic import Field from langchain_community.chains.graph_qa.prompts import ( CYPHER_QA_PROMPT, diff --git a/libs/community/langchain_community/chains/graph_qa/neptune_sparql.py b/libs/community/langchain_community/chains/graph_qa/neptune_sparql.py index 07042ed7bf5e6..62aa49079fc08 100644 --- a/libs/community/langchain_community/chains/graph_qa/neptune_sparql.py +++ b/libs/community/langchain_community/chains/graph_qa/neptune_sparql.py @@ -12,7 +12,7 @@ from langchain_core.language_models import BaseLanguageModel from langchain_core.prompts.base import BasePromptTemplate from langchain_core.prompts.prompt import PromptTemplate -from langchain_core.pydantic_v1 import Field +from pydantic import Field from langchain_community.chains.graph_qa.prompts import SPARQL_QA_PROMPT from langchain_community.graphs import NeptuneRdfGraph diff --git a/libs/community/langchain_community/chains/graph_qa/ontotext_graphdb.py b/libs/community/langchain_community/chains/graph_qa/ontotext_graphdb.py index ef15bc186121d..71081a99c48b8 100644 --- a/libs/community/langchain_community/chains/graph_qa/ontotext_graphdb.py +++ b/libs/community/langchain_community/chains/graph_qa/ontotext_graphdb.py @@ -12,7 +12,7 @@ from langchain_core.callbacks.manager import CallbackManager, CallbackManagerForChainRun from langchain_core.language_models import BaseLanguageModel from langchain_core.prompts.base import BasePromptTemplate -from langchain_core.pydantic_v1 import Field +from pydantic import Field from langchain_community.chains.graph_qa.prompts import ( GRAPHDB_QA_PROMPT, diff --git a/libs/community/langchain_community/chains/graph_qa/sparql.py b/libs/community/langchain_community/chains/graph_qa/sparql.py index 43d8135e4fe55..b61e262ffbaba 100644 --- a/libs/community/langchain_community/chains/graph_qa/sparql.py +++ b/libs/community/langchain_community/chains/graph_qa/sparql.py @@ -11,7 +11,7 @@ from langchain_core.callbacks import CallbackManagerForChainRun from langchain_core.language_models import BaseLanguageModel from langchain_core.prompts.base import BasePromptTemplate -from langchain_core.pydantic_v1 import Field +from pydantic import Field from langchain_community.chains.graph_qa.prompts import ( SPARQL_GENERATION_SELECT_PROMPT, diff --git a/libs/community/langchain_community/chains/llm_requests.py b/libs/community/langchain_community/chains/llm_requests.py index a7a42807dd1ee..6fc23683d2036 100644 --- a/libs/community/langchain_community/chains/llm_requests.py +++ b/libs/community/langchain_community/chains/llm_requests.py @@ -7,7 +7,7 @@ from langchain.chains import LLMChain from langchain.chains.base import Chain from langchain_core.callbacks import CallbackManagerForChainRun -from langchain_core.pydantic_v1 import Field, root_validator +from pydantic import ConfigDict, Field, model_validator from langchain_community.utilities.requests import TextRequestsWrapper @@ -38,9 +38,10 @@ class LLMRequestsChain(Chain): input_key: str = "url" #: :meta private: output_key: str = "output" #: :meta private: - class Config: - arbitrary_types_allowed = True - extra = "forbid" + model_config = ConfigDict( + arbitrary_types_allowed=True, + extra="forbid", + ) @property def input_keys(self) -> List[str]: @@ -58,8 +59,9 @@ def output_keys(self) -> List[str]: """ return [self.output_key] - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key and python package exists in environment.""" try: from bs4 import BeautifulSoup # noqa: F401 diff --git a/libs/community/langchain_community/chains/openapi/chain.py b/libs/community/langchain_community/chains/openapi/chain.py index 0ae9de6637d34..983efea7cc8da 100644 --- a/libs/community/langchain_community/chains/openapi/chain.py +++ b/libs/community/langchain_community/chains/openapi/chain.py @@ -11,7 +11,7 @@ from langchain.chains.llm import LLMChain from langchain_core.callbacks import CallbackManagerForChainRun, Callbacks from langchain_core.language_models import BaseLanguageModel -from langchain_core.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, Field from requests import Response from langchain_community.tools.openapi.utils.api_models import APIOperation diff --git a/libs/community/langchain_community/chains/pebblo_retrieval/base.py b/libs/community/langchain_community/chains/pebblo_retrieval/base.py index ee595061d95cf..2d4b550f1a999 100644 --- a/libs/community/langchain_community/chains/pebblo_retrieval/base.py +++ b/libs/community/langchain_community/chains/pebblo_retrieval/base.py @@ -17,8 +17,8 @@ ) from langchain_core.documents import Document from langchain_core.language_models import BaseLanguageModel -from langchain_core.pydantic_v1 import Field, validator from langchain_core.vectorstores import VectorStoreRetriever +from pydantic import ConfigDict, Field, validator from langchain_community.chains.pebblo_retrieval.enforcement_filters import ( SUPPORTED_VECTORSTORES, @@ -189,10 +189,11 @@ async def _acall( else: return {self.output_key: answer} - class Config: - allow_population_by_field_name = True - arbitrary_types_allowed = True - extra = "forbid" + model_config = ConfigDict( + populate_by_name=True, + arbitrary_types_allowed=True, + extra="forbid", + ) @property def input_keys(self) -> List[str]: diff --git a/libs/community/langchain_community/chains/pebblo_retrieval/models.py b/libs/community/langchain_community/chains/pebblo_retrieval/models.py index 315905d18ddf1..97e29769ced6f 100644 --- a/libs/community/langchain_community/chains/pebblo_retrieval/models.py +++ b/libs/community/langchain_community/chains/pebblo_retrieval/models.py @@ -2,7 +2,7 @@ from typing import Any, List, Optional, Union -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel class AuthContext(BaseModel): diff --git a/libs/community/langchain_community/chains/pebblo_retrieval/utilities.py b/libs/community/langchain_community/chains/pebblo_retrieval/utilities.py index e42fe6b00e0ea..e6e36a505a947 100644 --- a/libs/community/langchain_community/chains/pebblo_retrieval/utilities.py +++ b/libs/community/langchain_community/chains/pebblo_retrieval/utilities.py @@ -10,9 +10,9 @@ from aiohttp import ClientTimeout from langchain_core.documents import Document from langchain_core.env import get_runtime_environment -from langchain_core.pydantic_v1 import BaseModel from langchain_core.utils import get_from_dict_or_env from langchain_core.vectorstores import VectorStoreRetriever +from pydantic import BaseModel from requests import Response, request from requests.exceptions import RequestException diff --git a/libs/community/langchain_community/chat_models/anthropic.py b/libs/community/langchain_community/chat_models/anthropic.py index 7ee675e6bac56..cd7160eb554c8 100644 --- a/libs/community/langchain_community/chat_models/anthropic.py +++ b/libs/community/langchain_community/chat_models/anthropic.py @@ -20,6 +20,7 @@ ) from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult from langchain_core.prompt_values import PromptValue +from pydantic import ConfigDict from langchain_community.llms.anthropic import _AnthropicCommon @@ -91,9 +92,10 @@ class ChatAnthropic(BaseChatModel, _AnthropicCommon): model = ChatAnthropic(model="", anthropic_api_key="my-api-key") """ - class Config: - allow_population_by_field_name = True - arbitrary_types_allowed = True + model_config = ConfigDict( + populate_by_name=True, + arbitrary_types_allowed=True, + ) @property def lc_secrets(self) -> Dict[str, str]: diff --git a/libs/community/langchain_community/chat_models/anyscale.py b/libs/community/langchain_community/chat_models/anyscale.py index 570eaed04167b..bb1f83ad1b257 100644 --- a/libs/community/langchain_community/chat_models/anyscale.py +++ b/libs/community/langchain_community/chat_models/anyscale.py @@ -5,12 +5,12 @@ import logging import os import sys -from typing import TYPE_CHECKING, Dict, Optional, Set +from typing import TYPE_CHECKING, Any, Dict, Optional, Set import requests from langchain_core.messages import BaseMessage -from langchain_core.pydantic_v1 import Field, SecretStr, root_validator from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env +from pydantic import Field, SecretStr, model_validator from langchain_community.adapters.openai import convert_message_to_dict from langchain_community.chat_models.openai import ( @@ -102,8 +102,9 @@ def get_available_models( return {model["id"] for model in models_response.json()["data"]} - @root_validator(pre=True) - def validate_environment(cls, values: dict) -> dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: dict) -> Any: """Validate that api key and python package exists in environment.""" values["anyscale_api_key"] = convert_to_secret_str( get_from_dict_or_env( diff --git a/libs/community/langchain_community/chat_models/azure_openai.py b/libs/community/langchain_community/chat_models/azure_openai.py index 28e27876613fa..83bc551d86a1b 100644 --- a/libs/community/langchain_community/chat_models/azure_openai.py +++ b/libs/community/langchain_community/chat_models/azure_openai.py @@ -9,8 +9,8 @@ from langchain_core._api.deprecation import deprecated from langchain_core.outputs import ChatResult -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.utils import get_from_dict_or_env, pre_init +from pydantic import BaseModel, Field from langchain_community.chat_models.openai import ChatOpenAI from langchain_community.utils.openai import is_openai_v1 diff --git a/libs/community/langchain_community/chat_models/baichuan.py b/libs/community/langchain_community/chat_models/baichuan.py index ea426507253d1..73f1a671fa355 100644 --- a/libs/community/langchain_community/chat_models/baichuan.py +++ b/libs/community/langchain_community/chat_models/baichuan.py @@ -44,7 +44,6 @@ parse_tool_call, ) from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult -from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr, root_validator from langchain_core.runnables import Runnable from langchain_core.tools import BaseTool from langchain_core.utils import ( @@ -53,6 +52,13 @@ get_pydantic_field_names, ) from langchain_core.utils.function_calling import convert_to_openai_tool +from pydantic import ( + BaseModel, + ConfigDict, + Field, + SecretStr, + model_validator, +) from langchain_community.chat_models.llamacpp import ( _lc_invalid_tool_call_to_openai_tool_call, @@ -375,11 +381,13 @@ def lc_serializable(self) -> bool: model_kwargs: Dict[str, Any] = Field(default_factory=dict) """Holds any model parameters valid for API call not explicitly specified.""" - class Config: - allow_population_by_field_name = True + model_config = ConfigDict( + populate_by_name=True, + ) - @root_validator(pre=True) - def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def build_extra(cls, values: Dict[str, Any]) -> Any: """Build extra kwargs from additional params that were passed in.""" all_required_field_names = get_pydantic_field_names(cls) extra = values.get("model_kwargs", {}) @@ -404,8 +412,9 @@ def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: values["model_kwargs"] = extra return values - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: values["baichuan_api_base"] = get_from_dict_or_env( values, "baichuan_api_base", diff --git a/libs/community/langchain_community/chat_models/baidu_qianfan_endpoint.py b/libs/community/langchain_community/chat_models/baidu_qianfan_endpoint.py index d088054261494..a4fe51e3b4232 100644 --- a/libs/community/langchain_community/chat_models/baidu_qianfan_endpoint.py +++ b/libs/community/langchain_community/chat_models/baidu_qianfan_endpoint.py @@ -41,17 +41,18 @@ PydanticToolsParser, ) from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult -from langchain_core.pydantic_v1 import ( - BaseModel, - Field, - SecretStr, - root_validator, -) from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough from langchain_core.tools import BaseTool from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env from langchain_core.utils.function_calling import convert_to_openai_tool from langchain_core.utils.pydantic import get_fields, is_basemodel_subclass +from pydantic import ( + BaseModel, + ConfigDict, + Field, + SecretStr, + model_validator, +) logger = logging.getLogger(__name__) @@ -248,7 +249,7 @@ class QianfanChatEndpoint(BaseChatModel): Tool calling: .. code-block:: python - from langchain_core.pydantic_v1 import BaseModel, Field + from pydantic import BaseModel, Field class GetWeather(BaseModel): @@ -287,7 +288,7 @@ class GetPopulation(BaseModel): from typing import Optional - from langchain_core.pydantic_v1 import BaseModel, Field + from pydantic import BaseModel, Field class Joke(BaseModel): @@ -380,11 +381,13 @@ class Joke(BaseModel): endpoint: Optional[str] = None """Endpoint of the Qianfan LLM, required if custom model used.""" - class Config: - allow_population_by_field_name = True + model_config = ConfigDict( + populate_by_name=True, + ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: values["qianfan_ak"] = convert_to_secret_str( get_from_dict_or_env( values, ["qianfan_ak", "api_key"], "QIANFAN_AK", default="" @@ -747,7 +750,7 @@ def with_structured_output( .. code-block:: python from langchain_mistralai import QianfanChatEndpoint - from langchain_core.pydantic_v1 import BaseModel + from pydantic import BaseModel class AnswerWithJustification(BaseModel): '''An answer to the user question along with justification for the answer.''' @@ -768,7 +771,7 @@ class AnswerWithJustification(BaseModel): .. code-block:: python from langchain_mistralai import QianfanChatEndpoint - from langchain_core.pydantic_v1 import BaseModel + from pydantic import BaseModel class AnswerWithJustification(BaseModel): '''An answer to the user question along with justification for the answer.''' @@ -789,7 +792,7 @@ class AnswerWithJustification(BaseModel): .. code-block:: python from langchain_mistralai import QianfanChatEndpoint - from langchain_core.pydantic_v1 import BaseModel + from pydantic import BaseModel from langchain_core.utils.function_calling import convert_to_openai_tool class AnswerWithJustification(BaseModel): diff --git a/libs/community/langchain_community/chat_models/bedrock.py b/libs/community/langchain_community/chat_models/bedrock.py index db326d520f12b..6b36208390379 100644 --- a/libs/community/langchain_community/chat_models/bedrock.py +++ b/libs/community/langchain_community/chat_models/bedrock.py @@ -16,6 +16,7 @@ SystemMessage, ) from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult +from pydantic import ConfigDict from langchain_community.chat_models.anthropic import ( convert_messages_to_prompt_anthropic, @@ -231,8 +232,9 @@ def lc_attributes(self) -> Dict[str, Any]: return attributes - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) def _stream( self, diff --git a/libs/community/langchain_community/chat_models/cohere.py b/libs/community/langchain_community/chat_models/cohere.py index 857cd39dbadb1..d2e8560a15158 100644 --- a/libs/community/langchain_community/chat_models/cohere.py +++ b/libs/community/langchain_community/chat_models/cohere.py @@ -19,6 +19,7 @@ SystemMessage, ) from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult +from pydantic import ConfigDict from langchain_community.llms.cohere import BaseCohere @@ -117,9 +118,10 @@ class ChatCohere(BaseChatModel, BaseCohere): chat.invoke(messages) """ - class Config: - allow_population_by_field_name = True - arbitrary_types_allowed = True + model_config = ConfigDict( + populate_by_name=True, + arbitrary_types_allowed=True, + ) @property def _llm_type(self) -> str: diff --git a/libs/community/langchain_community/chat_models/coze.py b/libs/community/langchain_community/chat_models/coze.py index ce941b4a78663..4bce2fba14f38 100644 --- a/libs/community/langchain_community/chat_models/coze.py +++ b/libs/community/langchain_community/chat_models/coze.py @@ -19,11 +19,11 @@ HumanMessageChunk, ) from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult -from langchain_core.pydantic_v1 import Field, SecretStr, root_validator from langchain_core.utils import ( convert_to_secret_str, get_from_dict_or_env, ) +from pydantic import ConfigDict, Field, SecretStr, model_validator logger = logging.getLogger(__name__) @@ -111,11 +111,13 @@ def lc_serializable(self) -> bool: "Streaming response" will provide real-time response of the model to the client, and the client needs to assemble the final reply based on the type of message. """ - class Config: - allow_population_by_field_name = True + model_config = ConfigDict( + populate_by_name=True, + ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: values["coze_api_base"] = get_from_dict_or_env( values, "coze_api_base", diff --git a/libs/community/langchain_community/chat_models/dappier.py b/libs/community/langchain_community/chat_models/dappier.py index 457eac7dd2b16..fc32b5a95bcff 100644 --- a/libs/community/langchain_community/chat_models/dappier.py +++ b/libs/community/langchain_community/chat_models/dappier.py @@ -13,8 +13,8 @@ BaseMessage, ) from langchain_core.outputs import ChatGeneration, ChatResult -from langchain_core.pydantic_v1 import Field, SecretStr, root_validator from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env +from pydantic import ConfigDict, Field, SecretStr, model_validator from langchain_community.utilities.requests import Requests @@ -70,11 +70,13 @@ class ChatDappierAI(BaseChatModel): dappier_api_key: Optional[SecretStr] = Field(None, description="Dappier API Token") - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key exists in environment.""" values["dappier_api_key"] = convert_to_secret_str( get_from_dict_or_env(values, "dappier_api_key", "DAPPIER_API_KEY") diff --git a/libs/community/langchain_community/chat_models/deepinfra.py b/libs/community/langchain_community/chat_models/deepinfra.py index 3de532b0c4912..061bed9e8b0c7 100644 --- a/libs/community/langchain_community/chat_models/deepinfra.py +++ b/libs/community/langchain_community/chat_models/deepinfra.py @@ -54,11 +54,12 @@ ChatGenerationChunk, ChatResult, ) -from langchain_core.pydantic_v1 import BaseModel, Field, root_validator from langchain_core.runnables import Runnable from langchain_core.tools import BaseTool from langchain_core.utils import get_from_dict_or_env from langchain_core.utils.function_calling import convert_to_openai_tool +from pydantic import BaseModel, ConfigDict, Field, model_validator +from typing_extensions import Self from langchain_community.utilities.requests import Requests @@ -222,10 +223,9 @@ class ChatDeepInfra(BaseChatModel): streaming: bool = False max_retries: int = 1 - class Config: - """Configuration for this pydantic object.""" - - allow_population_by_field_name = True + model_config = ConfigDict( + populate_by_name=True, + ) @property def _default_params(self) -> Dict[str, Any]: @@ -291,8 +291,9 @@ async def _completion_with_retry(**kwargs: Any) -> Any: return await _completion_with_retry(**kwargs) - @root_validator(pre=True) - def init_defaults(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def init_defaults(cls, values: Dict) -> Any: """Validate api key, python package exists, temperature, top_p, and top_k.""" # For compatibility with LiteLLM api_key = get_from_dict_or_env( @@ -309,18 +310,18 @@ def init_defaults(cls, values: Dict) -> Dict: ) return values - @root_validator(pre=False, skip_on_failure=True) - def validate_environment(cls, values: Dict) -> Dict: - if values["temperature"] is not None and not 0 <= values["temperature"] <= 1: + @model_validator(mode="after") + def validate_environment(self) -> Self: + if self.temperature is not None and not 0 <= self.temperature <= 1: raise ValueError("temperature must be in the range [0.0, 1.0]") - if values["top_p"] is not None and not 0 <= values["top_p"] <= 1: + if self.top_p is not None and not 0 <= self.top_p <= 1: raise ValueError("top_p must be in the range [0.0, 1.0]") - if values["top_k"] is not None and values["top_k"] <= 0: + if self.top_k is not None and self.top_k <= 0: raise ValueError("top_k must be positive") - return values + return self def _generate( self, diff --git a/libs/community/langchain_community/chat_models/edenai.py b/libs/community/langchain_community/chat_models/edenai.py index 0e03f08d00a15..2a41e5e7d550f 100644 --- a/libs/community/langchain_community/chat_models/edenai.py +++ b/libs/community/langchain_community/chat_models/edenai.py @@ -47,16 +47,17 @@ PydanticToolsParser, ) from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult -from langchain_core.pydantic_v1 import ( - BaseModel, - Field, - SecretStr, -) from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough from langchain_core.tools import BaseTool from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init from langchain_core.utils.function_calling import convert_to_openai_tool from langchain_core.utils.pydantic import is_basemodel_subclass +from pydantic import ( + BaseModel, + ConfigDict, + Field, + SecretStr, +) from langchain_community.utilities.requests import Requests @@ -296,8 +297,9 @@ class ChatEdenAI(BaseChatModel): edenai_api_key: Optional[SecretStr] = Field(None, description="EdenAI API Token") - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) @pre_init def validate_environment(cls, values: Dict) -> Dict: diff --git a/libs/community/langchain_community/chat_models/ernie.py b/libs/community/langchain_community/chat_models/ernie.py index b8681fb867b60..41cf635cfaf9f 100644 --- a/libs/community/langchain_community/chat_models/ernie.py +++ b/libs/community/langchain_community/chat_models/ernie.py @@ -13,8 +13,8 @@ HumanMessage, ) from langchain_core.outputs import ChatGeneration, ChatResult -from langchain_core.pydantic_v1 import root_validator from langchain_core.utils import get_from_dict_or_env +from pydantic import model_validator logger = logging.getLogger(__name__) @@ -108,8 +108,9 @@ class ErnieBotChat(BaseChatModel): _lock = threading.Lock() - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: values["ernie_api_base"] = get_from_dict_or_env( values, "ernie_api_base", "ERNIE_API_BASE", "https://aip.baidubce.com" ) diff --git a/libs/community/langchain_community/chat_models/everlyai.py b/libs/community/langchain_community/chat_models/everlyai.py index 4122dae280cb7..160389b57c885 100644 --- a/libs/community/langchain_community/chat_models/everlyai.py +++ b/libs/community/langchain_community/chat_models/everlyai.py @@ -4,11 +4,11 @@ import logging import sys -from typing import TYPE_CHECKING, Dict, Optional, Set +from typing import TYPE_CHECKING, Any, Dict, Optional, Set from langchain_core.messages import BaseMessage -from langchain_core.pydantic_v1 import Field, root_validator from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env +from pydantic import Field, model_validator from langchain_community.adapters.openai import convert_message_to_dict from langchain_community.chat_models.openai import ( @@ -76,8 +76,9 @@ def get_available_models() -> Set[str]: ] ) - @root_validator(pre=True) - def validate_environment_override(cls, values: dict) -> dict: + @model_validator(mode="before") + @classmethod + def validate_environment_override(cls, values: dict) -> Any: """Validate that api key and python package exists in environment.""" values["openai_api_key"] = convert_to_secret_str( get_from_dict_or_env( diff --git a/libs/community/langchain_community/chat_models/fireworks.py b/libs/community/langchain_community/chat_models/fireworks.py index e3a5ac0e12da1..2211f186ae2f9 100644 --- a/libs/community/langchain_community/chat_models/fireworks.py +++ b/libs/community/langchain_community/chat_models/fireworks.py @@ -32,9 +32,9 @@ SystemMessageChunk, ) from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult -from langchain_core.pydantic_v1 import Field, SecretStr, root_validator from langchain_core.utils import convert_to_secret_str from langchain_core.utils.env import get_from_dict_or_env +from pydantic import Field, SecretStr, model_validator from langchain_community.adapters.openai import convert_message_to_dict @@ -112,8 +112,9 @@ def get_lc_namespace(cls) -> List[str]: """Get the namespace of the langchain object.""" return ["langchain", "chat_models", "fireworks"] - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key in environment.""" try: import fireworks.client diff --git a/libs/community/langchain_community/chat_models/google_palm.py b/libs/community/langchain_community/chat_models/google_palm.py index bbdc7efc32b90..77038256c7d82 100644 --- a/libs/community/langchain_community/chat_models/google_palm.py +++ b/libs/community/langchain_community/chat_models/google_palm.py @@ -21,8 +21,8 @@ ChatGeneration, ChatResult, ) -from langchain_core.pydantic_v1 import BaseModel, SecretStr from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init +from pydantic import BaseModel, SecretStr from tenacity import ( before_sleep_log, retry, diff --git a/libs/community/langchain_community/chat_models/gpt_router.py b/libs/community/langchain_community/chat_models/gpt_router.py index cff7df8421ba9..cac9f72b376de 100644 --- a/libs/community/langchain_community/chat_models/gpt_router.py +++ b/libs/community/langchain_community/chat_models/gpt_router.py @@ -30,8 +30,8 @@ from langchain_core.language_models.llms import create_base_retry_decorator from langchain_core.messages import AIMessageChunk, BaseMessage, BaseMessageChunk from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult -from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr, root_validator from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env +from pydantic import BaseModel, Field, SecretStr, model_validator, root_validator from langchain_community.adapters.openai import ( convert_dict_to_message, @@ -167,8 +167,9 @@ class GPTRouter(BaseChatModel): """Number of chat completions to generate for each prompt.""" max_tokens: int = 256 - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: values["gpt_router_api_base"] = get_from_dict_or_env( values, "gpt_router_api_base", diff --git a/libs/community/langchain_community/chat_models/huggingface.py b/libs/community/langchain_community/chat_models/huggingface.py index b5d3f905a2461..fe029d6d6ca7a 100644 --- a/libs/community/langchain_community/chat_models/huggingface.py +++ b/libs/community/langchain_community/chat_models/huggingface.py @@ -25,7 +25,8 @@ ChatResult, LLMResult, ) -from langchain_core.pydantic_v1 import root_validator +from pydantic import model_validator +from typing_extensions import Self from langchain_community.llms.huggingface_endpoint import HuggingFaceEndpoint from langchain_community.llms.huggingface_hub import HuggingFaceHub @@ -76,17 +77,17 @@ def __init__(self, **kwargs: Any): else self.tokenizer ) - @root_validator(pre=False, skip_on_failure=True) - def validate_llm(cls, values: dict) -> dict: + @model_validator(mode="after") + def validate_llm(self) -> Self: if not isinstance( - values["llm"], + self.llm, (HuggingFaceTextGenInference, HuggingFaceEndpoint, HuggingFaceHub), ): raise TypeError( "Expected llm to be one of HuggingFaceTextGenInference, " - f"HuggingFaceEndpoint, HuggingFaceHub, received {type(values['llm'])}" + f"HuggingFaceEndpoint, HuggingFaceHub, received {type(self.llm)}" ) - return values + return self def _stream( self, diff --git a/libs/community/langchain_community/chat_models/human.py b/libs/community/langchain_community/chat_models/human.py index 030516a822f7c..c24964a678edd 100644 --- a/libs/community/langchain_community/chat_models/human.py +++ b/libs/community/langchain_community/chat_models/human.py @@ -15,7 +15,7 @@ messages_to_dict, ) from langchain_core.outputs import ChatGeneration, ChatResult -from langchain_core.pydantic_v1 import Field +from pydantic import Field from langchain_community.llms.utils import enforce_stop_tokens diff --git a/libs/community/langchain_community/chat_models/hunyuan.py b/libs/community/langchain_community/chat_models/hunyuan.py index c16dc7d114173..cd744956263bd 100644 --- a/libs/community/langchain_community/chat_models/hunyuan.py +++ b/libs/community/langchain_community/chat_models/hunyuan.py @@ -19,13 +19,13 @@ SystemMessage, ) from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult -from langchain_core.pydantic_v1 import Field, SecretStr, root_validator from langchain_core.utils import ( convert_to_secret_str, get_from_dict_or_env, get_pydantic_field_names, pre_init, ) +from pydantic import ConfigDict, Field, SecretStr, model_validator logger = logging.getLogger(__name__) @@ -138,11 +138,13 @@ def lc_serializable(self) -> bool: model_kwargs: Dict[str, Any] = Field(default_factory=dict) """Holds any model parameters valid for API call not explicitly specified.""" - class Config: - allow_population_by_field_name = True + model_config = ConfigDict( + populate_by_name=True, + ) - @root_validator(pre=True) - def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def build_extra(cls, values: Dict[str, Any]) -> Any: """Build extra kwargs from additional params that were passed in.""" all_required_field_names = get_pydantic_field_names(cls) extra = values.get("model_kwargs", {}) diff --git a/libs/community/langchain_community/chat_models/javelin_ai_gateway.py b/libs/community/langchain_community/chat_models/javelin_ai_gateway.py index af7f401e1a4f9..d09fb3bcaf67b 100644 --- a/libs/community/langchain_community/chat_models/javelin_ai_gateway.py +++ b/libs/community/langchain_community/chat_models/javelin_ai_gateway.py @@ -18,7 +18,7 @@ ChatGeneration, ChatResult, ) -from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr +from pydantic import BaseModel, ConfigDict, Field, SecretStr logger = logging.getLogger(__name__) @@ -62,14 +62,15 @@ class ChatJavelinAIGateway(BaseChatModel): params: Optional[ChatParams] = None """Parameters for the Javelin AI Gateway LLM.""" - client: Any + client: Any = None """javelin client.""" javelin_api_key: Optional[SecretStr] = Field(None, alias="api_key") """The API key for the Javelin AI Gateway.""" - class Config: - allow_population_by_field_name = True + model_config = ConfigDict( + populate_by_name=True, + ) def __init__(self, **kwargs: Any): try: diff --git a/libs/community/langchain_community/chat_models/jinachat.py b/libs/community/langchain_community/chat_models/jinachat.py index ecda4528a4b7a..f258674856b86 100644 --- a/libs/community/langchain_community/chat_models/jinachat.py +++ b/libs/community/langchain_community/chat_models/jinachat.py @@ -40,13 +40,13 @@ SystemMessageChunk, ) from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult -from langchain_core.pydantic_v1 import Field, SecretStr, root_validator from langchain_core.utils import ( convert_to_secret_str, get_from_dict_or_env, get_pydantic_field_names, pre_init, ) +from pydantic import ConfigDict, Field, SecretStr, model_validator from tenacity import ( before_sleep_log, retry, @@ -188,11 +188,13 @@ def is_lc_serializable(cls) -> bool: max_tokens: Optional[int] = None """Maximum number of tokens to generate.""" - class Config: - allow_population_by_field_name = True + model_config = ConfigDict( + populate_by_name=True, + ) - @root_validator(pre=True) - def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def build_extra(cls, values: Dict[str, Any]) -> Any: """Build extra kwargs from additional params that were passed in.""" all_required_field_names = get_pydantic_field_names(cls) extra = values.get("model_kwargs", {}) diff --git a/libs/community/langchain_community/chat_models/kinetica.py b/libs/community/langchain_community/chat_models/kinetica.py index 9088afd950fed..1de6915a20188 100644 --- a/libs/community/langchain_community/chat_models/kinetica.py +++ b/libs/community/langchain_community/chat_models/kinetica.py @@ -26,7 +26,7 @@ ) from langchain_core.output_parsers.transform import BaseOutputParser from langchain_core.outputs import ChatGeneration, ChatResult, Generation -from langchain_core.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, ConfigDict, Field LOG = logging.getLogger(__name__) @@ -543,8 +543,9 @@ class KineticaSqlResponse(BaseModel): dataframe: Any = Field(default=None) """The Pandas dataframe containing the fetched data.""" - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) class KineticaSqlOutputParser(BaseOutputParser[KineticaSqlResponse]): @@ -582,8 +583,9 @@ class KineticaSqlOutputParser(BaseOutputParser[KineticaSqlResponse]): kdbc: Any = Field(exclude=True) """ Kinetica DB connection. """ - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) def parse(self, text: str) -> KineticaSqlResponse: df = self.kdbc.to_df(text) diff --git a/libs/community/langchain_community/chat_models/konko.py b/libs/community/langchain_community/chat_models/konko.py index d4ee329d835dc..a2d16e51179e6 100644 --- a/libs/community/langchain_community/chat_models/konko.py +++ b/libs/community/langchain_community/chat_models/konko.py @@ -23,8 +23,8 @@ ) from langchain_core.messages import AIMessageChunk, BaseMessage from langchain_core.outputs import ChatGenerationChunk, ChatResult -from langchain_core.pydantic_v1 import Field, SecretStr from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init +from pydantic import Field, SecretStr from langchain_community.adapters.openai import ( convert_message_to_dict, diff --git a/libs/community/langchain_community/chat_models/litellm.py b/libs/community/langchain_community/chat_models/litellm.py index f0cf69a71910d..97e6a3403fce2 100644 --- a/libs/community/langchain_community/chat_models/litellm.py +++ b/libs/community/langchain_community/chat_models/litellm.py @@ -52,11 +52,11 @@ ChatGenerationChunk, ChatResult, ) -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.runnables import Runnable from langchain_core.tools import BaseTool from langchain_core.utils import get_from_dict_or_env, pre_init from langchain_core.utils.function_calling import convert_to_openai_tool +from pydantic import BaseModel, Field logger = logging.getLogger(__name__) diff --git a/libs/community/langchain_community/chat_models/llama_edge.py b/libs/community/langchain_community/chat_models/llama_edge.py index 8edda85c3b023..0c096d89608de 100644 --- a/libs/community/langchain_community/chat_models/llama_edge.py +++ b/libs/community/langchain_community/chat_models/llama_edge.py @@ -21,8 +21,8 @@ SystemMessage, ) from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult -from langchain_core.pydantic_v1 import root_validator from langchain_core.utils import get_pydantic_field_names +from pydantic import ConfigDict, model_validator logger = logging.getLogger(__name__) @@ -84,11 +84,13 @@ class LlamaEdgeChatService(BaseChatModel): streaming: bool = False """Whether to stream the results or not.""" - class Config: - allow_population_by_field_name = True + model_config = ConfigDict( + populate_by_name=True, + ) - @root_validator(pre=True) - def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def build_extra(cls, values: Dict[str, Any]) -> Any: """Build extra kwargs from additional params that were passed in.""" all_required_field_names = get_pydantic_field_names(cls) extra = values.get("model_kwargs", {}) diff --git a/libs/community/langchain_community/chat_models/llamacpp.py b/libs/community/langchain_community/chat_models/llamacpp.py index a3b7f8a3a2527..ed8d0a528ada1 100644 --- a/libs/community/langchain_community/chat_models/llamacpp.py +++ b/libs/community/langchain_community/chat_models/llamacpp.py @@ -46,15 +46,16 @@ parse_tool_call, ) from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult -from langchain_core.pydantic_v1 import ( - BaseModel, - Field, - root_validator, -) from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough from langchain_core.tools import BaseTool from langchain_core.utils.function_calling import convert_to_openai_tool from langchain_core.utils.pydantic import is_basemodel_subclass +from pydantic import ( + BaseModel, + Field, + model_validator, +) +from typing_extensions import Self class ChatLlamaCpp(BaseChatModel): @@ -172,8 +173,8 @@ class ChatLlamaCpp(BaseChatModel): verbose: bool = True """Print verbose output to stderr.""" - @root_validator(pre=False, skip_on_failure=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="after") + def validate_environment(self) -> Self: """Validate that llama-cpp-python library is installed.""" try: from llama_cpp import Llama, LlamaGrammar @@ -184,7 +185,7 @@ def validate_environment(cls, values: Dict) -> Dict: "use this embedding model: pip install llama-cpp-python" ) - model_path = values["model_path"] + model_path = self.model_path model_param_names = [ "rope_freq_scale", "rope_freq_base", @@ -203,35 +204,35 @@ def validate_environment(cls, values: Dict) -> Dict: "last_n_tokens_size", "verbose", ] - model_params = {k: values[k] for k in model_param_names} + model_params = {k: getattr(self, k) for k in model_param_names} # For backwards compatibility, only include if non-null. - if values["n_gpu_layers"] is not None: - model_params["n_gpu_layers"] = values["n_gpu_layers"] + if self.n_gpu_layers is not None: + model_params["n_gpu_layers"] = self.n_gpu_layers - model_params.update(values["model_kwargs"]) + model_params.update(self.model_kwargs) try: - values["client"] = Llama(model_path, **model_params) + self.client = Llama(model_path, **model_params) except Exception as e: raise ValueError( f"Could not load Llama model from path: {model_path}. " f"Received error {e}" ) - if values["grammar"] and values["grammar_path"]: - grammar = values["grammar"] - grammar_path = values["grammar_path"] + if self.grammar and self.grammar_path: + grammar = self.grammar + grammar_path = self.grammar_path raise ValueError( "Can only pass in one of grammar and grammar_path. Received " f"{grammar=} and {grammar_path=}." ) - elif isinstance(values["grammar"], str): - values["grammar"] = LlamaGrammar.from_string(values["grammar"]) - elif values["grammar_path"]: - values["grammar"] = LlamaGrammar.from_file(values["grammar_path"]) + elif isinstance(self.grammar, str): + self.grammar = LlamaGrammar.from_string(self.grammar) + elif self.grammar_path: + self.grammar = LlamaGrammar.from_file(self.grammar_path) else: pass - return values + return self def _get_parameters(self, stop: Optional[List[str]]) -> Dict[str, Any]: """ @@ -433,7 +434,7 @@ def with_structured_output( .. code-block:: python from langchain_community.chat_models import ChatLlamaCpp - from langchain_core.pydantic_v1 import BaseModel + from pydantic import BaseModel class AnswerWithJustification(BaseModel): '''An answer to the user question along with justification for the answer.''' @@ -465,7 +466,7 @@ class AnswerWithJustification(BaseModel): .. code-block:: python from langchain_community.chat_models import ChatLlamaCpp - from langchain_core.pydantic_v1 import BaseModel + from pydantic import BaseModel class AnswerWithJustification(BaseModel): '''An answer to the user question along with justification for the answer.''' @@ -497,7 +498,7 @@ class AnswerWithJustification(BaseModel): .. code-block:: python from langchain_community.chat_models import ChatLlamaCpp - from langchain_core.pydantic_v1 import BaseModel + from pydantic import BaseModel from langchain_core.utils.function_calling import convert_to_openai_tool class AnswerWithJustification(BaseModel): diff --git a/libs/community/langchain_community/chat_models/maritalk.py b/libs/community/langchain_community/chat_models/maritalk.py index f06f2917e715d..9e089a174aeca 100644 --- a/libs/community/langchain_community/chat_models/maritalk.py +++ b/libs/community/langchain_community/chat_models/maritalk.py @@ -16,7 +16,7 @@ SystemMessage, ) from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult -from langchain_core.pydantic_v1 import Field +from pydantic import Field from requests import Response from requests.exceptions import HTTPError diff --git a/libs/community/langchain_community/chat_models/minimax.py b/libs/community/langchain_community/chat_models/minimax.py index b8b47ae838586..46cd4c1950165 100644 --- a/libs/community/langchain_community/chat_models/minimax.py +++ b/libs/community/langchain_community/chat_models/minimax.py @@ -44,12 +44,18 @@ PydanticToolsParser, ) from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult -from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr, root_validator from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough from langchain_core.tools import BaseTool from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env from langchain_core.utils.function_calling import convert_to_openai_tool from langchain_core.utils.pydantic import get_fields +from pydantic import ( + BaseModel, + ConfigDict, + Field, + SecretStr, + model_validator, +) logger = logging.getLogger(__name__) @@ -267,7 +273,7 @@ class MiniMaxChat(BaseChatModel): Tool calling: .. code-block:: python - from langchain_core.pydantic_v1 import BaseModel, Field + from pydantic import BaseModel, Field class GetWeather(BaseModel): @@ -307,7 +313,7 @@ class GetPopulation(BaseModel): from typing import Optional - from langchain_core.pydantic_v1 import BaseModel, Field + from pydantic import BaseModel, Field class Joke(BaseModel): @@ -384,11 +390,13 @@ def _default_params(self) -> Dict[str, Any]: streaming: bool = False """Whether to stream the results or not.""" - class Config: - allow_population_by_field_name = True + model_config = ConfigDict( + populate_by_name=True, + ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key and python package exists in environment.""" values["minimax_api_key"] = convert_to_secret_str( get_from_dict_or_env( @@ -694,7 +702,7 @@ def with_structured_output( .. code-block:: python from langchain_community.chat_models import MiniMaxChat - from langchain_core.pydantic_v1 import BaseModel + from pydantic import BaseModel class AnswerWithJustification(BaseModel): '''An answer to the user question along with justification for the answer.''' @@ -715,7 +723,7 @@ class AnswerWithJustification(BaseModel): .. code-block:: python from langchain_community.chat_models import MiniMaxChat - from langchain_core.pydantic_v1 import BaseModel + from pydantic import BaseModel class AnswerWithJustification(BaseModel): '''An answer to the user question along with justification for the answer.''' @@ -737,7 +745,7 @@ class AnswerWithJustification(BaseModel): .. code-block:: python from langchain_community.chat_models import MiniMaxChat - from langchain_core.pydantic_v1 import BaseModel + from pydantic import BaseModel from langchain_core.utils.function_calling import convert_to_openai_tool class AnswerWithJustification(BaseModel): diff --git a/libs/community/langchain_community/chat_models/mlflow.py b/libs/community/langchain_community/chat_models/mlflow.py index 101c2cb040c6b..9a1c528fd7538 100644 --- a/libs/community/langchain_community/chat_models/mlflow.py +++ b/libs/community/langchain_community/chat_models/mlflow.py @@ -42,14 +42,14 @@ parse_tool_call, ) from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult -from langchain_core.pydantic_v1 import ( +from langchain_core.runnables import Runnable, RunnableConfig +from langchain_core.tools import BaseTool +from langchain_core.utils.function_calling import convert_to_openai_tool +from pydantic import ( BaseModel, Field, PrivateAttr, ) -from langchain_core.runnables import Runnable, RunnableConfig -from langchain_core.tools import BaseTool -from langchain_core.utils.function_calling import convert_to_openai_tool logger = logging.getLogger(__name__) diff --git a/libs/community/langchain_community/chat_models/mlflow_ai_gateway.py b/libs/community/langchain_community/chat_models/mlflow_ai_gateway.py index f85eebed002e1..e33a656466eae 100644 --- a/libs/community/langchain_community/chat_models/mlflow_ai_gateway.py +++ b/libs/community/langchain_community/chat_models/mlflow_ai_gateway.py @@ -18,7 +18,7 @@ ChatGeneration, ChatResult, ) -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel logger = logging.getLogger(__name__) diff --git a/libs/community/langchain_community/chat_models/oci_generative_ai.py b/libs/community/langchain_community/chat_models/oci_generative_ai.py index 3207e2d93a693..85a3e763b8be9 100644 --- a/libs/community/langchain_community/chat_models/oci_generative_ai.py +++ b/libs/community/langchain_community/chat_models/oci_generative_ai.py @@ -38,10 +38,10 @@ PydanticToolsParser, ) from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult -from langchain_core.pydantic_v1 import BaseModel from langchain_core.runnables import Runnable from langchain_core.tools import BaseTool from langchain_core.utils.function_calling import convert_to_openai_function +from pydantic import BaseModel, ConfigDict from langchain_community.llms.oci_generative_ai import OCIGenAIBase from langchain_community.llms.utils import enforce_stop_tokens @@ -499,8 +499,10 @@ class ChatOCIGenAI(BaseChatModel, OCIGenAIBase): """ # noqa: E501 - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + arbitrary_types_allowed=True, + ) @property def _llm_type(self) -> str: diff --git a/libs/community/langchain_community/chat_models/octoai.py b/libs/community/langchain_community/chat_models/octoai.py index f79f9d192c077..77d7572d48da8 100644 --- a/libs/community/langchain_community/chat_models/octoai.py +++ b/libs/community/langchain_community/chat_models/octoai.py @@ -2,8 +2,8 @@ from typing import Dict -from langchain_core.pydantic_v1 import Field, SecretStr from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init +from pydantic import Field, SecretStr from langchain_community.chat_models.openai import ChatOpenAI from langchain_community.utils.openai import is_openai_v1 diff --git a/libs/community/langchain_community/chat_models/openai.py b/libs/community/langchain_community/chat_models/openai.py index 54326d0acc288..d67eb0f10b69f 100644 --- a/libs/community/langchain_community/chat_models/openai.py +++ b/libs/community/langchain_community/chat_models/openai.py @@ -44,13 +44,13 @@ ToolMessageChunk, ) from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult -from langchain_core.pydantic_v1 import BaseModel, Field, root_validator from langchain_core.runnables import Runnable from langchain_core.utils import ( get_from_dict_or_env, get_pydantic_field_names, pre_init, ) +from pydantic import BaseModel, ConfigDict, Field, model_validator from langchain_community.adapters.openai import ( convert_dict_to_message, @@ -244,11 +244,13 @@ def is_lc_serializable(cls) -> bool: http_client: Union[Any, None] = None """Optional httpx.Client.""" - class Config: - allow_population_by_field_name = True + model_config = ConfigDict( + populate_by_name=True, + ) - @root_validator(pre=True) - def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def build_extra(cls, values: Dict[str, Any]) -> Any: """Build extra kwargs from additional params that were passed in.""" all_required_field_names = get_pydantic_field_names(cls) extra = values.get("model_kwargs", {}) diff --git a/libs/community/langchain_community/chat_models/pai_eas_endpoint.py b/libs/community/langchain_community/chat_models/pai_eas_endpoint.py index 48c7eb9b42a0e..55dd0ccbf93c3 100644 --- a/libs/community/langchain_community/chat_models/pai_eas_endpoint.py +++ b/libs/community/langchain_community/chat_models/pai_eas_endpoint.py @@ -17,8 +17,8 @@ SystemMessage, ) from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult -from langchain_core.pydantic_v1 import root_validator from langchain_core.utils import get_from_dict_or_env +from pydantic import model_validator from langchain_community.llms.utils import enforce_stop_tokens @@ -67,8 +67,9 @@ class PaiEasChatEndpoint(BaseChatModel): timeout: Optional[int] = 5000 - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key and python package exists in environment.""" values["eas_service_url"] = get_from_dict_or_env( values, "eas_service_url", "EAS_SERVICE_URL" diff --git a/libs/community/langchain_community/chat_models/perplexity.py b/libs/community/langchain_community/chat_models/perplexity.py index ee93f48cbf894..88547c67d29db 100644 --- a/libs/community/langchain_community/chat_models/perplexity.py +++ b/libs/community/langchain_community/chat_models/perplexity.py @@ -35,8 +35,12 @@ ToolMessageChunk, ) from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult -from langchain_core.pydantic_v1 import Field, root_validator -from langchain_core.utils import get_from_dict_or_env, get_pydantic_field_names +from langchain_core.utils import ( + from_env, + get_pydantic_field_names, +) +from pydantic import ConfigDict, Field, model_validator +from typing_extensions import Self logger = logging.getLogger(__name__) @@ -60,14 +64,16 @@ class ChatPerplexity(BaseChatModel): ) """ - client: Any #: :meta private: + client: Any = None #: :meta private: model: str = "llama-3.1-sonar-small-128k-online" """Model name.""" temperature: float = 0.7 """What sampling temperature to use.""" model_kwargs: Dict[str, Any] = Field(default_factory=dict) """Holds any model parameters valid for `create` call not explicitly specified.""" - pplx_api_key: Optional[str] = Field(None, alias="api_key") + pplx_api_key: Optional[str] = Field( + default_factory=from_env("PPLX_API_KEY", default=None), alias="api_key" + ) """Base URL path for API requests, leave blank if not using a proxy or service emulator.""" request_timeout: Optional[Union[float, Tuple[float, float]]] = Field( @@ -81,15 +87,17 @@ class ChatPerplexity(BaseChatModel): max_tokens: Optional[int] = None """Maximum number of tokens to generate.""" - class Config: - allow_population_by_field_name = True + model_config = ConfigDict( + populate_by_name=True, + ) @property def lc_secrets(self) -> Dict[str, str]: return {"pplx_api_key": "PPLX_API_KEY"} - @root_validator(pre=True) - def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def build_extra(cls, values: Dict[str, Any]) -> Any: """Build extra kwargs from additional params that were passed in.""" all_required_field_names = get_pydantic_field_names(cls) extra = values.get("model_kwargs", {}) @@ -114,12 +122,9 @@ def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: values["model_kwargs"] = extra return values - @root_validator(pre=False, skip_on_failure=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="after") + def validate_environment(self) -> Self: """Validate that api key and python package exists in environment.""" - values["pplx_api_key"] = get_from_dict_or_env( - values, "pplx_api_key", "PPLX_API_KEY" - ) try: import openai except ImportError: @@ -128,8 +133,8 @@ def validate_environment(cls, values: Dict) -> Dict: "Please install it with `pip install openai`." ) try: - values["client"] = openai.OpenAI( - api_key=values["pplx_api_key"], base_url="https://api.perplexity.ai" + self.client = openai.OpenAI( + api_key=self.pplx_api_key, base_url="https://api.perplexity.ai" ) except AttributeError: raise ValueError( @@ -137,7 +142,7 @@ def validate_environment(cls, values: Dict) -> Dict: "due to an old version of the openai package. Try upgrading it " "with `pip install --upgrade openai`." ) - return values + return self @property def _default_params(self) -> Dict[str, Any]: diff --git a/libs/community/langchain_community/chat_models/premai.py b/libs/community/langchain_community/chat_models/premai.py index 5498ac9e40016..60fba53350867 100644 --- a/libs/community/langchain_community/chat_models/premai.py +++ b/libs/community/langchain_community/chat_models/premai.py @@ -38,15 +38,16 @@ ToolMessage, ) from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult -from langchain_core.pydantic_v1 import ( - BaseModel, - Field, - SecretStr, -) from langchain_core.runnables import Runnable from langchain_core.tools import BaseTool from langchain_core.utils import get_from_dict_or_env, pre_init from langchain_core.utils.function_calling import convert_to_openai_tool +from pydantic import ( + BaseModel, + ConfigDict, + Field, + SecretStr, +) if TYPE_CHECKING: from premai.api.chat_completions.v1_chat_completions_create import ( @@ -306,10 +307,11 @@ class ChatPremAI(BaseChatModel, BaseModel): client: Any - class Config: - allow_population_by_field_name = True - arbitrary_types_allowed = True - extra = "forbid" + model_config = ConfigDict( + populate_by_name=True, + arbitrary_types_allowed=True, + extra="forbid", + ) @pre_init def validate_environments(cls, values: Dict) -> Dict: diff --git a/libs/community/langchain_community/chat_models/snowflake.py b/libs/community/langchain_community/chat_models/snowflake.py index f5af38afdd2a2..524838188303d 100644 --- a/libs/community/langchain_community/chat_models/snowflake.py +++ b/libs/community/langchain_community/chat_models/snowflake.py @@ -11,7 +11,6 @@ SystemMessage, ) from langchain_core.outputs import ChatGeneration, ChatResult -from langchain_core.pydantic_v1 import Field, SecretStr, root_validator from langchain_core.utils import ( convert_to_secret_str, get_from_dict_or_env, @@ -19,6 +18,7 @@ pre_init, ) from langchain_core.utils.utils import build_extra_kwargs +from pydantic import Field, SecretStr, model_validator SUPPORTED_ROLES: List[str] = [ "system", @@ -126,8 +126,9 @@ class ChatSnowflakeCortex(BaseChatModel): snowflake_role: Optional[str] = Field(default=None, alias="role") """Automatically inferred from env var `SNOWFLAKE_ROLE` if not provided.""" - @root_validator(pre=True) - def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def build_extra(cls, values: Dict[str, Any]) -> Any: """Build extra kwargs from additional params that were passed in.""" all_required_field_names = get_pydantic_field_names(cls) extra = values.get("model_kwargs", {}) diff --git a/libs/community/langchain_community/chat_models/solar.py b/libs/community/langchain_community/chat_models/solar.py index d8a3cb04c89df..10eba5da5a7a7 100644 --- a/libs/community/langchain_community/chat_models/solar.py +++ b/libs/community/langchain_community/chat_models/solar.py @@ -3,8 +3,8 @@ from typing import Dict from langchain_core._api import deprecated -from langchain_core.pydantic_v1 import Field from langchain_core.utils import get_from_dict_or_env, pre_init +from pydantic import ConfigDict, Field from langchain_community.chat_models import ChatOpenAI from langchain_community.llms.solar import SOLAR_SERVICE_URL_BASE, SolarCommon @@ -30,10 +30,11 @@ class SolarChat(SolarCommon, ChatOpenAI): max_tokens: int = Field(default=1024) # this is needed to match ChatOpenAI superclass - class Config: - allow_population_by_field_name = True - arbitrary_types_allowed = True - extra = "ignore" + model_config = ConfigDict( + populate_by_name=True, + arbitrary_types_allowed=True, + extra="ignore", + ) @pre_init def validate_environment(cls, values: Dict) -> Dict: diff --git a/libs/community/langchain_community/chat_models/sparkllm.py b/libs/community/langchain_community/chat_models/sparkllm.py index adfeed5517d1b..996c8c2a2f67f 100644 --- a/libs/community/langchain_community/chat_models/sparkllm.py +++ b/libs/community/langchain_community/chat_models/sparkllm.py @@ -41,12 +41,12 @@ ChatGenerationChunk, ChatResult, ) -from langchain_core.pydantic_v1 import Field, root_validator from langchain_core.utils import ( get_from_dict_or_env, get_pydantic_field_names, ) from langchain_core.utils.pydantic import get_fields +from pydantic import ConfigDict, Field, model_validator logger = logging.getLogger(__name__) @@ -296,11 +296,13 @@ def lc_secrets(self) -> Dict[str, str]: model_kwargs: Dict[str, Any] = Field(default_factory=dict) """Holds any model parameters valid for API call not explicitly specified.""" - class Config: - allow_population_by_field_name = True + model_config = ConfigDict( + populate_by_name=True, + ) - @root_validator(pre=True) - def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def build_extra(cls, values: Dict[str, Any]) -> Any: """Build extra kwargs from additional params that were passed in.""" all_required_field_names = get_pydantic_field_names(cls) extra = values.get("model_kwargs", {}) @@ -326,8 +328,9 @@ def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: return values - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: values["spark_app_id"] = get_from_dict_or_env( values, ["spark_app_id", "app_id"], diff --git a/libs/community/langchain_community/chat_models/symblai_nebula.py b/libs/community/langchain_community/chat_models/symblai_nebula.py index e3638bada8f24..adacc508ca987 100644 --- a/libs/community/langchain_community/chat_models/symblai_nebula.py +++ b/libs/community/langchain_community/chat_models/symblai_nebula.py @@ -16,8 +16,8 @@ ) from langchain_core.messages import AIMessage, AIMessageChunk, BaseMessage from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult -from langchain_core.pydantic_v1 import Field, SecretStr from langchain_core.utils import convert_to_secret_str +from pydantic import ConfigDict, Field, SecretStr def _convert_role(role: str) -> str: @@ -89,11 +89,10 @@ class ChatNebula(BaseChatModel): nebula_api_key: Optional[SecretStr] = Field(None, description="Nebula API Token") - class Config: - """Configuration for this pydantic object.""" - - allow_population_by_field_name = True - arbitrary_types_allowed = True + model_config = ConfigDict( + populate_by_name=True, + arbitrary_types_allowed=True, + ) def __init__(self, **kwargs: Any) -> None: if "nebula_api_key" in kwargs: diff --git a/libs/community/langchain_community/chat_models/tongyi.py b/libs/community/langchain_community/chat_models/tongyi.py index 2e36987840de3..6e0a2d59787dd 100644 --- a/libs/community/langchain_community/chat_models/tongyi.py +++ b/libs/community/langchain_community/chat_models/tongyi.py @@ -53,16 +53,17 @@ ChatGenerationChunk, ChatResult, ) -from langchain_core.pydantic_v1 import ( - BaseModel, - Field, - SecretStr, -) from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough from langchain_core.tools import BaseTool from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init from langchain_core.utils.function_calling import convert_to_openai_tool from langchain_core.utils.pydantic import is_basemodel_subclass +from pydantic import ( + BaseModel, + ConfigDict, + Field, + SecretStr, +) from requests.exceptions import HTTPError from tenacity import ( before_sleep_log, @@ -348,7 +349,7 @@ class ChatTongyi(BaseChatModel): Tool calling: .. code-block:: python - from langchain_core.pydantic_v1 import BaseModel, Field + from pydantic import BaseModel, Field class GetWeather(BaseModel): @@ -386,7 +387,7 @@ class GetPopulation(BaseModel): from typing import Optional - from langchain_core.pydantic_v1 import BaseModel, Field + from pydantic import BaseModel, Field class Joke(BaseModel): @@ -457,8 +458,9 @@ def lc_secrets(self) -> Dict[str, str]: max_retries: int = 10 """Maximum number of retries to make when generating.""" - class Config: - allow_population_by_field_name = True + model_config = ConfigDict( + populate_by_name=True, + ) @property def _llm_type(self) -> str: diff --git a/libs/community/langchain_community/chat_models/yi.py b/libs/community/langchain_community/chat_models/yi.py index d5da5a0f95672..cb5c8f445743e 100644 --- a/libs/community/langchain_community/chat_models/yi.py +++ b/libs/community/langchain_community/chat_models/yi.py @@ -25,12 +25,12 @@ SystemMessage, ) from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult -from langchain_core.pydantic_v1 import Field, SecretStr from langchain_core.utils import ( convert_to_secret_str, get_from_dict_or_env, get_pydantic_field_names, ) +from pydantic import ConfigDict, Field, SecretStr logger = logging.getLogger(__name__) @@ -115,8 +115,9 @@ def lc_serializable(self) -> bool: top_p: float = 0.7 model_kwargs: Dict[str, Any] = Field(default_factory=dict) - class Config: - allow_population_by_field_name = True + model_config = ConfigDict( + populate_by_name=True, + ) def __init__(self, **kwargs: Any) -> None: kwargs["yi_api_key"] = convert_to_secret_str( diff --git a/libs/community/langchain_community/chat_models/yuan2.py b/libs/community/langchain_community/chat_models/yuan2.py index dc63eb83d246c..9266dadcadc58 100644 --- a/libs/community/langchain_community/chat_models/yuan2.py +++ b/libs/community/langchain_community/chat_models/yuan2.py @@ -40,12 +40,12 @@ SystemMessageChunk, ) from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult -from langchain_core.pydantic_v1 import BaseModel, Field, root_validator from langchain_core.utils import ( get_from_dict_or_env, get_pydantic_field_names, pre_init, ) +from pydantic import BaseModel, ConfigDict, Field, model_validator from tenacity import ( before_sleep_log, retry, @@ -122,8 +122,9 @@ class ChatYuan2(BaseChatModel): repeat_penalty: Optional[float] = 1.18 """The penalty to apply to repeated tokens.""" - class Config: - allow_population_by_field_name = True + model_config = ConfigDict( + populate_by_name=True, + ) @property def lc_secrets(self) -> Dict[str, str]: @@ -141,8 +142,9 @@ def lc_attributes(self) -> Dict[str, Any]: return attributes - @root_validator(pre=True) - def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def build_extra(cls, values: Dict[str, Any]) -> Any: """Build extra kwargs from additional params that were passed in.""" all_required_field_names = get_pydantic_field_names(cls) extra = values.get("model_kwargs", {}) diff --git a/libs/community/langchain_community/chat_models/zhipuai.py b/libs/community/langchain_community/chat_models/zhipuai.py index 8093e3cc52157..63994b3a6e1b8 100644 --- a/libs/community/langchain_community/chat_models/zhipuai.py +++ b/libs/community/langchain_community/chat_models/zhipuai.py @@ -50,11 +50,11 @@ PydanticToolsParser, ) from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult -from langchain_core.pydantic_v1 import BaseModel, Field, root_validator from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough from langchain_core.tools import BaseTool from langchain_core.utils import get_from_dict_or_env from langchain_core.utils.function_calling import convert_to_openai_tool +from pydantic import BaseModel, ConfigDict, Field, model_validator logger = logging.getLogger(__name__) @@ -331,7 +331,7 @@ class ChatZhipuAI(BaseChatModel): Tool calling: .. code-block:: python - from langchain_core.pydantic_v1 import BaseModel, Field + from pydantic import BaseModel, Field class GetWeather(BaseModel): @@ -371,7 +371,7 @@ class GetPopulation(BaseModel): from typing import Optional - from langchain_core.pydantic_v1 import BaseModel, Field + from pydantic import BaseModel, Field class Joke(BaseModel): @@ -480,11 +480,13 @@ def _default_params(self) -> Dict[str, Any]: max_tokens: Optional[int] = None """Maximum number of tokens to generate.""" - class Config: - allow_population_by_field_name = True + model_config = ConfigDict( + populate_by_name=True, + ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict[str, Any]) -> Any: values["zhipuai_api_key"] = get_from_dict_or_env( values, ["zhipuai_api_key", "api_key"], "ZHIPUAI_API_KEY" ) @@ -773,7 +775,7 @@ def with_structured_output( .. code-block:: python from langchain_community.chat_models import ChatZhipuAI - from langchain_core.pydantic_v1 import BaseModel + from pydantic import BaseModel class AnswerWithJustification(BaseModel): '''An answer to the user question along with justification for the answer.''' @@ -793,7 +795,7 @@ class AnswerWithJustification(BaseModel): .. code-block:: python from langchain_community.chat_models import ChatZhipuAI - from langchain_core.pydantic_v1 import BaseModel + from pydantic import BaseModel class AnswerWithJustification(BaseModel): '''An answer to the user question along with justification for the answer.''' @@ -814,7 +816,7 @@ class AnswerWithJustification(BaseModel): .. code-block:: python from langchain_community.chat_models import ChatZhipuAI - from langchain_core.pydantic_v1 import BaseModel + from pydantic import BaseModel from langchain_core.utils.function_calling import convert_to_openai_tool class AnswerWithJustification(BaseModel): diff --git a/libs/community/langchain_community/cross_encoders/fake.py b/libs/community/langchain_community/cross_encoders/fake.py index 91e1f702d8c8a..5c38175e50f4e 100644 --- a/libs/community/langchain_community/cross_encoders/fake.py +++ b/libs/community/langchain_community/cross_encoders/fake.py @@ -1,7 +1,7 @@ from difflib import SequenceMatcher from typing import List, Tuple -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel from langchain_community.cross_encoders.base import BaseCrossEncoder diff --git a/libs/community/langchain_community/cross_encoders/huggingface.py b/libs/community/langchain_community/cross_encoders/huggingface.py index 8d51d47cf940b..fa10f228d4cd1 100644 --- a/libs/community/langchain_community/cross_encoders/huggingface.py +++ b/libs/community/langchain_community/cross_encoders/huggingface.py @@ -1,6 +1,6 @@ from typing import Any, Dict, List, Tuple -from langchain_core.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, ConfigDict, Field from langchain_community.cross_encoders.base import BaseCrossEncoder @@ -45,8 +45,9 @@ def __init__(self, **kwargs: Any): self.model_name, **self.model_kwargs ) - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) def score(self, text_pairs: List[Tuple[str, str]]) -> List[float]: """Compute similarity scores using a HuggingFace transformer model. diff --git a/libs/community/langchain_community/cross_encoders/sagemaker_endpoint.py b/libs/community/langchain_community/cross_encoders/sagemaker_endpoint.py index 8da86cf6ddfdb..b809095532d30 100644 --- a/libs/community/langchain_community/cross_encoders/sagemaker_endpoint.py +++ b/libs/community/langchain_community/cross_encoders/sagemaker_endpoint.py @@ -1,7 +1,7 @@ import json from typing import Any, Dict, List, Optional, Tuple -from langchain_core.pydantic_v1 import BaseModel, root_validator +from pydantic import BaseModel, ConfigDict, model_validator from langchain_community.cross_encoders.base import BaseCrossEncoder @@ -89,12 +89,14 @@ class SagemakerEndpointCrossEncoder(BaseModel, BaseCrossEncoder): .. _boto3: """ - class Config: - arbitrary_types_allowed = True - extra = "forbid" + model_config = ConfigDict( + arbitrary_types_allowed=True, + extra="forbid", + ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that AWS credentials to and python package exists in environment.""" try: import boto3 diff --git a/libs/community/langchain_community/document_compressors/dashscope_rerank.py b/libs/community/langchain_community/document_compressors/dashscope_rerank.py index 702d0b3e8bad3..6e298c2c30989 100644 --- a/libs/community/langchain_community/document_compressors/dashscope_rerank.py +++ b/libs/community/langchain_community/document_compressors/dashscope_rerank.py @@ -5,8 +5,8 @@ from langchain_core.callbacks.base import Callbacks from langchain_core.documents import BaseDocumentCompressor, Document -from langchain_core.pydantic_v1 import Field, root_validator from langchain_core.utils import get_from_dict_or_env +from pydantic import ConfigDict, Field, model_validator class DashScopeRerank(BaseDocumentCompressor): @@ -25,13 +25,15 @@ class DashScopeRerank(BaseDocumentCompressor): """DashScope API key. Must be specified directly or via environment variable DASHSCOPE_API_KEY.""" - class Config: - allow_population_by_field_name = True - arbitrary_types_allowed = True - extra = "forbid" + model_config = ConfigDict( + populate_by_name=True, + arbitrary_types_allowed=True, + extra="forbid", + ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key and python package exists in environment.""" if not values.get("client"): diff --git a/libs/community/langchain_community/document_compressors/flashrank_rerank.py b/libs/community/langchain_community/document_compressors/flashrank_rerank.py index c21e17f8e639b..3b8e116fd72ac 100644 --- a/libs/community/langchain_community/document_compressors/flashrank_rerank.py +++ b/libs/community/langchain_community/document_compressors/flashrank_rerank.py @@ -1,10 +1,10 @@ from __future__ import annotations -from typing import TYPE_CHECKING, Dict, Optional, Sequence +from typing import TYPE_CHECKING, Any, Dict, Optional, Sequence from langchain_core.callbacks.manager import Callbacks from langchain_core.documents import BaseDocumentCompressor, Document -from langchain_core.pydantic_v1 import root_validator +from pydantic import ConfigDict, model_validator if TYPE_CHECKING: from flashrank import Ranker, RerankRequest @@ -33,12 +33,14 @@ class FlashrankRerank(BaseDocumentCompressor): prefix_metadata: str = "" """Prefix for flashrank_rerank metadata keys""" - class Config: - arbitrary_types_allowed = True - extra = "forbid" + model_config = ConfigDict( + arbitrary_types_allowed=True, + extra="forbid", + ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key and python package exists in environment.""" if "client" in values: return values diff --git a/libs/community/langchain_community/document_compressors/jina_rerank.py b/libs/community/langchain_community/document_compressors/jina_rerank.py index 60922334e872f..0a769b311e54e 100644 --- a/libs/community/langchain_community/document_compressors/jina_rerank.py +++ b/libs/community/langchain_community/document_compressors/jina_rerank.py @@ -6,8 +6,8 @@ import requests from langchain_core.callbacks import Callbacks from langchain_core.documents import BaseDocumentCompressor, Document -from langchain_core.pydantic_v1 import root_validator from langchain_core.utils import get_from_dict_or_env +from pydantic import ConfigDict, model_validator JINA_API_URL: str = "https://api.jina.ai/v1/rerank" @@ -27,12 +27,14 @@ class JinaRerank(BaseDocumentCompressor): user_agent: str = "langchain" """Identifier for the application making the request.""" - class Config: - arbitrary_types_allowed = True - extra = "forbid" + model_config = ConfigDict( + arbitrary_types_allowed=True, + extra="forbid", + ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key exists in environment.""" jina_api_key = get_from_dict_or_env(values, "jina_api_key", "JINA_API_KEY") user_agent = values.get("user_agent", "langchain") diff --git a/libs/community/langchain_community/document_compressors/llmlingua_filter.py b/libs/community/langchain_community/document_compressors/llmlingua_filter.py index dae183922462f..9e05c9318eaa5 100644 --- a/libs/community/langchain_community/document_compressors/llmlingua_filter.py +++ b/libs/community/langchain_community/document_compressors/llmlingua_filter.py @@ -8,7 +8,7 @@ from langchain_core.documents.compressor import ( BaseDocumentCompressor, ) -from langchain_core.pydantic_v1 import root_validator +from pydantic import ConfigDict, model_validator DEFAULT_LLM_LINGUA_INSTRUCTION = ( "Given this documents, please answer the final question" @@ -52,8 +52,9 @@ class LLMLinguaCompressor(BaseDocumentCompressor): lingua: Any """The instance of the llm linqua""" - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that the python package exists in environment.""" try: from llmlingua import PromptCompressor @@ -71,9 +72,10 @@ def validate_environment(cls, values: Dict) -> Dict: ) return values - class Config: - arbitrary_types_allowed = True - extra = "forbid" + model_config = ConfigDict( + arbitrary_types_allowed=True, + extra="forbid", + ) @staticmethod def _format_context(docs: Sequence[Document]) -> List[str]: diff --git a/libs/community/langchain_community/document_compressors/openvino_rerank.py b/libs/community/langchain_community/document_compressors/openvino_rerank.py index 24fb92673010d..9257a88f1890c 100644 --- a/libs/community/langchain_community/document_compressors/openvino_rerank.py +++ b/libs/community/langchain_community/document_compressors/openvino_rerank.py @@ -5,7 +5,7 @@ from langchain_core.callbacks import Callbacks from langchain_core.documents import Document from langchain_core.documents.compressor import BaseDocumentCompressor -from langchain_core.pydantic_v1 import Field +from pydantic import Field class RerankRequest: diff --git a/libs/community/langchain_community/document_compressors/rankllm_rerank.py b/libs/community/langchain_community/document_compressors/rankllm_rerank.py index 7e461ea878468..bcf18652928e2 100644 --- a/libs/community/langchain_community/document_compressors/rankllm_rerank.py +++ b/libs/community/langchain_community/document_compressors/rankllm_rerank.py @@ -7,8 +7,8 @@ from langchain.retrievers.document_compressors.base import BaseDocumentCompressor from langchain_core.callbacks.manager import Callbacks from langchain_core.documents import Document -from langchain_core.pydantic_v1 import Field, PrivateAttr, root_validator from langchain_core.utils import get_from_dict_or_env +from pydantic import ConfigDict, Field, PrivateAttr, model_validator if TYPE_CHECKING: from rank_llm.data import Candidate, Query, Request @@ -36,12 +36,14 @@ class RankLLMRerank(BaseDocumentCompressor): """OpenAI model name.""" _retriever: Any = PrivateAttr() - class Config: - arbitrary_types_allowed = True - extra = "forbid" + model_config = ConfigDict( + arbitrary_types_allowed=True, + extra="forbid", + ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate python package exists in environment.""" if not values.get("client"): diff --git a/libs/community/langchain_community/document_compressors/volcengine_rerank.py b/libs/community/langchain_community/document_compressors/volcengine_rerank.py index 9e516f7391575..e7b0cab2c1a8c 100644 --- a/libs/community/langchain_community/document_compressors/volcengine_rerank.py +++ b/libs/community/langchain_community/document_compressors/volcengine_rerank.py @@ -5,8 +5,8 @@ from langchain_core.callbacks.base import Callbacks from langchain_core.documents import BaseDocumentCompressor, Document -from langchain_core.pydantic_v1 import root_validator from langchain_core.utils import get_from_dict_or_env +from pydantic import ConfigDict, model_validator class VolcengineRerank(BaseDocumentCompressor): @@ -32,13 +32,15 @@ class VolcengineRerank(BaseDocumentCompressor): top_n: Optional[int] = 3 """Number of documents to return.""" - class Config: - allow_population_by_field_name = True - arbitrary_types_allowed = True - extra = "forbid" + model_config = ConfigDict( + populate_by_name=True, + arbitrary_types_allowed=True, + extra="forbid", + ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key and python package exists in environment.""" if not values.get("client"): diff --git a/libs/community/langchain_community/document_loaders/apify_dataset.py b/libs/community/langchain_community/document_loaders/apify_dataset.py index 339a25c1ede55..7148f1084c914 100644 --- a/libs/community/langchain_community/document_loaders/apify_dataset.py +++ b/libs/community/langchain_community/document_loaders/apify_dataset.py @@ -1,7 +1,7 @@ from typing import Any, Callable, Dict, List from langchain_core.documents import Document -from langchain_core.pydantic_v1 import BaseModel, root_validator +from pydantic import BaseModel, model_validator from langchain_community.document_loaders.base import BaseLoader @@ -49,8 +49,9 @@ def __init__( dataset_id=dataset_id, dataset_mapping_function=dataset_mapping_function ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate environment. Args: diff --git a/libs/community/langchain_community/document_loaders/base_o365.py b/libs/community/langchain_community/document_loaders/base_o365.py index 98c1188cf1760..f49d518981f8c 100644 --- a/libs/community/langchain_community/document_loaders/base_o365.py +++ b/libs/community/langchain_community/document_loaders/base_o365.py @@ -10,13 +10,13 @@ from pathlib import Path, PurePath from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Sequence, Union -from langchain_core.pydantic_v1 import ( +from pydantic import ( BaseModel, - BaseSettings, Field, FilePath, SecretStr, ) +from pydantic_settings import BaseSettings, SettingsConfigDict from langchain_community.document_loaders.base import BaseLoader from langchain_community.document_loaders.blob_loaders.file_system import ( @@ -37,10 +37,9 @@ class _O365Settings(BaseSettings): client_id: str = Field(..., env="O365_CLIENT_ID") client_secret: SecretStr = Field(..., env="O365_CLIENT_SECRET") - class Config: - case_sentive = False - env_file = ".env" - env_prefix = "" + model_config = SettingsConfigDict( + case_sentive=False, env_file=".env", env_prefix="" + ) class _O365TokenStorage(BaseSettings): diff --git a/libs/community/langchain_community/document_loaders/docugami.py b/libs/community/langchain_community/document_loaders/docugami.py index c3463a009bbb2..38c078351ba90 100644 --- a/libs/community/langchain_community/document_loaders/docugami.py +++ b/libs/community/langchain_community/document_loaders/docugami.py @@ -8,7 +8,7 @@ import requests from langchain_core._api.deprecation import deprecated from langchain_core.documents import Document -from langchain_core.pydantic_v1 import BaseModel, root_validator +from pydantic import BaseModel, model_validator from langchain_community.document_loaders.base import BaseLoader @@ -69,10 +69,10 @@ class DocugamiLoader(BaseLoader, BaseModel): """Set to False if you want to full whitespace formatting in the original XML doc, including indentation.""" - docset_id: Optional[str] + docset_id: Optional[str] = None """The Docugami API docset ID to use.""" - document_ids: Optional[Sequence[str]] + document_ids: Optional[Sequence[str]] = None """The Docugami API document IDs to use.""" file_paths: Optional[Sequence[Union[Path, str]]] @@ -81,8 +81,9 @@ class DocugamiLoader(BaseLoader, BaseModel): include_project_metadata_in_doc_metadata: bool = True """Set to True if you want to include the project metadata in the doc metadata.""" - @root_validator(pre=True) - def validate_local_or_remote(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def validate_local_or_remote(cls, values: Dict[str, Any]) -> Any: """Validate that either local file paths are given, or remote API docset ID. Args: diff --git a/libs/community/langchain_community/document_loaders/dropbox.py b/libs/community/langchain_community/document_loaders/dropbox.py index f2a7b603cefc3..c689e3a455633 100644 --- a/libs/community/langchain_community/document_loaders/dropbox.py +++ b/libs/community/langchain_community/document_loaders/dropbox.py @@ -12,7 +12,7 @@ from typing import Any, Dict, List, Optional from langchain_core.documents import Document -from langchain_core.pydantic_v1 import BaseModel, root_validator +from pydantic import BaseModel, model_validator from langchain_community.document_loaders.base import BaseLoader @@ -33,8 +33,9 @@ class DropboxLoader(BaseLoader, BaseModel): recursive: bool = False """Flag to indicate whether to load files recursively from subfolders.""" - @root_validator(pre=True) - def validate_inputs(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def validate_inputs(cls, values: Dict[str, Any]) -> Any: """Validate that either folder_path or file_paths is set, but not both.""" if ( values.get("dropbox_folder_path") is not None diff --git a/libs/community/langchain_community/document_loaders/github.py b/libs/community/langchain_community/document_loaders/github.py index df02a7894fbad..a92d854b7ea21 100644 --- a/libs/community/langchain_community/document_loaders/github.py +++ b/libs/community/langchain_community/document_loaders/github.py @@ -1,12 +1,12 @@ import base64 from abc import ABC from datetime import datetime -from typing import Callable, Dict, Iterator, List, Literal, Optional, Union +from typing import Any, Callable, Dict, Iterator, List, Literal, Optional, Union import requests from langchain_core.documents import Document -from langchain_core.pydantic_v1 import BaseModel, root_validator, validator from langchain_core.utils import get_from_dict_or_env +from pydantic import BaseModel, model_validator, validator from langchain_community.document_loaders.base import BaseLoader @@ -21,8 +21,9 @@ class BaseGitHubLoader(BaseLoader, BaseModel, ABC): github_api_url: str = "https://api.github.com" """URL of GitHub API""" - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that access token exists in environment.""" values["access_token"] = get_from_dict_or_env( values, "access_token", "GITHUB_PERSONAL_ACCESS_TOKEN" diff --git a/libs/community/langchain_community/document_loaders/googledrive.py b/libs/community/langchain_community/document_loaders/googledrive.py index 5646950a79064..ea525c10f17e6 100644 --- a/libs/community/langchain_community/document_loaders/googledrive.py +++ b/libs/community/langchain_community/document_loaders/googledrive.py @@ -12,7 +12,7 @@ from langchain_core._api.deprecation import deprecated from langchain_core.documents import Document -from langchain_core.pydantic_v1 import BaseModel, root_validator, validator +from pydantic import BaseModel, model_validator, validator from langchain_community.document_loaders.base import BaseLoader @@ -52,8 +52,9 @@ class GoogleDriveLoader(BaseLoader, BaseModel): file_loader_kwargs: Dict["str", Any] = {} """The file loader kwargs to use.""" - @root_validator(pre=True) - def validate_inputs(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def validate_inputs(cls, values: Dict[str, Any]) -> Any: """Validate that either folder_id or document_ids is set, but not both.""" if values.get("folder_id") and ( values.get("document_ids") or values.get("file_ids") diff --git a/libs/community/langchain_community/document_loaders/onedrive.py b/libs/community/langchain_community/document_loaders/onedrive.py index d33777dd6a081..ecc1d232bfe4c 100644 --- a/libs/community/langchain_community/document_loaders/onedrive.py +++ b/libs/community/langchain_community/document_loaders/onedrive.py @@ -6,7 +6,7 @@ from typing import TYPE_CHECKING, Iterator, List, Optional, Sequence, Union from langchain_core.documents import Document -from langchain_core.pydantic_v1 import Field +from pydantic import Field from langchain_community.document_loaders.base_o365 import ( O365BaseLoader, diff --git a/libs/community/langchain_community/document_loaders/onedrive_file.py b/libs/community/langchain_community/document_loaders/onedrive_file.py index fcb0c7c58f4de..d92cfdea91777 100644 --- a/libs/community/langchain_community/document_loaders/onedrive_file.py +++ b/libs/community/langchain_community/document_loaders/onedrive_file.py @@ -4,7 +4,7 @@ from typing import TYPE_CHECKING, List from langchain_core.documents import Document -from langchain_core.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, ConfigDict, Field from langchain_community.document_loaders.base import BaseLoader from langchain_community.document_loaders.unstructured import UnstructuredFileLoader @@ -21,8 +21,9 @@ class OneDriveFileLoader(BaseLoader, BaseModel): file: File = Field(...) """The file to load.""" - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) def load(self) -> List[Document]: """Load Documents""" diff --git a/libs/community/langchain_community/document_loaders/onenote.py b/libs/community/langchain_community/document_loaders/onenote.py index 13ba4a831bd71..747d9432005be 100644 --- a/libs/community/langchain_community/document_loaders/onenote.py +++ b/libs/community/langchain_community/document_loaders/onenote.py @@ -5,13 +5,13 @@ import requests from langchain_core.documents import Document -from langchain_core.pydantic_v1 import ( +from pydantic import ( BaseModel, - BaseSettings, Field, FilePath, SecretStr, ) +from pydantic_settings import BaseSettings from langchain_community.document_loaders.base import BaseLoader @@ -21,7 +21,7 @@ class _OneNoteGraphSettings(BaseSettings): client_secret: SecretStr = Field(..., env="MS_GRAPH_CLIENT_SECRET") class Config: - case_sentive = False + case_sensitive = False env_file = ".env" env_prefix = "" diff --git a/libs/community/langchain_community/document_loaders/sharepoint.py b/libs/community/langchain_community/document_loaders/sharepoint.py index e589a58447c4d..06426a7038fdd 100644 --- a/libs/community/langchain_community/document_loaders/sharepoint.py +++ b/libs/community/langchain_community/document_loaders/sharepoint.py @@ -9,7 +9,7 @@ import requests # type: ignore from langchain_core.document_loaders import BaseLoader from langchain_core.documents import Document -from langchain_core.pydantic_v1 import Field +from pydantic import Field from langchain_community.document_loaders.base_o365 import ( O365BaseLoader, diff --git a/libs/community/langchain_community/document_loaders/youtube.py b/libs/community/langchain_community/document_loaders/youtube.py index bfe1cb0b1391e..67c1569adf8d6 100644 --- a/libs/community/langchain_community/document_loaders/youtube.py +++ b/libs/community/langchain_community/document_loaders/youtube.py @@ -10,8 +10,8 @@ from xml.etree.ElementTree import ParseError # OK: trusted-source from langchain_core.documents import Document -from langchain_core.pydantic_v1 import root_validator -from langchain_core.pydantic_v1.dataclasses import dataclass +from pydantic import model_validator +from pydantic.dataclasses import dataclass from langchain_community.document_loaders.base import BaseLoader @@ -50,10 +50,9 @@ class GoogleApiClient: def __post_init__(self) -> None: self.creds = self._load_credentials() - @root_validator(pre=True) - def validate_channel_or_videoIds_is_set( - cls, values: Dict[str, Any] - ) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def validate_channel_or_videoIds_is_set(cls, values: Dict[str, Any]) -> Any: """Validate that either folder_id or document_ids is set, but not both.""" if not values.get("credentials_path") and not values.get( @@ -391,10 +390,9 @@ def _build_youtube_client(self, creds: Any) -> Any: return build("youtube", "v3", credentials=creds) - @root_validator(pre=True) - def validate_channel_or_videoIds_is_set( - cls, values: Dict[str, Any] - ) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def validate_channel_or_videoIds_is_set(cls, values: Dict[str, Any]) -> Any: """Validate that either folder_id or document_ids is set, but not both.""" if not values.get("channel_name") and not values.get("video_ids"): raise ValueError("Must specify either channel_name or video_ids") diff --git a/libs/community/langchain_community/document_transformers/embeddings_redundant_filter.py b/libs/community/langchain_community/document_transformers/embeddings_redundant_filter.py index 1675ad331730a..f46c55ae3656d 100644 --- a/libs/community/langchain_community/document_transformers/embeddings_redundant_filter.py +++ b/libs/community/langchain_community/document_transformers/embeddings_redundant_filter.py @@ -5,7 +5,7 @@ import numpy as np from langchain_core.documents import BaseDocumentTransformer, Document from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, ConfigDict, Field from langchain_community.utils.math import cosine_similarity @@ -153,8 +153,9 @@ class EmbeddingsRedundantFilter(BaseDocumentTransformer, BaseModel): """Threshold for determining when two documents are similar enough to be considered redundant.""" - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) def transform_documents( self, documents: Sequence[Document], **kwargs: Any @@ -201,8 +202,9 @@ class EmbeddingsClusteringFilter(BaseDocumentTransformer, BaseModel): clusters. """ - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) def transform_documents( self, documents: Sequence[Document], **kwargs: Any diff --git a/libs/community/langchain_community/document_transformers/long_context_reorder.py b/libs/community/langchain_community/document_transformers/long_context_reorder.py index 729138fa46762..2884b63f098cb 100644 --- a/libs/community/langchain_community/document_transformers/long_context_reorder.py +++ b/libs/community/langchain_community/document_transformers/long_context_reorder.py @@ -3,7 +3,7 @@ from typing import Any, List, Sequence from langchain_core.documents import BaseDocumentTransformer, Document -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel, ConfigDict def _litm_reordering(documents: List[Document]) -> List[Document]: @@ -29,8 +29,9 @@ class LongContextReorder(BaseDocumentTransformer, BaseModel): in the middle of long contexts. See: https://arxiv.org/abs//2307.03172""" - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) def transform_documents( self, documents: Sequence[Document], **kwargs: Any diff --git a/libs/community/langchain_community/document_transformers/openai_functions.py b/libs/community/langchain_community/document_transformers/openai_functions.py index 18415ab242271..7ba087a3e1315 100644 --- a/libs/community/langchain_community/document_transformers/openai_functions.py +++ b/libs/community/langchain_community/document_transformers/openai_functions.py @@ -5,7 +5,7 @@ from langchain_core.documents import BaseDocumentTransformer, Document from langchain_core.language_models import BaseLanguageModel from langchain_core.prompts import ChatPromptTemplate -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel class OpenAIMetadataTagger(BaseDocumentTransformer, BaseModel): diff --git a/libs/community/langchain_community/embeddings/aleph_alpha.py b/libs/community/langchain_community/embeddings/aleph_alpha.py index 409373d8bff8e..96426fdac8a91 100644 --- a/libs/community/langchain_community/embeddings/aleph_alpha.py +++ b/libs/community/langchain_community/embeddings/aleph_alpha.py @@ -1,8 +1,8 @@ from typing import Any, Dict, List, Optional from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel, root_validator from langchain_core.utils import get_from_dict_or_env +from pydantic import BaseModel, model_validator class AlephAlphaAsymmetricSemanticEmbedding(BaseModel, Embeddings): @@ -80,8 +80,9 @@ class AlephAlphaAsymmetricSemanticEmbedding(BaseModel, Embeddings): nice to other users by de-prioritizing your request below concurrent ones.""" - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key and python package exists in environment.""" aleph_alpha_api_key = get_from_dict_or_env( values, "aleph_alpha_api_key", "ALEPH_ALPHA_API_KEY" diff --git a/libs/community/langchain_community/embeddings/anyscale.py b/libs/community/langchain_community/embeddings/anyscale.py index 831946a4da5a5..76ba0132a1ec6 100644 --- a/libs/community/langchain_community/embeddings/anyscale.py +++ b/libs/community/langchain_community/embeddings/anyscale.py @@ -4,8 +4,8 @@ from typing import Dict -from langchain_core.pydantic_v1 import Field, SecretStr from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init +from pydantic import Field, SecretStr from langchain_community.embeddings.openai import OpenAIEmbeddings from langchain_community.utils.openai import is_openai_v1 diff --git a/libs/community/langchain_community/embeddings/ascend.py b/libs/community/langchain_community/embeddings/ascend.py index 2b94f4ff668ff..dd8c9c67fd94c 100644 --- a/libs/community/langchain_community/embeddings/ascend.py +++ b/libs/community/langchain_community/embeddings/ascend.py @@ -2,7 +2,7 @@ from typing import Any, Dict, List, Optional from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel, root_validator +from pydantic import BaseModel, model_validator class AscendEmbeddings(Embeddings, BaseModel): @@ -54,8 +54,9 @@ def __init__(self, *args: Any, **kwargs: Any) -> None: self.model.half() self.encode([f"warmup {i} times" for i in range(10)]) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: if "model_path" not in values: raise ValueError("model_path is required") if not os.access(values["model_path"], os.F_OK): diff --git a/libs/community/langchain_community/embeddings/awa.py b/libs/community/langchain_community/embeddings/awa.py index 6f09b2d064c04..27cb422423fde 100644 --- a/libs/community/langchain_community/embeddings/awa.py +++ b/libs/community/langchain_community/embeddings/awa.py @@ -1,7 +1,7 @@ from typing import Any, Dict, List from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel, root_validator +from pydantic import BaseModel, model_validator class AwaEmbeddings(BaseModel, Embeddings): @@ -16,8 +16,9 @@ class AwaEmbeddings(BaseModel, Embeddings): client: Any #: :meta private: model: str = "all-mpnet-base-v2" - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that awadb library is installed.""" try: diff --git a/libs/community/langchain_community/embeddings/azure_openai.py b/libs/community/langchain_community/embeddings/azure_openai.py index cf06a795d0377..f760d796feb93 100644 --- a/libs/community/langchain_community/embeddings/azure_openai.py +++ b/libs/community/langchain_community/embeddings/azure_openai.py @@ -4,11 +4,12 @@ import os import warnings -from typing import Callable, Dict, Optional, Union +from typing import Any, Callable, Dict, Optional, Union from langchain_core._api.deprecation import deprecated -from langchain_core.pydantic_v1 import Field, root_validator from langchain_core.utils import get_from_dict_or_env +from pydantic import Field, model_validator +from typing_extensions import Self from langchain_community.embeddings.openai import OpenAIEmbeddings from langchain_community.utils.openai import is_openai_v1 @@ -54,8 +55,9 @@ class AzureOpenAIEmbeddings(OpenAIEmbeddings): """Automatically inferred from env var `OPENAI_API_VERSION` if not provided.""" validate_base_url: bool = True - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key and python package exists in environment.""" # Check OPENAI_KEY for backwards compatibility. # TODO: Remove OPENAI_API_KEY support to avoid possible conflict when using @@ -138,32 +140,32 @@ def validate_environment(cls, values: Dict) -> Dict: values["deployment"] = None return values - @root_validator(pre=False, skip_on_failure=True) - def post_init_validator(cls, values: Dict) -> Dict: + @model_validator(mode="after") + def post_init_validator(self) -> Self: """Validate that the base url is set.""" import openai if is_openai_v1(): client_params = { - "api_version": values["openai_api_version"], - "azure_endpoint": values["azure_endpoint"], - "azure_deployment": values["deployment"], - "api_key": values["openai_api_key"], - "azure_ad_token": values["azure_ad_token"], - "azure_ad_token_provider": values["azure_ad_token_provider"], - "organization": values["openai_organization"], - "base_url": values["openai_api_base"], - "timeout": values["request_timeout"], - "max_retries": values["max_retries"], - "default_headers": values["default_headers"], - "default_query": values["default_query"], - "http_client": values["http_client"], + "api_version": self.openai_api_version, + "azure_endpoint": self.azure_endpoint, + "azure_deployment": self.deployment, + "api_key": self.openai_api_key, + "azure_ad_token": self.azure_ad_token, + "azure_ad_token_provider": self.azure_ad_token_provider, + "organization": self.openai_organization, + "base_url": self.openai_api_base, + "timeout": self.request_timeout, + "max_retries": self.max_retries, + "default_headers": self.default_headers, + "default_query": self.default_query, + "http_client": self.http_client, } - values["client"] = openai.AzureOpenAI(**client_params).embeddings - values["async_client"] = openai.AsyncAzureOpenAI(**client_params).embeddings + self.client = openai.AzureOpenAI(**client_params).embeddings + self.async_client = openai.AsyncAzureOpenAI(**client_params).embeddings else: - values["client"] = openai.Embedding - return values + self.client = openai.Embedding + return self @property def _llm_type(self) -> str: diff --git a/libs/community/langchain_community/embeddings/baichuan.py b/libs/community/langchain_community/embeddings/baichuan.py index fe81993000208..f6bce4956e890 100644 --- a/libs/community/langchain_community/embeddings/baichuan.py +++ b/libs/community/langchain_community/embeddings/baichuan.py @@ -1,14 +1,19 @@ -from typing import Any, Dict, List, Optional +from typing import Any, List, Optional import requests from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr, root_validator from langchain_core.utils import ( - convert_to_secret_str, - get_from_dict_or_env, secret_from_env, ) +from pydantic import ( + BaseModel, + ConfigDict, + Field, + SecretStr, + model_validator, +) from requests import RequestException +from typing_extensions import Self BAICHUAN_API_URL: str = "http://api.baichuan-ai.com/v1/embeddings" @@ -59,40 +64,29 @@ class BaichuanTextEmbeddings(BaseModel, Embeddings): """The model used to embed the documents.""" baichuan_api_key: Optional[SecretStr] = Field( alias="api_key", - default_factory=secret_from_env("BAICHUAN_API_KEY", default=None), + default_factory=secret_from_env(["BAICHUAN_API_KEY", "BAICHUAN_AUTH_TOKEN"]), ) """Automatically inferred from env var `BAICHUAN_API_KEY` if not provided.""" chunk_size: int = 16 """Chunk size when multiple texts are input""" - class Config: - allow_population_by_field_name = True + model_config = ConfigDict( + populate_by_name=True, + ) - @root_validator(pre=False, skip_on_failure=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="after") + def validate_environment(self) -> Self: """Validate that auth token exists in environment.""" - if values["baichuan_api_key"] is None: - # This is likely here for some backwards compatibility with - # BAICHUAN_AUTH_TOKEN - baichuan_api_key = convert_to_secret_str( - get_from_dict_or_env( - values, "baichuan_auth_token", "BAICHUAN_AUTH_TOKEN" - ) - ) - values["baichuan_api_key"] = baichuan_api_key - else: - baichuan_api_key = values["baichuan_api_key"] - session = requests.Session() session.headers.update( { - "Authorization": f"Bearer {baichuan_api_key.get_secret_value()}", + "Authorization": f"Bearer {self.baichuan_api_key.get_secret_value()}", "Accept-Encoding": "identity", "Content-type": "application/json", } ) - values["session"] = session - return values + self.session = session + return self def _embed(self, texts: List[str]) -> Optional[List[List[float]]]: """Internal method to call Baichuan Embedding API and return embeddings. diff --git a/libs/community/langchain_community/embeddings/baidu_qianfan_endpoint.py b/libs/community/langchain_community/embeddings/baidu_qianfan_endpoint.py index 6aa9df92364a5..981378eb19f3c 100644 --- a/libs/community/langchain_community/embeddings/baidu_qianfan_endpoint.py +++ b/libs/community/langchain_community/embeddings/baidu_qianfan_endpoint.py @@ -4,8 +4,8 @@ from typing import Any, Dict, List, Optional from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init +from pydantic import BaseModel, Field, SecretStr logger = logging.getLogger(__name__) diff --git a/libs/community/langchain_community/embeddings/bedrock.py b/libs/community/langchain_community/embeddings/bedrock.py index 70d4f41191449..671073e743fd4 100644 --- a/libs/community/langchain_community/embeddings/bedrock.py +++ b/libs/community/langchain_community/embeddings/bedrock.py @@ -6,8 +6,9 @@ import numpy as np from langchain_core._api.deprecation import deprecated from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel, root_validator from langchain_core.runnables.config import run_in_executor +from pydantic import BaseModel, ConfigDict, model_validator +from typing_extensions import Self @deprecated( @@ -74,33 +75,34 @@ class BedrockEmbeddings(BaseModel, Embeddings): normalize: bool = False """Whether the embeddings should be normalized to unit vectors""" - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) - @root_validator(pre=False, skip_on_failure=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="after") + def validate_environment(self) -> Self: """Validate that AWS credentials to and python package exists in environment.""" - if values["client"] is not None: - return values + if self.client is not None: + return self try: import boto3 - if values["credentials_profile_name"] is not None: - session = boto3.Session(profile_name=values["credentials_profile_name"]) + if self.credentials_profile_name is not None: + session = boto3.Session(profile_name=self.credentials_profile_name) else: # use default credentials session = boto3.Session() client_params = {} - if values["region_name"]: - client_params["region_name"] = values["region_name"] + if self.region_name: + client_params["region_name"] = self.region_name - if values["endpoint_url"]: - client_params["endpoint_url"] = values["endpoint_url"] + if self.endpoint_url: + client_params["endpoint_url"] = self.endpoint_url - values["client"] = session.client("bedrock-runtime", **client_params) + self.client = session.client("bedrock-runtime", **client_params) except ImportError: raise ImportError( @@ -114,7 +116,7 @@ def validate_environment(cls, values: Dict) -> Dict: f"profile name are valid. Bedrock error: {e}" ) from e - return values + return self def _embedding_func(self, text: str) -> List[float]: """Call out to Bedrock embedding endpoint.""" diff --git a/libs/community/langchain_community/embeddings/bookend.py b/libs/community/langchain_community/embeddings/bookend.py index f8388712d7cf4..c5390e19e71f0 100644 --- a/libs/community/langchain_community/embeddings/bookend.py +++ b/libs/community/langchain_community/embeddings/bookend.py @@ -5,7 +5,7 @@ import requests from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, Field API_URL = "https://api.bookend.ai/" DEFAULT_TASK = "embeddings" diff --git a/libs/community/langchain_community/embeddings/clarifai.py b/libs/community/langchain_community/embeddings/clarifai.py index a465fc737eee2..4c35fcf75b804 100644 --- a/libs/community/langchain_community/embeddings/clarifai.py +++ b/libs/community/langchain_community/embeddings/clarifai.py @@ -2,7 +2,7 @@ from typing import Any, Dict, List, Optional from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel, Field, root_validator +from pydantic import BaseModel, ConfigDict, Field, model_validator logger = logging.getLogger(__name__) @@ -43,11 +43,13 @@ class ClarifaiEmbeddings(BaseModel, Embeddings): model: Any = Field(default=None, exclude=True) #: :meta private: api_base: str = "https://api.clarifai.com" - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that we have all required info to access Clarifai platform and python package exists in environment.""" diff --git a/libs/community/langchain_community/embeddings/cloudflare_workersai.py b/libs/community/langchain_community/embeddings/cloudflare_workersai.py index 03b6617bf2e62..45fadb39c6885 100644 --- a/libs/community/langchain_community/embeddings/cloudflare_workersai.py +++ b/libs/community/langchain_community/embeddings/cloudflare_workersai.py @@ -2,7 +2,7 @@ import requests from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel, ConfigDict DEFAULT_MODEL_NAME = "@cf/baai/bge-base-en-v1.5" @@ -43,8 +43,9 @@ def __init__(self, **kwargs: Any): self.headers = {"Authorization": f"Bearer {self.api_token}"} - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) def embed_documents(self, texts: List[str]) -> List[List[float]]: """Compute doc embeddings using Cloudflare Workers AI. diff --git a/libs/community/langchain_community/embeddings/clova.py b/libs/community/langchain_community/embeddings/clova.py index 1e4cecc0ed2b3..551dc63526192 100644 --- a/libs/community/langchain_community/embeddings/clova.py +++ b/libs/community/langchain_community/embeddings/clova.py @@ -1,11 +1,11 @@ from __future__ import annotations -from typing import Dict, List, Optional, cast +from typing import Any, Dict, List, Optional, cast import requests from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel, SecretStr, root_validator from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env +from pydantic import BaseModel, ConfigDict, SecretStr, model_validator class ClovaEmbeddings(BaseModel, Embeddings): @@ -53,11 +53,13 @@ class ClovaEmbeddings(BaseModel, Embeddings): app_id: Optional[SecretStr] = None """Application ID for identifying your application.""" - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate api key exists in environment.""" values["clova_emb_api_key"] = convert_to_secret_str( get_from_dict_or_env(values, "clova_emb_api_key", "CLOVA_EMB_API_KEY") diff --git a/libs/community/langchain_community/embeddings/cohere.py b/libs/community/langchain_community/embeddings/cohere.py index 8c3ea9e101826..2b4e0efe57bd1 100644 --- a/libs/community/langchain_community/embeddings/cohere.py +++ b/libs/community/langchain_community/embeddings/cohere.py @@ -2,8 +2,8 @@ from langchain_core._api.deprecation import deprecated from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel, root_validator from langchain_core.utils import get_from_dict_or_env +from pydantic import BaseModel, ConfigDict, model_validator from langchain_community.llms.cohere import _create_retry_decorator @@ -49,11 +49,13 @@ class CohereEmbeddings(BaseModel, Embeddings): user_agent: str = "langchain" """Identifier for the application making the request.""" - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key and python package exists in environment.""" cohere_api_key = get_from_dict_or_env( values, "cohere_api_key", "COHERE_API_KEY" diff --git a/libs/community/langchain_community/embeddings/dashscope.py b/libs/community/langchain_community/embeddings/dashscope.py index 33ad8095c5609..e2f69712145fd 100644 --- a/libs/community/langchain_community/embeddings/dashscope.py +++ b/libs/community/langchain_community/embeddings/dashscope.py @@ -10,8 +10,8 @@ ) from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel, root_validator from langchain_core.utils import get_from_dict_or_env +from pydantic import BaseModel, ConfigDict, model_validator from requests.exceptions import HTTPError from tenacity import ( before_sleep_log, @@ -108,11 +108,13 @@ class DashScopeEmbeddings(BaseModel, Embeddings): max_retries: int = 5 """Maximum number of retries to make when generating.""" - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: import dashscope """Validate that api key and python package exists in environment.""" diff --git a/libs/community/langchain_community/embeddings/deepinfra.py b/libs/community/langchain_community/embeddings/deepinfra.py index b115b8c00ec83..05f3ed982b40e 100644 --- a/libs/community/langchain_community/embeddings/deepinfra.py +++ b/libs/community/langchain_community/embeddings/deepinfra.py @@ -2,8 +2,8 @@ import requests from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel from langchain_core.utils import get_from_dict_or_env, pre_init +from pydantic import BaseModel, ConfigDict DEFAULT_MODEL_ID = "sentence-transformers/clip-ViT-B-32" MAX_BATCH_SIZE = 1024 @@ -54,8 +54,9 @@ class DeepInfraEmbeddings(BaseModel, Embeddings): batch_size: int = MAX_BATCH_SIZE """Batch size for embedding requests.""" - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) @pre_init def validate_environment(cls, values: Dict) -> Dict: diff --git a/libs/community/langchain_community/embeddings/edenai.py b/libs/community/langchain_community/embeddings/edenai.py index bddbd8824c55a..097c730ae423a 100644 --- a/libs/community/langchain_community/embeddings/edenai.py +++ b/libs/community/langchain_community/embeddings/edenai.py @@ -1,12 +1,13 @@ from typing import Any, Dict, List, Optional from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import ( +from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init +from pydantic import ( BaseModel, + ConfigDict, Field, SecretStr, ) -from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init from langchain_community.utilities.requests import Requests @@ -28,8 +29,9 @@ class EdenAiEmbeddings(BaseModel, Embeddings): available models are shown on https://docs.edenai.co/ under 'available providers' """ - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) @pre_init def validate_environment(cls, values: Dict) -> Dict: diff --git a/libs/community/langchain_community/embeddings/embaas.py b/libs/community/langchain_community/embeddings/embaas.py index 861cab09280f6..78fd42bf8501d 100644 --- a/libs/community/langchain_community/embeddings/embaas.py +++ b/libs/community/langchain_community/embeddings/embaas.py @@ -2,8 +2,8 @@ import requests from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel, SecretStr from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init +from pydantic import BaseModel, ConfigDict, SecretStr from requests.adapters import HTTPAdapter, Retry from typing_extensions import NotRequired, TypedDict @@ -56,8 +56,9 @@ class EmbaasEmbeddings(BaseModel, Embeddings): """request timeout in seconds""" timeout: Optional[int] = 30 - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) @pre_init def validate_environment(cls, values: Dict) -> Dict: diff --git a/libs/community/langchain_community/embeddings/ernie.py b/libs/community/langchain_community/embeddings/ernie.py index c640680f9c8f2..42a49e2c6ca6f 100644 --- a/libs/community/langchain_community/embeddings/ernie.py +++ b/libs/community/langchain_community/embeddings/ernie.py @@ -6,9 +6,9 @@ import requests from langchain_core._api.deprecation import deprecated from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel from langchain_core.runnables.config import run_in_executor from langchain_core.utils import get_from_dict_or_env, pre_init +from pydantic import BaseModel logger = logging.getLogger(__name__) diff --git a/libs/community/langchain_community/embeddings/fake.py b/libs/community/langchain_community/embeddings/fake.py index fcc33496aa863..67dc1d6b6f5b5 100644 --- a/libs/community/langchain_community/embeddings/fake.py +++ b/libs/community/langchain_community/embeddings/fake.py @@ -3,7 +3,7 @@ import numpy as np from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel class FakeEmbeddings(Embeddings, BaseModel): diff --git a/libs/community/langchain_community/embeddings/fastembed.py b/libs/community/langchain_community/embeddings/fastembed.py index 3a0940c866c4f..fe654aa44594f 100644 --- a/libs/community/langchain_community/embeddings/fastembed.py +++ b/libs/community/langchain_community/embeddings/fastembed.py @@ -4,8 +4,8 @@ import numpy as np from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel from langchain_core.utils import pre_init +from pydantic import BaseModel, ConfigDict MIN_VERSION = "0.2.0" @@ -67,8 +67,9 @@ class FastEmbedEmbeddings(BaseModel, Embeddings): _model: Any # : :meta private: - class Config: - extra = "allow" + model_config = ConfigDict( + extra="allow", + ) @pre_init def validate_environment(cls, values: Dict) -> Dict: diff --git a/libs/community/langchain_community/embeddings/gigachat.py b/libs/community/langchain_community/embeddings/gigachat.py index 09ac5b3757851..eea276e2444b1 100644 --- a/libs/community/langchain_community/embeddings/gigachat.py +++ b/libs/community/langchain_community/embeddings/gigachat.py @@ -5,9 +5,9 @@ from typing import Any, Dict, List, Optional from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel from langchain_core.utils import pre_init from langchain_core.utils.pydantic import get_fields +from pydantic import BaseModel logger = logging.getLogger(__name__) diff --git a/libs/community/langchain_community/embeddings/google_palm.py b/libs/community/langchain_community/embeddings/google_palm.py index 2c960c93e84c1..363214caead72 100644 --- a/libs/community/langchain_community/embeddings/google_palm.py +++ b/libs/community/langchain_community/embeddings/google_palm.py @@ -4,8 +4,8 @@ from typing import Any, Callable, Dict, List, Optional from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel from langchain_core.utils import get_from_dict_or_env, pre_init +from pydantic import BaseModel from tenacity import ( before_sleep_log, retry, diff --git a/libs/community/langchain_community/embeddings/gpt4all.py b/libs/community/langchain_community/embeddings/gpt4all.py index 338acc50389ce..ce92ab84b6d23 100644 --- a/libs/community/langchain_community/embeddings/gpt4all.py +++ b/libs/community/langchain_community/embeddings/gpt4all.py @@ -1,7 +1,7 @@ from typing import Any, Dict, List, Optional from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel, root_validator +from pydantic import BaseModel, model_validator class GPT4AllEmbeddings(BaseModel, Embeddings): @@ -28,8 +28,9 @@ class GPT4AllEmbeddings(BaseModel, Embeddings): gpt4all_kwargs: Optional[dict] = {} client: Any #: :meta private: - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that GPT4All library is installed.""" try: from gpt4all import Embed4All diff --git a/libs/community/langchain_community/embeddings/gradient_ai.py b/libs/community/langchain_community/embeddings/gradient_ai.py index 6e6f0cedf76a3..f33c80a6eca31 100644 --- a/libs/community/langchain_community/embeddings/gradient_ai.py +++ b/libs/community/langchain_community/embeddings/gradient_ai.py @@ -1,9 +1,10 @@ from typing import Any, Dict, List, Optional from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel, root_validator from langchain_core.utils import get_from_dict_or_env from packaging.version import parse +from pydantic import BaseModel, ConfigDict, model_validator +from typing_extensions import Self __all__ = ["GradientEmbeddings"] @@ -50,11 +51,13 @@ class GradientEmbeddings(BaseModel, Embeddings): """Gradient client.""" # LLM call kwargs - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key and python package exists in environment.""" values["gradient_access_token"] = get_from_dict_or_env( @@ -72,8 +75,8 @@ def validate_environment(cls, values: Dict) -> Dict: ) return values - @root_validator(pre=False, skip_on_failure=True) - def post_init(cls, values: Dict) -> Dict: + @model_validator(mode="after") + def post_init(self) -> Self: try: import gradientai except ImportError: @@ -87,12 +90,12 @@ def post_init(cls, values: Dict) -> Dict: ) gradient = gradientai.Gradient( - access_token=values["gradient_access_token"], - workspace_id=values["gradient_workspace_id"], - host=values["gradient_api_url"], + access_token=self.gradient_access_token, + workspace_id=self.gradient_workspace_id, + host=self.gradient_api_url, ) - values["client"] = gradient.get_embeddings_model(slug=values["model"]) - return values + self.client = gradient.get_embeddings_model(slug=self.model) + return self def embed_documents(self, texts: List[str]) -> List[List[float]]: """Call out to Gradient's embedding endpoint. diff --git a/libs/community/langchain_community/embeddings/huggingface.py b/libs/community/langchain_community/embeddings/huggingface.py index ed188c7e61e3a..f4c2f38f8fdf8 100644 --- a/libs/community/langchain_community/embeddings/huggingface.py +++ b/libs/community/langchain_community/embeddings/huggingface.py @@ -4,7 +4,7 @@ import requests from langchain_core._api import deprecated, warn_deprecated from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr +from pydantic import BaseModel, ConfigDict, Field, SecretStr DEFAULT_MODEL_NAME = "sentence-transformers/all-mpnet-base-v2" DEFAULT_INSTRUCT_MODEL = "hkunlp/instructor-large" @@ -93,8 +93,9 @@ def __init__(self, **kwargs: Any): self.model_name, cache_folder=self.cache_folder, **self.model_kwargs ) - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) def embed_documents(self, texts: List[str]) -> List[List[float]]: """Compute doc embeddings using a HuggingFace transformer model. @@ -208,8 +209,9 @@ def __init__(self, **kwargs: Any): ) self.show_progress = self.encode_kwargs.pop("show_progress_bar") - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) def embed_documents(self, texts: List[str]) -> List[List[float]]: """Compute doc embeddings using a HuggingFace instruct model. @@ -348,8 +350,9 @@ def __init__(self, **kwargs: Any): ) self.show_progress = self.encode_kwargs.pop("show_progress_bar") - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) def embed_documents(self, texts: List[str]) -> List[List[float]]: """Compute doc embeddings using a HuggingFace transformer model. diff --git a/libs/community/langchain_community/embeddings/huggingface_hub.py b/libs/community/langchain_community/embeddings/huggingface_hub.py index 6b688913b9b95..cea56ad11c753 100644 --- a/libs/community/langchain_community/embeddings/huggingface_hub.py +++ b/libs/community/langchain_community/embeddings/huggingface_hub.py @@ -3,8 +3,9 @@ from langchain_core._api import deprecated from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel, root_validator from langchain_core.utils import get_from_dict_or_env +from pydantic import BaseModel, ConfigDict, model_validator +from typing_extensions import Self DEFAULT_MODEL = "sentence-transformers/all-mpnet-base-v2" VALID_TASKS = ("feature-extraction",) @@ -47,11 +48,13 @@ class HuggingFaceHubEmbeddings(BaseModel, Embeddings): huggingfacehub_api_token: Optional[str] = None - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key and python package exists in environment.""" huggingfacehub_api_token = get_from_dict_or_env( values, "huggingfacehub_api_token", "HUGGINGFACEHUB_API_TOKEN" @@ -88,15 +91,15 @@ def validate_environment(cls, values: Dict) -> Dict: ) return values - @root_validator(pre=False, skip_on_failure=True) - def post_init(cls, values: Dict) -> Dict: + @model_validator(mode="after") + def post_init(self) -> Self: """Post init validation for the class.""" - if values["task"] not in VALID_TASKS: + if self.task not in VALID_TASKS: raise ValueError( - f"Got invalid task {values['task']}, " + f"Got invalid task {self.task}, " f"currently only {VALID_TASKS} are supported" ) - return values + return self def embed_documents(self, texts: List[str]) -> List[List[float]]: """Call out to HuggingFaceHub's embedding endpoint for embedding search docs. diff --git a/libs/community/langchain_community/embeddings/infinity.py b/libs/community/langchain_community/embeddings/infinity.py index f9032427d5dc9..dbbc9f83142f6 100644 --- a/libs/community/langchain_community/embeddings/infinity.py +++ b/libs/community/langchain_community/embeddings/infinity.py @@ -8,8 +8,8 @@ import numpy as np import requests from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel, root_validator from langchain_core.utils import get_from_dict_or_env +from pydantic import BaseModel, ConfigDict, model_validator __all__ = ["InfinityEmbeddings"] @@ -44,11 +44,13 @@ class InfinityEmbeddings(BaseModel, Embeddings): """Infinity client.""" # LLM call kwargs - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key and python package exists in environment.""" values["infinity_api_url"] = get_from_dict_or_env( diff --git a/libs/community/langchain_community/embeddings/infinity_local.py b/libs/community/langchain_community/embeddings/infinity_local.py index 6697d63a83bc9..610ccaf3e88f0 100644 --- a/libs/community/langchain_community/embeddings/infinity_local.py +++ b/libs/community/langchain_community/embeddings/infinity_local.py @@ -2,10 +2,11 @@ import asyncio from logging import getLogger -from typing import Any, Dict, List, Optional +from typing import Any, List, Optional from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel, root_validator +from pydantic import BaseModel, ConfigDict, model_validator +from typing_extensions import Self __all__ = ["InfinityEmbeddingsLocal"] @@ -57,11 +58,12 @@ class InfinityEmbeddingsLocal(BaseModel, Embeddings): """Infinity's AsyncEmbeddingEngine.""" # LLM call kwargs - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) - @root_validator(pre=False, skip_on_failure=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="after") + def validate_environment(self) -> Self: """Validate that api key and python package exists in environment.""" try: @@ -72,17 +74,15 @@ def validate_environment(cls, values: Dict) -> Dict: "`pip install 'infinity_emb[optimum,torch]>=0.0.24'` " "package to use the InfinityEmbeddingsLocal." ) - logger.debug(f"Using InfinityEmbeddingsLocal with kwargs {values}") - - values["engine"] = AsyncEmbeddingEngine( - model_name_or_path=values["model"], - device=values["device"], - revision=values["revision"], - model_warmup=values["model_warmup"], - batch_size=values["batch_size"], - engine=values["backend"], + self.engine = AsyncEmbeddingEngine( + model_name_or_path=self.model, + device=self.device, + revision=self.revision, + model_warmup=self.model_warmup, + batch_size=self.batch_size, + engine=self.backend, ) - return values + return self async def __aenter__(self) -> None: """start the background worker. diff --git a/libs/community/langchain_community/embeddings/ipex_llm.py b/libs/community/langchain_community/embeddings/ipex_llm.py index 5b1a6a4a25e0e..be18e2f346104 100644 --- a/libs/community/langchain_community/embeddings/ipex_llm.py +++ b/libs/community/langchain_community/embeddings/ipex_llm.py @@ -4,7 +4,7 @@ from typing import Any, Dict, List, Optional from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, ConfigDict, Field DEFAULT_BGE_MODEL = "BAAI/bge-small-en-v1.5" DEFAULT_QUERY_BGE_INSTRUCTION_EN = ( @@ -106,8 +106,9 @@ def __init__(self, **kwargs: Any): if "-zh" in self.model_name: self.query_instruction = DEFAULT_QUERY_BGE_INSTRUCTION_ZH - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) def embed_documents(self, texts: List[str]) -> List[List[float]]: """Compute doc embeddings using a HuggingFace transformer model. diff --git a/libs/community/langchain_community/embeddings/itrex.py b/libs/community/langchain_community/embeddings/itrex.py index 67273acebc588..2fcd0afae3d7c 100644 --- a/libs/community/langchain_community/embeddings/itrex.py +++ b/libs/community/langchain_community/embeddings/itrex.py @@ -3,7 +3,7 @@ from typing import Any, Dict, List, Optional from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel, ConfigDict class QuantizedBgeEmbeddings(BaseModel, Embeddings): @@ -118,8 +118,9 @@ def load_model(self) -> None: onnx_model_path, use_embedding_runtime=True ) - class Config: - extra = "allow" + model_config = ConfigDict( + extra="allow", + ) def _embed(self, inputs: Any) -> Any: import torch diff --git a/libs/community/langchain_community/embeddings/javelin_ai_gateway.py b/libs/community/langchain_community/embeddings/javelin_ai_gateway.py index 61baac869da1f..205e58e1c30c5 100644 --- a/libs/community/langchain_community/embeddings/javelin_ai_gateway.py +++ b/libs/community/langchain_community/embeddings/javelin_ai_gateway.py @@ -3,7 +3,7 @@ from typing import Any, Iterator, List, Optional from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel def _chunk(texts: List[str], size: int) -> Iterator[List[str]]: diff --git a/libs/community/langchain_community/embeddings/jina.py b/libs/community/langchain_community/embeddings/jina.py index 73f75b24b8fb6..718345f1afd23 100644 --- a/libs/community/langchain_community/embeddings/jina.py +++ b/libs/community/langchain_community/embeddings/jina.py @@ -5,8 +5,8 @@ import requests from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel, SecretStr, root_validator from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env +from pydantic import BaseModel, SecretStr, model_validator JINA_API_URL: str = "https://api.jina.ai/v1/embeddings" @@ -46,8 +46,9 @@ class JinaEmbeddings(BaseModel, Embeddings): model_name: str = "jina-embeddings-v2-base-en" jina_api_key: Optional[SecretStr] = None - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that auth token exists in environment.""" try: jina_api_key = convert_to_secret_str( diff --git a/libs/community/langchain_community/embeddings/johnsnowlabs.py b/libs/community/langchain_community/embeddings/johnsnowlabs.py index 23296149eb491..4223114aa0b15 100644 --- a/libs/community/langchain_community/embeddings/johnsnowlabs.py +++ b/libs/community/langchain_community/embeddings/johnsnowlabs.py @@ -3,7 +3,7 @@ from typing import Any, List from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel, ConfigDict class JohnSnowLabsEmbeddings(BaseModel, Embeddings): @@ -58,8 +58,9 @@ def __init__( except Exception as exc: raise Exception("Failure loading model") from exc - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) def embed_documents(self, texts: List[str]) -> List[List[float]]: """Compute doc embeddings using a JohnSnowLabs transformer model. diff --git a/libs/community/langchain_community/embeddings/laser.py b/libs/community/langchain_community/embeddings/laser.py index 98bcaf26db411..4525bc2aee07e 100644 --- a/libs/community/langchain_community/embeddings/laser.py +++ b/libs/community/langchain_community/embeddings/laser.py @@ -2,8 +2,8 @@ import numpy as np from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel from langchain_core.utils import pre_init +from pydantic import BaseModel, ConfigDict LASER_MULTILINGUAL_MODEL: str = "laser2" @@ -37,8 +37,9 @@ class LaserEmbeddings(BaseModel, Embeddings): _encoder_pipeline: Any # : :meta private: - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) @pre_init def validate_environment(cls, values: Dict) -> Dict: diff --git a/libs/community/langchain_community/embeddings/llamacpp.py b/libs/community/langchain_community/embeddings/llamacpp.py index 49091b5aa6bfb..5a90b05aef93b 100644 --- a/libs/community/langchain_community/embeddings/llamacpp.py +++ b/libs/community/langchain_community/embeddings/llamacpp.py @@ -1,7 +1,8 @@ -from typing import Any, Dict, List, Optional +from typing import Any, List, Optional from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel, Field, root_validator +from pydantic import BaseModel, ConfigDict, Field, model_validator +from typing_extensions import Self class LlamaCppEmbeddings(BaseModel, Embeddings): @@ -60,13 +61,14 @@ class LlamaCppEmbeddings(BaseModel, Embeddings): device: Optional[str] = Field(None, alias="device") """Device type to use and pass to the model""" - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) - @root_validator(pre=False, skip_on_failure=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="after") + def validate_environment(self) -> Self: """Validate that llama-cpp-python library is installed.""" - model_path = values["model_path"] + model_path = self.model_path model_param_names = [ "n_ctx", "n_parts", @@ -80,15 +82,15 @@ def validate_environment(cls, values: Dict) -> Dict: "verbose", "device", ] - model_params = {k: values[k] for k in model_param_names} + model_params = {k: getattr(self, k) for k in model_param_names} # For backwards compatibility, only include if non-null. - if values["n_gpu_layers"] is not None: - model_params["n_gpu_layers"] = values["n_gpu_layers"] + if self.n_gpu_layers is not None: + model_params["n_gpu_layers"] = self.n_gpu_layers try: from llama_cpp import Llama - values["client"] = Llama(model_path, embedding=True, **model_params) + self.client = Llama(model_path, embedding=True, **model_params) except ImportError: raise ImportError( "Could not import llama-cpp-python library. " @@ -101,7 +103,7 @@ def validate_environment(cls, values: Dict) -> Dict: f"Received error {e}" ) - return values + return self def embed_documents(self, texts: List[str]) -> List[List[float]]: """Embed a list of documents using the Llama model. diff --git a/libs/community/langchain_community/embeddings/llamafile.py b/libs/community/langchain_community/embeddings/llamafile.py index 310f61a03e1d0..247b1a923ac58 100644 --- a/libs/community/langchain_community/embeddings/llamafile.py +++ b/libs/community/langchain_community/embeddings/llamafile.py @@ -3,7 +3,7 @@ import requests from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel logger = logging.getLogger(__name__) diff --git a/libs/community/langchain_community/embeddings/llm_rails.py b/libs/community/langchain_community/embeddings/llm_rails.py index 7de390ae72d03..92bb8c6a10cd3 100644 --- a/libs/community/langchain_community/embeddings/llm_rails.py +++ b/libs/community/langchain_community/embeddings/llm_rails.py @@ -4,8 +4,8 @@ import requests from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel, SecretStr from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init +from pydantic import BaseModel, ConfigDict, SecretStr class LLMRailsEmbeddings(BaseModel, Embeddings): @@ -32,8 +32,9 @@ class LLMRailsEmbeddings(BaseModel, Embeddings): api_key: Optional[SecretStr] = None """LLMRails API key.""" - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) @pre_init def validate_environment(cls, values: Dict) -> Dict: diff --git a/libs/community/langchain_community/embeddings/localai.py b/libs/community/langchain_community/embeddings/localai.py index 7a57eab2bc73e..b59433ad2e203 100644 --- a/libs/community/langchain_community/embeddings/localai.py +++ b/libs/community/langchain_community/embeddings/localai.py @@ -16,12 +16,12 @@ ) from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel, Field, root_validator from langchain_core.utils import ( get_from_dict_or_env, get_pydantic_field_names, pre_init, ) +from pydantic import BaseModel, ConfigDict, Field, model_validator from tenacity import ( AsyncRetrying, before_sleep_log, @@ -166,11 +166,13 @@ class LocalAIEmbeddings(BaseModel, Embeddings): model_kwargs: Dict[str, Any] = Field(default_factory=dict) """Holds any model parameters valid for `create` call not explicitly specified.""" - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) - @root_validator(pre=True) - def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def build_extra(cls, values: Dict[str, Any]) -> Any: """Build extra kwargs from additional params that were passed in.""" all_required_field_names = get_pydantic_field_names(cls) extra = values.get("model_kwargs", {}) diff --git a/libs/community/langchain_community/embeddings/minimax.py b/libs/community/langchain_community/embeddings/minimax.py index 3633029ed56b5..14262786158e5 100644 --- a/libs/community/langchain_community/embeddings/minimax.py +++ b/libs/community/langchain_community/embeddings/minimax.py @@ -5,8 +5,8 @@ import requests from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init +from pydantic import BaseModel, ConfigDict, Field, SecretStr from tenacity import ( before_sleep_log, retry, @@ -117,9 +117,10 @@ class MiniMaxEmbeddings(BaseModel, Embeddings): minimax_api_key: Optional[SecretStr] = Field(default=None, alias="api_key") """API Key for MiniMax API.""" - class Config: - allow_population_by_field_name = True - extra = "forbid" + model_config = ConfigDict( + populate_by_name=True, + extra="forbid", + ) @pre_init def validate_environment(cls, values: Dict) -> Dict: diff --git a/libs/community/langchain_community/embeddings/mlflow.py b/libs/community/langchain_community/embeddings/mlflow.py index 6261d76c16fd9..2dbff7c2eb8c2 100644 --- a/libs/community/langchain_community/embeddings/mlflow.py +++ b/libs/community/langchain_community/embeddings/mlflow.py @@ -4,7 +4,7 @@ from urllib.parse import urlparse from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel, PrivateAttr +from pydantic import BaseModel, PrivateAttr def _chunk(texts: List[str], size: int) -> Iterator[List[str]]: diff --git a/libs/community/langchain_community/embeddings/mlflow_gateway.py b/libs/community/langchain_community/embeddings/mlflow_gateway.py index e722d9e35ce6a..9a7a9643fe40f 100644 --- a/libs/community/langchain_community/embeddings/mlflow_gateway.py +++ b/libs/community/langchain_community/embeddings/mlflow_gateway.py @@ -4,7 +4,7 @@ from typing import Any, Iterator, List, Optional from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel def _chunk(texts: List[str], size: int) -> Iterator[List[str]]: diff --git a/libs/community/langchain_community/embeddings/modelscope_hub.py b/libs/community/langchain_community/embeddings/modelscope_hub.py index 77a53770bede4..878f05a4fe253 100644 --- a/libs/community/langchain_community/embeddings/modelscope_hub.py +++ b/libs/community/langchain_community/embeddings/modelscope_hub.py @@ -1,7 +1,7 @@ from typing import Any, List, Optional from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel, ConfigDict class ModelScopeEmbeddings(BaseModel, Embeddings): @@ -39,8 +39,9 @@ def __init__(self, **kwargs: Any): model_revision=self.model_revision, ) - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) def embed_documents(self, texts: List[str]) -> List[List[float]]: """Compute doc embeddings using a modelscope embedding model. diff --git a/libs/community/langchain_community/embeddings/mosaicml.py b/libs/community/langchain_community/embeddings/mosaicml.py index 01acee54ec580..cf5f8646b34ab 100644 --- a/libs/community/langchain_community/embeddings/mosaicml.py +++ b/libs/community/langchain_community/embeddings/mosaicml.py @@ -2,8 +2,8 @@ import requests from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel, root_validator from langchain_core.utils import get_from_dict_or_env +from pydantic import BaseModel, ConfigDict, model_validator class MosaicMLInstructorEmbeddings(BaseModel, Embeddings): @@ -41,11 +41,13 @@ class MosaicMLInstructorEmbeddings(BaseModel, Embeddings): mosaicml_api_token: Optional[str] = None - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key and python package exists in environment.""" mosaicml_api_token = get_from_dict_or_env( values, "mosaicml_api_token", "MOSAICML_API_TOKEN" diff --git a/libs/community/langchain_community/embeddings/nemo.py b/libs/community/langchain_community/embeddings/nemo.py index 95d00c3bbb4ce..38a20ffb45071 100644 --- a/libs/community/langchain_community/embeddings/nemo.py +++ b/libs/community/langchain_community/embeddings/nemo.py @@ -8,8 +8,8 @@ import requests from langchain_core._api.deprecation import deprecated from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel from langchain_core.utils import pre_init +from pydantic import BaseModel def is_endpoint_live(url: str, headers: Optional[dict], payload: Any) -> bool: diff --git a/libs/community/langchain_community/embeddings/nlpcloud.py b/libs/community/langchain_community/embeddings/nlpcloud.py index 69f08d28572f4..decf8ac2b803e 100644 --- a/libs/community/langchain_community/embeddings/nlpcloud.py +++ b/libs/community/langchain_community/embeddings/nlpcloud.py @@ -1,8 +1,8 @@ from typing import Any, Dict, List from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel from langchain_core.utils import get_from_dict_or_env, pre_init +from pydantic import BaseModel class NLPCloudEmbeddings(BaseModel, Embeddings): diff --git a/libs/community/langchain_community/embeddings/oci_generative_ai.py b/libs/community/langchain_community/embeddings/oci_generative_ai.py index cf5cec0e36253..ff735b905127d 100644 --- a/libs/community/langchain_community/embeddings/oci_generative_ai.py +++ b/libs/community/langchain_community/embeddings/oci_generative_ai.py @@ -2,8 +2,8 @@ from typing import Any, Dict, Iterator, List, Mapping, Optional from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel from langchain_core.utils import pre_init +from pydantic import BaseModel, ConfigDict CUSTOM_ENDPOINT_PREFIX = "ocid1.generativeaiendpoint" @@ -46,9 +46,9 @@ class OCIGenAIEmbeddings(BaseModel, Embeddings): ) """ - client: Any #: :meta private: + client: Any = None #: :meta private: - service_models: Any #: :meta private: + service_models: Any = None #: :meta private: auth_type: Optional[str] = "API_KEY" """Authentication type, could be @@ -66,16 +66,16 @@ class OCIGenAIEmbeddings(BaseModel, Embeddings): If not specified , DEFAULT will be used """ - model_id: str = None # type: ignore[assignment] + model_id: Optional[str] = None """Id of the model to call, e.g., cohere.embed-english-light-v2.0""" model_kwargs: Optional[Dict] = None """Keyword arguments to pass to the model""" - service_endpoint: str = None # type: ignore[assignment] + service_endpoint: Optional[str] = None """service endpoint url""" - compartment_id: str = None # type: ignore[assignment] + compartment_id: Optional[str] = None """OCID of compartment""" truncate: Optional[str] = "END" @@ -85,8 +85,9 @@ class OCIGenAIEmbeddings(BaseModel, Embeddings): """Batch size of OCI GenAI embedding requests. OCI GenAI may handle up to 96 texts per request""" - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) @pre_init def validate_environment(cls, values: Dict) -> Dict: # pylint: disable=no-self-argument diff --git a/libs/community/langchain_community/embeddings/octoai_embeddings.py b/libs/community/langchain_community/embeddings/octoai_embeddings.py index 287c04c264589..102ba313f54f8 100644 --- a/libs/community/langchain_community/embeddings/octoai_embeddings.py +++ b/libs/community/langchain_community/embeddings/octoai_embeddings.py @@ -1,7 +1,7 @@ from typing import Dict -from langchain_core.pydantic_v1 import Field, SecretStr from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init +from pydantic import Field, SecretStr from langchain_community.embeddings.openai import OpenAIEmbeddings from langchain_community.utils.openai import is_openai_v1 diff --git a/libs/community/langchain_community/embeddings/ollama.py b/libs/community/langchain_community/embeddings/ollama.py index 86774c45a5896..13be9a3cd84f0 100644 --- a/libs/community/langchain_community/embeddings/ollama.py +++ b/libs/community/langchain_community/embeddings/ollama.py @@ -3,7 +3,7 @@ import requests from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel, ConfigDict logger = logging.getLogger(__name__) @@ -141,8 +141,9 @@ def _identifying_params(self) -> Mapping[str, Any]: """Get the identifying parameters.""" return {**{"model": self.model}, **self._default_params} - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) def _process_emb_response(self, input: str) -> List[float]: """Process a response from the API. diff --git a/libs/community/langchain_community/embeddings/openai.py b/libs/community/langchain_community/embeddings/openai.py index 0c097b11aca03..a7abde76ea32a 100644 --- a/libs/community/langchain_community/embeddings/openai.py +++ b/libs/community/langchain_community/embeddings/openai.py @@ -21,12 +21,12 @@ import numpy as np from langchain_core._api.deprecation import deprecated from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel, Field, root_validator from langchain_core.utils import ( get_from_dict_or_env, get_pydantic_field_names, pre_init, ) +from pydantic import BaseModel, ConfigDict, Field, model_validator from tenacity import ( AsyncRetrying, before_sleep_log, @@ -254,12 +254,14 @@ class OpenAIEmbeddings(BaseModel, Embeddings): http_client: Union[Any, None] = None """Optional httpx.Client.""" - class Config: - allow_population_by_field_name = True - extra = "forbid" + model_config = ConfigDict( + populate_by_name=True, + extra="forbid", + ) - @root_validator(pre=True) - def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def build_extra(cls, values: Dict[str, Any]) -> Any: """Build extra kwargs from additional params that were passed in.""" all_required_field_names = get_pydantic_field_names(cls) extra = values.get("model_kwargs", {}) diff --git a/libs/community/langchain_community/embeddings/openvino.py b/libs/community/langchain_community/embeddings/openvino.py index f8dfc4077c819..8a83cfd16792a 100644 --- a/libs/community/langchain_community/embeddings/openvino.py +++ b/libs/community/langchain_community/embeddings/openvino.py @@ -2,7 +2,7 @@ from typing import Any, Dict, List from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, ConfigDict, Field DEFAULT_QUERY_INSTRUCTION = ( "Represent the question for retrieving supporting documents: " @@ -254,8 +254,9 @@ def run_mean_pooling(model_output: Any, attention_mask: Any) -> Any: return all_embeddings - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) def embed_documents(self, texts: List[str]) -> List[List[float]]: """Compute doc embeddings using a HuggingFace transformer model. diff --git a/libs/community/langchain_community/embeddings/optimum_intel.py b/libs/community/langchain_community/embeddings/optimum_intel.py index a9c40ff4e185b..657cba32c78c4 100644 --- a/libs/community/langchain_community/embeddings/optimum_intel.py +++ b/libs/community/langchain_community/embeddings/optimum_intel.py @@ -1,7 +1,7 @@ from typing import Any, Dict, List, Optional from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel, ConfigDict class QuantizedBiEncoderEmbeddings(BaseModel, Embeddings): @@ -100,8 +100,9 @@ def load_model(self) -> None: ) self.transformer_model.eval() - class Config: - extra = "allow" + model_config = ConfigDict( + extra="allow", + ) def _embed(self, inputs: Any) -> Any: try: diff --git a/libs/community/langchain_community/embeddings/oracleai.py b/libs/community/langchain_community/embeddings/oracleai.py index a7adbf6cad813..4b81787206ebd 100644 --- a/libs/community/langchain_community/embeddings/oracleai.py +++ b/libs/community/langchain_community/embeddings/oracleai.py @@ -14,7 +14,7 @@ from typing import TYPE_CHECKING, Any, Dict, List, Optional from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel, ConfigDict if TYPE_CHECKING: from oracledb import Connection @@ -37,8 +37,9 @@ class OracleEmbeddings(BaseModel, Embeddings): def __init__(self, **kwargs: Any): super().__init__(**kwargs) - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) """ 1 - user needs to have create procedure, diff --git a/libs/community/langchain_community/embeddings/ovhcloud.py b/libs/community/langchain_community/embeddings/ovhcloud.py index a943e2a70edf8..51b0dd2f34aa6 100644 --- a/libs/community/langchain_community/embeddings/ovhcloud.py +++ b/libs/community/langchain_community/embeddings/ovhcloud.py @@ -4,7 +4,7 @@ import requests from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel, ConfigDict logger = logging.getLogger(__name__) @@ -23,8 +23,9 @@ class OVHCloudEmbeddings(BaseModel, Embeddings): """ OVHcloud AI Endpoints region""" region: str = "kepler" - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) def __init__(self, **kwargs: Any): super().__init__(**kwargs) diff --git a/libs/community/langchain_community/embeddings/premai.py b/libs/community/langchain_community/embeddings/premai.py index 0ae42759776b0..ed4a74534439b 100644 --- a/libs/community/langchain_community/embeddings/premai.py +++ b/libs/community/langchain_community/embeddings/premai.py @@ -5,8 +5,8 @@ from langchain_core.embeddings import Embeddings from langchain_core.language_models.llms import create_base_retry_decorator -from langchain_core.pydantic_v1 import BaseModel, SecretStr from langchain_core.utils import get_from_dict_or_env, pre_init +from pydantic import BaseModel, SecretStr logger = logging.getLogger(__name__) diff --git a/libs/community/langchain_community/embeddings/sagemaker_endpoint.py b/libs/community/langchain_community/embeddings/sagemaker_endpoint.py index dfa58056ab47c..d5fa48f3b03c9 100644 --- a/libs/community/langchain_community/embeddings/sagemaker_endpoint.py +++ b/libs/community/langchain_community/embeddings/sagemaker_endpoint.py @@ -1,8 +1,8 @@ from typing import Any, Dict, List, Optional from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel from langchain_core.utils import pre_init +from pydantic import BaseModel, ConfigDict from langchain_community.llms.sagemaker_endpoint import ContentHandlerBase @@ -110,9 +110,10 @@ def transform_output(self, output: bytes) -> List[List[float]]: .. _boto3: """ - class Config: - arbitrary_types_allowed = True - extra = "forbid" + model_config = ConfigDict( + arbitrary_types_allowed=True, + extra="forbid", + ) @pre_init def validate_environment(cls, values: Dict) -> Dict: diff --git a/libs/community/langchain_community/embeddings/sambanova.py b/libs/community/langchain_community/embeddings/sambanova.py index 57601b5c6294a..0687295c8d9e5 100644 --- a/libs/community/langchain_community/embeddings/sambanova.py +++ b/libs/community/langchain_community/embeddings/sambanova.py @@ -3,8 +3,8 @@ import requests from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel from langchain_core.utils import get_from_dict_or_env, pre_init +from pydantic import BaseModel class SambaStudioEmbeddings(BaseModel, Embeddings): diff --git a/libs/community/langchain_community/embeddings/self_hosted.py b/libs/community/langchain_community/embeddings/self_hosted.py index 48188dedeea2f..8099c7018ed41 100644 --- a/libs/community/langchain_community/embeddings/self_hosted.py +++ b/libs/community/langchain_community/embeddings/self_hosted.py @@ -1,6 +1,7 @@ from typing import Any, Callable, List from langchain_core.embeddings import Embeddings +from pydantic import ConfigDict from langchain_community.llms.self_hosted import SelfHostedPipeline @@ -65,8 +66,9 @@ def get_pipeline(): inference_kwargs: Any = None """Any kwargs to pass to the model's inference function.""" - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) def embed_documents(self, texts: List[str]) -> List[List[float]]: """Compute doc embeddings using a HuggingFace transformer model. diff --git a/libs/community/langchain_community/embeddings/solar.py b/libs/community/langchain_community/embeddings/solar.py index e441b0cea1727..e730d7e6e9625 100644 --- a/libs/community/langchain_community/embeddings/solar.py +++ b/libs/community/langchain_community/embeddings/solar.py @@ -6,8 +6,8 @@ import requests from langchain_core._api import deprecated from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel, SecretStr from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init +from pydantic import BaseModel, ConfigDict, SecretStr from tenacity import ( before_sleep_log, retry, @@ -75,8 +75,9 @@ class SolarEmbeddings(BaseModel, Embeddings): solar_api_key: Optional[SecretStr] = None """API Key for Solar API.""" - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) @pre_init def validate_environment(cls, values: Dict) -> Dict: diff --git a/libs/community/langchain_community/embeddings/spacy_embeddings.py b/libs/community/langchain_community/embeddings/spacy_embeddings.py index d424ad70bc59d..63c71e07c3f4f 100644 --- a/libs/community/langchain_community/embeddings/spacy_embeddings.py +++ b/libs/community/langchain_community/embeddings/spacy_embeddings.py @@ -2,7 +2,7 @@ from typing import Any, Dict, List, Optional from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel, root_validator +from pydantic import BaseModel, ConfigDict, model_validator class SpacyEmbeddings(BaseModel, Embeddings): @@ -22,11 +22,13 @@ class SpacyEmbeddings(BaseModel, Embeddings): model_name: str = "en_core_web_sm" nlp: Optional[Any] = None - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """ Validates that the spaCy package and the model are installed. diff --git a/libs/community/langchain_community/embeddings/sparkllm.py b/libs/community/langchain_community/embeddings/sparkllm.py index 5f98ee74de269..6f0f1a1055267 100644 --- a/libs/community/langchain_community/embeddings/sparkllm.py +++ b/libs/community/langchain_community/embeddings/sparkllm.py @@ -12,11 +12,11 @@ import numpy as np import requests from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr from langchain_core.utils import ( secret_from_env, ) from numpy import ndarray +from pydantic import BaseModel, ConfigDict, Field, SecretStr # SparkLLMTextEmbeddings is an embedding model provided by iFLYTEK Co., Ltd.. (https://iflytek.com/en/). @@ -124,8 +124,9 @@ class SparkLLMTextEmbeddings(BaseModel, Embeddings): If "para"(default), it belongs to document Embedding. If "query", it belongs to query Embedding.""" - class Config: - allow_population_by_field_name = True + model_config = ConfigDict( + populate_by_name=True, + ) def _embed(self, texts: List[str], host: str) -> Optional[List[List[float]]]: """Internal method to call Spark Embedding API and return embeddings. diff --git a/libs/community/langchain_community/embeddings/tensorflow_hub.py b/libs/community/langchain_community/embeddings/tensorflow_hub.py index 4b72c071eab80..15c329417482c 100644 --- a/libs/community/langchain_community/embeddings/tensorflow_hub.py +++ b/libs/community/langchain_community/embeddings/tensorflow_hub.py @@ -1,7 +1,7 @@ from typing import Any, List from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel, ConfigDict DEFAULT_MODEL_URL = "https://tfhub.dev/google/universal-sentence-encoder-multilingual/3" @@ -43,8 +43,9 @@ def __init__(self, **kwargs: Any): self.embed = tensorflow_hub.load(self.model_url) - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) def embed_documents(self, texts: List[str]) -> List[List[float]]: """Compute doc embeddings using a TensorflowHub embedding model. diff --git a/libs/community/langchain_community/embeddings/text2vec.py b/libs/community/langchain_community/embeddings/text2vec.py index 537249aa93d3d..9b0cf353ec006 100644 --- a/libs/community/langchain_community/embeddings/text2vec.py +++ b/libs/community/langchain_community/embeddings/text2vec.py @@ -3,7 +3,7 @@ from typing import Any, List, Optional from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel class Text2vecEmbeddings(Embeddings, BaseModel): diff --git a/libs/community/langchain_community/embeddings/textembed.py b/libs/community/langchain_community/embeddings/textembed.py index 5559acf804f1b..57fc85211abb0 100644 --- a/libs/community/langchain_community/embeddings/textembed.py +++ b/libs/community/langchain_community/embeddings/textembed.py @@ -17,8 +17,9 @@ import numpy as np import requests from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel, root_validator -from langchain_core.utils import get_from_dict_or_env +from langchain_core.utils import from_env, secret_from_env +from pydantic import BaseModel, ConfigDict, Field, SecretStr, model_validator +from typing_extensions import Self __all__ = ["TextEmbedEmbeddings"] @@ -50,35 +51,30 @@ class TextEmbedEmbeddings(BaseModel, Embeddings): model: str """Underlying TextEmbed model id.""" - api_url: str = "http://localhost:8000/v1" + api_url: str = Field( + default_factory=from_env( + "TEXTEMBED_API_URL", default="http://localhost:8000/v1" + ) + ) """Endpoint URL to use.""" - api_key: str = "None" + api_key: SecretStr = Field(default_factory=secret_from_env("TEXTEMBED_API_KEY")) """API Key for authentication""" client: Any = None """TextEmbed client.""" - class Config: - extra = "forbid" - - @root_validator(pre=False, skip_on_failure=True) - def validate_environment(cls, values: Dict) -> Dict: - """Validate that api key and URL exist in the environment. - - Args: - values (Dict): Dictionary of values to validate. - - Returns: - Dict: Validated values. - """ - values["api_url"] = get_from_dict_or_env(values, "api_url", "API_URL") - values["api_key"] = get_from_dict_or_env(values, "api_key", "API_KEY") + model_config = ConfigDict( + extra="forbid", + ) - values["client"] = AsyncOpenAITextEmbedEmbeddingClient( - host=values["api_url"], api_key=values["api_key"] + @model_validator(mode="after") + def validate_environment(self) -> Self: + """Validate that api key and URL exist in the environment.""" + self.client = AsyncOpenAITextEmbedEmbeddingClient( + host=self.api_url, api_key=self.api_key.get_secret_value() ) - return values + return self def embed_documents(self, texts: List[str]) -> List[List[float]]: """Call out to TextEmbed's embedding endpoint. diff --git a/libs/community/langchain_community/embeddings/titan_takeoff.py b/libs/community/langchain_community/embeddings/titan_takeoff.py index bf82f54936e31..b171be0f2918f 100644 --- a/libs/community/langchain_community/embeddings/titan_takeoff.py +++ b/libs/community/langchain_community/embeddings/titan_takeoff.py @@ -2,7 +2,7 @@ from typing import Any, Dict, List, Optional, Set, Union from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel, ConfigDict class TakeoffEmbeddingException(Exception): @@ -24,8 +24,9 @@ class Device(str, Enum): class ReaderConfig(BaseModel): """Configuration for the reader to be deployed in Takeoff.""" - class Config: - protected_namespaces = () + model_config = ConfigDict( + protected_namespaces=(), + ) model_name: str """The name of the model to use""" diff --git a/libs/community/langchain_community/embeddings/volcengine.py b/libs/community/langchain_community/embeddings/volcengine.py index 9b8e399264819..6417e5e5a388e 100644 --- a/libs/community/langchain_community/embeddings/volcengine.py +++ b/libs/community/langchain_community/embeddings/volcengine.py @@ -4,8 +4,8 @@ from typing import Any, Dict, List, Optional from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel from langchain_core.utils import get_from_dict_or_env, pre_init +from pydantic import BaseModel logger = logging.getLogger(__name__) diff --git a/libs/community/langchain_community/embeddings/voyageai.py b/libs/community/langchain_community/embeddings/voyageai.py index e9c24b666abb5..2ef1477ac6c71 100644 --- a/libs/community/langchain_community/embeddings/voyageai.py +++ b/libs/community/langchain_community/embeddings/voyageai.py @@ -16,8 +16,8 @@ import requests from langchain_core._api.deprecation import deprecated from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel, SecretStr, root_validator from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env +from pydantic import BaseModel, ConfigDict, SecretStr, model_validator from tenacity import ( before_sleep_log, retry, @@ -99,11 +99,13 @@ class VoyageEmbeddings(BaseModel, Embeddings): length, before vectorized by the embedding model. If False, an error will be raised if any given text exceeds the context length.""" - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key and python package exists in environment.""" values["voyage_api_key"] = convert_to_secret_str( get_from_dict_or_env(values, "voyage_api_key", "VOYAGE_API_KEY") diff --git a/libs/community/langchain_community/embeddings/yandex.py b/libs/community/langchain_community/embeddings/yandex.py index dbddf245234df..fdc6dc6873ebb 100644 --- a/libs/community/langchain_community/embeddings/yandex.py +++ b/libs/community/langchain_community/embeddings/yandex.py @@ -7,8 +7,8 @@ from typing import Any, Callable, Dict, List, Sequence from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init +from pydantic import BaseModel, ConfigDict, Field, SecretStr from tenacity import ( before_sleep_log, retry, @@ -71,8 +71,9 @@ class YandexGPTEmbeddings(BaseModel, Embeddings): If you provide personal data, confidential information, disable logging.""" grpc_metadata: Sequence - class Config: - allow_population_by_field_name = True + model_config = ConfigDict( + populate_by_name=True, + ) @pre_init def validate_environment(cls, values: Dict) -> Dict: diff --git a/libs/community/langchain_community/embeddings/zhipuai.py b/libs/community/langchain_community/embeddings/zhipuai.py index b8415aed74712..73ced5fa01934 100644 --- a/libs/community/langchain_community/embeddings/zhipuai.py +++ b/libs/community/langchain_community/embeddings/zhipuai.py @@ -1,8 +1,8 @@ from typing import Any, Dict, List, Optional from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel, Field, root_validator from langchain_core.utils import get_from_dict_or_env +from pydantic import BaseModel, Field, model_validator class ZhipuAIEmbeddings(BaseModel, Embeddings): @@ -76,8 +76,9 @@ class ZhipuAIEmbeddings(BaseModel, Embeddings): Only supported in `embedding-3` and later models. """ - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that auth token exists in environment.""" values["api_key"] = get_from_dict_or_env(values, "api_key", "ZHIPUAI_API_KEY") try: diff --git a/libs/community/langchain_community/example_selectors/ngram_overlap.py b/libs/community/langchain_community/example_selectors/ngram_overlap.py index 76f536f83cb6f..fdd2e34eaf7dd 100644 --- a/libs/community/langchain_community/example_selectors/ngram_overlap.py +++ b/libs/community/langchain_community/example_selectors/ngram_overlap.py @@ -4,12 +4,12 @@ https://aclanthology.org/P02-1040.pdf """ -from typing import Dict, List +from typing import Any, Dict, List import numpy as np from langchain_core.example_selectors import BaseExampleSelector from langchain_core.prompts import PromptTemplate -from langchain_core.pydantic_v1 import BaseModel, root_validator +from pydantic import BaseModel, model_validator def ngram_overlap_score(source: List[str], example: List[str]) -> float: @@ -65,8 +65,9 @@ class NGramOverlapExampleSelector(BaseExampleSelector, BaseModel): and excludes examples with no ngram overlap with input. """ - @root_validator(pre=True) - def check_dependencies(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def check_dependencies(cls, values: Dict) -> Any: """Check that valid dependencies exist.""" try: from nltk.translate.bleu_score import ( # noqa: F401 diff --git a/libs/community/langchain_community/graphs/graph_document.py b/libs/community/langchain_community/graphs/graph_document.py index 3e9a597cc565f..ff82ca4b4341a 100644 --- a/libs/community/langchain_community/graphs/graph_document.py +++ b/libs/community/langchain_community/graphs/graph_document.py @@ -4,7 +4,7 @@ from langchain_core.documents import Document from langchain_core.load.serializable import Serializable -from langchain_core.pydantic_v1 import Field +from pydantic import Field class Node(Serializable): diff --git a/libs/community/langchain_community/graphs/index_creator.py b/libs/community/langchain_community/graphs/index_creator.py index 9394da264e2f2..f686096092712 100644 --- a/libs/community/langchain_community/graphs/index_creator.py +++ b/libs/community/langchain_community/graphs/index_creator.py @@ -1,7 +1,7 @@ from typing import Optional, Type -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel from langchain_core.language_models import BaseLanguageModel from langchain_core.prompts import BasePromptTemplate from langchain_core.prompts.prompt import PromptTemplate diff --git a/libs/community/langchain_community/llms/ai21.py b/libs/community/langchain_community/llms/ai21.py index 9956af4e2cd44..08afd82a947fa 100644 --- a/libs/community/langchain_community/llms/ai21.py +++ b/libs/community/langchain_community/llms/ai21.py @@ -3,8 +3,8 @@ import requests from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models.llms import LLM -from langchain_core.pydantic_v1 import BaseModel, SecretStr from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init +from pydantic import BaseModel, ConfigDict, SecretStr class AI21PenaltyData(BaseModel): @@ -68,8 +68,9 @@ class AI21(LLM): base_url: Optional[str] = None """Base url to use, if None decides based on model name.""" - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) @pre_init def validate_environment(cls, values: Dict) -> Dict: diff --git a/libs/community/langchain_community/llms/aleph_alpha.py b/libs/community/langchain_community/llms/aleph_alpha.py index ec366c1649cd8..f0a6735ecf593 100644 --- a/libs/community/langchain_community/llms/aleph_alpha.py +++ b/libs/community/langchain_community/llms/aleph_alpha.py @@ -2,8 +2,8 @@ from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models.llms import LLM -from langchain_core.pydantic_v1 import SecretStr from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init +from pydantic import ConfigDict, SecretStr from langchain_community.llms.utils import enforce_stop_tokens @@ -162,8 +162,9 @@ class AlephAlpha(LLM): nice to other users by de-prioritizing your request below concurrent ones.""" - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) @pre_init def validate_environment(cls, values: Dict) -> Dict: diff --git a/libs/community/langchain_community/llms/amazon_api_gateway.py b/libs/community/langchain_community/llms/amazon_api_gateway.py index 61e589a7f7cfa..61c088c8cdfab 100644 --- a/libs/community/langchain_community/llms/amazon_api_gateway.py +++ b/libs/community/langchain_community/llms/amazon_api_gateway.py @@ -3,6 +3,7 @@ import requests from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models.llms import LLM +from pydantic import ConfigDict from langchain_community.llms.utils import enforce_stop_tokens @@ -43,8 +44,9 @@ class AmazonAPIGateway(LLM): and the endpoint. """ - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) @property def _identifying_params(self) -> Mapping[str, Any]: diff --git a/libs/community/langchain_community/llms/anthropic.py b/libs/community/langchain_community/llms/anthropic.py index f6b977ab4559f..07b40d6eda705 100644 --- a/libs/community/langchain_community/llms/anthropic.py +++ b/libs/community/langchain_community/llms/anthropic.py @@ -20,7 +20,6 @@ from langchain_core.language_models.llms import LLM from langchain_core.outputs import GenerationChunk from langchain_core.prompt_values import PromptValue -from langchain_core.pydantic_v1 import Field, SecretStr, root_validator from langchain_core.utils import ( check_package_version, get_from_dict_or_env, @@ -28,6 +27,7 @@ pre_init, ) from langchain_core.utils.utils import build_extra_kwargs, convert_to_secret_str +from pydantic import ConfigDict, Field, SecretStr, model_validator class _AnthropicCommon(BaseLanguageModel): @@ -66,8 +66,9 @@ class _AnthropicCommon(BaseLanguageModel): count_tokens: Optional[Callable[[str], int]] = None model_kwargs: Dict[str, Any] = Field(default_factory=dict) - @root_validator(pre=True) - def build_extra(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def build_extra(cls, values: Dict) -> Any: extra = values.get("model_kwargs", {}) all_required_field_names = get_pydantic_field_names(cls) values["model_kwargs"] = build_extra_kwargs( @@ -180,9 +181,10 @@ class Anthropic(LLM, _AnthropicCommon): response = model.invoke(prompt) """ - class Config: - allow_population_by_field_name = True - arbitrary_types_allowed = True + model_config = ConfigDict( + populate_by_name=True, + arbitrary_types_allowed=True, + ) @pre_init def raise_warning(cls, values: Dict) -> Dict: diff --git a/libs/community/langchain_community/llms/anyscale.py b/libs/community/langchain_community/llms/anyscale.py index 2e43fa38ae5a0..2ae364c390630 100644 --- a/libs/community/langchain_community/llms/anyscale.py +++ b/libs/community/langchain_community/llms/anyscale.py @@ -14,8 +14,8 @@ CallbackManagerForLLMRun, ) from langchain_core.outputs import Generation, GenerationChunk, LLMResult -from langchain_core.pydantic_v1 import Field, SecretStr from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init +from pydantic import Field, SecretStr from langchain_community.llms.openai import ( BaseOpenAI, diff --git a/libs/community/langchain_community/llms/aphrodite.py b/libs/community/langchain_community/llms/aphrodite.py index d8bf1c1871c1d..7d752b07e7986 100644 --- a/libs/community/langchain_community/llms/aphrodite.py +++ b/libs/community/langchain_community/llms/aphrodite.py @@ -3,8 +3,8 @@ from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models import BaseLLM from langchain_core.outputs import Generation, LLMResult -from langchain_core.pydantic_v1 import Field from langchain_core.utils import pre_init +from pydantic import Field class Aphrodite(BaseLLM): diff --git a/libs/community/langchain_community/llms/arcee.py b/libs/community/langchain_community/llms/arcee.py index 9bd7ff97ff9db..d838509617aec 100644 --- a/libs/community/langchain_community/llms/arcee.py +++ b/libs/community/langchain_community/llms/arcee.py @@ -2,8 +2,8 @@ from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models.llms import LLM -from langchain_core.pydantic_v1 import SecretStr, root_validator from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env +from pydantic import ConfigDict, SecretStr, model_validator from langchain_community.utilities.arcee import ArceeWrapper, DALMFilter @@ -51,9 +51,10 @@ class Arcee(LLM): model_kwargs: Optional[Dict[str, Any]] = None """Keyword arguments to pass to the model.""" - class Config: - extra = "forbid" - underscore_attrs_are_private = True + model_config = ConfigDict( + extra="forbid", + underscore_attrs_are_private=True, + ) @property def _llm_type(self) -> str: @@ -73,8 +74,9 @@ def __init__(self, **data: Any) -> None: model_name=self.model, ) - @root_validator(pre=True) - def validate_environments(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environments(cls, values: Dict) -> Any: """Validate Arcee environment variables.""" # validate env vars diff --git a/libs/community/langchain_community/llms/aviary.py b/libs/community/langchain_community/llms/aviary.py index 73dcbacb48d6a..95fd5730fac5d 100644 --- a/libs/community/langchain_community/llms/aviary.py +++ b/libs/community/langchain_community/llms/aviary.py @@ -5,8 +5,8 @@ import requests from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models.llms import LLM -from langchain_core.pydantic_v1 import root_validator from langchain_core.utils import get_from_dict_or_env +from pydantic import ConfigDict, model_validator from langchain_community.llms.utils import enforce_stop_tokens @@ -122,11 +122,13 @@ class Aviary(LLM): # API version to use for Aviary version: Optional[str] = None - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key and python package exists in environment.""" aviary_url = get_from_dict_or_env(values, "aviary_url", "AVIARY_URL") aviary_token = get_from_dict_or_env(values, "aviary_token", "AVIARY_TOKEN") diff --git a/libs/community/langchain_community/llms/azureml_endpoint.py b/libs/community/langchain_community/llms/azureml_endpoint.py index 7dd4f6aee4225..f2b5eed2e1c02 100644 --- a/libs/community/langchain_community/llms/azureml_endpoint.py +++ b/libs/community/langchain_community/llms/azureml_endpoint.py @@ -8,8 +8,8 @@ from langchain_core.callbacks.manager import CallbackManagerForLLMRun from langchain_core.language_models.llms import BaseLLM from langchain_core.outputs import Generation, LLMResult -from langchain_core.pydantic_v1 import BaseModel, SecretStr, root_validator, validator from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env +from pydantic import BaseModel, SecretStr, model_validator, validator DEFAULT_TIMEOUT = 50 @@ -382,8 +382,9 @@ class AzureMLBaseEndpoint(BaseModel): model_kwargs: Optional[dict] = None """Keyword arguments to pass to the model.""" - @root_validator(pre=True) - def validate_environ(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environ(cls, values: Dict) -> Any: values["endpoint_api_key"] = convert_to_secret_str( get_from_dict_or_env(values, "endpoint_api_key", "AZUREML_ENDPOINT_API_KEY") ) diff --git a/libs/community/langchain_community/llms/baichuan.py b/libs/community/langchain_community/llms/baichuan.py index 4cc614ce8ac4a..4026e14b90efe 100644 --- a/libs/community/langchain_community/llms/baichuan.py +++ b/libs/community/langchain_community/llms/baichuan.py @@ -7,8 +7,8 @@ import requests from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models.llms import LLM -from langchain_core.pydantic_v1 import Field, SecretStr from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init +from pydantic import Field, SecretStr from langchain_community.llms.utils import enforce_stop_tokens diff --git a/libs/community/langchain_community/llms/baidu_qianfan_endpoint.py b/libs/community/langchain_community/llms/baidu_qianfan_endpoint.py index 601f952bddbb4..6e4bc68b57fd9 100644 --- a/libs/community/langchain_community/llms/baidu_qianfan_endpoint.py +++ b/libs/community/langchain_community/llms/baidu_qianfan_endpoint.py @@ -16,8 +16,8 @@ ) from langchain_core.language_models.llms import LLM from langchain_core.outputs import GenerationChunk -from langchain_core.pydantic_v1 import Field, SecretStr from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init +from pydantic import Field, SecretStr logger = logging.getLogger(__name__) diff --git a/libs/community/langchain_community/llms/bananadev.py b/libs/community/langchain_community/llms/bananadev.py index c1be0bb1fb6a7..9ed76e816b336 100644 --- a/libs/community/langchain_community/llms/bananadev.py +++ b/libs/community/langchain_community/llms/bananadev.py @@ -3,9 +3,10 @@ from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models.llms import LLM -from langchain_core.pydantic_v1 import Field, SecretStr, root_validator -from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init -from langchain_core.utils.pydantic import get_fields +from langchain_core.utils import ( + secret_from_env, +) +from pydantic import ConfigDict, Field, SecretStr, model_validator from langchain_community.llms.utils import enforce_stop_tokens @@ -39,16 +40,19 @@ class Banana(LLM): """Holds any model parameters valid for `create` call not explicitly specified.""" - banana_api_key: Optional[SecretStr] = None + banana_api_key: Optional[SecretStr] = Field( + default_factory=secret_from_env("BANANA_API_KEY", default=None) + ) - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) - @root_validator(pre=True) - def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def build_extra(cls, values: Dict[str, Any]) -> Any: """Build extra kwargs from additional params that were passed in.""" - all_required_field_names = {field.alias for field in get_fields(cls).values()} - + all_required_field_names = set(list(cls.model_fields.keys())) extra = values.get("model_kwargs", {}) for field_name in list(values): if field_name not in all_required_field_names: @@ -62,15 +66,6 @@ def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: values["model_kwargs"] = extra return values - @pre_init - def validate_environment(cls, values: Dict) -> Dict: - """Validate that api key and python package exists in environment.""" - banana_api_key = convert_to_secret_str( - get_from_dict_or_env(values, "banana_api_key", "BANANA_API_KEY") - ) - values["banana_api_key"] = banana_api_key - return values - @property def _identifying_params(self) -> Mapping[str, Any]: """Get the identifying parameters.""" diff --git a/libs/community/langchain_community/llms/baseten.py b/libs/community/langchain_community/llms/baseten.py index 9859106f51143..5b7ce87eb827e 100644 --- a/libs/community/langchain_community/llms/baseten.py +++ b/libs/community/langchain_community/llms/baseten.py @@ -5,7 +5,7 @@ import requests from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models.llms import LLM -from langchain_core.pydantic_v1 import Field +from pydantic import Field logger = logging.getLogger(__name__) diff --git a/libs/community/langchain_community/llms/beam.py b/libs/community/langchain_community/llms/beam.py index 63d51499e9a1b..fe8bf5f5ff8c0 100644 --- a/libs/community/langchain_community/llms/beam.py +++ b/libs/community/langchain_community/llms/beam.py @@ -9,9 +9,9 @@ import requests from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models.llms import LLM -from langchain_core.pydantic_v1 import Field, root_validator from langchain_core.utils import get_from_dict_or_env, pre_init from langchain_core.utils.pydantic import get_fields +from pydantic import ConfigDict, Field, model_validator logger = logging.getLogger(__name__) @@ -73,11 +73,13 @@ class Beam(LLM): beam_client_secret: str = "" app_id: Optional[str] = None - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) - @root_validator(pre=True) - def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def build_extra(cls, values: Dict[str, Any]) -> Any: """Build extra kwargs from additional params that were passed in.""" all_required_field_names = {field.alias for field in get_fields(cls).values()} diff --git a/libs/community/langchain_community/llms/bedrock.py b/libs/community/langchain_community/llms/bedrock.py index d47a762973bea..444d50eb24b48 100644 --- a/libs/community/langchain_community/llms/bedrock.py +++ b/libs/community/langchain_community/llms/bedrock.py @@ -21,8 +21,8 @@ ) from langchain_core.language_models.llms import LLM from langchain_core.outputs import GenerationChunk -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.utils import get_from_dict_or_env, pre_init +from pydantic import BaseModel, ConfigDict, Field from langchain_community.llms.utils import enforce_stop_tokens from langchain_community.utilities.anthropic import ( @@ -778,8 +778,9 @@ def lc_attributes(self) -> Dict[str, Any]: return attributes - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) def _stream( self, diff --git a/libs/community/langchain_community/llms/cerebriumai.py b/libs/community/langchain_community/llms/cerebriumai.py index 9e69b026c9cb1..b267033721124 100644 --- a/libs/community/langchain_community/llms/cerebriumai.py +++ b/libs/community/langchain_community/llms/cerebriumai.py @@ -4,9 +4,8 @@ import requests from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models.llms import LLM -from langchain_core.pydantic_v1 import Field, SecretStr, root_validator from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init -from langchain_core.utils.pydantic import get_fields +from pydantic import ConfigDict, Field, SecretStr, model_validator from langchain_community.llms.utils import enforce_stop_tokens @@ -40,13 +39,15 @@ class CerebriumAI(LLM): cerebriumai_api_key: Optional[SecretStr] = None - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) - @root_validator(pre=True) - def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def build_extra(cls, values: Dict[str, Any]) -> Any: """Build extra kwargs from additional params that were passed in.""" - all_required_field_names = {field.alias for field in get_fields(cls).values()} + all_required_field_names = set(list(cls.model_fields.keys())) extra = values.get("model_kwargs", {}) for field_name in list(values): diff --git a/libs/community/langchain_community/llms/chatglm3.py b/libs/community/langchain_community/llms/chatglm3.py index aa3255a50f6df..796a592f4af88 100644 --- a/libs/community/langchain_community/llms/chatglm3.py +++ b/libs/community/langchain_community/llms/chatglm3.py @@ -11,7 +11,7 @@ HumanMessage, SystemMessage, ) -from langchain_core.pydantic_v1 import Field +from pydantic import Field from langchain_community.llms.utils import enforce_stop_tokens diff --git a/libs/community/langchain_community/llms/clarifai.py b/libs/community/langchain_community/llms/clarifai.py index 706e8bea2b993..c7d6fcde6a975 100644 --- a/libs/community/langchain_community/llms/clarifai.py +++ b/libs/community/langchain_community/llms/clarifai.py @@ -4,8 +4,8 @@ from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models.llms import LLM from langchain_core.outputs import Generation, LLMResult -from langchain_core.pydantic_v1 import Field from langchain_core.utils import pre_init +from pydantic import ConfigDict, Field from langchain_community.llms.utils import enforce_stop_tokens @@ -49,8 +49,9 @@ class Clarifai(LLM): model: Any = Field(default=None, exclude=True) #: :meta private: api_base: str = "https://api.clarifai.com" - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) @pre_init def validate_environment(cls, values: Dict) -> Dict: diff --git a/libs/community/langchain_community/llms/cohere.py b/libs/community/langchain_community/llms/cohere.py index 9d075b25a4506..875e9228122ca 100644 --- a/libs/community/langchain_community/llms/cohere.py +++ b/libs/community/langchain_community/llms/cohere.py @@ -10,8 +10,8 @@ ) from langchain_core.language_models.llms import LLM from langchain_core.load.serializable import Serializable -from langchain_core.pydantic_v1 import Field, SecretStr from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init +from pydantic import ConfigDict, Field, SecretStr from tenacity import ( before_sleep_log, retry, @@ -159,8 +159,9 @@ class Cohere(LLM, BaseCohere): max_retries: int = 10 """Maximum number of retries to make when generating.""" - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) @property def _default_params(self) -> Dict[str, Any]: diff --git a/libs/community/langchain_community/llms/ctranslate2.py b/libs/community/langchain_community/llms/ctranslate2.py index 22c5de078acc4..71bfeb1f79864 100644 --- a/libs/community/langchain_community/llms/ctranslate2.py +++ b/libs/community/langchain_community/llms/ctranslate2.py @@ -3,8 +3,8 @@ from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models.llms import BaseLLM from langchain_core.outputs import Generation, LLMResult -from langchain_core.pydantic_v1 import Field from langchain_core.utils import pre_init +from pydantic import Field class CTranslate2(BaseLLM): diff --git a/libs/community/langchain_community/llms/databricks.py b/libs/community/langchain_community/llms/databricks.py index 8e5ab49f7ef0b..d0ede9dbcd740 100644 --- a/libs/community/langchain_community/llms/databricks.py +++ b/libs/community/langchain_community/llms/databricks.py @@ -7,11 +7,12 @@ import requests from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models import LLM -from langchain_core.pydantic_v1 import ( +from pydantic import ( BaseModel, + ConfigDict, Field, PrivateAttr, - root_validator, + model_validator, validator, ) @@ -97,8 +98,9 @@ def __init__(self, **data: Any): def llm(self) -> bool: return self.task in ("llm/v1/chat", "llm/v1/completions", "llama2/chat") - @root_validator(pre=True) - def set_api_url(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def set_api_url(cls, values: Dict[str, Any]) -> Any: if "api_url" not in values: host = values["host"] endpoint_name = values["endpoint_name"] @@ -141,8 +143,9 @@ class _DatabricksClusterDriverProxyClient(_DatabricksClientBase): cluster_id: str cluster_driver_port: str - @root_validator(pre=True) - def set_api_url(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def set_api_url(cls, values: Dict[str, Any]) -> Any: if "api_url" not in values: host = values["host"] cluster_id = values["cluster_id"] @@ -395,9 +398,10 @@ class Databricks(LLM): _client: _DatabricksClientBase = PrivateAttr() - class Config: - extra = "forbid" - underscore_attrs_are_private = True + model_config = ConfigDict( + extra="forbid", + underscore_attrs_are_private=True, + ) @property def _llm_params(self) -> Dict[str, Any]: diff --git a/libs/community/langchain_community/llms/deepinfra.py b/libs/community/langchain_community/llms/deepinfra.py index 303113f191aa4..47551dd4e6520 100644 --- a/libs/community/langchain_community/llms/deepinfra.py +++ b/libs/community/langchain_community/llms/deepinfra.py @@ -9,6 +9,7 @@ from langchain_core.language_models.llms import LLM from langchain_core.outputs import GenerationChunk from langchain_core.utils import get_from_dict_or_env, pre_init +from pydantic import ConfigDict from langchain_community.utilities.requests import Requests @@ -37,8 +38,9 @@ class DeepInfra(LLM): deepinfra_api_token: Optional[str] = None - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) @pre_init def validate_environment(cls, values: Dict) -> Dict: diff --git a/libs/community/langchain_community/llms/deepsparse.py b/libs/community/langchain_community/llms/deepsparse.py index 106750e273146..9375a3f772264 100644 --- a/libs/community/langchain_community/llms/deepsparse.py +++ b/libs/community/langchain_community/llms/deepsparse.py @@ -2,7 +2,7 @@ from langchain_core.utils import pre_init from typing import Any, AsyncIterator, Dict, Iterator, List, Optional, Union from langchain_core.utils import pre_init -from langchain_core.pydantic_v1 import root_validator +from pydantic import root_validator from langchain_core.utils import pre_init from langchain_core.callbacks import ( AsyncCallbackManagerForLLMRun, @@ -33,7 +33,7 @@ class DeepSparse(LLM): model: str """The path to a model file or directory or the name of a SparseZoo model stub.""" - model_config: Optional[Dict[str, Any]] = None + model_configuration: Optional[Dict[str, Any]] = None """Keyword arguments passed to the pipeline construction. Common parameters are sequence_length, prompt_sequence_length""" @@ -51,7 +51,7 @@ def _identifying_params(self) -> Dict[str, Any]: """Get the identifying parameters.""" return { "model": self.model, - "model_config": self.model_config, + "model_config": self.model_configuration, "generation_config": self.generation_config, "streaming": self.streaming, } @@ -72,7 +72,7 @@ def validate_environment(cls, values: Dict) -> Dict: "Please install it with `pip install deepsparse[llm]`" ) - model_config = values["model_config"] or {} + model_config = values["model_configuration"] or {} values["pipeline"] = Pipeline.create( task="text_generation", diff --git a/libs/community/langchain_community/llms/edenai.py b/libs/community/langchain_community/llms/edenai.py index 31cdaee92aeda..a43e8a71bec68 100644 --- a/libs/community/langchain_community/llms/edenai.py +++ b/libs/community/langchain_community/llms/edenai.py @@ -9,9 +9,9 @@ CallbackManagerForLLMRun, ) from langchain_core.language_models.llms import LLM -from langchain_core.pydantic_v1 import Field, root_validator from langchain_core.utils import get_from_dict_or_env, pre_init from langchain_core.utils.pydantic import get_fields +from pydantic import ConfigDict, Field, model_validator from langchain_community.llms.utils import enforce_stop_tokens from langchain_community.utilities.requests import Requests @@ -69,8 +69,9 @@ class EdenAI(LLM): stop_sequences: Optional[List[str]] = None """Stop sequences to use.""" - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) @pre_init def validate_environment(cls, values: Dict) -> Dict: @@ -80,8 +81,9 @@ def validate_environment(cls, values: Dict) -> Dict: ) return values - @root_validator(pre=True) - def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def build_extra(cls, values: Dict[str, Any]) -> Any: """Build extra kwargs from additional params that were passed in.""" all_required_field_names = {field.alias for field in get_fields(cls).values()} diff --git a/libs/community/langchain_community/llms/exllamav2.py b/libs/community/langchain_community/llms/exllamav2.py index 3e577cbf97788..95de8468e197f 100644 --- a/libs/community/langchain_community/llms/exllamav2.py +++ b/libs/community/langchain_community/llms/exllamav2.py @@ -3,8 +3,8 @@ from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models import LLM from langchain_core.outputs import GenerationChunk -from langchain_core.pydantic_v1 import Field from langchain_core.utils import pre_init +from pydantic import Field class ExLlamaV2(LLM): @@ -41,7 +41,7 @@ class ExLlamaV2(LLM): settings: Any = None # Langchain parameters - logfunc = print + logfunc: callable = print stop_sequences: List[str] = Field("") """Sequences that immediately will stop the generator.""" diff --git a/libs/community/langchain_community/llms/fireworks.py b/libs/community/langchain_community/llms/fireworks.py index 73948062e6c76..f7af9e90b8d24 100644 --- a/libs/community/langchain_community/llms/fireworks.py +++ b/libs/community/langchain_community/llms/fireworks.py @@ -9,9 +9,9 @@ ) from langchain_core.language_models.llms import BaseLLM, create_base_retry_decorator from langchain_core.outputs import Generation, GenerationChunk, LLMResult -from langchain_core.pydantic_v1 import Field, SecretStr from langchain_core.utils import convert_to_secret_str, pre_init from langchain_core.utils.env import get_from_dict_or_env +from pydantic import Field, SecretStr def _stream_response_to_generation_chunk( diff --git a/libs/community/langchain_community/llms/forefrontai.py b/libs/community/langchain_community/llms/forefrontai.py index 9f1ac129ebbba..8c47304438982 100644 --- a/libs/community/langchain_community/llms/forefrontai.py +++ b/libs/community/langchain_community/llms/forefrontai.py @@ -3,8 +3,8 @@ import requests from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models.llms import LLM -from langchain_core.pydantic_v1 import SecretStr, root_validator from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env +from pydantic import ConfigDict, SecretStr, model_validator from langchain_community.llms.utils import enforce_stop_tokens @@ -46,11 +46,13 @@ class ForefrontAI(LLM): base_url: Optional[str] = None """Base url to use, if None decides based on model name.""" - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key exists in environment.""" values["forefrontai_api_key"] = convert_to_secret_str( get_from_dict_or_env(values, "forefrontai_api_key", "FOREFRONTAI_API_KEY") diff --git a/libs/community/langchain_community/llms/friendli.py b/libs/community/langchain_community/llms/friendli.py index 5f137b078e172..e6d13155cd834 100644 --- a/libs/community/langchain_community/llms/friendli.py +++ b/libs/community/langchain_community/llms/friendli.py @@ -10,10 +10,10 @@ from langchain_core.language_models.llms import LLM from langchain_core.load.serializable import Serializable from langchain_core.outputs import GenerationChunk, LLMResult -from langchain_core.pydantic_v1 import Field, SecretStr from langchain_core.utils import pre_init from langchain_core.utils.env import get_from_dict_or_env from langchain_core.utils.utils import convert_to_secret_str +from pydantic import Field, SecretStr def _stream_response_to_generation_chunk(stream_response: Any) -> GenerationChunk: diff --git a/libs/community/langchain_community/llms/gigachat.py b/libs/community/langchain_community/llms/gigachat.py index 4172081dac4de..468d68cd9f4a8 100644 --- a/libs/community/langchain_community/llms/gigachat.py +++ b/libs/community/langchain_community/llms/gigachat.py @@ -13,6 +13,7 @@ from langchain_core.outputs import Generation, GenerationChunk, LLMResult from langchain_core.utils import pre_init from langchain_core.utils.pydantic import get_fields +from pydantic import ConfigDict if TYPE_CHECKING: import gigachat @@ -330,5 +331,6 @@ async def _astream( if run_manager: await run_manager.on_llm_new_token(content) - class Config: - extra = "allow" + model_config = ConfigDict( + extra="allow", + ) diff --git a/libs/community/langchain_community/llms/google_palm.py b/libs/community/langchain_community/llms/google_palm.py index e996c67b92df1..715792a3a0619 100644 --- a/libs/community/langchain_community/llms/google_palm.py +++ b/libs/community/langchain_community/llms/google_palm.py @@ -6,8 +6,8 @@ from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models import LanguageModelInput from langchain_core.outputs import Generation, GenerationChunk, LLMResult -from langchain_core.pydantic_v1 import BaseModel, SecretStr from langchain_core.utils import get_from_dict_or_env, pre_init +from pydantic import BaseModel, SecretStr from langchain_community.llms import BaseLLM from langchain_community.utilities.vertexai import create_retry_decorator diff --git a/libs/community/langchain_community/llms/gooseai.py b/libs/community/langchain_community/llms/gooseai.py index 255908a20c8e8..8a2dbdf37dbfc 100644 --- a/libs/community/langchain_community/llms/gooseai.py +++ b/libs/community/langchain_community/llms/gooseai.py @@ -3,9 +3,12 @@ from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models.llms import LLM -from langchain_core.pydantic_v1 import Field, SecretStr, root_validator -from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init -from langchain_core.utils.pydantic import get_fields +from langchain_core.utils import ( + convert_to_secret_str, + get_from_dict_or_env, + get_pydantic_field_names, +) +from pydantic import ConfigDict, Field, SecretStr, model_validator logger = logging.getLogger(__name__) @@ -63,13 +66,15 @@ class GooseAI(LLM): gooseai_api_key: Optional[SecretStr] = None - class Config: - extra = "ignore" + model_config = ConfigDict( + extra="ignore", + ) - @root_validator(pre=True) - def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def build_extra(cls, values: Dict[str, Any]) -> Any: """Build extra kwargs from additional params that were passed in.""" - all_required_field_names = {field.alias for field in get_fields(cls).values()} + all_required_field_names = get_pydantic_field_names(cls) extra = values.get("model_kwargs", {}) for field_name in list(values): @@ -83,11 +88,7 @@ def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: ) extra[field_name] = values.pop(field_name) values["model_kwargs"] = extra - return values - @pre_init - def validate_environment(cls, values: Dict) -> Dict: - """Validate that api key and python package exists in environment.""" gooseai_api_key = convert_to_secret_str( get_from_dict_or_env(values, "gooseai_api_key", "GOOSEAI_API_KEY") ) diff --git a/libs/community/langchain_community/llms/gpt4all.py b/libs/community/langchain_community/llms/gpt4all.py index 0700410c9f89d..85c47ac691ec8 100644 --- a/libs/community/langchain_community/llms/gpt4all.py +++ b/libs/community/langchain_community/llms/gpt4all.py @@ -3,8 +3,8 @@ from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models.llms import LLM -from langchain_core.pydantic_v1 import Field from langchain_core.utils import pre_init +from pydantic import ConfigDict, Field from langchain_community.llms.utils import enforce_stop_tokens @@ -96,8 +96,9 @@ class GPT4All(LLM): client: Any = None #: :meta private: - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) @staticmethod def _model_param_names() -> Set[str]: diff --git a/libs/community/langchain_community/llms/gradient_ai.py b/libs/community/langchain_community/llms/gradient_ai.py index 0da21c79ac29b..b603fd498d9a2 100644 --- a/libs/community/langchain_community/llms/gradient_ai.py +++ b/libs/community/langchain_community/llms/gradient_ai.py @@ -11,8 +11,9 @@ ) from langchain_core.language_models.llms import BaseLLM from langchain_core.outputs import Generation, LLMResult -from langchain_core.pydantic_v1 import Field, root_validator from langchain_core.utils import get_from_dict_or_env +from pydantic import ConfigDict, Field, model_validator +from typing_extensions import Self from langchain_community.llms.utils import enforce_stop_tokens @@ -73,12 +74,14 @@ class GradientLLM(BaseLLM): """ClientSession, private, subject to change in upcoming releases.""" # LLM call kwargs - class Config: - allow_population_by_field_name = True - extra = "forbid" - - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + model_config = ConfigDict( + populate_by_name=True, + extra="forbid", + ) + + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key and python package exists in environment.""" values["gradient_access_token"] = get_from_dict_or_env( @@ -93,8 +96,8 @@ def validate_environment(cls, values: Dict) -> Dict: ) return values - @root_validator(pre=False, skip_on_failure=True) - def post_init(cls, values: Dict) -> Dict: + @model_validator(mode="after") + def post_init(self) -> Self: """Post init validation.""" # Can be most to post_init_validation try: @@ -108,20 +111,14 @@ def post_init(cls, values: Dict) -> Dict: pass # Can be most to post_init_validation - if ( - values["gradient_access_token"] is None - or len(values["gradient_access_token"]) < 10 - ): + if self.gradient_access_token is None or len(self.gradient_access_token) < 10: raise ValueError("env variable `GRADIENT_ACCESS_TOKEN` must be set") - if ( - values["gradient_workspace_id"] is None - or len(values["gradient_access_token"]) < 3 - ): + if self.gradient_workspace_id is None or len(self.gradient_access_token) < 3: raise ValueError("env variable `GRADIENT_WORKSPACE_ID` must be set") - if values["model_kwargs"]: - kw = values["model_kwargs"] + if self.model_kwargs: + kw = self.model_kwargs if not 0 <= kw.get("temperature", 0.5) <= 1: raise ValueError("`temperature` must be in the range [0.0, 1.0]") @@ -134,7 +131,7 @@ def post_init(cls, values: Dict) -> Dict: if 0 >= kw.get("max_generated_token_count", 1): raise ValueError("`max_generated_token_count` must be positive") - return values + return self @property def _identifying_params(self) -> Mapping[str, Any]: diff --git a/libs/community/langchain_community/llms/huggingface_endpoint.py b/libs/community/langchain_community/llms/huggingface_endpoint.py index 27c1adce07f12..e9b4279a7d6e4 100644 --- a/libs/community/langchain_community/llms/huggingface_endpoint.py +++ b/libs/community/langchain_community/llms/huggingface_endpoint.py @@ -10,11 +10,11 @@ ) from langchain_core.language_models.llms import LLM from langchain_core.outputs import GenerationChunk -from langchain_core.pydantic_v1 import Field, root_validator from langchain_core.utils import ( get_pydantic_field_names, pre_init, ) +from pydantic import ConfigDict, Field, model_validator logger = logging.getLogger(__name__) @@ -126,11 +126,13 @@ class HuggingFaceEndpoint(LLM): """Task to call the model with. Should be a task that returns `generated_text` or `summary_text`.""" - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) - @root_validator(pre=True) - def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def build_extra(cls, values: Dict[str, Any]) -> Any: """Build extra kwargs from additional params that were passed in.""" all_required_field_names = get_pydantic_field_names(cls) extra = values.get("model_kwargs", {}) diff --git a/libs/community/langchain_community/llms/huggingface_hub.py b/libs/community/langchain_community/llms/huggingface_hub.py index 9c207c491f32f..7d1472fc5d844 100644 --- a/libs/community/langchain_community/llms/huggingface_hub.py +++ b/libs/community/langchain_community/llms/huggingface_hub.py @@ -5,6 +5,7 @@ from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models.llms import LLM from langchain_core.utils import get_from_dict_or_env, pre_init +from pydantic import ConfigDict from langchain_community.llms.utils import enforce_stop_tokens @@ -55,8 +56,9 @@ class HuggingFaceHub(LLM): huggingfacehub_api_token: Optional[str] = None - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) @pre_init def validate_environment(cls, values: Dict) -> Dict: diff --git a/libs/community/langchain_community/llms/huggingface_pipeline.py b/libs/community/langchain_community/llms/huggingface_pipeline.py index 1ccbc76954a6e..852e49c9385d4 100644 --- a/libs/community/langchain_community/llms/huggingface_pipeline.py +++ b/libs/community/langchain_community/llms/huggingface_pipeline.py @@ -8,6 +8,7 @@ from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models.llms import BaseLLM from langchain_core.outputs import Generation, GenerationChunk, LLMResult +from pydantic import ConfigDict DEFAULT_MODEL_ID = "gpt2" DEFAULT_TASK = "text-generation" @@ -69,8 +70,9 @@ class HuggingFacePipeline(BaseLLM): batch_size: int = DEFAULT_BATCH_SIZE """Batch size to use when passing multiple documents to generate.""" - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) @classmethod def from_model_id( diff --git a/libs/community/langchain_community/llms/huggingface_text_gen_inference.py b/libs/community/langchain_community/llms/huggingface_text_gen_inference.py index 56bcf69166ed0..97acb3ce78caf 100644 --- a/libs/community/langchain_community/llms/huggingface_text_gen_inference.py +++ b/libs/community/langchain_community/llms/huggingface_text_gen_inference.py @@ -8,8 +8,8 @@ ) from langchain_core.language_models.llms import LLM from langchain_core.outputs import GenerationChunk -from langchain_core.pydantic_v1 import Field, root_validator from langchain_core.utils import get_pydantic_field_names, pre_init +from pydantic import ConfigDict, Field, model_validator logger = logging.getLogger(__name__) @@ -103,11 +103,13 @@ class HuggingFaceTextGenInference(LLM): client: Any async_client: Any - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) - @root_validator(pre=True) - def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def build_extra(cls, values: Dict[str, Any]) -> Any: """Build extra kwargs from additional params that were passed in.""" all_required_field_names = get_pydantic_field_names(cls) extra = values.get("model_kwargs", {}) diff --git a/libs/community/langchain_community/llms/human.py b/libs/community/langchain_community/llms/human.py index 39473c0ee1e0e..9a54b29aa10c2 100644 --- a/libs/community/langchain_community/llms/human.py +++ b/libs/community/langchain_community/llms/human.py @@ -2,7 +2,7 @@ from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models.llms import LLM -from langchain_core.pydantic_v1 import Field +from pydantic import Field from langchain_community.llms.utils import enforce_stop_tokens diff --git a/libs/community/langchain_community/llms/ipex_llm.py b/libs/community/langchain_community/llms/ipex_llm.py index c1f7f40c4e887..72d5afa342d25 100644 --- a/libs/community/langchain_community/llms/ipex_llm.py +++ b/libs/community/langchain_community/llms/ipex_llm.py @@ -3,6 +3,7 @@ from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models.llms import LLM +from pydantic import ConfigDict DEFAULT_MODEL_ID = "gpt2" @@ -31,8 +32,9 @@ class IpexLLM(LLM): streaming: bool = True """Whether to stream the results, token by token.""" - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) @classmethod def from_model_id( diff --git a/libs/community/langchain_community/llms/javelin_ai_gateway.py b/libs/community/langchain_community/llms/javelin_ai_gateway.py index 3ba7fa3ccf9f3..156350308fac7 100644 --- a/libs/community/langchain_community/llms/javelin_ai_gateway.py +++ b/libs/community/langchain_community/llms/javelin_ai_gateway.py @@ -7,7 +7,7 @@ CallbackManagerForLLMRun, ) from langchain_core.language_models.llms import LLM -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel # Ignoring type because below is valid pydantic code diff --git a/libs/community/langchain_community/llms/konko.py b/libs/community/langchain_community/llms/konko.py index 6cc422cd43c4b..0c2a62e92747f 100644 --- a/libs/community/langchain_community/llms/konko.py +++ b/libs/community/langchain_community/llms/konko.py @@ -9,7 +9,7 @@ CallbackManagerForLLMRun, ) from langchain_core.language_models.llms import LLM -from langchain_core.pydantic_v1 import SecretStr, root_validator +from pydantic import ConfigDict, SecretStr, model_validator from langchain_community.utils.openai import is_openai_v1 @@ -60,11 +60,13 @@ class Konko(LLM): the response for each token generation step. """ - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict[str, Any]) -> Any: """Validate that python package exists in environment.""" try: import konko diff --git a/libs/community/langchain_community/llms/layerup_security.py b/libs/community/langchain_community/llms/layerup_security.py index dd8bb819284b4..c15626eff2318 100644 --- a/libs/community/langchain_community/llms/layerup_security.py +++ b/libs/community/langchain_community/llms/layerup_security.py @@ -3,7 +3,7 @@ from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models.llms import LLM -from langchain_core.pydantic_v1 import root_validator +from pydantic import model_validator logger = logging.getLogger(__name__) @@ -47,8 +47,9 @@ class LayerupSecurity(LLM): ) client: Any #: :meta private: - @root_validator(pre=True) - def validate_layerup_sdk(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def validate_layerup_sdk(cls, values: Dict[str, Any]) -> Any: try: from layerup_security import LayerupSecurity as LayerupSecuritySDK diff --git a/libs/community/langchain_community/llms/llamacpp.py b/libs/community/langchain_community/llms/llamacpp.py index 0bd6d7e010c0b..d206f138e6ba7 100644 --- a/libs/community/langchain_community/llms/llamacpp.py +++ b/libs/community/langchain_community/llms/llamacpp.py @@ -7,9 +7,9 @@ from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models.llms import LLM from langchain_core.outputs import GenerationChunk -from langchain_core.pydantic_v1 import Field, root_validator from langchain_core.utils import get_pydantic_field_names, pre_init from langchain_core.utils.utils import build_extra_kwargs +from pydantic import Field, model_validator logger = logging.getLogger(__name__) @@ -194,8 +194,9 @@ def validate_environment(cls, values: Dict) -> Dict: pass return values - @root_validator(pre=True) - def build_model_kwargs(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def build_model_kwargs(cls, values: Dict[str, Any]) -> Any: """Build extra kwargs from additional params that were passed in.""" all_required_field_names = get_pydantic_field_names(cls) extra = values.get("model_kwargs", {}) diff --git a/libs/community/langchain_community/llms/llamafile.py b/libs/community/langchain_community/llms/llamafile.py index 1f102bab52801..e168572c1b32a 100644 --- a/libs/community/langchain_community/llms/llamafile.py +++ b/libs/community/langchain_community/llms/llamafile.py @@ -9,6 +9,7 @@ from langchain_core.language_models.llms import LLM from langchain_core.outputs import GenerationChunk from langchain_core.utils import get_pydantic_field_names +from pydantic import ConfigDict class Llamafile(LLM): @@ -112,8 +113,9 @@ class Llamafile(LLM): mirostat_eta: float = 0.1 """Set the Mirostat learning rate, parameter eta. Default: 0.1.""" - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) @property def _llm_type(self) -> str: diff --git a/libs/community/langchain_community/llms/manifest.py b/libs/community/langchain_community/llms/manifest.py index 4806d8c219fe0..745de7e595401 100644 --- a/libs/community/langchain_community/llms/manifest.py +++ b/libs/community/langchain_community/llms/manifest.py @@ -3,6 +3,7 @@ from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models.llms import LLM from langchain_core.utils import pre_init +from pydantic import ConfigDict class ManifestWrapper(LLM): @@ -11,8 +12,9 @@ class ManifestWrapper(LLM): client: Any #: :meta private: llm_kwargs: Optional[Dict] = None - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) @pre_init def validate_environment(cls, values: Dict) -> Dict: diff --git a/libs/community/langchain_community/llms/minimax.py b/libs/community/langchain_community/llms/minimax.py index 18a71cfdab38a..e508eb4f959c8 100644 --- a/libs/community/langchain_community/llms/minimax.py +++ b/libs/community/langchain_community/llms/minimax.py @@ -15,8 +15,8 @@ CallbackManagerForLLMRun, ) from langchain_core.language_models.llms import LLM -from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr, root_validator from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init +from pydantic import BaseModel, Field, SecretStr, model_validator from langchain_community.llms.utils import enforce_stop_tokens @@ -31,8 +31,9 @@ class _MinimaxEndpointClient(BaseModel): api_key: SecretStr api_url: str - @root_validator(pre=True) - def set_api_url(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def set_api_url(cls, values: Dict[str, Any]) -> Any: if "api_url" not in values: host = values["host"] group_id = values["group_id"] diff --git a/libs/community/langchain_community/llms/mlflow.py b/libs/community/langchain_community/llms/mlflow.py index 9b27ed57d8721..d8422629bcde3 100644 --- a/libs/community/langchain_community/llms/mlflow.py +++ b/libs/community/langchain_community/llms/mlflow.py @@ -5,7 +5,7 @@ from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models import LLM -from langchain_core.pydantic_v1 import Field, PrivateAttr +from pydantic import Field, PrivateAttr class Mlflow(LLM): diff --git a/libs/community/langchain_community/llms/mlflow_ai_gateway.py b/libs/community/langchain_community/llms/mlflow_ai_gateway.py index bd064c3dbff93..95196838e94fb 100644 --- a/libs/community/langchain_community/llms/mlflow_ai_gateway.py +++ b/libs/community/langchain_community/llms/mlflow_ai_gateway.py @@ -5,7 +5,7 @@ from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models.llms import LLM -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel # Ignoring type because below is valid pydantic code diff --git a/libs/community/langchain_community/llms/mlx_pipeline.py b/libs/community/langchain_community/llms/mlx_pipeline.py index 3486aa33e5895..0937c8f39d517 100644 --- a/libs/community/langchain_community/llms/mlx_pipeline.py +++ b/libs/community/langchain_community/llms/mlx_pipeline.py @@ -6,6 +6,7 @@ from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models.llms import LLM from langchain_core.outputs import GenerationChunk +from pydantic import ConfigDict DEFAULT_MODEL_ID = "mlx-community/quantized-gemma-2b" @@ -74,8 +75,9 @@ class MLXPipeline(LLM): """ - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) @classmethod def from_model_id( diff --git a/libs/community/langchain_community/llms/modal.py b/libs/community/langchain_community/llms/modal.py index 010127d4d5438..a68aa985d3d1d 100644 --- a/libs/community/langchain_community/llms/modal.py +++ b/libs/community/langchain_community/llms/modal.py @@ -4,8 +4,8 @@ import requests from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models.llms import LLM -from langchain_core.pydantic_v1 import Field, root_validator from langchain_core.utils.pydantic import get_fields +from pydantic import ConfigDict, Field, model_validator from langchain_community.llms.utils import enforce_stop_tokens @@ -35,11 +35,13 @@ class Modal(LLM): """Holds any model parameters valid for `create` call not explicitly specified.""" - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) - @root_validator(pre=True) - def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def build_extra(cls, values: Dict[str, Any]) -> Any: """Build extra kwargs from additional params that were passed in.""" all_required_field_names = {field.alias for field in get_fields(cls).values()} diff --git a/libs/community/langchain_community/llms/moonshot.py b/libs/community/langchain_community/llms/moonshot.py index 390113c1f9c23..eedc482053fb6 100644 --- a/libs/community/langchain_community/llms/moonshot.py +++ b/libs/community/langchain_community/llms/moonshot.py @@ -3,8 +3,14 @@ import requests from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models import LLM -from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr, root_validator from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init +from pydantic import ( + BaseModel, + ConfigDict, + Field, + SecretStr, + model_validator, +) from langchain_community.llms.utils import enforce_stop_tokens @@ -44,8 +50,9 @@ class MoonshotCommon(BaseModel): temperature: float = 0.3 """Temperature parameter (higher values make the model more creative).""" - class Config: - allow_population_by_field_name = True + model_config = ConfigDict( + populate_by_name=True, + ) @property def lc_secrets(self) -> dict: @@ -69,8 +76,9 @@ def _default_params(self) -> Dict[str, Any]: def _invocation_params(self) -> Dict[str, Any]: return {**{"model": self.model_name}, **self._default_params} - @root_validator(pre=True) - def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def build_extra(cls, values: Dict[str, Any]) -> Any: """Build extra parameters. Override the superclass method, prevent the model parameter from being overridden. @@ -112,8 +120,9 @@ class Moonshot(MoonshotCommon, LLM): moonshot = Moonshot(model="moonshot-v1-8k") """ - class Config: - allow_population_by_field_name = True + model_config = ConfigDict( + populate_by_name=True, + ) def _call( self, diff --git a/libs/community/langchain_community/llms/mosaicml.py b/libs/community/langchain_community/llms/mosaicml.py index a0a7eca2ed751..15464dc829d9a 100644 --- a/libs/community/langchain_community/llms/mosaicml.py +++ b/libs/community/langchain_community/llms/mosaicml.py @@ -4,6 +4,7 @@ from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models.llms import LLM from langchain_core.utils import get_from_dict_or_env, pre_init +from pydantic import ConfigDict from langchain_community.llms.utils import enforce_stop_tokens @@ -58,8 +59,9 @@ class MosaicML(LLM): mosaicml_api_token: Optional[str] = None - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) @pre_init def validate_environment(cls, values: Dict) -> Dict: diff --git a/libs/community/langchain_community/llms/nlpcloud.py b/libs/community/langchain_community/llms/nlpcloud.py index 31c5b0f8b25d3..cd014f8581f1f 100644 --- a/libs/community/langchain_community/llms/nlpcloud.py +++ b/libs/community/langchain_community/llms/nlpcloud.py @@ -2,8 +2,8 @@ from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models.llms import LLM -from langchain_core.pydantic_v1 import SecretStr from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init +from pydantic import ConfigDict, SecretStr class NLPCloud(LLM): @@ -51,8 +51,9 @@ class NLPCloud(LLM): nlpcloud_api_key: Optional[SecretStr] = None - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) @pre_init def validate_environment(cls, values: Dict) -> Dict: diff --git a/libs/community/langchain_community/llms/oci_data_science_model_deployment_endpoint.py b/libs/community/langchain_community/llms/oci_data_science_model_deployment_endpoint.py index 972d245302662..04f551ce27c42 100644 --- a/libs/community/langchain_community/llms/oci_data_science_model_deployment_endpoint.py +++ b/libs/community/langchain_community/llms/oci_data_science_model_deployment_endpoint.py @@ -4,8 +4,8 @@ import requests from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models.llms import LLM -from langchain_core.pydantic_v1 import Field from langchain_core.utils import get_from_dict_or_env, pre_init +from pydantic import Field logger = logging.getLogger(__name__) diff --git a/libs/community/langchain_community/llms/oci_generative_ai.py b/libs/community/langchain_community/llms/oci_generative_ai.py index 5c996fe3191dd..8d06381264b2e 100644 --- a/libs/community/langchain_community/llms/oci_generative_ai.py +++ b/libs/community/langchain_community/llms/oci_generative_ai.py @@ -8,8 +8,8 @@ from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models.llms import LLM from langchain_core.outputs import GenerationChunk -from langchain_core.pydantic_v1 import BaseModel from langchain_core.utils import pre_init +from pydantic import BaseModel, ConfigDict, Field from langchain_community.llms.utils import enforce_stop_tokens @@ -61,7 +61,7 @@ class OCIAuthType(Enum): class OCIGenAIBase(BaseModel, ABC): """Base class for OCI GenAI models""" - client: Any #: :meta private: + client: Any = Field(default=None, exclude=True) #: :meta private: auth_type: Optional[str] = "API_KEY" """Authentication type, could be @@ -79,10 +79,10 @@ class OCIGenAIBase(BaseModel, ABC): If not specified , DEFAULT will be used """ - model_id: str = None # type: ignore[assignment] + model_id: Optional[str] = None """Id of the model to call, e.g., cohere.command""" - provider: str = None # type: ignore[assignment] + provider: Optional[str] = None """Provider name of the model. Default to None, will try to be derived from the model_id otherwise, requires user input @@ -91,15 +91,20 @@ class OCIGenAIBase(BaseModel, ABC): model_kwargs: Optional[Dict] = None """Keyword arguments to pass to the model""" - service_endpoint: str = None # type: ignore[assignment] + service_endpoint: Optional[str] = None """service endpoint url""" - compartment_id: str = None # type: ignore[assignment] + compartment_id: Optional[str] = None """OCID of compartment""" is_stream: bool = False """Whether to stream back partial progress""" + model_config = ConfigDict( + extra="forbid", + arbitrary_types_allowed=True, + ) + @pre_init def validate_environment(cls, values: Dict) -> Dict: """Validate that OCI config and python package exists in environment.""" @@ -230,8 +235,10 @@ class OCIGenAI(LLM, OCIGenAIBase): ) """ - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + arbitrary_types_allowed=True, + ) @property def _llm_type(self) -> str: diff --git a/libs/community/langchain_community/llms/octoai_endpoint.py b/libs/community/langchain_community/llms/octoai_endpoint.py index ff5302c3ff058..7a30c7c65b4e5 100644 --- a/libs/community/langchain_community/llms/octoai_endpoint.py +++ b/libs/community/langchain_community/llms/octoai_endpoint.py @@ -1,7 +1,7 @@ from typing import Any, Dict -from langchain_core.pydantic_v1 import Field, SecretStr from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init +from pydantic import Field, SecretStr from langchain_community.llms.openai import BaseOpenAI from langchain_community.utils.openai import is_openai_v1 diff --git a/libs/community/langchain_community/llms/ollama.py b/libs/community/langchain_community/llms/ollama.py index afb540573dbb2..580ed68b330b6 100644 --- a/libs/community/langchain_community/llms/ollama.py +++ b/libs/community/langchain_community/llms/ollama.py @@ -23,6 +23,7 @@ from langchain_core.language_models import BaseLanguageModel from langchain_core.language_models.llms import BaseLLM from langchain_core.outputs import GenerationChunk, LLMResult +from pydantic import ConfigDict def _stream_response_to_generation_chunk( @@ -397,8 +398,9 @@ class Ollama(BaseLLM, _OllamaCommon): ollama = Ollama(model="llama2") """ - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) @property def _llm_type(self) -> str: diff --git a/libs/community/langchain_community/llms/opaqueprompts.py b/libs/community/langchain_community/llms/opaqueprompts.py index 39c8d3ee88000..46a2a2b36fb71 100644 --- a/libs/community/langchain_community/llms/opaqueprompts.py +++ b/libs/community/langchain_community/llms/opaqueprompts.py @@ -6,6 +6,7 @@ from langchain_core.language_models.llms import LLM from langchain_core.messages import AIMessage from langchain_core.utils import get_from_dict_or_env, pre_init +from pydantic import ConfigDict logger = logging.getLogger(__name__) @@ -32,8 +33,9 @@ class OpaquePrompts(LLM): base_llm: BaseLanguageModel """The base LLM to use.""" - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) @pre_init def validate_environment(cls, values: Dict) -> Dict: diff --git a/libs/community/langchain_community/llms/openai.py b/libs/community/langchain_community/llms/openai.py index c912f8e41500f..aeb16883fd434 100644 --- a/libs/community/langchain_community/llms/openai.py +++ b/libs/community/langchain_community/llms/openai.py @@ -28,7 +28,6 @@ ) from langchain_core.language_models.llms import BaseLLM, create_base_retry_decorator from langchain_core.outputs import Generation, GenerationChunk, LLMResult -from langchain_core.pydantic_v1 import Field, root_validator from langchain_core.utils import ( get_from_dict_or_env, get_pydantic_field_names, @@ -36,6 +35,7 @@ ) from langchain_core.utils.pydantic import get_fields from langchain_core.utils.utils import build_extra_kwargs +from pydantic import ConfigDict, Field, model_validator from langchain_community.utils.openai import is_openai_v1 @@ -259,11 +259,13 @@ def __new__(cls, **data: Any) -> Union[OpenAIChat, BaseOpenAI]: # type: ignore return OpenAIChat(**data) return super().__new__(cls) - class Config: - allow_population_by_field_name = True + model_config = ConfigDict( + populate_by_name=True, + ) - @root_validator(pre=True) - def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def build_extra(cls, values: Dict[str, Any]) -> Any: """Build extra kwargs from additional params that were passed in.""" all_required_field_names = get_pydantic_field_names(cls) extra = values.get("model_kwargs", {}) @@ -1012,8 +1014,9 @@ class OpenAIChat(BaseLLM): disallowed_special: Union[Literal["all"], Collection[str]] = "all" """Set of special tokens that are not allowed。""" - @root_validator(pre=True) - def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def build_extra(cls, values: Dict[str, Any]) -> Any: """Build extra kwargs from additional params that were passed in.""" all_required_field_names = {field.alias for field in get_fields(cls).values()} diff --git a/libs/community/langchain_community/llms/openllm.py b/libs/community/langchain_community/llms/openllm.py index 7fa00b0d79122..21f4849538011 100644 --- a/libs/community/langchain_community/llms/openllm.py +++ b/libs/community/langchain_community/llms/openllm.py @@ -20,7 +20,7 @@ CallbackManagerForLLMRun, ) from langchain_core.language_models.llms import LLM -from langchain_core.pydantic_v1 import PrivateAttr +from pydantic import ConfigDict, PrivateAttr if TYPE_CHECKING: import openllm @@ -97,8 +97,9 @@ class OpenLLM(LLM): PrivateAttr(default=None) ) - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) @overload def __init__( diff --git a/libs/community/langchain_community/llms/petals.py b/libs/community/langchain_community/llms/petals.py index 7506e3bf5c802..3f8e4f87333a9 100644 --- a/libs/community/langchain_community/llms/petals.py +++ b/libs/community/langchain_community/llms/petals.py @@ -3,9 +3,9 @@ from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models.llms import LLM -from langchain_core.pydantic_v1 import Field, SecretStr, root_validator from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init from langchain_core.utils.pydantic import get_fields +from pydantic import ConfigDict, Field, SecretStr, model_validator from langchain_community.llms.utils import enforce_stop_tokens @@ -63,11 +63,13 @@ class Petals(LLM): huggingface_api_key: Optional[SecretStr] = None - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) - @root_validator(pre=True) - def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def build_extra(cls, values: Dict[str, Any]) -> Any: """Build extra kwargs from additional params that were passed in.""" all_required_field_names = {field.alias for field in get_fields(cls).values()} diff --git a/libs/community/langchain_community/llms/pipelineai.py b/libs/community/langchain_community/llms/pipelineai.py index d3616a4f60dc2..8d0f6e1579de4 100644 --- a/libs/community/langchain_community/llms/pipelineai.py +++ b/libs/community/langchain_community/llms/pipelineai.py @@ -3,14 +3,14 @@ from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models.llms import LLM -from langchain_core.pydantic_v1 import ( +from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init +from pydantic import ( BaseModel, + ConfigDict, Field, SecretStr, - root_validator, + model_validator, ) -from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init -from langchain_core.utils.pydantic import get_fields from langchain_community.llms.utils import enforce_stop_tokens @@ -42,13 +42,15 @@ class PipelineAI(LLM, BaseModel): pipeline_api_key: Optional[SecretStr] = None - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) - @root_validator(pre=True) - def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def build_extra(cls, values: Dict[str, Any]) -> Any: """Build extra kwargs from additional params that were passed in.""" - all_required_field_names = {field.alias for field in get_fields(cls).values()} + all_required_field_names = set(list(cls.model_fields.keys())) extra = values.get("pipeline_kwargs", {}) for field_name in list(values): diff --git a/libs/community/langchain_community/llms/predibase.py b/libs/community/langchain_community/llms/predibase.py index 398c169b4a22e..fbabdc04e70a8 100644 --- a/libs/community/langchain_community/llms/predibase.py +++ b/libs/community/langchain_community/llms/predibase.py @@ -3,7 +3,7 @@ from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models.llms import LLM -from langchain_core.pydantic_v1 import Field, SecretStr +from pydantic import Field, SecretStr class Predibase(LLM): @@ -34,8 +34,7 @@ class Predibase(LLM): { "max_new_tokens": 256, "temperature": 0.1, - }, - const=True, + } ) @property diff --git a/libs/community/langchain_community/llms/predictionguard.py b/libs/community/langchain_community/llms/predictionguard.py index 65196476e5d7f..9e85daf4ba66e 100644 --- a/libs/community/langchain_community/llms/predictionguard.py +++ b/libs/community/langchain_community/llms/predictionguard.py @@ -4,6 +4,7 @@ from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models.llms import LLM from langchain_core.utils import get_from_dict_or_env, pre_init +from pydantic import ConfigDict from langchain_community.llms.utils import enforce_stop_tokens @@ -47,8 +48,9 @@ class PredictionGuard(LLM): stop: Optional[List[str]] = None - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) @pre_init def validate_environment(cls, values: Dict) -> Dict: diff --git a/libs/community/langchain_community/llms/replicate.py b/libs/community/langchain_community/llms/replicate.py index 7235e57d7f4b4..ad4dceaf8f301 100644 --- a/libs/community/langchain_community/llms/replicate.py +++ b/libs/community/langchain_community/llms/replicate.py @@ -6,9 +6,9 @@ from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models.llms import LLM from langchain_core.outputs import GenerationChunk -from langchain_core.pydantic_v1 import Field, root_validator from langchain_core.utils import get_from_dict_or_env, pre_init from langchain_core.utils.pydantic import get_fields +from pydantic import ConfigDict, Field, model_validator if TYPE_CHECKING: from replicate.prediction import Prediction @@ -56,9 +56,10 @@ class Replicate(LLM): stop: List[str] = Field(default_factory=list) """Stop sequences to early-terminate generation.""" - class Config: - allow_population_by_field_name = True - extra = "forbid" + model_config = ConfigDict( + populate_by_name=True, + extra="forbid", + ) @property def lc_secrets(self) -> Dict[str, str]: @@ -73,8 +74,9 @@ def get_lc_namespace(cls) -> List[str]: """Get the namespace of the langchain object.""" return ["langchain", "llms", "replicate"] - @root_validator(pre=True) - def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def build_extra(cls, values: Dict[str, Any]) -> Any: """Build extra kwargs from additional params that were passed in.""" all_required_field_names = {field.alias for field in get_fields(cls).values()} diff --git a/libs/community/langchain_community/llms/rwkv.py b/libs/community/langchain_community/llms/rwkv.py index 0f1aaf74777ea..9273467c7fb09 100644 --- a/libs/community/langchain_community/llms/rwkv.py +++ b/libs/community/langchain_community/llms/rwkv.py @@ -8,8 +8,8 @@ from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models.llms import LLM -from langchain_core.pydantic_v1 import BaseModel from langchain_core.utils import pre_init +from pydantic import BaseModel, ConfigDict from langchain_community.llms.utils import enforce_stop_tokens @@ -74,8 +74,9 @@ class RWKV(LLM, BaseModel): model_state: Any = None #: :meta private: - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) @property def _default_params(self) -> Dict[str, Any]: diff --git a/libs/community/langchain_community/llms/sagemaker_endpoint.py b/libs/community/langchain_community/llms/sagemaker_endpoint.py index 80025eebb3841..db86beb9e0fa2 100644 --- a/libs/community/langchain_community/llms/sagemaker_endpoint.py +++ b/libs/community/langchain_community/llms/sagemaker_endpoint.py @@ -8,6 +8,7 @@ from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models.llms import LLM from langchain_core.utils import pre_init +from pydantic import ConfigDict from langchain_community.llms.utils import enforce_stop_tokens @@ -244,8 +245,9 @@ def transform_output(self, output: bytes) -> str: .. _boto3: """ - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) @pre_init def validate_environment(cls, values: Dict) -> Dict: diff --git a/libs/community/langchain_community/llms/sambanova.py b/libs/community/langchain_community/llms/sambanova.py index 187c1708fe425..5441f53678d75 100644 --- a/libs/community/langchain_community/llms/sambanova.py +++ b/libs/community/langchain_community/llms/sambanova.py @@ -6,6 +6,7 @@ from langchain_core.language_models.llms import LLM from langchain_core.outputs import GenerationChunk from langchain_core.utils import get_from_dict_or_env, pre_init +from pydantic import ConfigDict class SVEndpointHandler: @@ -209,8 +210,9 @@ class Sambaverse(LLM): streaming: Optional[bool] = False """Streaming flag to get streamed response.""" - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) @classmethod def is_lc_serializable(cls) -> bool: @@ -727,8 +729,9 @@ class SambaStudio(LLM): streaming: Optional[bool] = False """Streaming flag to get streamed response.""" - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) @classmethod def is_lc_serializable(cls) -> bool: diff --git a/libs/community/langchain_community/llms/self_hosted.py b/libs/community/langchain_community/llms/self_hosted.py index 320a4a651e2d2..5026c2108a7db 100644 --- a/libs/community/langchain_community/llms/self_hosted.py +++ b/libs/community/langchain_community/llms/self_hosted.py @@ -5,6 +5,7 @@ from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models.llms import LLM +from pydantic import ConfigDict from langchain_community.llms.utils import enforce_stop_tokens @@ -143,8 +144,9 @@ def inference_fn(pipeline, prompt, stop = None): loading compromised data. """ - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) def __init__(self, **kwargs: Any): """Init the pipeline with an auxiliary function. diff --git a/libs/community/langchain_community/llms/self_hosted_hugging_face.py b/libs/community/langchain_community/llms/self_hosted_hugging_face.py index 7358d4c54822d..5003a86aeb332 100644 --- a/libs/community/langchain_community/llms/self_hosted_hugging_face.py +++ b/libs/community/langchain_community/llms/self_hosted_hugging_face.py @@ -3,6 +3,7 @@ from typing import Any, Callable, List, Mapping, Optional from langchain_core.callbacks import CallbackManagerForLLMRun +from pydantic import ConfigDict from langchain_community.llms.self_hosted import SelfHostedPipeline from langchain_community.llms.utils import enforce_stop_tokens @@ -168,8 +169,9 @@ def get_pipeline(): inference_fn: Callable = _generate_text #: :meta private: """Inference function to send to the remote hardware.""" - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) def __init__(self, **kwargs: Any): """Construct the pipeline remotely using an auxiliary function. diff --git a/libs/community/langchain_community/llms/solar.py b/libs/community/langchain_community/llms/solar.py index 4afc6bd97e225..32dc00b98b29b 100644 --- a/libs/community/langchain_community/llms/solar.py +++ b/libs/community/langchain_community/llms/solar.py @@ -3,8 +3,14 @@ import requests from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models import LLM -from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr, root_validator from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init +from pydantic import ( + BaseModel, + ConfigDict, + Field, + SecretStr, + model_validator, +) from langchain_community.llms.utils import enforce_stop_tokens @@ -43,10 +49,11 @@ class SolarCommon(BaseModel): max_tokens: int = Field(default=1024) temperature: float = 0.3 - class Config: - allow_population_by_field_name = True - arbitrary_types_allowed = True - extra = "ignore" + model_config = ConfigDict( + populate_by_name=True, + arbitrary_types_allowed=True, + extra="ignore", + ) @property def lc_secrets(self) -> dict: @@ -64,8 +71,9 @@ def _default_params(self) -> Dict[str, Any]: def _invocation_params(self) -> Dict[str, Any]: return {**{"model": self.model_name}, **self._default_params} - @root_validator(pre=True) - def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def build_extra(cls, values: Dict[str, Any]) -> Any: return values @pre_init @@ -100,8 +108,9 @@ class Solar(SolarCommon, LLM): Referenced from https://console.upstage.ai/services/solar """ - class Config: - allow_population_by_field_name = True + model_config = ConfigDict( + populate_by_name=True, + ) def _call( self, diff --git a/libs/community/langchain_community/llms/sparkllm.py b/libs/community/langchain_community/llms/sparkllm.py index d3741aa93d9b3..8f0ead4d27d98 100644 --- a/libs/community/langchain_community/llms/sparkllm.py +++ b/libs/community/langchain_community/llms/sparkllm.py @@ -17,8 +17,8 @@ from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models.llms import LLM from langchain_core.outputs import GenerationChunk -from langchain_core.pydantic_v1 import Field from langchain_core.utils import get_from_dict_or_env, pre_init +from pydantic import Field logger = logging.getLogger(__name__) diff --git a/libs/community/langchain_community/llms/stochasticai.py b/libs/community/langchain_community/llms/stochasticai.py index d97fd1e5ee2e2..0e999bcb3ae45 100644 --- a/libs/community/langchain_community/llms/stochasticai.py +++ b/libs/community/langchain_community/llms/stochasticai.py @@ -5,9 +5,8 @@ import requests from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models.llms import LLM -from langchain_core.pydantic_v1 import Field, SecretStr, root_validator from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init -from langchain_core.utils.pydantic import get_fields +from pydantic import ConfigDict, Field, SecretStr, model_validator from langchain_community.llms.utils import enforce_stop_tokens @@ -36,13 +35,15 @@ class StochasticAI(LLM): stochasticai_api_key: Optional[SecretStr] = None - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) - @root_validator(pre=True) - def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def build_extra(cls, values: Dict[str, Any]) -> Any: """Build extra kwargs from additional params that were passed in.""" - all_required_field_names = {field.alias for field in get_fields(cls).values()} + all_required_field_names = set(list(cls.model_fields.keys())) extra = values.get("model_kwargs", {}) for field_name in list(values): diff --git a/libs/community/langchain_community/llms/symblai_nebula.py b/libs/community/langchain_community/llms/symblai_nebula.py index 1b8d07be8275a..63bb29a9a506a 100644 --- a/libs/community/langchain_community/llms/symblai_nebula.py +++ b/libs/community/langchain_community/llms/symblai_nebula.py @@ -5,8 +5,8 @@ import requests from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models.llms import LLM -from langchain_core.pydantic_v1 import SecretStr from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init +from pydantic import ConfigDict, SecretStr from requests import ConnectTimeout, ReadTimeout, RequestException from tenacity import ( before_sleep_log, @@ -60,8 +60,9 @@ class Nebula(LLM): stop_sequences: Optional[List[str]] = None max_retries: Optional[int] = 10 - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) @pre_init def validate_environment(cls, values: Dict) -> Dict: diff --git a/libs/community/langchain_community/llms/textgen.py b/libs/community/langchain_community/llms/textgen.py index aa3a490fa2958..a9a100d402625 100644 --- a/libs/community/langchain_community/llms/textgen.py +++ b/libs/community/langchain_community/llms/textgen.py @@ -9,7 +9,7 @@ ) from langchain_core.language_models.llms import LLM from langchain_core.outputs import GenerationChunk -from langchain_core.pydantic_v1 import Field +from pydantic import Field logger = logging.getLogger(__name__) diff --git a/libs/community/langchain_community/llms/titan_takeoff.py b/libs/community/langchain_community/llms/titan_takeoff.py index 5ff1f20a568f4..f89a1f5e3ac76 100644 --- a/libs/community/langchain_community/llms/titan_takeoff.py +++ b/libs/community/langchain_community/llms/titan_takeoff.py @@ -4,7 +4,7 @@ from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models.llms import LLM from langchain_core.outputs import GenerationChunk -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel, ConfigDict from langchain_community.llms.utils import enforce_stop_tokens @@ -19,8 +19,9 @@ class Device(str, Enum): class ReaderConfig(BaseModel): """Configuration for the reader to be deployed in Titan Takeoff API.""" - class Config: - protected_namespaces = () + model_config = ConfigDict( + protected_namespaces=(), + ) model_name: str """The name of the model to use""" diff --git a/libs/community/langchain_community/llms/together.py b/libs/community/langchain_community/llms/together.py index a0729c3723a42..e5e7b8d68bd85 100644 --- a/libs/community/langchain_community/llms/together.py +++ b/libs/community/langchain_community/llms/together.py @@ -10,8 +10,8 @@ CallbackManagerForLLMRun, ) from langchain_core.language_models.llms import LLM -from langchain_core.pydantic_v1 import SecretStr, root_validator from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env +from pydantic import ConfigDict, SecretStr, model_validator from langchain_community.utilities.requests import Requests @@ -66,11 +66,13 @@ class Together(LLM): the response for each token generation step. """ - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key exists in environment.""" values["together_api_key"] = convert_to_secret_str( get_from_dict_or_env(values, "together_api_key", "TOGETHER_API_KEY") diff --git a/libs/community/langchain_community/llms/tongyi.py b/libs/community/langchain_community/llms/tongyi.py index b4d79a52c1c2c..65bdd47ceaed7 100644 --- a/libs/community/langchain_community/llms/tongyi.py +++ b/libs/community/langchain_community/llms/tongyi.py @@ -24,8 +24,8 @@ ) from langchain_core.language_models.llms import BaseLLM from langchain_core.outputs import Generation, GenerationChunk, LLMResult -from langchain_core.pydantic_v1 import Field from langchain_core.utils import get_from_dict_or_env, pre_init +from pydantic import Field from requests.exceptions import HTTPError from tenacity import ( before_sleep_log, diff --git a/libs/community/langchain_community/llms/vertexai.py b/libs/community/langchain_community/llms/vertexai.py index 19409c004dea8..35fd883fdd7b5 100644 --- a/libs/community/langchain_community/llms/vertexai.py +++ b/libs/community/langchain_community/llms/vertexai.py @@ -10,8 +10,8 @@ ) from langchain_core.language_models.llms import BaseLLM from langchain_core.outputs import Generation, GenerationChunk, LLMResult -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.utils import pre_init +from pydantic import BaseModel, Field from langchain_community.utilities.vertexai import ( create_retry_decorator, diff --git a/libs/community/langchain_community/llms/vllm.py b/libs/community/langchain_community/llms/vllm.py index f4eddf46350ef..83e28b7ac71d5 100644 --- a/libs/community/langchain_community/llms/vllm.py +++ b/libs/community/langchain_community/llms/vllm.py @@ -3,8 +3,8 @@ from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models.llms import BaseLLM from langchain_core.outputs import Generation, LLMResult -from langchain_core.pydantic_v1 import Field from langchain_core.utils import pre_init +from pydantic import Field from langchain_community.llms.openai import BaseOpenAI from langchain_community.utils.openai import is_openai_v1 diff --git a/libs/community/langchain_community/llms/volcengine_maas.py b/libs/community/langchain_community/llms/volcengine_maas.py index dab4989adf3af..1046eb4544b09 100644 --- a/libs/community/langchain_community/llms/volcengine_maas.py +++ b/libs/community/langchain_community/llms/volcengine_maas.py @@ -5,8 +5,8 @@ from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models.llms import LLM from langchain_core.outputs import GenerationChunk -from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init +from pydantic import BaseModel, Field, SecretStr class VolcEngineMaasBase(BaseModel): diff --git a/libs/community/langchain_community/llms/watsonxllm.py b/libs/community/langchain_community/llms/watsonxllm.py index 2e910b23323bb..a19909e2da2f9 100644 --- a/libs/community/langchain_community/llms/watsonxllm.py +++ b/libs/community/langchain_community/llms/watsonxllm.py @@ -6,8 +6,8 @@ from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models.llms import BaseLLM from langchain_core.outputs import Generation, GenerationChunk, LLMResult -from langchain_core.pydantic_v1 import SecretStr from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init +from pydantic import ConfigDict, SecretStr logger = logging.getLogger(__name__) @@ -95,8 +95,9 @@ class WatsonxLLM(BaseLLM): watsonx_model: Any - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) @classmethod def is_lc_serializable(cls) -> bool: diff --git a/libs/community/langchain_community/llms/weight_only_quantization.py b/libs/community/langchain_community/llms/weight_only_quantization.py index ddce10c976525..35302ff6fd578 100644 --- a/libs/community/langchain_community/llms/weight_only_quantization.py +++ b/libs/community/langchain_community/llms/weight_only_quantization.py @@ -3,6 +3,7 @@ from langchain_core.callbacks.manager import CallbackManagerForLLMRun from langchain_core.language_models.llms import LLM +from pydantic import ConfigDict from langchain_community.llms.utils import enforce_stop_tokens @@ -71,8 +72,9 @@ class WeightOnlyQuantPipeline(LLM): pipeline_kwargs: Optional[dict] = None """Key word arguments passed to the pipeline.""" - class Config: - extra = "allow" + model_config = ConfigDict( + extra="allow", + ) @classmethod def from_model_id( diff --git a/libs/community/langchain_community/llms/writer.py b/libs/community/langchain_community/llms/writer.py index feccd05a282a1..d82a346c43616 100644 --- a/libs/community/langchain_community/llms/writer.py +++ b/libs/community/langchain_community/llms/writer.py @@ -4,6 +4,7 @@ from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models.llms import LLM from langchain_core.utils import get_from_dict_or_env, pre_init +from pydantic import ConfigDict from langchain_community.llms.utils import enforce_stop_tokens @@ -63,8 +64,9 @@ class Writer(LLM): base_url: Optional[str] = None """Base url to use, if None decides based on model name.""" - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) @pre_init def validate_environment(cls, values: Dict) -> Dict: diff --git a/libs/community/langchain_community/llms/yandex.py b/libs/community/langchain_community/llms/yandex.py index b8761acd62b3a..cc615ac6d7390 100644 --- a/libs/community/langchain_community/llms/yandex.py +++ b/libs/community/langchain_community/llms/yandex.py @@ -9,8 +9,8 @@ ) from langchain_core.language_models.llms import LLM from langchain_core.load.serializable import Serializable -from langchain_core.pydantic_v1 import SecretStr from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init +from pydantic import SecretStr from tenacity import ( before_sleep_log, retry, diff --git a/libs/community/langchain_community/llms/yi.py b/libs/community/langchain_community/llms/yi.py index 8a4557ab199f6..6b41e8d33789f 100644 --- a/libs/community/langchain_community/llms/yi.py +++ b/libs/community/langchain_community/llms/yi.py @@ -7,8 +7,8 @@ import requests from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models.llms import LLM -from langchain_core.pydantic_v1 import Field, SecretStr from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env +from pydantic import Field, SecretStr from langchain_community.llms.utils import enforce_stop_tokens diff --git a/libs/community/langchain_community/llms/you.py b/libs/community/langchain_community/llms/you.py index 54c1c31bfec9c..20ba6ca451bfe 100644 --- a/libs/community/langchain_community/llms/you.py +++ b/libs/community/langchain_community/llms/you.py @@ -5,7 +5,7 @@ from langchain_core.callbacks.manager import CallbackManagerForLLMRun from langchain_core.language_models.llms import LLM from langchain_core.outputs import GenerationChunk -from langchain_core.pydantic_v1 import Field +from pydantic import Field SMART_ENDPOINT = "https://chat-api.you.com/smart" RESEARCH_ENDPOINT = "https://chat-api.you.com/research" diff --git a/libs/community/langchain_community/llms/yuan2.py b/libs/community/langchain_community/llms/yuan2.py index 4373ba622e79f..1087d0cb006d5 100644 --- a/libs/community/langchain_community/llms/yuan2.py +++ b/libs/community/langchain_community/llms/yuan2.py @@ -5,7 +5,7 @@ import requests from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models.llms import LLM -from langchain_core.pydantic_v1 import Field +from pydantic import Field from langchain_community.llms.utils import enforce_stop_tokens diff --git a/libs/community/langchain_community/memory/kg.py b/libs/community/langchain_community/memory/kg.py index 09787d396c7cc..2a4828d81ff8d 100644 --- a/libs/community/langchain_community/memory/kg.py +++ b/libs/community/langchain_community/memory/kg.py @@ -3,7 +3,7 @@ from langchain_core.language_models import BaseLanguageModel from langchain_core.messages import BaseMessage, SystemMessage, get_buffer_string from langchain_core.prompts import BasePromptTemplate -from langchain_core.pydantic_v1 import Field +from pydantic import Field from langchain_community.graphs import NetworkxEntityGraph from langchain_community.graphs.networkx_graph import ( diff --git a/libs/community/langchain_community/output_parsers/ernie_functions.py b/libs/community/langchain_community/output_parsers/ernie_functions.py index 93fe9771cb2a6..8b80134af22df 100644 --- a/libs/community/langchain_community/output_parsers/ernie_functions.py +++ b/libs/community/langchain_community/output_parsers/ernie_functions.py @@ -13,7 +13,7 @@ ChatGeneration, Generation, ) -from langchain_core.pydantic_v1 import BaseModel, root_validator +from pydantic import BaseModel, model_validator class OutputFunctionsParser(BaseGenerationOutputParser[Any]): @@ -143,8 +143,9 @@ class PydanticOutputFunctionsParser(OutputFunctionsParser): pydantic_schema: Union[Type[BaseModel], Dict[str, Type[BaseModel]]] """The pydantic schema to parse the output with.""" - @root_validator(pre=True) - def validate_schema(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_schema(cls, values: Dict) -> Any: schema = values["pydantic_schema"] if "args_only" not in values: values["args_only"] = isinstance(schema, type) and issubclass( diff --git a/libs/community/langchain_community/retrievers/arcee.py b/libs/community/langchain_community/retrievers/arcee.py index 6d2b6f0b8009a..a602f4347c580 100644 --- a/libs/community/langchain_community/retrievers/arcee.py +++ b/libs/community/langchain_community/retrievers/arcee.py @@ -2,9 +2,9 @@ from langchain_core.callbacks import CallbackManagerForRetrieverRun from langchain_core.documents import Document -from langchain_core.pydantic_v1 import SecretStr from langchain_core.retrievers import BaseRetriever from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init +from pydantic import ConfigDict, SecretStr from langchain_community.utilities.arcee import ArceeWrapper, DALMFilter @@ -49,9 +49,10 @@ class ArceeRetriever(BaseRetriever): model_kwargs: Optional[Dict[str, Any]] = None """Keyword arguments to pass to the model.""" - class Config: - extra = "forbid" - underscore_attrs_are_private = True + model_config = ConfigDict( + extra="forbid", + underscore_attrs_are_private=True, + ) def __init__(self, **data: Any) -> None: """Initializes private fields.""" diff --git a/libs/community/langchain_community/retrievers/azure_ai_search.py b/libs/community/langchain_community/retrievers/azure_ai_search.py index e2ab7f74f90b4..beb0dc05972ad 100644 --- a/libs/community/langchain_community/retrievers/azure_ai_search.py +++ b/libs/community/langchain_community/retrievers/azure_ai_search.py @@ -1,7 +1,7 @@ from __future__ import annotations import json -from typing import Dict, List, Optional +from typing import Any, Dict, List, Optional import aiohttp import requests @@ -10,9 +10,9 @@ CallbackManagerForRetrieverRun, ) from langchain_core.documents import Document -from langchain_core.pydantic_v1 import root_validator from langchain_core.retrievers import BaseRetriever from langchain_core.utils import get_from_dict_or_env, get_from_env +from pydantic import ConfigDict, model_validator DEFAULT_URL_SUFFIX = "search.windows.net" """Default URL Suffix for endpoint connection - commercial cloud""" @@ -103,12 +103,14 @@ def format_docs(docs): filter: Optional[str] = None """OData $filter expression to apply to the search query.""" - class Config: - arbitrary_types_allowed = True - extra = "forbid" + model_config = ConfigDict( + arbitrary_types_allowed=True, + extra="forbid", + ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that service name, index name and api key exists in environment.""" values["service_name"] = get_from_dict_or_env( values, "service_name", "AZURE_AI_SEARCH_SERVICE_NAME" diff --git a/libs/community/langchain_community/retrievers/bedrock.py b/libs/community/langchain_community/retrievers/bedrock.py index a2a05f77496e5..6415bc9767833 100644 --- a/libs/community/langchain_community/retrievers/bedrock.py +++ b/libs/community/langchain_community/retrievers/bedrock.py @@ -2,8 +2,8 @@ from langchain_core.callbacks import CallbackManagerForRetrieverRun from langchain_core.documents import Document -from langchain_core.pydantic_v1 import BaseModel, root_validator from langchain_core.retrievers import BaseRetriever +from pydantic import BaseModel, model_validator class VectorSearchConfig(BaseModel, extra="allow"): # type: ignore[call-arg] @@ -105,8 +105,9 @@ def format_docs(docs): client: Any retrieval_config: RetrievalConfig - @root_validator(pre=True) - def create_client(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def create_client(cls, values: Dict[str, Any]) -> Any: if values.get("client") is not None: return values diff --git a/libs/community/langchain_community/retrievers/bm25.py b/libs/community/langchain_community/retrievers/bm25.py index ae16ee2df3db6..a654d3626ab8c 100644 --- a/libs/community/langchain_community/retrievers/bm25.py +++ b/libs/community/langchain_community/retrievers/bm25.py @@ -4,8 +4,8 @@ from langchain_core.callbacks import CallbackManagerForRetrieverRun from langchain_core.documents import Document -from langchain_core.pydantic_v1 import Field from langchain_core.retrievers import BaseRetriever +from pydantic import ConfigDict, Field def default_preprocessing_func(text: str) -> List[str]: @@ -24,8 +24,9 @@ class BM25Retriever(BaseRetriever): preprocess_func: Callable[[str], List[str]] = default_preprocessing_func """ Preprocessing function to use on the text before BM25 vectorization.""" - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) @classmethod def from_texts( diff --git a/libs/community/langchain_community/retrievers/chatgpt_plugin_retriever.py b/libs/community/langchain_community/retrievers/chatgpt_plugin_retriever.py index 178d99c8a178d..08559110bca89 100644 --- a/libs/community/langchain_community/retrievers/chatgpt_plugin_retriever.py +++ b/libs/community/langchain_community/retrievers/chatgpt_plugin_retriever.py @@ -10,6 +10,7 @@ ) from langchain_core.documents import Document from langchain_core.retrievers import BaseRetriever +from pydantic import ConfigDict class ChatGPTPluginRetriever(BaseRetriever): @@ -26,8 +27,9 @@ class ChatGPTPluginRetriever(BaseRetriever): aiosession: Optional[aiohttp.ClientSession] = None """Aiohttp session to use for requests.""" - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) def _get_relevant_documents( self, query: str, *, run_manager: CallbackManagerForRetrieverRun diff --git a/libs/community/langchain_community/retrievers/cohere_rag_retriever.py b/libs/community/langchain_community/retrievers/cohere_rag_retriever.py index dd0f8d21872d7..f76aafa2900f7 100644 --- a/libs/community/langchain_community/retrievers/cohere_rag_retriever.py +++ b/libs/community/langchain_community/retrievers/cohere_rag_retriever.py @@ -10,8 +10,8 @@ from langchain_core.documents import Document from langchain_core.language_models.chat_models import BaseChatModel from langchain_core.messages import HumanMessage -from langchain_core.pydantic_v1 import Field from langchain_core.retrievers import BaseRetriever +from pydantic import ConfigDict, Field if TYPE_CHECKING: from langchain_core.messages import BaseMessage @@ -61,8 +61,9 @@ class CohereRagRetriever(BaseRetriever): llm: BaseChatModel """Cohere ChatModel to use.""" - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) def _get_relevant_documents( self, query: str, *, run_manager: CallbackManagerForRetrieverRun, **kwargs: Any diff --git a/libs/community/langchain_community/retrievers/docarray.py b/libs/community/langchain_community/retrievers/docarray.py index e258735be2c2a..4531028fb6ecf 100644 --- a/libs/community/langchain_community/retrievers/docarray.py +++ b/libs/community/langchain_community/retrievers/docarray.py @@ -7,6 +7,7 @@ from langchain_core.embeddings import Embeddings from langchain_core.retrievers import BaseRetriever from langchain_core.utils.pydantic import get_fields +from pydantic import ConfigDict from langchain_community.vectorstores.utils import maximal_marginal_relevance @@ -45,8 +46,9 @@ class DocArrayRetriever(BaseRetriever): top_k: int = 1 filters: Optional[Any] = None - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) def _get_relevant_documents( self, diff --git a/libs/community/langchain_community/retrievers/google_vertex_ai_search.py b/libs/community/langchain_community/retrievers/google_vertex_ai_search.py index 6b836389a77ff..332199012aa73 100644 --- a/libs/community/langchain_community/retrievers/google_vertex_ai_search.py +++ b/libs/community/langchain_community/retrievers/google_vertex_ai_search.py @@ -7,9 +7,9 @@ from langchain_core._api.deprecation import deprecated from langchain_core.callbacks import CallbackManagerForRetrieverRun from langchain_core.documents import Document -from langchain_core.pydantic_v1 import BaseModel, Field, root_validator from langchain_core.retrievers import BaseRetriever from langchain_core.utils import get_from_dict_or_env +from pydantic import BaseModel, ConfigDict, Field, model_validator from langchain_community.utilities.vertexai import get_client_info @@ -46,8 +46,9 @@ class _BaseGoogleVertexAISearchRetriever(BaseModel): 3 - Blended search """ - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validates the environment.""" try: from google.cloud import discoveryengine_v1beta # noqa: F401 @@ -245,10 +246,11 @@ class GoogleVertexAISearchRetriever(BaseRetriever, _BaseGoogleVertexAISearchRetr _client: SearchServiceClient _serving_config: str - class Config: - arbitrary_types_allowed = True - extra = "ignore" - underscore_attrs_are_private = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + extra="ignore", + underscore_attrs_are_private=True, + ) def __init__(self, **kwargs: Any) -> None: """Initializes private fields.""" @@ -410,10 +412,11 @@ class GoogleVertexAIMultiTurnSearchRetriever( _client: ConversationalSearchServiceClient _serving_config: str - class Config: - arbitrary_types_allowed = True - extra = "ignore" - underscore_attrs_are_private = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + extra="ignore", + underscore_attrs_are_private=True, + ) def __init__(self, **kwargs: Any): super().__init__(**kwargs) diff --git a/libs/community/langchain_community/retrievers/kendra.py b/libs/community/langchain_community/retrievers/kendra.py index c5c3023ea78d7..7c824b0eacf9c 100644 --- a/libs/community/langchain_community/retrievers/kendra.py +++ b/libs/community/langchain_community/retrievers/kendra.py @@ -13,13 +13,13 @@ from langchain_core.callbacks import CallbackManagerForRetrieverRun from langchain_core.documents import Document -from langchain_core.pydantic_v1 import ( +from langchain_core.retrievers import BaseRetriever +from pydantic import ( BaseModel, Field, - root_validator, + model_validator, validator, ) -from langchain_core.retrievers import BaseRetriever from typing_extensions import Annotated @@ -382,8 +382,9 @@ def validate_top_k(cls, value: int) -> int: raise ValueError(f"top_k ({value}) cannot be negative.") return value - @root_validator(pre=True) - def create_client(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def create_client(cls, values: Dict[str, Any]) -> Any: if values.get("client") is not None: return values diff --git a/libs/community/langchain_community/retrievers/knn.py b/libs/community/langchain_community/retrievers/knn.py index 2266f98f20121..ed76aa41b7bdb 100644 --- a/libs/community/langchain_community/retrievers/knn.py +++ b/libs/community/langchain_community/retrievers/knn.py @@ -12,6 +12,7 @@ from langchain_core.documents import Document from langchain_core.embeddings import Embeddings from langchain_core.retrievers import BaseRetriever +from pydantic import ConfigDict def create_index(contexts: List[str], embeddings: Embeddings) -> np.ndarray: @@ -45,8 +46,9 @@ class KNNRetriever(BaseRetriever): relevancy_threshold: Optional[float] = None """Threshold for relevancy.""" - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) @classmethod def from_texts( diff --git a/libs/community/langchain_community/retrievers/llama_index.py b/libs/community/langchain_community/retrievers/llama_index.py index 9106c058d7548..433def090011f 100644 --- a/libs/community/langchain_community/retrievers/llama_index.py +++ b/libs/community/langchain_community/retrievers/llama_index.py @@ -2,8 +2,8 @@ from langchain_core.callbacks import CallbackManagerForRetrieverRun from langchain_core.documents import Document -from langchain_core.pydantic_v1 import Field from langchain_core.retrievers import BaseRetriever +from pydantic import Field class LlamaIndexRetriever(BaseRetriever): diff --git a/libs/community/langchain_community/retrievers/metal.py b/libs/community/langchain_community/retrievers/metal.py index 6eefd8312e3dc..df2f57f2357dc 100644 --- a/libs/community/langchain_community/retrievers/metal.py +++ b/libs/community/langchain_community/retrievers/metal.py @@ -2,8 +2,8 @@ from langchain_core.callbacks import CallbackManagerForRetrieverRun from langchain_core.documents import Document -from langchain_core.pydantic_v1 import root_validator from langchain_core.retrievers import BaseRetriever +from pydantic import model_validator class MetalRetriever(BaseRetriever): @@ -14,8 +14,9 @@ class MetalRetriever(BaseRetriever): params: Optional[dict] = None """The parameters to pass to the Metal client.""" - @root_validator(pre=True) - def validate_client(cls, values: dict) -> dict: + @model_validator(mode="before") + @classmethod + def validate_client(cls, values: dict) -> Any: """Validate that the client is of the correct type.""" from metal_sdk.metal import Metal diff --git a/libs/community/langchain_community/retrievers/milvus.py b/libs/community/langchain_community/retrievers/milvus.py index 7e8cd3fdb3ab0..23d8890341a5c 100644 --- a/libs/community/langchain_community/retrievers/milvus.py +++ b/libs/community/langchain_community/retrievers/milvus.py @@ -6,8 +6,8 @@ from langchain_core.callbacks import CallbackManagerForRetrieverRun from langchain_core.documents import Document from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import root_validator from langchain_core.retrievers import BaseRetriever +from pydantic import model_validator from langchain_community.vectorstores.milvus import Milvus @@ -93,8 +93,9 @@ def format_docs(docs): store: Milvus retriever: BaseRetriever - @root_validator(pre=True) - def create_retriever(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def create_retriever(cls, values: Dict) -> Any: """Create the Milvus store and retriever.""" values["store"] = Milvus( values["embedding_function"], diff --git a/libs/community/langchain_community/retrievers/nanopq.py b/libs/community/langchain_community/retrievers/nanopq.py index 1a52d1d7756b7..3ce6069327a42 100644 --- a/libs/community/langchain_community/retrievers/nanopq.py +++ b/libs/community/langchain_community/retrievers/nanopq.py @@ -8,6 +8,7 @@ from langchain_core.documents import Document from langchain_core.embeddings import Embeddings from langchain_core.retrievers import BaseRetriever +from pydantic import ConfigDict def create_index(contexts: List[str], embeddings: Embeddings) -> np.ndarray: @@ -45,8 +46,9 @@ class NanoPQRetriever(BaseRetriever): clusters: int = 128 """No of clusters to be created""" - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) @classmethod def from_texts( diff --git a/libs/community/langchain_community/retrievers/pinecone_hybrid_search.py b/libs/community/langchain_community/retrievers/pinecone_hybrid_search.py index dcbf35e38d7cc..b2e2d10d8121e 100644 --- a/libs/community/langchain_community/retrievers/pinecone_hybrid_search.py +++ b/libs/community/langchain_community/retrievers/pinecone_hybrid_search.py @@ -8,6 +8,7 @@ from langchain_core.embeddings import Embeddings from langchain_core.retrievers import BaseRetriever from langchain_core.utils import pre_init +from pydantic import ConfigDict def hash_text(text: str) -> str: @@ -114,9 +115,10 @@ class PineconeHybridSearchRetriever(BaseRetriever): namespace: Optional[str] = None """Namespace value for index partition.""" - class Config: - arbitrary_types_allowed = True - extra = "forbid" + model_config = ConfigDict( + arbitrary_types_allowed=True, + extra="forbid", + ) def add_texts( self, diff --git a/libs/community/langchain_community/retrievers/qdrant_sparse_vector_retriever.py b/libs/community/langchain_community/retrievers/qdrant_sparse_vector_retriever.py index 3226a3b6899a6..1e9fde744a267 100644 --- a/libs/community/langchain_community/retrievers/qdrant_sparse_vector_retriever.py +++ b/libs/community/langchain_community/retrievers/qdrant_sparse_vector_retriever.py @@ -18,6 +18,7 @@ from langchain_core.documents import Document from langchain_core.retrievers import BaseRetriever from langchain_core.utils import pre_init +from pydantic import ConfigDict from langchain_community.vectorstores.qdrant import Qdrant, QdrantException @@ -54,9 +55,10 @@ class QdrantSparseVectorRetriever(BaseRetriever): search_options: Dict[str, Any] = {} """Additional search options to pass to qdrant_client.QdrantClient.search().""" - class Config: - arbitrary_types_allowed = True - extra = "forbid" + model_config = ConfigDict( + arbitrary_types_allowed=True, + extra="forbid", + ) @pre_init def validate_environment(cls, values: Dict) -> Dict: diff --git a/libs/community/langchain_community/retrievers/svm.py b/libs/community/langchain_community/retrievers/svm.py index c19f83e58d38d..6b21e7fec5292 100644 --- a/libs/community/langchain_community/retrievers/svm.py +++ b/libs/community/langchain_community/retrievers/svm.py @@ -8,6 +8,7 @@ from langchain_core.documents import Document from langchain_core.embeddings import Embeddings from langchain_core.retrievers import BaseRetriever +from pydantic import ConfigDict def create_index(contexts: List[str], embeddings: Embeddings) -> np.ndarray: @@ -45,8 +46,9 @@ class SVMRetriever(BaseRetriever): relevancy_threshold: Optional[float] = None """Threshold for relevancy.""" - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) @classmethod def from_texts( diff --git a/libs/community/langchain_community/retrievers/tfidf.py b/libs/community/langchain_community/retrievers/tfidf.py index f556a8745ec2e..4101ac9af50f1 100644 --- a/libs/community/langchain_community/retrievers/tfidf.py +++ b/libs/community/langchain_community/retrievers/tfidf.py @@ -7,6 +7,7 @@ from langchain_core.callbacks import CallbackManagerForRetrieverRun from langchain_core.documents import Document from langchain_core.retrievers import BaseRetriever +from pydantic import ConfigDict class TFIDFRetriever(BaseRetriever): @@ -25,8 +26,9 @@ class TFIDFRetriever(BaseRetriever): k: int = 4 """Number of documents to return.""" - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) @classmethod def from_texts( diff --git a/libs/community/langchain_community/retrievers/thirdai_neuraldb.py b/libs/community/langchain_community/retrievers/thirdai_neuraldb.py index c1d9d140edc8c..48380db786ae4 100644 --- a/libs/community/langchain_community/retrievers/thirdai_neuraldb.py +++ b/libs/community/langchain_community/retrievers/thirdai_neuraldb.py @@ -7,9 +7,9 @@ from langchain_core.callbacks import CallbackManagerForRetrieverRun from langchain_core.documents import Document -from langchain_core.pydantic_v1 import SecretStr from langchain_core.retrievers import BaseRetriever from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init +from pydantic import ConfigDict, SecretStr class NeuralDBRetriever(BaseRetriever): @@ -21,9 +21,10 @@ class NeuralDBRetriever(BaseRetriever): db: Any = None #: :meta private: """NeuralDB instance""" - class Config: - extra = "forbid" - underscore_attrs_are_private = True + model_config = ConfigDict( + extra="forbid", + underscore_attrs_are_private=True, + ) @staticmethod def _verify_thirdai_library(thirdai_key: Optional[str] = None) -> None: diff --git a/libs/community/langchain_community/retrievers/weaviate_hybrid_search.py b/libs/community/langchain_community/retrievers/weaviate_hybrid_search.py index 198091f4f1b05..6e98d076690da 100644 --- a/libs/community/langchain_community/retrievers/weaviate_hybrid_search.py +++ b/libs/community/langchain_community/retrievers/weaviate_hybrid_search.py @@ -5,8 +5,8 @@ from langchain_core.callbacks import CallbackManagerForRetrieverRun from langchain_core.documents import Document -from langchain_core.pydantic_v1 import root_validator from langchain_core.retrievers import BaseRetriever +from pydantic import ConfigDict, model_validator class WeaviateHybridSearchRetriever(BaseRetriever): @@ -31,11 +31,12 @@ class WeaviateHybridSearchRetriever(BaseRetriever): create_schema_if_missing: bool = True """Whether to create the schema if it doesn't exist.""" - @root_validator(pre=True) + @model_validator(mode="before") + @classmethod def validate_client( cls, values: Dict[str, Any], - ) -> Dict[str, Any]: + ) -> Any: try: import weaviate except ImportError: @@ -65,8 +66,9 @@ def validate_client( return values - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) # added text_key def add_documents(self, docs: List[Document], **kwargs: Any) -> List[str]: diff --git a/libs/community/langchain_community/retrievers/web_research.py b/libs/community/langchain_community/retrievers/web_research.py index 8dfadb49cf22f..aa604dd84132a 100644 --- a/libs/community/langchain_community/retrievers/web_research.py +++ b/libs/community/langchain_community/retrievers/web_research.py @@ -12,10 +12,10 @@ from langchain_core.language_models import BaseLLM from langchain_core.output_parsers import BaseOutputParser from langchain_core.prompts import BasePromptTemplate, PromptTemplate -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.retrievers import BaseRetriever from langchain_core.vectorstores import VectorStore from langchain_text_splitters import RecursiveCharacterTextSplitter, TextSplitter +from pydantic import BaseModel, Field from langchain_community.document_loaders import AsyncHtmlLoader from langchain_community.document_transformers import Html2TextTransformer diff --git a/libs/community/langchain_community/retrievers/zep.py b/libs/community/langchain_community/retrievers/zep.py index 2060788e4cd34..d59aa00781566 100644 --- a/libs/community/langchain_community/retrievers/zep.py +++ b/libs/community/langchain_community/retrievers/zep.py @@ -8,8 +8,8 @@ CallbackManagerForRetrieverRun, ) from langchain_core.documents import Document -from langchain_core.pydantic_v1 import root_validator from langchain_core.retrievers import BaseRetriever +from pydantic import model_validator if TYPE_CHECKING: from zep_python.memory import MemorySearchResult @@ -79,8 +79,9 @@ class ZepRetriever(BaseRetriever): mmr_lambda: Optional[float] = None """Lambda value for MMR search.""" - @root_validator(pre=True) - def create_client(cls, values: dict) -> dict: + @model_validator(mode="before") + @classmethod + def create_client(cls, values: dict) -> Any: try: from zep_python import ZepClient except ImportError: diff --git a/libs/community/langchain_community/retrievers/zep_cloud.py b/libs/community/langchain_community/retrievers/zep_cloud.py index 96758c71d9861..c4e3f11040cc5 100644 --- a/libs/community/langchain_community/retrievers/zep_cloud.py +++ b/libs/community/langchain_community/retrievers/zep_cloud.py @@ -7,8 +7,8 @@ CallbackManagerForRetrieverRun, ) from langchain_core.documents import Document -from langchain_core.pydantic_v1 import root_validator from langchain_core.retrievers import BaseRetriever +from pydantic import model_validator if TYPE_CHECKING: from zep_cloud import MemorySearchResult, SearchScope, SearchType @@ -61,8 +61,9 @@ class ZepCloudRetriever(BaseRetriever): mmr_lambda: Optional[float] = None """Lambda value for MMR search.""" - @root_validator(pre=True) - def create_client(cls, values: dict) -> dict: + @model_validator(mode="before") + @classmethod + def create_client(cls, values: dict) -> Any: try: from zep_cloud.client import AsyncZep, Zep except ImportError: diff --git a/libs/community/langchain_community/retrievers/zilliz.py b/libs/community/langchain_community/retrievers/zilliz.py index b273ab6e5766a..d7e614942010f 100644 --- a/libs/community/langchain_community/retrievers/zilliz.py +++ b/libs/community/langchain_community/retrievers/zilliz.py @@ -4,8 +4,8 @@ from langchain_core.callbacks import CallbackManagerForRetrieverRun from langchain_core.documents import Document from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import root_validator from langchain_core.retrievers import BaseRetriever +from pydantic import model_validator from langchain_community.vectorstores.zilliz import Zilliz @@ -30,8 +30,9 @@ class ZillizRetriever(BaseRetriever): retriever: BaseRetriever """The underlying retriever.""" - @root_validator(pre=True) - def create_client(cls, values: dict) -> dict: + @model_validator(mode="before") + @classmethod + def create_client(cls, values: dict) -> Any: values["store"] = Zilliz( values["embedding_function"], values["collection_name"], diff --git a/libs/community/langchain_community/tools/ainetwork/app.py b/libs/community/langchain_community/tools/ainetwork/app.py index faef6120f9f9b..8175a210b7067 100644 --- a/libs/community/langchain_community/tools/ainetwork/app.py +++ b/libs/community/langchain_community/tools/ainetwork/app.py @@ -4,7 +4,7 @@ from typing import List, Optional, Type, Union from langchain_core.callbacks import AsyncCallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, Field from langchain_community.tools.ainetwork.base import AINBaseTool diff --git a/libs/community/langchain_community/tools/ainetwork/base.py b/libs/community/langchain_community/tools/ainetwork/base.py index 2f94127576983..00e4fc7f7a298 100644 --- a/libs/community/langchain_community/tools/ainetwork/base.py +++ b/libs/community/langchain_community/tools/ainetwork/base.py @@ -6,8 +6,8 @@ from typing import TYPE_CHECKING, Any, Optional from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import Field from langchain_core.tools import BaseTool +from pydantic import Field from langchain_community.tools.ainetwork.utils import authenticate diff --git a/libs/community/langchain_community/tools/ainetwork/owner.py b/libs/community/langchain_community/tools/ainetwork/owner.py index a89134f2a0597..13d41d9327350 100644 --- a/libs/community/langchain_community/tools/ainetwork/owner.py +++ b/libs/community/langchain_community/tools/ainetwork/owner.py @@ -3,7 +3,7 @@ from typing import List, Optional, Type, Union from langchain_core.callbacks import AsyncCallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, Field from langchain_community.tools.ainetwork.base import AINBaseTool, OperationType diff --git a/libs/community/langchain_community/tools/ainetwork/rule.py b/libs/community/langchain_community/tools/ainetwork/rule.py index 309010f5ba3ba..5a24c9e5aba79 100644 --- a/libs/community/langchain_community/tools/ainetwork/rule.py +++ b/libs/community/langchain_community/tools/ainetwork/rule.py @@ -3,7 +3,7 @@ from typing import Optional, Type from langchain_core.callbacks import AsyncCallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, Field from langchain_community.tools.ainetwork.base import AINBaseTool, OperationType diff --git a/libs/community/langchain_community/tools/ainetwork/transfer.py b/libs/community/langchain_community/tools/ainetwork/transfer.py index deab34ad9c2b5..81d630af2e3bd 100644 --- a/libs/community/langchain_community/tools/ainetwork/transfer.py +++ b/libs/community/langchain_community/tools/ainetwork/transfer.py @@ -2,7 +2,7 @@ from typing import Optional, Type from langchain_core.callbacks import AsyncCallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, Field from langchain_community.tools.ainetwork.base import AINBaseTool diff --git a/libs/community/langchain_community/tools/ainetwork/value.py b/libs/community/langchain_community/tools/ainetwork/value.py index 300e36c573c92..be6414727f53f 100644 --- a/libs/community/langchain_community/tools/ainetwork/value.py +++ b/libs/community/langchain_community/tools/ainetwork/value.py @@ -3,7 +3,7 @@ from typing import Optional, Type, Union from langchain_core.callbacks import AsyncCallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, Field from langchain_community.tools.ainetwork.base import AINBaseTool, OperationType diff --git a/libs/community/langchain_community/tools/amadeus/base.py b/libs/community/langchain_community/tools/amadeus/base.py index 96b4380f4e2e1..3fd3f377ce2dd 100644 --- a/libs/community/langchain_community/tools/amadeus/base.py +++ b/libs/community/langchain_community/tools/amadeus/base.py @@ -4,8 +4,8 @@ from typing import TYPE_CHECKING -from langchain_core.pydantic_v1 import Field from langchain_core.tools import BaseTool +from pydantic import Field from langchain_community.tools.amadeus.utils import authenticate diff --git a/libs/community/langchain_community/tools/amadeus/closest_airport.py b/libs/community/langchain_community/tools/amadeus/closest_airport.py index 1a3f4c4da2cb2..9523f73afbb6d 100644 --- a/libs/community/langchain_community/tools/amadeus/closest_airport.py +++ b/libs/community/langchain_community/tools/amadeus/closest_airport.py @@ -2,7 +2,7 @@ from langchain_core.callbacks import CallbackManagerForToolRun from langchain_core.language_models import BaseLanguageModel -from langchain_core.pydantic_v1 import BaseModel, Field, root_validator +from pydantic import BaseModel, Field, model_validator from langchain_community.chat_models import ChatOpenAI from langchain_community.tools.amadeus.base import AmadeusBaseTool @@ -39,8 +39,9 @@ class AmadeusClosestAirport(AmadeusBaseTool): llm: Optional[BaseLanguageModel] = Field(default=None) """Tool's llm used for calculating the closest airport. Defaults to `ChatOpenAI`.""" - @root_validator(pre=True) - def set_llm(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def set_llm(cls, values: Dict[str, Any]) -> Any: if not values.get("llm"): # For backward-compatibility values["llm"] = ChatOpenAI(temperature=0) diff --git a/libs/community/langchain_community/tools/amadeus/flight_search.py b/libs/community/langchain_community/tools/amadeus/flight_search.py index 708a53054dc0d..c3cd8fe7bb9d2 100644 --- a/libs/community/langchain_community/tools/amadeus/flight_search.py +++ b/libs/community/langchain_community/tools/amadeus/flight_search.py @@ -3,7 +3,7 @@ from typing import Dict, Optional, Type from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, Field from langchain_community.tools.amadeus.base import AmadeusBaseTool diff --git a/libs/community/langchain_community/tools/arxiv/tool.py b/libs/community/langchain_community/tools/arxiv/tool.py index 5c8ca77f1bb80..601022a1325c3 100644 --- a/libs/community/langchain_community/tools/arxiv/tool.py +++ b/libs/community/langchain_community/tools/arxiv/tool.py @@ -3,8 +3,8 @@ from typing import Optional, Type from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.tools import BaseTool +from pydantic import BaseModel, Field from langchain_community.utilities.arxiv import ArxivAPIWrapper diff --git a/libs/community/langchain_community/tools/asknews/tool.py b/libs/community/langchain_community/tools/asknews/tool.py index e3d39027aa701..7589d11078bd6 100644 --- a/libs/community/langchain_community/tools/asknews/tool.py +++ b/libs/community/langchain_community/tools/asknews/tool.py @@ -12,8 +12,8 @@ AsyncCallbackManagerForToolRun, CallbackManagerForToolRun, ) -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.tools import BaseTool +from pydantic import BaseModel, Field from langchain_community.utilities.asknews import AskNewsAPIWrapper diff --git a/libs/community/langchain_community/tools/audio/huggingface_text_to_speech_inference.py b/libs/community/langchain_community/tools/audio/huggingface_text_to_speech_inference.py index 62d73f9191b16..bbe5f7d47c295 100644 --- a/libs/community/langchain_community/tools/audio/huggingface_text_to_speech_inference.py +++ b/libs/community/langchain_community/tools/audio/huggingface_text_to_speech_inference.py @@ -6,8 +6,8 @@ import requests from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import SecretStr from langchain_core.tools import BaseTool +from pydantic import SecretStr logger = logging.getLogger(__name__) @@ -48,10 +48,12 @@ def __init__( destination_dir: str = "./tts", file_naming_func: Literal["uuid", "timestamp"] = "uuid", huggingface_api_key: Optional[SecretStr] = None, + _HUGGINGFACE_API_KEY_ENV_NAME: str = "HUGGINGFACE_API_KEY", + _HUGGINGFACE_API_URL_ROOT: str = "https://api-inference.huggingface.co/models", ) -> None: if not huggingface_api_key: huggingface_api_key = SecretStr( - os.getenv(self._HUGGINGFACE_API_KEY_ENV_NAME, "") + os.getenv(_HUGGINGFACE_API_KEY_ENV_NAME, "") ) if ( @@ -60,7 +62,7 @@ def __init__( or huggingface_api_key.get_secret_value() == "" ): raise ValueError( - f"'{self._HUGGINGFACE_API_KEY_ENV_NAME}' must be or set or passed" + f"'{_HUGGINGFACE_API_KEY_ENV_NAME}' must be or set or passed" ) if file_naming_func == "uuid": @@ -75,10 +77,12 @@ def __init__( super().__init__( # type: ignore[call-arg] model=model, file_extension=file_extension, - api_url=f"{self._HUGGINGFACE_API_URL_ROOT}/{model}", + api_url=f"{_HUGGINGFACE_API_URL_ROOT}/{model}", destination_dir=destination_dir, file_namer=file_namer, huggingface_api_key=huggingface_api_key, + _HUGGINGFACE_API_KEY_ENV_NAME=_HUGGINGFACE_API_KEY_ENV_NAME, + _HUGGINGFACE_API_URL_ROOT=_HUGGINGFACE_API_URL_ROOT, ) def _run( diff --git a/libs/community/langchain_community/tools/azure_ai_services/document_intelligence.py b/libs/community/langchain_community/tools/azure_ai_services/document_intelligence.py index a5e9e9e481ad9..cd0ac25018ee4 100644 --- a/libs/community/langchain_community/tools/azure_ai_services/document_intelligence.py +++ b/libs/community/langchain_community/tools/azure_ai_services/document_intelligence.py @@ -4,9 +4,9 @@ from typing import Any, Dict, List, Optional from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import root_validator from langchain_core.tools import BaseTool from langchain_core.utils import get_from_dict_or_env +from pydantic import model_validator from langchain_community.tools.azure_ai_services.utils import ( detect_file_src_type, @@ -34,8 +34,9 @@ class AzureAiServicesDocumentIntelligenceTool(BaseTool): "Input should be a url to a document." ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key and endpoint exists in environment.""" azure_ai_services_key = get_from_dict_or_env( values, "azure_ai_services_key", "AZURE_AI_SERVICES_KEY" diff --git a/libs/community/langchain_community/tools/azure_ai_services/image_analysis.py b/libs/community/langchain_community/tools/azure_ai_services/image_analysis.py index ade4ef2bdbad6..0a1e206e45e7d 100644 --- a/libs/community/langchain_community/tools/azure_ai_services/image_analysis.py +++ b/libs/community/langchain_community/tools/azure_ai_services/image_analysis.py @@ -4,9 +4,9 @@ from typing import Any, Dict, Optional from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import root_validator from langchain_core.tools import BaseTool from langchain_core.utils import get_from_dict_or_env +from pydantic import model_validator from langchain_community.tools.azure_ai_services.utils import ( detect_file_src_type, @@ -34,8 +34,9 @@ class AzureAiServicesImageAnalysisTool(BaseTool): "Input should be a url to an image." ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key and endpoint exists in environment.""" azure_ai_services_key = get_from_dict_or_env( values, "azure_ai_services_key", "AZURE_AI_SERVICES_KEY" diff --git a/libs/community/langchain_community/tools/azure_ai_services/speech_to_text.py b/libs/community/langchain_community/tools/azure_ai_services/speech_to_text.py index 28d490d7ab843..15e08d27222e1 100644 --- a/libs/community/langchain_community/tools/azure_ai_services/speech_to_text.py +++ b/libs/community/langchain_community/tools/azure_ai_services/speech_to_text.py @@ -5,9 +5,9 @@ from typing import Any, Dict, Optional from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import root_validator from langchain_core.tools import BaseTool from langchain_core.utils import get_from_dict_or_env +from pydantic import model_validator from langchain_community.tools.azure_ai_services.utils import ( detect_file_src_type, @@ -36,8 +36,9 @@ class AzureAiServicesSpeechToTextTool(BaseTool): "Input should be a url to an audio file." ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key and endpoint exists in environment.""" azure_ai_services_key = get_from_dict_or_env( values, "azure_ai_services_key", "AZURE_AI_SERVICES_KEY" diff --git a/libs/community/langchain_community/tools/azure_ai_services/text_analytics_for_health.py b/libs/community/langchain_community/tools/azure_ai_services/text_analytics_for_health.py index 869c93657166d..6447c5de4185e 100644 --- a/libs/community/langchain_community/tools/azure_ai_services/text_analytics_for_health.py +++ b/libs/community/langchain_community/tools/azure_ai_services/text_analytics_for_health.py @@ -4,9 +4,9 @@ from typing import Any, Dict, Optional from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import root_validator from langchain_core.tools import BaseTool from langchain_core.utils import get_from_dict_or_env +from pydantic import model_validator logger = logging.getLogger(__name__) @@ -29,8 +29,9 @@ class AzureAiServicesTextAnalyticsForHealthTool(BaseTool): "Input should be text." ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key and endpoint exists in environment.""" azure_ai_services_key = get_from_dict_or_env( values, "azure_ai_services_key", "AZURE_AI_SERVICES_KEY" diff --git a/libs/community/langchain_community/tools/azure_ai_services/text_to_speech.py b/libs/community/langchain_community/tools/azure_ai_services/text_to_speech.py index 67fde6bad8534..1291e2dac5641 100644 --- a/libs/community/langchain_community/tools/azure_ai_services/text_to_speech.py +++ b/libs/community/langchain_community/tools/azure_ai_services/text_to_speech.py @@ -5,9 +5,9 @@ from typing import Any, Dict, Optional from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import root_validator from langchain_core.tools import BaseTool from langchain_core.utils import get_from_dict_or_env +from pydantic import model_validator logger = logging.getLogger(__name__) @@ -31,8 +31,9 @@ class AzureAiServicesTextToSpeechTool(BaseTool): speech_language: str = "en-US" #: :meta private: speech_config: Any #: :meta private: - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key and endpoint exists in environment.""" azure_ai_services_key = get_from_dict_or_env( values, "azure_ai_services_key", "AZURE_AI_SERVICES_KEY" diff --git a/libs/community/langchain_community/tools/azure_cognitive_services/form_recognizer.py b/libs/community/langchain_community/tools/azure_cognitive_services/form_recognizer.py index 42d11b4ac4ed7..937b1fc7930f3 100644 --- a/libs/community/langchain_community/tools/azure_cognitive_services/form_recognizer.py +++ b/libs/community/langchain_community/tools/azure_cognitive_services/form_recognizer.py @@ -4,9 +4,9 @@ from typing import Any, Dict, List, Optional from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import root_validator from langchain_core.tools import BaseTool from langchain_core.utils import get_from_dict_or_env +from pydantic import model_validator from langchain_community.tools.azure_cognitive_services.utils import ( detect_file_src_type, @@ -34,8 +34,9 @@ class AzureCogsFormRecognizerTool(BaseTool): "Input should be a url to a document." ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key and endpoint exists in environment.""" azure_cogs_key = get_from_dict_or_env( values, "azure_cogs_key", "AZURE_COGS_KEY" diff --git a/libs/community/langchain_community/tools/azure_cognitive_services/image_analysis.py b/libs/community/langchain_community/tools/azure_cognitive_services/image_analysis.py index 801aa57bd2d7b..ce076243a7b14 100644 --- a/libs/community/langchain_community/tools/azure_cognitive_services/image_analysis.py +++ b/libs/community/langchain_community/tools/azure_cognitive_services/image_analysis.py @@ -4,9 +4,9 @@ from typing import Any, Dict, Optional from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import root_validator from langchain_core.tools import BaseTool from langchain_core.utils import get_from_dict_or_env +from pydantic import model_validator from langchain_community.tools.azure_cognitive_services.utils import ( detect_file_src_type, @@ -34,8 +34,9 @@ class AzureCogsImageAnalysisTool(BaseTool): "Input should be a url to an image." ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key and endpoint exists in environment.""" azure_cogs_key = get_from_dict_or_env( values, "azure_cogs_key", "AZURE_COGS_KEY" diff --git a/libs/community/langchain_community/tools/azure_cognitive_services/speech2text.py b/libs/community/langchain_community/tools/azure_cognitive_services/speech2text.py index b1aa90cb7013d..125c910df1ba7 100644 --- a/libs/community/langchain_community/tools/azure_cognitive_services/speech2text.py +++ b/libs/community/langchain_community/tools/azure_cognitive_services/speech2text.py @@ -5,9 +5,9 @@ from typing import Any, Dict, Optional from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import root_validator from langchain_core.tools import BaseTool from langchain_core.utils import get_from_dict_or_env +from pydantic import model_validator from langchain_community.tools.azure_cognitive_services.utils import ( detect_file_src_type, @@ -36,8 +36,9 @@ class AzureCogsSpeech2TextTool(BaseTool): "Input should be a url to an audio file." ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key and endpoint exists in environment.""" azure_cogs_key = get_from_dict_or_env( values, "azure_cogs_key", "AZURE_COGS_KEY" diff --git a/libs/community/langchain_community/tools/azure_cognitive_services/text2speech.py b/libs/community/langchain_community/tools/azure_cognitive_services/text2speech.py index f65049f13a966..343653fe9c3e1 100644 --- a/libs/community/langchain_community/tools/azure_cognitive_services/text2speech.py +++ b/libs/community/langchain_community/tools/azure_cognitive_services/text2speech.py @@ -5,9 +5,9 @@ from typing import Any, Dict, Optional from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import root_validator from langchain_core.tools import BaseTool from langchain_core.utils import get_from_dict_or_env +from pydantic import model_validator logger = logging.getLogger(__name__) @@ -30,8 +30,9 @@ class AzureCogsText2SpeechTool(BaseTool): "Useful for when you need to convert text to speech. " ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key and endpoint exists in environment.""" azure_cogs_key = get_from_dict_or_env( values, "azure_cogs_key", "AZURE_COGS_KEY" diff --git a/libs/community/langchain_community/tools/azure_cognitive_services/text_analytics_health.py b/libs/community/langchain_community/tools/azure_cognitive_services/text_analytics_health.py index ce5332436c7ea..1de8566528fe4 100644 --- a/libs/community/langchain_community/tools/azure_cognitive_services/text_analytics_health.py +++ b/libs/community/langchain_community/tools/azure_cognitive_services/text_analytics_health.py @@ -4,9 +4,9 @@ from typing import Any, Dict, Optional from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import root_validator from langchain_core.tools import BaseTool from langchain_core.utils import get_from_dict_or_env +from pydantic import model_validator logger = logging.getLogger(__name__) @@ -29,8 +29,9 @@ class AzureCogsTextAnalyticsHealthTool(BaseTool): "Input should be text." ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key and endpoint exists in environment.""" azure_cogs_key = get_from_dict_or_env( values, "azure_cogs_key", "AZURE_COGS_KEY" diff --git a/libs/community/langchain_community/tools/bearly/tool.py b/libs/community/langchain_community/tools/bearly/tool.py index 64a2625716053..1196e79acf504 100644 --- a/libs/community/langchain_community/tools/bearly/tool.py +++ b/libs/community/langchain_community/tools/bearly/tool.py @@ -6,8 +6,8 @@ from typing import Dict, List, Type import requests -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.tools import Tool +from pydantic import BaseModel, Field def strip_markdown_code(md_string: str) -> str: diff --git a/libs/community/langchain_community/tools/cassandra_database/tool.py b/libs/community/langchain_community/tools/cassandra_database/tool.py index 10074ecabb0f5..eb03146e712a3 100644 --- a/libs/community/langchain_community/tools/cassandra_database/tool.py +++ b/libs/community/langchain_community/tools/cassandra_database/tool.py @@ -6,8 +6,8 @@ from typing import TYPE_CHECKING, Any, Dict, Optional, Sequence, Type, Union from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.tools import BaseTool +from pydantic import BaseModel, Field from langchain_community.utilities.cassandra_database import CassandraDatabase diff --git a/libs/community/langchain_community/tools/clickup/tool.py b/libs/community/langchain_community/tools/clickup/tool.py index 8e40c97c456b6..03b3fde586c18 100644 --- a/libs/community/langchain_community/tools/clickup/tool.py +++ b/libs/community/langchain_community/tools/clickup/tool.py @@ -20,8 +20,8 @@ from typing import Optional from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import Field from langchain_core.tools import BaseTool +from pydantic import Field from langchain_community.utilities.clickup import ClickupAPIWrapper diff --git a/libs/community/langchain_community/tools/cogniswitch/tool.py b/libs/community/langchain_community/tools/cogniswitch/tool.py index d88120e5076f8..41c9663fb5eb0 100644 --- a/libs/community/langchain_community/tools/cogniswitch/tool.py +++ b/libs/community/langchain_community/tools/cogniswitch/tool.py @@ -98,7 +98,7 @@ class CogniswitchKnowledgeStatus(BaseTool): cs_token: str OAI_token: str apiKey: str - knowledge_status_url = ( + knowledge_status_url: str = ( "https://api.cogniswitch.ai:8243/cs-api/0.0.1/cs/knowledgeSource/status" ) @@ -200,7 +200,7 @@ class CogniswitchKnowledgeSourceFile(BaseTool): cs_token: str OAI_token: str apiKey: str - knowledgesource_file = ( + knowledgesource_file: str = ( "https://api.cogniswitch.ai:8243/cs-api/0.0.1/cs/knowledgeSource/file" ) @@ -312,7 +312,7 @@ class CogniswitchKnowledgeSourceURL(BaseTool): cs_token: str OAI_token: str apiKey: str - knowledgesource_url = ( + knowledgesource_url: str = ( "https://api.cogniswitch.ai:8243/cs-api/0.0.1/cs/knowledgeSource/url" ) diff --git a/libs/community/langchain_community/tools/connery/models.py b/libs/community/langchain_community/tools/connery/models.py index a8292e62b9e42..92554ea754d6b 100644 --- a/libs/community/langchain_community/tools/connery/models.py +++ b/libs/community/langchain_community/tools/connery/models.py @@ -1,6 +1,6 @@ from typing import List, Optional -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel class Validation(BaseModel): diff --git a/libs/community/langchain_community/tools/connery/service.py b/libs/community/langchain_community/tools/connery/service.py index 11c921c1bc2cb..bbe4cc8c183bd 100644 --- a/libs/community/langchain_community/tools/connery/service.py +++ b/libs/community/langchain_community/tools/connery/service.py @@ -1,9 +1,9 @@ import json -from typing import Dict, List, Optional +from typing import Any, Dict, List, Optional import requests -from langchain_core.pydantic_v1 import BaseModel, root_validator from langchain_core.utils.env import get_from_dict_or_env +from pydantic import BaseModel, model_validator from langchain_community.tools.connery.models import Action from langchain_community.tools.connery.tool import ConneryAction @@ -20,8 +20,9 @@ class ConneryService(BaseModel): runner_url: Optional[str] = None api_key: Optional[str] = None - @root_validator(pre=True) - def validate_attributes(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_attributes(cls, values: Dict) -> Any: """ Validate the attributes of the ConneryService class. Parameters: diff --git a/libs/community/langchain_community/tools/connery/tool.py b/libs/community/langchain_community/tools/connery/tool.py index da7f802696ed4..b5c9d5f5f79ed 100644 --- a/libs/community/langchain_community/tools/connery/tool.py +++ b/libs/community/langchain_community/tools/connery/tool.py @@ -6,8 +6,8 @@ AsyncCallbackManagerForToolRun, CallbackManagerForToolRun, ) -from langchain_core.pydantic_v1 import BaseModel, Field, create_model, root_validator from langchain_core.tools import BaseTool +from pydantic import BaseModel, Field, create_model, model_validator from langchain_community.tools.connery.models import Action, Parameter @@ -63,8 +63,9 @@ def get_schema_json(self) -> str: return self.args_schema.schema_json(indent=2) - @root_validator(pre=True) - def validate_attributes(cls, values: dict) -> dict: + @model_validator(mode="before") + @classmethod + def validate_attributes(cls, values: dict) -> Any: """ Validate the attributes of the ConneryAction class. Parameters: diff --git a/libs/community/langchain_community/tools/databricks/tool.py b/libs/community/langchain_community/tools/databricks/tool.py index 7bf30e2d20b3c..2255caf4d08a1 100644 --- a/libs/community/langchain_community/tools/databricks/tool.py +++ b/libs/community/langchain_community/tools/databricks/tool.py @@ -4,15 +4,17 @@ from hashlib import md5 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Type, Union -from langchain_core.pydantic_v1 import BaseModel, Field, create_model from langchain_core.tools import BaseTool, StructuredTool from langchain_core.tools.base import BaseToolkit +from pydantic import BaseModel, Field, create_model from typing_extensions import Self if TYPE_CHECKING: from databricks.sdk import WorkspaceClient from databricks.sdk.service.catalog import FunctionInfo +from pydantic import ConfigDict + from langchain_community.tools.databricks._execution import execute_function @@ -142,8 +144,9 @@ class UCFunctionToolkit(BaseToolkit): tools: Dict[str, BaseTool] = Field(default_factory=dict) - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) def include(self, *function_names: str, **kwargs: Any) -> Self: """ diff --git a/libs/community/langchain_community/tools/dataforseo_api_search/tool.py b/libs/community/langchain_community/tools/dataforseo_api_search/tool.py index 4d601df59adcf..65bf1ec22cd75 100644 --- a/libs/community/langchain_community/tools/dataforseo_api_search/tool.py +++ b/libs/community/langchain_community/tools/dataforseo_api_search/tool.py @@ -6,8 +6,8 @@ AsyncCallbackManagerForToolRun, CallbackManagerForToolRun, ) -from langchain_core.pydantic_v1 import Field from langchain_core.tools import BaseTool +from pydantic import Field from langchain_community.utilities.dataforseo_api_search import DataForSeoAPIWrapper diff --git a/libs/community/langchain_community/tools/dataherald/tool.py b/libs/community/langchain_community/tools/dataherald/tool.py index 90c4cef7102d8..2a2546a328378 100644 --- a/libs/community/langchain_community/tools/dataherald/tool.py +++ b/libs/community/langchain_community/tools/dataherald/tool.py @@ -3,8 +3,8 @@ from typing import Optional, Type from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.tools import BaseTool +from pydantic import BaseModel, Field from langchain_community.utilities.dataherald import DataheraldAPIWrapper diff --git a/libs/community/langchain_community/tools/ddg_search/tool.py b/libs/community/langchain_community/tools/ddg_search/tool.py index fe707a1a201c6..f2889daa716b2 100644 --- a/libs/community/langchain_community/tools/ddg_search/tool.py +++ b/libs/community/langchain_community/tools/ddg_search/tool.py @@ -4,8 +4,8 @@ from typing import Any, List, Optional, Type from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.tools import BaseTool +from pydantic import BaseModel, Field from langchain_community.utilities.duckduckgo_search import DuckDuckGoSearchAPIWrapper diff --git a/libs/community/langchain_community/tools/e2b_data_analysis/tool.py b/libs/community/langchain_community/tools/e2b_data_analysis/tool.py index fee95b4af5bf9..7e780c60a6433 100644 --- a/libs/community/langchain_community/tools/e2b_data_analysis/tool.py +++ b/libs/community/langchain_community/tools/e2b_data_analysis/tool.py @@ -12,8 +12,8 @@ CallbackManager, CallbackManagerForToolRun, ) -from langchain_core.pydantic_v1 import BaseModel, Field, PrivateAttr from langchain_core.tools import BaseTool, Tool +from pydantic import BaseModel, Field, PrivateAttr from langchain_community.tools.e2b_data_analysis.unparse import Unparser diff --git a/libs/community/langchain_community/tools/edenai/audio_speech_to_text.py b/libs/community/langchain_community/tools/edenai/audio_speech_to_text.py index 342f33d2b2a42..c0fc870ef2d94 100644 --- a/libs/community/langchain_community/tools/edenai/audio_speech_to_text.py +++ b/libs/community/langchain_community/tools/edenai/audio_speech_to_text.py @@ -7,7 +7,7 @@ import requests from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Field, HttpUrl, validator +from pydantic import BaseModel, Field, HttpUrl, validator from langchain_community.tools.edenai.edenai_base_tool import EdenaiTool @@ -30,7 +30,7 @@ class EdenAiSpeechToTextTool(EdenaiTool): """ name: str = "edenai_speech_to_text" - description = ( + description: str = ( "A wrapper around edenai Services speech to text " "Useful for when you have to convert audio to text." "Input should be a url to an audio file." diff --git a/libs/community/langchain_community/tools/edenai/audio_text_to_speech.py b/libs/community/langchain_community/tools/edenai/audio_text_to_speech.py index e78cf35419b1e..4bf79180b39d0 100644 --- a/libs/community/langchain_community/tools/edenai/audio_text_to_speech.py +++ b/libs/community/langchain_community/tools/edenai/audio_text_to_speech.py @@ -1,11 +1,11 @@ from __future__ import annotations import logging -from typing import Dict, List, Literal, Optional, Type +from typing import Any, Dict, List, Literal, Optional, Type import requests from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Field, root_validator, validator +from pydantic import BaseModel, Field, model_validator, validator from langchain_community.tools.edenai.edenai_base_tool import EdenaiTool @@ -28,7 +28,7 @@ class EdenAiTextToSpeechTool(EdenaiTool): """ name: str = "edenai_text_to_speech" - description = ( + description: str = ( "A wrapper around edenai Services text to speech." "Useful for when you need to convert text to speech." """the output is a string representing the URL of the audio file, @@ -70,8 +70,9 @@ def check_only_one_provider_selected(cls, v: List[str]) -> List[str]: ) return v - @root_validator(pre=True) - def check_voice_models_key_is_provider_name(cls, values: dict) -> dict: + @model_validator(mode="before") + @classmethod + def check_voice_models_key_is_provider_name(cls, values: dict) -> Any: for key in values.get("voice_models", {}).keys(): if key not in values.get("providers", []): raise ValueError( diff --git a/libs/community/langchain_community/tools/edenai/edenai_base_tool.py b/libs/community/langchain_community/tools/edenai/edenai_base_tool.py index 2f83ef37bc487..c4f5547999234 100644 --- a/libs/community/langchain_community/tools/edenai/edenai_base_tool.py +++ b/libs/community/langchain_community/tools/edenai/edenai_base_tool.py @@ -6,9 +6,9 @@ import requests from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import Field, SecretStr from langchain_core.tools import BaseTool from langchain_core.utils import secret_from_env +from pydantic import Field, SecretStr logger = logging.getLogger(__name__) diff --git a/libs/community/langchain_community/tools/edenai/image_explicitcontent.py b/libs/community/langchain_community/tools/edenai/image_explicitcontent.py index 8ca1d7739cb7f..50f9f24338a3f 100644 --- a/libs/community/langchain_community/tools/edenai/image_explicitcontent.py +++ b/libs/community/langchain_community/tools/edenai/image_explicitcontent.py @@ -4,7 +4,7 @@ from typing import Optional, Type from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Field, HttpUrl +from pydantic import BaseModel, Field, HttpUrl from langchain_community.tools.edenai.edenai_base_tool import EdenaiTool diff --git a/libs/community/langchain_community/tools/edenai/image_objectdetection.py b/libs/community/langchain_community/tools/edenai/image_objectdetection.py index 1098e8e37f6da..aedfe56b88389 100644 --- a/libs/community/langchain_community/tools/edenai/image_objectdetection.py +++ b/libs/community/langchain_community/tools/edenai/image_objectdetection.py @@ -4,7 +4,7 @@ from typing import Optional, Type from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Field, HttpUrl +from pydantic import BaseModel, Field, HttpUrl from langchain_community.tools.edenai.edenai_base_tool import EdenaiTool diff --git a/libs/community/langchain_community/tools/edenai/ocr_identityparser.py b/libs/community/langchain_community/tools/edenai/ocr_identityparser.py index 2e208dbb54340..f2270345917b4 100644 --- a/libs/community/langchain_community/tools/edenai/ocr_identityparser.py +++ b/libs/community/langchain_community/tools/edenai/ocr_identityparser.py @@ -4,7 +4,7 @@ from typing import Optional, Type from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Field, HttpUrl +from pydantic import BaseModel, Field, HttpUrl from langchain_community.tools.edenai.edenai_base_tool import EdenaiTool diff --git a/libs/community/langchain_community/tools/edenai/ocr_invoiceparser.py b/libs/community/langchain_community/tools/edenai/ocr_invoiceparser.py index 75c8425154aa2..d5266476784c9 100644 --- a/libs/community/langchain_community/tools/edenai/ocr_invoiceparser.py +++ b/libs/community/langchain_community/tools/edenai/ocr_invoiceparser.py @@ -4,7 +4,7 @@ from typing import Optional, Type from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Field, HttpUrl +from pydantic import BaseModel, Field, HttpUrl from langchain_community.tools.edenai.edenai_base_tool import EdenaiTool diff --git a/libs/community/langchain_community/tools/edenai/text_moderation.py b/libs/community/langchain_community/tools/edenai/text_moderation.py index 9aed36f0b7343..f5f8497ff3ca6 100644 --- a/libs/community/langchain_community/tools/edenai/text_moderation.py +++ b/libs/community/langchain_community/tools/edenai/text_moderation.py @@ -4,7 +4,7 @@ from typing import Optional, Type from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, Field from langchain_community.tools.edenai.edenai_base_tool import EdenaiTool diff --git a/libs/community/langchain_community/tools/eleven_labs/text2speech.py b/libs/community/langchain_community/tools/eleven_labs/text2speech.py index d1a68ba7a5c6f..333dfb6bb6432 100644 --- a/libs/community/langchain_community/tools/eleven_labs/text2speech.py +++ b/libs/community/langchain_community/tools/eleven_labs/text2speech.py @@ -3,9 +3,9 @@ from typing import Any, Dict, Optional, Union from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import root_validator from langchain_core.tools import BaseTool from langchain_core.utils import get_from_dict_or_env +from pydantic import model_validator def _import_elevenlabs() -> Any: @@ -42,8 +42,9 @@ class ElevenLabsText2SpeechTool(BaseTool): "Spanish, Italian, French, Portuguese, and Hindi. " ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key exists in environment.""" _ = get_from_dict_or_env(values, "eleven_api_key", "ELEVEN_API_KEY") diff --git a/libs/community/langchain_community/tools/file_management/copy.py b/libs/community/langchain_community/tools/file_management/copy.py index f91081003d14c..7679e3c43bee0 100644 --- a/libs/community/langchain_community/tools/file_management/copy.py +++ b/libs/community/langchain_community/tools/file_management/copy.py @@ -2,8 +2,8 @@ from typing import Optional, Type from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.tools import BaseTool +from pydantic import BaseModel, Field from langchain_community.tools.file_management.utils import ( INVALID_PATH_TEMPLATE, diff --git a/libs/community/langchain_community/tools/file_management/delete.py b/libs/community/langchain_community/tools/file_management/delete.py index c2694762aede3..33f4b70b28dd6 100644 --- a/libs/community/langchain_community/tools/file_management/delete.py +++ b/libs/community/langchain_community/tools/file_management/delete.py @@ -2,8 +2,8 @@ from typing import Optional, Type from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.tools import BaseTool +from pydantic import BaseModel, Field from langchain_community.tools.file_management.utils import ( INVALID_PATH_TEMPLATE, diff --git a/libs/community/langchain_community/tools/file_management/file_search.py b/libs/community/langchain_community/tools/file_management/file_search.py index c77abd0bef95d..a00aee40b4ba7 100644 --- a/libs/community/langchain_community/tools/file_management/file_search.py +++ b/libs/community/langchain_community/tools/file_management/file_search.py @@ -3,8 +3,8 @@ from typing import Optional, Type from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.tools import BaseTool +from pydantic import BaseModel, Field from langchain_community.tools.file_management.utils import ( INVALID_PATH_TEMPLATE, diff --git a/libs/community/langchain_community/tools/file_management/list_dir.py b/libs/community/langchain_community/tools/file_management/list_dir.py index d8b700134ae9f..a8bfdc8e3abe1 100644 --- a/libs/community/langchain_community/tools/file_management/list_dir.py +++ b/libs/community/langchain_community/tools/file_management/list_dir.py @@ -2,8 +2,8 @@ from typing import Optional, Type from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.tools import BaseTool +from pydantic import BaseModel, Field from langchain_community.tools.file_management.utils import ( INVALID_PATH_TEMPLATE, diff --git a/libs/community/langchain_community/tools/file_management/move.py b/libs/community/langchain_community/tools/file_management/move.py index fc3bb778d8ef8..935625172e9a3 100644 --- a/libs/community/langchain_community/tools/file_management/move.py +++ b/libs/community/langchain_community/tools/file_management/move.py @@ -2,8 +2,8 @@ from typing import Optional, Type from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.tools import BaseTool +from pydantic import BaseModel, Field from langchain_community.tools.file_management.utils import ( INVALID_PATH_TEMPLATE, diff --git a/libs/community/langchain_community/tools/file_management/read.py b/libs/community/langchain_community/tools/file_management/read.py index 42cc1515e7e70..9f746ed16c105 100644 --- a/libs/community/langchain_community/tools/file_management/read.py +++ b/libs/community/langchain_community/tools/file_management/read.py @@ -1,8 +1,8 @@ from typing import Optional, Type from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.tools import BaseTool +from pydantic import BaseModel, Field from langchain_community.tools.file_management.utils import ( INVALID_PATH_TEMPLATE, diff --git a/libs/community/langchain_community/tools/file_management/utils.py b/libs/community/langchain_community/tools/file_management/utils.py index b2a3632ecaa88..788823fecd739 100644 --- a/libs/community/langchain_community/tools/file_management/utils.py +++ b/libs/community/langchain_community/tools/file_management/utils.py @@ -2,7 +2,7 @@ from pathlib import Path from typing import Optional -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel def is_relative_to(path: Path, root: Path) -> bool: diff --git a/libs/community/langchain_community/tools/file_management/write.py b/libs/community/langchain_community/tools/file_management/write.py index b42b70256b7fe..1d62065bb74e4 100644 --- a/libs/community/langchain_community/tools/file_management/write.py +++ b/libs/community/langchain_community/tools/file_management/write.py @@ -1,8 +1,8 @@ from typing import Optional, Type from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.tools import BaseTool +from pydantic import BaseModel, Field from langchain_community.tools.file_management.utils import ( INVALID_PATH_TEMPLATE, diff --git a/libs/community/langchain_community/tools/financial_datasets/balance_sheets.py b/libs/community/langchain_community/tools/financial_datasets/balance_sheets.py index 5bbc9093dc3be..a2374e35c7cbb 100644 --- a/libs/community/langchain_community/tools/financial_datasets/balance_sheets.py +++ b/libs/community/langchain_community/tools/financial_datasets/balance_sheets.py @@ -1,8 +1,8 @@ from typing import Optional, Type from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.tools import BaseTool +from pydantic import BaseModel, Field from langchain_community.utilities.financial_datasets import FinancialDatasetsAPIWrapper diff --git a/libs/community/langchain_community/tools/financial_datasets/cash_flow_statements.py b/libs/community/langchain_community/tools/financial_datasets/cash_flow_statements.py index 65f95a3e0c993..e8ac4a5b2bff6 100644 --- a/libs/community/langchain_community/tools/financial_datasets/cash_flow_statements.py +++ b/libs/community/langchain_community/tools/financial_datasets/cash_flow_statements.py @@ -1,8 +1,8 @@ from typing import Optional, Type from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.tools import BaseTool +from pydantic import BaseModel, Field from langchain_community.utilities.financial_datasets import FinancialDatasetsAPIWrapper diff --git a/libs/community/langchain_community/tools/financial_datasets/income_statements.py b/libs/community/langchain_community/tools/financial_datasets/income_statements.py index 8e4f197d529e3..e9db4e5cc1626 100644 --- a/libs/community/langchain_community/tools/financial_datasets/income_statements.py +++ b/libs/community/langchain_community/tools/financial_datasets/income_statements.py @@ -1,8 +1,8 @@ from typing import Optional, Type from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.tools import BaseTool +from pydantic import BaseModel, Field from langchain_community.utilities.financial_datasets import FinancialDatasetsAPIWrapper diff --git a/libs/community/langchain_community/tools/github/tool.py b/libs/community/langchain_community/tools/github/tool.py index cb1433b6551c9..af6933fe9b80e 100644 --- a/libs/community/langchain_community/tools/github/tool.py +++ b/libs/community/langchain_community/tools/github/tool.py @@ -11,8 +11,8 @@ from typing import Any, Optional, Type from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.tools import BaseTool +from pydantic import BaseModel, Field from langchain_community.utilities.github import GitHubAPIWrapper diff --git a/libs/community/langchain_community/tools/gitlab/tool.py b/libs/community/langchain_community/tools/gitlab/tool.py index bce7ca950cc13..8dfe2bb32fdbd 100644 --- a/libs/community/langchain_community/tools/gitlab/tool.py +++ b/libs/community/langchain_community/tools/gitlab/tool.py @@ -11,8 +11,8 @@ from typing import Optional from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import Field from langchain_core.tools import BaseTool +from pydantic import Field from langchain_community.utilities.gitlab import GitLabAPIWrapper diff --git a/libs/community/langchain_community/tools/gmail/base.py b/libs/community/langchain_community/tools/gmail/base.py index 1bbecb252326f..d55b0d30f8a45 100644 --- a/libs/community/langchain_community/tools/gmail/base.py +++ b/libs/community/langchain_community/tools/gmail/base.py @@ -4,8 +4,8 @@ from typing import TYPE_CHECKING -from langchain_core.pydantic_v1 import Field from langchain_core.tools import BaseTool +from pydantic import Field from langchain_community.tools.gmail.utils import build_resource_service diff --git a/libs/community/langchain_community/tools/gmail/create_draft.py b/libs/community/langchain_community/tools/gmail/create_draft.py index b8e6ac93c6178..2fdfcf37dd2c8 100644 --- a/libs/community/langchain_community/tools/gmail/create_draft.py +++ b/libs/community/langchain_community/tools/gmail/create_draft.py @@ -3,7 +3,7 @@ from typing import List, Optional, Type from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, Field from langchain_community.tools.gmail.base import GmailBaseTool diff --git a/libs/community/langchain_community/tools/gmail/get_message.py b/libs/community/langchain_community/tools/gmail/get_message.py index 3be2db0853312..6155cb499f4d9 100644 --- a/libs/community/langchain_community/tools/gmail/get_message.py +++ b/libs/community/langchain_community/tools/gmail/get_message.py @@ -3,7 +3,7 @@ from typing import Dict, Optional, Type from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, Field from langchain_community.tools.gmail.base import GmailBaseTool from langchain_community.tools.gmail.utils import clean_email_body diff --git a/libs/community/langchain_community/tools/gmail/get_thread.py b/libs/community/langchain_community/tools/gmail/get_thread.py index c42c90924b6dc..5e61bd8bb98ab 100644 --- a/libs/community/langchain_community/tools/gmail/get_thread.py +++ b/libs/community/langchain_community/tools/gmail/get_thread.py @@ -1,7 +1,7 @@ from typing import Dict, Optional, Type from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, Field from langchain_community.tools.gmail.base import GmailBaseTool diff --git a/libs/community/langchain_community/tools/gmail/search.py b/libs/community/langchain_community/tools/gmail/search.py index 72125762d5464..eb61968429520 100644 --- a/libs/community/langchain_community/tools/gmail/search.py +++ b/libs/community/langchain_community/tools/gmail/search.py @@ -4,7 +4,7 @@ from typing import Any, Dict, List, Optional, Type from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, Field from langchain_community.tools.gmail.base import GmailBaseTool from langchain_community.tools.gmail.utils import clean_email_body diff --git a/libs/community/langchain_community/tools/gmail/send_message.py b/libs/community/langchain_community/tools/gmail/send_message.py index c059b31f3f4b3..3ccd2185048e6 100644 --- a/libs/community/langchain_community/tools/gmail/send_message.py +++ b/libs/community/langchain_community/tools/gmail/send_message.py @@ -6,7 +6,7 @@ from typing import Any, Dict, List, Optional, Type, Union from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, Field from langchain_community.tools.gmail.base import GmailBaseTool diff --git a/libs/community/langchain_community/tools/google_places/tool.py b/libs/community/langchain_community/tools/google_places/tool.py index de33e0196a177..b97705c9742dd 100644 --- a/libs/community/langchain_community/tools/google_places/tool.py +++ b/libs/community/langchain_community/tools/google_places/tool.py @@ -4,8 +4,8 @@ from langchain_core._api.deprecation import deprecated from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.tools import BaseTool +from pydantic import BaseModel, Field from langchain_community.utilities.google_places_api import GooglePlacesAPIWrapper diff --git a/libs/community/langchain_community/tools/google_serper/tool.py b/libs/community/langchain_community/tools/google_serper/tool.py index b94771a894f38..562dd012a1ac6 100644 --- a/libs/community/langchain_community/tools/google_serper/tool.py +++ b/libs/community/langchain_community/tools/google_serper/tool.py @@ -6,8 +6,8 @@ AsyncCallbackManagerForToolRun, CallbackManagerForToolRun, ) -from langchain_core.pydantic_v1 import Field from langchain_core.tools import BaseTool +from pydantic import Field from langchain_community.utilities.google_serper import GoogleSerperAPIWrapper diff --git a/libs/community/langchain_community/tools/graphql/tool.py b/libs/community/langchain_community/tools/graphql/tool.py index bca6211d5be8e..0530f8cae07fe 100644 --- a/libs/community/langchain_community/tools/graphql/tool.py +++ b/libs/community/langchain_community/tools/graphql/tool.py @@ -3,6 +3,7 @@ from langchain_core.callbacks import CallbackManagerForToolRun from langchain_core.tools import BaseTool +from pydantic import ConfigDict from langchain_community.utilities.graphql import GraphQLAPIWrapper @@ -22,8 +23,9 @@ class BaseGraphQLTool(BaseTool): Example Input: query {{ allUsers {{ id, name, email }} }}\ """ # noqa: E501 - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) def _run( self, diff --git a/libs/community/langchain_community/tools/human/tool.py b/libs/community/langchain_community/tools/human/tool.py index e4987e44fa6c3..d9e238b93c97b 100644 --- a/libs/community/langchain_community/tools/human/tool.py +++ b/libs/community/langchain_community/tools/human/tool.py @@ -3,8 +3,8 @@ from typing import Callable, Optional from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import Field from langchain_core.tools import BaseTool +from pydantic import Field def _print_func(text: str) -> None: diff --git a/libs/community/langchain_community/tools/jina_search/tool.py b/libs/community/langchain_community/tools/jina_search/tool.py index 4ddb098eebb4c..a6961e685b382 100644 --- a/libs/community/langchain_community/tools/jina_search/tool.py +++ b/libs/community/langchain_community/tools/jina_search/tool.py @@ -3,8 +3,8 @@ from typing import Optional from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.tools import BaseTool +from pydantic import BaseModel, Field from langchain_community.utilities.jina_search import JinaSearchAPIWrapper diff --git a/libs/community/langchain_community/tools/jira/tool.py b/libs/community/langchain_community/tools/jira/tool.py index a80044b2a10cd..70868947004ac 100644 --- a/libs/community/langchain_community/tools/jira/tool.py +++ b/libs/community/langchain_community/tools/jira/tool.py @@ -23,8 +23,8 @@ from typing import Optional from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import Field from langchain_core.tools import BaseTool +from pydantic import Field from langchain_community.utilities.jira import JiraAPIWrapper diff --git a/libs/community/langchain_community/tools/json/tool.py b/libs/community/langchain_community/tools/json/tool.py index bcb04fbf1f2f2..6e7fddff6d7d6 100644 --- a/libs/community/langchain_community/tools/json/tool.py +++ b/libs/community/langchain_community/tools/json/tool.py @@ -8,7 +8,7 @@ from pathlib import Path from typing import Dict, List, Optional, Union -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel from langchain_core.callbacks import ( AsyncCallbackManagerForToolRun, diff --git a/libs/community/langchain_community/tools/memorize/tool.py b/libs/community/langchain_community/tools/memorize/tool.py index 30d416b1fd774..87badf9ac31f1 100644 --- a/libs/community/langchain_community/tools/memorize/tool.py +++ b/libs/community/langchain_community/tools/memorize/tool.py @@ -5,8 +5,8 @@ AsyncCallbackManagerForToolRun, CallbackManagerForToolRun, ) -from langchain_core.pydantic_v1 import Field from langchain_core.tools import BaseTool +from pydantic import Field from langchain_community.llms.gradient_ai import TrainResult diff --git a/libs/community/langchain_community/tools/multion/close_session.py b/libs/community/langchain_community/tools/multion/close_session.py index 023c07539e0f1..28f0abd013ba7 100644 --- a/libs/community/langchain_community/tools/multion/close_session.py +++ b/libs/community/langchain_community/tools/multion/close_session.py @@ -3,8 +3,8 @@ from langchain_core.callbacks import ( CallbackManagerForToolRun, ) -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.tools import BaseTool +from pydantic import BaseModel, Field if TYPE_CHECKING: # This is for linting and IDE typehints diff --git a/libs/community/langchain_community/tools/multion/create_session.py b/libs/community/langchain_community/tools/multion/create_session.py index de6983cb4bc2d..53388a5a973f7 100644 --- a/libs/community/langchain_community/tools/multion/create_session.py +++ b/libs/community/langchain_community/tools/multion/create_session.py @@ -3,8 +3,8 @@ from langchain_core.callbacks import ( CallbackManagerForToolRun, ) -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.tools import BaseTool +from pydantic import BaseModel, Field if TYPE_CHECKING: # This is for linting and IDE typehints diff --git a/libs/community/langchain_community/tools/multion/update_session.py b/libs/community/langchain_community/tools/multion/update_session.py index 10fe4c9fa6384..b535861e2ac76 100644 --- a/libs/community/langchain_community/tools/multion/update_session.py +++ b/libs/community/langchain_community/tools/multion/update_session.py @@ -3,8 +3,8 @@ from langchain_core.callbacks import ( CallbackManagerForToolRun, ) -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.tools import BaseTool +from pydantic import BaseModel, Field if TYPE_CHECKING: # This is for linting and IDE typehints diff --git a/libs/community/langchain_community/tools/nasa/tool.py b/libs/community/langchain_community/tools/nasa/tool.py index 74c24c78449a4..b9f2caa455584 100644 --- a/libs/community/langchain_community/tools/nasa/tool.py +++ b/libs/community/langchain_community/tools/nasa/tool.py @@ -6,8 +6,8 @@ from typing import Optional from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import Field from langchain_core.tools import BaseTool +from pydantic import Field from langchain_community.utilities.nasa import NasaAPIWrapper diff --git a/libs/community/langchain_community/tools/nuclia/tool.py b/libs/community/langchain_community/tools/nuclia/tool.py index fcba67a9618d4..a575f7d1d054d 100644 --- a/libs/community/langchain_community/tools/nuclia/tool.py +++ b/libs/community/langchain_community/tools/nuclia/tool.py @@ -20,8 +20,8 @@ AsyncCallbackManagerForToolRun, CallbackManagerForToolRun, ) -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.tools import BaseTool +from pydantic import BaseModel, Field logger = logging.getLogger(__name__) diff --git a/libs/community/langchain_community/tools/office365/base.py b/libs/community/langchain_community/tools/office365/base.py index 24aed733a771b..55160bd5e509e 100644 --- a/libs/community/langchain_community/tools/office365/base.py +++ b/libs/community/langchain_community/tools/office365/base.py @@ -4,8 +4,8 @@ from typing import TYPE_CHECKING -from langchain_core.pydantic_v1 import Field from langchain_core.tools import BaseTool +from pydantic import Field from langchain_community.tools.office365.utils import authenticate diff --git a/libs/community/langchain_community/tools/office365/create_draft_message.py b/libs/community/langchain_community/tools/office365/create_draft_message.py index 88c94578a83f6..02915ffedcf86 100644 --- a/libs/community/langchain_community/tools/office365/create_draft_message.py +++ b/libs/community/langchain_community/tools/office365/create_draft_message.py @@ -1,7 +1,7 @@ from typing import List, Optional, Type from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, Field from langchain_community.tools.office365.base import O365BaseTool diff --git a/libs/community/langchain_community/tools/office365/events_search.py b/libs/community/langchain_community/tools/office365/events_search.py index 514eb4025df1a..f23dd86b0870c 100644 --- a/libs/community/langchain_community/tools/office365/events_search.py +++ b/libs/community/langchain_community/tools/office365/events_search.py @@ -8,7 +8,7 @@ from typing import Any, Dict, List, Optional, Type from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, ConfigDict, Field from langchain_community.tools.office365.base import O365BaseTool from langchain_community.tools.office365.utils import UTC_FORMAT, clean_body @@ -70,8 +70,9 @@ class O365SearchEvents(O365BaseTool): "is busy during meetings. Any times without events are free for the user. " ) - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) def _run( self, diff --git a/libs/community/langchain_community/tools/office365/messages_search.py b/libs/community/langchain_community/tools/office365/messages_search.py index dac0ceac30e3b..5b66dc63644b3 100644 --- a/libs/community/langchain_community/tools/office365/messages_search.py +++ b/libs/community/langchain_community/tools/office365/messages_search.py @@ -7,7 +7,7 @@ from typing import Any, Dict, List, Optional, Type from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, ConfigDict, Field from langchain_community.tools.office365.base import O365BaseTool from langchain_community.tools.office365.utils import UTC_FORMAT, clean_body @@ -66,8 +66,9 @@ class O365SearchEmails(O365BaseTool): " The output is a JSON list of the requested resource." ) - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) def _run( self, diff --git a/libs/community/langchain_community/tools/office365/send_event.py b/libs/community/langchain_community/tools/office365/send_event.py index e8b9d023af7a5..6a824deef759d 100644 --- a/libs/community/langchain_community/tools/office365/send_event.py +++ b/libs/community/langchain_community/tools/office365/send_event.py @@ -8,7 +8,7 @@ from typing import List, Optional, Type from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, Field from langchain_community.tools.office365.base import O365BaseTool from langchain_community.tools.office365.utils import UTC_FORMAT diff --git a/libs/community/langchain_community/tools/office365/send_message.py b/libs/community/langchain_community/tools/office365/send_message.py index 828e9e4fa614d..6ebc8883714ed 100644 --- a/libs/community/langchain_community/tools/office365/send_message.py +++ b/libs/community/langchain_community/tools/office365/send_message.py @@ -1,7 +1,7 @@ from typing import List, Optional, Type from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, Field from langchain_community.tools.office365.base import O365BaseTool diff --git a/libs/community/langchain_community/tools/openapi/utils/api_models.py b/libs/community/langchain_community/tools/openapi/utils/api_models.py index 4c253e31e20a7..fef0b849ae37d 100644 --- a/libs/community/langchain_community/tools/openapi/utils/api_models.py +++ b/libs/community/langchain_community/tools/openapi/utils/api_models.py @@ -16,7 +16,7 @@ Union, ) -from langchain_core.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, Field from langchain_community.tools.openapi.utils.openapi_utils import HTTPVerb, OpenAPISpec diff --git a/libs/community/langchain_community/tools/openweathermap/tool.py b/libs/community/langchain_community/tools/openweathermap/tool.py index d716b8098d840..f1e7758d2d0c2 100644 --- a/libs/community/langchain_community/tools/openweathermap/tool.py +++ b/libs/community/langchain_community/tools/openweathermap/tool.py @@ -3,8 +3,8 @@ from typing import Optional from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import Field from langchain_core.tools import BaseTool +from pydantic import Field from langchain_community.utilities.openweathermap import OpenWeatherMapAPIWrapper diff --git a/libs/community/langchain_community/tools/passio_nutrition_ai/tool.py b/libs/community/langchain_community/tools/passio_nutrition_ai/tool.py index 275760d4cbca0..939e1a41bc9fe 100644 --- a/libs/community/langchain_community/tools/passio_nutrition_ai/tool.py +++ b/libs/community/langchain_community/tools/passio_nutrition_ai/tool.py @@ -3,8 +3,8 @@ from typing import Dict, Optional, Type from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.tools import BaseTool +from pydantic import BaseModel, Field from langchain_community.utilities.passio_nutrition_ai import NutritionAIAPI diff --git a/libs/community/langchain_community/tools/playwright/base.py b/libs/community/langchain_community/tools/playwright/base.py index f06b964e4d2d2..e85cc8479360c 100644 --- a/libs/community/langchain_community/tools/playwright/base.py +++ b/libs/community/langchain_community/tools/playwright/base.py @@ -1,10 +1,10 @@ from __future__ import annotations -from typing import TYPE_CHECKING, Optional, Tuple, Type +from typing import TYPE_CHECKING, Any, Optional, Tuple, Type -from langchain_core.pydantic_v1 import root_validator from langchain_core.tools import BaseTool from langchain_core.utils import guard_import +from pydantic import model_validator if TYPE_CHECKING: from playwright.async_api import Browser as AsyncBrowser @@ -38,8 +38,9 @@ class BaseBrowserTool(BaseTool): sync_browser: Optional["SyncBrowser"] = None async_browser: Optional["AsyncBrowser"] = None - @root_validator(pre=True) - def validate_browser_provided(cls, values: dict) -> dict: + @model_validator(mode="before") + @classmethod + def validate_browser_provided(cls, values: dict) -> Any: """Check that the arguments are valid.""" lazy_import_playwright_browsers() if values.get("async_browser") is None and values.get("sync_browser") is None: diff --git a/libs/community/langchain_community/tools/playwright/click.py b/libs/community/langchain_community/tools/playwright/click.py index 5cf5cdda148ee..22c6a23bf9c33 100644 --- a/libs/community/langchain_community/tools/playwright/click.py +++ b/libs/community/langchain_community/tools/playwright/click.py @@ -6,7 +6,7 @@ AsyncCallbackManagerForToolRun, CallbackManagerForToolRun, ) -from langchain_core.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, Field from langchain_community.tools.playwright.base import BaseBrowserTool from langchain_community.tools.playwright.utils import ( diff --git a/libs/community/langchain_community/tools/playwright/current_page.py b/libs/community/langchain_community/tools/playwright/current_page.py index 9ea5e0e1f9dd9..94abcaf1f1c25 100644 --- a/libs/community/langchain_community/tools/playwright/current_page.py +++ b/libs/community/langchain_community/tools/playwright/current_page.py @@ -6,7 +6,7 @@ AsyncCallbackManagerForToolRun, CallbackManagerForToolRun, ) -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel from langchain_community.tools.playwright.base import BaseBrowserTool from langchain_community.tools.playwright.utils import ( diff --git a/libs/community/langchain_community/tools/playwright/extract_hyperlinks.py b/libs/community/langchain_community/tools/playwright/extract_hyperlinks.py index 0dcd3fd2e06b6..00a5e290274b5 100644 --- a/libs/community/langchain_community/tools/playwright/extract_hyperlinks.py +++ b/libs/community/langchain_community/tools/playwright/extract_hyperlinks.py @@ -7,7 +7,7 @@ AsyncCallbackManagerForToolRun, CallbackManagerForToolRun, ) -from langchain_core.pydantic_v1 import BaseModel, Field, root_validator +from pydantic import BaseModel, Field, model_validator from langchain_community.tools.playwright.base import BaseBrowserTool from langchain_community.tools.playwright.utils import ( @@ -35,8 +35,9 @@ class ExtractHyperlinksTool(BaseBrowserTool): description: str = "Extract all hyperlinks on the current webpage" args_schema: Type[BaseModel] = ExtractHyperlinksToolInput - @root_validator(pre=True) - def check_bs_import(cls, values: dict) -> dict: + @model_validator(mode="before") + @classmethod + def check_bs_import(cls, values: dict) -> Any: """Check that the arguments are valid.""" try: from bs4 import BeautifulSoup # noqa: F401 diff --git a/libs/community/langchain_community/tools/playwright/extract_text.py b/libs/community/langchain_community/tools/playwright/extract_text.py index 0cf112b0fb2e0..b04e367952afa 100644 --- a/libs/community/langchain_community/tools/playwright/extract_text.py +++ b/libs/community/langchain_community/tools/playwright/extract_text.py @@ -1,12 +1,12 @@ from __future__ import annotations -from typing import Optional, Type +from typing import Any, Optional, Type from langchain_core.callbacks import ( AsyncCallbackManagerForToolRun, CallbackManagerForToolRun, ) -from langchain_core.pydantic_v1 import BaseModel, root_validator +from pydantic import BaseModel, model_validator from langchain_community.tools.playwright.base import BaseBrowserTool from langchain_community.tools.playwright.utils import ( @@ -22,8 +22,9 @@ class ExtractTextTool(BaseBrowserTool): description: str = "Extract all the text on the current webpage" args_schema: Type[BaseModel] = BaseModel - @root_validator(pre=True) - def check_acheck_bs_importrgs(cls, values: dict) -> dict: + @model_validator(mode="before") + @classmethod + def check_acheck_bs_importrgs(cls, values: dict) -> Any: """Check that the arguments are valid.""" try: from bs4 import BeautifulSoup # noqa: F401 diff --git a/libs/community/langchain_community/tools/playwright/get_elements.py b/libs/community/langchain_community/tools/playwright/get_elements.py index 3b88529fc7588..11e43c01696aa 100644 --- a/libs/community/langchain_community/tools/playwright/get_elements.py +++ b/libs/community/langchain_community/tools/playwright/get_elements.py @@ -7,7 +7,7 @@ AsyncCallbackManagerForToolRun, CallbackManagerForToolRun, ) -from langchain_core.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, Field from langchain_community.tools.playwright.base import BaseBrowserTool from langchain_community.tools.playwright.utils import ( diff --git a/libs/community/langchain_community/tools/playwright/navigate.py b/libs/community/langchain_community/tools/playwright/navigate.py index 82f6349ff064a..5c0de271ce9af 100644 --- a/libs/community/langchain_community/tools/playwright/navigate.py +++ b/libs/community/langchain_community/tools/playwright/navigate.py @@ -7,7 +7,7 @@ AsyncCallbackManagerForToolRun, CallbackManagerForToolRun, ) -from langchain_core.pydantic_v1 import BaseModel, Field, validator +from pydantic import BaseModel, Field, validator from langchain_community.tools.playwright.base import BaseBrowserTool from langchain_community.tools.playwright.utils import ( diff --git a/libs/community/langchain_community/tools/playwright/navigate_back.py b/libs/community/langchain_community/tools/playwright/navigate_back.py index 5988fa7fe894a..908e20bd57100 100644 --- a/libs/community/langchain_community/tools/playwright/navigate_back.py +++ b/libs/community/langchain_community/tools/playwright/navigate_back.py @@ -6,7 +6,7 @@ AsyncCallbackManagerForToolRun, CallbackManagerForToolRun, ) -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel from langchain_community.tools.playwright.base import BaseBrowserTool from langchain_community.tools.playwright.utils import ( diff --git a/libs/community/langchain_community/tools/plugin.py b/libs/community/langchain_community/tools/plugin.py index 0966ed5bedee2..102451e72dade 100644 --- a/libs/community/langchain_community/tools/plugin.py +++ b/libs/community/langchain_community/tools/plugin.py @@ -9,8 +9,8 @@ AsyncCallbackManagerForToolRun, CallbackManagerForToolRun, ) -from langchain_core.pydantic_v1 import BaseModel from langchain_core.tools import BaseTool +from pydantic import BaseModel class ApiConfig(BaseModel): diff --git a/libs/community/langchain_community/tools/polygon/aggregates.py b/libs/community/langchain_community/tools/polygon/aggregates.py index e4c07761727f9..26cb62d4677de 100644 --- a/libs/community/langchain_community/tools/polygon/aggregates.py +++ b/libs/community/langchain_community/tools/polygon/aggregates.py @@ -1,8 +1,8 @@ from typing import Optional, Type from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.tools import BaseTool +from pydantic import BaseModel, Field from langchain_community.utilities.polygon import PolygonAPIWrapper diff --git a/libs/community/langchain_community/tools/polygon/financials.py b/libs/community/langchain_community/tools/polygon/financials.py index aae19cad06ab7..8400e7498b469 100644 --- a/libs/community/langchain_community/tools/polygon/financials.py +++ b/libs/community/langchain_community/tools/polygon/financials.py @@ -1,8 +1,8 @@ from typing import Optional, Type from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel from langchain_core.tools import BaseTool +from pydantic import BaseModel from langchain_community.utilities.polygon import PolygonAPIWrapper diff --git a/libs/community/langchain_community/tools/polygon/last_quote.py b/libs/community/langchain_community/tools/polygon/last_quote.py index ce6ebe750ea57..76c768113b3b5 100644 --- a/libs/community/langchain_community/tools/polygon/last_quote.py +++ b/libs/community/langchain_community/tools/polygon/last_quote.py @@ -1,8 +1,8 @@ from typing import Optional, Type from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel from langchain_core.tools import BaseTool +from pydantic import BaseModel from langchain_community.utilities.polygon import PolygonAPIWrapper diff --git a/libs/community/langchain_community/tools/polygon/ticker_news.py b/libs/community/langchain_community/tools/polygon/ticker_news.py index 9d858ebc8f498..d4c4a2017abbd 100644 --- a/libs/community/langchain_community/tools/polygon/ticker_news.py +++ b/libs/community/langchain_community/tools/polygon/ticker_news.py @@ -1,8 +1,8 @@ from typing import Optional, Type from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel from langchain_core.tools import BaseTool +from pydantic import BaseModel from langchain_community.utilities.polygon import PolygonAPIWrapper diff --git a/libs/community/langchain_community/tools/powerbi/tool.py b/libs/community/langchain_community/tools/powerbi/tool.py index 2a2602f28ad9f..9d55f431c2fc5 100644 --- a/libs/community/langchain_community/tools/powerbi/tool.py +++ b/libs/community/langchain_community/tools/powerbi/tool.py @@ -8,8 +8,8 @@ AsyncCallbackManagerForToolRun, CallbackManagerForToolRun, ) -from langchain_core.pydantic_v1 import Field, validator from langchain_core.tools import BaseTool +from pydantic import ConfigDict, Field, validator from langchain_community.chat_models.openai import _import_tiktoken from langchain_community.tools.powerbi.prompt import ( @@ -39,8 +39,9 @@ class QueryPowerBITool(BaseTool): output_token_limit: int = 4000 tiktoken_model_name: Optional[str] = None # "cl100k_base" - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) @validator("llm_chain") def validate_llm_chain_input_variables( # pylint: disable=E0213 @@ -225,8 +226,9 @@ class InfoPowerBITool(BaseTool): """ # noqa: E501 powerbi: PowerBIDataset = Field(exclude=True) - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) def _run( self, @@ -251,8 +253,9 @@ class ListPowerBITool(BaseTool): description: str = "Input is an empty string, output is a comma separated list of tables in the database." # noqa: E501 # pylint: disable=C0301 powerbi: PowerBIDataset = Field(exclude=True) - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) def _run( self, diff --git a/libs/community/langchain_community/tools/pubmed/tool.py b/libs/community/langchain_community/tools/pubmed/tool.py index 7eda0fd3a5778..fe8338635449f 100644 --- a/libs/community/langchain_community/tools/pubmed/tool.py +++ b/libs/community/langchain_community/tools/pubmed/tool.py @@ -1,8 +1,8 @@ from typing import Optional from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import Field from langchain_core.tools import BaseTool +from pydantic import Field from langchain_community.utilities.pubmed import PubMedAPIWrapper diff --git a/libs/community/langchain_community/tools/reddit_search/tool.py b/libs/community/langchain_community/tools/reddit_search/tool.py index e245286e840b4..fc823c2b23dcd 100644 --- a/libs/community/langchain_community/tools/reddit_search/tool.py +++ b/libs/community/langchain_community/tools/reddit_search/tool.py @@ -3,8 +3,8 @@ from typing import Optional, Type from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.tools import BaseTool +from pydantic import BaseModel, Field from langchain_community.utilities.reddit_search import RedditSearchAPIWrapper diff --git a/libs/community/langchain_community/tools/requests/tool.py b/libs/community/langchain_community/tools/requests/tool.py index 49c2b1734f514..c91b348053f0e 100644 --- a/libs/community/langchain_community/tools/requests/tool.py +++ b/libs/community/langchain_community/tools/requests/tool.py @@ -4,7 +4,7 @@ import json from typing import Any, Dict, Optional, Union -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel from langchain_core.callbacks import ( AsyncCallbackManagerForToolRun, CallbackManagerForToolRun, diff --git a/libs/community/langchain_community/tools/riza/command.py b/libs/community/langchain_community/tools/riza/command.py index cfc36865768af..f0fbe382d46cd 100644 --- a/libs/community/langchain_community/tools/riza/command.py +++ b/libs/community/langchain_community/tools/riza/command.py @@ -10,8 +10,8 @@ from langchain_core.callbacks import ( CallbackManagerForToolRun, ) -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.tools import BaseTool, ToolException +from pydantic import BaseModel, Field class ExecPythonInput(BaseModel): diff --git a/libs/community/langchain_community/tools/scenexplain/tool.py b/libs/community/langchain_community/tools/scenexplain/tool.py index 94ed70e699102..fdc085ab1c8fd 100644 --- a/libs/community/langchain_community/tools/scenexplain/tool.py +++ b/libs/community/langchain_community/tools/scenexplain/tool.py @@ -3,8 +3,8 @@ from typing import Optional from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.tools import BaseTool +from pydantic import BaseModel, Field from langchain_community.utilities.scenexplain import SceneXplainAPIWrapper diff --git a/libs/community/langchain_community/tools/searchapi/tool.py b/libs/community/langchain_community/tools/searchapi/tool.py index 5e7f5e8917cff..205d59880deaa 100644 --- a/libs/community/langchain_community/tools/searchapi/tool.py +++ b/libs/community/langchain_community/tools/searchapi/tool.py @@ -6,8 +6,8 @@ AsyncCallbackManagerForToolRun, CallbackManagerForToolRun, ) -from langchain_core.pydantic_v1 import Field from langchain_core.tools import BaseTool +from pydantic import Field from langchain_community.utilities.searchapi import SearchApiAPIWrapper diff --git a/libs/community/langchain_community/tools/searx_search/tool.py b/libs/community/langchain_community/tools/searx_search/tool.py index db8bca859c3b4..d16739e88f2f1 100644 --- a/libs/community/langchain_community/tools/searx_search/tool.py +++ b/libs/community/langchain_community/tools/searx_search/tool.py @@ -6,8 +6,8 @@ AsyncCallbackManagerForToolRun, CallbackManagerForToolRun, ) -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.tools import BaseTool +from pydantic import BaseModel, ConfigDict, Field from langchain_community.utilities.searx_search import SearxSearchWrapper @@ -62,8 +62,9 @@ class SearxSearchResults(BaseTool): kwargs: dict = Field(default_factory=dict) args_schema: Type[BaseModel] = SearxSearchQueryInput - class Config: - extra = "allow" + model_config = ConfigDict( + extra="allow", + ) def _run( self, diff --git a/libs/community/langchain_community/tools/semanticscholar/tool.py b/libs/community/langchain_community/tools/semanticscholar/tool.py index d9bf773ea9368..ce53fa4bab525 100644 --- a/libs/community/langchain_community/tools/semanticscholar/tool.py +++ b/libs/community/langchain_community/tools/semanticscholar/tool.py @@ -3,8 +3,8 @@ from typing import Optional, Type from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.tools import BaseTool +from pydantic import BaseModel, Field from langchain_community.utilities.semanticscholar import SemanticScholarAPIWrapper diff --git a/libs/community/langchain_community/tools/shell/tool.py b/libs/community/langchain_community/tools/shell/tool.py index cbc77ea16ffa6..c4ff4d1b60596 100644 --- a/libs/community/langchain_community/tools/shell/tool.py +++ b/libs/community/langchain_community/tools/shell/tool.py @@ -6,8 +6,8 @@ from langchain_core.callbacks import ( CallbackManagerForToolRun, ) -from langchain_core.pydantic_v1 import BaseModel, Field, root_validator from langchain_core.tools import BaseTool +from pydantic import BaseModel, Field, model_validator logger = logging.getLogger(__name__) @@ -21,8 +21,9 @@ class ShellInput(BaseModel): ) """List of shell commands to run.""" - @root_validator(pre=True) - def _validate_commands(cls, values: dict) -> dict: + @model_validator(mode="before") + @classmethod + def _validate_commands(cls, values: dict) -> Any: """Validate commands.""" # TODO: Add real validators commands = values.get("commands") diff --git a/libs/community/langchain_community/tools/slack/base.py b/libs/community/langchain_community/tools/slack/base.py index 1b87779490729..6579e6ce44e30 100644 --- a/libs/community/langchain_community/tools/slack/base.py +++ b/libs/community/langchain_community/tools/slack/base.py @@ -4,8 +4,8 @@ from typing import TYPE_CHECKING -from langchain_core.pydantic_v1 import Field from langchain_core.tools import BaseTool +from pydantic import Field from langchain_community.tools.slack.utils import login diff --git a/libs/community/langchain_community/tools/slack/get_message.py b/libs/community/langchain_community/tools/slack/get_message.py index 06ec9a9f57031..733f16979e428 100644 --- a/libs/community/langchain_community/tools/slack/get_message.py +++ b/libs/community/langchain_community/tools/slack/get_message.py @@ -3,7 +3,7 @@ from typing import Optional, Type from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, Field from langchain_community.tools.slack.base import SlackBaseTool diff --git a/libs/community/langchain_community/tools/slack/schedule_message.py b/libs/community/langchain_community/tools/slack/schedule_message.py index 90edc9bcb9d71..c4a561f5aae17 100644 --- a/libs/community/langchain_community/tools/slack/schedule_message.py +++ b/libs/community/langchain_community/tools/slack/schedule_message.py @@ -3,7 +3,7 @@ from typing import Optional, Type from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, Field from langchain_community.tools.slack.base import SlackBaseTool from langchain_community.tools.slack.utils import UTC_FORMAT diff --git a/libs/community/langchain_community/tools/slack/send_message.py b/libs/community/langchain_community/tools/slack/send_message.py index c5e8a875ad0fe..87223830d431a 100644 --- a/libs/community/langchain_community/tools/slack/send_message.py +++ b/libs/community/langchain_community/tools/slack/send_message.py @@ -1,7 +1,7 @@ from typing import Optional, Type from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, Field from langchain_community.tools.slack.base import SlackBaseTool diff --git a/libs/community/langchain_community/tools/sleep/tool.py b/libs/community/langchain_community/tools/sleep/tool.py index 27ea267e6e930..e39a272a79f79 100644 --- a/libs/community/langchain_community/tools/sleep/tool.py +++ b/libs/community/langchain_community/tools/sleep/tool.py @@ -8,8 +8,8 @@ AsyncCallbackManagerForToolRun, CallbackManagerForToolRun, ) -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.tools import BaseTool +from pydantic import BaseModel, Field class SleepInput(BaseModel): diff --git a/libs/community/langchain_community/tools/spark_sql/tool.py b/libs/community/langchain_community/tools/spark_sql/tool.py index 7c40bebf17b70..91bbc87f29549 100644 --- a/libs/community/langchain_community/tools/spark_sql/tool.py +++ b/libs/community/langchain_community/tools/spark_sql/tool.py @@ -3,7 +3,7 @@ from typing import Any, Dict, Optional -from langchain_core.pydantic_v1 import BaseModel, Field, root_validator +from pydantic import BaseModel, Field, root_validator, model_validator, ConfigDict from langchain_core.language_models import BaseLanguageModel from langchain_core.callbacks import ( @@ -21,8 +21,9 @@ class BaseSparkSQLTool(BaseModel): db: SparkSQL = Field(exclude=True) - class Config(BaseTool.Config): - pass + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) class QuerySparkSQLTool(BaseSparkSQLTool, BaseTool): @@ -92,8 +93,9 @@ class QueryCheckerTool(BaseSparkSQLTool, BaseTool): Always use this tool before executing a query with query_sql_db! """ - @root_validator(pre=True) - def initialize_llm_chain(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def initialize_llm_chain(cls, values: Dict[str, Any]) -> Any: if "llm_chain" not in values: from langchain.chains.llm import LLMChain diff --git a/libs/community/langchain_community/tools/sql_database/tool.py b/libs/community/langchain_community/tools/sql_database/tool.py index be89eefbf0912..1bc119860d6ed 100644 --- a/libs/community/langchain_community/tools/sql_database/tool.py +++ b/libs/community/langchain_community/tools/sql_database/tool.py @@ -5,7 +5,7 @@ from sqlalchemy.engine import Result -from langchain_core.pydantic_v1 import BaseModel, Field, root_validator +from pydantic import BaseModel, Field, root_validator, model_validator, ConfigDict from langchain_core.language_models import BaseLanguageModel from langchain_core.callbacks import ( @@ -23,8 +23,9 @@ class BaseSQLDatabaseTool(BaseModel): db: SQLDatabase = Field(exclude=True) - class Config(BaseTool.Config): - pass + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) class _QuerySQLDataBaseToolInput(BaseModel): @@ -117,8 +118,9 @@ class QuerySQLCheckerTool(BaseSQLDatabaseTool, BaseTool): """ args_schema: Type[BaseModel] = _QuerySQLCheckerToolInput - @root_validator(pre=True) - def initialize_llm_chain(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def initialize_llm_chain(cls, values: Dict[str, Any]) -> Any: if "llm_chain" not in values: from langchain.chains.llm import LLMChain diff --git a/libs/community/langchain_community/tools/steamship_image_generation/tool.py b/libs/community/langchain_community/tools/steamship_image_generation/tool.py index 20d3956209f7e..ea1c4b6860df0 100644 --- a/libs/community/langchain_community/tools/steamship_image_generation/tool.py +++ b/libs/community/langchain_community/tools/steamship_image_generation/tool.py @@ -15,12 +15,12 @@ from __future__ import annotations from enum import Enum -from typing import TYPE_CHECKING, Dict, Optional +from typing import TYPE_CHECKING, Any, Dict, Optional from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import root_validator from langchain_core.tools import BaseTool from langchain_core.utils import get_from_dict_or_env +from pydantic import model_validator from langchain_community.tools.steamship_image_generation.utils import make_image_public @@ -56,8 +56,9 @@ class SteamshipImageGenerationTool(BaseTool): "Output: the UUID of a generated image" ) - @root_validator(pre=True) - def validate_size(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_size(cls, values: Dict) -> Any: if "size" in values: size = values["size"] model_name = values["model_name"] @@ -66,8 +67,9 @@ def validate_size(cls, values: Dict) -> Dict: return values - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key and python package exists in environment.""" steamship_api_key = get_from_dict_or_env( values, "steamship_api_key", "STEAMSHIP_API_KEY" diff --git a/libs/community/langchain_community/tools/tavily_search/tool.py b/libs/community/langchain_community/tools/tavily_search/tool.py index 6a5b246dd1e86..53164d9340698 100644 --- a/libs/community/langchain_community/tools/tavily_search/tool.py +++ b/libs/community/langchain_community/tools/tavily_search/tool.py @@ -6,8 +6,8 @@ AsyncCallbackManagerForToolRun, CallbackManagerForToolRun, ) -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.tools import BaseTool +from pydantic import BaseModel, Field from langchain_community.utilities.tavily_search import TavilySearchAPIWrapper diff --git a/libs/community/langchain_community/tools/vectorstore/tool.py b/libs/community/langchain_community/tools/vectorstore/tool.py index 7f9b965f08cc8..e2479929df6ba 100644 --- a/libs/community/langchain_community/tools/vectorstore/tool.py +++ b/libs/community/langchain_community/tools/vectorstore/tool.py @@ -8,9 +8,9 @@ CallbackManagerForToolRun, ) from langchain_core.language_models import BaseLanguageModel -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.tools import BaseTool from langchain_core.vectorstores import VectorStore +from pydantic import BaseModel, ConfigDict, Field from langchain_community.llms.openai import OpenAI @@ -21,8 +21,9 @@ class BaseVectorStoreTool(BaseModel): vectorstore: VectorStore = Field(exclude=True) llm: BaseLanguageModel = Field(default_factory=lambda: OpenAI(temperature=0)) - class Config(BaseTool.Config): - pass + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) def _create_description_from_template(values: Dict[str, Any]) -> Dict[str, Any]: diff --git a/libs/community/langchain_community/tools/wikipedia/tool.py b/libs/community/langchain_community/tools/wikipedia/tool.py index 66af94e41a055..127117f89d642 100644 --- a/libs/community/langchain_community/tools/wikipedia/tool.py +++ b/libs/community/langchain_community/tools/wikipedia/tool.py @@ -3,8 +3,8 @@ from typing import Optional, Type from langchain_core.callbacks import CallbackManagerForToolRun -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.tools import BaseTool +from pydantic import BaseModel, Field from langchain_community.utilities.wikipedia import WikipediaAPIWrapper diff --git a/libs/community/langchain_community/tools/yahoo_finance_news.py b/libs/community/langchain_community/tools/yahoo_finance_news.py index 4e120e45ce41b..7c844f0c59052 100644 --- a/libs/community/langchain_community/tools/yahoo_finance_news.py +++ b/libs/community/langchain_community/tools/yahoo_finance_news.py @@ -2,8 +2,8 @@ from langchain_core.callbacks import CallbackManagerForToolRun from langchain_core.documents import Document -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.tools import BaseTool +from pydantic import BaseModel, Field from requests.exceptions import HTTPError, ReadTimeout from urllib3.exceptions import ConnectionError diff --git a/libs/community/langchain_community/tools/you/tool.py b/libs/community/langchain_community/tools/you/tool.py index 949eca08274d7..59990e71ff545 100644 --- a/libs/community/langchain_community/tools/you/tool.py +++ b/libs/community/langchain_community/tools/you/tool.py @@ -5,8 +5,8 @@ CallbackManagerForToolRun, ) from langchain_core.documents import Document -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.tools import BaseTool +from pydantic import BaseModel, Field from langchain_community.utilities.you import YouSearchAPIWrapper diff --git a/libs/community/langchain_community/tools/zapier/tool.py b/libs/community/langchain_community/tools/zapier/tool.py index 7cc97132a7c13..44d7fb04c0af7 100644 --- a/libs/community/langchain_community/tools/zapier/tool.py +++ b/libs/community/langchain_community/tools/zapier/tool.py @@ -75,9 +75,9 @@ AsyncCallbackManagerForToolRun, CallbackManagerForToolRun, ) -from langchain_core.pydantic_v1 import Field from langchain_core.tools import BaseTool from langchain_core.utils import pre_init +from pydantic import Field from langchain_community.tools.zapier.prompt import BASE_ZAPIER_TOOL_PROMPT from langchain_community.utilities.zapier import ZapierNLAWrapper diff --git a/libs/community/langchain_community/tools/zenguard/tool.py b/libs/community/langchain_community/tools/zenguard/tool.py index f7ddbc4defd4b..0e4a686eb2209 100644 --- a/libs/community/langchain_community/tools/zenguard/tool.py +++ b/libs/community/langchain_community/tools/zenguard/tool.py @@ -1,10 +1,10 @@ import os from enum import Enum -from typing import Any, Dict, List, Optional +from typing import Any, Dict, List, Optional, Type import requests -from langchain_core.pydantic_v1 import BaseModel, Field, ValidationError, validator from langchain_core.tools import BaseTool +from pydantic import BaseModel, Field, ValidationError, validator class Detector(str, Enum): @@ -50,7 +50,7 @@ class ZenGuardTool(BaseTool): description: str = ( "ZenGuard AI integration package. ZenGuard AI - the fastest GenAI guardrails." ) - args_schema = ZenGuardInput + args_schema: Type[BaseModel] = ZenGuardInput return_direct: bool = True zenguard_api_key: Optional[str] = Field(default=None) diff --git a/libs/community/langchain_community/utilities/alpha_vantage.py b/libs/community/langchain_community/utilities/alpha_vantage.py index 59eb65ac910dc..e6354affbfae0 100644 --- a/libs/community/langchain_community/utilities/alpha_vantage.py +++ b/libs/community/langchain_community/utilities/alpha_vantage.py @@ -3,8 +3,8 @@ from typing import Any, Dict, List, Optional import requests -from langchain_core.pydantic_v1 import BaseModel, root_validator from langchain_core.utils import get_from_dict_or_env +from pydantic import BaseModel, ConfigDict, model_validator class AlphaVantageAPIWrapper(BaseModel): @@ -18,11 +18,13 @@ class AlphaVantageAPIWrapper(BaseModel): alphavantage_api_key: Optional[str] = None - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key exists in environment.""" values["alphavantage_api_key"] = get_from_dict_or_env( values, "alphavantage_api_key", "ALPHAVANTAGE_API_KEY" diff --git a/libs/community/langchain_community/utilities/apify.py b/libs/community/langchain_community/utilities/apify.py index f80deb496d4d3..077bcc12b2985 100644 --- a/libs/community/langchain_community/utilities/apify.py +++ b/libs/community/langchain_community/utilities/apify.py @@ -1,8 +1,8 @@ from typing import TYPE_CHECKING, Any, Callable, Dict, Optional from langchain_core.documents import Document -from langchain_core.pydantic_v1 import BaseModel, root_validator from langchain_core.utils import get_from_dict_or_env +from pydantic import BaseModel, model_validator if TYPE_CHECKING: from langchain_community.document_loaders import ApifyDatasetLoader @@ -19,8 +19,9 @@ class ApifyWrapper(BaseModel): apify_client_async: Any apify_api_token: Optional[str] = None - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate environment. Validate that an Apify API token is set and the apify-client Python package exists in the current environment. diff --git a/libs/community/langchain_community/utilities/arcee.py b/libs/community/langchain_community/utilities/arcee.py index 3064fd16b4d6d..badbf9cdc9139 100644 --- a/libs/community/langchain_community/utilities/arcee.py +++ b/libs/community/langchain_community/utilities/arcee.py @@ -6,8 +6,8 @@ from typing import Any, Dict, List, Literal, Mapping, Optional, Union import requests -from langchain_core.pydantic_v1 import BaseModel, SecretStr, root_validator from langchain_core.retrievers import Document +from pydantic import BaseModel, SecretStr, model_validator class ArceeRoute(str, Enum): @@ -51,8 +51,9 @@ class DALMFilter(BaseModel): value: str _is_metadata: bool = False - @root_validator(pre=True) - def set_meta(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def set_meta(cls, values: Dict) -> Any: """document and name are reserved arcee keys. Anything else is metadata""" values["_is_meta"] = values.get("field_name") not in ["document", "name"] return values diff --git a/libs/community/langchain_community/utilities/arxiv.py b/libs/community/langchain_community/utilities/arxiv.py index 55767a339b723..a4c6cf5aceaad 100644 --- a/libs/community/langchain_community/utilities/arxiv.py +++ b/libs/community/langchain_community/utilities/arxiv.py @@ -6,7 +6,7 @@ from typing import Any, Dict, Iterator, List, Optional from langchain_core.documents import Document -from langchain_core.pydantic_v1 import BaseModel, root_validator +from pydantic import BaseModel, model_validator logger = logging.getLogger(__name__) @@ -73,8 +73,9 @@ def is_arxiv_identifier(self, query: str) -> bool: return False return True - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that the python package exists in environment.""" try: import arxiv diff --git a/libs/community/langchain_community/utilities/asknews.py b/libs/community/langchain_community/utilities/asknews.py index 4f7051bbc1be5..e61a1e23f3d9c 100644 --- a/libs/community/langchain_community/utilities/asknews.py +++ b/libs/community/langchain_community/utilities/asknews.py @@ -5,8 +5,8 @@ from datetime import datetime, timedelta from typing import Any, Dict, Optional -from langchain_core.pydantic_v1 import BaseModel, root_validator from langchain_core.utils import get_from_dict_or_env +from pydantic import BaseModel, ConfigDict, model_validator class AskNewsAPIWrapper(BaseModel): @@ -19,11 +19,13 @@ class AskNewsAPIWrapper(BaseModel): asknews_client_secret: Optional[str] = None """Client Secret for the AskNews API.""" - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api credentials and python package exists in environment.""" asknews_client_id = get_from_dict_or_env( diff --git a/libs/community/langchain_community/utilities/awslambda.py b/libs/community/langchain_community/utilities/awslambda.py index 1bc77295061b7..9abd0e7958f50 100644 --- a/libs/community/langchain_community/utilities/awslambda.py +++ b/libs/community/langchain_community/utilities/awslambda.py @@ -3,7 +3,7 @@ import json from typing import Any, Dict, Optional -from langchain_core.pydantic_v1 import BaseModel, root_validator +from pydantic import BaseModel, ConfigDict, model_validator class LambdaWrapper(BaseModel): @@ -30,11 +30,13 @@ class LambdaWrapper(BaseModel): awslambda_tool_description: Optional[str] = None """If passing to an agent as a tool, the description""" - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that python package exists in environment.""" try: diff --git a/libs/community/langchain_community/utilities/bibtex.py b/libs/community/langchain_community/utilities/bibtex.py index 101b88bfaac86..a3bf82ab73889 100644 --- a/libs/community/langchain_community/utilities/bibtex.py +++ b/libs/community/langchain_community/utilities/bibtex.py @@ -3,7 +3,7 @@ import logging from typing import Any, Dict, List, Mapping -from langchain_core.pydantic_v1 import BaseModel, root_validator +from pydantic import BaseModel, ConfigDict, model_validator logger = logging.getLogger(__name__) @@ -36,11 +36,13 @@ class BibtexparserWrapper(BaseModel): a bibtex file and fetch document summaries. """ - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that the python package exists in environment.""" try: import bibtexparser # noqa diff --git a/libs/community/langchain_community/utilities/bing_search.py b/libs/community/langchain_community/utilities/bing_search.py index ee85cbe216bb8..a3dc03fdea9b5 100644 --- a/libs/community/langchain_community/utilities/bing_search.py +++ b/libs/community/langchain_community/utilities/bing_search.py @@ -1,10 +1,10 @@ """Util that calls Bing Search.""" -from typing import Dict, List +from typing import Any, Dict, List import requests -from langchain_core.pydantic_v1 import BaseModel, Field, root_validator from langchain_core.utils import get_from_dict_or_env +from pydantic import BaseModel, ConfigDict, Field, model_validator # BING_SEARCH_ENDPOINT is the default endpoint for Bing Web Search API. # Currently There are two web-based Bing Search services available on Azure, @@ -34,8 +34,9 @@ class BingSearchAPIWrapper(BaseModel): search_kwargs: dict = Field(default_factory=dict) """Additional keyword arguments to pass to the search request.""" - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) def _bing_search_results(self, search_term: str, count: int) -> List[dict]: headers = {"Ocp-Apim-Subscription-Key": self.bing_subscription_key} @@ -57,8 +58,9 @@ def _bing_search_results(self, search_term: str, count: int) -> List[dict]: return search_results["webPages"]["value"] return [] - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key and endpoint exists in environment.""" bing_subscription_key = get_from_dict_or_env( values, "bing_subscription_key", "BING_SUBSCRIPTION_KEY" diff --git a/libs/community/langchain_community/utilities/brave_search.py b/libs/community/langchain_community/utilities/brave_search.py index cb822e55f43d1..41dd9015f2e40 100644 --- a/libs/community/langchain_community/utilities/brave_search.py +++ b/libs/community/langchain_community/utilities/brave_search.py @@ -3,7 +3,7 @@ import requests from langchain_core.documents import Document -from langchain_core.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, Field class BraveSearchWrapper(BaseModel): diff --git a/libs/community/langchain_community/utilities/cassandra_database.py b/libs/community/langchain_community/utilities/cassandra_database.py index 90eba0cae1b16..4ec1973b1668e 100644 --- a/libs/community/langchain_community/utilities/cassandra_database.py +++ b/libs/community/langchain_community/utilities/cassandra_database.py @@ -5,7 +5,8 @@ import re from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Tuple, Union -from langchain_core.pydantic_v1 import BaseModel, Field, root_validator +from pydantic import BaseModel, ConfigDict, Field, model_validator +from typing_extensions import Self if TYPE_CHECKING: from cassandra.cluster import ResultSet, Session @@ -480,16 +481,17 @@ class Table(BaseModel): clustering: List[Tuple[str, str]] = Field(default_factory=list) indexes: List[Tuple[str, str, str]] = Field(default_factory=list) - class Config: - frozen = True + model_config = ConfigDict( + frozen=True, + ) - @root_validator(pre=False, skip_on_failure=True) - def check_required_fields(cls, class_values: dict) -> dict: - if not class_values["columns"]: + @model_validator(mode="after") + def check_required_fields(self) -> Self: + if not self.columns: raise ValueError("non-empty column list for must be provided") - if not class_values["partition"]: + if not self.partition: raise ValueError("non-empty partition list must be provided") - return class_values + return self @classmethod def from_database( diff --git a/libs/community/langchain_community/utilities/clickup.py b/libs/community/langchain_community/utilities/clickup.py index 2c099a8623a78..f5f00b22a0884 100644 --- a/libs/community/langchain_community/utilities/clickup.py +++ b/libs/community/langchain_community/utilities/clickup.py @@ -6,8 +6,8 @@ from typing import Any, Dict, List, Mapping, Optional, Tuple, Type, Union import requests -from langchain_core.pydantic_v1 import BaseModel, root_validator from langchain_core.utils import get_from_dict_or_env +from pydantic import BaseModel, ConfigDict, model_validator DEFAULT_URL = "https://api.clickup.com/api/v2" @@ -282,8 +282,9 @@ class ClickupAPIWrapper(BaseModel): folder_id: Optional[str] = None list_id: Optional[str] = None - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) @classmethod def get_access_code_url( @@ -321,8 +322,9 @@ def get_access_token( return data["access_token"] - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key and python package exists in environment.""" values["access_token"] = get_from_dict_or_env( values, "access_token", "CLICKUP_ACCESS_TOKEN" diff --git a/libs/community/langchain_community/utilities/dalle_image_generator.py b/libs/community/langchain_community/utilities/dalle_image_generator.py index 6ce9b3b5686b8..c0cbf00a437ba 100644 --- a/libs/community/langchain_community/utilities/dalle_image_generator.py +++ b/libs/community/langchain_community/utilities/dalle_image_generator.py @@ -1,14 +1,15 @@ """Utility that calls OpenAI's Dall-E Image Generator.""" import logging -import os from typing import Any, Dict, Mapping, Optional, Tuple, Union -from langchain_core.pydantic_v1 import BaseModel, Field, root_validator from langchain_core.utils import ( - get_from_dict_or_env, + from_env, get_pydantic_field_names, + secret_from_env, ) +from pydantic import BaseModel, ConfigDict, Field, Secret, model_validator +from typing_extensions import Self from langchain_community.utils.openai import is_openai_v1 @@ -30,15 +31,28 @@ class DallEAPIWrapper(BaseModel): async_client: Any = Field(default=None, exclude=True) #: :meta private: model_name: str = Field(default="dall-e-2", alias="model") model_kwargs: Dict[str, Any] = Field(default_factory=dict) - openai_api_key: Optional[str] = Field(default=None, alias="api_key") + openai_api_key: Secret[str] = Field( + alias="api_key", + default_factory=secret_from_env( + "OPENAI_API_KEY", + default=None, + ), + ) """Automatically inferred from env var `OPENAI_API_KEY` if not provided.""" - openai_api_base: Optional[str] = Field(default=None, alias="base_url") + openai_api_base: Optional[str] = Field( + alias="base_url", default_factory=from_env("OPENAI_API_BASE", default=None) + ) """Base URL path for API requests, leave blank if not using a proxy or service emulator.""" - openai_organization: Optional[str] = Field(default=None, alias="organization") + openai_organization: Optional[str] = Field( + alias="organization", + default_factory=from_env( + ["OPENAI_ORG_ID", "OPENAI_ORGANIZATION"], default=None + ), + ) """Automatically inferred from env var `OPENAI_ORG_ID` if not provided.""" # to support explicit proxy for OpenAI - openai_proxy: Optional[str] = None + openai_proxy: str = Field(default_factory=from_env("OPENAI_PROXY", default="")) request_timeout: Union[float, Tuple[float, float], Any, None] = Field( default=None, alias="timeout" ) @@ -59,11 +73,13 @@ class DallEAPIWrapper(BaseModel): http_client: Union[Any, None] = None """Optional httpx.Client.""" - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) - @root_validator(pre=True) - def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def build_extra(cls, values: Dict[str, Any]) -> Any: """Build extra kwargs from additional params that were passed in.""" all_required_field_names = get_pydantic_field_names(cls) extra = values.get("model_kwargs", {}) @@ -88,29 +104,9 @@ def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: values["model_kwargs"] = extra return values - @root_validator(pre=False, skip_on_failure=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="after") + def validate_environment(self) -> Self: """Validate that api key and python package exists in environment.""" - values["openai_api_key"] = get_from_dict_or_env( - values, "openai_api_key", "OPENAI_API_KEY" - ) - # Check OPENAI_ORGANIZATION for backwards compatibility. - values["openai_organization"] = ( - values["openai_organization"] - or os.getenv("OPENAI_ORG_ID") - or os.getenv("OPENAI_ORGANIZATION") - or None - ) - values["openai_api_base"] = values["openai_api_base"] or os.getenv( - "OPENAI_API_BASE" - ) - values["openai_proxy"] = get_from_dict_or_env( - values, - "openai_proxy", - "OPENAI_PROXY", - default="", - ) - try: import openai @@ -122,25 +118,25 @@ def validate_environment(cls, values: Dict) -> Dict: if is_openai_v1(): client_params = { - "api_key": values["openai_api_key"], - "organization": values["openai_organization"], - "base_url": values["openai_api_base"], - "timeout": values["request_timeout"], - "max_retries": values["max_retries"], - "default_headers": values["default_headers"], - "default_query": values["default_query"], - "http_client": values["http_client"], + "api_key": self.openai_api_key, + "organization": self.openai_organization, + "base_url": self.openai_api_base, + "timeout": self.request_timeout, + "max_retries": self.max_retries, + "default_headers": self.default_headers, + "default_query": self.default_query, + "http_client": self.http_client, } - if not values.get("client"): - values["client"] = openai.OpenAI(**client_params).images - if not values.get("async_client"): - values["async_client"] = openai.AsyncOpenAI(**client_params).images - elif not values.get("client"): - values["client"] = openai.Image + if not self.client: + self.client = openai.OpenAI(**client_params).images + if not self.async_client: + self.async_client = openai.AsyncOpenAI(**client_params).images + elif not self.client: + self.client = openai.Image else: pass - return values + return self def run(self, query: str) -> str: """Run query through OpenAI and parse result.""" diff --git a/libs/community/langchain_community/utilities/dataforseo_api_search.py b/libs/community/langchain_community/utilities/dataforseo_api_search.py index 4c705dabcbf97..7e0c7e085db90 100644 --- a/libs/community/langchain_community/utilities/dataforseo_api_search.py +++ b/libs/community/langchain_community/utilities/dataforseo_api_search.py @@ -1,19 +1,20 @@ import base64 -from typing import Dict, Optional +from typing import Any, Dict, Optional from urllib.parse import quote import aiohttp import requests -from langchain_core.pydantic_v1 import BaseModel, Field, root_validator from langchain_core.utils import get_from_dict_or_env +from pydantic import BaseModel, ConfigDict, Field, model_validator class DataForSeoAPIWrapper(BaseModel): """Wrapper around the DataForSeo API.""" - class Config: - arbitrary_types_allowed = True - extra = "forbid" + model_config = ConfigDict( + arbitrary_types_allowed=True, + extra="forbid", + ) default_params: dict = Field( default={ @@ -40,8 +41,9 @@ class Config: aiosession: Optional[aiohttp.ClientSession] = None """The aiohttp session to use for the DataForSEO SERP API.""" - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that login and password exists in environment.""" login = get_from_dict_or_env(values, "api_login", "DATAFORSEO_LOGIN") password = get_from_dict_or_env(values, "api_password", "DATAFORSEO_PASSWORD") diff --git a/libs/community/langchain_community/utilities/dataherald.py b/libs/community/langchain_community/utilities/dataherald.py index fd0c28aeff648..bf69d8813af19 100644 --- a/libs/community/langchain_community/utilities/dataherald.py +++ b/libs/community/langchain_community/utilities/dataherald.py @@ -2,8 +2,8 @@ from typing import Any, Dict, Optional -from langchain_core.pydantic_v1 import BaseModel, root_validator from langchain_core.utils import get_from_dict_or_env +from pydantic import BaseModel, ConfigDict, model_validator class DataheraldAPIWrapper(BaseModel): @@ -22,11 +22,13 @@ class DataheraldAPIWrapper(BaseModel): db_connection_id: str dataherald_api_key: Optional[str] = None - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key and python package exists in environment.""" dataherald_api_key = get_from_dict_or_env( values, "dataherald_api_key", "DATAHERALD_API_KEY" diff --git a/libs/community/langchain_community/utilities/duckduckgo_search.py b/libs/community/langchain_community/utilities/duckduckgo_search.py index 1d69f47c19c85..d8017c28ae661 100644 --- a/libs/community/langchain_community/utilities/duckduckgo_search.py +++ b/libs/community/langchain_community/utilities/duckduckgo_search.py @@ -4,9 +4,9 @@ https://pypi.org/project/duckduckgo-search/ """ -from typing import Dict, List, Optional +from typing import Any, Dict, List, Optional -from langchain_core.pydantic_v1 import BaseModel, root_validator +from pydantic import BaseModel, ConfigDict, model_validator class DuckDuckGoSearchAPIWrapper(BaseModel): @@ -37,11 +37,13 @@ class DuckDuckGoSearchAPIWrapper(BaseModel): Options: text, news """ - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that python package exists in environment.""" try: from duckduckgo_search import DDGS # noqa: F401 diff --git a/libs/community/langchain_community/utilities/financial_datasets.py b/libs/community/langchain_community/utilities/financial_datasets.py index d8e769442e1e3..b3bfd2165113b 100644 --- a/libs/community/langchain_community/utilities/financial_datasets.py +++ b/libs/community/langchain_community/utilities/financial_datasets.py @@ -7,8 +7,8 @@ from typing import Any, List, Optional import requests -from langchain_core.pydantic_v1 import BaseModel from langchain_core.utils import get_from_dict_or_env +from pydantic import BaseModel FINANCIAL_DATASETS_BASE_URL = "https://api.financialdatasets.ai/" diff --git a/libs/community/langchain_community/utilities/github.py b/libs/community/langchain_community/utilities/github.py index f650fd2b46898..6fee6c27cf9a6 100644 --- a/libs/community/langchain_community/utilities/github.py +++ b/libs/community/langchain_community/utilities/github.py @@ -6,8 +6,8 @@ from typing import TYPE_CHECKING, Any, Dict, List, Optional import requests -from langchain_core.pydantic_v1 import BaseModel, root_validator from langchain_core.utils import get_from_dict_or_env +from pydantic import BaseModel, ConfigDict, model_validator if TYPE_CHECKING: from github.Issue import Issue @@ -37,11 +37,13 @@ class GitHubAPIWrapper(BaseModel): active_branch: Optional[str] = None github_base_branch: Optional[str] = None - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key and python package exists in environment.""" github_repository = get_from_dict_or_env( values, "github_repository", "GITHUB_REPOSITORY" diff --git a/libs/community/langchain_community/utilities/gitlab.py b/libs/community/langchain_community/utilities/gitlab.py index 4907958d8f77b..12e2e1ecd22ad 100644 --- a/libs/community/langchain_community/utilities/gitlab.py +++ b/libs/community/langchain_community/utilities/gitlab.py @@ -5,8 +5,8 @@ import json from typing import TYPE_CHECKING, Any, Dict, List, Optional -from langchain_core.pydantic_v1 import BaseModel, root_validator from langchain_core.utils import get_from_dict_or_env +from pydantic import BaseModel, ConfigDict, model_validator if TYPE_CHECKING: from gitlab.v4.objects import Issue @@ -30,11 +30,13 @@ class GitLabAPIWrapper(BaseModel): Usually 'main' or 'master'. Defaults to 'main'. """ - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key and python package exists in environment.""" gitlab_url = get_from_dict_or_env( diff --git a/libs/community/langchain_community/utilities/golden_query.py b/libs/community/langchain_community/utilities/golden_query.py index 6e0204eef24f4..74cad57f64418 100644 --- a/libs/community/langchain_community/utilities/golden_query.py +++ b/libs/community/langchain_community/utilities/golden_query.py @@ -1,11 +1,11 @@ """Util that calls Golden.""" import json -from typing import Dict, Optional +from typing import Any, Dict, Optional import requests -from langchain_core.pydantic_v1 import BaseModel, root_validator from langchain_core.utils import get_from_dict_or_env +from pydantic import BaseModel, ConfigDict, model_validator GOLDEN_BASE_URL = "https://golden.com" GOLDEN_TIMEOUT = 5000 @@ -24,11 +24,13 @@ class GoldenQueryAPIWrapper(BaseModel): golden_api_key: Optional[str] = None - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key and python package exists in environment.""" golden_api_key = get_from_dict_or_env( values, "golden_api_key", "GOLDEN_API_KEY" diff --git a/libs/community/langchain_community/utilities/google_finance.py b/libs/community/langchain_community/utilities/google_finance.py index da35a2383edea..650bf3611aa85 100644 --- a/libs/community/langchain_community/utilities/google_finance.py +++ b/libs/community/langchain_community/utilities/google_finance.py @@ -2,8 +2,8 @@ from typing import Any, Dict, Optional, cast -from langchain_core.pydantic_v1 import BaseModel, SecretStr, root_validator from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env +from pydantic import BaseModel, ConfigDict, SecretStr, model_validator class GoogleFinanceAPIWrapper(BaseModel): @@ -25,11 +25,13 @@ class GoogleFinanceAPIWrapper(BaseModel): serp_search_engine: Any serp_api_key: Optional[SecretStr] = None - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key and python package exists in environment.""" values["serp_api_key"] = convert_to_secret_str( get_from_dict_or_env(values, "serp_api_key", "SERPAPI_API_KEY") diff --git a/libs/community/langchain_community/utilities/google_jobs.py b/libs/community/langchain_community/utilities/google_jobs.py index bd7be4c7fd220..d036ff38e8bae 100644 --- a/libs/community/langchain_community/utilities/google_jobs.py +++ b/libs/community/langchain_community/utilities/google_jobs.py @@ -2,8 +2,8 @@ from typing import Any, Dict, Optional, cast -from langchain_core.pydantic_v1 import BaseModel, SecretStr, root_validator from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env +from pydantic import BaseModel, ConfigDict, SecretStr, model_validator class GoogleJobsAPIWrapper(BaseModel): @@ -25,11 +25,13 @@ class GoogleJobsAPIWrapper(BaseModel): serp_search_engine: Any serp_api_key: Optional[SecretStr] = None - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key and python package exists in environment.""" values["serp_api_key"] = convert_to_secret_str( get_from_dict_or_env(values, "serp_api_key", "SERPAPI_API_KEY") diff --git a/libs/community/langchain_community/utilities/google_lens.py b/libs/community/langchain_community/utilities/google_lens.py index 34bab1def7988..2aad581c995d2 100644 --- a/libs/community/langchain_community/utilities/google_lens.py +++ b/libs/community/langchain_community/utilities/google_lens.py @@ -3,8 +3,8 @@ from typing import Any, Dict, Optional, cast import requests -from langchain_core.pydantic_v1 import BaseModel, SecretStr, root_validator from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env +from pydantic import BaseModel, ConfigDict, SecretStr, model_validator class GoogleLensAPIWrapper(BaseModel): @@ -30,11 +30,13 @@ class GoogleLensAPIWrapper(BaseModel): serp_search_engine: Any serp_api_key: Optional[SecretStr] = None - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key and python package exists in environment.""" values["serp_api_key"] = convert_to_secret_str( get_from_dict_or_env(values, "serp_api_key", "SERPAPI_API_KEY") diff --git a/libs/community/langchain_community/utilities/google_places_api.py b/libs/community/langchain_community/utilities/google_places_api.py index 5143aeb9e6095..e5e15898ab1b0 100644 --- a/libs/community/langchain_community/utilities/google_places_api.py +++ b/libs/community/langchain_community/utilities/google_places_api.py @@ -4,8 +4,8 @@ from typing import Any, Dict, Optional from langchain_core._api.deprecation import deprecated -from langchain_core.pydantic_v1 import BaseModel, root_validator from langchain_core.utils import get_from_dict_or_env +from pydantic import BaseModel, ConfigDict, model_validator @deprecated( @@ -37,12 +37,14 @@ class GooglePlacesAPIWrapper(BaseModel): google_map_client: Any #: :meta private: top_k_results: Optional[int] = None - class Config: - arbitrary_types_allowed = True - extra = "forbid" + model_config = ConfigDict( + arbitrary_types_allowed=True, + extra="forbid", + ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key is in your environment variable.""" gplaces_api_key = get_from_dict_or_env( values, "gplaces_api_key", "GPLACES_API_KEY" diff --git a/libs/community/langchain_community/utilities/google_scholar.py b/libs/community/langchain_community/utilities/google_scholar.py index e2c0445fd2944..ffc94848a61a5 100644 --- a/libs/community/langchain_community/utilities/google_scholar.py +++ b/libs/community/langchain_community/utilities/google_scholar.py @@ -1,9 +1,9 @@ """Util that calls Google Scholar Search.""" -from typing import Dict, Optional +from typing import Any, Dict, Optional -from langchain_core.pydantic_v1 import BaseModel, root_validator from langchain_core.utils import get_from_dict_or_env +from pydantic import BaseModel, ConfigDict, model_validator class GoogleScholarAPIWrapper(BaseModel): @@ -46,11 +46,13 @@ class GoogleScholarAPIWrapper(BaseModel): lr: str = "lang_en" serp_api_key: Optional[str] = None - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key and python package exists in environment.""" serp_api_key = get_from_dict_or_env(values, "serp_api_key", "SERP_API_KEY") values["SERP_API_KEY"] = serp_api_key diff --git a/libs/community/langchain_community/utilities/google_search.py b/libs/community/langchain_community/utilities/google_search.py index 997df2f9acf88..13e1a41c3b3cd 100644 --- a/libs/community/langchain_community/utilities/google_search.py +++ b/libs/community/langchain_community/utilities/google_search.py @@ -3,8 +3,8 @@ from typing import Any, Dict, List, Optional from langchain_core._api.deprecation import deprecated -from langchain_core.pydantic_v1 import BaseModel, root_validator from langchain_core.utils import get_from_dict_or_env +from pydantic import BaseModel, ConfigDict, model_validator @deprecated( @@ -57,8 +57,9 @@ class GoogleSearchAPIWrapper(BaseModel): k: int = 10 siterestrict: bool = False - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) def _google_search_results(self, search_term: str, **kwargs: Any) -> List[dict]: cse = self.search_engine.cse() @@ -67,8 +68,9 @@ def _google_search_results(self, search_term: str, **kwargs: Any) -> List[dict]: res = cse.list(q=search_term, cx=self.google_cse_id, **kwargs).execute() return res.get("items", []) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key and python package exists in environment.""" google_api_key = get_from_dict_or_env( values, "google_api_key", "GOOGLE_API_KEY" diff --git a/libs/community/langchain_community/utilities/google_serper.py b/libs/community/langchain_community/utilities/google_serper.py index 442ad4bf44584..49f75eaf275b8 100644 --- a/libs/community/langchain_community/utilities/google_serper.py +++ b/libs/community/langchain_community/utilities/google_serper.py @@ -4,8 +4,8 @@ import aiohttp import requests -from langchain_core.pydantic_v1 import BaseModel, root_validator from langchain_core.utils import get_from_dict_or_env +from pydantic import BaseModel, ConfigDict, model_validator from typing_extensions import Literal @@ -31,7 +31,7 @@ class GoogleSerperAPIWrapper(BaseModel): # "places" and "images" is available from Serper but not implemented in the # parser of run(). They can be used in results() type: Literal["news", "search", "places", "images"] = "search" - result_key_for_type = { + result_key_for_type: dict = { "news": "news", "places": "places", "images": "images", @@ -42,11 +42,13 @@ class GoogleSerperAPIWrapper(BaseModel): serper_api_key: Optional[str] = None aiosession: Optional[aiohttp.ClientSession] = None - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key exists in environment.""" serper_api_key = get_from_dict_or_env( values, "serper_api_key", "SERPER_API_KEY" diff --git a/libs/community/langchain_community/utilities/google_trends.py b/libs/community/langchain_community/utilities/google_trends.py index 7ad121a09b59b..38163a43bbddf 100644 --- a/libs/community/langchain_community/utilities/google_trends.py +++ b/libs/community/langchain_community/utilities/google_trends.py @@ -2,8 +2,8 @@ from typing import Any, Dict, Optional, cast -from langchain_core.pydantic_v1 import BaseModel, SecretStr, root_validator from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env +from pydantic import BaseModel, ConfigDict, SecretStr, model_validator class GoogleTrendsAPIWrapper(BaseModel): @@ -29,11 +29,13 @@ class GoogleTrendsAPIWrapper(BaseModel): serp_search_engine: Any serp_api_key: Optional[SecretStr] = None - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key and python package exists in environment.""" values["serp_api_key"] = convert_to_secret_str( get_from_dict_or_env(values, "serp_api_key", "SERPAPI_API_KEY") diff --git a/libs/community/langchain_community/utilities/graphql.py b/libs/community/langchain_community/utilities/graphql.py index 2b27357305df6..2e1dcc181b70e 100644 --- a/libs/community/langchain_community/utilities/graphql.py +++ b/libs/community/langchain_community/utilities/graphql.py @@ -1,7 +1,7 @@ import json from typing import Any, Callable, Dict, Optional -from langchain_core.pydantic_v1 import BaseModel, root_validator +from pydantic import BaseModel, ConfigDict, model_validator class GraphQLAPIWrapper(BaseModel): @@ -17,11 +17,13 @@ class GraphQLAPIWrapper(BaseModel): gql_client: Any #: :meta private: gql_function: Callable[[str], Any] #: :meta private: - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that the python package exists in the environment.""" try: from gql import Client, gql diff --git a/libs/community/langchain_community/utilities/infobip.py b/libs/community/langchain_community/utilities/infobip.py index f73e73b81f46d..a036627daa46e 100644 --- a/libs/community/langchain_community/utilities/infobip.py +++ b/libs/community/langchain_community/utilities/infobip.py @@ -1,10 +1,10 @@ """Util that sends messages via Infobip.""" -from typing import Dict, List, Optional +from typing import Any, Dict, List, Optional import requests -from langchain_core.pydantic_v1 import BaseModel, root_validator from langchain_core.utils import get_from_dict_or_env +from pydantic import BaseModel, ConfigDict, model_validator from requests.adapters import HTTPAdapter from urllib3.util import Retry @@ -15,11 +15,13 @@ class InfobipAPIWrapper(BaseModel): infobip_api_key: Optional[str] = None infobip_base_url: Optional[str] = "https://api.infobip.com" - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key exists in environment.""" values["infobip_api_key"] = get_from_dict_or_env( values, "infobip_api_key", "INFOBIP_API_KEY" diff --git a/libs/community/langchain_community/utilities/jina_search.py b/libs/community/langchain_community/utilities/jina_search.py index eba067a11b2c8..b35b31f6830ad 100644 --- a/libs/community/langchain_community/utilities/jina_search.py +++ b/libs/community/langchain_community/utilities/jina_search.py @@ -3,7 +3,7 @@ import requests from langchain_core.documents import Document -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel from yarl import URL diff --git a/libs/community/langchain_community/utilities/jira.py b/libs/community/langchain_community/utilities/jira.py index 1657f13234e1f..811e7136086a2 100644 --- a/libs/community/langchain_community/utilities/jira.py +++ b/libs/community/langchain_community/utilities/jira.py @@ -2,8 +2,8 @@ from typing import Any, Dict, List, Optional -from langchain_core.pydantic_v1 import BaseModel, root_validator from langchain_core.utils import get_from_dict_or_env +from pydantic import BaseModel, ConfigDict, model_validator # TODO: think about error handling, more specific api specs, and jql/project limits @@ -17,11 +17,13 @@ class JiraAPIWrapper(BaseModel): jira_instance_url: Optional[str] = None jira_cloud: Optional[bool] = None - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key and python package exists in environment.""" jira_username = get_from_dict_or_env( values, "jira_username", "JIRA_USERNAME", default="" diff --git a/libs/community/langchain_community/utilities/merriam_webster.py b/libs/community/langchain_community/utilities/merriam_webster.py index bf02a7ce1961a..8cf9e18a107e8 100644 --- a/libs/community/langchain_community/utilities/merriam_webster.py +++ b/libs/community/langchain_community/utilities/merriam_webster.py @@ -1,12 +1,12 @@ """Util that calls Merriam-Webster.""" import json -from typing import Dict, Iterator, List, Optional +from typing import Any, Dict, Iterator, List, Optional from urllib.parse import quote import requests -from langchain_core.pydantic_v1 import BaseModel, root_validator from langchain_core.utils import get_from_dict_or_env +from pydantic import BaseModel, ConfigDict, model_validator MERRIAM_WEBSTER_API_URL = ( "https://www.dictionaryapi.com/api/v3/references/collegiate/json" @@ -28,11 +28,13 @@ class MerriamWebsterAPIWrapper(BaseModel): merriam_webster_api_key: Optional[str] = None - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key exists in environment.""" merriam_webster_api_key = get_from_dict_or_env( values, "merriam_webster_api_key", "MERRIAM_WEBSTER_API_KEY" diff --git a/libs/community/langchain_community/utilities/metaphor_search.py b/libs/community/langchain_community/utilities/metaphor_search.py index 4269d6c31c751..cbfab0d35e8ee 100644 --- a/libs/community/langchain_community/utilities/metaphor_search.py +++ b/libs/community/langchain_community/utilities/metaphor_search.py @@ -4,12 +4,12 @@ """ import json -from typing import Dict, List, Optional +from typing import Any, Dict, List, Optional import aiohttp import requests -from langchain_core.pydantic_v1 import BaseModel, root_validator from langchain_core.utils import get_from_dict_or_env +from pydantic import BaseModel, ConfigDict, model_validator METAPHOR_API_URL = "https://api.metaphor.systems" @@ -20,8 +20,9 @@ class MetaphorSearchAPIWrapper(BaseModel): metaphor_api_key: str k: int = 10 - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) def _metaphor_search_results( self, @@ -58,8 +59,9 @@ def _metaphor_search_results( search_results = response.json() return search_results["results"] - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key and endpoint exists in environment.""" metaphor_api_key = get_from_dict_or_env( values, "metaphor_api_key", "METAPHOR_API_KEY" diff --git a/libs/community/langchain_community/utilities/mojeek_search.py b/libs/community/langchain_community/utilities/mojeek_search.py index 8f48059dcb9c3..eb5e688cbb667 100644 --- a/libs/community/langchain_community/utilities/mojeek_search.py +++ b/libs/community/langchain_community/utilities/mojeek_search.py @@ -2,7 +2,7 @@ from typing import List import requests -from langchain_core.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, Field class MojeekSearchAPIWrapper(BaseModel): diff --git a/libs/community/langchain_community/utilities/nasa.py b/libs/community/langchain_community/utilities/nasa.py index a0e2904f875ed..2726cae8c1dbe 100644 --- a/libs/community/langchain_community/utilities/nasa.py +++ b/libs/community/langchain_community/utilities/nasa.py @@ -3,7 +3,7 @@ import json import requests -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel IMAGE_AND_VIDEO_LIBRARY_URL = "https://images-api.nasa.gov" diff --git a/libs/community/langchain_community/utilities/nvidia_riva.py b/libs/community/langchain_community/utilities/nvidia_riva.py index c3c887b14f079..c798935e2a7e8 100644 --- a/libs/community/langchain_community/utilities/nvidia_riva.py +++ b/libs/community/langchain_community/utilities/nvidia_riva.py @@ -25,7 +25,8 @@ from langchain_core.messages import AnyMessage, BaseMessage from langchain_core.prompt_values import PromptValue -from langchain_core.pydantic_v1 import ( +from langchain_core.runnables import RunnableConfig, RunnableSerializable +from pydantic import ( AnyHttpUrl, BaseModel, Field, @@ -33,7 +34,6 @@ root_validator, validator, ) -from langchain_core.runnables import RunnableConfig, RunnableSerializable if TYPE_CHECKING: import riva.client @@ -110,7 +110,7 @@ class RivaAuthMixin(BaseModel): """Configuration for the authentication to a Riva service connection.""" url: Union[AnyHttpUrl, str] = Field( - AnyHttpUrl("http://localhost:50051", scheme="http"), + AnyHttpUrl("http://localhost:50051"), description="The full URL where the Riva service can be found.", examples=["http://localhost:50051", "https://user@pass:riva.example.com"], ) diff --git a/libs/community/langchain_community/utilities/openapi.py b/libs/community/langchain_community/utilities/openapi.py index 6bd9182713a8b..1d99f7e182301 100644 --- a/libs/community/langchain_community/utilities/openapi.py +++ b/libs/community/langchain_community/utilities/openapi.py @@ -12,7 +12,7 @@ import requests import yaml -from langchain_core.pydantic_v1 import ValidationError +from pydantic import ValidationError logger = logging.getLogger(__name__) diff --git a/libs/community/langchain_community/utilities/openweathermap.py b/libs/community/langchain_community/utilities/openweathermap.py index 07f4517038af5..42eb2674929a9 100644 --- a/libs/community/langchain_community/utilities/openweathermap.py +++ b/libs/community/langchain_community/utilities/openweathermap.py @@ -2,8 +2,8 @@ from typing import Any, Dict, Optional -from langchain_core.pydantic_v1 import BaseModel, root_validator from langchain_core.utils import get_from_dict_or_env +from pydantic import BaseModel, ConfigDict, model_validator class OpenWeatherMapAPIWrapper(BaseModel): @@ -19,11 +19,13 @@ class OpenWeatherMapAPIWrapper(BaseModel): owm: Any openweathermap_api_key: Optional[str] = None - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key exists in environment.""" openweathermap_api_key = get_from_dict_or_env( values, "openweathermap_api_key", "OPENWEATHERMAP_API_KEY" diff --git a/libs/community/langchain_community/utilities/outline.py b/libs/community/langchain_community/utilities/outline.py index 152c69a8fe0c1..a1106bd4f29ed 100644 --- a/libs/community/langchain_community/utilities/outline.py +++ b/libs/community/langchain_community/utilities/outline.py @@ -5,8 +5,8 @@ import requests from langchain_core.documents import Document -from langchain_core.pydantic_v1 import BaseModel, root_validator from langchain_core.utils import get_from_dict_or_env +from pydantic import BaseModel, model_validator logger = logging.getLogger(__name__) @@ -28,8 +28,9 @@ class OutlineAPIWrapper(BaseModel): outline_api_key: Optional[str] = None outline_search_endpoint: str = "/api/documents.search" - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that instance url and api key exists in environment.""" outline_instance_url = get_from_dict_or_env( values, "outline_instance_url", "OUTLINE_INSTANCE_URL" diff --git a/libs/community/langchain_community/utilities/passio_nutrition_ai.py b/libs/community/langchain_community/utilities/passio_nutrition_ai.py index d4854c348ba39..41caef5eb0079 100644 --- a/libs/community/langchain_community/utilities/passio_nutrition_ai.py +++ b/libs/community/langchain_community/utilities/passio_nutrition_ai.py @@ -4,8 +4,8 @@ from typing import Any, Callable, Dict, Optional, final import requests -from langchain_core.pydantic_v1 import BaseModel, Field, root_validator from langchain_core.utils import get_from_dict_or_env +from pydantic import BaseModel, ConfigDict, Field, model_validator class NoDiskStorage: @@ -120,9 +120,10 @@ class NutritionAIAPI(BaseModel): more_kwargs: dict = Field(default_factory=dict) auth_: ManagedPassioLifeAuth - class Config: - arbitrary_types_allowed = True - extra = "forbid" + model_config = ConfigDict( + arbitrary_types_allowed=True, + extra="forbid", + ) @retry( retry=retry_if_result(is_http_retryable), @@ -144,8 +145,9 @@ def _api_call_results(self, search_term: str) -> dict: rsp.raise_for_status() return rsp.json() - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key and endpoint exists in environment.""" nutritionai_subscription_key = get_from_dict_or_env( values, "nutritionai_subscription_key", "NUTRITIONAI_SUBSCRIPTION_KEY" diff --git a/libs/community/langchain_community/utilities/pebblo.py b/libs/community/langchain_community/utilities/pebblo.py index eacf90ed8aaff..d68ec6e9ff75c 100644 --- a/libs/community/langchain_community/utilities/pebblo.py +++ b/libs/community/langchain_community/utilities/pebblo.py @@ -11,8 +11,8 @@ from langchain_core.documents import Document from langchain_core.env import get_runtime_environment -from langchain_core.pydantic_v1 import BaseModel from langchain_core.utils import get_from_dict_or_env +from pydantic import BaseModel from requests import Response, request from requests.exceptions import RequestException diff --git a/libs/community/langchain_community/utilities/polygon.py b/libs/community/langchain_community/utilities/polygon.py index 36e21abefcaad..c7ab49f405467 100644 --- a/libs/community/langchain_community/utilities/polygon.py +++ b/libs/community/langchain_community/utilities/polygon.py @@ -7,8 +7,8 @@ from typing import Any, Dict, Optional import requests -from langchain_core.pydantic_v1 import BaseModel, root_validator from langchain_core.utils import get_from_dict_or_env +from pydantic import BaseModel, model_validator POLYGON_BASE_URL = "https://api.polygon.io/" @@ -18,8 +18,9 @@ class PolygonAPIWrapper(BaseModel): polygon_api_key: Optional[str] = None - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key in environment.""" polygon_api_key = get_from_dict_or_env( values, "polygon_api_key", "POLYGON_API_KEY" diff --git a/libs/community/langchain_community/utilities/powerbi.py b/libs/community/langchain_community/utilities/powerbi.py index 0390ffcab0897..88aee2ae08789 100644 --- a/libs/community/langchain_community/utilities/powerbi.py +++ b/libs/community/langchain_community/utilities/powerbi.py @@ -10,7 +10,13 @@ import aiohttp import requests from aiohttp import ClientTimeout, ServerTimeoutError -from langchain_core.pydantic_v1 import BaseModel, Field, root_validator, validator +from pydantic import ( + BaseModel, + ConfigDict, + Field, + model_validator, + validator, +) from requests.exceptions import Timeout logger = logging.getLogger(__name__) @@ -40,16 +46,18 @@ class PowerBIDataset(BaseModel): schemas: Dict[str, str] = Field(default_factory=dict) aiosession: Optional[aiohttp.ClientSession] = None - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) @validator("table_names", allow_reuse=True) def fix_table_names(cls, table_names: List[str]) -> List[str]: """Fix the table names.""" return [fix_table_name(table) for table in table_names] - @root_validator(pre=True) - def token_or_credential_present(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def token_or_credential_present(cls, values: Dict[str, Any]) -> Any: """Validate that at least one of token and credentials is present.""" if "token" in values or "credential" in values: return values diff --git a/libs/community/langchain_community/utilities/pubmed.py b/libs/community/langchain_community/utilities/pubmed.py index ab541b02b31f3..e3b23cfa0adfb 100644 --- a/libs/community/langchain_community/utilities/pubmed.py +++ b/libs/community/langchain_community/utilities/pubmed.py @@ -7,7 +7,7 @@ from typing import Any, Dict, Iterator, List from langchain_core.documents import Document -from langchain_core.pydantic_v1 import BaseModel, root_validator +from pydantic import BaseModel, model_validator logger = logging.getLogger(__name__) @@ -48,8 +48,9 @@ class PubMedAPIWrapper(BaseModel): doc_content_chars_max: int = 2000 email: str = "your_email@example.com" - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that the python package exists in environment.""" try: import xmltodict diff --git a/libs/community/langchain_community/utilities/reddit_search.py b/libs/community/langchain_community/utilities/reddit_search.py index 6192c033b3672..ae4300c5109f8 100644 --- a/libs/community/langchain_community/utilities/reddit_search.py +++ b/libs/community/langchain_community/utilities/reddit_search.py @@ -2,8 +2,8 @@ from typing import Any, Dict, List, Optional -from langchain_core.pydantic_v1 import BaseModel, root_validator from langchain_core.utils import get_from_dict_or_env +from pydantic import BaseModel, model_validator class RedditSearchAPIWrapper(BaseModel): @@ -30,8 +30,9 @@ class RedditSearchAPIWrapper(BaseModel): reddit_client_secret: Optional[str] reddit_user_agent: Optional[str] - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that the API ID, secret and user agent exists in environment and check that praw module is present. """ diff --git a/libs/community/langchain_community/utilities/rememberizer.py b/libs/community/langchain_community/utilities/rememberizer.py index 03d7a3a40a906..402b76ee0126e 100644 --- a/libs/community/langchain_community/utilities/rememberizer.py +++ b/libs/community/langchain_community/utilities/rememberizer.py @@ -1,11 +1,11 @@ """Wrapper for Rememberizer APIs.""" -from typing import Dict, List, Optional, cast +from typing import Any, Dict, List, Optional, cast import requests from langchain_core.documents import Document -from langchain_core.pydantic_v1 import BaseModel, root_validator from langchain_core.utils import get_from_dict_or_env +from pydantic import BaseModel, model_validator class RememberizerAPIWrapper(BaseModel): @@ -14,8 +14,9 @@ class RememberizerAPIWrapper(BaseModel): top_k_results: int = 10 rememberizer_api_key: Optional[str] = None - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key in environment.""" rememberizer_api_key = get_from_dict_or_env( values, "rememberizer_api_key", "REMEMBERIZER_API_KEY" diff --git a/libs/community/langchain_community/utilities/requests.py b/libs/community/langchain_community/utilities/requests.py index 59d37221b1880..d23218e116281 100644 --- a/libs/community/langchain_community/utilities/requests.py +++ b/libs/community/langchain_community/utilities/requests.py @@ -5,7 +5,7 @@ import aiohttp import requests -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel, ConfigDict from requests import Response @@ -21,9 +21,10 @@ class Requests(BaseModel): auth: Optional[Any] = None verify: Optional[bool] = True - class Config: - arbitrary_types_allowed = True - extra = "forbid" + model_config = ConfigDict( + arbitrary_types_allowed=True, + extra="forbid", + ) def get(self, url: str, **kwargs: Any) -> requests.Response: """GET the URL and return the text.""" @@ -145,9 +146,10 @@ class GenericRequestsWrapper(BaseModel): response_content_type: Literal["text", "json"] = "text" verify: bool = True - class Config: - arbitrary_types_allowed = True - extra = "forbid" + model_config = ConfigDict( + arbitrary_types_allowed=True, + extra="forbid", + ) @property def requests(self) -> Requests: diff --git a/libs/community/langchain_community/utilities/scenexplain.py b/libs/community/langchain_community/utilities/scenexplain.py index 82c35a06091c2..30eff00fdfb14 100644 --- a/libs/community/langchain_community/utilities/scenexplain.py +++ b/libs/community/langchain_community/utilities/scenexplain.py @@ -6,14 +6,14 @@ - Navigate to the API Access page (https://scenex.jina.ai/api) and create a new API key. """ -from typing import Dict +from typing import Any, Dict import requests -from langchain_core.pydantic_v1 import BaseModel, BaseSettings, Field, root_validator -from langchain_core.utils import get_from_dict_or_env +from langchain_core.utils import from_env, get_from_dict_or_env +from pydantic import BaseModel, Field, model_validator -class SceneXplainAPIWrapper(BaseSettings, BaseModel): +class SceneXplainAPIWrapper(BaseModel): """Wrapper for SceneXplain API. In order to set this up, you need API key for the SceneXplain API. @@ -23,7 +23,7 @@ class SceneXplainAPIWrapper(BaseSettings, BaseModel): and create a new API key. """ - scenex_api_key: str = Field(..., env="SCENEX_API_KEY") + scenex_api_key: str = Field(..., default_factory=from_env("SCENEX_API_KEY")) scenex_api_url: str = "https://api.scenex.jina.ai/v1/describe" def _describe_image(self, image: str) -> str: @@ -47,8 +47,9 @@ def _describe_image(self, image: str) -> str: return img.get("text", "") - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key exists in environment.""" scenex_api_key = get_from_dict_or_env( values, "scenex_api_key", "SCENEX_API_KEY" diff --git a/libs/community/langchain_community/utilities/searchapi.py b/libs/community/langchain_community/utilities/searchapi.py index c4edcd8fb0be4..9e08df68d94da 100644 --- a/libs/community/langchain_community/utilities/searchapi.py +++ b/libs/community/langchain_community/utilities/searchapi.py @@ -2,8 +2,8 @@ import aiohttp import requests -from langchain_core.pydantic_v1 import BaseModel, root_validator from langchain_core.utils import get_from_dict_or_env +from pydantic import BaseModel, ConfigDict, model_validator class SearchApiAPIWrapper(BaseModel): @@ -27,11 +27,13 @@ class SearchApiAPIWrapper(BaseModel): searchapi_api_key: Optional[str] = None aiosession: Optional[aiohttp.ClientSession] = None - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that API key exists in environment.""" searchapi_api_key = get_from_dict_or_env( values, "searchapi_api_key", "SEARCHAPI_API_KEY" diff --git a/libs/community/langchain_community/utilities/searx_search.py b/libs/community/langchain_community/utilities/searx_search.py index 5debde2dfef8d..65780a7e340b7 100644 --- a/libs/community/langchain_community/utilities/searx_search.py +++ b/libs/community/langchain_community/utilities/searx_search.py @@ -132,14 +132,15 @@ import aiohttp import requests -from langchain_core.pydantic_v1 import ( +from langchain_core.utils import get_from_dict_or_env +from pydantic import ( BaseModel, + ConfigDict, Field, PrivateAttr, - root_validator, + model_validator, validator, ) -from langchain_core.utils import get_from_dict_or_env def _get_default_params() -> dict: @@ -228,8 +229,9 @@ def disable_ssl_warnings(cls, v: bool) -> bool: return v - @root_validator(pre=True) - def validate_params(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_params(cls, values: Dict) -> Any: """Validate that custom searx params are merged with default ones.""" user_params = values.get("params", {}) default = _get_default_params() @@ -257,8 +259,9 @@ def validate_params(cls, values: Dict) -> Dict: return values - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) def _searx_api_query(self, params: dict) -> SearxResults: """Actual request to searx API.""" diff --git a/libs/community/langchain_community/utilities/semanticscholar.py b/libs/community/langchain_community/utilities/semanticscholar.py index 14a9333c6b85e..896b0e599c360 100644 --- a/libs/community/langchain_community/utilities/semanticscholar.py +++ b/libs/community/langchain_community/utilities/semanticscholar.py @@ -1,9 +1,9 @@ """Utils for interacting with the Semantic Scholar API.""" import logging -from typing import Any, Dict, Optional +from typing import Any, Dict, List, Optional -from langchain_core.pydantic_v1 import BaseModel, root_validator +from pydantic import BaseModel, model_validator logger = logging.getLogger(__name__) @@ -39,7 +39,7 @@ class SemanticScholarAPIWrapper(BaseModel): S2_MAX_QUERY_LENGTH: int = 300 load_max_docs: int = 100 doc_content_chars_max: Optional[int] = 4000 - returned_fields = [ + returned_fields: List[str] = [ "title", "abstract", "venue", @@ -51,8 +51,9 @@ class SemanticScholarAPIWrapper(BaseModel): "externalIds", ] - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that the python package exists in environment.""" try: from semanticscholar import SemanticScholar diff --git a/libs/community/langchain_community/utilities/serpapi.py b/libs/community/langchain_community/utilities/serpapi.py index c3152a06d0cbc..a9281d587e347 100644 --- a/libs/community/langchain_community/utilities/serpapi.py +++ b/libs/community/langchain_community/utilities/serpapi.py @@ -8,8 +8,8 @@ from typing import Any, Dict, Optional, Tuple import aiohttp -from langchain_core.pydantic_v1 import BaseModel, Field, root_validator from langchain_core.utils import get_from_dict_or_env +from pydantic import BaseModel, ConfigDict, Field, model_validator class HiddenPrints: @@ -52,12 +52,14 @@ class SerpAPIWrapper(BaseModel): serpapi_api_key: Optional[str] = None aiosession: Optional[aiohttp.ClientSession] = None - class Config: - arbitrary_types_allowed = True - extra = "forbid" + model_config = ConfigDict( + arbitrary_types_allowed=True, + extra="forbid", + ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key and python package exists in environment.""" serpapi_api_key = get_from_dict_or_env( values, "serpapi_api_key", "SERPAPI_API_KEY" diff --git a/libs/community/langchain_community/utilities/stackexchange.py b/libs/community/langchain_community/utilities/stackexchange.py index 777022cdc828a..7d09a480f39ee 100644 --- a/libs/community/langchain_community/utilities/stackexchange.py +++ b/libs/community/langchain_community/utilities/stackexchange.py @@ -1,7 +1,7 @@ import html from typing import Any, Dict, Literal -from langchain_core.pydantic_v1 import BaseModel, Field, root_validator +from pydantic import BaseModel, Field, model_validator class StackExchangeAPIWrapper(BaseModel): @@ -19,8 +19,9 @@ class StackExchangeAPIWrapper(BaseModel): result_separator: str = "\n\n" """Separator between question,answer pairs.""" - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that the required Python package exists.""" try: from stackapi import StackAPI diff --git a/libs/community/langchain_community/utilities/steam.py b/libs/community/langchain_community/utilities/steam.py index 304412d2bd4cd..e3197b5295427 100644 --- a/libs/community/langchain_community/utilities/steam.py +++ b/libs/community/langchain_community/utilities/steam.py @@ -2,7 +2,12 @@ from typing import Any, List -from langchain_core.pydantic_v1 import BaseModel, root_validator +from pydantic import BaseModel, ConfigDict, model_validator + +from langchain_community.tools.steam.prompt import ( + STEAM_GET_GAMES_DETAILS, + STEAM_GET_RECOMMENDED_GAMES, +) class SteamWebAPIWrapper(BaseModel): @@ -10,11 +15,6 @@ class SteamWebAPIWrapper(BaseModel): steam: Any # for python-steam-api - from langchain_community.tools.steam.prompt import ( - STEAM_GET_GAMES_DETAILS, - STEAM_GET_RECOMMENDED_GAMES, - ) - # operations: a list of dictionaries, each representing a specific operation that # can be performed with the API operations: List[dict] = [ @@ -30,15 +30,17 @@ class SteamWebAPIWrapper(BaseModel): }, ] - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) def get_operations(self) -> List[dict]: """Return a list of operations.""" return self.operations - @root_validator(pre=True) - def validate_environment(cls, values: dict) -> dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: dict) -> Any: """Validate api key and python package has been configured.""" # check if the python package is installed diff --git a/libs/community/langchain_community/utilities/tavily_search.py b/libs/community/langchain_community/utilities/tavily_search.py index 3c5666d0257cd..84e9815ee9ea6 100644 --- a/libs/community/langchain_community/utilities/tavily_search.py +++ b/libs/community/langchain_community/utilities/tavily_search.py @@ -5,12 +5,12 @@ """ import json -from typing import Dict, List, Optional +from typing import Any, Dict, List, Optional import aiohttp import requests -from langchain_core.pydantic_v1 import BaseModel, SecretStr, root_validator from langchain_core.utils import get_from_dict_or_env +from pydantic import BaseModel, ConfigDict, SecretStr, model_validator TAVILY_API_URL = "https://api.tavily.com" @@ -20,11 +20,13 @@ class TavilySearchAPIWrapper(BaseModel): tavily_api_key: SecretStr - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key and endpoint exists in environment.""" tavily_api_key = get_from_dict_or_env( values, "tavily_api_key", "TAVILY_API_KEY" diff --git a/libs/community/langchain_community/utilities/tensorflow_datasets.py b/libs/community/langchain_community/utilities/tensorflow_datasets.py index 197c2f4c0f8d1..6fe5abefcd0f6 100644 --- a/libs/community/langchain_community/utilities/tensorflow_datasets.py +++ b/libs/community/langchain_community/utilities/tensorflow_datasets.py @@ -2,7 +2,7 @@ from typing import Any, Callable, Dict, Iterator, List, Optional from langchain_core.documents import Document -from langchain_core.pydantic_v1 import BaseModel, root_validator +from pydantic import BaseModel, model_validator logger = logging.getLogger(__name__) @@ -60,8 +60,9 @@ def mlqaen_example_to_document(example: dict) -> Document: sample_to_document_function: Optional[Callable[[Dict], Document]] = None dataset: Any #: :meta private: - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that the python package exists in environment.""" try: import tensorflow # noqa: F401 diff --git a/libs/community/langchain_community/utilities/twilio.py b/libs/community/langchain_community/utilities/twilio.py index 5e7d6e6d8e776..ceef3b2f26a64 100644 --- a/libs/community/langchain_community/utilities/twilio.py +++ b/libs/community/langchain_community/utilities/twilio.py @@ -2,8 +2,8 @@ from typing import Any, Dict, Optional -from langchain_core.pydantic_v1 import BaseModel, root_validator from langchain_core.utils import get_from_dict_or_env +from pydantic import BaseModel, ConfigDict, model_validator class TwilioAPIWrapper(BaseModel): @@ -43,12 +43,14 @@ class TwilioAPIWrapper(BaseModel): must be empty. """ - class Config: - arbitrary_types_allowed = False - extra = "forbid" + model_config = ConfigDict( + arbitrary_types_allowed=False, + extra="forbid", + ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key and python package exists in environment.""" try: from twilio.rest import Client diff --git a/libs/community/langchain_community/utilities/wikidata.py b/libs/community/langchain_community/utilities/wikidata.py index 8ea4122d915ec..3b2d877766200 100644 --- a/libs/community/langchain_community/utilities/wikidata.py +++ b/libs/community/langchain_community/utilities/wikidata.py @@ -4,7 +4,7 @@ from typing import Any, Dict, List, Optional from langchain_core.documents import Document -from langchain_core.pydantic_v1 import BaseModel, root_validator +from pydantic import BaseModel, model_validator logger = logging.getLogger(__name__) @@ -92,8 +92,9 @@ class WikidataAPIWrapper(BaseModel): wikidata_props: List[str] = DEFAULT_PROPERTIES lang: str = DEFAULT_LANG_CODE - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that the python package exists in environment.""" try: from mediawikiapi import MediaWikiAPI diff --git a/libs/community/langchain_community/utilities/wikipedia.py b/libs/community/langchain_community/utilities/wikipedia.py index ede156656b119..271a165ebdeb0 100644 --- a/libs/community/langchain_community/utilities/wikipedia.py +++ b/libs/community/langchain_community/utilities/wikipedia.py @@ -4,7 +4,7 @@ from typing import Any, Dict, Iterator, List, Optional from langchain_core.documents import Document -from langchain_core.pydantic_v1 import BaseModel, root_validator +from pydantic import BaseModel, model_validator logger = logging.getLogger(__name__) @@ -27,8 +27,9 @@ class WikipediaAPIWrapper(BaseModel): load_all_available_meta: bool = False doc_content_chars_max: int = 4000 - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that the python package exists in environment.""" try: import wikipedia diff --git a/libs/community/langchain_community/utilities/wolfram_alpha.py b/libs/community/langchain_community/utilities/wolfram_alpha.py index 35fe64308e228..aed9b9ddc675b 100644 --- a/libs/community/langchain_community/utilities/wolfram_alpha.py +++ b/libs/community/langchain_community/utilities/wolfram_alpha.py @@ -2,8 +2,8 @@ from typing import Any, Dict, Optional -from langchain_core.pydantic_v1 import BaseModel, root_validator from langchain_core.utils import get_from_dict_or_env +from pydantic import BaseModel, ConfigDict, model_validator class WolframAlphaAPIWrapper(BaseModel): @@ -21,11 +21,13 @@ class WolframAlphaAPIWrapper(BaseModel): wolfram_client: Any #: :meta private: wolfram_alpha_appid: Optional[str] = None - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key and python package exists in environment.""" wolfram_alpha_appid = get_from_dict_or_env( values, "wolfram_alpha_appid", "WOLFRAM_ALPHA_APPID" diff --git a/libs/community/langchain_community/utilities/you.py b/libs/community/langchain_community/utilities/you.py index 1cd17bdc55643..dadb2309c2f8a 100644 --- a/libs/community/langchain_community/utilities/you.py +++ b/libs/community/langchain_community/utilities/you.py @@ -10,8 +10,9 @@ import aiohttp import requests from langchain_core.documents import Document -from langchain_core.pydantic_v1 import BaseModel, Field, root_validator from langchain_core.utils import get_from_dict_or_env +from pydantic import BaseModel, Field, model_validator +from typing_extensions import Self YOU_API_URL = "https://api.ydc-index.io" @@ -106,30 +107,31 @@ class YouSearchAPIWrapper(BaseModel): # should deprecate n_hits n_hits: Optional[int] = None - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key exists in environment.""" ydc_api_key = get_from_dict_or_env(values, "ydc_api_key", "YDC_API_KEY") values["ydc_api_key"] = ydc_api_key return values - @root_validator(pre=False, skip_on_failure=True) - def warn_if_set_fields_have_no_effect(cls, values: Dict) -> Dict: - if values["endpoint_type"] != "news": + @model_validator(mode="after") + def warn_if_set_fields_have_no_effect(self) -> Self: + if self.endpoint_type != "news": news_api_fields = ("search_lang", "ui_lang", "spellcheck") for field in news_api_fields: - if values[field]: + if getattr(self, field): warnings.warn( ( f"News API-specific field '{field}' is set but " - f"`endpoint_type=\"{values['endpoint_type']}\"`. " + f'`endpoint_type="{self.endpoint_type}"`. ' "This will have no effect." ), UserWarning, ) - if values["endpoint_type"] not in ("search", "snippet"): - if values["n_snippets_per_hit"]: + if self.endpoint_type not in ("search", "snippet"): + if self.n_snippets_per_hit: warnings.warn( ( "Field 'n_snippets_per_hit' only has effect on " @@ -137,19 +139,19 @@ def warn_if_set_fields_have_no_effect(cls, values: Dict) -> Dict: ), UserWarning, ) - return values + return self - @root_validator(pre=False, skip_on_failure=True) - def warn_if_deprecated_endpoints_are_used(cls, values: Dict) -> Dict: - if values["endpoint_type"] == "snippets": + @model_validator(mode="after") + def warn_if_deprecated_endpoints_are_used(self) -> Self: + if self.endpoint_type == "snippets": warnings.warn( ( - f"`endpoint_type=\"{values['endpoint_type']}\"` is deprecated. " + f'`endpoint_type="{self.endpoint_type}"` is deprecated. ' 'Use `endpoint_type="search"` instead.' ), DeprecationWarning, ) - return values + return self def _generate_params(self, query: str, **kwargs: Any) -> Dict: """ diff --git a/libs/community/langchain_community/utilities/zapier.py b/libs/community/langchain_community/utilities/zapier.py index 38f3ba7ff8475..56e5dc5dda4c2 100644 --- a/libs/community/langchain_community/utilities/zapier.py +++ b/libs/community/langchain_community/utilities/zapier.py @@ -17,8 +17,8 @@ import aiohttp import requests -from langchain_core.pydantic_v1 import BaseModel, root_validator from langchain_core.utils import get_from_dict_or_env +from pydantic import BaseModel, ConfigDict, model_validator from requests import Request, Session @@ -45,8 +45,9 @@ class ZapierNLAWrapper(BaseModel): zapier_nla_oauth_access_token: str zapier_nla_api_base: str = "https://nla.zapier.com/api/v1/" - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) def _format_headers(self) -> Dict[str, str]: """Format headers for requests.""" @@ -108,8 +109,9 @@ def _create_action_request( # type: ignore[no-untyped-def] json=data, ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key exists in environment.""" zapier_nla_api_key_default = None diff --git a/libs/community/langchain_community/utils/ernie_functions.py b/libs/community/langchain_community/utils/ernie_functions.py index 4166de1bfd383..fcbc705e33d42 100644 --- a/libs/community/langchain_community/utils/ernie_functions.py +++ b/libs/community/langchain_community/utils/ernie_functions.py @@ -1,7 +1,7 @@ from typing import Literal, Optional, Type, TypedDict -from langchain_core.pydantic_v1 import BaseModel from langchain_core.utils.json_schema import dereference_refs +from pydantic import BaseModel class FunctionDescription(TypedDict): diff --git a/libs/community/langchain_community/vectorstores/apache_doris.py b/libs/community/langchain_community/vectorstores/apache_doris.py index c6c929074c02c..07f72d1508dab 100644 --- a/libs/community/langchain_community/vectorstores/apache_doris.py +++ b/libs/community/langchain_community/vectorstores/apache_doris.py @@ -8,8 +8,8 @@ from langchain_core.documents import Document from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseSettings from langchain_core.vectorstores import VectorStore +from pydantic_settings import BaseSettings, SettingsConfigDict logger = logging.getLogger() DEBUG = False @@ -61,10 +61,9 @@ class ApacheDorisSettings(BaseSettings): def __getitem__(self, item: str) -> Any: return getattr(self, item) - class Config: - env_file = ".env" - env_file_encoding = "utf-8" - env_prefix = "apache_doris_" + model_config = SettingsConfigDict( + env_file=".env", env_file_encoding="utf-8", env_prefix="apache_doris_" + ) class ApacheDoris(VectorStore): diff --git a/libs/community/langchain_community/vectorstores/azuresearch.py b/libs/community/langchain_community/vectorstores/azuresearch.py index 4dd4eb8177038..fa2a989631106 100644 --- a/libs/community/langchain_community/vectorstores/azuresearch.py +++ b/libs/community/langchain_community/vectorstores/azuresearch.py @@ -32,10 +32,10 @@ from langchain_core.documents import Document from langchain_core.embeddings import Embeddings from langchain_core.exceptions import LangChainException -from langchain_core.pydantic_v1 import root_validator from langchain_core.retrievers import BaseRetriever from langchain_core.utils import get_from_env from langchain_core.vectorstores import VectorStore +from pydantic import ConfigDict, model_validator from langchain_community.vectorstores.utils import maximal_marginal_relevance @@ -1580,11 +1580,13 @@ class AzureSearchVectorStoreRetriever(BaseRetriever): "semantic_hybrid_score_threshold", ) - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) - @root_validator(pre=True) - def validate_search_type(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_search_type(cls, values: Dict) -> Any: """Validate search type.""" if "search_type" in values: search_type = values["search_type"] diff --git a/libs/community/langchain_community/vectorstores/clickhouse.py b/libs/community/langchain_community/vectorstores/clickhouse.py index edea983b976e9..83f5af6698b7e 100644 --- a/libs/community/langchain_community/vectorstores/clickhouse.py +++ b/libs/community/langchain_community/vectorstores/clickhouse.py @@ -8,8 +8,8 @@ from langchain_core.documents import Document from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseSettings from langchain_core.vectorstores import VectorStore +from pydantic_settings import BaseSettings, SettingsConfigDict logger = logging.getLogger() @@ -95,10 +95,9 @@ class ClickhouseSettings(BaseSettings): def __getitem__(self, item: str) -> Any: return getattr(self, item) - class Config: - env_file = ".env" - env_file_encoding = "utf-8" - env_prefix = "clickhouse_" + model_config = SettingsConfigDict( + env_file=".env", env_file_encoding="utf-8", env_prefix="clickhouse_" + ) class Clickhouse(VectorStore): diff --git a/libs/community/langchain_community/vectorstores/docarray/base.py b/libs/community/langchain_community/vectorstores/docarray/base.py index 9f66e79bfb775..47e1e76ea5184 100644 --- a/libs/community/langchain_community/vectorstores/docarray/base.py +++ b/libs/community/langchain_community/vectorstores/docarray/base.py @@ -4,8 +4,8 @@ import numpy as np from langchain_core.documents import Document from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import Field from langchain_core.vectorstores import VectorStore +from pydantic import Field from langchain_community.vectorstores.utils import maximal_marginal_relevance diff --git a/libs/community/langchain_community/vectorstores/kinetica.py b/libs/community/langchain_community/vectorstores/kinetica.py index 2ecca3b65e8e4..464f71de2c547 100644 --- a/libs/community/langchain_community/vectorstores/kinetica.py +++ b/libs/community/langchain_community/vectorstores/kinetica.py @@ -14,8 +14,8 @@ import numpy as np from langchain_core.documents import Document from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseSettings from langchain_core.vectorstores import VectorStore +from pydantic_settings import BaseSettings, SettingsConfigDict from langchain_community.vectorstores.utils import maximal_marginal_relevance @@ -79,10 +79,9 @@ class KineticaSettings(BaseSettings): def __getitem__(self, item: str) -> Any: return getattr(self, item) - class Config: - env_file = ".env" - env_file_encoding = "utf-8" - env_prefix = "kinetica_" + model_config = SettingsConfigDict( + env_file=".env", env_file_encoding="utf-8", env_prefix="kinetica_" + ) class Kinetica(VectorStore): diff --git a/libs/community/langchain_community/vectorstores/llm_rails.py b/libs/community/langchain_community/vectorstores/llm_rails.py index 3684f3b3b581c..16277161280a0 100644 --- a/libs/community/langchain_community/vectorstores/llm_rails.py +++ b/libs/community/langchain_community/vectorstores/llm_rails.py @@ -11,8 +11,8 @@ import requests from langchain_core.documents import Document from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import Field from langchain_core.vectorstores import VectorStore, VectorStoreRetriever +from pydantic import Field class LLMRails(VectorStore): diff --git a/libs/community/langchain_community/vectorstores/manticore_search.py b/libs/community/langchain_community/vectorstores/manticore_search.py index 0a452deb32197..4566189388fdc 100644 --- a/libs/community/langchain_community/vectorstores/manticore_search.py +++ b/libs/community/langchain_community/vectorstores/manticore_search.py @@ -8,8 +8,8 @@ from langchain_core.documents import Document from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseSettings from langchain_core.vectorstores import VectorStore +from pydantic_settings import BaseSettings, SettingsConfigDict logger = logging.getLogger() DEFAULT_K = 4 # Number of Documents to return. @@ -56,10 +56,9 @@ def get_connection_string(self) -> str: def __getitem__(self, item: str) -> Any: return getattr(self, item) - class Config: - env_file = ".env" - env_file_encoding = "utf-8" - env_prefix = "manticore_" + model_config = SettingsConfigDict( + env_file=".env", env_file_encoding="utf-8", env_prefix="manticore_" + ) class ManticoreSearch(VectorStore): diff --git a/libs/community/langchain_community/vectorstores/myscale.py b/libs/community/langchain_community/vectorstores/myscale.py index b91962ed07bc2..8140840c6bbc6 100644 --- a/libs/community/langchain_community/vectorstores/myscale.py +++ b/libs/community/langchain_community/vectorstores/myscale.py @@ -8,8 +8,8 @@ from langchain_core.documents import Document from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseSettings from langchain_core.vectorstores import VectorStore +from pydantic_settings import BaseSettings, SettingsConfigDict logger = logging.getLogger() @@ -85,10 +85,9 @@ class MyScaleSettings(BaseSettings): def __getitem__(self, item: str) -> Any: return getattr(self, item) - class Config: - env_file = ".env" - env_file_encoding = "utf-8" - env_prefix = "myscale_" + model_config = SettingsConfigDict( + env_file=".env", env_file_encoding="utf-8", env_prefix="myscale_" + ) class MyScale(VectorStore): diff --git a/libs/community/langchain_community/vectorstores/redis/base.py b/libs/community/langchain_community/vectorstores/redis/base.py index 93d0fd5a28443..5a50b8dd310b2 100644 --- a/libs/community/langchain_community/vectorstores/redis/base.py +++ b/libs/community/langchain_community/vectorstores/redis/base.py @@ -31,6 +31,7 @@ from langchain_core.embeddings import Embeddings from langchain_core.utils import get_from_dict_or_env from langchain_core.vectorstores import VectorStore, VectorStoreRetriever +from pydantic import ConfigDict from langchain_community.utilities.redis import ( _array_to_buffer, @@ -1452,8 +1453,9 @@ class RedisVectorStoreRetriever(VectorStoreRetriever): ] """Allowed search types.""" - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) def _get_relevant_documents( self, query: str, *, run_manager: CallbackManagerForRetrieverRun diff --git a/libs/community/langchain_community/vectorstores/redis/schema.py b/libs/community/langchain_community/vectorstores/redis/schema.py index 50b20245fe252..2cf1ee2d927cd 100644 --- a/libs/community/langchain_community/vectorstores/redis/schema.py +++ b/libs/community/langchain_community/vectorstores/redis/schema.py @@ -7,8 +7,8 @@ import numpy as np import yaml -from langchain_core.pydantic_v1 import BaseModel, Field, validator from langchain_core.utils.pydantic import get_fields +from pydantic import BaseModel, Field, field_validator, validator from typing_extensions import TYPE_CHECKING, Literal from langchain_community.vectorstores.redis.constants import REDIS_VECTOR_DTYPE_MAP @@ -100,7 +100,8 @@ class RedisVectorField(RedisField): distance_metric: RedisDistanceMetric = Field(default="COSINE") initial_cap: Optional[int] = None - @validator("algorithm", "datatype", "distance_metric", pre=True, each_item=True) + @field_validator("algorithm", "datatype", "distance_metric", mode="before") + @classmethod def uppercase_strings(cls, v: str) -> str: return v.upper() diff --git a/libs/community/langchain_community/vectorstores/starrocks.py b/libs/community/langchain_community/vectorstores/starrocks.py index 2360b75f4e036..f17d7b650fdaf 100644 --- a/libs/community/langchain_community/vectorstores/starrocks.py +++ b/libs/community/langchain_community/vectorstores/starrocks.py @@ -8,8 +8,8 @@ from langchain_core.documents import Document from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseSettings from langchain_core.vectorstores import VectorStore +from pydantic_settings import BaseSettings, SettingsConfigDict logger = logging.getLogger() DEBUG = False @@ -112,10 +112,9 @@ class StarRocksSettings(BaseSettings): def __getitem__(self, item: str) -> Any: return getattr(self, item) - class Config: - env_file = ".env" - env_file_encoding = "utf-8" - env_prefix = "starrocks_" + model_config = SettingsConfigDict( + env_file=".env", env_file_encoding="utf-8", env_prefix="starrocks_" + ) class StarRocks(VectorStore): diff --git a/libs/community/langchain_community/vectorstores/tencentvectordb.py b/libs/community/langchain_community/vectorstores/tencentvectordb.py index ffa2beb650146..c0dc41965fb84 100644 --- a/libs/community/langchain_community/vectorstores/tencentvectordb.py +++ b/libs/community/langchain_community/vectorstores/tencentvectordb.py @@ -11,9 +11,9 @@ import numpy as np from langchain_core.documents import Document from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel from langchain_core.utils import guard_import from langchain_core.vectorstores import VectorStore +from pydantic import BaseModel from langchain_community.vectorstores.utils import maximal_marginal_relevance diff --git a/libs/community/langchain_community/vectorstores/thirdai_neuraldb.py b/libs/community/langchain_community/vectorstores/thirdai_neuraldb.py index d30aeb1383b3b..f2040271ad330 100644 --- a/libs/community/langchain_community/vectorstores/thirdai_neuraldb.py +++ b/libs/community/langchain_community/vectorstores/thirdai_neuraldb.py @@ -7,6 +7,7 @@ from langchain_core.documents import Document from langchain_core.embeddings import Embeddings from langchain_core.vectorstores import VectorStore +from pydantic import ConfigDict class NeuralDBVectorStore(VectorStore): @@ -30,9 +31,10 @@ def __init__(self, db: Any) -> None: db: Any = None #: :meta private: """NeuralDB instance""" - class Config: - extra = "forbid" - underscore_attrs_are_private = True + model_config = ConfigDict( + extra="forbid", + underscore_attrs_are_private=True, + ) @staticmethod def _verify_thirdai_library(thirdai_key: Optional[str] = None): # type: ignore[no-untyped-def] @@ -330,9 +332,10 @@ def __init__(self, db: Any) -> None: db: Any = None #: :meta private: """NeuralDB Client instance""" - class Config: - extra = "forbid" - underscore_attrs_are_private = True + model_config = ConfigDict( + extra="forbid", + underscore_attrs_are_private=True, + ) def similarity_search( self, query: str, k: int = 10, **kwargs: Any diff --git a/libs/community/langchain_community/vectorstores/vectara.py b/libs/community/langchain_community/vectorstores/vectara.py index a83a220885ee6..2d217333f8394 100644 --- a/libs/community/langchain_community/vectorstores/vectara.py +++ b/libs/community/langchain_community/vectorstores/vectara.py @@ -16,6 +16,7 @@ from langchain_core.embeddings import Embeddings from langchain_core.runnables import Runnable, RunnableConfig from langchain_core.vectorstores import VectorStore, VectorStoreRetriever +from pydantic import ConfigDict logger = logging.getLogger(__name__) @@ -731,8 +732,9 @@ class VectaraRetriever(VectorStoreRetriever): config: VectaraQueryConfig """Configuration for this retriever.""" - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) def _get_relevant_documents( self, query: str, *, run_manager: CallbackManagerForRetrieverRun diff --git a/libs/community/poetry.lock b/libs/community/poetry.lock index a90e3edec89c1..34324f2ee6fe0 100644 --- a/libs/community/poetry.lock +++ b/libs/community/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. 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"sha256:ae294808afde1b14a1a69aa86a69cadfe391848bbb233a5332a8065e4081cabc"}, {file = "sqlalchemy-2.0.33.tar.gz", hash = "sha256:91c93333c2b37ff721dc83b37e28c29de4c502b5612f2d093468037b86aa2be0"}, ] @@ -4323,4 +4391,4 @@ type = ["pytest-mypy"] [metadata] lock-version = "2.0" python-versions = ">=3.8.1,<4.0" -content-hash = "b69b2186cc152c64945d8000e883e92d7872a38b7113f60b1db5c7e1c9aed7c6" +content-hash = "3d399069b8e59e6912a11435123974e0ab8a7c2fbf66af3e09fd982027ed2dbb" diff --git a/libs/community/pyproject.toml b/libs/community/pyproject.toml index 3a4758e5cf058..c943b0b7fcad2 100644 --- a/libs/community/pyproject.toml +++ b/libs/community/pyproject.toml @@ -39,6 +39,7 @@ aiohttp = "^3.8.3" tenacity = "^8.1.0,!=8.4.0" dataclasses-json = ">= 0.5.7, < 0.7" langsmith = "^0.1.0" +pydantic-settings = "^2.4.0" [[tool.poetry.dependencies.numpy]] version = "^1" python = "<3.12" diff --git a/libs/community/scripts/check_pydantic.sh b/libs/community/scripts/check_pydantic.sh deleted file mode 100755 index 1b091d0f4eb8a..0000000000000 --- a/libs/community/scripts/check_pydantic.sh +++ /dev/null @@ -1,68 +0,0 @@ -#!/bin/bash -# -# This script searches for lines starting with "import pydantic" or "from pydantic" -# in tracked files within a Git repository. -# -# Usage: ./scripts/check_pydantic.sh /path/to/repository - -# Check if a path argument is provided -if [ $# -ne 1 ]; then - echo "Usage: $0 /path/to/repository" - exit 1 -fi - -repository_path="$1" - -# Check that we are not using features that cannot be captured via init. -# pre-init is a custom decorator that we introduced to capture the same semantics -# as @root_validator(pre=False, skip_on_failure=False) available in pydantic 1. -count=$(git grep -E '(@root_validator)|(@validator)|(@pre_init)' -- "*.py" | wc -l) -# PRs that increase the current count will not be accepted. -# PRs that decrease update the code in the repository -# and allow decreasing the count of are welcome! -current_count=336 - -if [ "$count" -gt "$current_count" ]; then - echo "The PR seems to be introducing new usage of @root_validator and/or @field_validator." - echo "git grep -E '(@root_validator)|(@validator)' | wc -l returned $count" - echo "whereas the expected count should be equal or less than $current_count" - echo "Please update the code to instead use __init__" - echo "For examples, please see: " - echo "https://gist.github.com/eyurtsev/d1dcba10c2f35626e302f1b98a0f5a3c " - echo "This linter is here to make sure that its easier to upgrade pydantic in the future." - exit 1 -elif [ "$count" -lt "$current_count" ]; then - echo "Please update the $current_count variable in ./scripts/check_pydantic.sh to $count" - exit 1 -fi - - -# Search for lines matching the pattern within the specified repository -result=$(git -C "$repository_path" grep -En '^import pydantic|^from pydantic') - -# Check if any matching lines were found -if [ -n "$result" ]; then - echo "ERROR: The following lines need to be updated:" - echo "$result" - echo "Please replace the code with an import from langchain_core.pydantic_v1." - echo "For example, replace 'from pydantic import BaseModel'" - echo "with 'from langchain_core.pydantic_v1 import BaseModel'" - exit 1 -fi - -# Forbid vanilla usage of @root_validator -# This prevents the code from using either @root_validator or @root_validator() -# Search for lines matching the pattern within the specified repository -result=$(git -C "$repository_path" grep -En '(@root_validator\s*$)|(@root_validator\(\)|@root_validator\(pre=False\))' -- '*.py') - -# Check if any matching lines were found -if [ -n "$result" ]; then - echo "ERROR: The following lines need to be updated:" - echo - echo "$result" - echo - echo "Please replace @root_validator or @root_validator() with either:" - echo - echo "@root_validator(pre=True) or @root_validator(pre=False, skip_on_failure=True)" - exit 1 -fi diff --git a/libs/community/tests/integration_tests/chat_models/test_deepinfra.py b/libs/community/tests/integration_tests/chat_models/test_deepinfra.py index 0834d88476d4b..37fdc68ccb4c7 100644 --- a/libs/community/tests/integration_tests/chat_models/test_deepinfra.py +++ b/libs/community/tests/integration_tests/chat_models/test_deepinfra.py @@ -6,8 +6,8 @@ from langchain_core.messages.ai import AIMessage from langchain_core.messages.tool import ToolMessage from langchain_core.outputs import ChatGeneration, LLMResult -from langchain_core.pydantic_v1 import BaseModel from langchain_core.runnables.base import RunnableBinding +from pydantic import BaseModel from langchain_community.chat_models.deepinfra import ChatDeepInfra from tests.unit_tests.callbacks.fake_callback_handler import FakeCallbackHandler diff --git a/libs/community/tests/integration_tests/chat_models/test_gpt_router.py b/libs/community/tests/integration_tests/chat_models/test_gpt_router.py index 0b5230e98c993..a65515363f3b3 100644 --- a/libs/community/tests/integration_tests/chat_models/test_gpt_router.py +++ b/libs/community/tests/integration_tests/chat_models/test_gpt_router.py @@ -8,7 +8,7 @@ ) from langchain_core.messages import AIMessage, BaseMessage, HumanMessage from langchain_core.outputs import ChatGeneration, LLMResult -from langchain_core.pydantic_v1 import SecretStr +from pydantic import SecretStr from pytest import CaptureFixture from langchain_community.chat_models.gpt_router import GPTRouter, GPTRouterModel diff --git a/libs/community/tests/integration_tests/chat_models/test_jinachat.py b/libs/community/tests/integration_tests/chat_models/test_jinachat.py index a43b955d7eabf..50b641311cf61 100644 --- a/libs/community/tests/integration_tests/chat_models/test_jinachat.py +++ b/libs/community/tests/integration_tests/chat_models/test_jinachat.py @@ -6,7 +6,7 @@ from langchain_core.callbacks import CallbackManager from langchain_core.messages import BaseMessage, HumanMessage, SystemMessage from langchain_core.outputs import ChatGeneration, LLMResult -from langchain_core.pydantic_v1 import SecretStr +from pydantic import SecretStr from pytest import CaptureFixture, MonkeyPatch from langchain_community.chat_models.jinachat import JinaChat diff --git a/libs/community/tests/integration_tests/chat_models/test_konko.py b/libs/community/tests/integration_tests/chat_models/test_konko.py index a4b9977de39f4..1980aa9c6d030 100644 --- a/libs/community/tests/integration_tests/chat_models/test_konko.py +++ b/libs/community/tests/integration_tests/chat_models/test_konko.py @@ -6,7 +6,7 @@ from langchain_core.callbacks import CallbackManager from langchain_core.messages import BaseMessage, HumanMessage, SystemMessage from langchain_core.outputs import ChatGeneration, ChatResult, LLMResult -from langchain_core.pydantic_v1 import SecretStr +from pydantic import SecretStr from pytest import CaptureFixture, MonkeyPatch from langchain_community.chat_models.konko import ChatKonko diff --git a/libs/community/tests/integration_tests/chat_models/test_minimax.py b/libs/community/tests/integration_tests/chat_models/test_minimax.py index 8826f7d64e6f3..88199f07535e9 100644 --- a/libs/community/tests/integration_tests/chat_models/test_minimax.py +++ b/libs/community/tests/integration_tests/chat_models/test_minimax.py @@ -1,8 +1,8 @@ import os from langchain_core.messages import AIMessage, HumanMessage, ToolMessage -from langchain_core.pydantic_v1 import BaseModel from langchain_core.tools import tool +from pydantic import BaseModel from langchain_community.chat_models import MiniMaxChat diff --git a/libs/community/tests/integration_tests/chat_models/test_moonshot.py b/libs/community/tests/integration_tests/chat_models/test_moonshot.py index bb29175a2d0a5..68d9f43b5d8f0 100644 --- a/libs/community/tests/integration_tests/chat_models/test_moonshot.py +++ b/libs/community/tests/integration_tests/chat_models/test_moonshot.py @@ -4,8 +4,8 @@ import pytest from langchain_core.language_models import BaseChatModel -from langchain_core.pydantic_v1 import SecretStr from langchain_standard_tests.integration_tests import ChatModelIntegrationTests +from pydantic import SecretStr from langchain_community.chat_models.moonshot import MoonshotChat diff --git a/libs/community/tests/integration_tests/chat_models/test_openai.py b/libs/community/tests/integration_tests/chat_models/test_openai.py index 4c1982f3923be..551cfdd7e84ca 100644 --- a/libs/community/tests/integration_tests/chat_models/test_openai.py +++ b/libs/community/tests/integration_tests/chat_models/test_openai.py @@ -11,7 +11,7 @@ LLMResult, ) from langchain_core.prompts import ChatPromptTemplate -from langchain_core.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, Field from langchain_community.chat_models.openai import ChatOpenAI from tests.unit_tests.callbacks.fake_callback_handler import FakeCallbackHandler diff --git a/libs/community/tests/integration_tests/chat_models/test_qianfan_endpoint.py b/libs/community/tests/integration_tests/chat_models/test_qianfan_endpoint.py index e34bee531b6bc..d286aca33b8b2 100644 --- a/libs/community/tests/integration_tests/chat_models/test_qianfan_endpoint.py +++ b/libs/community/tests/integration_tests/chat_models/test_qianfan_endpoint.py @@ -13,7 +13,7 @@ ) from langchain_core.outputs import ChatGeneration, LLMResult from langchain_core.prompts import ChatPromptTemplate, HumanMessagePromptTemplate -from langchain_core.pydantic_v1 import SecretStr +from pydantic import SecretStr from pytest import CaptureFixture, MonkeyPatch from langchain_community.chat_models.baidu_qianfan_endpoint import ( diff --git a/libs/community/tests/integration_tests/chat_models/test_tongyi.py b/libs/community/tests/integration_tests/chat_models/test_tongyi.py index a395e800c9b16..e6884e6535346 100644 --- a/libs/community/tests/integration_tests/chat_models/test_tongyi.py +++ b/libs/community/tests/integration_tests/chat_models/test_tongyi.py @@ -8,7 +8,7 @@ from langchain_core.messages.tool import ToolCall, ToolMessage from langchain_core.outputs import ChatGeneration, LLMResult from langchain_core.prompts import ChatPromptTemplate, HumanMessagePromptTemplate -from langchain_core.pydantic_v1 import BaseModel, SecretStr +from pydantic import BaseModel, SecretStr from pytest import CaptureFixture from langchain_community.chat_models.tongyi import ChatTongyi diff --git a/libs/community/tests/integration_tests/document_loaders/test_tensorflow_datasets.py b/libs/community/tests/integration_tests/document_loaders/test_tensorflow_datasets.py index 5dae638e04646..9f6cb9190a411 100644 --- a/libs/community/tests/integration_tests/document_loaders/test_tensorflow_datasets.py +++ b/libs/community/tests/integration_tests/document_loaders/test_tensorflow_datasets.py @@ -6,7 +6,7 @@ import pytest from langchain_core.documents import Document -from langchain_core.pydantic_v1 import ValidationError +from pydantic import ValidationError from langchain_community.document_loaders.tensorflow_datasets import ( TensorflowDatasetLoader, diff --git a/libs/community/tests/integration_tests/embeddings/test_minimax.py b/libs/community/tests/integration_tests/embeddings/test_minimax.py index bd48277932b16..3bb949d953554 100644 --- a/libs/community/tests/integration_tests/embeddings/test_minimax.py +++ b/libs/community/tests/integration_tests/embeddings/test_minimax.py @@ -1,6 +1,6 @@ from typing import cast -from langchain_core.pydantic_v1 import SecretStr +from pydantic import SecretStr from langchain_community.embeddings import MiniMaxEmbeddings diff --git a/libs/community/tests/integration_tests/embeddings/test_qianfan_endpoint.py b/libs/community/tests/integration_tests/embeddings/test_qianfan_endpoint.py index a0edf46a6f946..6be6ecc5f8fba 100644 --- a/libs/community/tests/integration_tests/embeddings/test_qianfan_endpoint.py +++ b/libs/community/tests/integration_tests/embeddings/test_qianfan_endpoint.py @@ -2,7 +2,7 @@ from typing import cast -from langchain_core.pydantic_v1 import SecretStr +from pydantic import SecretStr from langchain_community.embeddings.baidu_qianfan_endpoint import ( QianfanEmbeddingsEndpoint, diff --git a/libs/community/tests/integration_tests/llms/test_arcee.py b/libs/community/tests/integration_tests/llms/test_arcee.py index e195a7818dbc7..d36a98b294126 100644 --- a/libs/community/tests/integration_tests/llms/test_arcee.py +++ b/libs/community/tests/integration_tests/llms/test_arcee.py @@ -1,6 +1,6 @@ from unittest.mock import MagicMock, patch -from langchain_core.pydantic_v1 import SecretStr +from pydantic import SecretStr from pytest import CaptureFixture, MonkeyPatch from langchain_community.llms.arcee import Arcee diff --git a/libs/community/tests/integration_tests/llms/test_azureml_endpoint.py b/libs/community/tests/integration_tests/llms/test_azureml_endpoint.py index cda463d719c87..a02c5244cc01a 100644 --- a/libs/community/tests/integration_tests/llms/test_azureml_endpoint.py +++ b/libs/community/tests/integration_tests/llms/test_azureml_endpoint.py @@ -7,7 +7,7 @@ from urllib.request import HTTPError import pytest -from langchain_core.pydantic_v1 import ValidationError +from pydantic import ValidationError from langchain_community.llms.azureml_endpoint import ( AzureMLOnlineEndpoint, diff --git a/libs/community/tests/integration_tests/llms/test_cohere.py b/libs/community/tests/integration_tests/llms/test_cohere.py index 5fad016491804..02404d20665a3 100644 --- a/libs/community/tests/integration_tests/llms/test_cohere.py +++ b/libs/community/tests/integration_tests/llms/test_cohere.py @@ -2,7 +2,7 @@ from pathlib import Path -from langchain_core.pydantic_v1 import SecretStr +from pydantic import SecretStr from pytest import MonkeyPatch from langchain_community.llms.cohere import Cohere diff --git a/libs/community/tests/integration_tests/llms/test_edenai.py b/libs/community/tests/integration_tests/llms/test_edenai.py index 3d8e0b9e230da..233ee2bf8dc2b 100644 --- a/libs/community/tests/integration_tests/llms/test_edenai.py +++ b/libs/community/tests/integration_tests/llms/test_edenai.py @@ -9,7 +9,7 @@ You'll then need to set EDENAI_API_KEY environment variable to your api key. """ -from langchain_core.pydantic_v1 import SecretStr +from pydantic import SecretStr from langchain_community.llms import EdenAI diff --git a/libs/community/tests/integration_tests/llms/test_nlpcloud.py b/libs/community/tests/integration_tests/llms/test_nlpcloud.py index 85a67e3be6a6e..3645359e8563e 100644 --- a/libs/community/tests/integration_tests/llms/test_nlpcloud.py +++ b/libs/community/tests/integration_tests/llms/test_nlpcloud.py @@ -3,7 +3,7 @@ from pathlib import Path from typing import cast -from langchain_core.pydantic_v1 import SecretStr +from pydantic import SecretStr from pytest import CaptureFixture, MonkeyPatch from langchain_community.llms.loading import load_llm diff --git a/libs/community/tests/integration_tests/llms/test_petals.py b/libs/community/tests/integration_tests/llms/test_petals.py index 82a7857ec0e11..1689efb26b3f9 100644 --- a/libs/community/tests/integration_tests/llms/test_petals.py +++ b/libs/community/tests/integration_tests/llms/test_petals.py @@ -1,6 +1,6 @@ """Test Petals API wrapper.""" -from langchain_core.pydantic_v1 import SecretStr +from pydantic import SecretStr from pytest import CaptureFixture from langchain_community.llms.petals import Petals diff --git a/libs/community/tests/integration_tests/llms/test_qianfan_endpoint.py b/libs/community/tests/integration_tests/llms/test_qianfan_endpoint.py index afc654dfc6447..35c63e8d878db 100644 --- a/libs/community/tests/integration_tests/llms/test_qianfan_endpoint.py +++ b/libs/community/tests/integration_tests/llms/test_qianfan_endpoint.py @@ -3,7 +3,7 @@ from typing import Generator, cast from langchain_core.outputs import LLMResult -from langchain_core.pydantic_v1 import SecretStr +from pydantic import SecretStr from langchain_community.llms.baidu_qianfan_endpoint import QianfanLLMEndpoint diff --git a/libs/community/tests/integration_tests/llms/test_volcengine_maas.py b/libs/community/tests/integration_tests/llms/test_volcengine_maas.py index 7cf3e29081957..321e830eeb432 100644 --- a/libs/community/tests/integration_tests/llms/test_volcengine_maas.py +++ b/libs/community/tests/integration_tests/llms/test_volcengine_maas.py @@ -3,7 +3,7 @@ from typing import Generator from langchain_core.outputs import LLMResult -from langchain_core.pydantic_v1 import SecretStr +from pydantic import SecretStr from pytest import CaptureFixture from langchain_community.llms.volcengine_maas import ( diff --git a/libs/community/tests/integration_tests/retrievers/docarray/fixtures.py b/libs/community/tests/integration_tests/retrievers/docarray/fixtures.py index d9e717e042934..ea3a5d4815a3c 100644 --- a/libs/community/tests/integration_tests/retrievers/docarray/fixtures.py +++ b/libs/community/tests/integration_tests/retrievers/docarray/fixtures.py @@ -5,7 +5,7 @@ import numpy as np import pytest -from langchain_core.pydantic_v1 import Field +from pydantic import Field if TYPE_CHECKING: from docarray.index import ( diff --git a/libs/community/tests/integration_tests/utilities/test_tensorflow_datasets.py b/libs/community/tests/integration_tests/utilities/test_tensorflow_datasets.py index 1b25fd522b3b9..973e7d0b9b696 100644 --- a/libs/community/tests/integration_tests/utilities/test_tensorflow_datasets.py +++ b/libs/community/tests/integration_tests/utilities/test_tensorflow_datasets.py @@ -6,7 +6,7 @@ import pytest from langchain_core.documents import Document -from langchain_core.pydantic_v1 import ValidationError +from pydantic import ValidationError from langchain_community.utilities.tensorflow_datasets import TensorflowDatasets diff --git a/libs/community/tests/unit_tests/agents/test_serialization.py b/libs/community/tests/unit_tests/agents/test_serialization.py deleted file mode 100644 index 338f33e093280..0000000000000 --- a/libs/community/tests/unit_tests/agents/test_serialization.py +++ /dev/null @@ -1,26 +0,0 @@ -from pathlib import Path -from tempfile import TemporaryDirectory - -from langchain.agents.agent_types import AgentType -from langchain.agents.initialize import initialize_agent, load_agent -from langchain_core.language_models import FakeListLLM -from langchain_core.tools import Tool - - -def test_mrkl_serialization() -> None: - agent = initialize_agent( - [ - Tool( - name="Test tool", - func=lambda x: x, - description="Test description", - ) - ], - FakeListLLM(responses=[]), - agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, - verbose=True, - ) - with TemporaryDirectory() as tempdir: - file = Path(tempdir) / "agent.json" - agent.save_agent(file) - load_agent(file) diff --git a/libs/community/tests/unit_tests/callbacks/fake_callback_handler.py b/libs/community/tests/unit_tests/callbacks/fake_callback_handler.py index 2b0889748a8f5..b6838b3c85cf6 100644 --- a/libs/community/tests/unit_tests/callbacks/fake_callback_handler.py +++ b/libs/community/tests/unit_tests/callbacks/fake_callback_handler.py @@ -6,7 +6,7 @@ from langchain_core.callbacks import AsyncCallbackHandler, BaseCallbackHandler from langchain_core.messages import BaseMessage -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel class BaseFakeCallbackHandler(BaseModel): @@ -254,7 +254,7 @@ def on_retriever_error( ) -> Any: self.on_retriever_error_common() - def __deepcopy__(self, memo: dict) -> "FakeCallbackHandler": + def __deepcopy__(self, memo: dict) -> "FakeCallbackHandler": # type: ignore return self @@ -388,5 +388,5 @@ async def on_text( ) -> None: self.on_text_common() - def __deepcopy__(self, memo: dict) -> "FakeAsyncCallbackHandler": + def __deepcopy__(self, memo: dict) -> "FakeAsyncCallbackHandler": # type: ignore return self diff --git a/libs/community/tests/unit_tests/chains/test_llm.py b/libs/community/tests/unit_tests/chains/test_llm.py index cef101f8db242..d7f58f6a39de4 100644 --- a/libs/community/tests/unit_tests/chains/test_llm.py +++ b/libs/community/tests/unit_tests/chains/test_llm.py @@ -1,8 +1,6 @@ """Test LLM chain.""" -from tempfile import TemporaryDirectory from typing import Dict, List, Union -from unittest.mock import patch import pytest from langchain.chains.llm import LLMChain @@ -27,21 +25,6 @@ def fake_llm_chain() -> LLMChain: return LLMChain(prompt=prompt, llm=FakeLLM(), output_key="text1") -@patch( - "langchain_community.llms.loading.get_type_to_cls_dict", - lambda: {"fake": lambda: FakeLLM}, -) -def test_serialization(fake_llm_chain: LLMChain) -> None: - """Test serialization.""" - from langchain.chains.loading import load_chain - - with TemporaryDirectory() as temp_dir: - file = temp_dir + "/llm.json" - fake_llm_chain.save(file) - loaded_chain = load_chain(file) - assert loaded_chain == fake_llm_chain - - def test_missing_inputs(fake_llm_chain: LLMChain) -> None: """Test error is raised if inputs are missing.""" with pytest.raises(ValueError): diff --git a/libs/community/tests/unit_tests/chains/test_pebblo_retrieval.py b/libs/community/tests/unit_tests/chains/test_pebblo_retrieval.py index 3b49a0d5174c9..a2fb1dbd00920 100644 --- a/libs/community/tests/unit_tests/chains/test_pebblo_retrieval.py +++ b/libs/community/tests/unit_tests/chains/test_pebblo_retrieval.py @@ -45,18 +45,6 @@ async def _aget_relevant_documents( return [Document(page_content=query)] -@pytest.fixture -def unsupported_retriever() -> FakeRetriever: - """ - Create a FakeRetriever instance - """ - retriever = FakeRetriever() - retriever.search_kwargs = {} - # Set the class of vectorstore - retriever.vectorstore.__class__ = InMemoryVectorStore - return retriever - - @pytest.fixture def retriever() -> FakeRetriever: """ @@ -110,9 +98,7 @@ def test_invoke(pebblo_retrieval_qa: PebbloRetrievalQA) -> None: assert response is not None -def test_validate_vectorstore( - retriever: FakeRetriever, unsupported_retriever: FakeRetriever -) -> None: +def test_validate_vectorstore(retriever: FakeRetriever) -> None: """ Test vectorstore validation """ @@ -127,6 +113,11 @@ def test_validate_vectorstore( app_name="app_name", ) + unsupported_retriever = FakeRetriever() + unsupported_retriever.search_kwargs = {} + # Set the class of vectorstore + unsupported_retriever.vectorstore.__class__ = InMemoryVectorStore + # validate_vectorstore method should raise a ValueError for unsupported vectorstores with pytest.raises(ValueError) as exc_info: _ = PebbloRetrievalQA.from_chain_type( diff --git a/libs/community/tests/unit_tests/chat_models/test_azureml_endpoint.py b/libs/community/tests/unit_tests/chat_models/test_azureml_endpoint.py index bf01dcae2a968..20bb38f367e00 100644 --- a/libs/community/tests/unit_tests/chat_models/test_azureml_endpoint.py +++ b/libs/community/tests/unit_tests/chat_models/test_azureml_endpoint.py @@ -3,7 +3,7 @@ import os import pytest -from langchain_core.pydantic_v1 import SecretStr +from pydantic import SecretStr from pytest import CaptureFixture, FixtureRequest from langchain_community.chat_models.azureml_endpoint import AzureMLChatOnlineEndpoint diff --git a/libs/community/tests/unit_tests/chat_models/test_baichuan.py b/libs/community/tests/unit_tests/chat_models/test_baichuan.py index 31d588274a70a..d869f0042bc0d 100644 --- a/libs/community/tests/unit_tests/chat_models/test_baichuan.py +++ b/libs/community/tests/unit_tests/chat_models/test_baichuan.py @@ -10,7 +10,7 @@ SystemMessage, ToolMessage, ) -from langchain_core.pydantic_v1 import SecretStr +from pydantic import SecretStr from pytest import CaptureFixture, MonkeyPatch from langchain_community.chat_models.baichuan import ( diff --git a/libs/community/tests/unit_tests/chat_models/test_fireworks.py b/libs/community/tests/unit_tests/chat_models/test_fireworks.py index 5b9c69b4d0fea..61548fa52c442 100644 --- a/libs/community/tests/unit_tests/chat_models/test_fireworks.py +++ b/libs/community/tests/unit_tests/chat_models/test_fireworks.py @@ -3,7 +3,7 @@ import sys import pytest -from langchain_core.pydantic_v1 import SecretStr +from pydantic import SecretStr from pytest import CaptureFixture from langchain_community.chat_models import ChatFireworks diff --git a/libs/community/tests/unit_tests/chat_models/test_friendli.py b/libs/community/tests/unit_tests/chat_models/test_friendli.py index e101533fb8731..c752171299122 100644 --- a/libs/community/tests/unit_tests/chat_models/test_friendli.py +++ b/libs/community/tests/unit_tests/chat_models/test_friendli.py @@ -3,7 +3,7 @@ from unittest.mock import AsyncMock, MagicMock, Mock import pytest -from langchain_core.pydantic_v1 import SecretStr +from pydantic import SecretStr from pytest import CaptureFixture, MonkeyPatch from langchain_community.adapters.openai import aenumerate diff --git a/libs/community/tests/unit_tests/chat_models/test_javelin_ai_gateway.py b/libs/community/tests/unit_tests/chat_models/test_javelin_ai_gateway.py index c612747dd5dcc..f3e7e361917ca 100644 --- a/libs/community/tests/unit_tests/chat_models/test_javelin_ai_gateway.py +++ b/libs/community/tests/unit_tests/chat_models/test_javelin_ai_gateway.py @@ -1,7 +1,7 @@ """Test `Javelin AI Gateway` chat models""" import pytest -from langchain_core.pydantic_v1 import SecretStr +from pydantic import SecretStr from langchain_community.chat_models import ChatJavelinAIGateway diff --git a/libs/community/tests/unit_tests/chat_models/test_kinetica.py b/libs/community/tests/unit_tests/chat_models/test_kinetica.py index 87824c775a697..5d83e92cdc7d1 100644 --- a/libs/community/tests/unit_tests/chat_models/test_kinetica.py +++ b/libs/community/tests/unit_tests/chat_models/test_kinetica.py @@ -14,6 +14,7 @@ class TestChatKinetica: test_ctx_json: str = """ { "payload":{ + "question": "foo", "context":[ { "table":"demo.test_profiles", diff --git a/libs/community/tests/unit_tests/chat_models/test_mlflow.py b/libs/community/tests/unit_tests/chat_models/test_mlflow.py index d526086c490e0..af2efd5202dcf 100644 --- a/libs/community/tests/unit_tests/chat_models/test_mlflow.py +++ b/libs/community/tests/unit_tests/chat_models/test_mlflow.py @@ -19,8 +19,8 @@ ToolMessageChunk, ) from langchain_core.prompts import ChatPromptTemplate -from langchain_core.pydantic_v1 import _PYDANTIC_MAJOR_VERSION, BaseModel from langchain_core.tools import StructuredTool +from pydantic import BaseModel from langchain_community.chat_models.mlflow import ChatMlflow @@ -199,10 +199,6 @@ def mock_stream(*args: Any, **kwargs: Any) -> Any: @pytest.mark.requires("mlflow") -@pytest.mark.skipif( - _PYDANTIC_MAJOR_VERSION < 2, - reason="The tool mock is not compatible with pydantic 1.x", -) def test_chat_mlflow_bind_tools( llm: ChatMlflow, mock_predict_stream_result: List[dict] ) -> None: @@ -226,14 +222,18 @@ def mock_stream(*args: Any, **kwargs: Any) -> Any: ] ) - def mock_func(*args: Any, **kwargs: Any) -> str: + def mock_func(x: int, y: int) -> str: return "36939 x 8922.4 = 329,511,111.6" + class ArgsSchema(BaseModel): + x: int + y: int + tools = [ StructuredTool( name="name", description="description", - args_schema=BaseModel, + args_schema=ArgsSchema, func=mock_func, ) ] diff --git a/libs/community/tests/unit_tests/chat_models/test_octoai.py b/libs/community/tests/unit_tests/chat_models/test_octoai.py index b0d66eae36d3b..fb639a7399b73 100644 --- a/libs/community/tests/unit_tests/chat_models/test_octoai.py +++ b/libs/community/tests/unit_tests/chat_models/test_octoai.py @@ -1,5 +1,5 @@ import pytest -from langchain_core.pydantic_v1 import SecretStr, ValidationError +from pydantic import SecretStr, ValidationError from langchain_community.chat_models.octoai import ChatOctoAI diff --git a/libs/community/tests/unit_tests/chat_models/test_ollama.py b/libs/community/tests/unit_tests/chat_models/test_ollama.py index 3d3cb6c32addf..96075dda33497 100644 --- a/libs/community/tests/unit_tests/chat_models/test_ollama.py +++ b/libs/community/tests/unit_tests/chat_models/test_ollama.py @@ -1,7 +1,7 @@ from typing import List, Literal, Optional import pytest -from langchain_core.pydantic_v1 import BaseModel, ValidationError +from pydantic import BaseModel, ValidationError from langchain_community.chat_models import ChatOllama @@ -12,8 +12,8 @@ class ExpectedParams(BaseModel): ls_model_name: str ls_model_type: Literal["chat", "llm"] ls_temperature: Optional[float] - ls_max_tokens: Optional[int] - ls_stop: Optional[List[str]] + ls_max_tokens: Optional[int] = None + ls_stop: Optional[List[str]] = None model = ChatOllama(model="llama3") ls_params = model._get_ls_params() diff --git a/libs/community/tests/unit_tests/chat_models/test_premai.py b/libs/community/tests/unit_tests/chat_models/test_premai.py index 22327e3aa2fb3..09118f48ea528 100644 --- a/libs/community/tests/unit_tests/chat_models/test_premai.py +++ b/libs/community/tests/unit_tests/chat_models/test_premai.py @@ -4,7 +4,7 @@ import pytest from langchain_core.messages import AIMessage, HumanMessage, SystemMessage, ToolMessage -from langchain_core.pydantic_v1 import SecretStr +from pydantic import SecretStr from pytest import CaptureFixture from langchain_community.chat_models import ChatPremAI diff --git a/libs/community/tests/unit_tests/document_loaders/blob_loaders/test_schema.py b/libs/community/tests/unit_tests/document_loaders/blob_loaders/test_schema.py deleted file mode 100644 index 8829b227cf429..0000000000000 --- a/libs/community/tests/unit_tests/document_loaders/blob_loaders/test_schema.py +++ /dev/null @@ -1,154 +0,0 @@ -import os -from contextlib import contextmanager -from pathlib import Path -from tempfile import NamedTemporaryFile -from typing import Generator, Iterable, Optional - -import pytest - -from langchain_community.document_loaders.blob_loaders.schema import ( - Blob, - BlobLoader, - PathLike, -) - - -@contextmanager -def get_temp_file( - content: bytes, suffix: Optional[str] = None -) -> Generator[Path, None, None]: - """Yield a temporary field with some content.""" - with NamedTemporaryFile(suffix=suffix, delete=False) as temp_file: - temp_file.write(content) - path = Path(temp_file.name) - try: - yield path - finally: - os.remove(str(path)) - - -def test_blob_initialized_with_binary_data() -> None: - """Test reading blob IO if blob content hasn't been read yet.""" - data = b"Hello, World!" - blob = Blob(data=data) - assert blob.as_string() == "Hello, World!" - assert blob.as_bytes() == data - assert blob.source is None - with blob.as_bytes_io() as bytes_io: - assert bytes_io.read() == data - - -def test_blob_from_pure_path() -> None: - """Test reading blob from a file path.""" - content = b"Hello, World!" - - with get_temp_file(content, suffix=".html") as temp_path: - assert isinstance(temp_path, Path) - blob = Blob.from_path(temp_path) - assert blob.encoding == "utf-8" # Default encoding - assert blob.path == temp_path - assert blob.mimetype == "text/html" - assert blob.source == str(temp_path) - assert blob.data is None - assert blob.as_bytes() == content - assert blob.as_string() == "Hello, World!" - with blob.as_bytes_io() as bytes_io: - assert bytes_io.read() == content - - -def test_blob_from_str_path() -> None: - """Test reading blob from a file path.""" - content = b"Hello, World!" - - with get_temp_file(content) as temp_path: - str_path = str(temp_path) - assert isinstance(str_path, str) - blob = Blob.from_path(str_path) - assert blob.encoding == "utf-8" # Default encoding - assert blob.path == str(temp_path) - assert blob.source == str(temp_path) - assert blob.data is None - assert blob.as_bytes() == content - assert blob.as_string() == "Hello, World!" - with blob.as_bytes_io() as bytes_io: - assert bytes_io.read() == content - - -def test_blob_from_str_data() -> None: - """Test reading blob from a file path.""" - content = b"Hello, World!" - blob = Blob.from_data(content) - assert blob.encoding == "utf-8" # Default encoding - assert blob.path is None - assert blob.mimetype is None - assert blob.source is None - assert blob.data == b"Hello, World!" - assert blob.as_bytes() == content - assert blob.as_string() == "Hello, World!" - with blob.as_bytes_io() as bytes_io: - assert bytes_io.read() == content - - -def test_blob_mimetype_from_str_data() -> None: - """Test reading blob from a file path.""" - content = b"Hello, World!" - mimetype = "text/html" - blob = Blob.from_data(content, mime_type=mimetype) - assert blob.mimetype == mimetype - - -@pytest.mark.parametrize( - "path, mime_type, guess_type, expected_mime_type", - [ - ("test.txt", None, True, "text/plain"), - ("test.txt", None, False, None), - ("test.html", None, True, "text/html"), - ("test.html", None, False, None), - ("test.html", "user_forced_value", True, "user_forced_value"), - (Path("test.html"), "user_forced_value", True, "user_forced_value"), - (Path("test.html"), None, True, "text/html"), - ], -) -def test_mime_type_inference( - path: PathLike, mime_type: str, guess_type: bool, expected_mime_type: Optional[str] -) -> None: - """Tests mimetype inference based on options and path.""" - blob = Blob.from_path(path, mime_type=mime_type, guess_type=guess_type) - assert blob.mimetype == expected_mime_type - - -def test_blob_initialization_validator() -> None: - """Test that blob initialization validates the arguments.""" - with pytest.raises(ValueError, match="Either data or path must be provided"): - Blob() # type: ignore[call-arg] - - assert Blob(data=b"Hello, World!") is not None - assert Blob(path="some_path") is not None # type: ignore[call-arg] - - -def test_blob_loader() -> None: - """Simple test that verifies that we can implement a blob loader.""" - - class TestLoader(BlobLoader): - def yield_blobs(self) -> Iterable[Blob]: - """Yield blob implementation.""" - yield Blob(data=b"Hello, World!") - - assert list(TestLoader().yield_blobs()) == [Blob(data=b"Hello, World!")] - - -def test_metadata_and_source() -> None: - """Test metadata and source""" - blob = Blob(path="some_file", data="b") - assert blob.source == "some_file" - assert blob.metadata == {} - blob = Blob(data=b"", metadata={"source": "hello"}) - assert blob.source == "hello" - assert blob.metadata == {"source": "hello"} - - blob = Blob.from_data("data", metadata={"source": "somewhere"}) - assert blob.source == "somewhere" - - with get_temp_file(b"hello") as path: - blob = Blob.from_path(path, metadata={"source": "somewhere"}) - assert blob.source == "somewhere" diff --git a/libs/community/tests/unit_tests/document_loaders/loaders/vendors/test_docugami.py b/libs/community/tests/unit_tests/document_loaders/loaders/vendors/test_docugami.py index cfbf8ba82e105..7b107f32789c5 100644 --- a/libs/community/tests/unit_tests/document_loaders/loaders/vendors/test_docugami.py +++ b/libs/community/tests/unit_tests/document_loaders/loaders/vendors/test_docugami.py @@ -25,4 +25,6 @@ def test_docugami_loader_local() -> None: def test_docugami_initialization() -> None: """Test correct initialization in remote mode.""" - DocugamiLoader(access_token="test", docset_id="123") # type: ignore[call-arg] + DocugamiLoader( + access_token="test", docset_id="123", document_ids=None, file_paths=None + ) diff --git a/libs/community/tests/unit_tests/document_loaders/test_github.py b/libs/community/tests/unit_tests/document_loaders/test_github.py index 544681f0ae660..c37eb2db9ee7a 100644 --- a/libs/community/tests/unit_tests/document_loaders/test_github.py +++ b/libs/community/tests/unit_tests/document_loaders/test_github.py @@ -171,10 +171,11 @@ def test_github_file_content_get_file_paths(mocker: MockerFixture) -> None: assert files[0]["path"] == "readme.md" # case2: didn't add file_filter - loader = GithubFileLoader( # type: ignore[call-arg] + loader = GithubFileLoader( repo="shufanhao/langchain", access_token="access_token", github_api_url="https://github.com", + file_filter=None, ) # Call the load method @@ -220,10 +221,11 @@ def test_github_file_content_loader(mocker: MockerFixture) -> None: mocker.patch("requests.get", side_effect=[file_path_res, file_content_res]) # case1: file_extension=".md" - loader = GithubFileLoader( # type: ignore[call-arg] + loader = GithubFileLoader( repo="shufanhao/langchain", access_token="access_token", github_api_url="https://github.com", + file_filter=None, ) # Call the load method diff --git a/libs/community/tests/unit_tests/document_loaders/test_onenote.py b/libs/community/tests/unit_tests/document_loaders/test_onenote.py index 41a58b2ec0e19..7d8e1590ccc83 100644 --- a/libs/community/tests/unit_tests/document_loaders/test_onenote.py +++ b/libs/community/tests/unit_tests/document_loaders/test_onenote.py @@ -15,6 +15,7 @@ def test_initialization() -> None: os.environ["MS_GRAPH_CLIENT_SECRET"] = "CLIENT_SECRET" loader = OneNoteLoader( + settings={"client_id": "", "client_secret": ""}, notebook_name="test_notebook", section_name="test_section", page_title="test_title", @@ -39,6 +40,7 @@ def test_load(mocker: MockerFixture) -> None: return_value=mocker.MagicMock(json=lambda: {"value": []}, links=None), ) loader = OneNoteLoader( + settings={"client_id": "", "client_secret": ""}, notebook_name="test_notebook", section_name="test_section", page_title="test_title", @@ -54,7 +56,11 @@ def test_load(mocker: MockerFixture) -> None: "

Test Content

" ), ) - loader = OneNoteLoader(object_ids=["test_id"], access_token="access_token") + loader = OneNoteLoader( + settings={"client_id": "", "client_secret": ""}, + object_ids=["test_id"], + access_token="access_token", + ) documents = loader.load() assert documents == [ Document( @@ -79,6 +85,7 @@ def test_msal_import(monkeypatch: MonkeyPatch, mocker: MockerFixture) -> None: return_value=FakeConfidentialClientApplication(), ) loader = OneNoteLoader( + settings={"client_id": "", "client_secret": ""}, notebook_name="test_notebook", section_name="test_section", page_title="test_title", @@ -92,6 +99,7 @@ def test_url() -> None: os.environ["MS_GRAPH_CLIENT_SECRET"] = "CLIENT_SECRET" loader = OneNoteLoader( + settings={"client_id": "", "client_secret": ""}, notebook_name="test_notebook", section_name="test_section", page_title="test_title", @@ -107,6 +115,7 @@ def test_url() -> None: ) loader = OneNoteLoader( + settings={"client_id": "", "client_secret": ""}, notebook_name="test_notebook", section_name="test_section", access_token="access_token", @@ -120,6 +129,7 @@ def test_url() -> None: ) loader = OneNoteLoader( + settings={"client_id": "", "client_secret": ""}, notebook_name="test_notebook", access_token="access_token", onenote_api_base_url="https://graph.microsoft.com/v1.0/me/onenote", @@ -131,6 +141,7 @@ def test_url() -> None: ) loader = OneNoteLoader( + settings={"client_id": "", "client_secret": ""}, section_name="test_section", access_token="access_token", onenote_api_base_url="https://graph.microsoft.com/v1.0/me/onenote", @@ -142,6 +153,7 @@ def test_url() -> None: ) loader = OneNoteLoader( + settings={"client_id": "", "client_secret": ""}, section_name="test_section", page_title="test_title", access_token="access_token", @@ -155,6 +167,7 @@ def test_url() -> None: ) loader = OneNoteLoader( + settings={"client_id": "", "client_secret": ""}, page_title="test_title", access_token="access_token", onenote_api_base_url="https://graph.microsoft.com/v1.0/me/onenote", diff --git a/libs/community/tests/unit_tests/embeddings/test_baichuan.py b/libs/community/tests/unit_tests/embeddings/test_baichuan.py index 3c7961af2dc68..47fb9f032ecb6 100644 --- a/libs/community/tests/unit_tests/embeddings/test_baichuan.py +++ b/libs/community/tests/unit_tests/embeddings/test_baichuan.py @@ -1,15 +1,16 @@ from typing import cast -from langchain_core.pydantic_v1 import SecretStr +from pydantic import SecretStr from langchain_community.embeddings import BaichuanTextEmbeddings def test_sparkllm_initialization_by_alias() -> None: # Effective initialization - embeddings = BaichuanTextEmbeddings( # type: ignore[call-arg] + embeddings = BaichuanTextEmbeddings( model="embedding_model", api_key="your-api-key", # type: ignore[arg-type] + session=None, ) assert embeddings.model_name == "embedding_model" assert ( diff --git a/libs/community/tests/unit_tests/embeddings/test_edenai.py b/libs/community/tests/unit_tests/embeddings/test_edenai.py index d80c2279f8afc..164c27f1af10f 100644 --- a/libs/community/tests/unit_tests/embeddings/test_edenai.py +++ b/libs/community/tests/unit_tests/embeddings/test_edenai.py @@ -1,6 +1,6 @@ """Test EdenAiEmbeddings embeddings""" -from langchain_core.pydantic_v1 import SecretStr +from pydantic import SecretStr from pytest import CaptureFixture from langchain_community.embeddings import EdenAiEmbeddings diff --git a/libs/community/tests/unit_tests/embeddings/test_embaas.py b/libs/community/tests/unit_tests/embeddings/test_embaas.py index d631be8036bc8..1297f80658c89 100644 --- a/libs/community/tests/unit_tests/embeddings/test_embaas.py +++ b/libs/community/tests/unit_tests/embeddings/test_embaas.py @@ -1,6 +1,6 @@ """Test EmbaasEmbeddings embeddings""" -from langchain_core.pydantic_v1 import SecretStr +from pydantic import SecretStr from pytest import CaptureFixture from langchain_community.embeddings import EmbaasEmbeddings diff --git a/libs/community/tests/unit_tests/embeddings/test_llm_rails.py b/libs/community/tests/unit_tests/embeddings/test_llm_rails.py index ccc6ff0d77d6a..0ddaa79006bf7 100644 --- a/libs/community/tests/unit_tests/embeddings/test_llm_rails.py +++ b/libs/community/tests/unit_tests/embeddings/test_llm_rails.py @@ -1,6 +1,6 @@ """Test LLMRailsEmbeddings embeddings""" -from langchain_core.pydantic_v1 import SecretStr +from pydantic import SecretStr from pytest import CaptureFixture from langchain_community.embeddings import LLMRailsEmbeddings diff --git a/libs/community/tests/unit_tests/embeddings/test_premai.py b/libs/community/tests/unit_tests/embeddings/test_premai.py index 3d1b2b77447fa..8c75a67d20c05 100644 --- a/libs/community/tests/unit_tests/embeddings/test_premai.py +++ b/libs/community/tests/unit_tests/embeddings/test_premai.py @@ -1,7 +1,7 @@ """Test EmbaasEmbeddings embeddings""" import pytest -from langchain_core.pydantic_v1 import SecretStr +from pydantic import SecretStr from pytest import CaptureFixture from langchain_community.embeddings import PremAIEmbeddings diff --git a/libs/community/tests/unit_tests/embeddings/test_sparkllm.py b/libs/community/tests/unit_tests/embeddings/test_sparkllm.py index 41f8ca4bd2edd..faff133c5c057 100644 --- a/libs/community/tests/unit_tests/embeddings/test_sparkllm.py +++ b/libs/community/tests/unit_tests/embeddings/test_sparkllm.py @@ -2,7 +2,7 @@ from typing import cast import pytest -from langchain_core.pydantic_v1 import SecretStr +from pydantic import SecretStr from langchain_community.embeddings import SparkLLMTextEmbeddings diff --git a/libs/community/tests/unit_tests/llms/fake_llm.py b/libs/community/tests/unit_tests/llms/fake_llm.py index 6f457f00e63aa..a8e9eb67eacea 100644 --- a/libs/community/tests/unit_tests/llms/fake_llm.py +++ b/libs/community/tests/unit_tests/llms/fake_llm.py @@ -4,7 +4,7 @@ from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models.llms import LLM -from langchain_core.pydantic_v1 import validator +from pydantic import validator class FakeLLM(LLM): diff --git a/libs/community/tests/unit_tests/llms/test_ai21.py b/libs/community/tests/unit_tests/llms/test_ai21.py index 788364da0c5a8..f743fe7503b2b 100644 --- a/libs/community/tests/unit_tests/llms/test_ai21.py +++ b/libs/community/tests/unit_tests/llms/test_ai21.py @@ -2,7 +2,7 @@ from typing import cast -from langchain_core.pydantic_v1 import SecretStr +from pydantic import SecretStr from pytest import CaptureFixture, MonkeyPatch from langchain_community.llms.ai21 import AI21 diff --git a/libs/community/tests/unit_tests/llms/test_aleph_alpha.py b/libs/community/tests/unit_tests/llms/test_aleph_alpha.py index 57cd544d7eb58..24b2d0433dc7e 100644 --- a/libs/community/tests/unit_tests/llms/test_aleph_alpha.py +++ b/libs/community/tests/unit_tests/llms/test_aleph_alpha.py @@ -1,7 +1,7 @@ """Test Aleph Alpha specific stuff.""" import pytest -from langchain_core.pydantic_v1 import SecretStr +from pydantic import SecretStr from pytest import CaptureFixture, MonkeyPatch from langchain_community.llms.aleph_alpha import AlephAlpha diff --git a/libs/community/tests/unit_tests/llms/test_anyscale.py b/libs/community/tests/unit_tests/llms/test_anyscale.py index c5896f540cae2..481738de81d23 100644 --- a/libs/community/tests/unit_tests/llms/test_anyscale.py +++ b/libs/community/tests/unit_tests/llms/test_anyscale.py @@ -1,7 +1,7 @@ """Test Anyscale llm""" import pytest -from langchain_core.pydantic_v1 import SecretStr +from pydantic import SecretStr from pytest import CaptureFixture, MonkeyPatch from langchain_community.llms.anyscale import Anyscale diff --git a/libs/community/tests/unit_tests/llms/test_bananadev.py b/libs/community/tests/unit_tests/llms/test_bananadev.py index 026cf134d3a6e..caa5f49a3cdd7 100644 --- a/libs/community/tests/unit_tests/llms/test_bananadev.py +++ b/libs/community/tests/unit_tests/llms/test_bananadev.py @@ -2,7 +2,7 @@ from typing import cast -from langchain_core.pydantic_v1 import SecretStr +from pydantic import SecretStr from pytest import CaptureFixture, MonkeyPatch from langchain_community.llms.bananadev import Banana diff --git a/libs/community/tests/unit_tests/llms/test_cerebriumai.py b/libs/community/tests/unit_tests/llms/test_cerebriumai.py index 87e0439870e88..6d57408378aed 100644 --- a/libs/community/tests/unit_tests/llms/test_cerebriumai.py +++ b/libs/community/tests/unit_tests/llms/test_cerebriumai.py @@ -1,6 +1,6 @@ """Test CerebriumAI llm""" -from langchain_core.pydantic_v1 import SecretStr +from pydantic import SecretStr from pytest import CaptureFixture, MonkeyPatch from langchain_community.llms.cerebriumai import CerebriumAI diff --git a/libs/community/tests/unit_tests/llms/test_fireworks.py b/libs/community/tests/unit_tests/llms/test_fireworks.py index 1487fdc0fba19..a5030d3ad53ca 100644 --- a/libs/community/tests/unit_tests/llms/test_fireworks.py +++ b/libs/community/tests/unit_tests/llms/test_fireworks.py @@ -3,7 +3,7 @@ import sys import pytest -from langchain_core.pydantic_v1 import SecretStr +from pydantic import SecretStr from pytest import CaptureFixture from langchain_community.llms import Fireworks diff --git a/libs/community/tests/unit_tests/llms/test_forefrontai.py b/libs/community/tests/unit_tests/llms/test_forefrontai.py index 711cb2186be71..52a2c578387a7 100644 --- a/libs/community/tests/unit_tests/llms/test_forefrontai.py +++ b/libs/community/tests/unit_tests/llms/test_forefrontai.py @@ -2,7 +2,7 @@ from typing import cast -from langchain_core.pydantic_v1 import SecretStr +from pydantic import SecretStr from pytest import CaptureFixture, MonkeyPatch from langchain_community.llms.forefrontai import ForefrontAI diff --git a/libs/community/tests/unit_tests/llms/test_friendli.py b/libs/community/tests/unit_tests/llms/test_friendli.py index d55d6d14fa812..6fd4593d93d3b 100644 --- a/libs/community/tests/unit_tests/llms/test_friendli.py +++ b/libs/community/tests/unit_tests/llms/test_friendli.py @@ -3,7 +3,7 @@ from unittest.mock import AsyncMock, MagicMock, Mock import pytest -from langchain_core.pydantic_v1 import SecretStr +from pydantic import SecretStr from pytest import CaptureFixture, MonkeyPatch from langchain_community.adapters.openai import aenumerate diff --git a/libs/community/tests/unit_tests/llms/test_gooseai.py b/libs/community/tests/unit_tests/llms/test_gooseai.py index be467b61758a9..6ec2f0aadc905 100644 --- a/libs/community/tests/unit_tests/llms/test_gooseai.py +++ b/libs/community/tests/unit_tests/llms/test_gooseai.py @@ -1,7 +1,7 @@ """Test GooseAI""" import pytest -from langchain_core.pydantic_v1 import SecretStr +from pydantic import SecretStr from pytest import MonkeyPatch from langchain_community.llms.gooseai import GooseAI diff --git a/libs/community/tests/unit_tests/llms/test_minimax.py b/libs/community/tests/unit_tests/llms/test_minimax.py index ef128ea1d5192..a244cf0b2492a 100644 --- a/libs/community/tests/unit_tests/llms/test_minimax.py +++ b/libs/community/tests/unit_tests/llms/test_minimax.py @@ -2,7 +2,7 @@ from typing import cast -from langchain_core.pydantic_v1 import SecretStr +from pydantic import SecretStr from pytest import CaptureFixture, MonkeyPatch from langchain_community.llms.minimax import Minimax diff --git a/libs/community/tests/unit_tests/llms/test_pipelineai.py b/libs/community/tests/unit_tests/llms/test_pipelineai.py index 3a8513650fc04..ea56cd322278d 100644 --- a/libs/community/tests/unit_tests/llms/test_pipelineai.py +++ b/libs/community/tests/unit_tests/llms/test_pipelineai.py @@ -1,4 +1,4 @@ -from langchain_core.pydantic_v1 import SecretStr +from pydantic import SecretStr from pytest import CaptureFixture from langchain_community.llms.pipelineai import PipelineAI diff --git a/libs/community/tests/unit_tests/llms/test_predibase.py b/libs/community/tests/unit_tests/llms/test_predibase.py index bb8245f1ef32b..4812492776a35 100644 --- a/libs/community/tests/unit_tests/llms/test_predibase.py +++ b/libs/community/tests/unit_tests/llms/test_predibase.py @@ -1,4 +1,4 @@ -from langchain_core.pydantic_v1 import SecretStr +from pydantic import SecretStr from pytest import CaptureFixture from langchain_community.llms.predibase import Predibase diff --git a/libs/community/tests/unit_tests/llms/test_stochasticai.py b/libs/community/tests/unit_tests/llms/test_stochasticai.py index 285d4368f2a72..894b1df5a5693 100644 --- a/libs/community/tests/unit_tests/llms/test_stochasticai.py +++ b/libs/community/tests/unit_tests/llms/test_stochasticai.py @@ -1,4 +1,4 @@ -from langchain_core.pydantic_v1 import SecretStr +from pydantic import SecretStr from pytest import CaptureFixture from langchain_community.llms.stochasticai import StochasticAI diff --git a/libs/community/tests/unit_tests/llms/test_symblai_nebula.py b/libs/community/tests/unit_tests/llms/test_symblai_nebula.py index 9109ee3c45be7..07654cdcd6b08 100644 --- a/libs/community/tests/unit_tests/llms/test_symblai_nebula.py +++ b/libs/community/tests/unit_tests/llms/test_symblai_nebula.py @@ -1,6 +1,6 @@ """Test the Nebula model by Symbl.ai""" -from langchain_core.pydantic_v1 import SecretStr +from pydantic import SecretStr from pytest import CaptureFixture, MonkeyPatch from langchain_community.llms.symblai_nebula import Nebula diff --git a/libs/community/tests/unit_tests/llms/test_together.py b/libs/community/tests/unit_tests/llms/test_together.py index 0d6cf975035c0..2012a36590d7e 100644 --- a/libs/community/tests/unit_tests/llms/test_together.py +++ b/libs/community/tests/unit_tests/llms/test_together.py @@ -2,7 +2,7 @@ from typing import cast -from langchain_core.pydantic_v1 import SecretStr +from pydantic import SecretStr from pytest import CaptureFixture, MonkeyPatch from langchain_community.llms.together import Together diff --git a/libs/community/tests/unit_tests/load/__snapshots__/test_dump.ambr b/libs/community/tests/unit_tests/load/__snapshots__/test_dump.ambr index 2789472be2212..0c30949ab703e 100644 --- a/libs/community/tests/unit_tests/load/__snapshots__/test_dump.ambr +++ b/libs/community/tests/unit_tests/load/__snapshots__/test_dump.ambr @@ -71,6 +71,7 @@ "LLMChain" ], "kwargs": { + "verbose": false, "prompt": { "lc": 1, "type": "constructor", @@ -298,6 +299,7 @@ "LLMChain" ], "kwargs": { + "verbose": false, "prompt": { "lc": 1, "type": "constructor", @@ -587,6 +589,7 @@ "LLMChain" ], "kwargs": { + "verbose": false, "prompt": { "lc": 1, "type": "constructor", diff --git a/libs/community/tests/unit_tests/load/test_dump.py b/libs/community/tests/unit_tests/load/test_dump.py index c46233a874a72..ef86c654575de 100644 --- a/libs/community/tests/unit_tests/load/test_dump.py +++ b/libs/community/tests/unit_tests/load/test_dump.py @@ -11,8 +11,8 @@ from langchain_core.load.serializable import Serializable from langchain_core.prompts.chat import ChatPromptTemplate, HumanMessagePromptTemplate from langchain_core.prompts.prompt import PromptTemplate -from langchain_core.pydantic_v1 import Field, root_validator from langchain_core.tracers.langchain import LangChainTracer +from pydantic import ConfigDict, Field, model_validator class Person(Serializable): @@ -182,11 +182,13 @@ class TestClass(Serializable): my_favorite_secret: str = Field(alias="my_favorite_secret_alias") my_other_secret: str = Field() - class Config: - allow_population_by_field_name = True + model_config = ConfigDict( + populate_by_name=True, + ) - @root_validator(pre=True) - def get_from_env(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def get_from_env(cls, values: Dict) -> Any: """Get the values from the environment.""" if "my_favorite_secret" not in values: values["my_favorite_secret"] = os.getenv("MY_FAVORITE_SECRET") diff --git a/libs/community/tests/unit_tests/load/test_serializable.py b/libs/community/tests/unit_tests/load/test_serializable.py index 7ca4d27da9167..f86d2f707396b 100644 --- a/libs/community/tests/unit_tests/load/test_serializable.py +++ b/libs/community/tests/unit_tests/load/test_serializable.py @@ -102,6 +102,24 @@ def test_serializable_mapping() -> None: "modifier", "RemoveMessage", ), + ("langchain", "chat_models", "mistralai", "ChatMistralAI"): ( + "langchain_mistralai", + "chat_models", + "ChatMistralAI", + ), + ("langchain_groq", "chat_models", "ChatGroq"): ( + "langchain_groq", + "chat_models", + "ChatGroq", + ), + # TODO(0.3): For now we're skipping this test. Need to fix + # so that it only runs when langchain-aws is installed. + ("langchain", "chat_models", "bedrock", "ChatBedrock"): ( + "langchain_aws", + "chat_models", + "bedrock", + "ChatBedrock", + ), } serializable_modules = import_all_modules("langchain") diff --git a/libs/community/tests/unit_tests/retrievers/test_bedrock.py b/libs/community/tests/unit_tests/retrievers/test_bedrock.py index ff72d193e4ad6..1f24891b4b8c1 100644 --- a/libs/community/tests/unit_tests/retrievers/test_bedrock.py +++ b/libs/community/tests/unit_tests/retrievers/test_bedrock.py @@ -29,7 +29,9 @@ def amazon_retriever( def test_create_client(amazon_retriever: AmazonKnowledgeBasesRetriever) -> None: - with pytest.raises(ImportError): + # Import error if boto3 is not installed + # Value error if credentials are not supplied. + with pytest.raises((ImportError, ValueError)): amazon_retriever.create_client({}) diff --git a/libs/community/tests/unit_tests/test_dependencies.py b/libs/community/tests/unit_tests/test_dependencies.py index e68f5e4fa6009..47239c945d2ad 100644 --- a/libs/community/tests/unit_tests/test_dependencies.py +++ b/libs/community/tests/unit_tests/test_dependencies.py @@ -48,6 +48,7 @@ def test_required_dependencies(poetry_conf: Mapping[str, Any]) -> None: "numpy", "python", "requests", + "pydantic-settings", "tenacity", "langchain", ] diff --git a/libs/community/tests/unit_tests/tools/audio/test_tools.py b/libs/community/tests/unit_tests/tools/audio/test_tools.py index 03055ad36c25d..30cacb7b7b0b5 100644 --- a/libs/community/tests/unit_tests/tools/audio/test_tools.py +++ b/libs/community/tests/unit_tests/tools/audio/test_tools.py @@ -6,7 +6,7 @@ from unittest.mock import Mock, mock_open, patch import pytest -from langchain_core.pydantic_v1 import SecretStr +from pydantic import SecretStr from langchain_community.tools.audio import HuggingFaceTextToSpeechModelInference diff --git a/libs/community/tests/unit_tests/tools/openapi/__init__.py b/libs/community/tests/unit_tests/tools/openapi/__init__.py deleted file mode 100644 index e69de29bb2d1d..0000000000000 diff --git a/libs/community/tests/unit_tests/tools/openapi/test_api_models.py b/libs/community/tests/unit_tests/tools/openapi/test_api_models.py deleted file mode 100644 index e871349a7bc59..0000000000000 --- a/libs/community/tests/unit_tests/tools/openapi/test_api_models.py +++ /dev/null @@ -1,224 +0,0 @@ -"""Test the APIOperation class.""" - -import json -import os -from pathlib import Path -from typing import Iterable, List, Tuple - -import pytest - -# Keep at top of file to ensure that pydantic test can be skipped before -# pydantic v1 related imports are attempted by openapi_pydantic. -from langchain_core.pydantic_v1 import _PYDANTIC_MAJOR_VERSION - -if _PYDANTIC_MAJOR_VERSION != 1: - pytest.skip( - f"Pydantic major version {_PYDANTIC_MAJOR_VERSION} is not supported.", - allow_module_level=True, - ) - -import pytest -import yaml - -from langchain_community.tools.openapi.utils.api_models import ( - APIOperation, - APIRequestBody, - APIRequestBodyProperty, -) -from langchain_community.tools.openapi.utils.openapi_utils import HTTPVerb, OpenAPISpec - -SPECS_DIR = Path(__file__).parents[2] / "examples" / "test_specs" - - -def _get_test_specs() -> Iterable[Path]: - """Walk the test_specs directory and collect all files with the name 'apispec' - in them. - """ - if not SPECS_DIR.exists(): - raise ValueError - return ( - Path(root) / file - for root, _, files in os.walk(SPECS_DIR) - for file in files - if file.startswith("apispec") - ) - - -def _get_paths_and_methods_from_spec_dictionary( - spec: dict, -) -> Iterable[Tuple[str, str]]: - """Return a tuple (paths, methods) for every path in spec.""" - valid_methods = [verb.value for verb in HTTPVerb] - for path_name, path_item in spec["paths"].items(): - for method in valid_methods: - if method in path_item: - yield (path_name, method) - - -def http_paths_and_methods() -> List[Tuple[str, OpenAPISpec, str, str]]: - """Return a args for every method in cached OpenAPI spec in test_specs.""" - http_paths_and_methods = [] - for test_spec in _get_test_specs(): - spec_name = test_spec.parent.name - if test_spec.suffix == ".json": - with test_spec.open("r") as f: - spec = json.load(f) - else: - with test_spec.open("r") as f: - spec = yaml.safe_load(f.read()) - parsed_spec = OpenAPISpec.from_file(test_spec) - for path, method in _get_paths_and_methods_from_spec_dictionary(spec): - http_paths_and_methods.append( - ( - spec_name, - parsed_spec, - path, - method, - ) - ) - return http_paths_and_methods - - -@pytest.mark.requires("openapi_pydantic") -def test_parse_api_operations() -> None: - """Test the APIOperation class.""" - for spec_name, spec, path, method in http_paths_and_methods(): - try: - APIOperation.from_openapi_spec(spec, path, method) - except Exception as e: - raise AssertionError(f"Error processing {spec_name}: {e} ") from e - - -@pytest.mark.requires("openapi_pydantic") -@pytest.fixture -def raw_spec() -> OpenAPISpec: - """Return a raw OpenAPI spec.""" - from openapi_pydantic import Info - - return OpenAPISpec( - info=Info(title="Test API", version="1.0.0"), - ) - - -@pytest.mark.requires("openapi_pydantic") -def test_api_request_body_from_request_body_with_ref(raw_spec: OpenAPISpec) -> None: - """Test instantiating APIRequestBody from RequestBody with a reference.""" - from openapi_pydantic import ( - Components, - MediaType, - Reference, - RequestBody, - Schema, - ) - - raw_spec.components = Components( - schemas={ - "Foo": Schema( - type="object", - properties={ - "foo": Schema(type="string"), - "bar": Schema(type="number"), - }, - required=["foo"], - ) - } - ) - media_type = MediaType( - schema=Reference( - ref="#/components/schemas/Foo", - ) - ) - request_body = RequestBody(content={"application/json": media_type}) - api_request_body = APIRequestBody.from_request_body(request_body, raw_spec) - assert api_request_body.description is None - assert len(api_request_body.properties) == 2 - foo_prop = api_request_body.properties[0] - assert foo_prop.name == "foo" - assert foo_prop.required is True - bar_prop = api_request_body.properties[1] - assert bar_prop.name == "bar" - assert bar_prop.required is False - assert api_request_body.media_type == "application/json" - - -@pytest.mark.requires("openapi_pydantic") -def test_api_request_body_from_request_body_with_schema(raw_spec: OpenAPISpec) -> None: - """Test instantiating APIRequestBody from RequestBody with a schema.""" - from openapi_pydantic import ( - MediaType, - RequestBody, - Schema, - ) - - request_body = RequestBody( - content={ - "application/json": MediaType( - schema=Schema(type="object", properties={"foo": Schema(type="string")}) - ) - } - ) - api_request_body = APIRequestBody.from_request_body(request_body, raw_spec) - assert api_request_body.properties == [ - APIRequestBodyProperty( - name="foo", - required=False, - type="string", - default=None, - description=None, - properties=[], - references_used=[], - ) - ] - assert api_request_body.media_type == "application/json" - - -@pytest.mark.requires("openapi_pydantic") -def test_api_request_body_property_from_schema(raw_spec: OpenAPISpec) -> None: - from openapi_pydantic import ( - Components, - Reference, - Schema, - ) - - raw_spec.components = Components( - schemas={ - "Bar": Schema( - type="number", - ) - } - ) - schema = Schema( - type="object", - properties={ - "foo": Schema(type="string"), - "bar": Reference(ref="#/components/schemas/Bar"), - }, - required=["bar"], - ) - api_request_body_property = APIRequestBodyProperty.from_schema( - schema, "test", required=True, spec=raw_spec - ) - expected_sub_properties = [ - APIRequestBodyProperty( - name="foo", - required=False, - type="string", - default=None, - description=None, - properties=[], - references_used=[], - ), - APIRequestBodyProperty( - name="bar", - required=True, - type="number", - default=None, - description=None, - properties=[], - references_used=["Bar"], - ), - ] - assert api_request_body_property.properties[0] == expected_sub_properties[0] - assert api_request_body_property.properties[1] == expected_sub_properties[1] - assert api_request_body_property.type == "object" - assert api_request_body_property.properties[1].references_used == ["Bar"] diff --git a/libs/community/tests/unit_tests/tools/test_exported.py b/libs/community/tests/unit_tests/tools/test_exported.py index 6dd98bd0d7790..e5503369873ed 100644 --- a/libs/community/tests/unit_tests/tools/test_exported.py +++ b/libs/community/tests/unit_tests/tools/test_exported.py @@ -23,13 +23,15 @@ def _get_tool_classes(skip_tools_without_default_names: bool) -> List[Type[BaseT if isinstance(tool_class, type) and issubclass(tool_class, BaseTool): if tool_class in _EXCLUDE: continue - if skip_tools_without_default_names and get_fields(tool_class)[ - "name" - ].default in [ # type: ignore + default_name = get_fields(tool_class)["name"].default + if skip_tools_without_default_names and default_name in [ # type: ignore None, "", ]: continue + if not isinstance(default_name, str): + continue + results.append(tool_class) return results @@ -37,6 +39,6 @@ def _get_tool_classes(skip_tools_without_default_names: bool) -> List[Type[BaseT def test_tool_names_unique() -> None: """Test that the default names for our core tools are unique.""" tool_classes = _get_tool_classes(skip_tools_without_default_names=True) - names = sorted([get_fields(tool_cls)["name"].default for tool_cls in tool_classes]) + names = sorted([tool_cls.model_fields["name"].default for tool_cls in tool_classes]) duplicated_names = [name for name in names if names.count(name) > 1] assert not duplicated_names diff --git a/libs/community/tests/unit_tests/tools/test_zapier.py b/libs/community/tests/unit_tests/tools/test_zapier.py index d2b0661943506..8df158a765c16 100644 --- a/libs/community/tests/unit_tests/tools/test_zapier.py +++ b/libs/community/tests/unit_tests/tools/test_zapier.py @@ -16,7 +16,9 @@ def test_default_base_prompt() -> None: action_id="test", zapier_description="test", params_schema={"test": "test"}, - api_wrapper=ZapierNLAWrapper(zapier_nla_api_key="test"), # type: ignore[call-arg] + api_wrapper=ZapierNLAWrapper( + zapier_nla_api_key="test", zapier_nla_oauth_access_token="" + ), ) # Test that the base prompt was successfully assigned to the default prompt diff --git a/libs/community/tests/unit_tests/utilities/test_nvidia_riva_asr.py b/libs/community/tests/unit_tests/utilities/test_nvidia_riva_asr.py index e95e41dc4f8a6..13a1b2ecffc47 100644 --- a/libs/community/tests/unit_tests/utilities/test_nvidia_riva_asr.py +++ b/libs/community/tests/unit_tests/utilities/test_nvidia_riva_asr.py @@ -4,6 +4,7 @@ from unittest.mock import patch import pytest +from pydantic import AnyHttpUrl from langchain_community.utilities.nvidia_riva import ( AudioStream, @@ -126,7 +127,10 @@ def stream() -> AudioStream: def test_init(asr: RivaASR) -> None: """Test that ASR accepts valid arguments.""" for key, expected_val in CONFIG.items(): - assert getattr(asr, key, None) == expected_val + if key == "url": + assert asr.url == AnyHttpUrl(expected_val) + else: + assert getattr(asr, key, None) == expected_val @pytest.mark.requires("riva.client") @@ -162,7 +166,7 @@ def test_get_service(asr: RivaASR) -> None: svc = asr._get_service() assert str(svc.auth.ssl_cert) == CONFIG["ssl_cert"] assert svc.auth.use_ssl == SVC_USE_SSL - assert svc.auth.uri == SVC_URI + assert str(svc.auth.uri) == SVC_URI @pytest.mark.requires("riva.client") diff --git a/libs/community/tests/unit_tests/utilities/test_nvidia_riva_tts.py b/libs/community/tests/unit_tests/utilities/test_nvidia_riva_tts.py index 3024ff5885a8a..055f5872e3f63 100644 --- a/libs/community/tests/unit_tests/utilities/test_nvidia_riva_tts.py +++ b/libs/community/tests/unit_tests/utilities/test_nvidia_riva_tts.py @@ -57,7 +57,10 @@ def tts() -> RivaTTS: def test_init(tts: RivaTTS) -> None: """Test that ASR accepts valid arguments.""" for key, expected_val in CONFIG.items(): - assert getattr(tts, key, None) == expected_val + if key == "url": + assert str(tts.url) == expected_val + "/" + else: + assert getattr(tts, key, None) == expected_val @pytest.mark.requires("riva.client") diff --git a/libs/community/tests/unit_tests/vectorstores/redis/test_redis_schema.py b/libs/community/tests/unit_tests/vectorstores/redis/test_redis_schema.py index 90196a7f3e661..a3afbfc5ad5cf 100644 --- a/libs/community/tests/unit_tests/vectorstores/redis/test_redis_schema.py +++ b/libs/community/tests/unit_tests/vectorstores/redis/test_redis_schema.py @@ -36,7 +36,7 @@ def test_numeric_field_schema_creation() -> None: def test_redis_vector_field_validation() -> None: """Test validation for RedisVectorField's datatype.""" - from langchain_core.pydantic_v1 import ValidationError + from pydantic import ValidationError with pytest.raises(ValidationError): RedisVectorField( diff --git a/libs/community/tests/unit_tests/vectorstores/test_inmemory.py b/libs/community/tests/unit_tests/vectorstores/test_inmemory.py index 7381335103a0a..6facca3429b2a 100644 --- a/libs/community/tests/unit_tests/vectorstores/test_inmemory.py +++ b/libs/community/tests/unit_tests/vectorstores/test_inmemory.py @@ -19,6 +19,13 @@ def __eq__(self, other: Any) -> bool: return isinstance(other, str) +def _AnyDocument(**kwargs: Any) -> Document: + """Create a Document with an any id field.""" + doc = Document(**kwargs) + doc.id = AnyStr() + return doc + + class TestInMemoryReadWriteTestSuite(ReadWriteTestSuite): @pytest.fixture def vectorstore(self) -> InMemoryVectorStore: @@ -37,12 +44,12 @@ async def test_inmemory() -> None: ["foo", "bar", "baz"], ConsistentFakeEmbeddings() ) output = await store.asimilarity_search("foo", k=1) - assert output == [Document(page_content="foo", id=AnyStr())] + assert output == [_AnyDocument(page_content="foo")] output = await store.asimilarity_search("bar", k=2) assert output == [ - Document(page_content="bar", id=AnyStr()), - Document(page_content="baz", id=AnyStr()), + _AnyDocument(page_content="bar"), + _AnyDocument(page_content="baz"), ] output2 = await store.asimilarity_search_with_score("bar", k=2) @@ -70,8 +77,8 @@ async def test_inmemory_mmr() -> None: "foo", k=10, lambda_mult=0.1 ) assert len(output) == len(texts) - assert output[0] == Document(page_content="foo", id=AnyStr()) - assert output[1] == Document(page_content="foy", id=AnyStr()) + assert output[0] == _AnyDocument(page_content="foo") + assert output[1] == _AnyDocument(page_content="foy") async def test_inmemory_dump_load(tmp_path: Path) -> None: @@ -99,4 +106,4 @@ async def test_inmemory_filter() -> None: output = await store.asimilarity_search( "baz", filter=lambda doc: doc.metadata["id"] == 1 ) - assert output == [Document(page_content="foo", metadata={"id": 1}, id=AnyStr())] + assert output == [_AnyDocument(page_content="foo", metadata={"id": 1})] diff --git a/libs/core/Makefile b/libs/core/Makefile index 694946791075b..1582fcd478483 100644 --- a/libs/core/Makefile +++ b/libs/core/Makefile @@ -39,7 +39,6 @@ lint_tests: PYTHON_FILES=tests lint_tests: MYPY_CACHE=.mypy_cache_test lint lint_diff lint_package lint_tests: - ./scripts/check_pydantic.sh . ./scripts/lint_imports.sh [ "$(PYTHON_FILES)" = "" ] || poetry run ruff check $(PYTHON_FILES) [ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) --diff diff --git a/libs/core/langchain_core/_api/deprecation.py b/libs/core/langchain_core/_api/deprecation.py index cf5dbd608a93e..7f46ff2802f90 100644 --- a/libs/core/langchain_core/_api/deprecation.py +++ b/libs/core/langchain_core/_api/deprecation.py @@ -144,7 +144,7 @@ def deprecate( _package: str = package, ) -> T: """Implementation of the decorator returned by `deprecated`.""" - from langchain_core.utils.pydantic import FieldInfoV1 + from langchain_core.utils.pydantic import FieldInfoV1, FieldInfoV2 def emit_warning() -> None: """Emit the warning.""" @@ -238,6 +238,25 @@ def finalize(wrapper: Callable[..., Any], new_doc: str) -> T: exclude=obj.exclude, ), ) + elif isinstance(obj, FieldInfoV2): + wrapped = None + if not _obj_type: + _obj_type = "attribute" + if not _name: + raise ValueError(f"Field {obj} must have a name to be deprecated.") + old_doc = obj.description + + def finalize(wrapper: Callable[..., Any], new_doc: str) -> T: + return cast( + T, + FieldInfoV2( + default=obj.default, + default_factory=obj.default_factory, + description=new_doc, + alias=obj.alias, + exclude=obj.exclude, + ), + ) elif isinstance(obj, property): if not _obj_type: diff --git a/libs/core/langchain_core/beta/runnables/context.py b/libs/core/langchain_core/beta/runnables/context.py index 13767e352dd5a..3b95bf2859b4d 100644 --- a/libs/core/langchain_core/beta/runnables/context.py +++ b/libs/core/langchain_core/beta/runnables/context.py @@ -18,6 +18,8 @@ Union, ) +from pydantic import ConfigDict + from langchain_core._api.beta_decorator import beta from langchain_core.runnables.base import ( Runnable, @@ -229,8 +231,9 @@ class ContextSet(RunnableSerializable): keys: Mapping[str, Optional[Runnable]] - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) def __init__( self, diff --git a/libs/core/langchain_core/chat_history.py b/libs/core/langchain_core/chat_history.py index d080b24a505f3..dd5a8afaa88be 100644 --- a/libs/core/langchain_core/chat_history.py +++ b/libs/core/langchain_core/chat_history.py @@ -20,13 +20,14 @@ from abc import ABC, abstractmethod from typing import List, Sequence, Union +from pydantic import BaseModel, Field + from langchain_core.messages import ( AIMessage, BaseMessage, HumanMessage, get_buffer_string, ) -from langchain_core.pydantic_v1 import BaseModel, Field class BaseChatMessageHistory(ABC): diff --git a/libs/core/langchain_core/documents/base.py b/libs/core/langchain_core/documents/base.py index 61b506a0b9674..6c79f99651433 100644 --- a/libs/core/langchain_core/documents/base.py +++ b/libs/core/langchain_core/documents/base.py @@ -4,10 +4,12 @@ import mimetypes from io import BufferedReader, BytesIO from pathlib import PurePath -from typing import Any, Generator, List, Literal, Mapping, Optional, Union, cast +from typing import Any, Dict, Generator, List, Literal, Optional, Union, cast + +from pydantic import ConfigDict, Field, model_validator from langchain_core.load.serializable import Serializable -from langchain_core.pydantic_v1 import Field, root_validator +from langchain_core.utils.pydantic import v1_repr PathLike = Union[str, PurePath] @@ -110,9 +112,10 @@ class Blob(BaseMedia): path: Optional[PathLike] = None """Location where the original content was found.""" - class Config: - arbitrary_types_allowed = True - frozen = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + frozen=True, + ) @property def source(self) -> Optional[str]: @@ -127,8 +130,9 @@ def source(self) -> Optional[str]: return cast(Optional[str], self.metadata["source"]) return str(self.path) if self.path else None - @root_validator(pre=True) - def check_blob_is_valid(cls, values: Mapping[str, Any]) -> Mapping[str, Any]: + @model_validator(mode="before") + @classmethod + def check_blob_is_valid(cls, values: Dict[str, Any]) -> Any: """Verify that either data or path is provided.""" if "data" not in values and "path" not in values: raise ValueError("Either data or path must be provided") @@ -293,3 +297,7 @@ def __str__(self) -> str: return f"page_content='{self.page_content}' metadata={self.metadata}" else: return f"page_content='{self.page_content}'" + + def __repr__(self) -> str: + # TODO(0.3): Remove this override after confirming unit tests! + return v1_repr(self) diff --git a/libs/core/langchain_core/documents/compressor.py b/libs/core/langchain_core/documents/compressor.py index 895079fc12724..95c8cd9a96394 100644 --- a/libs/core/langchain_core/documents/compressor.py +++ b/libs/core/langchain_core/documents/compressor.py @@ -3,9 +3,10 @@ from abc import ABC, abstractmethod from typing import Optional, Sequence +from pydantic import BaseModel + from langchain_core.callbacks import Callbacks from langchain_core.documents import Document -from langchain_core.pydantic_v1 import BaseModel from langchain_core.runnables import run_in_executor diff --git a/libs/core/langchain_core/embeddings/fake.py b/libs/core/langchain_core/embeddings/fake.py index e7a31fab1adb5..2c8221425ccd5 100644 --- a/libs/core/langchain_core/embeddings/fake.py +++ b/libs/core/langchain_core/embeddings/fake.py @@ -4,8 +4,9 @@ import hashlib from typing import List +from pydantic import BaseModel + from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel class FakeEmbeddings(Embeddings, BaseModel): diff --git a/libs/core/langchain_core/example_selectors/length_based.py b/libs/core/langchain_core/example_selectors/length_based.py index 8f3511ad7672f..810d1bc4f413d 100644 --- a/libs/core/langchain_core/example_selectors/length_based.py +++ b/libs/core/langchain_core/example_selectors/length_based.py @@ -3,9 +3,10 @@ import re from typing import Callable, Dict, List +from pydantic import BaseModel, validator + from langchain_core.example_selectors.base import BaseExampleSelector from langchain_core.prompts.prompt import PromptTemplate -from langchain_core.pydantic_v1 import BaseModel, validator def _get_length_based(text: str) -> int: diff --git a/libs/core/langchain_core/example_selectors/semantic_similarity.py b/libs/core/langchain_core/example_selectors/semantic_similarity.py index c14428a0c11d6..77c7186088dc8 100644 --- a/libs/core/langchain_core/example_selectors/semantic_similarity.py +++ b/libs/core/langchain_core/example_selectors/semantic_similarity.py @@ -5,9 +5,10 @@ from abc import ABC from typing import TYPE_CHECKING, Any, Dict, List, Optional, Type +from pydantic import BaseModel, ConfigDict + from langchain_core.documents import Document from langchain_core.example_selectors.base import BaseExampleSelector -from langchain_core.pydantic_v1 import BaseModel, Extra from langchain_core.vectorstores import VectorStore if TYPE_CHECKING: @@ -42,9 +43,10 @@ class _VectorStoreExampleSelector(BaseExampleSelector, BaseModel, ABC): vectorstore_kwargs: Optional[Dict[str, Any]] = None """Extra arguments passed to similarity_search function of the vectorstore.""" - class Config: - arbitrary_types_allowed = True - extra = Extra.forbid + model_config = ConfigDict( + arbitrary_types_allowed=True, + extra="forbid", + ) @staticmethod def _example_to_text( diff --git a/libs/core/langchain_core/graph_vectorstores/base.py b/libs/core/langchain_core/graph_vectorstores/base.py index adc9064cb488d..4110be32e888f 100644 --- a/libs/core/langchain_core/graph_vectorstores/base.py +++ b/libs/core/langchain_core/graph_vectorstores/base.py @@ -12,6 +12,8 @@ Optional, ) +from pydantic import Field + from langchain_core._api import beta from langchain_core.callbacks import ( AsyncCallbackManagerForRetrieverRun, @@ -20,7 +22,6 @@ from langchain_core.documents import Document from langchain_core.graph_vectorstores.links import METADATA_LINKS_KEY, Link from langchain_core.load import Serializable -from langchain_core.pydantic_v1 import Field from langchain_core.runnables import run_in_executor from langchain_core.vectorstores import VectorStore, VectorStoreRetriever diff --git a/libs/core/langchain_core/indexing/api.py b/libs/core/langchain_core/indexing/api.py index 115eb72b9772b..5f0fb5bba4644 100644 --- a/libs/core/langchain_core/indexing/api.py +++ b/libs/core/langchain_core/indexing/api.py @@ -25,10 +25,11 @@ cast, ) +from pydantic import model_validator + from langchain_core.document_loaders.base import BaseLoader from langchain_core.documents import Document from langchain_core.indexing.base import DocumentIndex, RecordManager -from langchain_core.pydantic_v1 import root_validator from langchain_core.vectorstores import VectorStore # Magic UUID to use as a namespace for hashing. @@ -68,8 +69,9 @@ class _HashedDocument(Document): def is_lc_serializable(cls) -> bool: return False - @root_validator(pre=True) - def calculate_hashes(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def calculate_hashes(cls, values: Dict[str, Any]) -> Any: """Root validator to calculate content and metadata hash.""" content = values.get("page_content", "") metadata = values.get("metadata", {}) diff --git a/libs/core/langchain_core/indexing/in_memory.py b/libs/core/langchain_core/indexing/in_memory.py index 0154103031210..3d062643c909d 100644 --- a/libs/core/langchain_core/indexing/in_memory.py +++ b/libs/core/langchain_core/indexing/in_memory.py @@ -1,12 +1,13 @@ import uuid from typing import Any, Dict, List, Optional, Sequence, cast +from pydantic import Field + from langchain_core._api import beta from langchain_core.callbacks import CallbackManagerForRetrieverRun from langchain_core.documents import Document from langchain_core.indexing import UpsertResponse from langchain_core.indexing.base import DeleteResponse, DocumentIndex -from langchain_core.pydantic_v1 import Field @beta(message="Introduced in version 0.2.29. Underlying abstraction subject to change.") diff --git a/libs/core/langchain_core/language_models/base.py b/libs/core/langchain_core/language_models/base.py index d097cb4791e57..f2f0d47a251b4 100644 --- a/libs/core/langchain_core/language_models/base.py +++ b/libs/core/langchain_core/language_models/base.py @@ -18,6 +18,7 @@ Union, ) +from pydantic import BaseModel, ConfigDict, Field, validator from typing_extensions import TypeAlias, TypedDict from langchain_core._api import deprecated @@ -28,7 +29,6 @@ get_buffer_string, ) from langchain_core.prompt_values import PromptValue -from langchain_core.pydantic_v1 import BaseModel, Field, validator from langchain_core.runnables import Runnable, RunnableSerializable from langchain_core.utils import get_pydantic_field_names @@ -113,7 +113,11 @@ class BaseLanguageModel( Caching is not currently supported for streaming methods of models. """ - verbose: bool = Field(default_factory=_get_verbosity) + # Repr = False is consistent with pydantic 1 if verbose = False + # We can relax this for pydantic 2? + # TODO(0.3): Resolve repr for verbose + # Modified just to get unit tests to pass. + verbose: bool = Field(default_factory=_get_verbosity, exclude=True, repr=False) """Whether to print out response text.""" callbacks: Callbacks = Field(default=None, exclude=True) """Callbacks to add to the run trace.""" @@ -126,6 +130,10 @@ class BaseLanguageModel( ) """Optional encoder to use for counting tokens.""" + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) + @validator("verbose", pre=True, always=True, allow_reuse=True) def set_verbose(cls, verbose: Optional[bool]) -> bool: """If verbose is None, set it. diff --git a/libs/core/langchain_core/language_models/chat_models.py b/libs/core/langchain_core/language_models/chat_models.py index 0ac867a9c66f1..1b382aec06e00 100644 --- a/libs/core/langchain_core/language_models/chat_models.py +++ b/libs/core/langchain_core/language_models/chat_models.py @@ -23,6 +23,13 @@ cast, ) +from pydantic import ( + BaseModel, + ConfigDict, + Field, + model_validator, +) + from langchain_core._api import deprecated from langchain_core.caches import BaseCache from langchain_core.callbacks import ( @@ -57,11 +64,6 @@ RunInfo, ) from langchain_core.prompt_values import ChatPromptValue, PromptValue, StringPromptValue -from langchain_core.pydantic_v1 import ( - BaseModel, - Field, - root_validator, -) from langchain_core.rate_limiters import BaseRateLimiter from langchain_core.runnables import RunnableMap, RunnablePassthrough from langchain_core.runnables.config import ensure_config, run_in_executor @@ -193,14 +195,20 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC): """ # noqa: E501 - callback_manager: Optional[BaseCallbackManager] = deprecated( - name="callback_manager", since="0.1.7", removal="1.0", alternative="callbacks" - )( - Field( - default=None, - exclude=True, - description="Callback manager to add to the run trace.", - ) + # TODO(0.3): Figure out how to re-apply deprecated decorator + # callback_manager: Optional[BaseCallbackManager] = deprecated( + # name="callback_manager", since="0.1.7", removal="1.0", alternative="callbacks" + # )( + # Field( + # default=None, + # exclude=True, + # description="Callback manager to add to the run trace.", + # ) + # ) + callback_manager: Optional[BaseCallbackManager] = Field( + default=None, + exclude=True, + description="Callback manager to add to the run trace.", ) rate_limiter: Optional[BaseRateLimiter] = Field(default=None, exclude=True) @@ -218,8 +226,9 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC): - If False (default), will always use streaming case if available. """ - @root_validator(pre=True) - def raise_deprecation(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def raise_deprecation(cls, values: Dict) -> Any: """Raise deprecation warning if callback_manager is used. Args: @@ -240,8 +249,9 @@ def raise_deprecation(cls, values: Dict) -> Dict: values["callbacks"] = values.pop("callback_manager", None) return values - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) # --- Runnable methods --- diff --git a/libs/core/langchain_core/language_models/llms.py b/libs/core/langchain_core/language_models/llms.py index 56679dd4209ca..91f7956005b07 100644 --- a/libs/core/langchain_core/language_models/llms.py +++ b/libs/core/langchain_core/language_models/llms.py @@ -27,6 +27,7 @@ ) import yaml +from pydantic import ConfigDict, Field, model_validator from tenacity import ( RetryCallState, before_sleep_log, @@ -62,7 +63,6 @@ ) from langchain_core.outputs import Generation, GenerationChunk, LLMResult, RunInfo from langchain_core.prompt_values import ChatPromptValue, PromptValue, StringPromptValue -from langchain_core.pydantic_v1 import Field, root_validator from langchain_core.runnables import RunnableConfig, ensure_config, get_config_list from langchain_core.runnables.config import run_in_executor @@ -300,11 +300,13 @@ class BaseLLM(BaseLanguageModel[str], ABC): callback_manager: Optional[BaseCallbackManager] = Field(default=None, exclude=True) """[DEPRECATED]""" - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) - @root_validator(pre=True) - def raise_deprecation(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def raise_deprecation(cls, values: Dict) -> Any: """Raise deprecation warning if callback_manager is used.""" if values.get("callback_manager") is not None: warnings.warn( diff --git a/libs/core/langchain_core/load/load.py b/libs/core/langchain_core/load/load.py index f7db2ff6f022f..fb6e3ec2a1da5 100644 --- a/libs/core/langchain_core/load/load.py +++ b/libs/core/langchain_core/load/load.py @@ -17,6 +17,7 @@ "langchain_core", "langchain_community", "langchain_anthropic", + "langchain_groq", ] ALL_SERIALIZABLE_MAPPINGS = { diff --git a/libs/core/langchain_core/load/mapping.py b/libs/core/langchain_core/load/mapping.py index 84c3878c5d810..dccabf3dbff56 100644 --- a/libs/core/langchain_core/load/mapping.py +++ b/libs/core/langchain_core/load/mapping.py @@ -271,6 +271,11 @@ "chat_models", "ChatAnthropic", ), + ("langchain_groq", "chat_models", "ChatGroq"): ( + "langchain_groq", + "chat_models", + "ChatGroq", + ), ("langchain", "chat_models", "fireworks", "ChatFireworks"): ( "langchain_fireworks", "chat_models", @@ -287,6 +292,17 @@ "chat_models", "ChatVertexAI", ), + ("langchain", "chat_models", "mistralai", "ChatMistralAI"): ( + "langchain_mistralai", + "chat_models", + "ChatMistralAI", + ), + ("langchain", "chat_models", "bedrock", "ChatBedrock"): ( + "langchain_aws", + "chat_models", + "bedrock", + "ChatBedrock", + ), ("langchain", "schema", "output", "ChatGenerationChunk"): ( "langchain_core", "outputs", diff --git a/libs/core/langchain_core/load/serializable.py b/libs/core/langchain_core/load/serializable.py index 06e46b2667c0b..dbb9a4edaf5c7 100644 --- a/libs/core/langchain_core/load/serializable.py +++ b/libs/core/langchain_core/load/serializable.py @@ -10,9 +10,10 @@ cast, ) +from pydantic import BaseModel, ConfigDict from typing_extensions import NotRequired -from langchain_core.pydantic_v1 import BaseModel +from langchain_core.utils.pydantic import v1_repr class BaseSerialized(TypedDict): @@ -80,7 +81,7 @@ def try_neq_default(value: Any, key: str, model: BaseModel) -> bool: Exception: If the key is not in the model. """ try: - return model.__fields__[key].get_default() != value + return model.model_fields[key].get_default() != value except Exception: return True @@ -161,16 +162,25 @@ def lc_id(cls) -> List[str]: For example, for the class `langchain.llms.openai.OpenAI`, the id is ["langchain", "llms", "openai", "OpenAI"]. """ - return [*cls.get_lc_namespace(), cls.__name__] - - class Config: - extra = "ignore" + # Pydantic generics change the class name. So we need to do the following + if ( + "origin" in cls.__pydantic_generic_metadata__ + and cls.__pydantic_generic_metadata__["origin"] is not None + ): + original_name = cls.__pydantic_generic_metadata__["origin"].__name__ + else: + original_name = cls.__name__ + return [*cls.get_lc_namespace(), original_name] + + model_config = ConfigDict( + extra="ignore", + ) def __repr_args__(self) -> Any: return [ (k, v) for k, v in super().__repr_args__() - if (k not in self.__fields__ or try_neq_default(v, k, self)) + if (k not in self.model_fields or try_neq_default(v, k, self)) ] def to_json(self) -> Union[SerializedConstructor, SerializedNotImplemented]: @@ -184,12 +194,15 @@ def to_json(self) -> Union[SerializedConstructor, SerializedNotImplemented]: secrets = dict() # Get latest values for kwargs if there is an attribute with same name - lc_kwargs = { - k: getattr(self, k, v) - for k, v in self - if not (self.__exclude_fields__ or {}).get(k, False) # type: ignore - and _is_field_useful(self, k, v) - } + lc_kwargs = {} + for k, v in self: + if not _is_field_useful(self, k, v): + continue + # Do nothing if the field is excluded + if k in self.model_fields and self.model_fields[k].exclude: + continue + + lc_kwargs[k] = getattr(self, k, v) # Merge the lc_secrets and lc_attributes from every class in the MRO for cls in [None, *self.__class__.mro()]: @@ -221,8 +234,10 @@ def to_json(self) -> Union[SerializedConstructor, SerializedNotImplemented]: # that are not present in the fields. for key in list(secrets): value = secrets[key] - if key in this.__fields__: - secrets[this.__fields__[key].alias] = value + if key in this.model_fields: + alias = this.model_fields[key].alias + if alias is not None: + secrets[alias] = value lc_kwargs.update(this.lc_attributes) # include all secrets, even if not specified in kwargs @@ -244,6 +259,10 @@ def to_json(self) -> Union[SerializedConstructor, SerializedNotImplemented]: def to_json_not_implemented(self) -> SerializedNotImplemented: return to_json_not_implemented(self) + def __repr__(self) -> str: + # TODO(0.3): Remove this override after confirming unit tests! + return v1_repr(self) + def _is_field_useful(inst: Serializable, key: str, value: Any) -> bool: """Check if a field is useful as a constructor argument. @@ -259,9 +278,13 @@ def _is_field_useful(inst: Serializable, key: str, value: Any) -> bool: If the field is not required and the value is None, it is useful if the default value is different from the value. """ - field = inst.__fields__.get(key) + field = inst.model_fields.get(key) if not field: return False + + if field.is_required(): + return True + # Handle edge case: a value cannot be converted to a boolean (e.g. a # Pandas DataFrame). try: @@ -269,6 +292,17 @@ def _is_field_useful(inst: Serializable, key: str, value: Any) -> bool: except Exception as _: value_is_truthy = False + if value_is_truthy: + return True + + # Value is still falsy here! + if field.default_factory is dict and isinstance(value, dict): + return False + + # Value is still falsy here! + if field.default_factory is list and isinstance(value, list): + return False + # Handle edge case: inequality of two objects does not evaluate to a bool (e.g. two # Pandas DataFrames). try: @@ -282,7 +316,8 @@ def _is_field_useful(inst: Serializable, key: str, value: Any) -> bool: except Exception as _: value_neq_default = False - return field.required is True or value_is_truthy or value_neq_default + # If value is falsy and does not match the default + return value_is_truthy or value_neq_default def _replace_secrets( diff --git a/libs/core/langchain_core/memory.py b/libs/core/langchain_core/memory.py index 743c68b68b6a1..cdbe5be656dde 100644 --- a/libs/core/langchain_core/memory.py +++ b/libs/core/langchain_core/memory.py @@ -13,6 +13,8 @@ from abc import ABC, abstractmethod from typing import Any, Dict, List +from pydantic import ConfigDict + from langchain_core.load.serializable import Serializable from langchain_core.runnables import run_in_executor @@ -47,8 +49,9 @@ def clear(self) -> None: pass """ # noqa: E501 - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) @property @abstractmethod diff --git a/libs/core/langchain_core/messages/ai.py b/libs/core/langchain_core/messages/ai.py index 51aeed441d728..b7c4cd9d92150 100644 --- a/libs/core/langchain_core/messages/ai.py +++ b/libs/core/langchain_core/messages/ai.py @@ -1,7 +1,8 @@ import json from typing import Any, Dict, List, Literal, Optional, Union -from typing_extensions import TypedDict +from pydantic import model_validator +from typing_extensions import Self, TypedDict from langchain_core.messages.base import ( BaseMessage, @@ -24,7 +25,6 @@ from langchain_core.messages.tool import ( tool_call_chunk as create_tool_call_chunk, ) -from langchain_core.pydantic_v1 import root_validator from langchain_core.utils._merge import merge_dicts, merge_lists from langchain_core.utils.json import parse_partial_json @@ -111,8 +111,9 @@ def lc_attributes(self) -> Dict: "invalid_tool_calls": self.invalid_tool_calls, } - @root_validator(pre=True) - def _backwards_compat_tool_calls(cls, values: dict) -> dict: + @model_validator(mode="before") + @classmethod + def _backwards_compat_tool_calls(cls, values: dict) -> Any: check_additional_kwargs = not any( values.get(k) for k in ("tool_calls", "invalid_tool_calls", "tool_call_chunks") @@ -204,7 +205,7 @@ def _format_tool_args(tc: Union[ToolCall, InvalidToolCall]) -> List[str]: return (base.strip() + "\n" + "\n".join(lines)).strip() -AIMessage.update_forward_refs() +AIMessage.model_rebuild() class AIMessageChunk(AIMessage, BaseMessageChunk): @@ -238,8 +239,8 @@ def lc_attributes(self) -> Dict: "invalid_tool_calls": self.invalid_tool_calls, } - @root_validator(pre=False, skip_on_failure=True) - def init_tool_calls(cls, values: dict) -> dict: + @model_validator(mode="after") + def init_tool_calls(self) -> Self: """Initialize tool calls from tool call chunks. Args: @@ -251,35 +252,35 @@ def init_tool_calls(cls, values: dict) -> dict: Raises: ValueError: If the tool call chunks are malformed. """ - if not values["tool_call_chunks"]: - if values["tool_calls"]: - values["tool_call_chunks"] = [ + if not self.tool_call_chunks: + if self.tool_calls: + self.tool_call_chunks = [ create_tool_call_chunk( name=tc["name"], args=json.dumps(tc["args"]), id=tc["id"], index=None, ) - for tc in values["tool_calls"] + for tc in self.tool_calls ] - if values["invalid_tool_calls"]: - tool_call_chunks = values.get("tool_call_chunks", []) + if self.invalid_tool_calls: + tool_call_chunks = self.tool_call_chunks tool_call_chunks.extend( [ create_tool_call_chunk( name=tc["name"], args=tc["args"], id=tc["id"], index=None ) - for tc in values["invalid_tool_calls"] + for tc in self.invalid_tool_calls ] ) - values["tool_call_chunks"] = tool_call_chunks + self.tool_call_chunks = tool_call_chunks - return values + return self tool_calls = [] invalid_tool_calls = [] - for chunk in values["tool_call_chunks"]: + for chunk in self.tool_call_chunks: try: - args_ = parse_partial_json(chunk["args"]) if chunk["args"] != "" else {} + args_ = parse_partial_json(chunk["args"]) if chunk["args"] != "" else {} # type: ignore[arg-type] if isinstance(args_, dict): tool_calls.append( create_tool_call( @@ -299,9 +300,9 @@ def init_tool_calls(cls, values: dict) -> dict: error=None, ) ) - values["tool_calls"] = tool_calls - values["invalid_tool_calls"] = invalid_tool_calls - return values + self.tool_calls = tool_calls + self.invalid_tool_calls = invalid_tool_calls + return self def __add__(self, other: Any) -> BaseMessageChunk: # type: ignore if isinstance(other, AIMessageChunk): diff --git a/libs/core/langchain_core/messages/base.py b/libs/core/langchain_core/messages/base.py index c0607f6d95758..2e0344265bc91 100644 --- a/libs/core/langchain_core/messages/base.py +++ b/libs/core/langchain_core/messages/base.py @@ -2,11 +2,13 @@ from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Union, cast +from pydantic import ConfigDict, Field + from langchain_core.load.serializable import Serializable -from langchain_core.pydantic_v1 import Extra, Field from langchain_core.utils import get_bolded_text from langchain_core.utils._merge import merge_dicts, merge_lists from langchain_core.utils.interactive_env import is_interactive_env +from langchain_core.utils.pydantic import v1_repr if TYPE_CHECKING: from langchain_core.prompts.chat import ChatPromptTemplate @@ -51,8 +53,9 @@ class BaseMessage(Serializable): """An optional unique identifier for the message. This should ideally be provided by the provider/model which created the message.""" - class Config: - extra = Extra.allow + model_config = ConfigDict( + extra="allow", + ) def __init__( self, content: Union[str, List[Union[str, Dict]]], **kwargs: Any @@ -108,6 +111,10 @@ def pretty_repr(self, html: bool = False) -> str: def pretty_print(self) -> None: print(self.pretty_repr(html=is_interactive_env())) # noqa: T201 + def __repr__(self) -> str: + # TODO(0.3): Remove this override after confirming unit tests! + return v1_repr(self) + def merge_content( first_content: Union[str, List[Union[str, Dict]]], diff --git a/libs/core/langchain_core/messages/chat.py b/libs/core/langchain_core/messages/chat.py index dbbe05a2b894f..7d6205239b18c 100644 --- a/libs/core/langchain_core/messages/chat.py +++ b/libs/core/langchain_core/messages/chat.py @@ -25,7 +25,7 @@ def get_lc_namespace(cls) -> List[str]: return ["langchain", "schema", "messages"] -ChatMessage.update_forward_refs() +ChatMessage.model_rebuild() class ChatMessageChunk(ChatMessage, BaseMessageChunk): diff --git a/libs/core/langchain_core/messages/function.py b/libs/core/langchain_core/messages/function.py index 5625a3214dadd..86b108d9ad822 100644 --- a/libs/core/langchain_core/messages/function.py +++ b/libs/core/langchain_core/messages/function.py @@ -32,7 +32,7 @@ def get_lc_namespace(cls) -> List[str]: return ["langchain", "schema", "messages"] -FunctionMessage.update_forward_refs() +FunctionMessage.model_rebuild() class FunctionMessageChunk(FunctionMessage, BaseMessageChunk): diff --git a/libs/core/langchain_core/messages/human.py b/libs/core/langchain_core/messages/human.py index b61c0616af15c..631d5052cb32a 100644 --- a/libs/core/langchain_core/messages/human.py +++ b/libs/core/langchain_core/messages/human.py @@ -56,7 +56,7 @@ def __init__( super().__init__(content=content, **kwargs) -HumanMessage.update_forward_refs() +HumanMessage.model_rebuild() class HumanMessageChunk(HumanMessage, BaseMessageChunk): diff --git a/libs/core/langchain_core/messages/modifier.py b/libs/core/langchain_core/messages/modifier.py index 66d6dd531d6d2..4e6f44f914ca6 100644 --- a/libs/core/langchain_core/messages/modifier.py +++ b/libs/core/langchain_core/messages/modifier.py @@ -33,4 +33,4 @@ def get_lc_namespace(cls) -> List[str]: return ["langchain", "schema", "messages"] -RemoveMessage.update_forward_refs() +RemoveMessage.model_rebuild() diff --git a/libs/core/langchain_core/messages/system.py b/libs/core/langchain_core/messages/system.py index 07bcbe64ddd48..7d94776a50cc5 100644 --- a/libs/core/langchain_core/messages/system.py +++ b/libs/core/langchain_core/messages/system.py @@ -50,7 +50,7 @@ def __init__( super().__init__(content=content, **kwargs) -SystemMessage.update_forward_refs() +SystemMessage.model_rebuild() class SystemMessageChunk(SystemMessage, BaseMessageChunk): diff --git a/libs/core/langchain_core/messages/tool.py b/libs/core/langchain_core/messages/tool.py index 61baa32de58c8..3e4dd3740b4af 100644 --- a/libs/core/langchain_core/messages/tool.py +++ b/libs/core/langchain_core/messages/tool.py @@ -1,6 +1,7 @@ import json from typing import Any, Dict, List, Literal, Optional, Tuple, Union +from pydantic import Field from typing_extensions import NotRequired, TypedDict from langchain_core.messages.base import BaseMessage, BaseMessageChunk, merge_content @@ -70,6 +71,11 @@ class ToolMessage(BaseMessage): .. versionadded:: 0.2.24 """ + additional_kwargs: dict = Field(default_factory=dict, repr=False) + """Currently inherited from BaseMessage, but not used.""" + response_metadata: dict = Field(default_factory=dict, repr=False) + """Currently inherited from BaseMessage, but not used.""" + @classmethod def get_lc_namespace(cls) -> List[str]: """Get the namespace of the langchain object. @@ -88,7 +94,7 @@ def __init__( super().__init__(content=content, **kwargs) -ToolMessage.update_forward_refs() +ToolMessage.model_rebuild() class ToolMessageChunk(ToolMessage, BaseMessageChunk): diff --git a/libs/core/langchain_core/messages/utils.py b/libs/core/langchain_core/messages/utils.py index eb0b7df86a222..8f5fffdf60621 100644 --- a/libs/core/langchain_core/messages/utils.py +++ b/libs/core/langchain_core/messages/utils.py @@ -52,6 +52,12 @@ SystemMessage, FunctionMessage, ToolMessage, + AIMessageChunk, + HumanMessageChunk, + ChatMessageChunk, + SystemMessageChunk, + FunctionMessageChunk, + ToolMessageChunk, ] diff --git a/libs/core/langchain_core/output_parsers/base.py b/libs/core/langchain_core/output_parsers/base.py index caac385b6bd87..6e1420d9b714c 100644 --- a/libs/core/langchain_core/output_parsers/base.py +++ b/libs/core/langchain_core/output_parsers/base.py @@ -13,8 +13,6 @@ Union, ) -from typing_extensions import get_args - from langchain_core.language_models import LanguageModelOutput from langchain_core.messages import AnyMessage, BaseMessage from langchain_core.outputs import ChatGeneration, Generation @@ -166,10 +164,11 @@ def OutputType(self) -> Type[T]: Raises: TypeError: If the class doesn't have an inferable OutputType. """ - for cls in self.__class__.__orig_bases__: # type: ignore[attr-defined] - type_args = get_args(cls) - if type_args and len(type_args) == 1: - return type_args[0] + for base in self.__class__.mro(): + if hasattr(base, "__pydantic_generic_metadata__"): + metadata = base.__pydantic_generic_metadata__ + if "args" in metadata and len(metadata["args"]) > 0: + return metadata["args"][0] raise TypeError( f"Runnable {self.__class__.__name__} doesn't have an inferable OutputType. " diff --git a/libs/core/langchain_core/output_parsers/json.py b/libs/core/langchain_core/output_parsers/json.py index 58a7090be704d..5f2732273475b 100644 --- a/libs/core/langchain_core/output_parsers/json.py +++ b/libs/core/langchain_core/output_parsers/json.py @@ -5,7 +5,9 @@ from typing import Any, List, Optional, Type, TypeVar, Union import jsonpatch # type: ignore[import] -import pydantic # pydantic: ignore +import pydantic +from pydantic import SkipValidation +from typing_extensions import Annotated from langchain_core.exceptions import OutputParserException from langchain_core.output_parsers.format_instructions import JSON_FORMAT_INSTRUCTIONS @@ -22,7 +24,7 @@ PydanticBaseModel = pydantic.BaseModel else: - from pydantic.v1 import BaseModel # pydantic: ignore + from pydantic.v1 import BaseModel # Union type needs to be last assignment to PydanticBaseModel to make mypy happy. PydanticBaseModel = Union[BaseModel, pydantic.BaseModel] # type: ignore @@ -40,7 +42,7 @@ class JsonOutputParser(BaseCumulativeTransformOutputParser[Any]): describing the difference between the previous and the current object. """ - pydantic_object: Optional[Type[TBaseModel]] = None # type: ignore + pydantic_object: Annotated[Optional[Type[TBaseModel]], SkipValidation()] = None # type: ignore """The Pydantic object to use for validation. If None, no validation is performed.""" diff --git a/libs/core/langchain_core/output_parsers/list.py b/libs/core/langchain_core/output_parsers/list.py index 34371946b6e3a..8227fa8c613c2 100644 --- a/libs/core/langchain_core/output_parsers/list.py +++ b/libs/core/langchain_core/output_parsers/list.py @@ -4,6 +4,7 @@ from abc import abstractmethod from collections import deque from typing import AsyncIterator, Deque, Iterator, List, TypeVar, Union +from typing import Optional as Optional from langchain_core.messages import BaseMessage from langchain_core.output_parsers.transform import BaseTransformOutputParser @@ -122,6 +123,9 @@ async def _atransform( yield [part] +ListOutputParser.model_rebuild() + + class CommaSeparatedListOutputParser(ListOutputParser): """Parse the output of an LLM call to a comma-separated list.""" diff --git a/libs/core/langchain_core/output_parsers/openai_functions.py b/libs/core/langchain_core/output_parsers/openai_functions.py index 73c13b0fabca5..ab706acfd8074 100644 --- a/libs/core/langchain_core/output_parsers/openai_functions.py +++ b/libs/core/langchain_core/output_parsers/openai_functions.py @@ -3,6 +3,7 @@ from typing import Any, Dict, List, Optional, Type, Union import jsonpatch # type: ignore[import] +from pydantic import BaseModel, model_validator from langchain_core.exceptions import OutputParserException from langchain_core.output_parsers import ( @@ -11,7 +12,6 @@ ) from langchain_core.output_parsers.json import parse_partial_json from langchain_core.outputs import ChatGeneration, Generation -from langchain_core.pydantic_v1 import BaseModel, root_validator class OutputFunctionsParser(BaseGenerationOutputParser[Any]): @@ -230,8 +230,9 @@ class Dog(BaseModel): determine which schema to use. """ - @root_validator(pre=True) - def validate_schema(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_schema(cls, values: Dict) -> Any: """Validate the pydantic schema. Args: @@ -267,11 +268,17 @@ def parse_result(self, result: List[Generation], *, partial: bool = False) -> An """ _result = super().parse_result(result) if self.args_only: - pydantic_args = self.pydantic_schema.parse_raw(_result) # type: ignore + if hasattr(self.pydantic_schema, "model_validate_json"): + pydantic_args = self.pydantic_schema.model_validate_json(_result) + else: + pydantic_args = self.pydantic_schema.parse_raw(_result) # type: ignore else: fn_name = _result["name"] _args = _result["arguments"] - pydantic_args = self.pydantic_schema[fn_name].parse_raw(_args) # type: ignore + if hasattr(self.pydantic_schema, "model_validate_json"): + pydantic_args = self.pydantic_schema[fn_name].model_validate_json(_args) # type: ignore + else: + pydantic_args = self.pydantic_schema[fn_name].parse_raw(_args) # type: ignore return pydantic_args diff --git a/libs/core/langchain_core/output_parsers/openai_tools.py b/libs/core/langchain_core/output_parsers/openai_tools.py index 5c533a3e0959b..9a803c3d8ccd4 100644 --- a/libs/core/langchain_core/output_parsers/openai_tools.py +++ b/libs/core/langchain_core/output_parsers/openai_tools.py @@ -3,13 +3,15 @@ from json import JSONDecodeError from typing import Any, Dict, List, Optional +from pydantic import SkipValidation, ValidationError +from typing_extensions import Annotated + from langchain_core.exceptions import OutputParserException from langchain_core.messages import AIMessage, InvalidToolCall from langchain_core.messages.tool import invalid_tool_call from langchain_core.messages.tool import tool_call as create_tool_call from langchain_core.output_parsers.transform import BaseCumulativeTransformOutputParser from langchain_core.outputs import ChatGeneration, Generation -from langchain_core.pydantic_v1 import ValidationError from langchain_core.utils.json import parse_partial_json from langchain_core.utils.pydantic import TypeBaseModel @@ -252,7 +254,7 @@ def parse_result(self, result: List[Generation], *, partial: bool = False) -> An class PydanticToolsParser(JsonOutputToolsParser): """Parse tools from OpenAI response.""" - tools: List[TypeBaseModel] + tools: Annotated[List[TypeBaseModel], SkipValidation()] """The tools to parse.""" # TODO: Support more granular streaming of objects. Currently only streams once all diff --git a/libs/core/langchain_core/output_parsers/pydantic.py b/libs/core/langchain_core/output_parsers/pydantic.py index 584784a41cf0c..e7ddae3ac5aef 100644 --- a/libs/core/langchain_core/output_parsers/pydantic.py +++ b/libs/core/langchain_core/output_parsers/pydantic.py @@ -1,7 +1,9 @@ import json from typing import Generic, List, Optional, Type -import pydantic # pydantic: ignore +import pydantic +from pydantic import SkipValidation +from typing_extensions import Annotated from langchain_core.exceptions import OutputParserException from langchain_core.output_parsers import JsonOutputParser @@ -16,7 +18,7 @@ class PydanticOutputParser(JsonOutputParser, Generic[TBaseModel]): """Parse an output using a pydantic model.""" - pydantic_object: Type[TBaseModel] # type: ignore + pydantic_object: Annotated[Type[TBaseModel], SkipValidation()] # type: ignore """The pydantic model to parse.""" def _parse_obj(self, obj: dict) -> TBaseModel: @@ -111,6 +113,9 @@ def OutputType(self) -> Type[TBaseModel]: return self.pydantic_object +PydanticOutputParser.model_rebuild() + + _PYDANTIC_FORMAT_INSTRUCTIONS = """The output should be formatted as a JSON instance that conforms to the JSON schema below. As an example, for the schema {{"properties": {{"foo": {{"title": "Foo", "description": "a list of strings", "type": "array", "items": {{"type": "string"}}}}}}, "required": ["foo"]}} diff --git a/libs/core/langchain_core/output_parsers/string.py b/libs/core/langchain_core/output_parsers/string.py index 12350bf0d84bb..ef231b893c9b7 100644 --- a/libs/core/langchain_core/output_parsers/string.py +++ b/libs/core/langchain_core/output_parsers/string.py @@ -1,4 +1,5 @@ from typing import List +from typing import Optional as Optional from langchain_core.output_parsers.transform import BaseTransformOutputParser @@ -24,3 +25,6 @@ def _type(self) -> str: def parse(self, text: str) -> str: """Returns the input text with no changes.""" return text + + +StrOutputParser.model_rebuild() diff --git a/libs/core/langchain_core/outputs/chat_generation.py b/libs/core/langchain_core/outputs/chat_generation.py index 740d856029071..d64015ed6a226 100644 --- a/libs/core/langchain_core/outputs/chat_generation.py +++ b/libs/core/langchain_core/outputs/chat_generation.py @@ -1,10 +1,12 @@ from __future__ import annotations -from typing import Any, Dict, List, Literal, Union +from typing import List, Literal, Union + +from pydantic import model_validator +from typing_extensions import Self from langchain_core.messages import BaseMessage, BaseMessageChunk from langchain_core.outputs.generation import Generation -from langchain_core.pydantic_v1 import root_validator from langchain_core.utils._merge import merge_dicts @@ -30,8 +32,8 @@ class ChatGeneration(Generation): type: Literal["ChatGeneration"] = "ChatGeneration" # type: ignore[assignment] """Type is used exclusively for serialization purposes.""" - @root_validator(pre=False, skip_on_failure=True) - def set_text(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="after") + def set_text(self) -> Self: """Set the text attribute to be the contents of the message. Args: @@ -45,12 +47,12 @@ def set_text(cls, values: Dict[str, Any]) -> Dict[str, Any]: """ try: text = "" - if isinstance(values["message"].content, str): - text = values["message"].content + if isinstance(self.message.content, str): + text = self.message.content # HACK: Assumes text in content blocks in OpenAI format. # Uses first text block. - elif isinstance(values["message"].content, list): - for block in values["message"].content: + elif isinstance(self.message.content, list): + for block in self.message.content: if isinstance(block, str): text = block break @@ -61,10 +63,10 @@ def set_text(cls, values: Dict[str, Any]) -> Dict[str, Any]: pass else: pass - values["text"] = text + self.text = text except (KeyError, AttributeError) as e: raise ValueError("Error while initializing ChatGeneration") from e - return values + return self @classmethod def get_lc_namespace(cls) -> List[str]: diff --git a/libs/core/langchain_core/outputs/chat_result.py b/libs/core/langchain_core/outputs/chat_result.py index 2895c2a5cf604..511b13bae3c1c 100644 --- a/libs/core/langchain_core/outputs/chat_result.py +++ b/libs/core/langchain_core/outputs/chat_result.py @@ -1,7 +1,8 @@ from typing import List, Optional +from pydantic import BaseModel + from langchain_core.outputs.chat_generation import ChatGeneration -from langchain_core.pydantic_v1 import BaseModel class ChatResult(BaseModel): diff --git a/libs/core/langchain_core/outputs/llm_result.py b/libs/core/langchain_core/outputs/llm_result.py index ce4b3cd1132b4..ae6757c5173b6 100644 --- a/libs/core/langchain_core/outputs/llm_result.py +++ b/libs/core/langchain_core/outputs/llm_result.py @@ -1,11 +1,13 @@ from __future__ import annotations from copy import deepcopy -from typing import List, Optional +from typing import List, Optional, Union -from langchain_core.outputs.generation import Generation +from pydantic import BaseModel + +from langchain_core.outputs.chat_generation import ChatGeneration, ChatGenerationChunk +from langchain_core.outputs.generation import Generation, GenerationChunk from langchain_core.outputs.run_info import RunInfo -from langchain_core.pydantic_v1 import BaseModel class LLMResult(BaseModel): @@ -16,7 +18,9 @@ class LLMResult(BaseModel): wants to return. """ - generations: List[List[Generation]] + generations: List[ + List[Union[Generation, ChatGeneration, GenerationChunk, ChatGenerationChunk]] + ] """Generated outputs. The first dimension of the list represents completions for different input diff --git a/libs/core/langchain_core/outputs/run_info.py b/libs/core/langchain_core/outputs/run_info.py index a54e3b49cfc1c..4b20c9bd701cf 100644 --- a/libs/core/langchain_core/outputs/run_info.py +++ b/libs/core/langchain_core/outputs/run_info.py @@ -2,7 +2,7 @@ from uuid import UUID -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel class RunInfo(BaseModel): diff --git a/libs/core/langchain_core/prompts/base.py b/libs/core/langchain_core/prompts/base.py index 5b4951e6d1a4a..d565efb1ffe51 100644 --- a/libs/core/langchain_core/prompts/base.py +++ b/libs/core/langchain_core/prompts/base.py @@ -18,6 +18,8 @@ ) import yaml +from pydantic import BaseModel, ConfigDict, Field, model_validator +from typing_extensions import Self from langchain_core.output_parsers.base import BaseOutputParser from langchain_core.prompt_values import ( @@ -25,7 +27,6 @@ PromptValue, StringPromptValue, ) -from langchain_core.pydantic_v1 import BaseModel, Field, root_validator from langchain_core.runnables import RunnableConfig, RunnableSerializable from langchain_core.runnables.config import ensure_config from langchain_core.runnables.utils import create_model @@ -64,28 +65,26 @@ class BasePromptTemplate( tags: Optional[List[str]] = None """Tags to be used for tracing.""" - @root_validator(pre=False, skip_on_failure=True) - def validate_variable_names(cls, values: Dict) -> Dict: + @model_validator(mode="after") + def validate_variable_names(self) -> Self: """Validate variable names do not include restricted names.""" - if "stop" in values["input_variables"]: + if "stop" in self.input_variables: raise ValueError( "Cannot have an input variable named 'stop', as it is used internally," " please rename." ) - if "stop" in values["partial_variables"]: + if "stop" in self.partial_variables: raise ValueError( "Cannot have an partial variable named 'stop', as it is used " "internally, please rename." ) - overall = set(values["input_variables"]).intersection( - values["partial_variables"] - ) + overall = set(self.input_variables).intersection(self.partial_variables) if overall: raise ValueError( f"Found overlapping input and partial variables: {overall}" ) - return values + return self @classmethod def get_lc_namespace(cls) -> List[str]: @@ -99,8 +98,9 @@ def is_lc_serializable(cls) -> bool: Returns True.""" return True - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) @property def OutputType(self) -> Any: diff --git a/libs/core/langchain_core/prompts/chat.py b/libs/core/langchain_core/prompts/chat.py index 4041964f04b33..0c3ec402cb360 100644 --- a/libs/core/langchain_core/prompts/chat.py +++ b/libs/core/langchain_core/prompts/chat.py @@ -21,6 +21,14 @@ overload, ) +from pydantic import ( + Field, + PositiveInt, + SkipValidation, + model_validator, +) +from typing_extensions import Annotated + from langchain_core._api import deprecated from langchain_core.load import Serializable from langchain_core.messages import ( @@ -38,7 +46,6 @@ from langchain_core.prompts.image import ImagePromptTemplate from langchain_core.prompts.prompt import PromptTemplate from langchain_core.prompts.string import StringPromptTemplate, get_template_variables -from langchain_core.pydantic_v1 import Field, PositiveInt, root_validator from langchain_core.utils import get_colored_text from langchain_core.utils.interactive_env import is_interactive_env @@ -207,8 +214,14 @@ def get_lc_namespace(cls) -> List[str]: """Get the namespace of the langchain object.""" return ["langchain", "prompts", "chat"] - def __init__(self, variable_name: str, *, optional: bool = False, **kwargs: Any): - super().__init__(variable_name=variable_name, optional=optional, **kwargs) + def __init__( + self, variable_name: str, *, optional: bool = False, **kwargs: Any + ) -> None: + # mypy can't detect the init which is defined in the parent class + # b/c these are BaseModel classes. + super().__init__( # type: ignore + variable_name=variable_name, optional=optional, **kwargs + ) def format_messages(self, **kwargs: Any) -> List[BaseMessage]: """Format messages from kwargs. @@ -922,7 +935,7 @@ class ChatPromptTemplate(BaseChatPromptTemplate): """ # noqa: E501 - messages: List[MessageLike] + messages: Annotated[List[MessageLike], SkipValidation()] """List of messages consisting of either message prompt templates or messages.""" validate_template: bool = False """Whether or not to try validating the template.""" @@ -1038,8 +1051,9 @@ def __add__(self, other: Any) -> ChatPromptTemplate: else: raise NotImplementedError(f"Unsupported operand type for +: {type(other)}") - @root_validator(pre=True) - def validate_input_variables(cls, values: dict) -> dict: + @model_validator(mode="before") + @classmethod + def validate_input_variables(cls, values: dict) -> Any: """Validate input variables. If input_variables is not set, it will be set to the union of diff --git a/libs/core/langchain_core/prompts/few_shot.py b/libs/core/langchain_core/prompts/few_shot.py index 934ac88a81a45..a28c87cf0c8a0 100644 --- a/libs/core/langchain_core/prompts/few_shot.py +++ b/libs/core/langchain_core/prompts/few_shot.py @@ -5,6 +5,14 @@ from pathlib import Path from typing import Any, Dict, List, Literal, Optional, Union +from pydantic import ( + BaseModel, + ConfigDict, + Field, + model_validator, +) +from typing_extensions import Self + from langchain_core.example_selectors import BaseExampleSelector from langchain_core.messages import BaseMessage, get_buffer_string from langchain_core.prompts.chat import ( @@ -18,7 +26,6 @@ check_valid_template, get_template_variables, ) -from langchain_core.pydantic_v1 import BaseModel, Extra, Field, root_validator class _FewShotPromptTemplateMixin(BaseModel): @@ -32,12 +39,14 @@ class _FewShotPromptTemplateMixin(BaseModel): """ExampleSelector to choose the examples to format into the prompt. Either this or examples should be provided.""" - class Config: - arbitrary_types_allowed = True - extra = Extra.forbid + model_config = ConfigDict( + arbitrary_types_allowed=True, + extra="forbid", + ) - @root_validator(pre=True) - def check_examples_and_selector(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def check_examples_and_selector(cls, values: Dict) -> Any: """Check that one and only one of examples/example_selector are provided. Args: @@ -139,28 +148,29 @@ def __init__(self, **kwargs: Any) -> None: kwargs["input_variables"] = kwargs["example_prompt"].input_variables super().__init__(**kwargs) - @root_validator(pre=False, skip_on_failure=True) - def template_is_valid(cls, values: Dict) -> Dict: + @model_validator(mode="after") + def template_is_valid(self) -> Self: """Check that prefix, suffix, and input variables are consistent.""" - if values["validate_template"]: + if self.validate_template: check_valid_template( - values["prefix"] + values["suffix"], - values["template_format"], - values["input_variables"] + list(values["partial_variables"]), + self.prefix + self.suffix, + self.template_format, + self.input_variables + list(self.partial_variables), ) - elif values.get("template_format"): - values["input_variables"] = [ + elif self.template_format or None: + self.input_variables = [ var for var in get_template_variables( - values["prefix"] + values["suffix"], values["template_format"] + self.prefix + self.suffix, self.template_format ) - if var not in values["partial_variables"] + if var not in self.partial_variables ] - return values + return self - class Config: - arbitrary_types_allowed = True - extra = Extra.forbid + model_config = ConfigDict( + arbitrary_types_allowed=True, + extra="forbid", + ) def format(self, **kwargs: Any) -> str: """Format the prompt with inputs generating a string. @@ -365,9 +375,10 @@ def is_lc_serializable(cls) -> bool: """Return whether or not the class is serializable.""" return False - class Config: - arbitrary_types_allowed = True - extra = Extra.forbid + model_config = ConfigDict( + arbitrary_types_allowed=True, + extra="forbid", + ) def format_messages(self, **kwargs: Any) -> List[BaseMessage]: """Format kwargs into a list of messages. diff --git a/libs/core/langchain_core/prompts/few_shot_with_templates.py b/libs/core/langchain_core/prompts/few_shot_with_templates.py index 6b98694d44bcd..075eaac85182a 100644 --- a/libs/core/langchain_core/prompts/few_shot_with_templates.py +++ b/libs/core/langchain_core/prompts/few_shot_with_templates.py @@ -3,12 +3,14 @@ from pathlib import Path from typing import Any, Dict, List, Optional, Union +from pydantic import ConfigDict, model_validator +from typing_extensions import Self + from langchain_core.prompts.prompt import PromptTemplate from langchain_core.prompts.string import ( DEFAULT_FORMATTER_MAPPING, StringPromptTemplate, ) -from langchain_core.pydantic_v1 import Extra, root_validator class FewShotPromptWithTemplates(StringPromptTemplate): @@ -45,8 +47,9 @@ def get_lc_namespace(cls) -> List[str]: """Get the namespace of the langchain object.""" return ["langchain", "prompts", "few_shot_with_templates"] - @root_validator(pre=True) - def check_examples_and_selector(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def check_examples_and_selector(cls, values: Dict) -> Any: """Check that one and only one of examples/example_selector are provided.""" examples = values.get("examples", None) example_selector = values.get("example_selector", None) @@ -62,15 +65,15 @@ def check_examples_and_selector(cls, values: Dict) -> Dict: return values - @root_validator(pre=False, skip_on_failure=True) - def template_is_valid(cls, values: Dict) -> Dict: + @model_validator(mode="after") + def template_is_valid(self) -> Self: """Check that prefix, suffix, and input variables are consistent.""" - if values["validate_template"]: - input_variables = values["input_variables"] - expected_input_variables = set(values["suffix"].input_variables) - expected_input_variables |= set(values["partial_variables"]) - if values["prefix"] is not None: - expected_input_variables |= set(values["prefix"].input_variables) + if self.validate_template: + input_variables = self.input_variables + expected_input_variables = set(self.suffix.input_variables) + expected_input_variables |= set(self.partial_variables) + if self.prefix is not None: + expected_input_variables |= set(self.prefix.input_variables) missing_vars = expected_input_variables.difference(input_variables) if missing_vars: raise ValueError( @@ -78,16 +81,17 @@ def template_is_valid(cls, values: Dict) -> Dict: f"prefix/suffix expected {expected_input_variables}" ) else: - values["input_variables"] = sorted( - set(values["suffix"].input_variables) - | set(values["prefix"].input_variables if values["prefix"] else []) - - set(values["partial_variables"]) + self.input_variables = sorted( + set(self.suffix.input_variables) + | set(self.prefix.input_variables if self.prefix else []) + - set(self.partial_variables) ) - return values + return self - class Config: - arbitrary_types_allowed = True - extra = Extra.forbid + model_config = ConfigDict( + arbitrary_types_allowed=True, + extra="forbid", + ) def _get_examples(self, **kwargs: Any) -> List[dict]: if self.examples is not None: diff --git a/libs/core/langchain_core/prompts/image.py b/libs/core/langchain_core/prompts/image.py index cc0bebfc5b4d1..fdad76cc92bde 100644 --- a/libs/core/langchain_core/prompts/image.py +++ b/libs/core/langchain_core/prompts/image.py @@ -1,8 +1,9 @@ from typing import Any, List +from pydantic import Field + from langchain_core.prompt_values import ImagePromptValue, ImageURL, PromptValue from langchain_core.prompts.base import BasePromptTemplate -from langchain_core.pydantic_v1 import Field from langchain_core.runnables import run_in_executor from langchain_core.utils import image as image_utils diff --git a/libs/core/langchain_core/prompts/pipeline.py b/libs/core/langchain_core/prompts/pipeline.py index 49d3c9664343c..c1ddc815f32e2 100644 --- a/libs/core/langchain_core/prompts/pipeline.py +++ b/libs/core/langchain_core/prompts/pipeline.py @@ -1,9 +1,11 @@ from typing import Any, Dict, List, Tuple +from typing import Optional as Optional + +from pydantic import model_validator from langchain_core.prompt_values import PromptValue from langchain_core.prompts.base import BasePromptTemplate from langchain_core.prompts.chat import BaseChatPromptTemplate -from langchain_core.pydantic_v1 import root_validator def _get_inputs(inputs: dict, input_variables: List[str]) -> dict: @@ -34,8 +36,9 @@ def get_lc_namespace(cls) -> List[str]: """Get the namespace of the langchain object.""" return ["langchain", "prompts", "pipeline"] - @root_validator(pre=True) - def get_input_variables(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def get_input_variables(cls, values: Dict) -> Any: """Get input variables.""" created_variables = set() all_variables = set() @@ -106,3 +109,6 @@ async def aformat(self, **kwargs: Any) -> str: @property def _prompt_type(self) -> str: raise ValueError + + +PipelinePromptTemplate.model_rebuild() diff --git a/libs/core/langchain_core/prompts/prompt.py b/libs/core/langchain_core/prompts/prompt.py index 6b86b54566b80..9824721210fff 100644 --- a/libs/core/langchain_core/prompts/prompt.py +++ b/libs/core/langchain_core/prompts/prompt.py @@ -6,6 +6,8 @@ from pathlib import Path from typing import Any, Dict, List, Literal, Optional, Union +from pydantic import BaseModel, model_validator + from langchain_core.prompts.string import ( DEFAULT_FORMATTER_MAPPING, StringPromptTemplate, @@ -13,7 +15,6 @@ get_template_variables, mustache_schema, ) -from langchain_core.pydantic_v1 import BaseModel, root_validator from langchain_core.runnables.config import RunnableConfig @@ -73,8 +74,9 @@ def get_lc_namespace(cls) -> List[str]: validate_template: bool = False """Whether or not to try validating the template.""" - @root_validator(pre=True) - def pre_init_validation(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def pre_init_validation(cls, values: Dict) -> Any: """Check that template and input variables are consistent.""" if values.get("template") is None: # Will let pydantic fail with a ValidationError if template diff --git a/libs/core/langchain_core/prompts/string.py b/libs/core/langchain_core/prompts/string.py index b2377512c5402..2c08549d0bd71 100644 --- a/libs/core/langchain_core/prompts/string.py +++ b/libs/core/langchain_core/prompts/string.py @@ -7,10 +7,11 @@ from string import Formatter from typing import Any, Callable, Dict, List, Set, Tuple, Type +from pydantic import BaseModel, create_model + import langchain_core.utils.mustache as mustache from langchain_core.prompt_values import PromptValue, StringPromptValue from langchain_core.prompts.base import BasePromptTemplate -from langchain_core.pydantic_v1 import BaseModel, create_model from langchain_core.utils import get_colored_text from langchain_core.utils.formatting import formatter from langchain_core.utils.interactive_env import is_interactive_env diff --git a/libs/core/langchain_core/prompts/structured.py b/libs/core/langchain_core/prompts/structured.py index 8ccb17733853b..12114e80338b4 100644 --- a/libs/core/langchain_core/prompts/structured.py +++ b/libs/core/langchain_core/prompts/structured.py @@ -11,13 +11,14 @@ Union, ) +from pydantic import BaseModel, Field + from langchain_core._api.beta_decorator import beta from langchain_core.language_models.base import BaseLanguageModel from langchain_core.prompts.chat import ( ChatPromptTemplate, MessageLikeRepresentation, ) -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.runnables.base import ( Other, Runnable, diff --git a/libs/core/langchain_core/retrievers.py b/libs/core/langchain_core/retrievers.py index 2e51a95897961..438377948f5f1 100644 --- a/libs/core/langchain_core/retrievers.py +++ b/libs/core/langchain_core/retrievers.py @@ -26,6 +26,7 @@ from inspect import signature from typing import TYPE_CHECKING, Any, Dict, List, Optional +from pydantic import ConfigDict from typing_extensions import TypedDict from langchain_core._api import deprecated @@ -126,8 +127,9 @@ def _get_relevant_documents(self, query: str) -> List[Document]: return [self.docs[i] for i in results.argsort()[-self.k :][::-1]] """ # noqa: E501 - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) _new_arg_supported: bool = False _expects_other_args: bool = False diff --git a/libs/core/langchain_core/runnables/base.py b/libs/core/langchain_core/runnables/base.py index 21ec7caad386c..444b191c2f55d 100644 --- a/libs/core/langchain_core/runnables/base.py +++ b/libs/core/langchain_core/runnables/base.py @@ -35,7 +35,8 @@ overload, ) -from typing_extensions import Literal, get_args +from pydantic import BaseModel, ConfigDict, Field, RootModel +from typing_extensions import Literal, get_args, get_type_hints from langchain_core._api import beta_decorator from langchain_core.load.dump import dumpd @@ -44,7 +45,6 @@ SerializedConstructor, SerializedNotImplemented, ) -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.runnables.config import ( RunnableConfig, _set_config_context, @@ -83,7 +83,6 @@ ) from langchain_core.utils.aiter import aclosing, atee, py_anext from langchain_core.utils.iter import safetee -from langchain_core.utils.pydantic import is_basemodel_subclass if TYPE_CHECKING: from langchain_core.callbacks.manager import ( @@ -236,25 +235,58 @@ def buggy_double(y: int) -> int: For a UI (and much more) checkout LangSmith: https://docs.smith.langchain.com/ """ # noqa: E501 - name: Optional[str] = None + name: Optional[str] """The name of the Runnable. Used for debugging and tracing.""" def get_name( self, suffix: Optional[str] = None, *, name: Optional[str] = None ) -> str: """Get the name of the Runnable.""" - name = name or self.name or self.__class__.__name__ + if name: + name_ = name + elif hasattr(self, "name") and self.name: + name_ = self.name + else: + # Here we handle a case where the runnable subclass is also a pydantic + # model. + cls = self.__class__ + # Then it's a pydantic sub-class, and we have to check + # whether it's a generic, and if so recover the original name. + if ( + hasattr( + cls, + "__pydantic_generic_metadata__", + ) + and "origin" in cls.__pydantic_generic_metadata__ + and cls.__pydantic_generic_metadata__["origin"] is not None + ): + name_ = cls.__pydantic_generic_metadata__["origin"].__name__ + else: + name_ = cls.__name__ + if suffix: - if name[0].isupper(): - return name + suffix.title() + if name_[0].isupper(): + return name_ + suffix.title() else: - return name + "_" + suffix.lower() + return name_ + "_" + suffix.lower() else: - return name + return name_ @property def InputType(self) -> Type[Input]: """The type of input this Runnable accepts specified as a type annotation.""" + # First loop through all parent classes and if any of them is + # a pydantic model, we will pick up the generic parameterization + # from that model via the __pydantic_generic_metadata__ attribute. + for base in self.__class__.mro(): + if hasattr(base, "__pydantic_generic_metadata__"): + metadata = base.__pydantic_generic_metadata__ + if "args" in metadata and len(metadata["args"]) == 2: + return metadata["args"][0] + + # If we didn't find a pydantic model in the parent classes, + # then loop through __orig_bases__. This corresponds to + # Runnables that are not pydantic models. for cls in self.__class__.__orig_bases__: # type: ignore[attr-defined] type_args = get_args(cls) if type_args and len(type_args) == 2: @@ -268,6 +300,14 @@ def InputType(self) -> Type[Input]: @property def OutputType(self) -> Type[Output]: """The type of output this Runnable produces specified as a type annotation.""" + # First loop through bases -- this will help generic + # any pydantic models. + for base in self.__class__.mro(): + if hasattr(base, "__pydantic_generic_metadata__"): + metadata = base.__pydantic_generic_metadata__ + if "args" in metadata and len(metadata["args"]) == 2: + return metadata["args"][1] + for cls in self.__class__.__orig_bases__: # type: ignore[attr-defined] type_args = get_args(cls) if type_args and len(type_args) == 2: @@ -302,14 +342,42 @@ def get_input_schema( """ root_type = self.InputType - if inspect.isclass(root_type) and is_basemodel_subclass(root_type): + if inspect.isclass(root_type) and issubclass(root_type, BaseModel): return root_type return create_model( self.get_name("Input"), - __root__=(root_type, None), + __root__=root_type, ) + def get_input_jsonschema( + self, config: Optional[RunnableConfig] = None + ) -> Dict[str, Any]: + """Get a JSON schema that represents the input to the Runnable. + + Args: + config: A config to use when generating the schema. + + Returns: + A JSON schema that represents the input to the Runnable. + + Example: + + .. code-block:: python + + from langchain_core.runnables import RunnableLambda + + def add_one(x: int) -> int: + return x + 1 + + runnable = RunnableLambda(add_one) + + print(runnable.get_input_jsonschema()) + + .. versionadded:: 0.3.0 + """ + return self.get_input_schema(config).model_json_schema() + @property def output_schema(self) -> Type[BaseModel]: """The type of output this Runnable produces specified as a pydantic model.""" @@ -334,14 +402,42 @@ def get_output_schema( """ root_type = self.OutputType - if inspect.isclass(root_type) and is_basemodel_subclass(root_type): + if inspect.isclass(root_type) and issubclass(root_type, BaseModel): return root_type return create_model( self.get_name("Output"), - __root__=(root_type, None), + __root__=root_type, ) + def get_output_jsonschema( + self, config: Optional[RunnableConfig] = None + ) -> Dict[str, Any]: + """Get a JSON schema that represents the output of the Runnable. + + Args: + config: A config to use when generating the schema. + + Returns: + A JSON schema that represents the output of the Runnable. + + Example: + + .. code-block:: python + + from langchain_core.runnables import RunnableLambda + + def add_one(x: int) -> int: + return x + 1 + + runnable = RunnableLambda(add_one) + + print(runnable.get_output_jsonschema()) + + .. versionadded:: 0.3.0 + """ + return self.get_output_schema(config).model_json_schema() + @property def config_specs(self) -> List[ConfigurableFieldSpec]: """List configurable fields for this Runnable.""" @@ -381,15 +477,34 @@ def config_schema( else None ) - return create_model( # type: ignore[call-overload] - self.get_name("Config"), + # Many need to create a typed dict instead to implement NotRequired! + all_fields = { **({"configurable": (configurable, None)} if configurable else {}), **{ field_name: (field_type, None) - for field_name, field_type in RunnableConfig.__annotations__.items() + for field_name, field_type in get_type_hints(RunnableConfig).items() if field_name in [i for i in include if i != "configurable"] }, + } + model = create_model( # type: ignore[call-overload] + self.get_name("Config"), **all_fields ) + return model + + def get_config_jsonschema( + self, *, include: Optional[Sequence[str]] = None + ) -> Dict[str, Any]: + """Get a JSON schema that represents the output of the Runnable. + + Args: + include: A list of fields to include in the config schema. + + Returns: + A JSON schema that represents the output of the Runnable. + + .. versionadded:: 0.3.0 + """ + return self.config_schema(include=include).model_json_schema() def get_graph(self, config: Optional[RunnableConfig] = None) -> Graph: """Return a graph representation of this Runnable.""" @@ -579,7 +694,7 @@ def assign( """ from langchain_core.runnables.passthrough import RunnableAssign - return self | RunnableAssign(RunnableParallel(kwargs)) + return self | RunnableAssign(RunnableParallel[Dict[str, Any]](kwargs)) """ --- Public API --- """ @@ -2129,7 +2244,6 @@ async def _atransform_stream_with_config( name=config.get("run_name") or self.get_name(), run_id=config.pop("run_id", None), ) - iterator_ = None try: child_config = patch_config(config, callbacks=run_manager.get_child()) if accepts_config(transformer): @@ -2314,7 +2428,12 @@ class RunnableSerializable(Serializable, Runnable[Input, Output]): """Runnable that can be serialized to JSON.""" name: Optional[str] = None - """The name of the Runnable. Used for debugging and tracing.""" + + model_config = ConfigDict( + # Suppress warnings from pydantic protected namespaces + # (e.g., `model_`) + protected_namespaces=(), + ) def to_json(self) -> Union[SerializedConstructor, SerializedNotImplemented]: """Serialize the Runnable to JSON. @@ -2369,10 +2488,10 @@ def configurable_fields( from langchain_core.runnables.configurable import RunnableConfigurableFields for key in kwargs: - if key not in self.__fields__: + if key not in self.model_fields: raise ValueError( f"Configuration key {key} not found in {self}: " - f"available keys are {self.__fields__.keys()}" + f"available keys are {self.model_fields.keys()}" ) return RunnableConfigurableFields(default=self, fields=kwargs) @@ -2447,13 +2566,13 @@ def _seq_input_schema( return first.get_input_schema(config) elif isinstance(first, RunnableAssign): next_input_schema = _seq_input_schema(steps[1:], config) - if not next_input_schema.__custom_root_type__: + if not issubclass(next_input_schema, RootModel): # it's a dict as expected return create_model( # type: ignore[call-overload] "RunnableSequenceInput", **{ k: (v.annotation, v.default) - for k, v in next_input_schema.__fields__.items() + for k, v in next_input_schema.model_fields.items() if k not in first.mapper.steps__ }, ) @@ -2474,36 +2593,36 @@ def _seq_output_schema( elif isinstance(last, RunnableAssign): mapper_output_schema = last.mapper.get_output_schema(config) prev_output_schema = _seq_output_schema(steps[:-1], config) - if not prev_output_schema.__custom_root_type__: + if not issubclass(prev_output_schema, RootModel): # it's a dict as expected return create_model( # type: ignore[call-overload] "RunnableSequenceOutput", **{ **{ k: (v.annotation, v.default) - for k, v in prev_output_schema.__fields__.items() + for k, v in prev_output_schema.model_fields.items() }, **{ k: (v.annotation, v.default) - for k, v in mapper_output_schema.__fields__.items() + for k, v in mapper_output_schema.model_fields.items() }, }, ) elif isinstance(last, RunnablePick): prev_output_schema = _seq_output_schema(steps[:-1], config) - if not prev_output_schema.__custom_root_type__: + if not issubclass(prev_output_schema, RootModel): # it's a dict as expected if isinstance(last.keys, list): return create_model( # type: ignore[call-overload] "RunnableSequenceOutput", **{ k: (v.annotation, v.default) - for k, v in prev_output_schema.__fields__.items() + for k, v in prev_output_schema.model_fields.items() if k in last.keys }, ) else: - field = prev_output_schema.__fields__[last.keys] + field = prev_output_schema.model_fields[last.keys] return create_model( # type: ignore[call-overload] "RunnableSequenceOutput", __root__=(field.annotation, field.default), @@ -2665,8 +2784,9 @@ def is_lc_serializable(cls) -> bool: """ return True - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) @property def InputType(self) -> Type[Input]: @@ -3402,8 +3522,9 @@ def get_lc_namespace(cls) -> List[str]: """Get the namespace of the langchain object.""" return ["langchain", "schema", "runnable"] - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) def get_name( self, suffix: Optional[str] = None, *, name: Optional[str] = None @@ -3450,7 +3571,7 @@ def get_input_schema( **{ k: (v.annotation, v.default) for step in self.steps__.values() - for k, v in step.get_input_schema(config).__fields__.items() + for k, v in step.get_input_schema(config).model_fields.items() if k != "__root__" }, ) @@ -3468,11 +3589,8 @@ def get_output_schema( Returns: The output schema of the Runnable. """ - # This is correct, but pydantic typings/mypy don't think so. - return create_model( # type: ignore[call-overload] - self.get_name("Output"), - **{k: (v.OutputType, None) for k, v in self.steps__.items()}, - ) + fields = {k: (v.OutputType, ...) for k, v in self.steps__.items()} + return create_model(self.get_name("Output"), **fields) @property def config_specs(self) -> List[ConfigurableFieldSpec]: @@ -3882,6 +4000,8 @@ def __init__( atransform: Optional[ Callable[[AsyncIterator[Input]], AsyncIterator[Output]] ] = None, + *, + name: Optional[str] = None, ) -> None: """Initialize a RunnableGenerator. @@ -3909,9 +4029,9 @@ def __init__( ) try: - self.name = func_for_name.__name__ + self.name = name or func_for_name.__name__ except AttributeError: - pass + self.name = "RunnableGenerator" @property def InputType(self) -> Any: @@ -4183,15 +4303,13 @@ def get_input_schema( if all( item[0] == "'" and item[-1] == "'" and len(item) > 2 for item in items ): + fields = {item[1:-1]: (Any, ...) for item in items} # It's a dict, lol - return create_model( - self.get_name("Input"), - **{item[1:-1]: (Any, None) for item in items}, # type: ignore - ) + return create_model(self.get_name("Input"), **fields) else: return create_model( self.get_name("Input"), - __root__=(List[Any], None), + __root__=List[Any], ) if self.InputType != Any: @@ -4200,7 +4318,7 @@ def get_input_schema( if dict_keys := get_function_first_arg_dict_keys(func): return create_model( self.get_name("Input"), - **{key: (Any, None) for key in dict_keys}, # type: ignore + **{key: (Any, ...) for key in dict_keys}, # type: ignore ) return super().get_input_schema(config) @@ -4728,8 +4846,9 @@ class RunnableEachBase(RunnableSerializable[List[Input], List[Output]]): bound: Runnable[Input, Output] - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) @property def InputType(self) -> Any: @@ -4756,10 +4875,7 @@ def get_output_schema( schema = self.bound.get_output_schema(config) return create_model( self.get_name("Output"), - __root__=( - List[schema], # type: ignore - None, - ), + __root__=List[schema], # type: ignore[valid-type] ) @property @@ -4979,8 +5095,9 @@ class RunnableBindingBase(RunnableSerializable[Input, Output]): The type can be a pydantic model, or a type annotation (e.g., `List[str]`). """ - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) def __init__( self, @@ -5316,7 +5433,7 @@ async def atransform( yield item -RunnableBindingBase.update_forward_refs(RunnableConfig=RunnableConfig) +RunnableBindingBase.model_rebuild() class RunnableBinding(RunnableBindingBase[Input, Output]): diff --git a/libs/core/langchain_core/runnables/branch.py b/libs/core/langchain_core/runnables/branch.py index 9b52d04091b5f..6aba17437ca7b 100644 --- a/libs/core/langchain_core/runnables/branch.py +++ b/libs/core/langchain_core/runnables/branch.py @@ -14,8 +14,9 @@ cast, ) +from pydantic import BaseModel, ConfigDict + from langchain_core.load.dump import dumpd -from langchain_core.pydantic_v1 import BaseModel from langchain_core.runnables.base import ( Runnable, RunnableLike, @@ -134,10 +135,21 @@ def __init__( runnable = coerce_to_runnable(runnable) _branches.append((condition, runnable)) - super().__init__(branches=_branches, default=default_) # type: ignore[call-arg] - - class Config: - arbitrary_types_allowed = True + super().__init__( + branches=_branches, + default=default_, + # Hard-coding a name here because RunnableBranch is a generic + # and with pydantic 2, the class name with pydantic will capture + # include the parameterized type, which is not what we want. + # e.g., we'd get RunnableBranch[Input, Output] instead of RunnableBranch + # for the name. This information is already captured in the + # input and output types. + name="RunnableBranch", + ) # type: ignore[call-arg] + + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) @classmethod def is_lc_serializable(cls) -> bool: diff --git a/libs/core/langchain_core/runnables/configurable.py b/libs/core/langchain_core/runnables/configurable.py index cf7e9ce1dc7bc..bc332475d63a1 100644 --- a/libs/core/langchain_core/runnables/configurable.py +++ b/libs/core/langchain_core/runnables/configurable.py @@ -20,7 +20,8 @@ ) from weakref import WeakValueDictionary -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel, ConfigDict + from langchain_core.runnables.base import Runnable, RunnableSerializable from langchain_core.runnables.config import ( RunnableConfig, @@ -58,8 +59,9 @@ class DynamicRunnable(RunnableSerializable[Input, Output]): config: Optional[RunnableConfig] = None - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) @classmethod def is_lc_serializable(cls) -> bool: @@ -373,28 +375,33 @@ def config_specs(self) -> List[ConfigurableFieldSpec]: Returns: List[ConfigurableFieldSpec]: The configuration specs. """ - return get_unique_config_specs( - [ - ( + # TODO(0.3): This change removes field_info which isn't needed in pydantic 2 + config_specs = [] + + for field_name, spec in self.fields.items(): + if isinstance(spec, ConfigurableField): + config_specs.append( ConfigurableFieldSpec( id=spec.id, name=spec.name, description=spec.description - or self.default.__fields__[field_name].field_info.description, + or self.default.model_fields[field_name].description, annotation=spec.annotation - or self.default.__fields__[field_name].annotation, + or self.default.model_fields[field_name].annotation, default=getattr(self.default, field_name), is_shared=spec.is_shared, ) - if isinstance(spec, ConfigurableField) - else make_options_spec( - spec, self.default.__fields__[field_name].field_info.description + ) + else: + config_specs.append( + make_options_spec( + spec, self.default.model_fields[field_name].description ) ) - for field_name, spec in self.fields.items() - ] - + list(self.default.config_specs) - ) + + config_specs.extend(self.default.config_specs) + + return get_unique_config_specs(config_specs) def configurable_fields( self, **kwargs: AnyConfigurableField @@ -436,7 +443,7 @@ def _prepare( init_params = { k: v for k, v in self.default.__dict__.items() - if k in self.default.__fields__ + if k in self.default.model_fields } return ( self.default.__class__(**{**init_params, **configurable}), diff --git a/libs/core/langchain_core/runnables/fallbacks.py b/libs/core/langchain_core/runnables/fallbacks.py index e3a1b2b1cfb33..2ae98cac656a7 100644 --- a/libs/core/langchain_core/runnables/fallbacks.py +++ b/libs/core/langchain_core/runnables/fallbacks.py @@ -18,8 +18,9 @@ cast, ) +from pydantic import BaseModel, ConfigDict + from langchain_core.load.dump import dumpd -from langchain_core.pydantic_v1 import BaseModel from langchain_core.runnables.base import Runnable, RunnableSerializable from langchain_core.runnables.config import ( RunnableConfig, @@ -107,8 +108,9 @@ def when_all_is_lost(inputs): will not be passed to fallbacks. If used, the base Runnable and its fallbacks must accept a dictionary as input.""" - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) @property def InputType(self) -> Type[Input]: diff --git a/libs/core/langchain_core/runnables/graph.py b/libs/core/langchain_core/runnables/graph.py index ae3a9c1b8995f..d7db269e66d3a 100644 --- a/libs/core/langchain_core/runnables/graph.py +++ b/libs/core/langchain_core/runnables/graph.py @@ -22,8 +22,9 @@ ) from uuid import UUID, uuid4 -from langchain_core.pydantic_v1 import BaseModel -from langchain_core.utils.pydantic import is_basemodel_subclass +from pydantic import BaseModel + +from langchain_core.utils.pydantic import _IgnoreUnserializable, is_basemodel_subclass if TYPE_CHECKING: from langchain_core.runnables.base import Runnable as RunnableType @@ -235,7 +236,9 @@ def node_data_json( json = ( { "type": "schema", - "data": node.data.schema(), + "data": node.data.model_json_schema( + schema_generator=_IgnoreUnserializable + ), } if with_schemas else { diff --git a/libs/core/langchain_core/runnables/history.py b/libs/core/langchain_core/runnables/history.py index e3197bec9000a..6df1a4bea5807 100644 --- a/libs/core/langchain_core/runnables/history.py +++ b/libs/core/langchain_core/runnables/history.py @@ -13,9 +13,10 @@ Union, ) +from pydantic import BaseModel + from langchain_core.chat_history import BaseChatMessageHistory from langchain_core.load.load import load -from langchain_core.pydantic_v1 import BaseModel from langchain_core.runnables.base import Runnable, RunnableBindingBase, RunnableLambda from langchain_core.runnables.passthrough import RunnablePassthrough from langchain_core.runnables.utils import ( @@ -372,28 +373,25 @@ def config_specs(self) -> List[ConfigurableFieldSpec]: def get_input_schema( self, config: Optional[RunnableConfig] = None ) -> Type[BaseModel]: - super_schema = super().get_input_schema(config) - if super_schema.__custom_root_type__ or not super_schema.schema().get( - "properties" - ): - from langchain_core.messages import BaseMessage - - fields: Dict = {} - if self.input_messages_key and self.history_messages_key: - fields[self.input_messages_key] = ( - Union[str, BaseMessage, Sequence[BaseMessage]], - ..., - ) - elif self.input_messages_key: - fields[self.input_messages_key] = (Sequence[BaseMessage], ...) - else: - fields["__root__"] = (Sequence[BaseMessage], ...) - return create_model( # type: ignore[call-overload] - "RunnableWithChatHistoryInput", - **fields, + # TODO(0.3): Verify that this change was correct + # Not enough tests and unclear on why the previous implementation was + # necessary. + from langchain_core.messages import BaseMessage + + fields: Dict = {} + if self.input_messages_key and self.history_messages_key: + fields[self.input_messages_key] = ( + Union[str, BaseMessage, Sequence[BaseMessage]], + ..., ) + elif self.input_messages_key: + fields[self.input_messages_key] = (Sequence[BaseMessage], ...) else: - return super_schema + fields["__root__"] = (Sequence[BaseMessage], ...) + return create_model( # type: ignore[call-overload] + "RunnableWithChatHistoryInput", + **fields, + ) def _is_not_async(self, *args: Sequence[Any], **kwargs: Dict[str, Any]) -> bool: return False diff --git a/libs/core/langchain_core/runnables/passthrough.py b/libs/core/langchain_core/runnables/passthrough.py index d486c60412169..bdec3b7205ebc 100644 --- a/libs/core/langchain_core/runnables/passthrough.py +++ b/libs/core/langchain_core/runnables/passthrough.py @@ -21,7 +21,8 @@ cast, ) -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel, RootModel + from langchain_core.runnables.base import ( Other, Runnable, @@ -227,7 +228,7 @@ def assign( A Runnable that merges the Dict input with the output produced by the mapping argument. """ - return RunnableAssign(RunnableParallel(kwargs)) + return RunnableAssign(RunnableParallel[Dict[str, Any]](kwargs)) def invoke( self, input: Other, config: Optional[RunnableConfig] = None, **kwargs: Any @@ -419,7 +420,7 @@ def get_input_schema( self, config: Optional[RunnableConfig] = None ) -> Type[BaseModel]: map_input_schema = self.mapper.get_input_schema(config) - if not map_input_schema.__custom_root_type__: + if not issubclass(map_input_schema, RootModel): # ie. it's a dict return map_input_schema @@ -430,20 +431,22 @@ def get_output_schema( ) -> Type[BaseModel]: map_input_schema = self.mapper.get_input_schema(config) map_output_schema = self.mapper.get_output_schema(config) - if ( - not map_input_schema.__custom_root_type__ - and not map_output_schema.__custom_root_type__ + if not issubclass(map_input_schema, RootModel) and not issubclass( + map_output_schema, RootModel ): - # ie. both are dicts + fields = {} + + for name, field_info in map_input_schema.model_fields.items(): + fields[name] = (field_info.annotation, field_info.default) + + for name, field_info in map_output_schema.model_fields.items(): + fields[name] = (field_info.annotation, field_info.default) + return create_model( # type: ignore[call-overload] "RunnableAssignOutput", - **{ - k: (v.type_, v.default) - for s in (map_input_schema, map_output_schema) - for k, v in s.__fields__.items() - }, + **fields, ) - elif not map_output_schema.__custom_root_type__: + elif not issubclass(map_output_schema, RootModel): # ie. only map output is a dict # ie. input type is either unknown or inferred incorrectly return map_output_schema diff --git a/libs/core/langchain_core/runnables/router.py b/libs/core/langchain_core/runnables/router.py index 0e1224f079263..b8a5faaaa8792 100644 --- a/libs/core/langchain_core/runnables/router.py +++ b/libs/core/langchain_core/runnables/router.py @@ -12,6 +12,7 @@ cast, ) +from pydantic import ConfigDict from typing_extensions import TypedDict from langchain_core.runnables.base import ( @@ -83,8 +84,9 @@ def __init__( runnables={key: coerce_to_runnable(r) for key, r in runnables.items()} ) - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) @classmethod def is_lc_serializable(cls) -> bool: diff --git a/libs/core/langchain_core/runnables/utils.py b/libs/core/langchain_core/runnables/utils.py index 712ba0372ebe0..018ef1d674ea1 100644 --- a/libs/core/langchain_core/runnables/utils.py +++ b/libs/core/langchain_core/runnables/utils.py @@ -28,12 +28,18 @@ Type, TypeVar, Union, + cast, ) +from pydantic import BaseModel, ConfigDict, RootModel +from pydantic import create_model as _create_model_base # pydantic :ignore +from pydantic.json_schema import ( + DEFAULT_REF_TEMPLATE, + GenerateJsonSchema, + JsonSchemaMode, +) from typing_extensions import TypeGuard -from langchain_core.pydantic_v1 import BaseConfig, BaseModel -from langchain_core.pydantic_v1 import create_model as _create_model_base from langchain_core.runnables.schema import StreamEvent Input = TypeVar("Input", contravariant=True) @@ -350,7 +356,7 @@ def get_function_first_arg_dict_keys(func: Callable) -> Optional[List[str]]: tree = ast.parse(textwrap.dedent(code)) visitor = IsFunctionArgDict() visitor.visit(tree) - return list(visitor.keys) if visitor.keys else None + return sorted(visitor.keys) if visitor.keys else None except (SyntaxError, TypeError, OSError, SystemError): return None @@ -699,9 +705,57 @@ def include_event(self, event: StreamEvent, root_type: str) -> bool: return include -class _SchemaConfig(BaseConfig): - arbitrary_types_allowed = True - frozen = True +_SchemaConfig = ConfigDict(arbitrary_types_allowed=True, frozen=True) + +NO_DEFAULT = object() + + +def create_base_class( + name: str, type_: Any, default_: object = NO_DEFAULT +) -> Type[BaseModel]: + """Create a base class.""" + + def schema( + cls: Type[BaseModel], + by_alias: bool = True, + ref_template: str = DEFAULT_REF_TEMPLATE, + ) -> Dict[str, Any]: + # Complains about schema not being defined in superclass + schema_ = super(cls, cls).schema( # type: ignore[misc] + by_alias=by_alias, ref_template=ref_template + ) + schema_["title"] = name + return schema_ + + def model_json_schema( + cls: Type[BaseModel], + by_alias: bool = True, + ref_template: str = DEFAULT_REF_TEMPLATE, + schema_generator: type[GenerateJsonSchema] = GenerateJsonSchema, + mode: JsonSchemaMode = "validation", + ) -> Dict[str, Any]: + # Complains about model_json_schema not being defined in superclass + schema_ = super(cls, cls).model_json_schema( # type: ignore[misc] + by_alias=by_alias, + ref_template=ref_template, + schema_generator=schema_generator, + mode=mode, + ) + schema_["title"] = name + return schema_ + + base_class_attributes = { + "__annotations__": {"root": type_}, + "model_config": ConfigDict(arbitrary_types_allowed=True), + "schema": classmethod(schema), + "model_json_schema": classmethod(model_json_schema), + "__module__": "langchain_core.runnables.utils", + } + + if default_ is not NO_DEFAULT: + base_class_attributes["root"] = default_ + custom_root_type = type(name, (RootModel,), base_class_attributes) + return cast(Type[BaseModel], custom_root_type) def create_model( @@ -717,6 +771,21 @@ def create_model( Returns: Type[BaseModel]: The created model. """ + + # Move this to caching path + if "__root__" in field_definitions: + if len(field_definitions) > 1: + raise NotImplementedError( + "When specifying __root__ no other " + f"fields should be provided. Got {field_definitions}" + ) + + arg = field_definitions["__root__"] + if isinstance(arg, tuple): + named_root_model = create_base_class(__model_name, arg[0], arg[1]) + else: + named_root_model = create_base_class(__model_name, arg) + return named_root_model try: return _create_model_cached(__model_name, **field_definitions) except TypeError: diff --git a/libs/core/langchain_core/structured_query.py b/libs/core/langchain_core/structured_query.py index bbbf84f64737c..fcde3be9b270c 100644 --- a/libs/core/langchain_core/structured_query.py +++ b/libs/core/langchain_core/structured_query.py @@ -6,7 +6,7 @@ from enum import Enum from typing import Any, List, Optional, Sequence, Union -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel class Visitor(ABC): @@ -127,7 +127,8 @@ class Comparison(FilterDirective): def __init__( self, comparator: Comparator, attribute: str, value: Any, **kwargs: Any ) -> None: - super().__init__( + # super exists from BaseModel + super().__init__( # type: ignore[call-arg] comparator=comparator, attribute=attribute, value=value, **kwargs ) @@ -145,8 +146,11 @@ class Operation(FilterDirective): def __init__( self, operator: Operator, arguments: List[FilterDirective], **kwargs: Any - ): - super().__init__(operator=operator, arguments=arguments, **kwargs) + ) -> None: + # super exists from BaseModel + super().__init__( # type: ignore[call-arg] + operator=operator, arguments=arguments, **kwargs + ) class StructuredQuery(Expr): @@ -165,5 +169,8 @@ def __init__( filter: Optional[FilterDirective], limit: Optional[int] = None, **kwargs: Any, - ): - super().__init__(query=query, filter=filter, limit=limit, **kwargs) + ) -> None: + # super exists from BaseModel + super().__init__( # type: ignore[call-arg] + query=query, filter=filter, limit=limit, **kwargs + ) diff --git a/libs/core/langchain_core/tools/base.py b/libs/core/langchain_core/tools/base.py index 9d35aa614fa2d..c150a0b35d522 100644 --- a/libs/core/langchain_core/tools/base.py +++ b/libs/core/langchain_core/tools/base.py @@ -19,12 +19,25 @@ Sequence, Tuple, Type, + TypeVar, Union, cast, + get_args, + get_origin, get_type_hints, ) -from typing_extensions import Annotated, TypeVar, get_args, get_origin +from pydantic import ( + BaseModel, + ConfigDict, + Extra, + Field, + SkipValidation, + ValidationError, + model_validator, + validate_arguments, +) +from typing_extensions import Annotated from langchain_core._api import deprecated from langchain_core.callbacks import ( @@ -33,16 +46,7 @@ CallbackManager, Callbacks, ) -from langchain_core.load import Serializable -from langchain_core.messages import ToolCall, ToolMessage -from langchain_core.pydantic_v1 import ( - BaseModel, - Extra, - Field, - ValidationError, - root_validator, - validate_arguments, -) +from langchain_core.messages.tool import ToolCall, ToolMessage from langchain_core.runnables import ( RunnableConfig, RunnableSerializable, @@ -59,6 +63,7 @@ from langchain_core.utils.pydantic import ( TypeBaseModel, _create_subset_model, + get_fields, is_basemodel_subclass, is_pydantic_v1_subclass, is_pydantic_v2_subclass, @@ -204,20 +209,64 @@ def create_schema_from_function( """ # https://docs.pydantic.dev/latest/usage/validation_decorator/ validated = validate_arguments(func, config=_SchemaConfig) # type: ignore + + sig = inspect.signature(func) + + # Let's ignore `self` and `cls` arguments for class and instance methods + if func.__qualname__ and "." in func.__qualname__: + # Then it likely belongs in a class namespace + in_class = True + else: + in_class = False + + has_args = False + has_kwargs = False + + for param in sig.parameters.values(): + if param.kind == param.VAR_POSITIONAL: + has_args = True + elif param.kind == param.VAR_KEYWORD: + has_kwargs = True + inferred_model = validated.model # type: ignore - filter_args = filter_args if filter_args is not None else FILTERED_ARGS - for arg in filter_args: - if arg in inferred_model.__fields__: - del inferred_model.__fields__[arg] + + if filter_args: + filter_args_ = filter_args + else: + # Handle classmethods and instance methods + existing_params: List[str] = list(sig.parameters.keys()) + if existing_params and existing_params[0] in ("self", "cls") and in_class: + filter_args_ = [existing_params[0]] + list(FILTERED_ARGS) + else: + filter_args_ = list(FILTERED_ARGS) + + for existing_param in existing_params: + if not include_injected and _is_injected_arg_type( + sig.parameters[existing_param].annotation + ): + filter_args_.append(existing_param) + description, arg_descriptions = _infer_arg_descriptions( func, parse_docstring=parse_docstring, error_on_invalid_docstring=error_on_invalid_docstring, ) # Pydantic adds placeholder virtual fields we need to strip - valid_properties = _get_filtered_args( - inferred_model, func, filter_args=filter_args, include_injected=include_injected - ) + valid_properties = [] + for field in get_fields(inferred_model): + if not has_args: + if field == "args": + continue + if not has_kwargs: + if field == "kwargs": + continue + + if field == "v__duplicate_kwargs": # Internal pydantic field + continue + + if field not in filter_args_: + valid_properties.append(field) + return _create_subset_model( f"{model_name}Schema", inferred_model, @@ -274,7 +323,10 @@ class ChildTool(BaseTool): You can provide few-shot examples as a part of the description. """ - args_schema: Optional[TypeBaseModel] = None + + args_schema: Annotated[Optional[TypeBaseModel], SkipValidation()] = Field( + default=None, description="The tool schema." + ) """Pydantic model class to validate and parse the tool's input arguments. Args schema should be either: @@ -345,8 +397,9 @@ def __init__(self, **kwargs: Any) -> None: ) super().__init__(**kwargs) - class Config(Serializable.Config): - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) @property def is_single_input(self) -> bool: @@ -416,7 +469,7 @@ def _parse_input(self, tool_input: Union[str, Dict]) -> Union[str, Dict[str, Any input_args = self.args_schema if isinstance(tool_input, str): if input_args is not None: - key_ = next(iter(input_args.__fields__.keys())) + key_ = next(iter(get_fields(input_args).keys())) input_args.validate({key_: tool_input}) return tool_input else: @@ -429,8 +482,9 @@ def _parse_input(self, tool_input: Union[str, Dict]) -> Union[str, Dict[str, Any } return tool_input - @root_validator(pre=True) - def raise_deprecation(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def raise_deprecation(cls, values: Dict) -> Any: """Raise deprecation warning if callback_manager is used. Args: diff --git a/libs/core/langchain_core/tools/convert.py b/libs/core/langchain_core/tools/convert.py index 7428b69e3864c..c0c2a214a529f 100644 --- a/libs/core/langchain_core/tools/convert.py +++ b/libs/core/langchain_core/tools/convert.py @@ -1,8 +1,9 @@ import inspect from typing import Any, Callable, Dict, Literal, Optional, Type, Union, get_type_hints +from pydantic import BaseModel, Field, create_model + from langchain_core.callbacks import Callbacks -from langchain_core.pydantic_v1 import BaseModel, Field, create_model from langchain_core.runnables import Runnable from langchain_core.tools.base import BaseTool from langchain_core.tools.simple import Tool diff --git a/libs/core/langchain_core/tools/retriever.py b/libs/core/langchain_core/tools/retriever.py index 3dba4442b7aa0..b59b49a3d317a 100644 --- a/libs/core/langchain_core/tools/retriever.py +++ b/libs/core/langchain_core/tools/retriever.py @@ -3,6 +3,8 @@ from functools import partial from typing import Optional +from pydantic import BaseModel, Field + from langchain_core.callbacks import Callbacks from langchain_core.prompts import ( BasePromptTemplate, @@ -10,7 +12,6 @@ aformat_document, format_document, ) -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.retrievers import BaseRetriever from langchain_core.tools.simple import Tool diff --git a/libs/core/langchain_core/tools/simple.py b/libs/core/langchain_core/tools/simple.py index c8d77a31ec8d2..b80b264ea590e 100644 --- a/libs/core/langchain_core/tools/simple.py +++ b/libs/core/langchain_core/tools/simple.py @@ -1,14 +1,24 @@ from __future__ import annotations from inspect import signature -from typing import Any, Awaitable, Callable, Dict, Optional, Tuple, Type, Union +from typing import ( + Any, + Awaitable, + Callable, + Dict, + Optional, + Tuple, + Type, + Union, +) + +from pydantic import BaseModel from langchain_core.callbacks import ( AsyncCallbackManagerForToolRun, CallbackManagerForToolRun, ) from langchain_core.messages import ToolCall -from langchain_core.pydantic_v1 import BaseModel from langchain_core.runnables import RunnableConfig, run_in_executor from langchain_core.tools.base import ( BaseTool, @@ -155,3 +165,6 @@ def from_function( args_schema=args_schema, **kwargs, ) + + +Tool.model_rebuild() diff --git a/libs/core/langchain_core/tools/structured.py b/libs/core/langchain_core/tools/structured.py index 8da6d654559d2..96d6e4ca494cb 100644 --- a/libs/core/langchain_core/tools/structured.py +++ b/libs/core/langchain_core/tools/structured.py @@ -2,14 +2,26 @@ import textwrap from inspect import signature -from typing import Any, Awaitable, Callable, Dict, List, Literal, Optional, Type, Union +from typing import ( + Any, + Awaitable, + Callable, + Dict, + List, + Literal, + Optional, + Type, + Union, +) + +from pydantic import BaseModel, Field, SkipValidation +from typing_extensions import Annotated from langchain_core.callbacks import ( AsyncCallbackManagerForToolRun, CallbackManagerForToolRun, ) from langchain_core.messages import ToolCall -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.runnables import RunnableConfig, run_in_executor from langchain_core.tools.base import ( FILTERED_ARGS, @@ -24,7 +36,9 @@ class StructuredTool(BaseTool): """Tool that can operate on any number of inputs.""" description: str = "" - args_schema: TypeBaseModel = Field(..., description="The tool schema.") + args_schema: Annotated[TypeBaseModel, SkipValidation()] = Field( + ..., description="The tool schema." + ) """The input arguments' schema.""" func: Optional[Callable[..., Any]] """The function to run when the tool is called.""" diff --git a/libs/core/langchain_core/tracers/schemas.py b/libs/core/langchain_core/tracers/schemas.py index 4a40e2cc56309..2725a3a8b537c 100644 --- a/libs/core/langchain_core/tracers/schemas.py +++ b/libs/core/langchain_core/tracers/schemas.py @@ -11,7 +11,6 @@ from langsmith.schemas import RunTypeEnum as RunTypeEnumDep from langchain_core._api import deprecated -from langchain_core.outputs import LLMResult from langchain_core.pydantic_v1 import BaseModel, Field, root_validator @@ -83,7 +82,8 @@ class LLMRun(BaseRun): """Class for LLMRun.""" prompts: List[str] - response: Optional[LLMResult] = None + # Temporarily, remove but we will completely remove LLMRun + # response: Optional[LLMResult] = None @deprecated("0.1.0", alternative="Run", removal="1.0") diff --git a/libs/core/langchain_core/utils/function_calling.py b/libs/core/langchain_core/utils/function_calling.py index c5c4d82461dc2..f94dfca75c1da 100644 --- a/libs/core/langchain_core/utils/function_calling.py +++ b/libs/core/langchain_core/utils/function_calling.py @@ -23,11 +23,11 @@ cast, ) +from pydantic import BaseModel from typing_extensions import Annotated, TypedDict, get_args, get_origin, is_typeddict from langchain_core._api import deprecated from langchain_core.messages import AIMessage, BaseMessage, HumanMessage, ToolMessage -from langchain_core.pydantic_v1 import BaseModel, Field, create_model from langchain_core.utils.json_schema import dereference_refs from langchain_core.utils.pydantic import is_basemodel_subclass @@ -85,7 +85,7 @@ def _rm_titles(kv: dict, prev_key: str = "") -> dict: removal="1.0", ) def convert_pydantic_to_openai_function( - model: Type[BaseModel], + model: Type, *, name: Optional[str] = None, description: Optional[str] = None, @@ -109,7 +109,10 @@ def convert_pydantic_to_openai_function( else: schema = model.schema() # Pydantic 1 schema = dereference_refs(schema) - schema.pop("definitions", None) + if "definitions" in schema: # pydantic 1 + schema.pop("definitions", None) + if "$defs" in schema: # pydantic 2 + schema.pop("$defs", None) title = schema.pop("title", "") default_description = schema.pop("description", "") return { @@ -193,11 +196,13 @@ def convert_python_function_to_openai_function( def _convert_typed_dict_to_openai_function(typed_dict: Type) -> FunctionDescription: visited: Dict = {} + from pydantic.v1 import BaseModel + model = cast( Type[BaseModel], _convert_any_typed_dicts_to_pydantic(typed_dict, visited=visited), ) - return convert_pydantic_to_openai_function(model) + return convert_pydantic_to_openai_function(model) # type: ignore _MAX_TYPED_DICT_RECURSION = 25 @@ -209,6 +214,9 @@ def _convert_any_typed_dicts_to_pydantic( visited: Dict, depth: int = 0, ) -> Type: + from pydantic.v1 import Field as Field_v1 + from pydantic.v1 import create_model as create_model_v1 + if type_ in visited: return visited[type_] elif depth >= _MAX_TYPED_DICT_RECURSION: @@ -242,7 +250,7 @@ def _convert_any_typed_dicts_to_pydantic( field_kwargs["description"] = arg_desc else: pass - fields[arg] = (new_arg_type, Field(**field_kwargs)) + fields[arg] = (new_arg_type, Field_v1(**field_kwargs)) else: new_arg_type = _convert_any_typed_dicts_to_pydantic( arg_type, depth=depth + 1, visited=visited @@ -250,8 +258,8 @@ def _convert_any_typed_dicts_to_pydantic( field_kwargs = {"default": ...} if arg_desc := arg_descriptions.get(arg): field_kwargs["description"] = arg_desc - fields[arg] = (new_arg_type, Field(**field_kwargs)) - model = create_model(typed_dict.__name__, **fields) + fields[arg] = (new_arg_type, Field_v1(**field_kwargs)) + model = create_model_v1(typed_dict.__name__, **fields) model.__doc__ = description visited[typed_dict] = model return model diff --git a/libs/core/langchain_core/utils/pydantic.py b/libs/core/langchain_core/utils/pydantic.py index edb48d9ceff8d..fb4a72a29c711 100644 --- a/libs/core/langchain_core/utils/pydantic.py +++ b/libs/core/langchain_core/utils/pydantic.py @@ -7,9 +7,10 @@ from functools import wraps from typing import Any, Callable, Dict, List, Optional, Type, TypeVar, Union, overload -import pydantic # pydantic: ignore - -from langchain_core.pydantic_v1 import BaseModel, root_validator +import pydantic +from pydantic import BaseModel, root_validator +from pydantic.json_schema import GenerateJsonSchema, JsonSchemaValue +from pydantic_core import core_schema def get_pydantic_major_version() -> int: @@ -76,13 +77,13 @@ def is_basemodel_subclass(cls: Type) -> bool: return False if PYDANTIC_MAJOR_VERSION == 1: - from pydantic import BaseModel as BaseModelV1Proper # pydantic: ignore + from pydantic import BaseModel as BaseModelV1Proper if issubclass(cls, BaseModelV1Proper): return True elif PYDANTIC_MAJOR_VERSION == 2: - from pydantic import BaseModel as BaseModelV2 # pydantic: ignore - from pydantic.v1 import BaseModel as BaseModelV1 # pydantic: ignore + from pydantic import BaseModel as BaseModelV2 + from pydantic.v1 import BaseModel as BaseModelV1 if issubclass(cls, BaseModelV2): return True @@ -104,13 +105,13 @@ def is_basemodel_instance(obj: Any) -> bool: * pydantic.v1.BaseModel in Pydantic 2.x """ if PYDANTIC_MAJOR_VERSION == 1: - from pydantic import BaseModel as BaseModelV1Proper # pydantic: ignore + from pydantic import BaseModel as BaseModelV1Proper if isinstance(obj, BaseModelV1Proper): return True elif PYDANTIC_MAJOR_VERSION == 2: - from pydantic import BaseModel as BaseModelV2 # pydantic: ignore - from pydantic.v1 import BaseModel as BaseModelV1 # pydantic: ignore + from pydantic import BaseModel as BaseModelV2 + from pydantic.v1 import BaseModel as BaseModelV1 if isinstance(obj, BaseModelV2): return True @@ -146,7 +147,7 @@ def wrapper(cls: Type[BaseModel], values: Dict[str, Any]) -> Dict[str, Any]: Dict[str, Any]: The values to initialize the model with. """ # Insert default values - fields = cls.__fields__ + fields = cls.model_fields for name, field_info in fields.items(): # Check if allow_population_by_field_name is enabled # If yes, then set the field name to the alias @@ -155,9 +156,13 @@ def wrapper(cls: Type[BaseModel], values: Dict[str, Any]) -> Dict[str, Any]: if cls.Config.allow_population_by_field_name: if field_info.alias in values: values[name] = values.pop(field_info.alias) + if hasattr(cls, "model_config"): + if cls.model_config.get("populate_by_name"): + if field_info.alias in values: + values[name] = values.pop(field_info.alias) if name not in values or values[name] is None: - if not field_info.required: + if not field_info.is_required(): if field_info.default_factory is not None: values[name] = field_info.default_factory() else: @@ -169,6 +174,46 @@ def wrapper(cls: Type[BaseModel], values: Dict[str, Any]) -> Dict[str, Any]: return wrapper +class _IgnoreUnserializable(GenerateJsonSchema): + """A JSON schema generator that ignores unknown types. + + https://docs.pydantic.dev/latest/concepts/json_schema/#customizing-the-json-schema-generation-process + """ + + def handle_invalid_for_json_schema( + self, schema: core_schema.CoreSchema, error_info: str + ) -> JsonSchemaValue: + return {} + + +def v1_repr(obj: BaseModel) -> str: + """Return the schema of the object as a string. + + Get a repr for the pydantic object which is consistent with pydantic.v1. + """ + if not is_basemodel_instance(obj): + raise TypeError(f"Expected a pydantic BaseModel, got {type(obj)}") + repr_ = [] + for name, field in get_fields(obj).items(): + value = getattr(obj, name) + + if isinstance(value, BaseModel): + repr_.append(f"{name}={v1_repr(value)}") + else: + if field.exclude: + continue + if not field.is_required(): + if not value: + continue + if field.default == value: + continue + + repr_.append(f"{name}={repr(value)}") + + args = ", ".join(repr_) + return f"{obj.__class__.__name__}({args})" + + def _create_subset_model_v1( name: str, model: Type[BaseModel], @@ -178,12 +223,20 @@ def _create_subset_model_v1( fn_description: Optional[str] = None, ) -> Type[BaseModel]: """Create a pydantic model with only a subset of model's fields.""" - from langchain_core.pydantic_v1 import create_model + if PYDANTIC_MAJOR_VERSION == 1: + from pydantic import create_model + elif PYDANTIC_MAJOR_VERSION == 2: + from pydantic.v1 import create_model # type: ignore + else: + raise NotImplementedError( + f"Unsupported pydantic version: {PYDANTIC_MAJOR_VERSION}" + ) fields = {} for field_name in field_names: - field = model.__fields__[field_name] + # Using pydantic v1 so can access __fields__ as a dict. + field = model.__fields__[field_name] # type: ignore t = ( # this isn't perfect but should work for most functions field.outer_type_ @@ -208,8 +261,8 @@ def _create_subset_model_v2( fn_description: Optional[str] = None, ) -> Type[pydantic.BaseModel]: """Create a pydantic model with a subset of the model fields.""" - from pydantic import create_model # pydantic: ignore - from pydantic.fields import FieldInfo # pydantic: ignore + from pydantic import create_model + from pydantic.fields import FieldInfo descriptions_ = descriptions or {} fields = {} @@ -222,6 +275,17 @@ def _create_subset_model_v2( fields[field_name] = (field.annotation, field_info) rtn = create_model(name, **fields) # type: ignore + # TODO(0.3): Determine if there is a more "pydantic" way to preserve annotations. + # This is done to preserve __annotations__ when working with pydantic 2.x + # and using the Annotated type with TypedDict. + # Comment out the following line, to trigger the relevant test case. + selected_annotations = [ + (name, annotation) + for name, annotation in model.__annotations__.items() + if name in field_names + ] + + rtn.__annotations__ = dict(selected_annotations) rtn.__doc__ = textwrap.dedent(fn_description or model.__doc__ or "") return rtn @@ -248,7 +312,7 @@ def _create_subset_model( fn_description=fn_description, ) elif PYDANTIC_MAJOR_VERSION == 2: - from pydantic.v1 import BaseModel as BaseModelV1 # pydantic: ignore + from pydantic.v1 import BaseModel as BaseModelV1 if issubclass(model, BaseModelV1): return _create_subset_model_v1( diff --git a/libs/core/langchain_core/utils/utils.py b/libs/core/langchain_core/utils/utils.py index d4a29da926260..d38657cc91cd0 100644 --- a/libs/core/langchain_core/utils/utils.py +++ b/libs/core/langchain_core/utils/utils.py @@ -10,9 +10,9 @@ from typing import Any, Callable, Dict, Optional, Sequence, Set, Tuple, Union, overload from packaging.version import parse +from pydantic import SecretStr from requests import HTTPError, Response -from langchain_core.pydantic_v1 import SecretStr from langchain_core.utils.pydantic import ( is_pydantic_v1_subclass, ) @@ -353,7 +353,7 @@ def get_from_env_fn() -> Optional[str]: @overload -def secret_from_env(key: str, /) -> Callable[[], SecretStr]: ... +def secret_from_env(key: Union[str, Sequence[str]], /) -> Callable[[], SecretStr]: ... @overload diff --git a/libs/core/langchain_core/vectorstores/base.py b/libs/core/langchain_core/vectorstores/base.py index b4e2f1c00019f..737b5c32d7dd2 100644 --- a/libs/core/langchain_core/vectorstores/base.py +++ b/libs/core/langchain_core/vectorstores/base.py @@ -42,8 +42,9 @@ TypeVar, ) +from pydantic import ConfigDict, Field, model_validator + from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import Field, root_validator from langchain_core.retrievers import BaseRetriever, LangSmithRetrieverParams from langchain_core.runnables.config import run_in_executor @@ -984,11 +985,13 @@ class VectorStoreRetriever(BaseRetriever): "mmr", ) - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) - @root_validator(pre=True) - def validate_search_type(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_search_type(cls, values: Dict) -> Any: """Validate search type. Args: diff --git a/libs/core/langchain_core/vectorstores/utils.py b/libs/core/langchain_core/vectorstores/utils.py index 956052c1115f1..73b9aac9cbe22 100644 --- a/libs/core/langchain_core/vectorstores/utils.py +++ b/libs/core/langchain_core/vectorstores/utils.py @@ -51,7 +51,7 @@ def _cosine_similarity(X: Matrix, Y: Matrix) -> np.ndarray: f"and Y has shape {Y.shape}." ) try: - import simsimd as simd + import simsimd as simd # type: ignore[import-not-found] X = np.array(X, dtype=np.float32) Y = np.array(Y, dtype=np.float32) diff --git a/libs/core/poetry.lock b/libs/core/poetry.lock index 6b99c525c966b..beba6f841841e 100644 --- a/libs/core/poetry.lock +++ b/libs/core/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. 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{file = "pytz-2024.1-py2.py3-none-any.whl", hash = "sha256:328171f4e3623139da4983451950b28e95ac706e13f3f2630a879749e7a8b319"}, - {file = "pytz-2024.1.tar.gz", hash = "sha256:2a29735ea9c18baf14b448846bde5a48030ed267578472d8955cd0e7443a9812"}, -] - [[package]] name = "pywin32" version = "306" @@ -3219,5 +3128,5 @@ type = ["pytest-mypy"] [metadata] lock-version = "2.0" -python-versions = ">=3.8.1,<4.0" -content-hash = "e86e28d75744b77f8c2173b58e950c2d23c95cacad8ee5d3110309c4248f7c09" +python-versions = ">=3.9,<4.0" +content-hash = "7f2ce36878754aeb498d961452c156ab63e95bc5c6bcf3ca29acae325062aed9" diff --git a/libs/core/pyproject.toml b/libs/core/pyproject.toml index ae040a018b545..4f0c12661e89d 100644 --- a/libs/core/pyproject.toml +++ b/libs/core/pyproject.toml @@ -23,7 +23,7 @@ ignore_missing_imports = true "Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-core%3D%3D0%22&expanded=true" [tool.poetry.dependencies] -python = ">=3.8.1,<4.0" +python = ">=3.9,<4.0" langsmith = "^0.1.75" tenacity = "^8.1.0,!=8.4.0" jsonpatch = "^1.33" diff --git a/libs/core/tests/unit_tests/_api/test_beta_decorator.py b/libs/core/tests/unit_tests/_api/test_beta_decorator.py index ef7dd110687e1..1c9ab67700699 100644 --- a/libs/core/tests/unit_tests/_api/test_beta_decorator.py +++ b/libs/core/tests/unit_tests/_api/test_beta_decorator.py @@ -3,9 +3,9 @@ from typing import Any, Dict import pytest +from pydantic import BaseModel from langchain_core._api.beta_decorator import beta, warn_beta -from langchain_core.pydantic_v1 import BaseModel @pytest.mark.parametrize( diff --git a/libs/core/tests/unit_tests/_api/test_deprecation.py b/libs/core/tests/unit_tests/_api/test_deprecation.py index e1a2849635474..16407e9d89bbc 100644 --- a/libs/core/tests/unit_tests/_api/test_deprecation.py +++ b/libs/core/tests/unit_tests/_api/test_deprecation.py @@ -3,13 +3,13 @@ from typing import Any, Dict import pytest +from pydantic import BaseModel from langchain_core._api.deprecation import ( deprecated, rename_parameter, warn_deprecated, ) -from langchain_core.pydantic_v1 import BaseModel @pytest.mark.parametrize( diff --git a/libs/core/tests/unit_tests/fake/callbacks.py b/libs/core/tests/unit_tests/fake/callbacks.py index 71a6dea0cef02..21efc2c176cd8 100644 --- a/libs/core/tests/unit_tests/fake/callbacks.py +++ b/libs/core/tests/unit_tests/fake/callbacks.py @@ -4,9 +4,10 @@ from typing import Any, Dict, List, Optional, Union from uuid import UUID +from pydantic import BaseModel + from langchain_core.callbacks.base import AsyncCallbackHandler, BaseCallbackHandler from langchain_core.messages import BaseMessage -from langchain_core.pydantic_v1 import BaseModel class BaseFakeCallbackHandler(BaseModel): @@ -256,7 +257,8 @@ def on_retriever_error( ) -> Any: self.on_retriever_error_common() - def __deepcopy__(self, memo: dict) -> "FakeCallbackHandler": + # Overriding since BaseModel has __deepcopy__ method as well + def __deepcopy__(self, memo: dict) -> "FakeCallbackHandler": # type: ignore return self @@ -390,5 +392,6 @@ async def on_text( ) -> None: self.on_text_common() - def __deepcopy__(self, memo: dict) -> "FakeAsyncCallbackHandler": + # Overriding since BaseModel has __deepcopy__ method as well + def __deepcopy__(self, memo: dict) -> "FakeAsyncCallbackHandler": # type: ignore return self diff --git a/libs/core/tests/unit_tests/fake/test_fake_chat_model.py b/libs/core/tests/unit_tests/fake/test_fake_chat_model.py index feff8a4a7bafa..873ca5deec3be 100644 --- a/libs/core/tests/unit_tests/fake/test_fake_chat_model.py +++ b/libs/core/tests/unit_tests/fake/test_fake_chat_model.py @@ -9,7 +9,6 @@ from langchain_core.messages import AIMessage, AIMessageChunk, BaseMessage from langchain_core.outputs import ChatGenerationChunk, GenerationChunk from tests.unit_tests.stubs import ( - AnyStr, _AnyIdAIMessage, _AnyIdAIMessageChunk, _AnyIdHumanMessage, @@ -70,8 +69,8 @@ async def test_generic_fake_chat_model_stream() -> None: model = GenericFakeChatModel(messages=cycle([message])) chunks = [chunk async for chunk in model.astream("meow")] assert chunks == [ - AIMessageChunk(content="", additional_kwargs={"foo": 42}, id=AnyStr()), - AIMessageChunk(content="", additional_kwargs={"bar": 24}, id=AnyStr()), + _AnyIdAIMessageChunk(content="", additional_kwargs={"foo": 42}), + _AnyIdAIMessageChunk(content="", additional_kwargs={"bar": 24}), ] assert len({chunk.id for chunk in chunks}) == 1 @@ -89,29 +88,23 @@ async def test_generic_fake_chat_model_stream() -> None: chunks = [chunk async for chunk in model.astream("meow")] assert chunks == [ - AIMessageChunk( - content="", - additional_kwargs={"function_call": {"name": "move_file"}}, - id=AnyStr(), + _AnyIdAIMessageChunk( + content="", additional_kwargs={"function_call": {"name": "move_file"}} ), - AIMessageChunk( + _AnyIdAIMessageChunk( content="", additional_kwargs={ "function_call": {"arguments": '{\n "source_path": "foo"'}, }, - id=AnyStr(), ), - AIMessageChunk( - content="", - additional_kwargs={"function_call": {"arguments": ","}}, - id=AnyStr(), + _AnyIdAIMessageChunk( + content="", additional_kwargs={"function_call": {"arguments": ","}} ), - AIMessageChunk( + _AnyIdAIMessageChunk( content="", additional_kwargs={ "function_call": {"arguments": '\n "destination_path": "bar"\n}'}, }, - id=AnyStr(), ), ] assert len({chunk.id for chunk in chunks}) == 1 diff --git a/libs/core/tests/unit_tests/language_models/chat_models/test_rate_limiting.py b/libs/core/tests/unit_tests/language_models/chat_models/test_rate_limiting.py index ac633b8263fc4..3547cc8af6b59 100644 --- a/libs/core/tests/unit_tests/language_models/chat_models/test_rate_limiting.py +++ b/libs/core/tests/unit_tests/language_models/chat_models/test_rate_limiting.py @@ -1,4 +1,5 @@ import time +from typing import Optional as Optional from langchain_core.caches import InMemoryCache from langchain_core.language_models import GenericFakeChatModel @@ -220,6 +221,9 @@ def is_lc_serializable(cls) -> bool: return True +SerializableModel.model_rebuild() + + def test_serialization_with_rate_limiter() -> None: """Test model serialization with rate limiter.""" from langchain_core.load import dumps diff --git a/libs/core/tests/unit_tests/load/test_serializable.py b/libs/core/tests/unit_tests/load/test_serializable.py index 9ef83a6c89c20..e451506c68cb3 100644 --- a/libs/core/tests/unit_tests/load/test_serializable.py +++ b/libs/core/tests/unit_tests/load/test_serializable.py @@ -1,8 +1,9 @@ from typing import Dict +from pydantic import ConfigDict, Field + from langchain_core.load import Serializable, dumpd from langchain_core.load.serializable import _is_field_useful -from langchain_core.pydantic_v1 import Field def test_simple_serialization() -> None: @@ -40,8 +41,9 @@ def is_lc_serializable(cls) -> bool: def test_simple_serialization_secret() -> None: """Test handling of secrets.""" + from pydantic import SecretStr + from langchain_core.load import Serializable - from langchain_core.pydantic_v1 import SecretStr class Foo(Serializable): bar: int @@ -97,8 +99,9 @@ class Foo(Serializable): # Make sure works for fields without default. z: ArrayObj - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) foo = Foo(x=ArrayObj(), y=NonBoolObj(), z=ArrayObj()) assert _is_field_useful(foo, "x", foo.x) diff --git a/libs/core/tests/unit_tests/output_parsers/test_base_parsers.py b/libs/core/tests/unit_tests/output_parsers/test_base_parsers.py index 3ac0b56655d6e..6e9eea83ba8b8 100644 --- a/libs/core/tests/unit_tests/output_parsers/test_base_parsers.py +++ b/libs/core/tests/unit_tests/output_parsers/test_base_parsers.py @@ -1,6 +1,7 @@ """Module to test base parser implementations.""" from typing import List +from typing import Optional as Optional from langchain_core.exceptions import OutputParserException from langchain_core.language_models import GenericFakeChatModel @@ -46,6 +47,8 @@ def parse_result( assert isinstance(content, str) return content.swapcase() # type: ignore + StrInvertCase.model_rebuild() + model = GenericFakeChatModel(messages=iter([AIMessage(content="hEllo")])) chain = model | StrInvertCase() assert chain.invoke("") == "HeLLO" diff --git a/libs/core/tests/unit_tests/output_parsers/test_json.py b/libs/core/tests/unit_tests/output_parsers/test_json.py index c468adcc1274a..0cc9dd699f218 100644 --- a/libs/core/tests/unit_tests/output_parsers/test_json.py +++ b/libs/core/tests/unit_tests/output_parsers/test_json.py @@ -2,12 +2,12 @@ from typing import Any, AsyncIterator, Iterator, Tuple import pytest +from pydantic import BaseModel from langchain_core.exceptions import OutputParserException from langchain_core.output_parsers.json import ( SimpleJsonOutputParser, ) -from langchain_core.pydantic_v1 import BaseModel from langchain_core.utils.function_calling import convert_to_openai_function from langchain_core.utils.json import parse_json_markdown, parse_partial_json from tests.unit_tests.pydantic_utils import _schema diff --git a/libs/core/tests/unit_tests/output_parsers/test_openai_functions.py b/libs/core/tests/unit_tests/output_parsers/test_openai_functions.py index 11f61d655ca41..0e20295c15f8a 100644 --- a/libs/core/tests/unit_tests/output_parsers/test_openai_functions.py +++ b/libs/core/tests/unit_tests/output_parsers/test_openai_functions.py @@ -2,6 +2,7 @@ from typing import Any, Dict import pytest +from pydantic import BaseModel from langchain_core.exceptions import OutputParserException from langchain_core.messages import AIMessage, BaseMessage, HumanMessage @@ -10,7 +11,6 @@ PydanticOutputFunctionsParser, ) from langchain_core.outputs import ChatGeneration -from langchain_core.pydantic_v1 import BaseModel def test_json_output_function_parser() -> None: diff --git a/libs/core/tests/unit_tests/output_parsers/test_openai_tools.py b/libs/core/tests/unit_tests/output_parsers/test_openai_tools.py index d5ca880ee785b..c961170dea079 100644 --- a/libs/core/tests/unit_tests/output_parsers/test_openai_tools.py +++ b/libs/core/tests/unit_tests/output_parsers/test_openai_tools.py @@ -1,6 +1,7 @@ from typing import Any, AsyncIterator, Iterator, List import pytest +from pydantic import BaseModel, Field from langchain_core.messages import ( AIMessage, @@ -14,7 +15,6 @@ PydanticToolsParser, ) from langchain_core.outputs import ChatGeneration -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.utils.pydantic import PYDANTIC_MAJOR_VERSION STREAMED_MESSAGES: list = [ @@ -531,7 +531,7 @@ async def test_partial_pydantic_output_parser_async() -> None: @pytest.mark.skipif(PYDANTIC_MAJOR_VERSION != 2, reason="This test is for pydantic 2") def test_parse_with_different_pydantic_2_v1() -> None: """Test with pydantic.v1.BaseModel from pydantic 2.""" - import pydantic # pydantic: ignore + import pydantic class Forecast(pydantic.v1.BaseModel): temperature: int @@ -566,7 +566,7 @@ class Forecast(pydantic.v1.BaseModel): @pytest.mark.skipif(PYDANTIC_MAJOR_VERSION != 2, reason="This test is for pydantic 2") def test_parse_with_different_pydantic_2_proper() -> None: """Test with pydantic.BaseModel from pydantic 2.""" - import pydantic # pydantic: ignore + import pydantic class Forecast(pydantic.BaseModel): temperature: int @@ -601,7 +601,7 @@ class Forecast(pydantic.BaseModel): @pytest.mark.skipif(PYDANTIC_MAJOR_VERSION != 1, reason="This test is for pydantic 1") def test_parse_with_different_pydantic_1_proper() -> None: """Test with pydantic.BaseModel from pydantic 1.""" - import pydantic # pydantic: ignore + import pydantic class Forecast(pydantic.BaseModel): temperature: int diff --git a/libs/core/tests/unit_tests/output_parsers/test_pydantic_parser.py b/libs/core/tests/unit_tests/output_parsers/test_pydantic_parser.py index 2a748bd65bed7..bdf8799dc145e 100644 --- a/libs/core/tests/unit_tests/output_parsers/test_pydantic_parser.py +++ b/libs/core/tests/unit_tests/output_parsers/test_pydantic_parser.py @@ -3,22 +3,17 @@ from enum import Enum from typing import Literal, Optional -import pydantic # pydantic: ignore +import pydantic import pytest +from pydantic import BaseModel, Field +from pydantic.v1 import BaseModel as V1BaseModel from langchain_core.exceptions import OutputParserException from langchain_core.language_models import ParrotFakeChatModel from langchain_core.output_parsers import PydanticOutputParser from langchain_core.output_parsers.json import JsonOutputParser from langchain_core.prompts.prompt import PromptTemplate -from langchain_core.pydantic_v1 import BaseModel, Field -from langchain_core.utils.pydantic import PYDANTIC_MAJOR_VERSION, TBaseModel - -V1BaseModel = pydantic.BaseModel -if PYDANTIC_MAJOR_VERSION == 2: - from pydantic.v1 import BaseModel # pydantic: ignore - - V1BaseModel = BaseModel # type: ignore +from langchain_core.utils.pydantic import TBaseModel class ForecastV2(pydantic.BaseModel): @@ -194,7 +189,7 @@ class SampleModel(BaseModel): def test_format_instructions_preserves_language() -> None: """Test format instructions does not attempt to encode into ascii.""" - from langchain_core.pydantic_v1 import BaseModel, Field + from pydantic import BaseModel, Field description = ( "你好, こんにちは, नमस्ते, Bonjour, Hola, " diff --git a/libs/core/tests/unit_tests/prompts/__snapshots__/test_chat.ambr b/libs/core/tests/unit_tests/prompts/__snapshots__/test_chat.ambr index 901b35e75c719..29ab354d126ac 100644 --- a/libs/core/tests/unit_tests/prompts/__snapshots__/test_chat.ambr +++ b/libs/core/tests/unit_tests/prompts/__snapshots__/test_chat.ambr @@ -1,8 +1,9 @@ # serializer version: 1 # name: test_chat_input_schema[partial] dict({ - 'definitions': dict({ + '$defs': dict({ 'AIMessage': dict({ + 'additionalProperties': True, 'description': ''' Message from an AI. @@ -44,21 +45,37 @@ 'type': 'boolean', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', }), 'invalid_tool_calls': dict({ 'default': list([ ]), 'items': dict({ - '$ref': '#/definitions/InvalidToolCall', + '$ref': '#/$defs/InvalidToolCall', }), 'title': 'Invalid Tool Calls', 'type': 'array', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Name', - 'type': 'string', }), 'response_metadata': dict({ 'title': 'Response Metadata', @@ -68,12 +85,13 @@ 'default': list([ ]), 'items': dict({ - '$ref': '#/definitions/ToolCall', + '$ref': '#/$defs/ToolCall', }), 'title': 'Tool Calls', 'type': 'array', }), 'type': dict({ + 'const': 'ai', 'default': 'ai', 'enum': list([ 'ai', @@ -82,7 +100,15 @@ 'type': 'string', }), 'usage_metadata': dict({ - '$ref': '#/definitions/UsageMetadata', + 'anyOf': list([ + dict({ + '$ref': '#/$defs/UsageMetadata', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, }), }), 'required': list([ @@ -91,8 +117,9 @@ 'title': 'AIMessage', 'type': 'object', }), - 'ChatMessage': dict({ - 'description': 'Message that can be assigned an arbitrary speaker (i.e. role).', + 'AIMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'Message chunk from an AI.', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -119,49 +146,96 @@ ]), 'title': 'Content', }), + 'example': dict({ + 'default': False, + 'title': 'Example', + 'type': 'boolean', + }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', + }), + 'invalid_tool_calls': dict({ + 'default': list([ + ]), + 'items': dict({ + '$ref': '#/$defs/InvalidToolCall', + }), + 'title': 'Invalid Tool Calls', + 'type': 'array', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Name', - 'type': 'string', }), 'response_metadata': dict({ 'title': 'Response Metadata', 'type': 'object', }), - 'role': dict({ - 'title': 'Role', - 'type': 'string', + 'tool_call_chunks': dict({ + 'default': list([ + ]), + 'items': dict({ + '$ref': '#/$defs/ToolCallChunk', + }), + 'title': 'Tool Call Chunks', + 'type': 'array', + }), + 'tool_calls': dict({ + 'default': list([ + ]), + 'items': dict({ + '$ref': '#/$defs/ToolCall', + }), + 'title': 'Tool Calls', + 'type': 'array', }), 'type': dict({ - 'default': 'chat', + 'const': 'AIMessageChunk', + 'default': 'AIMessageChunk', 'enum': list([ - 'chat', + 'AIMessageChunk', ]), 'title': 'Type', 'type': 'string', }), + 'usage_metadata': dict({ + 'anyOf': list([ + dict({ + '$ref': '#/$defs/UsageMetadata', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + }), }), 'required': list([ 'content', - 'role', ]), - 'title': 'ChatMessage', + 'title': 'AIMessageChunk', 'type': 'object', }), - 'FunctionMessage': dict({ - 'description': ''' - Message for passing the result of executing a tool back to a model. - - FunctionMessage are an older version of the ToolMessage schema, and - do not contain the tool_call_id field. - - The tool_call_id field is used to associate the tool call request with the - tool call response. This is useful in situations where a chat model is able - to request multiple tool calls in parallel. - ''', + 'ChatMessage': dict({ + 'additionalProperties': True, + 'description': 'Message that can be assigned an arbitrary speaker (i.e. role).', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -189,21 +263,42 @@ 'title': 'Content', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Name', - 'type': 'string', }), 'response_metadata': dict({ 'title': 'Response Metadata', 'type': 'object', }), + 'role': dict({ + 'title': 'Role', + 'type': 'string', + }), 'type': dict({ - 'default': 'function', + 'const': 'chat', + 'default': 'chat', 'enum': list([ - 'function', + 'chat', ]), 'title': 'Type', 'type': 'string', @@ -211,36 +306,14 @@ }), 'required': list([ 'content', - 'name', + 'role', ]), - 'title': 'FunctionMessage', + 'title': 'ChatMessage', 'type': 'object', }), - 'HumanMessage': dict({ - 'description': ''' - Message from a human. - - HumanMessages are messages that are passed in from a human to the model. - - Example: - - .. code-block:: python - - from langchain_core.messages import HumanMessage, SystemMessage - - messages = [ - SystemMessage( - content="You are a helpful assistant! Your name is Bob." - ), - HumanMessage( - content="What is your name?" - ) - ] - - # Instantiate a chat model and invoke it with the messages - model = ... - print(model.invoke(messages)) - ''', + 'ChatMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'Chat Message chunk.', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -267,97 +340,66 @@ ]), 'title': 'Content', }), - 'example': dict({ - 'default': False, - 'title': 'Example', - 'type': 'boolean', - }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Name', - 'type': 'string', }), 'response_metadata': dict({ 'title': 'Response Metadata', 'type': 'object', }), - 'type': dict({ - 'default': 'human', - 'enum': list([ - 'human', - ]), - 'title': 'Type', - 'type': 'string', - }), - }), - 'required': list([ - 'content', - ]), - 'title': 'HumanMessage', - 'type': 'object', - }), - 'InvalidToolCall': dict({ - 'properties': dict({ - 'args': dict({ - 'title': 'Args', - 'type': 'string', - }), - 'error': dict({ - 'title': 'Error', - 'type': 'string', - }), - 'id': dict({ - 'title': 'Id', - 'type': 'string', - }), - 'name': dict({ - 'title': 'Name', + 'role': dict({ + 'title': 'Role', 'type': 'string', }), 'type': dict({ + 'const': 'ChatMessageChunk', + 'default': 'ChatMessageChunk', 'enum': list([ - 'invalid_tool_call', + 'ChatMessageChunk', ]), 'title': 'Type', 'type': 'string', }), }), 'required': list([ - 'name', - 'args', - 'id', - 'error', + 'content', + 'role', ]), - 'title': 'InvalidToolCall', + 'title': 'ChatMessageChunk', 'type': 'object', }), - 'SystemMessage': dict({ + 'FunctionMessage': dict({ + 'additionalProperties': True, 'description': ''' - Message for priming AI behavior. - - The system message is usually passed in as the first of a sequence - of input messages. - - Example: - - .. code-block:: python - - from langchain_core.messages import HumanMessage, SystemMessage + Message for passing the result of executing a tool back to a model. - messages = [ - SystemMessage( - content="You are a helpful assistant! Your name is Bob." - ), - HumanMessage( - content="What is your name?" - ) - ] + FunctionMessage are an older version of the ToolMessage schema, and + do not contain the tool_call_id field. - # Define a chat model and invoke it with the messages - print(model.invoke(messages)) + The tool_call_id field is used to associate the tool call request with the + tool call response. This is useful in situations where a chat model is able + to request multiple tool calls in parallel. ''', 'properties': dict({ 'additional_kwargs': dict({ @@ -386,8 +428,16 @@ 'title': 'Content', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', }), 'name': dict({ 'title': 'Name', @@ -398,9 +448,10 @@ 'type': 'object', }), 'type': dict({ - 'default': 'system', + 'const': 'function', + 'default': 'function', 'enum': list([ - 'system', + 'function', ]), 'title': 'Type', 'type': 'string', @@ -408,89 +459,108 @@ }), 'required': list([ 'content', + 'name', ]), - 'title': 'SystemMessage', + 'title': 'FunctionMessage', 'type': 'object', }), - 'ToolCall': dict({ + 'FunctionMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'Function Message chunk.', 'properties': dict({ - 'args': dict({ - 'title': 'Args', - 'type': 'object', + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', }), 'name': dict({ 'title': 'Name', 'type': 'string', }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), 'type': dict({ + 'const': 'FunctionMessageChunk', + 'default': 'FunctionMessageChunk', 'enum': list([ - 'tool_call', + 'FunctionMessageChunk', ]), 'title': 'Type', 'type': 'string', }), }), 'required': list([ + 'content', 'name', - 'args', - 'id', ]), - 'title': 'ToolCall', + 'title': 'FunctionMessageChunk', 'type': 'object', }), - 'ToolMessage': dict({ + 'HumanMessage': dict({ + 'additionalProperties': True, 'description': ''' - Message for passing the result of executing a tool back to a model. - - ToolMessages contain the result of a tool invocation. Typically, the result - is encoded inside the `content` field. - - Example: A ToolMessage representing a result of 42 from a tool call with id - - .. code-block:: python - - from langchain_core.messages import ToolMessage - - ToolMessage(content='42', tool_call_id='call_Jja7J89XsjrOLA5r!MEOW!SL') - + Message from a human. - Example: A ToolMessage where only part of the tool output is sent to the model - and the full output is passed in to artifact. + HumanMessages are messages that are passed in from a human to the model. - .. versionadded:: 0.2.17 + Example: .. code-block:: python - from langchain_core.messages import ToolMessage - - tool_output = { - "stdout": "From the graph we can see that the correlation between x and y is ...", - "stderr": None, - "artifacts": {"type": "image", "base64_data": "/9j/4gIcSU..."}, - } + from langchain_core.messages import HumanMessage, SystemMessage - ToolMessage( - content=tool_output["stdout"], - artifact=tool_output, - tool_call_id='call_Jja7J89XsjrOLA5r!MEOW!SL', - ) + messages = [ + SystemMessage( + content="You are a helpful assistant! Your name is Bob." + ), + HumanMessage( + content="What is your name?" + ) + ] - The tool_call_id field is used to associate the tool call request with the - tool call response. This is useful in situations where a chat model is able - to request multiple tool calls in parallel. + # Instantiate a chat model and invoke it with the messages + model = ... + print(model.invoke(messages)) ''', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', 'type': 'object', }), - 'artifact': dict({ - 'title': 'Artifact', - }), 'content': dict({ 'anyOf': list([ dict({ @@ -512,35 +582,44 @@ ]), 'title': 'Content', }), + 'example': dict({ + 'default': False, + 'title': 'Example', + 'type': 'boolean', + }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Name', - 'type': 'string', }), 'response_metadata': dict({ 'title': 'Response Metadata', 'type': 'object', }), - 'status': dict({ - 'default': 'success', - 'enum': list([ - 'success', - 'error', - ]), - 'title': 'Status', - 'type': 'string', - }), - 'tool_call_id': dict({ - 'title': 'Tool Call Id', - 'type': 'string', - }), 'type': dict({ - 'default': 'tool', + 'const': 'human', + 'default': 'human', 'enum': list([ - 'tool', + 'human', ]), 'title': 'Type', 'type': 'string', @@ -548,87 +627,13 @@ }), 'required': list([ 'content', - 'tool_call_id', - ]), - 'title': 'ToolMessage', - 'type': 'object', - }), - 'UsageMetadata': dict({ - 'properties': dict({ - 'input_tokens': dict({ - 'title': 'Input Tokens', - 'type': 'integer', - }), - 'output_tokens': dict({ - 'title': 'Output Tokens', - 'type': 'integer', - }), - 'total_tokens': dict({ - 'title': 'Total Tokens', - 'type': 'integer', - }), - }), - 'required': list([ - 'input_tokens', - 'output_tokens', - 'total_tokens', ]), - 'title': 'UsageMetadata', + 'title': 'HumanMessage', 'type': 'object', }), - }), - 'properties': dict({ - 'history': dict({ - 'items': dict({ - 'anyOf': list([ - dict({ - '$ref': '#/definitions/AIMessage', - }), - dict({ - '$ref': '#/definitions/HumanMessage', - }), - dict({ - '$ref': '#/definitions/ChatMessage', - }), - dict({ - '$ref': '#/definitions/SystemMessage', - }), - dict({ - '$ref': '#/definitions/FunctionMessage', - }), - dict({ - '$ref': '#/definitions/ToolMessage', - }), - ]), - }), - 'title': 'History', - 'type': 'array', - }), - 'input': dict({ - 'title': 'Input', - 'type': 'string', - }), - }), - 'required': list([ - 'input', - ]), - 'title': 'PromptInput', - 'type': 'object', - }) -# --- -# name: test_chat_input_schema[required] - dict({ - 'definitions': dict({ - 'AIMessage': dict({ - 'description': ''' - Message from an AI. - - AIMessage is returned from a chat model as a response to a prompt. - - This message represents the output of the model and consists of both - the raw output as returned by the model together standardized fields - (e.g., tool calls, usage metadata) added by the LangChain framework. - ''', + 'HumanMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'Human Message chunk.', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -661,55 +666,1145 @@ 'type': 'boolean', }), 'id': dict({ - 'title': 'Id', - 'type': 'string', - }), - 'invalid_tool_calls': dict({ - 'default': list([ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), ]), - 'items': dict({ - '$ref': '#/definitions/InvalidToolCall', - }), - 'title': 'Invalid Tool Calls', - 'type': 'array', + 'default': None, + 'title': 'Id', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Name', - 'type': 'string', }), 'response_metadata': dict({ 'title': 'Response Metadata', 'type': 'object', }), - 'tool_calls': dict({ - 'default': list([ - ]), - 'items': dict({ - '$ref': '#/definitions/ToolCall', - }), - 'title': 'Tool Calls', - 'type': 'array', - }), 'type': dict({ - 'default': 'ai', + 'const': 'HumanMessageChunk', + 'default': 'HumanMessageChunk', 'enum': list([ - 'ai', + 'HumanMessageChunk', ]), 'title': 'Type', 'type': 'string', }), - 'usage_metadata': dict({ - '$ref': '#/definitions/UsageMetadata', + }), + 'required': list([ + 'content', + ]), + 'title': 'HumanMessageChunk', + 'type': 'object', + }), + 'InvalidToolCall': dict({ + 'description': ''' + Allowance for errors made by LLM. + + Here we add an `error` key to surface errors made during generation + (e.g., invalid JSON arguments.) + ''', + 'properties': dict({ + 'args': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Args', + }), + 'error': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Error', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Id', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Name', + }), + 'type': dict({ + 'const': 'invalid_tool_call', + 'enum': list([ + 'invalid_tool_call', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'name', + 'args', + 'id', + 'error', + ]), + 'title': 'InvalidToolCall', + 'type': 'object', + }), + 'SystemMessage': dict({ + 'additionalProperties': True, + 'description': ''' + Message for priming AI behavior. + + The system message is usually passed in as the first of a sequence + of input messages. + + Example: + + .. code-block:: python + + from langchain_core.messages import HumanMessage, SystemMessage + + messages = [ + SystemMessage( + content="You are a helpful assistant! Your name is Bob." + ), + HumanMessage( + content="What is your name?" + ) + ] + + # Define a chat model and invoke it with the messages + print(model.invoke(messages)) + ''', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'type': dict({ + 'const': 'system', + 'default': 'system', + 'enum': list([ + 'system', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'content', + ]), + 'title': 'SystemMessage', + 'type': 'object', + }), + 'SystemMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'System Message chunk.', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'type': dict({ + 'const': 'SystemMessageChunk', + 'default': 'SystemMessageChunk', + 'enum': list([ + 'SystemMessageChunk', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'content', + ]), + 'title': 'SystemMessageChunk', + 'type': 'object', + }), + 'ToolCall': dict({ + 'description': ''' + Represents a request to call a tool. + + Example: + + .. code-block:: python + + { + "name": "foo", + "args": {"a": 1}, + "id": "123" + } + + This represents a request to call the tool named "foo" with arguments {"a": 1} + and an identifier of "123". + ''', + 'properties': dict({ + 'args': dict({ + 'title': 'Args', + 'type': 'object', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Id', + }), + 'name': dict({ + 'title': 'Name', + 'type': 'string', + }), + 'type': dict({ + 'const': 'tool_call', + 'enum': list([ + 'tool_call', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'name', + 'args', + 'id', + ]), + 'title': 'ToolCall', + 'type': 'object', + }), + 'ToolCallChunk': dict({ + 'description': ''' + A chunk of a tool call (e.g., as part of a stream). + + When merging ToolCallChunks (e.g., via AIMessageChunk.__add__), + all string attributes are concatenated. Chunks are only merged if their + values of `index` are equal and not None. + + Example: + + .. code-block:: python + + left_chunks = [ToolCallChunk(name="foo", args='{"a":', index=0)] + right_chunks = [ToolCallChunk(name=None, args='1}', index=0)] + + ( + AIMessageChunk(content="", tool_call_chunks=left_chunks) + + AIMessageChunk(content="", tool_call_chunks=right_chunks) + ).tool_call_chunks == [ToolCallChunk(name='foo', args='{"a":1}', index=0)] + ''', + 'properties': dict({ + 'args': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Args', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Id', + }), + 'index': dict({ + 'anyOf': list([ + dict({ + 'type': 'integer', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Index', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Name', + }), + 'type': dict({ + 'const': 'tool_call_chunk', + 'enum': list([ + 'tool_call_chunk', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'name', + 'args', + 'id', + 'index', + ]), + 'title': 'ToolCallChunk', + 'type': 'object', + }), + 'ToolMessage': dict({ + 'additionalProperties': True, + 'description': ''' + Message for passing the result of executing a tool back to a model. + + ToolMessages contain the result of a tool invocation. Typically, the result + is encoded inside the `content` field. + + Example: A ToolMessage representing a result of 42 from a tool call with id + + .. code-block:: python + + from langchain_core.messages import ToolMessage + + ToolMessage(content='42', tool_call_id='call_Jja7J89XsjrOLA5r!MEOW!SL') + + + Example: A ToolMessage where only part of the tool output is sent to the model + and the full output is passed in to artifact. + + .. versionadded:: 0.2.17 + + .. code-block:: python + + from langchain_core.messages import ToolMessage + + tool_output = { + "stdout": "From the graph we can see that the correlation between x and y is ...", + "stderr": None, + "artifacts": {"type": "image", "base64_data": "/9j/4gIcSU..."}, + } + + ToolMessage( + content=tool_output["stdout"], + artifact=tool_output, + tool_call_id='call_Jja7J89XsjrOLA5r!MEOW!SL', + ) + + The tool_call_id field is used to associate the tool call request with the + tool call response. This is useful in situations where a chat model is able + to request multiple tool calls in parallel. + ''', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'artifact': dict({ + 'default': None, + 'title': 'Artifact', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'status': dict({ + 'default': 'success', + 'enum': list([ + 'success', + 'error', + ]), + 'title': 'Status', + 'type': 'string', + }), + 'tool_call_id': dict({ + 'title': 'Tool Call Id', + 'type': 'string', + }), + 'type': dict({ + 'const': 'tool', + 'default': 'tool', + 'enum': list([ + 'tool', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'content', + 'tool_call_id', + ]), + 'title': 'ToolMessage', + 'type': 'object', + }), + 'ToolMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'Tool Message chunk.', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'artifact': dict({ + 'default': None, + 'title': 'Artifact', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'status': dict({ + 'default': 'success', + 'enum': list([ + 'success', + 'error', + ]), + 'title': 'Status', + 'type': 'string', + }), + 'tool_call_id': dict({ + 'title': 'Tool Call Id', + 'type': 'string', + }), + 'type': dict({ + 'const': 'ToolMessageChunk', + 'default': 'ToolMessageChunk', + 'enum': list([ + 'ToolMessageChunk', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'content', + 'tool_call_id', + ]), + 'title': 'ToolMessageChunk', + 'type': 'object', + }), + 'UsageMetadata': dict({ + 'description': ''' + Usage metadata for a message, such as token counts. + + This is a standard representation of token usage that is consistent across models. + + Example: + + .. code-block:: python + + { + "input_tokens": 10, + "output_tokens": 20, + "total_tokens": 30 + } + ''', + 'properties': dict({ + 'input_tokens': dict({ + 'title': 'Input Tokens', + 'type': 'integer', + }), + 'output_tokens': dict({ + 'title': 'Output Tokens', + 'type': 'integer', + }), + 'total_tokens': dict({ + 'title': 'Total Tokens', + 'type': 'integer', + }), + }), + 'required': list([ + 'input_tokens', + 'output_tokens', + 'total_tokens', + ]), + 'title': 'UsageMetadata', + 'type': 'object', + }), + }), + 'properties': dict({ + 'history': dict({ + 'default': None, + 'items': dict({ + 'anyOf': list([ + dict({ + '$ref': '#/$defs/AIMessage', + }), + dict({ + '$ref': '#/$defs/HumanMessage', + }), + dict({ + '$ref': '#/$defs/ChatMessage', + }), + dict({ + '$ref': '#/$defs/SystemMessage', + }), + dict({ + '$ref': '#/$defs/FunctionMessage', + }), + dict({ + '$ref': '#/$defs/ToolMessage', + }), + dict({ + '$ref': '#/$defs/AIMessageChunk', + }), + dict({ + '$ref': '#/$defs/HumanMessageChunk', + }), + dict({ + '$ref': '#/$defs/ChatMessageChunk', + }), + dict({ + '$ref': '#/$defs/SystemMessageChunk', + }), + dict({ + '$ref': '#/$defs/FunctionMessageChunk', + }), + dict({ + '$ref': '#/$defs/ToolMessageChunk', + }), + ]), + }), + 'title': 'History', + 'type': 'array', + }), + 'input': dict({ + 'title': 'Input', + 'type': 'string', + }), + }), + 'required': list([ + 'input', + ]), + 'title': 'PromptInput', + 'type': 'object', + }) +# --- +# name: test_chat_input_schema[required] + dict({ + '$defs': dict({ + 'AIMessage': dict({ + 'additionalProperties': True, + 'description': ''' + Message from an AI. + + AIMessage is returned from a chat model as a response to a prompt. + + This message represents the output of the model and consists of both + the raw output as returned by the model together standardized fields + (e.g., tool calls, usage metadata) added by the LangChain framework. + ''', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'example': dict({ + 'default': False, + 'title': 'Example', + 'type': 'boolean', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'invalid_tool_calls': dict({ + 'default': list([ + ]), + 'items': dict({ + '$ref': '#/$defs/InvalidToolCall', + }), + 'title': 'Invalid Tool Calls', + 'type': 'array', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'tool_calls': dict({ + 'default': list([ + ]), + 'items': dict({ + '$ref': '#/$defs/ToolCall', + }), + 'title': 'Tool Calls', + 'type': 'array', + }), + 'type': dict({ + 'const': 'ai', + 'default': 'ai', + 'enum': list([ + 'ai', + ]), + 'title': 'Type', + 'type': 'string', + }), + 'usage_metadata': dict({ + 'anyOf': list([ + dict({ + '$ref': '#/$defs/UsageMetadata', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + }), + }), + 'required': list([ + 'content', + ]), + 'title': 'AIMessage', + 'type': 'object', + }), + 'AIMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'Message chunk from an AI.', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'example': dict({ + 'default': False, + 'title': 'Example', + 'type': 'boolean', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'invalid_tool_calls': dict({ + 'default': list([ + ]), + 'items': dict({ + '$ref': '#/$defs/InvalidToolCall', + }), + 'title': 'Invalid Tool Calls', + 'type': 'array', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'tool_call_chunks': dict({ + 'default': list([ + ]), + 'items': dict({ + '$ref': '#/$defs/ToolCallChunk', + }), + 'title': 'Tool Call Chunks', + 'type': 'array', + }), + 'tool_calls': dict({ + 'default': list([ + ]), + 'items': dict({ + '$ref': '#/$defs/ToolCall', + }), + 'title': 'Tool Calls', + 'type': 'array', + }), + 'type': dict({ + 'const': 'AIMessageChunk', + 'default': 'AIMessageChunk', + 'enum': list([ + 'AIMessageChunk', + ]), + 'title': 'Type', + 'type': 'string', + }), + 'usage_metadata': dict({ + 'anyOf': list([ + dict({ + '$ref': '#/$defs/UsageMetadata', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + }), + }), + 'required': list([ + 'content', + ]), + 'title': 'AIMessageChunk', + 'type': 'object', + }), + 'ChatMessage': dict({ + 'additionalProperties': True, + 'description': 'Message that can be assigned an arbitrary speaker (i.e. role).', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'role': dict({ + 'title': 'Role', + 'type': 'string', + }), + 'type': dict({ + 'const': 'chat', + 'default': 'chat', + 'enum': list([ + 'chat', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'content', + 'role', + ]), + 'title': 'ChatMessage', + 'type': 'object', + }), + 'ChatMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'Chat Message chunk.', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'role': dict({ + 'title': 'Role', + 'type': 'string', + }), + 'type': dict({ + 'const': 'ChatMessageChunk', + 'default': 'ChatMessageChunk', + 'enum': list([ + 'ChatMessageChunk', + ]), + 'title': 'Type', + 'type': 'string', }), }), 'required': list([ 'content', + 'role', ]), - 'title': 'AIMessage', + 'title': 'ChatMessageChunk', 'type': 'object', }), - 'ChatMessage': dict({ - 'description': 'Message that can be assigned an arbitrary speaker (i.e. role).', + 'FunctionMessage': dict({ + 'additionalProperties': True, + 'description': ''' + Message for passing the result of executing a tool back to a model. + + FunctionMessage are an older version of the ToolMessage schema, and + do not contain the tool_call_id field. + + The tool_call_id field is used to associate the tool call request with the + tool call response. This is useful in situations where a chat model is able + to request multiple tool calls in parallel. + ''', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -737,8 +1832,16 @@ 'title': 'Content', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', }), 'name': dict({ 'title': 'Name', @@ -748,14 +1851,11 @@ 'title': 'Response Metadata', 'type': 'object', }), - 'role': dict({ - 'title': 'Role', - 'type': 'string', - }), 'type': dict({ - 'default': 'chat', + 'const': 'function', + 'default': 'function', 'enum': list([ - 'chat', + 'function', ]), 'title': 'Type', 'type': 'string', @@ -763,22 +1863,14 @@ }), 'required': list([ 'content', - 'role', + 'name', ]), - 'title': 'ChatMessage', + 'title': 'FunctionMessage', 'type': 'object', }), - 'FunctionMessage': dict({ - 'description': ''' - Message for passing the result of executing a tool back to a model. - - FunctionMessage are an older version of the ToolMessage schema, and - do not contain the tool_call_id field. - - The tool_call_id field is used to associate the tool call request with the - tool call response. This is useful in situations where a chat model is able - to request multiple tool calls in parallel. - ''', + 'FunctionMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'Function Message chunk.', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -806,8 +1898,16 @@ 'title': 'Content', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', }), 'name': dict({ 'title': 'Name', @@ -818,9 +1918,10 @@ 'type': 'object', }), 'type': dict({ - 'default': 'function', + 'const': 'FunctionMessageChunk', + 'default': 'FunctionMessageChunk', 'enum': list([ - 'function', + 'FunctionMessageChunk', ]), 'title': 'Type', 'type': 'string', @@ -830,10 +1931,11 @@ 'content', 'name', ]), - 'title': 'FunctionMessage', + 'title': 'FunctionMessageChunk', 'type': 'object', }), 'HumanMessage': dict({ + 'additionalProperties': True, 'description': ''' Message from a human. @@ -890,18 +1992,35 @@ 'type': 'boolean', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Name', - 'type': 'string', }), 'response_metadata': dict({ 'title': 'Response Metadata', 'type': 'object', }), 'type': dict({ + 'const': 'human', 'default': 'human', 'enum': list([ 'human', @@ -916,66 +2035,253 @@ 'title': 'HumanMessage', 'type': 'object', }), + 'HumanMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'Human Message chunk.', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'example': dict({ + 'default': False, + 'title': 'Example', + 'type': 'boolean', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'type': dict({ + 'const': 'HumanMessageChunk', + 'default': 'HumanMessageChunk', + 'enum': list([ + 'HumanMessageChunk', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'content', + ]), + 'title': 'HumanMessageChunk', + 'type': 'object', + }), 'InvalidToolCall': dict({ + 'description': ''' + Allowance for errors made by LLM. + + Here we add an `error` key to surface errors made during generation + (e.g., invalid JSON arguments.) + ''', 'properties': dict({ 'args': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), 'title': 'Args', - 'type': 'string', }), 'error': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), 'title': 'Error', - 'type': 'string', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), 'title': 'Id', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Name', + }), + 'type': dict({ + 'const': 'invalid_tool_call', + 'enum': list([ + 'invalid_tool_call', + ]), + 'title': 'Type', 'type': 'string', }), + }), + 'required': list([ + 'name', + 'args', + 'id', + 'error', + ]), + 'title': 'InvalidToolCall', + 'type': 'object', + }), + 'SystemMessage': dict({ + 'additionalProperties': True, + 'description': ''' + Message for priming AI behavior. + + The system message is usually passed in as the first of a sequence + of input messages. + + Example: + + .. code-block:: python + + from langchain_core.messages import HumanMessage, SystemMessage + + messages = [ + SystemMessage( + content="You are a helpful assistant! Your name is Bob." + ), + HumanMessage( + content="What is your name?" + ) + ] + + # Define a chat model and invoke it with the messages + print(model.invoke(messages)) + ''', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Name', - 'type': 'string', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', }), 'type': dict({ + 'const': 'system', + 'default': 'system', 'enum': list([ - 'invalid_tool_call', + 'system', ]), 'title': 'Type', 'type': 'string', }), }), 'required': list([ - 'name', - 'args', - 'id', - 'error', + 'content', ]), - 'title': 'InvalidToolCall', + 'title': 'SystemMessage', 'type': 'object', }), - 'SystemMessage': dict({ - 'description': ''' - Message for priming AI behavior. - - The system message is usually passed in as the first of a sequence - of input messages. - - Example: - - .. code-block:: python - - from langchain_core.messages import HumanMessage, SystemMessage - - messages = [ - SystemMessage( - content="You are a helpful assistant! Your name is Bob." - ), - HumanMessage( - content="What is your name?" - ) - ] - - # Define a chat model and invoke it with the messages - print(model.invoke(messages)) - ''', + 'SystemMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'System Message chunk.', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -1003,21 +2309,38 @@ 'title': 'Content', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Name', - 'type': 'string', }), 'response_metadata': dict({ 'title': 'Response Metadata', 'type': 'object', }), 'type': dict({ - 'default': 'system', + 'const': 'SystemMessageChunk', + 'default': 'SystemMessageChunk', 'enum': list([ - 'system', + 'SystemMessageChunk', ]), 'title': 'Type', 'type': 'string', @@ -1026,24 +2349,48 @@ 'required': list([ 'content', ]), - 'title': 'SystemMessage', + 'title': 'SystemMessageChunk', 'type': 'object', }), 'ToolCall': dict({ + 'description': ''' + Represents a request to call a tool. + + Example: + + .. code-block:: python + + { + "name": "foo", + "args": {"a": 1}, + "id": "123" + } + + This represents a request to call the tool named "foo" with arguments {"a": 1} + and an identifier of "123". + ''', 'properties': dict({ 'args': dict({ 'title': 'Args', 'type': 'object', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), 'title': 'Id', - 'type': 'string', }), 'name': dict({ 'title': 'Name', 'type': 'string', }), 'type': dict({ + 'const': 'tool_call', 'enum': list([ 'tool_call', ]), @@ -1059,7 +2406,91 @@ 'title': 'ToolCall', 'type': 'object', }), + 'ToolCallChunk': dict({ + 'description': ''' + A chunk of a tool call (e.g., as part of a stream). + + When merging ToolCallChunks (e.g., via AIMessageChunk.__add__), + all string attributes are concatenated. Chunks are only merged if their + values of `index` are equal and not None. + + Example: + + .. code-block:: python + + left_chunks = [ToolCallChunk(name="foo", args='{"a":', index=0)] + right_chunks = [ToolCallChunk(name=None, args='1}', index=0)] + + ( + AIMessageChunk(content="", tool_call_chunks=left_chunks) + + AIMessageChunk(content="", tool_call_chunks=right_chunks) + ).tool_call_chunks == [ToolCallChunk(name='foo', args='{"a":1}', index=0)] + ''', + 'properties': dict({ + 'args': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Args', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Id', + }), + 'index': dict({ + 'anyOf': list([ + dict({ + 'type': 'integer', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Index', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Name', + }), + 'type': dict({ + 'const': 'tool_call_chunk', + 'enum': list([ + 'tool_call_chunk', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'name', + 'args', + 'id', + 'index', + ]), + 'title': 'ToolCallChunk', + 'type': 'object', + }), 'ToolMessage': dict({ + 'additionalProperties': True, 'description': ''' Message for passing the result of executing a tool back to a model. @@ -1106,6 +2537,7 @@ 'type': 'object', }), 'artifact': dict({ + 'default': None, 'title': 'Artifact', }), 'content': dict({ @@ -1130,12 +2562,28 @@ 'title': 'Content', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Name', - 'type': 'string', }), 'response_metadata': dict({ 'title': 'Response Metadata', @@ -1155,6 +2603,7 @@ 'type': 'string', }), 'type': dict({ + 'const': 'tool', 'default': 'tool', 'enum': list([ 'tool', @@ -1170,7 +2619,113 @@ 'title': 'ToolMessage', 'type': 'object', }), + 'ToolMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'Tool Message chunk.', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'artifact': dict({ + 'default': None, + 'title': 'Artifact', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'status': dict({ + 'default': 'success', + 'enum': list([ + 'success', + 'error', + ]), + 'title': 'Status', + 'type': 'string', + }), + 'tool_call_id': dict({ + 'title': 'Tool Call Id', + 'type': 'string', + }), + 'type': dict({ + 'const': 'ToolMessageChunk', + 'default': 'ToolMessageChunk', + 'enum': list([ + 'ToolMessageChunk', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'content', + 'tool_call_id', + ]), + 'title': 'ToolMessageChunk', + 'type': 'object', + }), 'UsageMetadata': dict({ + 'description': ''' + Usage metadata for a message, such as token counts. + + This is a standard representation of token usage that is consistent across models. + + Example: + + .. code-block:: python + + { + "input_tokens": 10, + "output_tokens": 20, + "total_tokens": 30 + } + ''', 'properties': dict({ 'input_tokens': dict({ 'title': 'Input Tokens', @@ -1199,22 +2754,40 @@ 'items': dict({ 'anyOf': list([ dict({ - '$ref': '#/definitions/AIMessage', + '$ref': '#/$defs/AIMessage', + }), + dict({ + '$ref': '#/$defs/HumanMessage', + }), + dict({ + '$ref': '#/$defs/ChatMessage', + }), + dict({ + '$ref': '#/$defs/SystemMessage', + }), + dict({ + '$ref': '#/$defs/FunctionMessage', + }), + dict({ + '$ref': '#/$defs/ToolMessage', + }), + dict({ + '$ref': '#/$defs/AIMessageChunk', }), dict({ - '$ref': '#/definitions/HumanMessage', + '$ref': '#/$defs/HumanMessageChunk', }), dict({ - '$ref': '#/definitions/ChatMessage', + '$ref': '#/$defs/ChatMessageChunk', }), dict({ - '$ref': '#/definitions/SystemMessage', + '$ref': '#/$defs/SystemMessageChunk', }), dict({ - '$ref': '#/definitions/FunctionMessage', + '$ref': '#/$defs/FunctionMessageChunk', }), dict({ - '$ref': '#/definitions/ToolMessage', + '$ref': '#/$defs/ToolMessageChunk', }), ]), }), @@ -1436,7 +3009,7 @@ 'type': 'constructor', }) # --- -# name: test_chat_prompt_w_msgs_placeholder_ser_des[placholder] +# name: test_chat_prompt_w_msgs_placeholder_ser_des[placeholder] dict({ 'id': list([ 'langchain', diff --git a/libs/core/tests/unit_tests/prompts/test_chat.py b/libs/core/tests/unit_tests/prompts/test_chat.py index a280ff1dbb670..a5145fbfaf970 100644 --- a/libs/core/tests/unit_tests/prompts/test_chat.py +++ b/libs/core/tests/unit_tests/prompts/test_chat.py @@ -4,9 +4,12 @@ from typing import Any, List, Tuple, Union, cast import pytest +from pydantic import ValidationError from syrupy import SnapshotAssertion -from langchain_core._api.deprecation import LangChainPendingDeprecationWarning +from langchain_core._api.deprecation import ( + LangChainPendingDeprecationWarning, +) from langchain_core.load import dumpd, load from langchain_core.messages import ( AIMessage, @@ -28,8 +31,6 @@ SystemMessagePromptTemplate, _convert_to_message, ) -from langchain_core.pydantic_v1 import ValidationError -from tests.unit_tests.pydantic_utils import _schema @pytest.fixture @@ -794,21 +795,21 @@ def test_chat_input_schema(snapshot: SnapshotAssertion) -> None: assert prompt_all_required.optional_variables == [] with pytest.raises(ValidationError): prompt_all_required.input_schema(input="") - assert _schema(prompt_all_required.input_schema) == snapshot(name="required") + assert prompt_all_required.get_input_jsonschema() == snapshot(name="required") prompt_optional = ChatPromptTemplate( messages=[MessagesPlaceholder("history", optional=True), ("user", "${input}")] ) # input variables only lists required variables assert set(prompt_optional.input_variables) == {"input"} prompt_optional.input_schema(input="") # won't raise error - assert _schema(prompt_optional.input_schema) == snapshot(name="partial") + assert prompt_optional.get_input_jsonschema() == snapshot(name="partial") def test_chat_prompt_w_msgs_placeholder_ser_des(snapshot: SnapshotAssertion) -> None: prompt = ChatPromptTemplate.from_messages( [("system", "foo"), MessagesPlaceholder("bar"), ("human", "baz")] ) - assert dumpd(MessagesPlaceholder("bar")) == snapshot(name="placholder") + assert dumpd(MessagesPlaceholder("bar")) == snapshot(name="placeholder") assert load(dumpd(MessagesPlaceholder("bar"))) == MessagesPlaceholder("bar") assert dumpd(prompt) == snapshot(name="chat_prompt") assert load(dumpd(prompt)) == prompt diff --git a/libs/core/tests/unit_tests/prompts/test_prompt.py b/libs/core/tests/unit_tests/prompts/test_prompt.py index a9bee11ba8933..396114c718d29 100644 --- a/libs/core/tests/unit_tests/prompts/test_prompt.py +++ b/libs/core/tests/unit_tests/prompts/test_prompt.py @@ -7,7 +7,6 @@ from langchain_core.prompts.prompt import PromptTemplate from langchain_core.tracers.run_collector import RunCollectorCallbackHandler -from tests.unit_tests.pydantic_utils import _schema def test_prompt_valid() -> None: @@ -70,10 +69,10 @@ def test_mustache_prompt_from_template() -> None: prompt = PromptTemplate.from_template(template, template_format="mustache") assert prompt.format(foo="bar") == "This is a bar test." assert prompt.input_variables == ["foo"] - assert _schema(prompt.input_schema) == { + assert prompt.get_input_jsonschema() == { "title": "PromptInput", "type": "object", - "properties": {"foo": {"title": "Foo", "type": "string"}}, + "properties": {"foo": {"title": "Foo", "type": "string", "default": None}}, } # Multiple input variables. @@ -81,12 +80,12 @@ def test_mustache_prompt_from_template() -> None: prompt = PromptTemplate.from_template(template, template_format="mustache") assert prompt.format(bar="baz", foo="bar") == "This baz is a bar test." assert prompt.input_variables == ["bar", "foo"] - assert _schema(prompt.input_schema) == { + assert prompt.get_input_jsonschema() == { "title": "PromptInput", "type": "object", "properties": { - "bar": {"title": "Bar", "type": "string"}, - "foo": {"title": "Foo", "type": "string"}, + "bar": {"title": "Bar", "type": "string", "default": None}, + "foo": {"title": "Foo", "type": "string", "default": None}, }, } @@ -95,12 +94,12 @@ def test_mustache_prompt_from_template() -> None: prompt = PromptTemplate.from_template(template, template_format="mustache") assert prompt.format(bar="baz", foo="bar") == "This baz is a bar test bar." assert prompt.input_variables == ["bar", "foo"] - assert _schema(prompt.input_schema) == { + assert prompt.get_input_jsonschema() == { "title": "PromptInput", "type": "object", "properties": { - "bar": {"title": "Bar", "type": "string"}, - "foo": {"title": "Foo", "type": "string"}, + "bar": {"title": "Bar", "type": "string", "default": None}, + "foo": {"title": "Foo", "type": "string", "default": None}, }, } @@ -111,23 +110,23 @@ def test_mustache_prompt_from_template() -> None: "This foo is a bar test baz." ) assert prompt.input_variables == ["foo", "obj"] - assert _schema(prompt.input_schema) == { - "title": "PromptInput", - "type": "object", - "properties": { - "foo": {"title": "Foo", "type": "string"}, - "obj": {"$ref": "#/definitions/obj"}, - }, - "definitions": { + assert prompt.get_input_jsonschema() == { + "$defs": { "obj": { - "title": "obj", - "type": "object", "properties": { - "foo": {"title": "Foo", "type": "string"}, - "bar": {"title": "Bar", "type": "string"}, + "bar": {"default": None, "title": "Bar", "type": "string"}, + "foo": {"default": None, "title": "Foo", "type": "string"}, }, + "title": "obj", + "type": "object", } }, + "properties": { + "foo": {"default": None, "title": "Foo", "type": "string"}, + "obj": {"allOf": [{"$ref": "#/$defs/obj"}], "default": None}, + }, + "title": "PromptInput", + "type": "object", } # . variables @@ -135,7 +134,7 @@ def test_mustache_prompt_from_template() -> None: prompt = PromptTemplate.from_template(template, template_format="mustache") assert prompt.format(foo="baz") == ("This {'foo': 'baz'} is a test.") assert prompt.input_variables == [] - assert _schema(prompt.input_schema) == { + assert prompt.get_input_jsonschema() == { "title": "PromptInput", "type": "object", "properties": {}, @@ -152,17 +151,19 @@ def test_mustache_prompt_from_template() -> None: is a test.""" ) assert prompt.input_variables == ["foo"] - assert _schema(prompt.input_schema) == { - "title": "PromptInput", - "type": "object", - "properties": {"foo": {"$ref": "#/definitions/foo"}}, - "definitions": { + assert prompt.get_input_jsonschema() == { + "$defs": { "foo": { + "properties": { + "bar": {"default": None, "title": "Bar", "type": "string"} + }, "title": "foo", "type": "object", - "properties": {"bar": {"title": "Bar", "type": "string"}}, } }, + "properties": {"foo": {"allOf": [{"$ref": "#/$defs/foo"}], "default": None}}, + "title": "PromptInput", + "type": "object", } # more complex nested section/context variables @@ -184,26 +185,28 @@ def test_mustache_prompt_from_template() -> None: is a test.""" ) assert prompt.input_variables == ["foo"] - assert _schema(prompt.input_schema) == { - "title": "PromptInput", - "type": "object", - "properties": {"foo": {"$ref": "#/definitions/foo"}}, - "definitions": { - "foo": { - "title": "foo", - "type": "object", + assert prompt.get_input_jsonschema() == { + "$defs": { + "baz": { "properties": { - "bar": {"title": "Bar", "type": "string"}, - "baz": {"$ref": "#/definitions/baz"}, - "quux": {"title": "Quux", "type": "string"}, + "qux": {"default": None, "title": "Qux", "type": "string"} }, - }, - "baz": { "title": "baz", "type": "object", - "properties": {"qux": {"title": "Qux", "type": "string"}}, + }, + "foo": { + "properties": { + "bar": {"default": None, "title": "Bar", "type": "string"}, + "baz": {"allOf": [{"$ref": "#/$defs/baz"}], "default": None}, + "quux": {"default": None, "title": "Quux", "type": "string"}, + }, + "title": "foo", + "type": "object", }, }, + "properties": {"foo": {"allOf": [{"$ref": "#/$defs/foo"}], "default": None}}, + "title": "PromptInput", + "type": "object", } # triply nested section/context variables @@ -239,39 +242,43 @@ def test_mustache_prompt_from_template() -> None: is a test.""" ) assert prompt.input_variables == ["foo"] - assert _schema(prompt.input_schema) == { - "title": "PromptInput", - "type": "object", - "properties": {"foo": {"$ref": "#/definitions/foo"}}, - "definitions": { - "foo": { - "title": "foo", - "type": "object", + assert prompt.get_input_jsonschema() == { + "$defs": { + "barfoo": { "properties": { - "bar": {"title": "Bar", "type": "string"}, - "baz": {"$ref": "#/definitions/baz"}, - "quux": {"title": "Quux", "type": "string"}, + "foobar": {"default": None, "title": "Foobar", "type": "string"} }, + "title": "barfoo", + "type": "object", }, "baz": { + "properties": { + "qux": {"allOf": [{"$ref": "#/$defs/qux"}], "default": None} + }, "title": "baz", "type": "object", - "properties": {"qux": {"$ref": "#/definitions/qux"}}, }, - "qux": { - "title": "qux", - "type": "object", + "foo": { "properties": { - "foobar": {"title": "Foobar", "type": "string"}, - "barfoo": {"$ref": "#/definitions/barfoo"}, + "bar": {"default": None, "title": "Bar", "type": "string"}, + "baz": {"allOf": [{"$ref": "#/$defs/baz"}], "default": None}, + "quux": {"default": None, "title": "Quux", "type": "string"}, }, + "title": "foo", + "type": "object", }, - "barfoo": { - "title": "barfoo", + "qux": { + "properties": { + "barfoo": {"allOf": [{"$ref": "#/$defs/barfoo"}], "default": None}, + "foobar": {"default": None, "title": "Foobar", "type": "string"}, + }, + "title": "qux", "type": "object", - "properties": {"foobar": {"title": "Foobar", "type": "string"}}, }, }, + "properties": {"foo": {"allOf": [{"$ref": "#/$defs/foo"}], "default": None}}, + "title": "PromptInput", + "type": "object", } # section/context variables with repeats @@ -287,19 +294,20 @@ def test_mustache_prompt_from_template() -> None: is a test.""" ) assert prompt.input_variables == ["foo"] - assert _schema(prompt.input_schema) == { - "title": "PromptInput", - "type": "object", - "properties": {"foo": {"$ref": "#/definitions/foo"}}, - "definitions": { + assert prompt.get_input_jsonschema() == { + "$defs": { "foo": { + "properties": { + "bar": {"default": None, "title": "Bar", "type": "string"} + }, "title": "foo", "type": "object", - "properties": {"bar": {"title": "Bar", "type": "string"}}, } }, + "properties": {"foo": {"allOf": [{"$ref": "#/$defs/foo"}], "default": None}}, + "title": "PromptInput", + "type": "object", } - template = """This{{^foo}} no foos {{/foo}}is a test.""" @@ -310,10 +318,10 @@ def test_mustache_prompt_from_template() -> None: is a test.""" ) assert prompt.input_variables == ["foo"] - assert _schema(prompt.input_schema) == { + assert prompt.get_input_jsonschema() == { + "properties": {"foo": {"default": None, "title": "Foo", "type": "object"}}, "title": "PromptInput", "type": "object", - "properties": {"foo": {"title": "Foo", "type": "object"}}, } diff --git a/libs/core/tests/unit_tests/prompts/test_structured.py b/libs/core/tests/unit_tests/prompts/test_structured.py index 923a69e97d84d..eb17bcd0ca6d8 100644 --- a/libs/core/tests/unit_tests/prompts/test_structured.py +++ b/libs/core/tests/unit_tests/prompts/test_structured.py @@ -1,12 +1,14 @@ from functools import partial from inspect import isclass from typing import Any, Dict, Type, Union, cast +from typing import Optional as Optional + +from pydantic import BaseModel from langchain_core.language_models import FakeListChatModel from langchain_core.load.dump import dumps from langchain_core.load.load import loads from langchain_core.prompts.structured import StructuredPrompt -from langchain_core.pydantic_v1 import BaseModel from langchain_core.runnables.base import Runnable, RunnableLambda from langchain_core.utils.pydantic import is_basemodel_subclass @@ -34,6 +36,9 @@ def _llm_type(self) -> str: return "fake-messages-list-chat-model" +FakeStructuredChatModel.model_rebuild() + + def test_structured_prompt_pydantic() -> None: class OutputSchema(BaseModel): name: str diff --git a/libs/core/tests/unit_tests/pydantic_utils.py b/libs/core/tests/unit_tests/pydantic_utils.py index 7d7c06df6f615..438eb9d0be11e 100644 --- a/libs/core/tests/unit_tests/pydantic_utils.py +++ b/libs/core/tests/unit_tests/pydantic_utils.py @@ -1,20 +1,10 @@ -"""Helper utilities for pydantic. - -This module includes helper utilities to ease the migration from pydantic v1 to v2. - -They're meant to be used in the following way: - -1) Use utility code to help (selected) unit tests pass without modifications -2) Upgrade the unit tests to match pydantic 2 -3) Stop using the utility code -""" - from typing import Any +from langchain_core.utils.pydantic import is_basemodel_subclass + # Function to replace allOf with $ref -def _replace_all_of_with_ref(schema: Any) -> None: - """Replace allOf with $ref in the schema.""" +def replace_all_of_with_ref(schema: Any) -> None: if isinstance(schema, dict): # If the schema has an allOf key with a single item that contains a $ref if ( @@ -30,13 +20,13 @@ def _replace_all_of_with_ref(schema: Any) -> None: # Recursively process nested schemas for value in schema.values(): if isinstance(value, (dict, list)): - _replace_all_of_with_ref(value) + replace_all_of_with_ref(value) elif isinstance(schema, list): for item in schema: - _replace_all_of_with_ref(item) + replace_all_of_with_ref(item) -def _remove_bad_none_defaults(schema: Any) -> None: +def remove_all_none_default(schema: Any) -> None: """Removing all none defaults. Pydantic v1 did not generate these, but Pydantic v2 does. @@ -56,39 +46,48 @@ def _remove_bad_none_defaults(schema: Any) -> None: break # Null type explicitly defined else: del value["default"] - _remove_bad_none_defaults(value) + remove_all_none_default(value) elif isinstance(value, list): for item in value: - _remove_bad_none_defaults(item) + remove_all_none_default(item) elif isinstance(schema, list): for item in schema: - _remove_bad_none_defaults(item) + remove_all_none_default(item) -def _schema(obj: Any) -> dict: - """Get the schema of a pydantic model in the pydantic v1 style. +def _remove_enum_description(obj: Any) -> None: + """Remove the description from enums.""" + if isinstance(obj, dict): + if "enum" in obj: + if "description" in obj and obj["description"] == "An enumeration.": + del obj["description"] + for value in obj.values(): + _remove_enum_description(value) + elif isinstance(obj, list): + for item in obj: + _remove_enum_description(item) - This will attempt to map the schema as close as possible to the pydantic v1 schema. - """ + +def _schema(obj: Any) -> dict: + """Return the schema of the object.""" # Remap to old style schema + if not is_basemodel_subclass(obj): + raise TypeError( + f"Object must be a Pydantic BaseModel subclass. Got {type(obj)}" + ) if not hasattr(obj, "model_json_schema"): # V1 model return obj.schema() - # Then we're using V2 models internally. - raise AssertionError( - "Hi there! Looks like you're attempting to upgrade to Pydantic v2. If so: \n" - "1) remove this exception\n" - "2) confirm that the old unit tests pass, and if not look for difference\n" - "3) update the unit tests to match the new schema\n" - "4) remove this utility function\n" - ) - schema_ = obj.model_json_schema(ref_template="#/definitions/{model}") if "$defs" in schema_: schema_["definitions"] = schema_["$defs"] del schema_["$defs"] - _replace_all_of_with_ref(schema_) - _remove_bad_none_defaults(schema_) + if "default" in schema_ and schema_["default"] is None: + del schema_["default"] + + replace_all_of_with_ref(schema_) + remove_all_none_default(schema_) + _remove_enum_description(schema_) return schema_ diff --git a/libs/core/tests/unit_tests/runnables/__snapshots__/test_graph.ambr b/libs/core/tests/unit_tests/runnables/__snapshots__/test_graph.ambr index d1f1b20a49ca3..2a6ff6ad79cce 100644 --- a/libs/core/tests/unit_tests/runnables/__snapshots__/test_graph.ambr +++ b/libs/core/tests/unit_tests/runnables/__snapshots__/test_graph.ambr @@ -1,4 +1,31 @@ # serializer version: 1 +# name: test_double_nested_subgraph_mermaid[mermaid] + ''' + %%{init: {'flowchart': {'curve': 'linear'}}}%% + graph TD; + __start__([__start__]):::first + parent_1(parent_1) + child_child_1_grandchild_1(grandchild_1) + child_child_1_grandchild_2(grandchild_2
__interrupt = before) + child_child_2(child_2) + parent_2(parent_2) + __end__([__end__]):::last + __start__ --> parent_1; + child_child_2 --> parent_2; + parent_1 --> child_child_1_grandchild_1; + parent_2 --> __end__; + subgraph child + child_child_1_grandchild_2 --> child_child_2; + subgraph child_1 + child_child_1_grandchild_1 --> child_child_1_grandchild_2; + end + end + classDef default fill:#f2f0ff,line-height:1.2 + classDef first fill-opacity:0 + classDef last fill:#bfb6fc + + ''' +# --- # name: test_graph_sequence[ascii] ''' +-------------+ @@ -334,31 +361,9 @@ }), dict({ 'data': dict({ - 'anyOf': list([ - dict({ - 'type': 'string', - }), - dict({ - '$ref': '#/definitions/AIMessage', - }), - dict({ - '$ref': '#/definitions/HumanMessage', - }), - dict({ - '$ref': '#/definitions/ChatMessage', - }), - dict({ - '$ref': '#/definitions/SystemMessage', - }), - dict({ - '$ref': '#/definitions/FunctionMessage', - }), - dict({ - '$ref': '#/definitions/ToolMessage', - }), - ]), - 'definitions': dict({ + '$defs': dict({ 'AIMessage': dict({ + 'additionalProperties': True, 'description': ''' Message from an AI. @@ -400,21 +405,37 @@ 'type': 'boolean', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', }), 'invalid_tool_calls': dict({ 'default': list([ ]), 'items': dict({ - '$ref': '#/definitions/InvalidToolCall', + '$ref': '#/$defs/InvalidToolCall', }), 'title': 'Invalid Tool Calls', 'type': 'array', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Name', - 'type': 'string', }), 'response_metadata': dict({ 'title': 'Response Metadata', @@ -424,12 +445,13 @@ 'default': list([ ]), 'items': dict({ - '$ref': '#/definitions/ToolCall', + '$ref': '#/$defs/ToolCall', }), 'title': 'Tool Calls', 'type': 'array', }), 'type': dict({ + 'const': 'ai', 'default': 'ai', 'enum': list([ 'ai', @@ -438,7 +460,15 @@ 'type': 'string', }), 'usage_metadata': dict({ - '$ref': '#/definitions/UsageMetadata', + 'anyOf': list([ + dict({ + '$ref': '#/$defs/UsageMetadata', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, }), }), 'required': list([ @@ -447,7 +477,124 @@ 'title': 'AIMessage', 'type': 'object', }), + 'AIMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'Message chunk from an AI.', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'example': dict({ + 'default': False, + 'title': 'Example', + 'type': 'boolean', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'invalid_tool_calls': dict({ + 'default': list([ + ]), + 'items': dict({ + '$ref': '#/$defs/InvalidToolCall', + }), + 'title': 'Invalid Tool Calls', + 'type': 'array', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'tool_call_chunks': dict({ + 'default': list([ + ]), + 'items': dict({ + '$ref': '#/$defs/ToolCallChunk', + }), + 'title': 'Tool Call Chunks', + 'type': 'array', + }), + 'tool_calls': dict({ + 'default': list([ + ]), + 'items': dict({ + '$ref': '#/$defs/ToolCall', + }), + 'title': 'Tool Calls', + 'type': 'array', + }), + 'type': dict({ + 'const': 'AIMessageChunk', + 'default': 'AIMessageChunk', + 'enum': list([ + 'AIMessageChunk', + ]), + 'title': 'Type', + 'type': 'string', + }), + 'usage_metadata': dict({ + 'anyOf': list([ + dict({ + '$ref': '#/$defs/UsageMetadata', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + }), + }), + 'required': list([ + 'content', + ]), + 'title': 'AIMessageChunk', + 'type': 'object', + }), 'ChatMessage': dict({ + 'additionalProperties': True, 'description': 'Message that can be assigned an arbitrary speaker (i.e. role).', 'properties': dict({ 'additional_kwargs': dict({ @@ -476,12 +623,28 @@ 'title': 'Content', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Name', - 'type': 'string', }), 'response_metadata': dict({ 'title': 'Response Metadata', @@ -492,6 +655,7 @@ 'type': 'string', }), 'type': dict({ + 'const': 'chat', 'default': 'chat', 'enum': list([ 'chat', @@ -507,17 +671,9 @@ 'title': 'ChatMessage', 'type': 'object', }), - 'FunctionMessage': dict({ - 'description': ''' - Message for passing the result of executing a tool back to a model. - - FunctionMessage are an older version of the ToolMessage schema, and - do not contain the tool_call_id field. - - The tool_call_id field is used to associate the tool call request with the - tool call response. This is useful in situations where a chat model is able - to request multiple tool calls in parallel. - ''', + 'ChatMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'Chat Message chunk.', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -545,21 +701,42 @@ 'title': 'Content', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Name', - 'type': 'string', }), 'response_metadata': dict({ 'title': 'Response Metadata', 'type': 'object', }), + 'role': dict({ + 'title': 'Role', + 'type': 'string', + }), 'type': dict({ - 'default': 'function', + 'const': 'ChatMessageChunk', + 'default': 'ChatMessageChunk', 'enum': list([ - 'function', + 'ChatMessageChunk', ]), 'title': 'Type', 'type': 'string', @@ -567,35 +744,22 @@ }), 'required': list([ 'content', - 'name', + 'role', ]), - 'title': 'FunctionMessage', + 'title': 'ChatMessageChunk', 'type': 'object', }), - 'HumanMessage': dict({ + 'FunctionMessage': dict({ + 'additionalProperties': True, 'description': ''' - Message from a human. - - HumanMessages are messages that are passed in from a human to the model. - - Example: - - .. code-block:: python - - from langchain_core.messages import HumanMessage, SystemMessage + Message for passing the result of executing a tool back to a model. - messages = [ - SystemMessage( - content="You are a helpful assistant! Your name is Bob." - ), - HumanMessage( - content="What is your name?" - ) - ] + FunctionMessage are an older version of the ToolMessage schema, and + do not contain the tool_call_id field. - # Instantiate a chat model and invoke it with the messages - model = ... - print(model.invoke(messages)) + The tool_call_id field is used to associate the tool call request with the + tool call response. This is useful in situations where a chat model is able + to request multiple tool calls in parallel. ''', 'properties': dict({ 'additional_kwargs': dict({ @@ -623,14 +787,17 @@ ]), 'title': 'Content', }), - 'example': dict({ - 'default': False, - 'title': 'Example', - 'type': 'boolean', - }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', }), 'name': dict({ 'title': 'Name', @@ -641,6 +808,175 @@ 'type': 'object', }), 'type': dict({ + 'const': 'function', + 'default': 'function', + 'enum': list([ + 'function', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'content', + 'name', + ]), + 'title': 'FunctionMessage', + 'type': 'object', + }), + 'FunctionMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'Function Message chunk.', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'name': dict({ + 'title': 'Name', + 'type': 'string', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'type': dict({ + 'const': 'FunctionMessageChunk', + 'default': 'FunctionMessageChunk', + 'enum': list([ + 'FunctionMessageChunk', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'content', + 'name', + ]), + 'title': 'FunctionMessageChunk', + 'type': 'object', + }), + 'HumanMessage': dict({ + 'additionalProperties': True, + 'description': ''' + Message from a human. + + HumanMessages are messages that are passed in from a human to the model. + + Example: + + .. code-block:: python + + from langchain_core.messages import HumanMessage, SystemMessage + + messages = [ + SystemMessage( + content="You are a helpful assistant! Your name is Bob." + ), + HumanMessage( + content="What is your name?" + ) + ] + + # Instantiate a chat model and invoke it with the messages + model = ... + print(model.invoke(messages)) + ''', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'example': dict({ + 'default': False, + 'title': 'Example', + 'type': 'boolean', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'type': dict({ + 'const': 'human', 'default': 'human', 'enum': list([ 'human', @@ -655,25 +991,138 @@ 'title': 'HumanMessage', 'type': 'object', }), + 'HumanMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'Human Message chunk.', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'example': dict({ + 'default': False, + 'title': 'Example', + 'type': 'boolean', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'type': dict({ + 'const': 'HumanMessageChunk', + 'default': 'HumanMessageChunk', + 'enum': list([ + 'HumanMessageChunk', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'content', + ]), + 'title': 'HumanMessageChunk', + 'type': 'object', + }), 'InvalidToolCall': dict({ + 'description': ''' + Allowance for errors made by LLM. + + Here we add an `error` key to surface errors made during generation + (e.g., invalid JSON arguments.) + ''', 'properties': dict({ 'args': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), 'title': 'Args', - 'type': 'string', }), 'error': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), 'title': 'Error', - 'type': 'string', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), 'title': 'Id', - 'type': 'string', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), 'title': 'Name', - 'type': 'string', }), 'type': dict({ + 'const': 'invalid_tool_call', 'enum': list([ 'invalid_tool_call', ]), @@ -691,6 +1140,7 @@ 'type': 'object', }), 'SystemMessage': dict({ + 'additionalProperties': True, 'description': ''' Message for priming AI behavior. @@ -742,18 +1192,35 @@ 'title': 'Content', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Name', - 'type': 'string', }), 'response_metadata': dict({ 'title': 'Response Metadata', 'type': 'object', }), 'type': dict({ + 'const': 'system', 'default': 'system', 'enum': list([ 'system', @@ -768,23 +1235,202 @@ 'title': 'SystemMessage', 'type': 'object', }), + 'SystemMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'System Message chunk.', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'type': dict({ + 'const': 'SystemMessageChunk', + 'default': 'SystemMessageChunk', + 'enum': list([ + 'SystemMessageChunk', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'content', + ]), + 'title': 'SystemMessageChunk', + 'type': 'object', + }), 'ToolCall': dict({ + 'description': ''' + Represents a request to call a tool. + + Example: + + .. code-block:: python + + { + "name": "foo", + "args": {"a": 1}, + "id": "123" + } + + This represents a request to call the tool named "foo" with arguments {"a": 1} + and an identifier of "123". + ''', + 'properties': dict({ + 'args': dict({ + 'title': 'Args', + 'type': 'object', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Id', + }), + 'name': dict({ + 'title': 'Name', + 'type': 'string', + }), + 'type': dict({ + 'const': 'tool_call', + 'enum': list([ + 'tool_call', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'name', + 'args', + 'id', + ]), + 'title': 'ToolCall', + 'type': 'object', + }), + 'ToolCallChunk': dict({ + 'description': ''' + A chunk of a tool call (e.g., as part of a stream). + + When merging ToolCallChunks (e.g., via AIMessageChunk.__add__), + all string attributes are concatenated. Chunks are only merged if their + values of `index` are equal and not None. + + Example: + + .. code-block:: python + + left_chunks = [ToolCallChunk(name="foo", args='{"a":', index=0)] + right_chunks = [ToolCallChunk(name=None, args='1}', index=0)] + + ( + AIMessageChunk(content="", tool_call_chunks=left_chunks) + + AIMessageChunk(content="", tool_call_chunks=right_chunks) + ).tool_call_chunks == [ToolCallChunk(name='foo', args='{"a":1}', index=0)] + ''', 'properties': dict({ 'args': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), 'title': 'Args', - 'type': 'object', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), 'title': 'Id', - 'type': 'string', + }), + 'index': dict({ + 'anyOf': list([ + dict({ + 'type': 'integer', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Index', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), 'title': 'Name', - 'type': 'string', }), 'type': dict({ + 'const': 'tool_call_chunk', 'enum': list([ - 'tool_call', + 'tool_call_chunk', ]), 'title': 'Type', 'type': 'string', @@ -794,11 +1440,13 @@ 'name', 'args', 'id', + 'index', ]), - 'title': 'ToolCall', + 'title': 'ToolCallChunk', 'type': 'object', }), 'ToolMessage': dict({ + 'additionalProperties': True, 'description': ''' Message for passing the result of executing a tool back to a model. @@ -845,6 +1493,7 @@ 'type': 'object', }), 'artifact': dict({ + 'default': None, 'title': 'Artifact', }), 'content': dict({ @@ -869,12 +1518,28 @@ 'title': 'Content', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Name', - 'type': 'string', }), 'response_metadata': dict({ 'title': 'Response Metadata', @@ -894,6 +1559,7 @@ 'type': 'string', }), 'type': dict({ + 'const': 'tool', 'default': 'tool', 'enum': list([ 'tool', @@ -909,7 +1575,113 @@ 'title': 'ToolMessage', 'type': 'object', }), + 'ToolMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'Tool Message chunk.', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'artifact': dict({ + 'default': None, + 'title': 'Artifact', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'status': dict({ + 'default': 'success', + 'enum': list([ + 'success', + 'error', + ]), + 'title': 'Status', + 'type': 'string', + }), + 'tool_call_id': dict({ + 'title': 'Tool Call Id', + 'type': 'string', + }), + 'type': dict({ + 'const': 'ToolMessageChunk', + 'default': 'ToolMessageChunk', + 'enum': list([ + 'ToolMessageChunk', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'content', + 'tool_call_id', + ]), + 'title': 'ToolMessageChunk', + 'type': 'object', + }), 'UsageMetadata': dict({ + 'description': ''' + Usage metadata for a message, such as token counts. + + This is a standard representation of token usage that is consistent across models. + + Example: + + .. code-block:: python + + { + "input_tokens": 10, + "output_tokens": 20, + "total_tokens": 30 + } + ''', 'properties': dict({ 'input_tokens': dict({ 'title': 'Input Tokens', @@ -933,6 +1705,47 @@ 'type': 'object', }), }), + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + '$ref': '#/$defs/AIMessage', + }), + dict({ + '$ref': '#/$defs/HumanMessage', + }), + dict({ + '$ref': '#/$defs/ChatMessage', + }), + dict({ + '$ref': '#/$defs/SystemMessage', + }), + dict({ + '$ref': '#/$defs/FunctionMessage', + }), + dict({ + '$ref': '#/$defs/ToolMessage', + }), + dict({ + '$ref': '#/$defs/AIMessageChunk', + }), + dict({ + '$ref': '#/$defs/HumanMessageChunk', + }), + dict({ + '$ref': '#/$defs/ChatMessageChunk', + }), + dict({ + '$ref': '#/$defs/SystemMessageChunk', + }), + dict({ + '$ref': '#/$defs/FunctionMessageChunk', + }), + dict({ + '$ref': '#/$defs/ToolMessageChunk', + }), + ]), 'title': 'RunnableParallelInput', }), 'id': 3, @@ -952,6 +1765,10 @@ 'title': 'As Str', }), }), + 'required': list([ + 'as_list', + 'as_str', + ]), 'title': 'RunnableParallelOutput', 'type': 'object', }), @@ -1063,63 +1880,6 @@ ''' # --- -# name: test_parallel_subgraph_mermaid[mermaid] - ''' - %%{init: {'flowchart': {'curve': 'linear'}}}%% - graph TD; - __start__([__start__]):::first - outer_1(outer_1) - inner_1_inner_1(inner_1) - inner_1_inner_2(inner_2
__interrupt = before) - inner_2_inner_1(inner_1) - inner_2_inner_2(inner_2) - outer_2(outer_2) - __end__([__end__]):::last - __start__ --> outer_1; - inner_1_inner_2 --> outer_2; - inner_2_inner_2 --> outer_2; - outer_1 --> inner_1_inner_1; - outer_1 --> inner_2_inner_1; - outer_2 --> __end__; - subgraph inner_1 - inner_1_inner_1 --> inner_1_inner_2; - end - subgraph inner_2 - inner_2_inner_1 --> inner_2_inner_2; - end - classDef default fill:#f2f0ff,line-height:1.2 - classDef first fill-opacity:0 - classDef last fill:#bfb6fc - - ''' -# --- -# name: test_double_nested_subgraph_mermaid[mermaid] - ''' - %%{init: {'flowchart': {'curve': 'linear'}}}%% - graph TD; - __start__([__start__]):::first - parent_1(parent_1) - child_child_1_grandchild_1(grandchild_1) - child_child_1_grandchild_2(grandchild_2
__interrupt = before) - child_child_2(child_2) - parent_2(parent_2) - __end__([__end__]):::last - __start__ --> parent_1; - child_child_2 --> parent_2; - parent_1 --> child_child_1_grandchild_1; - parent_2 --> __end__; - subgraph child - child_child_1_grandchild_2 --> child_child_2; - subgraph child_1 - child_child_1_grandchild_1 --> child_child_1_grandchild_2; - end - end - classDef default fill:#f2f0ff,line-height:1.2 - classDef first fill-opacity:0 - classDef last fill:#bfb6fc - - ''' -# --- # name: test_graph_single_runnable[ascii] ''' +----------------------+ @@ -1154,6 +1914,36 @@ ''' # --- +# name: test_parallel_subgraph_mermaid[mermaid] + ''' + %%{init: {'flowchart': {'curve': 'linear'}}}%% + graph TD; + __start__([__start__]):::first + outer_1(outer_1) + inner_1_inner_1(inner_1) + inner_1_inner_2(inner_2
__interrupt = before) + inner_2_inner_1(inner_1) + inner_2_inner_2(inner_2) + outer_2(outer_2) + __end__([__end__]):::last + __start__ --> outer_1; + inner_1_inner_2 --> outer_2; + inner_2_inner_2 --> outer_2; + outer_1 --> inner_1_inner_1; + outer_1 --> inner_2_inner_1; + outer_2 --> __end__; + subgraph inner_1 + inner_1_inner_1 --> inner_1_inner_2; + end + subgraph inner_2 + inner_2_inner_1 --> inner_2_inner_2; + end + classDef default fill:#f2f0ff,line-height:1.2 + classDef first fill-opacity:0 + classDef last fill:#bfb6fc + + ''' +# --- # name: test_trim dict({ 'edges': list([ diff --git a/libs/core/tests/unit_tests/runnables/__snapshots__/test_runnable.ambr b/libs/core/tests/unit_tests/runnables/__snapshots__/test_runnable.ambr index c75e39a78ef1d..074fe7ddba3dc 100644 --- a/libs/core/tests/unit_tests/runnables/__snapshots__/test_runnable.ambr +++ b/libs/core/tests/unit_tests/runnables/__snapshots__/test_runnable.ambr @@ -5176,46 +5176,11 @@ } ''' # --- -# name: test_schemas +# name: test_schemas[chat_prompt_input_schema] dict({ - 'anyOf': list([ - dict({ - 'type': 'string', - }), - dict({ - '$ref': '#/definitions/StringPromptValue', - }), - dict({ - '$ref': '#/definitions/ChatPromptValueConcrete', - }), - dict({ - 'items': dict({ - 'anyOf': list([ - dict({ - '$ref': '#/definitions/AIMessage', - }), - dict({ - '$ref': '#/definitions/HumanMessage', - }), - dict({ - '$ref': '#/definitions/ChatMessage', - }), - dict({ - '$ref': '#/definitions/SystemMessage', - }), - dict({ - '$ref': '#/definitions/FunctionMessage', - }), - dict({ - '$ref': '#/definitions/ToolMessage', - }), - ]), - }), - 'type': 'array', - }), - ]), - 'definitions': dict({ + '$defs': dict({ 'AIMessage': dict({ + 'additionalProperties': True, 'description': ''' Message from an AI. @@ -5257,21 +5222,37 @@ 'type': 'boolean', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', }), 'invalid_tool_calls': dict({ 'default': list([ ]), 'items': dict({ - '$ref': '#/definitions/InvalidToolCall', + '$ref': '#/$defs/InvalidToolCall', }), 'title': 'Invalid Tool Calls', 'type': 'array', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Name', - 'type': 'string', }), 'response_metadata': dict({ 'title': 'Response Metadata', @@ -5281,12 +5262,13 @@ 'default': list([ ]), 'items': dict({ - '$ref': '#/definitions/ToolCall', + '$ref': '#/$defs/ToolCall', }), 'title': 'Tool Calls', 'type': 'array', }), 'type': dict({ + 'const': 'ai', 'default': 'ai', 'enum': list([ 'ai', @@ -5295,7 +5277,15 @@ 'type': 'string', }), 'usage_metadata': dict({ - '$ref': '#/definitions/UsageMetadata', + 'anyOf': list([ + dict({ + '$ref': '#/$defs/UsageMetadata', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, }), }), 'required': list([ @@ -5304,8 +5294,9 @@ 'title': 'AIMessage', 'type': 'object', }), - 'ChatMessage': dict({ - 'description': 'Message that can be assigned an arbitrary speaker (i.e. role).', + 'AIMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'Message chunk from an AI.', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -5332,96 +5323,96 @@ ]), 'title': 'Content', }), + 'example': dict({ + 'default': False, + 'title': 'Example', + 'type': 'boolean', + }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', + }), + 'invalid_tool_calls': dict({ + 'default': list([ + ]), + 'items': dict({ + '$ref': '#/$defs/InvalidToolCall', + }), + 'title': 'Invalid Tool Calls', + 'type': 'array', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Name', - 'type': 'string', }), 'response_metadata': dict({ 'title': 'Response Metadata', 'type': 'object', }), - 'role': dict({ - 'title': 'Role', - 'type': 'string', - }), - 'type': dict({ - 'default': 'chat', - 'enum': list([ - 'chat', + 'tool_call_chunks': dict({ + 'default': list([ ]), - 'title': 'Type', - 'type': 'string', + 'items': dict({ + '$ref': '#/$defs/ToolCallChunk', + }), + 'title': 'Tool Call Chunks', + 'type': 'array', }), - }), - 'required': list([ - 'content', - 'role', - ]), - 'title': 'ChatMessage', - 'type': 'object', - }), - 'ChatPromptValueConcrete': dict({ - 'description': ''' - Chat prompt value which explicitly lists out the message types it accepts. - For use in external schemas. - ''', - 'properties': dict({ - 'messages': dict({ + 'tool_calls': dict({ + 'default': list([ + ]), 'items': dict({ - 'anyOf': list([ - dict({ - '$ref': '#/definitions/AIMessage', - }), - dict({ - '$ref': '#/definitions/HumanMessage', - }), - dict({ - '$ref': '#/definitions/ChatMessage', - }), - dict({ - '$ref': '#/definitions/SystemMessage', - }), - dict({ - '$ref': '#/definitions/FunctionMessage', - }), - dict({ - '$ref': '#/definitions/ToolMessage', - }), - ]), + '$ref': '#/$defs/ToolCall', }), - 'title': 'Messages', + 'title': 'Tool Calls', 'type': 'array', }), 'type': dict({ - 'default': 'ChatPromptValueConcrete', + 'const': 'AIMessageChunk', + 'default': 'AIMessageChunk', 'enum': list([ - 'ChatPromptValueConcrete', + 'AIMessageChunk', ]), 'title': 'Type', 'type': 'string', }), + 'usage_metadata': dict({ + 'anyOf': list([ + dict({ + '$ref': '#/$defs/UsageMetadata', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + }), }), 'required': list([ - 'messages', + 'content', ]), - 'title': 'ChatPromptValueConcrete', + 'title': 'AIMessageChunk', 'type': 'object', }), - 'FunctionMessage': dict({ - 'description': ''' - Message for passing the result of executing a tool back to a model. - - FunctionMessage are an older version of the ToolMessage schema, and - do not contain the tool_call_id field. - - The tool_call_id field is used to associate the tool call request with the - tool call response. This is useful in situations where a chat model is able - to request multiple tool calls in parallel. - ''', + 'ChatMessage': dict({ + 'additionalProperties': True, + 'description': 'Message that can be assigned an arbitrary speaker (i.e. role).', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -5449,21 +5440,42 @@ 'title': 'Content', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Name', - 'type': 'string', }), 'response_metadata': dict({ 'title': 'Response Metadata', 'type': 'object', }), + 'role': dict({ + 'title': 'Role', + 'type': 'string', + }), 'type': dict({ - 'default': 'function', + 'const': 'chat', + 'default': 'chat', 'enum': list([ - 'function', + 'chat', ]), 'title': 'Type', 'type': 'string', @@ -5471,36 +5483,14 @@ }), 'required': list([ 'content', - 'name', + 'role', ]), - 'title': 'FunctionMessage', + 'title': 'ChatMessage', 'type': 'object', }), - 'HumanMessage': dict({ - 'description': ''' - Message from a human. - - HumanMessages are messages that are passed in from a human to the model. - - Example: - - .. code-block:: python - - from langchain_core.messages import HumanMessage, SystemMessage - - messages = [ - SystemMessage( - content="You are a helpful assistant! Your name is Bob." - ), - HumanMessage( - content="What is your name?" - ) - ] - - # Instantiate a chat model and invoke it with the messages - model = ... - print(model.invoke(messages)) - ''', + 'ChatMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'Chat Message chunk.', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -5527,27 +5517,43 @@ ]), 'title': 'Content', }), - 'example': dict({ - 'default': False, - 'title': 'Example', - 'type': 'boolean', - }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Name', - 'type': 'string', }), 'response_metadata': dict({ 'title': 'Response Metadata', 'type': 'object', }), + 'role': dict({ + 'title': 'Role', + 'type': 'string', + }), 'type': dict({ - 'default': 'human', + 'const': 'ChatMessageChunk', + 'default': 'ChatMessageChunk', 'enum': list([ - 'human', + 'ChatMessageChunk', ]), 'title': 'Type', 'type': 'string', @@ -5555,73 +5561,158 @@ }), 'required': list([ 'content', + 'role', ]), - 'title': 'HumanMessage', + 'title': 'ChatMessageChunk', 'type': 'object', }), - 'InvalidToolCall': dict({ + 'FunctionMessage': dict({ + 'additionalProperties': True, + 'description': ''' + Message for passing the result of executing a tool back to a model. + + FunctionMessage are an older version of the ToolMessage schema, and + do not contain the tool_call_id field. + + The tool_call_id field is used to associate the tool call request with the + tool call response. This is useful in situations where a chat model is able + to request multiple tool calls in parallel. + ''', 'properties': dict({ - 'args': dict({ - 'title': 'Args', - 'type': 'string', + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', }), - 'error': dict({ - 'title': 'Error', - 'type': 'string', + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', }), 'name': dict({ 'title': 'Name', 'type': 'string', }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), 'type': dict({ + 'const': 'function', + 'default': 'function', 'enum': list([ - 'invalid_tool_call', + 'function', ]), 'title': 'Type', 'type': 'string', }), }), 'required': list([ + 'content', 'name', - 'args', - 'id', - 'error', ]), - 'title': 'InvalidToolCall', + 'title': 'FunctionMessage', 'type': 'object', }), - 'StringPromptValue': dict({ - 'description': 'String prompt value.', + 'FunctionMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'Function Message chunk.', 'properties': dict({ - 'text': dict({ - 'title': 'Text', + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'name': dict({ + 'title': 'Name', 'type': 'string', }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), 'type': dict({ - 'default': 'StringPromptValue', + 'const': 'FunctionMessageChunk', + 'default': 'FunctionMessageChunk', 'enum': list([ - 'StringPromptValue', + 'FunctionMessageChunk', ]), 'title': 'Type', 'type': 'string', }), }), 'required': list([ - 'text', + 'content', + 'name', ]), - 'title': 'StringPromptValue', + 'title': 'FunctionMessageChunk', 'type': 'object', }), - 'SystemMessage': dict({ + 'HumanMessage': dict({ + 'additionalProperties': True, 'description': ''' - Message for priming AI behavior. + Message from a human. - The system message is usually passed in as the first of a sequence - of input messages. + HumanMessages are messages that are passed in from a human to the model. Example: @@ -5638,7 +5729,8 @@ ) ] - # Define a chat model and invoke it with the messages + # Instantiate a chat model and invoke it with the messages + model = ... print(model.invoke(messages)) ''', 'properties': dict({ @@ -5667,22 +5759,44 @@ ]), 'title': 'Content', }), + 'example': dict({ + 'default': False, + 'title': 'Example', + 'type': 'boolean', + }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Name', - 'type': 'string', }), 'response_metadata': dict({ 'title': 'Response Metadata', 'type': 'object', }), 'type': dict({ - 'default': 'system', + 'const': 'human', + 'default': 'human', 'enum': list([ - 'system', + 'human', ]), 'title': 'Type', 'type': 'string', @@ -5691,88 +5805,17 @@ 'required': list([ 'content', ]), - 'title': 'SystemMessage', - 'type': 'object', - }), - 'ToolCall': dict({ - 'properties': dict({ - 'args': dict({ - 'title': 'Args', - 'type': 'object', - }), - 'id': dict({ - 'title': 'Id', - 'type': 'string', - }), - 'name': dict({ - 'title': 'Name', - 'type': 'string', - }), - 'type': dict({ - 'enum': list([ - 'tool_call', - ]), - 'title': 'Type', - 'type': 'string', - }), - }), - 'required': list([ - 'name', - 'args', - 'id', - ]), - 'title': 'ToolCall', + 'title': 'HumanMessage', 'type': 'object', }), - 'ToolMessage': dict({ - 'description': ''' - Message for passing the result of executing a tool back to a model. - - ToolMessages contain the result of a tool invocation. Typically, the result - is encoded inside the `content` field. - - Example: A ToolMessage representing a result of 42 from a tool call with id - - .. code-block:: python - - from langchain_core.messages import ToolMessage - - ToolMessage(content='42', tool_call_id='call_Jja7J89XsjrOLA5r!MEOW!SL') - - - Example: A ToolMessage where only part of the tool output is sent to the model - and the full output is passed in to artifact. - - .. versionadded:: 0.2.17 - - .. code-block:: python - - from langchain_core.messages import ToolMessage - - tool_output = { - "stdout": "From the graph we can see that the correlation between x and y is ...", - "stderr": None, - "artifacts": {"type": "image", "base64_data": "/9j/4gIcSU..."}, - } - - ToolMessage( - content=tool_output["stdout"], - artifact=tool_output, - tool_call_id='call_Jja7J89XsjrOLA5r!MEOW!SL', - ) - - The tool_call_id field is used to associate the tool call request with the - tool call response. This is useful in situations where a chat model is able - to request multiple tool calls in parallel. - ''', + 'HumanMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'Human Message chunk.', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', 'type': 'object', }), - 'artifact': dict({ - 'title': 'Artifact', - }), 'content': dict({ 'anyOf': list([ dict({ @@ -5794,122 +5837,150 @@ ]), 'title': 'Content', }), + 'example': dict({ + 'default': False, + 'title': 'Example', + 'type': 'boolean', + }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Name', - 'type': 'string', }), 'response_metadata': dict({ 'title': 'Response Metadata', 'type': 'object', }), - 'status': dict({ - 'default': 'success', + 'type': dict({ + 'const': 'HumanMessageChunk', + 'default': 'HumanMessageChunk', 'enum': list([ - 'success', - 'error', + 'HumanMessageChunk', ]), - 'title': 'Status', - 'type': 'string', - }), - 'tool_call_id': dict({ - 'title': 'Tool Call Id', - 'type': 'string', - }), - 'type': dict({ - 'default': 'tool', - 'enum': list([ - 'tool', - ]), - 'title': 'Type', + 'title': 'Type', 'type': 'string', }), }), 'required': list([ 'content', - 'tool_call_id', ]), - 'title': 'ToolMessage', + 'title': 'HumanMessageChunk', 'type': 'object', }), - 'UsageMetadata': dict({ + 'InvalidToolCall': dict({ + 'description': ''' + Allowance for errors made by LLM. + + Here we add an `error` key to surface errors made during generation + (e.g., invalid JSON arguments.) + ''', 'properties': dict({ - 'input_tokens': dict({ - 'title': 'Input Tokens', - 'type': 'integer', + 'args': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Args', }), - 'output_tokens': dict({ - 'title': 'Output Tokens', - 'type': 'integer', + 'error': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Error', }), - 'total_tokens': dict({ - 'title': 'Total Tokens', - 'type': 'integer', + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Id', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Name', + }), + 'type': dict({ + 'const': 'invalid_tool_call', + 'enum': list([ + 'invalid_tool_call', + ]), + 'title': 'Type', + 'type': 'string', }), }), 'required': list([ - 'input_tokens', - 'output_tokens', - 'total_tokens', + 'name', + 'args', + 'id', + 'error', ]), - 'title': 'UsageMetadata', + 'title': 'InvalidToolCall', 'type': 'object', }), - }), - 'title': 'FakeListLLMInput', - }) -# --- -# name: test_schemas.1 - dict({ - 'anyOf': list([ - dict({ - 'type': 'string', - }), - dict({ - '$ref': '#/definitions/StringPromptValue', - }), - dict({ - '$ref': '#/definitions/ChatPromptValueConcrete', - }), - dict({ - 'items': dict({ - 'anyOf': list([ - dict({ - '$ref': '#/definitions/AIMessage', - }), - dict({ - '$ref': '#/definitions/HumanMessage', - }), - dict({ - '$ref': '#/definitions/ChatMessage', - }), - dict({ - '$ref': '#/definitions/SystemMessage', - }), - dict({ - '$ref': '#/definitions/FunctionMessage', - }), - dict({ - '$ref': '#/definitions/ToolMessage', - }), - ]), - }), - 'type': 'array', - }), - ]), - 'definitions': dict({ - 'AIMessage': dict({ + 'SystemMessage': dict({ + 'additionalProperties': True, 'description': ''' - Message from an AI. + Message for priming AI behavior. - AIMessage is returned from a chat model as a response to a prompt. + The system message is usually passed in as the first of a sequence + of input messages. - This message represents the output of the model and consists of both - the raw output as returned by the model together standardized fields - (e.g., tool calls, usage metadata) added by the LangChain framework. + Example: + + .. code-block:: python + + from langchain_core.messages import HumanMessage, SystemMessage + + messages = [ + SystemMessage( + content="You are a helpful assistant! Your name is Bob." + ), + HumanMessage( + content="What is your name?" + ) + ] + + # Define a chat model and invoke it with the messages + print(model.invoke(messages)) ''', 'properties': dict({ 'additional_kwargs': dict({ @@ -5937,61 +6008,53 @@ ]), 'title': 'Content', }), - 'example': dict({ - 'default': False, - 'title': 'Example', - 'type': 'boolean', - }), 'id': dict({ - 'title': 'Id', - 'type': 'string', - }), - 'invalid_tool_calls': dict({ - 'default': list([ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), ]), - 'items': dict({ - '$ref': '#/definitions/InvalidToolCall', - }), - 'title': 'Invalid Tool Calls', - 'type': 'array', + 'default': None, + 'title': 'Id', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Name', - 'type': 'string', }), 'response_metadata': dict({ 'title': 'Response Metadata', 'type': 'object', }), - 'tool_calls': dict({ - 'default': list([ - ]), - 'items': dict({ - '$ref': '#/definitions/ToolCall', - }), - 'title': 'Tool Calls', - 'type': 'array', - }), 'type': dict({ - 'default': 'ai', + 'const': 'system', + 'default': 'system', 'enum': list([ - 'ai', + 'system', ]), 'title': 'Type', 'type': 'string', }), - 'usage_metadata': dict({ - '$ref': '#/definitions/UsageMetadata', - }), }), 'required': list([ 'content', ]), - 'title': 'AIMessage', + 'title': 'SystemMessage', 'type': 'object', }), - 'ChatMessage': dict({ - 'description': 'Message that can be assigned an arbitrary speaker (i.e. role).', + 'SystemMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'System Message chunk.', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -6019,25 +6082,38 @@ 'title': 'Content', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Name', - 'type': 'string', }), 'response_metadata': dict({ 'title': 'Response Metadata', 'type': 'object', }), - 'role': dict({ - 'title': 'Role', - 'type': 'string', - }), 'type': dict({ - 'default': 'chat', + 'const': 'SystemMessageChunk', + 'default': 'SystemMessageChunk', 'enum': list([ - 'chat', + 'SystemMessageChunk', ]), 'title': 'Type', 'type': 'string', @@ -6045,153 +6121,198 @@ }), 'required': list([ 'content', - 'role', ]), - 'title': 'ChatMessage', + 'title': 'SystemMessageChunk', 'type': 'object', }), - 'ChatPromptValueConcrete': dict({ + 'ToolCall': dict({ 'description': ''' - Chat prompt value which explicitly lists out the message types it accepts. - For use in external schemas. + Represents a request to call a tool. + + Example: + + .. code-block:: python + + { + "name": "foo", + "args": {"a": 1}, + "id": "123" + } + + This represents a request to call the tool named "foo" with arguments {"a": 1} + and an identifier of "123". ''', 'properties': dict({ - 'messages': dict({ - 'items': dict({ - 'anyOf': list([ - dict({ - '$ref': '#/definitions/AIMessage', - }), - dict({ - '$ref': '#/definitions/HumanMessage', - }), - dict({ - '$ref': '#/definitions/ChatMessage', - }), - dict({ - '$ref': '#/definitions/SystemMessage', - }), - dict({ - '$ref': '#/definitions/FunctionMessage', - }), - dict({ - '$ref': '#/definitions/ToolMessage', - }), - ]), - }), - 'title': 'Messages', - 'type': 'array', - }), + 'args': dict({ + 'title': 'Args', + 'type': 'object', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Id', + }), + 'name': dict({ + 'title': 'Name', + 'type': 'string', + }), 'type': dict({ - 'default': 'ChatPromptValueConcrete', + 'const': 'tool_call', 'enum': list([ - 'ChatPromptValueConcrete', + 'tool_call', ]), 'title': 'Type', 'type': 'string', }), }), 'required': list([ - 'messages', + 'name', + 'args', + 'id', ]), - 'title': 'ChatPromptValueConcrete', + 'title': 'ToolCall', 'type': 'object', }), - 'FunctionMessage': dict({ + 'ToolCallChunk': dict({ 'description': ''' - Message for passing the result of executing a tool back to a model. + A chunk of a tool call (e.g., as part of a stream). - FunctionMessage are an older version of the ToolMessage schema, and - do not contain the tool_call_id field. + When merging ToolCallChunks (e.g., via AIMessageChunk.__add__), + all string attributes are concatenated. Chunks are only merged if their + values of `index` are equal and not None. - The tool_call_id field is used to associate the tool call request with the - tool call response. This is useful in situations where a chat model is able - to request multiple tool calls in parallel. + Example: + + .. code-block:: python + + left_chunks = [ToolCallChunk(name="foo", args='{"a":', index=0)] + right_chunks = [ToolCallChunk(name=None, args='1}', index=0)] + + ( + AIMessageChunk(content="", tool_call_chunks=left_chunks) + + AIMessageChunk(content="", tool_call_chunks=right_chunks) + ).tool_call_chunks == [ToolCallChunk(name='foo', args='{"a":1}', index=0)] ''', 'properties': dict({ - 'additional_kwargs': dict({ - 'title': 'Additional Kwargs', - 'type': 'object', - }), - 'content': dict({ + 'args': dict({ 'anyOf': list([ dict({ 'type': 'string', }), dict({ - 'items': dict({ - 'anyOf': list([ - dict({ - 'type': 'string', - }), - dict({ - 'type': 'object', - }), - ]), - }), - 'type': 'array', + 'type': 'null', }), ]), - 'title': 'Content', + 'title': 'Args', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), 'title': 'Id', - 'type': 'string', + }), + 'index': dict({ + 'anyOf': list([ + dict({ + 'type': 'integer', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Index', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), 'title': 'Name', - 'type': 'string', - }), - 'response_metadata': dict({ - 'title': 'Response Metadata', - 'type': 'object', }), 'type': dict({ - 'default': 'function', + 'const': 'tool_call_chunk', 'enum': list([ - 'function', + 'tool_call_chunk', ]), 'title': 'Type', 'type': 'string', }), }), 'required': list([ - 'content', 'name', + 'args', + 'id', + 'index', ]), - 'title': 'FunctionMessage', + 'title': 'ToolCallChunk', 'type': 'object', }), - 'HumanMessage': dict({ + 'ToolMessage': dict({ + 'additionalProperties': True, 'description': ''' - Message from a human. + Message for passing the result of executing a tool back to a model. - HumanMessages are messages that are passed in from a human to the model. + ToolMessages contain the result of a tool invocation. Typically, the result + is encoded inside the `content` field. - Example: + Example: A ToolMessage representing a result of 42 from a tool call with id .. code-block:: python - from langchain_core.messages import HumanMessage, SystemMessage + from langchain_core.messages import ToolMessage - messages = [ - SystemMessage( - content="You are a helpful assistant! Your name is Bob." - ), - HumanMessage( - content="What is your name?" - ) - ] + ToolMessage(content='42', tool_call_id='call_Jja7J89XsjrOLA5r!MEOW!SL') - # Instantiate a chat model and invoke it with the messages - model = ... - print(model.invoke(messages)) + + Example: A ToolMessage where only part of the tool output is sent to the model + and the full output is passed in to artifact. + + .. versionadded:: 0.2.17 + + .. code-block:: python + + from langchain_core.messages import ToolMessage + + tool_output = { + "stdout": "From the graph we can see that the correlation between x and y is ...", + "stderr": None, + "artifacts": {"type": "image", "base64_data": "/9j/4gIcSU..."}, + } + + ToolMessage( + content=tool_output["stdout"], + artifact=tool_output, + tool_call_id='call_Jja7J89XsjrOLA5r!MEOW!SL', + ) + + The tool_call_id field is used to associate the tool call request with the + tool call response. This is useful in situations where a chat model is able + to request multiple tool calls in parallel. ''', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', 'type': 'object', }), + 'artifact': dict({ + 'default': None, + 'title': 'Artifact', + }), 'content': dict({ 'anyOf': list([ dict({ @@ -6213,27 +6334,52 @@ ]), 'title': 'Content', }), - 'example': dict({ - 'default': False, - 'title': 'Example', - 'type': 'boolean', - }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Name', - 'type': 'string', }), 'response_metadata': dict({ 'title': 'Response Metadata', 'type': 'object', }), + 'status': dict({ + 'default': 'success', + 'enum': list([ + 'success', + 'error', + ]), + 'title': 'Status', + 'type': 'string', + }), + 'tool_call_id': dict({ + 'title': 'Tool Call Id', + 'type': 'string', + }), 'type': dict({ - 'default': 'human', + 'const': 'tool', + 'default': 'tool', 'enum': list([ - 'human', + 'tool', ]), 'title': 'Type', 'type': 'string', @@ -6241,92 +6387,4806 @@ }), 'required': list([ 'content', + 'tool_call_id', ]), - 'title': 'HumanMessage', + 'title': 'ToolMessage', 'type': 'object', }), - 'InvalidToolCall': dict({ + 'ToolMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'Tool Message chunk.', 'properties': dict({ - 'args': dict({ - 'title': 'Args', - 'type': 'string', + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', }), - 'error': dict({ - 'title': 'Error', - 'type': 'string', + 'artifact': dict({ + 'default': None, + 'title': 'Artifact', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'status': dict({ + 'default': 'success', + 'enum': list([ + 'success', + 'error', + ]), + 'title': 'Status', + 'type': 'string', + }), + 'tool_call_id': dict({ + 'title': 'Tool Call Id', 'type': 'string', }), 'type': dict({ + 'const': 'ToolMessageChunk', + 'default': 'ToolMessageChunk', 'enum': list([ - 'invalid_tool_call', + 'ToolMessageChunk', ]), 'title': 'Type', 'type': 'string', }), }), 'required': list([ - 'name', - 'args', - 'id', - 'error', + 'content', + 'tool_call_id', ]), - 'title': 'InvalidToolCall', + 'title': 'ToolMessageChunk', 'type': 'object', }), - 'StringPromptValue': dict({ - 'description': 'String prompt value.', + 'UsageMetadata': dict({ + 'description': ''' + Usage metadata for a message, such as token counts. + + This is a standard representation of token usage that is consistent across models. + + Example: + + .. code-block:: python + + { + "input_tokens": 10, + "output_tokens": 20, + "total_tokens": 30 + } + ''', + 'properties': dict({ + 'input_tokens': dict({ + 'title': 'Input Tokens', + 'type': 'integer', + }), + 'output_tokens': dict({ + 'title': 'Output Tokens', + 'type': 'integer', + }), + 'total_tokens': dict({ + 'title': 'Total Tokens', + 'type': 'integer', + }), + }), + 'required': list([ + 'input_tokens', + 'output_tokens', + 'total_tokens', + ]), + 'title': 'UsageMetadata', + 'type': 'object', + }), + }), + 'properties': dict({ + 'history': dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + '$ref': '#/$defs/AIMessage', + }), + dict({ + '$ref': '#/$defs/HumanMessage', + }), + dict({ + '$ref': '#/$defs/ChatMessage', + }), + dict({ + '$ref': '#/$defs/SystemMessage', + }), + dict({ + '$ref': '#/$defs/FunctionMessage', + }), + dict({ + '$ref': '#/$defs/ToolMessage', + }), + dict({ + '$ref': '#/$defs/AIMessageChunk', + }), + dict({ + '$ref': '#/$defs/HumanMessageChunk', + }), + dict({ + '$ref': '#/$defs/ChatMessageChunk', + }), + dict({ + '$ref': '#/$defs/SystemMessageChunk', + }), + dict({ + '$ref': '#/$defs/FunctionMessageChunk', + }), + dict({ + '$ref': '#/$defs/ToolMessageChunk', + }), + ]), + }), + 'title': 'History', + 'type': 'array', + }), + }), + 'required': list([ + 'history', + ]), + 'title': 'PromptInput', + 'type': 'object', + }) +# --- +# name: test_schemas[chat_prompt_output_schema] + dict({ + '$defs': dict({ + 'AIMessage': dict({ + 'additionalProperties': True, + 'description': ''' + Message from an AI. + + AIMessage is returned from a chat model as a response to a prompt. + + This message represents the output of the model and consists of both + the raw output as returned by the model together standardized fields + (e.g., tool calls, usage metadata) added by the LangChain framework. + ''', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'example': dict({ + 'default': False, + 'title': 'Example', + 'type': 'boolean', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'invalid_tool_calls': dict({ + 'default': list([ + ]), + 'items': dict({ + '$ref': '#/$defs/InvalidToolCall', + }), + 'title': 'Invalid Tool Calls', + 'type': 'array', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'tool_calls': dict({ + 'default': list([ + ]), + 'items': dict({ + '$ref': '#/$defs/ToolCall', + }), + 'title': 'Tool Calls', + 'type': 'array', + }), + 'type': dict({ + 'const': 'ai', + 'default': 'ai', + 'enum': list([ + 'ai', + ]), + 'title': 'Type', + 'type': 'string', + }), + 'usage_metadata': dict({ + 'anyOf': list([ + dict({ + '$ref': '#/$defs/UsageMetadata', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + }), + }), + 'required': list([ + 'content', + ]), + 'title': 'AIMessage', + 'type': 'object', + }), + 'AIMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'Message chunk from an AI.', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'example': dict({ + 'default': False, + 'title': 'Example', + 'type': 'boolean', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'invalid_tool_calls': dict({ + 'default': list([ + ]), + 'items': dict({ + '$ref': '#/$defs/InvalidToolCall', + }), + 'title': 'Invalid Tool Calls', + 'type': 'array', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'tool_call_chunks': dict({ + 'default': list([ + ]), + 'items': dict({ + '$ref': '#/$defs/ToolCallChunk', + }), + 'title': 'Tool Call Chunks', + 'type': 'array', + }), + 'tool_calls': dict({ + 'default': list([ + ]), + 'items': dict({ + '$ref': '#/$defs/ToolCall', + }), + 'title': 'Tool Calls', + 'type': 'array', + }), + 'type': dict({ + 'const': 'AIMessageChunk', + 'default': 'AIMessageChunk', + 'enum': list([ + 'AIMessageChunk', + ]), + 'title': 'Type', + 'type': 'string', + }), + 'usage_metadata': dict({ + 'anyOf': list([ + dict({ + '$ref': '#/$defs/UsageMetadata', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + }), + }), + 'required': list([ + 'content', + ]), + 'title': 'AIMessageChunk', + 'type': 'object', + }), + 'ChatMessage': dict({ + 'additionalProperties': True, + 'description': 'Message that can be assigned an arbitrary speaker (i.e. role).', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'role': dict({ + 'title': 'Role', + 'type': 'string', + }), + 'type': dict({ + 'const': 'chat', + 'default': 'chat', + 'enum': list([ + 'chat', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'content', + 'role', + ]), + 'title': 'ChatMessage', + 'type': 'object', + }), + 'ChatMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'Chat Message chunk.', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'role': dict({ + 'title': 'Role', + 'type': 'string', + }), + 'type': dict({ + 'const': 'ChatMessageChunk', + 'default': 'ChatMessageChunk', + 'enum': list([ + 'ChatMessageChunk', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'content', + 'role', + ]), + 'title': 'ChatMessageChunk', + 'type': 'object', + }), + 'ChatPromptValueConcrete': dict({ + 'description': ''' + Chat prompt value which explicitly lists out the message types it accepts. + For use in external schemas. + ''', + 'properties': dict({ + 'messages': dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + '$ref': '#/$defs/AIMessage', + }), + dict({ + '$ref': '#/$defs/HumanMessage', + }), + dict({ + '$ref': '#/$defs/ChatMessage', + }), + dict({ + '$ref': '#/$defs/SystemMessage', + }), + dict({ + '$ref': '#/$defs/FunctionMessage', + }), + dict({ + '$ref': '#/$defs/ToolMessage', + }), + dict({ + '$ref': '#/$defs/AIMessageChunk', + }), + dict({ + '$ref': '#/$defs/HumanMessageChunk', + }), + dict({ + '$ref': '#/$defs/ChatMessageChunk', + }), + dict({ + '$ref': '#/$defs/SystemMessageChunk', + }), + dict({ + '$ref': '#/$defs/FunctionMessageChunk', + }), + dict({ + '$ref': '#/$defs/ToolMessageChunk', + }), + ]), + }), + 'title': 'Messages', + 'type': 'array', + }), + 'type': dict({ + 'const': 'ChatPromptValueConcrete', + 'default': 'ChatPromptValueConcrete', + 'enum': list([ + 'ChatPromptValueConcrete', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'messages', + ]), + 'title': 'ChatPromptValueConcrete', + 'type': 'object', + }), + 'FunctionMessage': dict({ + 'additionalProperties': True, + 'description': ''' + Message for passing the result of executing a tool back to a model. + + FunctionMessage are an older version of the ToolMessage schema, and + do not contain the tool_call_id field. + + The tool_call_id field is used to associate the tool call request with the + tool call response. This is useful in situations where a chat model is able + to request multiple tool calls in parallel. + ''', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'name': dict({ + 'title': 'Name', + 'type': 'string', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'type': dict({ + 'const': 'function', + 'default': 'function', + 'enum': list([ + 'function', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'content', + 'name', + ]), + 'title': 'FunctionMessage', + 'type': 'object', + }), + 'FunctionMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'Function Message chunk.', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'name': dict({ + 'title': 'Name', + 'type': 'string', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'type': dict({ + 'const': 'FunctionMessageChunk', + 'default': 'FunctionMessageChunk', + 'enum': list([ + 'FunctionMessageChunk', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'content', + 'name', + ]), + 'title': 'FunctionMessageChunk', + 'type': 'object', + }), + 'HumanMessage': dict({ + 'additionalProperties': True, + 'description': ''' + Message from a human. + + HumanMessages are messages that are passed in from a human to the model. + + Example: + + .. code-block:: python + + from langchain_core.messages import HumanMessage, SystemMessage + + messages = [ + SystemMessage( + content="You are a helpful assistant! Your name is Bob." + ), + HumanMessage( + content="What is your name?" + ) + ] + + # Instantiate a chat model and invoke it with the messages + model = ... + print(model.invoke(messages)) + ''', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'example': dict({ + 'default': False, + 'title': 'Example', + 'type': 'boolean', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'type': dict({ + 'const': 'human', + 'default': 'human', + 'enum': list([ + 'human', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'content', + ]), + 'title': 'HumanMessage', + 'type': 'object', + }), + 'HumanMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'Human Message chunk.', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'example': dict({ + 'default': False, + 'title': 'Example', + 'type': 'boolean', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'type': dict({ + 'const': 'HumanMessageChunk', + 'default': 'HumanMessageChunk', + 'enum': list([ + 'HumanMessageChunk', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'content', + ]), + 'title': 'HumanMessageChunk', + 'type': 'object', + }), + 'InvalidToolCall': dict({ + 'description': ''' + Allowance for errors made by LLM. + + Here we add an `error` key to surface errors made during generation + (e.g., invalid JSON arguments.) + ''', + 'properties': dict({ + 'args': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Args', + }), + 'error': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Error', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Id', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Name', + }), + 'type': dict({ + 'const': 'invalid_tool_call', + 'enum': list([ + 'invalid_tool_call', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'name', + 'args', + 'id', + 'error', + ]), + 'title': 'InvalidToolCall', + 'type': 'object', + }), + 'StringPromptValue': dict({ + 'description': 'String prompt value.', + 'properties': dict({ + 'text': dict({ + 'title': 'Text', + 'type': 'string', + }), + 'type': dict({ + 'const': 'StringPromptValue', + 'default': 'StringPromptValue', + 'enum': list([ + 'StringPromptValue', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'text', + ]), + 'title': 'StringPromptValue', + 'type': 'object', + }), + 'SystemMessage': dict({ + 'additionalProperties': True, + 'description': ''' + Message for priming AI behavior. + + The system message is usually passed in as the first of a sequence + of input messages. + + Example: + + .. code-block:: python + + from langchain_core.messages import HumanMessage, SystemMessage + + messages = [ + SystemMessage( + content="You are a helpful assistant! Your name is Bob." + ), + HumanMessage( + content="What is your name?" + ) + ] + + # Define a chat model and invoke it with the messages + print(model.invoke(messages)) + ''', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'type': dict({ + 'const': 'system', + 'default': 'system', + 'enum': list([ + 'system', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'content', + ]), + 'title': 'SystemMessage', + 'type': 'object', + }), + 'SystemMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'System Message chunk.', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'type': dict({ + 'const': 'SystemMessageChunk', + 'default': 'SystemMessageChunk', + 'enum': list([ + 'SystemMessageChunk', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'content', + ]), + 'title': 'SystemMessageChunk', + 'type': 'object', + }), + 'ToolCall': dict({ + 'description': ''' + Represents a request to call a tool. + + Example: + + .. code-block:: python + + { + "name": "foo", + "args": {"a": 1}, + "id": "123" + } + + This represents a request to call the tool named "foo" with arguments {"a": 1} + and an identifier of "123". + ''', + 'properties': dict({ + 'args': dict({ + 'title': 'Args', + 'type': 'object', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Id', + }), + 'name': dict({ + 'title': 'Name', + 'type': 'string', + }), + 'type': dict({ + 'const': 'tool_call', + 'enum': list([ + 'tool_call', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'name', + 'args', + 'id', + ]), + 'title': 'ToolCall', + 'type': 'object', + }), + 'ToolCallChunk': dict({ + 'description': ''' + A chunk of a tool call (e.g., as part of a stream). + + When merging ToolCallChunks (e.g., via AIMessageChunk.__add__), + all string attributes are concatenated. Chunks are only merged if their + values of `index` are equal and not None. + + Example: + + .. code-block:: python + + left_chunks = [ToolCallChunk(name="foo", args='{"a":', index=0)] + right_chunks = [ToolCallChunk(name=None, args='1}', index=0)] + + ( + AIMessageChunk(content="", tool_call_chunks=left_chunks) + + AIMessageChunk(content="", tool_call_chunks=right_chunks) + ).tool_call_chunks == [ToolCallChunk(name='foo', args='{"a":1}', index=0)] + ''', + 'properties': dict({ + 'args': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Args', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Id', + }), + 'index': dict({ + 'anyOf': list([ + dict({ + 'type': 'integer', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Index', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Name', + }), + 'type': dict({ + 'const': 'tool_call_chunk', + 'enum': list([ + 'tool_call_chunk', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'name', + 'args', + 'id', + 'index', + ]), + 'title': 'ToolCallChunk', + 'type': 'object', + }), + 'ToolMessage': dict({ + 'additionalProperties': True, + 'description': ''' + Message for passing the result of executing a tool back to a model. + + ToolMessages contain the result of a tool invocation. Typically, the result + is encoded inside the `content` field. + + Example: A ToolMessage representing a result of 42 from a tool call with id + + .. code-block:: python + + from langchain_core.messages import ToolMessage + + ToolMessage(content='42', tool_call_id='call_Jja7J89XsjrOLA5r!MEOW!SL') + + + Example: A ToolMessage where only part of the tool output is sent to the model + and the full output is passed in to artifact. + + .. versionadded:: 0.2.17 + + .. code-block:: python + + from langchain_core.messages import ToolMessage + + tool_output = { + "stdout": "From the graph we can see that the correlation between x and y is ...", + "stderr": None, + "artifacts": {"type": "image", "base64_data": "/9j/4gIcSU..."}, + } + + ToolMessage( + content=tool_output["stdout"], + artifact=tool_output, + tool_call_id='call_Jja7J89XsjrOLA5r!MEOW!SL', + ) + + The tool_call_id field is used to associate the tool call request with the + tool call response. This is useful in situations where a chat model is able + to request multiple tool calls in parallel. + ''', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'artifact': dict({ + 'default': None, + 'title': 'Artifact', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'status': dict({ + 'default': 'success', + 'enum': list([ + 'success', + 'error', + ]), + 'title': 'Status', + 'type': 'string', + }), + 'tool_call_id': dict({ + 'title': 'Tool Call Id', + 'type': 'string', + }), + 'type': dict({ + 'const': 'tool', + 'default': 'tool', + 'enum': list([ + 'tool', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'content', + 'tool_call_id', + ]), + 'title': 'ToolMessage', + 'type': 'object', + }), + 'ToolMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'Tool Message chunk.', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'artifact': dict({ + 'default': None, + 'title': 'Artifact', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'status': dict({ + 'default': 'success', + 'enum': list([ + 'success', + 'error', + ]), + 'title': 'Status', + 'type': 'string', + }), + 'tool_call_id': dict({ + 'title': 'Tool Call Id', + 'type': 'string', + }), + 'type': dict({ + 'const': 'ToolMessageChunk', + 'default': 'ToolMessageChunk', + 'enum': list([ + 'ToolMessageChunk', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'content', + 'tool_call_id', + ]), + 'title': 'ToolMessageChunk', + 'type': 'object', + }), + 'UsageMetadata': dict({ + 'description': ''' + Usage metadata for a message, such as token counts. + + This is a standard representation of token usage that is consistent across models. + + Example: + + .. code-block:: python + + { + "input_tokens": 10, + "output_tokens": 20, + "total_tokens": 30 + } + ''', + 'properties': dict({ + 'input_tokens': dict({ + 'title': 'Input Tokens', + 'type': 'integer', + }), + 'output_tokens': dict({ + 'title': 'Output Tokens', + 'type': 'integer', + }), + 'total_tokens': dict({ + 'title': 'Total Tokens', + 'type': 'integer', + }), + }), + 'required': list([ + 'input_tokens', + 'output_tokens', + 'total_tokens', + ]), + 'title': 'UsageMetadata', + 'type': 'object', + }), + }), + 'anyOf': list([ + dict({ + '$ref': '#/$defs/StringPromptValue', + }), + dict({ + '$ref': '#/$defs/ChatPromptValueConcrete', + }), + ]), + 'title': 'ChatPromptTemplateOutput', + }) +# --- +# name: test_schemas[fake_chat_input_schema] + dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + '$ref': '#/definitions/StringPromptValue', + }), + dict({ + '$ref': '#/definitions/ChatPromptValueConcrete', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + '$ref': '#/definitions/AIMessage', + }), + dict({ + '$ref': '#/definitions/HumanMessage', + }), + dict({ + '$ref': '#/definitions/ChatMessage', + }), + dict({ + '$ref': '#/definitions/SystemMessage', + }), + dict({ + '$ref': '#/definitions/FunctionMessage', + }), + dict({ + '$ref': '#/definitions/ToolMessage', + }), + dict({ + '$ref': '#/definitions/AIMessageChunk', + }), + dict({ + '$ref': '#/definitions/HumanMessageChunk', + }), + dict({ + '$ref': '#/definitions/ChatMessageChunk', + }), + dict({ + '$ref': '#/definitions/SystemMessageChunk', + }), + dict({ + '$ref': '#/definitions/FunctionMessageChunk', + }), + dict({ + '$ref': '#/definitions/ToolMessageChunk', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'definitions': dict({ + 'AIMessage': dict({ + 'additionalProperties': True, + 'description': ''' + Message from an AI. + + AIMessage is returned from a chat model as a response to a prompt. + + This message represents the output of the model and consists of both + the raw output as returned by the model together standardized fields + (e.g., tool calls, usage metadata) added by the LangChain framework. + ''', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'example': dict({ + 'default': False, + 'title': 'Example', + 'type': 'boolean', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'invalid_tool_calls': dict({ + 'default': list([ + ]), + 'items': dict({ + '$ref': '#/definitions/InvalidToolCall', + }), + 'title': 'Invalid Tool Calls', + 'type': 'array', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'tool_calls': dict({ + 'default': list([ + ]), + 'items': dict({ + '$ref': '#/definitions/ToolCall', + }), + 'title': 'Tool Calls', + 'type': 'array', + }), + 'type': dict({ + 'const': 'ai', + 'default': 'ai', + 'enum': list([ + 'ai', + ]), + 'title': 'Type', + 'type': 'string', + }), + 'usage_metadata': dict({ + 'anyOf': list([ + dict({ + '$ref': '#/definitions/UsageMetadata', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + }), + }), + 'required': list([ + 'content', + ]), + 'title': 'AIMessage', + 'type': 'object', + }), + 'AIMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'Message chunk from an AI.', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'example': dict({ + 'default': False, + 'title': 'Example', + 'type': 'boolean', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'invalid_tool_calls': dict({ + 'default': list([ + ]), + 'items': dict({ + '$ref': '#/definitions/InvalidToolCall', + }), + 'title': 'Invalid Tool Calls', + 'type': 'array', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'tool_call_chunks': dict({ + 'default': list([ + ]), + 'items': dict({ + '$ref': '#/definitions/ToolCallChunk', + }), + 'title': 'Tool Call Chunks', + 'type': 'array', + }), + 'tool_calls': dict({ + 'default': list([ + ]), + 'items': dict({ + '$ref': '#/definitions/ToolCall', + }), + 'title': 'Tool Calls', + 'type': 'array', + }), + 'type': dict({ + 'const': 'AIMessageChunk', + 'default': 'AIMessageChunk', + 'enum': list([ + 'AIMessageChunk', + ]), + 'title': 'Type', + 'type': 'string', + }), + 'usage_metadata': dict({ + 'anyOf': list([ + dict({ + '$ref': '#/definitions/UsageMetadata', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + }), + }), + 'required': list([ + 'content', + ]), + 'title': 'AIMessageChunk', + 'type': 'object', + }), + 'ChatMessage': dict({ + 'additionalProperties': True, + 'description': 'Message that can be assigned an arbitrary speaker (i.e. role).', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'role': dict({ + 'title': 'Role', + 'type': 'string', + }), + 'type': dict({ + 'const': 'chat', + 'default': 'chat', + 'enum': list([ + 'chat', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'content', + 'role', + ]), + 'title': 'ChatMessage', + 'type': 'object', + }), + 'ChatMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'Chat Message chunk.', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'role': dict({ + 'title': 'Role', + 'type': 'string', + }), + 'type': dict({ + 'const': 'ChatMessageChunk', + 'default': 'ChatMessageChunk', + 'enum': list([ + 'ChatMessageChunk', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'content', + 'role', + ]), + 'title': 'ChatMessageChunk', + 'type': 'object', + }), + 'ChatPromptValueConcrete': dict({ + 'description': ''' + Chat prompt value which explicitly lists out the message types it accepts. + For use in external schemas. + ''', + 'properties': dict({ + 'messages': dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + '$ref': '#/definitions/AIMessage', + }), + dict({ + '$ref': '#/definitions/HumanMessage', + }), + dict({ + '$ref': '#/definitions/ChatMessage', + }), + dict({ + '$ref': '#/definitions/SystemMessage', + }), + dict({ + '$ref': '#/definitions/FunctionMessage', + }), + dict({ + '$ref': '#/definitions/ToolMessage', + }), + dict({ + '$ref': '#/definitions/AIMessageChunk', + }), + dict({ + '$ref': '#/definitions/HumanMessageChunk', + }), + dict({ + '$ref': '#/definitions/ChatMessageChunk', + }), + dict({ + '$ref': '#/definitions/SystemMessageChunk', + }), + dict({ + '$ref': '#/definitions/FunctionMessageChunk', + }), + dict({ + '$ref': '#/definitions/ToolMessageChunk', + }), + ]), + }), + 'title': 'Messages', + 'type': 'array', + }), + 'type': dict({ + 'const': 'ChatPromptValueConcrete', + 'default': 'ChatPromptValueConcrete', + 'enum': list([ + 'ChatPromptValueConcrete', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'messages', + ]), + 'title': 'ChatPromptValueConcrete', + 'type': 'object', + }), + 'FunctionMessage': dict({ + 'additionalProperties': True, + 'description': ''' + Message for passing the result of executing a tool back to a model. + + FunctionMessage are an older version of the ToolMessage schema, and + do not contain the tool_call_id field. + + The tool_call_id field is used to associate the tool call request with the + tool call response. This is useful in situations where a chat model is able + to request multiple tool calls in parallel. + ''', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'name': dict({ + 'title': 'Name', + 'type': 'string', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'type': dict({ + 'const': 'function', + 'default': 'function', + 'enum': list([ + 'function', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'content', + 'name', + ]), + 'title': 'FunctionMessage', + 'type': 'object', + }), + 'FunctionMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'Function Message chunk.', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'name': dict({ + 'title': 'Name', + 'type': 'string', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'type': dict({ + 'const': 'FunctionMessageChunk', + 'default': 'FunctionMessageChunk', + 'enum': list([ + 'FunctionMessageChunk', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'content', + 'name', + ]), + 'title': 'FunctionMessageChunk', + 'type': 'object', + }), + 'HumanMessage': dict({ + 'additionalProperties': True, + 'description': ''' + Message from a human. + + HumanMessages are messages that are passed in from a human to the model. + + Example: + + .. code-block:: python + + from langchain_core.messages import HumanMessage, SystemMessage + + messages = [ + SystemMessage( + content="You are a helpful assistant! Your name is Bob." + ), + HumanMessage( + content="What is your name?" + ) + ] + + # Instantiate a chat model and invoke it with the messages + model = ... + print(model.invoke(messages)) + ''', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'example': dict({ + 'default': False, + 'title': 'Example', + 'type': 'boolean', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'type': dict({ + 'const': 'human', + 'default': 'human', + 'enum': list([ + 'human', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'content', + ]), + 'title': 'HumanMessage', + 'type': 'object', + }), + 'HumanMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'Human Message chunk.', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'example': dict({ + 'default': False, + 'title': 'Example', + 'type': 'boolean', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'type': dict({ + 'const': 'HumanMessageChunk', + 'default': 'HumanMessageChunk', + 'enum': list([ + 'HumanMessageChunk', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'content', + ]), + 'title': 'HumanMessageChunk', + 'type': 'object', + }), + 'InvalidToolCall': dict({ + 'description': ''' + Allowance for errors made by LLM. + + Here we add an `error` key to surface errors made during generation + (e.g., invalid JSON arguments.) + ''', + 'properties': dict({ + 'args': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Args', + }), + 'error': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Error', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Id', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Name', + }), + 'type': dict({ + 'const': 'invalid_tool_call', + 'enum': list([ + 'invalid_tool_call', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'name', + 'args', + 'id', + 'error', + ]), + 'title': 'InvalidToolCall', + 'type': 'object', + }), + 'StringPromptValue': dict({ + 'description': 'String prompt value.', + 'properties': dict({ + 'text': dict({ + 'title': 'Text', + 'type': 'string', + }), + 'type': dict({ + 'const': 'StringPromptValue', + 'default': 'StringPromptValue', + 'enum': list([ + 'StringPromptValue', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'text', + ]), + 'title': 'StringPromptValue', + 'type': 'object', + }), + 'SystemMessage': dict({ + 'additionalProperties': True, + 'description': ''' + Message for priming AI behavior. + + The system message is usually passed in as the first of a sequence + of input messages. + + Example: + + .. code-block:: python + + from langchain_core.messages import HumanMessage, SystemMessage + + messages = [ + SystemMessage( + content="You are a helpful assistant! Your name is Bob." + ), + HumanMessage( + content="What is your name?" + ) + ] + + # Define a chat model and invoke it with the messages + print(model.invoke(messages)) + ''', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'type': dict({ + 'const': 'system', + 'default': 'system', + 'enum': list([ + 'system', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'content', + ]), + 'title': 'SystemMessage', + 'type': 'object', + }), + 'SystemMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'System Message chunk.', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'type': dict({ + 'const': 'SystemMessageChunk', + 'default': 'SystemMessageChunk', + 'enum': list([ + 'SystemMessageChunk', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'content', + ]), + 'title': 'SystemMessageChunk', + 'type': 'object', + }), + 'ToolCall': dict({ + 'description': ''' + Represents a request to call a tool. + + Example: + + .. code-block:: python + + { + "name": "foo", + "args": {"a": 1}, + "id": "123" + } + + This represents a request to call the tool named "foo" with arguments {"a": 1} + and an identifier of "123". + ''', + 'properties': dict({ + 'args': dict({ + 'title': 'Args', + 'type': 'object', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Id', + }), + 'name': dict({ + 'title': 'Name', + 'type': 'string', + }), + 'type': dict({ + 'const': 'tool_call', + 'enum': list([ + 'tool_call', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'name', + 'args', + 'id', + ]), + 'title': 'ToolCall', + 'type': 'object', + }), + 'ToolCallChunk': dict({ + 'description': ''' + A chunk of a tool call (e.g., as part of a stream). + + When merging ToolCallChunks (e.g., via AIMessageChunk.__add__), + all string attributes are concatenated. Chunks are only merged if their + values of `index` are equal and not None. + + Example: + + .. code-block:: python + + left_chunks = [ToolCallChunk(name="foo", args='{"a":', index=0)] + right_chunks = [ToolCallChunk(name=None, args='1}', index=0)] + + ( + AIMessageChunk(content="", tool_call_chunks=left_chunks) + + AIMessageChunk(content="", tool_call_chunks=right_chunks) + ).tool_call_chunks == [ToolCallChunk(name='foo', args='{"a":1}', index=0)] + ''', + 'properties': dict({ + 'args': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Args', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Id', + }), + 'index': dict({ + 'anyOf': list([ + dict({ + 'type': 'integer', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Index', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Name', + }), + 'type': dict({ + 'const': 'tool_call_chunk', + 'enum': list([ + 'tool_call_chunk', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'name', + 'args', + 'id', + 'index', + ]), + 'title': 'ToolCallChunk', + 'type': 'object', + }), + 'ToolMessage': dict({ + 'additionalProperties': True, + 'description': ''' + Message for passing the result of executing a tool back to a model. + + ToolMessages contain the result of a tool invocation. Typically, the result + is encoded inside the `content` field. + + Example: A ToolMessage representing a result of 42 from a tool call with id + + .. code-block:: python + + from langchain_core.messages import ToolMessage + + ToolMessage(content='42', tool_call_id='call_Jja7J89XsjrOLA5r!MEOW!SL') + + + Example: A ToolMessage where only part of the tool output is sent to the model + and the full output is passed in to artifact. + + .. versionadded:: 0.2.17 + + .. code-block:: python + + from langchain_core.messages import ToolMessage + + tool_output = { + "stdout": "From the graph we can see that the correlation between x and y is ...", + "stderr": None, + "artifacts": {"type": "image", "base64_data": "/9j/4gIcSU..."}, + } + + ToolMessage( + content=tool_output["stdout"], + artifact=tool_output, + tool_call_id='call_Jja7J89XsjrOLA5r!MEOW!SL', + ) + + The tool_call_id field is used to associate the tool call request with the + tool call response. This is useful in situations where a chat model is able + to request multiple tool calls in parallel. + ''', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'artifact': dict({ + 'title': 'Artifact', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'status': dict({ + 'default': 'success', + 'enum': list([ + 'success', + 'error', + ]), + 'title': 'Status', + 'type': 'string', + }), + 'tool_call_id': dict({ + 'title': 'Tool Call Id', + 'type': 'string', + }), + 'type': dict({ + 'const': 'tool', + 'default': 'tool', + 'enum': list([ + 'tool', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'content', + 'tool_call_id', + ]), + 'title': 'ToolMessage', + 'type': 'object', + }), + 'ToolMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'Tool Message chunk.', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'artifact': dict({ + 'title': 'Artifact', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'status': dict({ + 'default': 'success', + 'enum': list([ + 'success', + 'error', + ]), + 'title': 'Status', + 'type': 'string', + }), + 'tool_call_id': dict({ + 'title': 'Tool Call Id', + 'type': 'string', + }), + 'type': dict({ + 'const': 'ToolMessageChunk', + 'default': 'ToolMessageChunk', + 'enum': list([ + 'ToolMessageChunk', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'content', + 'tool_call_id', + ]), + 'title': 'ToolMessageChunk', + 'type': 'object', + }), + 'UsageMetadata': dict({ + 'description': ''' + Usage metadata for a message, such as token counts. + + This is a standard representation of token usage that is consistent across models. + + Example: + + .. code-block:: python + + { + "input_tokens": 10, + "output_tokens": 20, + "total_tokens": 30 + } + ''', + 'properties': dict({ + 'input_tokens': dict({ + 'title': 'Input Tokens', + 'type': 'integer', + }), + 'output_tokens': dict({ + 'title': 'Output Tokens', + 'type': 'integer', + }), + 'total_tokens': dict({ + 'title': 'Total Tokens', + 'type': 'integer', + }), + }), + 'required': list([ + 'input_tokens', + 'output_tokens', + 'total_tokens', + ]), + 'title': 'UsageMetadata', + 'type': 'object', + }), + }), + 'title': 'FakeListChatModelInput', + }) +# --- +# name: test_schemas[fake_chat_output_schema] + dict({ + 'anyOf': list([ + dict({ + '$ref': '#/definitions/AIMessage', + }), + dict({ + '$ref': '#/definitions/HumanMessage', + }), + dict({ + '$ref': '#/definitions/ChatMessage', + }), + dict({ + '$ref': '#/definitions/SystemMessage', + }), + dict({ + '$ref': '#/definitions/FunctionMessage', + }), + dict({ + '$ref': '#/definitions/ToolMessage', + }), + dict({ + '$ref': '#/definitions/AIMessageChunk', + }), + dict({ + '$ref': '#/definitions/HumanMessageChunk', + }), + dict({ + '$ref': '#/definitions/ChatMessageChunk', + }), + dict({ + '$ref': '#/definitions/SystemMessageChunk', + }), + dict({ + '$ref': '#/definitions/FunctionMessageChunk', + }), + dict({ + '$ref': '#/definitions/ToolMessageChunk', + }), + ]), + 'definitions': dict({ + 'AIMessage': dict({ + 'additionalProperties': True, + 'description': ''' + Message from an AI. + + AIMessage is returned from a chat model as a response to a prompt. + + This message represents the output of the model and consists of both + the raw output as returned by the model together standardized fields + (e.g., tool calls, usage metadata) added by the LangChain framework. + ''', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'example': dict({ + 'default': False, + 'title': 'Example', + 'type': 'boolean', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'invalid_tool_calls': dict({ + 'default': list([ + ]), + 'items': dict({ + '$ref': '#/definitions/InvalidToolCall', + }), + 'title': 'Invalid Tool Calls', + 'type': 'array', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'tool_calls': dict({ + 'default': list([ + ]), + 'items': dict({ + '$ref': '#/definitions/ToolCall', + }), + 'title': 'Tool Calls', + 'type': 'array', + }), + 'type': dict({ + 'const': 'ai', + 'default': 'ai', + 'enum': list([ + 'ai', + ]), + 'title': 'Type', + 'type': 'string', + }), + 'usage_metadata': dict({ + 'anyOf': list([ + dict({ + '$ref': '#/definitions/UsageMetadata', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + }), + }), + 'required': list([ + 'content', + ]), + 'title': 'AIMessage', + 'type': 'object', + }), + 'AIMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'Message chunk from an AI.', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'example': dict({ + 'default': False, + 'title': 'Example', + 'type': 'boolean', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'invalid_tool_calls': dict({ + 'default': list([ + ]), + 'items': dict({ + '$ref': '#/definitions/InvalidToolCall', + }), + 'title': 'Invalid Tool Calls', + 'type': 'array', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'tool_call_chunks': dict({ + 'default': list([ + ]), + 'items': dict({ + '$ref': '#/definitions/ToolCallChunk', + }), + 'title': 'Tool Call Chunks', + 'type': 'array', + }), + 'tool_calls': dict({ + 'default': list([ + ]), + 'items': dict({ + '$ref': '#/definitions/ToolCall', + }), + 'title': 'Tool Calls', + 'type': 'array', + }), + 'type': dict({ + 'const': 'AIMessageChunk', + 'default': 'AIMessageChunk', + 'enum': list([ + 'AIMessageChunk', + ]), + 'title': 'Type', + 'type': 'string', + }), + 'usage_metadata': dict({ + 'anyOf': list([ + dict({ + '$ref': '#/definitions/UsageMetadata', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + }), + }), + 'required': list([ + 'content', + ]), + 'title': 'AIMessageChunk', + 'type': 'object', + }), + 'ChatMessage': dict({ + 'additionalProperties': True, + 'description': 'Message that can be assigned an arbitrary speaker (i.e. role).', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'role': dict({ + 'title': 'Role', + 'type': 'string', + }), + 'type': dict({ + 'const': 'chat', + 'default': 'chat', + 'enum': list([ + 'chat', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'content', + 'role', + ]), + 'title': 'ChatMessage', + 'type': 'object', + }), + 'ChatMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'Chat Message chunk.', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'role': dict({ + 'title': 'Role', + 'type': 'string', + }), + 'type': dict({ + 'const': 'ChatMessageChunk', + 'default': 'ChatMessageChunk', + 'enum': list([ + 'ChatMessageChunk', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'content', + 'role', + ]), + 'title': 'ChatMessageChunk', + 'type': 'object', + }), + 'FunctionMessage': dict({ + 'additionalProperties': True, + 'description': ''' + Message for passing the result of executing a tool back to a model. + + FunctionMessage are an older version of the ToolMessage schema, and + do not contain the tool_call_id field. + + The tool_call_id field is used to associate the tool call request with the + tool call response. This is useful in situations where a chat model is able + to request multiple tool calls in parallel. + ''', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'name': dict({ + 'title': 'Name', + 'type': 'string', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'type': dict({ + 'const': 'function', + 'default': 'function', + 'enum': list([ + 'function', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'content', + 'name', + ]), + 'title': 'FunctionMessage', + 'type': 'object', + }), + 'FunctionMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'Function Message chunk.', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'name': dict({ + 'title': 'Name', + 'type': 'string', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'type': dict({ + 'const': 'FunctionMessageChunk', + 'default': 'FunctionMessageChunk', + 'enum': list([ + 'FunctionMessageChunk', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'content', + 'name', + ]), + 'title': 'FunctionMessageChunk', + 'type': 'object', + }), + 'HumanMessage': dict({ + 'additionalProperties': True, + 'description': ''' + Message from a human. + + HumanMessages are messages that are passed in from a human to the model. + + Example: + + .. code-block:: python + + from langchain_core.messages import HumanMessage, SystemMessage + + messages = [ + SystemMessage( + content="You are a helpful assistant! Your name is Bob." + ), + HumanMessage( + content="What is your name?" + ) + ] + + # Instantiate a chat model and invoke it with the messages + model = ... + print(model.invoke(messages)) + ''', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'example': dict({ + 'default': False, + 'title': 'Example', + 'type': 'boolean', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'type': dict({ + 'const': 'human', + 'default': 'human', + 'enum': list([ + 'human', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'content', + ]), + 'title': 'HumanMessage', + 'type': 'object', + }), + 'HumanMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'Human Message chunk.', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'example': dict({ + 'default': False, + 'title': 'Example', + 'type': 'boolean', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'type': dict({ + 'const': 'HumanMessageChunk', + 'default': 'HumanMessageChunk', + 'enum': list([ + 'HumanMessageChunk', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'content', + ]), + 'title': 'HumanMessageChunk', + 'type': 'object', + }), + 'InvalidToolCall': dict({ + 'description': ''' + Allowance for errors made by LLM. + + Here we add an `error` key to surface errors made during generation + (e.g., invalid JSON arguments.) + ''', + 'properties': dict({ + 'args': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Args', + }), + 'error': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Error', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Id', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Name', + }), + 'type': dict({ + 'const': 'invalid_tool_call', + 'enum': list([ + 'invalid_tool_call', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'name', + 'args', + 'id', + 'error', + ]), + 'title': 'InvalidToolCall', + 'type': 'object', + }), + 'SystemMessage': dict({ + 'additionalProperties': True, + 'description': ''' + Message for priming AI behavior. + + The system message is usually passed in as the first of a sequence + of input messages. + + Example: + + .. code-block:: python + + from langchain_core.messages import HumanMessage, SystemMessage + + messages = [ + SystemMessage( + content="You are a helpful assistant! Your name is Bob." + ), + HumanMessage( + content="What is your name?" + ) + ] + + # Define a chat model and invoke it with the messages + print(model.invoke(messages)) + ''', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'type': dict({ + 'const': 'system', + 'default': 'system', + 'enum': list([ + 'system', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'content', + ]), + 'title': 'SystemMessage', + 'type': 'object', + }), + 'SystemMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'System Message chunk.', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'type': dict({ + 'const': 'SystemMessageChunk', + 'default': 'SystemMessageChunk', + 'enum': list([ + 'SystemMessageChunk', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'content', + ]), + 'title': 'SystemMessageChunk', + 'type': 'object', + }), + 'ToolCall': dict({ + 'description': ''' + Represents a request to call a tool. + + Example: + + .. code-block:: python + + { + "name": "foo", + "args": {"a": 1}, + "id": "123" + } + + This represents a request to call the tool named "foo" with arguments {"a": 1} + and an identifier of "123". + ''', + 'properties': dict({ + 'args': dict({ + 'title': 'Args', + 'type': 'object', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Id', + }), + 'name': dict({ + 'title': 'Name', + 'type': 'string', + }), + 'type': dict({ + 'const': 'tool_call', + 'enum': list([ + 'tool_call', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'name', + 'args', + 'id', + ]), + 'title': 'ToolCall', + 'type': 'object', + }), + 'ToolCallChunk': dict({ + 'description': ''' + A chunk of a tool call (e.g., as part of a stream). + + When merging ToolCallChunks (e.g., via AIMessageChunk.__add__), + all string attributes are concatenated. Chunks are only merged if their + values of `index` are equal and not None. + + Example: + + .. code-block:: python + + left_chunks = [ToolCallChunk(name="foo", args='{"a":', index=0)] + right_chunks = [ToolCallChunk(name=None, args='1}', index=0)] + + ( + AIMessageChunk(content="", tool_call_chunks=left_chunks) + + AIMessageChunk(content="", tool_call_chunks=right_chunks) + ).tool_call_chunks == [ToolCallChunk(name='foo', args='{"a":1}', index=0)] + ''', + 'properties': dict({ + 'args': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Args', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Id', + }), + 'index': dict({ + 'anyOf': list([ + dict({ + 'type': 'integer', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Index', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Name', + }), + 'type': dict({ + 'const': 'tool_call_chunk', + 'enum': list([ + 'tool_call_chunk', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'name', + 'args', + 'id', + 'index', + ]), + 'title': 'ToolCallChunk', + 'type': 'object', + }), + 'ToolMessage': dict({ + 'additionalProperties': True, + 'description': ''' + Message for passing the result of executing a tool back to a model. + + ToolMessages contain the result of a tool invocation. Typically, the result + is encoded inside the `content` field. + + Example: A ToolMessage representing a result of 42 from a tool call with id + + .. code-block:: python + + from langchain_core.messages import ToolMessage + + ToolMessage(content='42', tool_call_id='call_Jja7J89XsjrOLA5r!MEOW!SL') + + + Example: A ToolMessage where only part of the tool output is sent to the model + and the full output is passed in to artifact. + + .. versionadded:: 0.2.17 + + .. code-block:: python + + from langchain_core.messages import ToolMessage + + tool_output = { + "stdout": "From the graph we can see that the correlation between x and y is ...", + "stderr": None, + "artifacts": {"type": "image", "base64_data": "/9j/4gIcSU..."}, + } + + ToolMessage( + content=tool_output["stdout"], + artifact=tool_output, + tool_call_id='call_Jja7J89XsjrOLA5r!MEOW!SL', + ) + + The tool_call_id field is used to associate the tool call request with the + tool call response. This is useful in situations where a chat model is able + to request multiple tool calls in parallel. + ''', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'artifact': dict({ + 'title': 'Artifact', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'status': dict({ + 'default': 'success', + 'enum': list([ + 'success', + 'error', + ]), + 'title': 'Status', + 'type': 'string', + }), + 'tool_call_id': dict({ + 'title': 'Tool Call Id', + 'type': 'string', + }), + 'type': dict({ + 'const': 'tool', + 'default': 'tool', + 'enum': list([ + 'tool', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'content', + 'tool_call_id', + ]), + 'title': 'ToolMessage', + 'type': 'object', + }), + 'ToolMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'Tool Message chunk.', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'artifact': dict({ + 'title': 'Artifact', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'status': dict({ + 'default': 'success', + 'enum': list([ + 'success', + 'error', + ]), + 'title': 'Status', + 'type': 'string', + }), + 'tool_call_id': dict({ + 'title': 'Tool Call Id', + 'type': 'string', + }), + 'type': dict({ + 'const': 'ToolMessageChunk', + 'default': 'ToolMessageChunk', + 'enum': list([ + 'ToolMessageChunk', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'content', + 'tool_call_id', + ]), + 'title': 'ToolMessageChunk', + 'type': 'object', + }), + 'UsageMetadata': dict({ + 'description': ''' + Usage metadata for a message, such as token counts. + + This is a standard representation of token usage that is consistent across models. + + Example: + + .. code-block:: python + + { + "input_tokens": 10, + "output_tokens": 20, + "total_tokens": 30 + } + ''', 'properties': dict({ - 'text': dict({ - 'title': 'Text', + 'input_tokens': dict({ + 'title': 'Input Tokens', + 'type': 'integer', + }), + 'output_tokens': dict({ + 'title': 'Output Tokens', + 'type': 'integer', + }), + 'total_tokens': dict({ + 'title': 'Total Tokens', + 'type': 'integer', + }), + }), + 'required': list([ + 'input_tokens', + 'output_tokens', + 'total_tokens', + ]), + 'title': 'UsageMetadata', + 'type': 'object', + }), + }), + 'title': 'FakeListChatModelOutput', + }) +# --- +# name: test_schemas[fake_llm_input_schema] + dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + '$ref': '#/definitions/StringPromptValue', + }), + dict({ + '$ref': '#/definitions/ChatPromptValueConcrete', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + '$ref': '#/definitions/AIMessage', + }), + dict({ + '$ref': '#/definitions/HumanMessage', + }), + dict({ + '$ref': '#/definitions/ChatMessage', + }), + dict({ + '$ref': '#/definitions/SystemMessage', + }), + dict({ + '$ref': '#/definitions/FunctionMessage', + }), + dict({ + '$ref': '#/definitions/ToolMessage', + }), + dict({ + '$ref': '#/definitions/AIMessageChunk', + }), + dict({ + '$ref': '#/definitions/HumanMessageChunk', + }), + dict({ + '$ref': '#/definitions/ChatMessageChunk', + }), + dict({ + '$ref': '#/definitions/SystemMessageChunk', + }), + dict({ + '$ref': '#/definitions/FunctionMessageChunk', + }), + dict({ + '$ref': '#/definitions/ToolMessageChunk', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'definitions': dict({ + 'AIMessage': dict({ + 'additionalProperties': True, + 'description': ''' + Message from an AI. + + AIMessage is returned from a chat model as a response to a prompt. + + This message represents the output of the model and consists of both + the raw output as returned by the model together standardized fields + (e.g., tool calls, usage metadata) added by the LangChain framework. + ''', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'example': dict({ + 'default': False, + 'title': 'Example', + 'type': 'boolean', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'invalid_tool_calls': dict({ + 'default': list([ + ]), + 'items': dict({ + '$ref': '#/definitions/InvalidToolCall', + }), + 'title': 'Invalid Tool Calls', + 'type': 'array', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'tool_calls': dict({ + 'default': list([ + ]), + 'items': dict({ + '$ref': '#/definitions/ToolCall', + }), + 'title': 'Tool Calls', + 'type': 'array', + }), + 'type': dict({ + 'const': 'ai', + 'default': 'ai', + 'enum': list([ + 'ai', + ]), + 'title': 'Type', 'type': 'string', }), + 'usage_metadata': dict({ + 'anyOf': list([ + dict({ + '$ref': '#/definitions/UsageMetadata', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + }), + }), + 'required': list([ + 'content', + ]), + 'title': 'AIMessage', + 'type': 'object', + }), + 'AIMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'Message chunk from an AI.', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'example': dict({ + 'default': False, + 'title': 'Example', + 'type': 'boolean', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'invalid_tool_calls': dict({ + 'default': list([ + ]), + 'items': dict({ + '$ref': '#/definitions/InvalidToolCall', + }), + 'title': 'Invalid Tool Calls', + 'type': 'array', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'tool_call_chunks': dict({ + 'default': list([ + ]), + 'items': dict({ + '$ref': '#/definitions/ToolCallChunk', + }), + 'title': 'Tool Call Chunks', + 'type': 'array', + }), + 'tool_calls': dict({ + 'default': list([ + ]), + 'items': dict({ + '$ref': '#/definitions/ToolCall', + }), + 'title': 'Tool Calls', + 'type': 'array', + }), 'type': dict({ - 'default': 'StringPromptValue', + 'const': 'AIMessageChunk', + 'default': 'AIMessageChunk', 'enum': list([ - 'StringPromptValue', + 'AIMessageChunk', ]), 'title': 'Type', 'type': 'string', }), + 'usage_metadata': dict({ + 'anyOf': list([ + dict({ + '$ref': '#/definitions/UsageMetadata', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + }), }), 'required': list([ - 'text', + 'content', ]), - 'title': 'StringPromptValue', + 'title': 'AIMessageChunk', 'type': 'object', }), - 'SystemMessage': dict({ - 'description': ''' - Message for priming AI behavior. - - The system message is usually passed in as the first of a sequence - of input messages. - - Example: - - .. code-block:: python - - from langchain_core.messages import HumanMessage, SystemMessage - - messages = [ - SystemMessage( - content="You are a helpful assistant! Your name is Bob." - ), - HumanMessage( - content="What is your name?" - ) - ] - - # Define a chat model and invoke it with the messages - print(model.invoke(messages)) - ''', + 'ChatMessage': dict({ + 'additionalProperties': True, + 'description': 'Message that can be assigned an arbitrary speaker (i.e. role).', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -6354,111 +11214,62 @@ 'title': 'Content', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Name', - 'type': 'string', }), 'response_metadata': dict({ 'title': 'Response Metadata', 'type': 'object', }), - 'type': dict({ - 'default': 'system', - 'enum': list([ - 'system', - ]), - 'title': 'Type', - 'type': 'string', - }), - }), - 'required': list([ - 'content', - ]), - 'title': 'SystemMessage', - 'type': 'object', - }), - 'ToolCall': dict({ - 'properties': dict({ - 'args': dict({ - 'title': 'Args', - 'type': 'object', - }), - 'id': dict({ - 'title': 'Id', - 'type': 'string', - }), - 'name': dict({ - 'title': 'Name', + 'role': dict({ + 'title': 'Role', 'type': 'string', }), 'type': dict({ + 'const': 'chat', + 'default': 'chat', 'enum': list([ - 'tool_call', + 'chat', ]), 'title': 'Type', 'type': 'string', }), }), 'required': list([ - 'name', - 'args', - 'id', + 'content', + 'role', ]), - 'title': 'ToolCall', + 'title': 'ChatMessage', 'type': 'object', }), - 'ToolMessage': dict({ - 'description': ''' - Message for passing the result of executing a tool back to a model. - - ToolMessages contain the result of a tool invocation. Typically, the result - is encoded inside the `content` field. - - Example: A ToolMessage representing a result of 42 from a tool call with id - - .. code-block:: python - - from langchain_core.messages import ToolMessage - - ToolMessage(content='42', tool_call_id='call_Jja7J89XsjrOLA5r!MEOW!SL') - - - Example: A ToolMessage where only part of the tool output is sent to the model - and the full output is passed in to artifact. - - .. versionadded:: 0.2.17 - - .. code-block:: python - - from langchain_core.messages import ToolMessage - - tool_output = { - "stdout": "From the graph we can see that the correlation between x and y is ...", - "stderr": None, - "artifacts": {"type": "image", "base64_data": "/9j/4gIcSU..."}, - } - - ToolMessage( - content=tool_output["stdout"], - artifact=tool_output, - tool_call_id='call_Jja7J89XsjrOLA5r!MEOW!SL', - ) - - The tool_call_id field is used to associate the tool call request with the - tool call response. This is useful in situations where a chat model is able - to request multiple tool calls in parallel. - ''', + 'ChatMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'Chat Message chunk.', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', 'type': 'object', }), - 'artifact': dict({ - 'title': 'Artifact', - }), 'content': dict({ 'anyOf': list([ dict({ @@ -6481,34 +11292,42 @@ 'title': 'Content', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Name', - 'type': 'string', }), 'response_metadata': dict({ 'title': 'Response Metadata', 'type': 'object', }), - 'status': dict({ - 'default': 'success', - 'enum': list([ - 'success', - 'error', - ]), - 'title': 'Status', - 'type': 'string', - }), - 'tool_call_id': dict({ - 'title': 'Tool Call Id', + 'role': dict({ + 'title': 'Role', 'type': 'string', }), 'type': dict({ - 'default': 'tool', + 'const': 'ChatMessageChunk', + 'default': 'ChatMessageChunk', 'enum': list([ - 'tool', + 'ChatMessageChunk', ]), 'title': 'Type', 'type': 'string', @@ -6516,70 +11335,88 @@ }), 'required': list([ 'content', - 'tool_call_id', + 'role', ]), - 'title': 'ToolMessage', + 'title': 'ChatMessageChunk', 'type': 'object', }), - 'UsageMetadata': dict({ + 'ChatPromptValueConcrete': dict({ + 'description': ''' + Chat prompt value which explicitly lists out the message types it accepts. + For use in external schemas. + ''', 'properties': dict({ - 'input_tokens': dict({ - 'title': 'Input Tokens', - 'type': 'integer', - }), - 'output_tokens': dict({ - 'title': 'Output Tokens', - 'type': 'integer', + 'messages': dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + '$ref': '#/definitions/AIMessage', + }), + dict({ + '$ref': '#/definitions/HumanMessage', + }), + dict({ + '$ref': '#/definitions/ChatMessage', + }), + dict({ + '$ref': '#/definitions/SystemMessage', + }), + dict({ + '$ref': '#/definitions/FunctionMessage', + }), + dict({ + '$ref': '#/definitions/ToolMessage', + }), + dict({ + '$ref': '#/definitions/AIMessageChunk', + }), + dict({ + '$ref': '#/definitions/HumanMessageChunk', + }), + dict({ + '$ref': '#/definitions/ChatMessageChunk', + }), + dict({ + '$ref': '#/definitions/SystemMessageChunk', + }), + dict({ + '$ref': '#/definitions/FunctionMessageChunk', + }), + dict({ + '$ref': '#/definitions/ToolMessageChunk', + }), + ]), + }), + 'title': 'Messages', + 'type': 'array', }), - 'total_tokens': dict({ - 'title': 'Total Tokens', - 'type': 'integer', + 'type': dict({ + 'const': 'ChatPromptValueConcrete', + 'default': 'ChatPromptValueConcrete', + 'enum': list([ + 'ChatPromptValueConcrete', + ]), + 'title': 'Type', + 'type': 'string', }), }), 'required': list([ - 'input_tokens', - 'output_tokens', - 'total_tokens', + 'messages', ]), - 'title': 'UsageMetadata', + 'title': 'ChatPromptValueConcrete', 'type': 'object', }), - }), - 'title': 'FakeListChatModelInput', - }) -# --- -# name: test_schemas.2 - dict({ - 'anyOf': list([ - dict({ - '$ref': '#/definitions/AIMessage', - }), - dict({ - '$ref': '#/definitions/HumanMessage', - }), - dict({ - '$ref': '#/definitions/ChatMessage', - }), - dict({ - '$ref': '#/definitions/SystemMessage', - }), - dict({ - '$ref': '#/definitions/FunctionMessage', - }), - dict({ - '$ref': '#/definitions/ToolMessage', - }), - ]), - 'definitions': dict({ - 'AIMessage': dict({ + 'FunctionMessage': dict({ + 'additionalProperties': True, 'description': ''' - Message from an AI. + Message for passing the result of executing a tool back to a model. - AIMessage is returned from a chat model as a response to a prompt. + FunctionMessage are an older version of the ToolMessage schema, and + do not contain the tool_call_id field. - This message represents the output of the model and consists of both - the raw output as returned by the model together standardized fields - (e.g., tool calls, usage metadata) added by the LangChain framework. + The tool_call_id field is used to associate the tool call request with the + tool call response. This is useful in situations where a chat model is able + to request multiple tool calls in parallel. ''', 'properties': dict({ 'additional_kwargs': dict({ @@ -6607,23 +11444,17 @@ ]), 'title': 'Content', }), - 'example': dict({ - 'default': False, - 'title': 'Example', - 'type': 'boolean', - }), 'id': dict({ - 'title': 'Id', - 'type': 'string', - }), - 'invalid_tool_calls': dict({ - 'default': list([ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), ]), - 'items': dict({ - '$ref': '#/definitions/InvalidToolCall', - }), - 'title': 'Invalid Tool Calls', - 'type': 'array', + 'default': None, + 'title': 'Id', }), 'name': dict({ 'title': 'Name', @@ -6633,35 +11464,26 @@ 'title': 'Response Metadata', 'type': 'object', }), - 'tool_calls': dict({ - 'default': list([ - ]), - 'items': dict({ - '$ref': '#/definitions/ToolCall', - }), - 'title': 'Tool Calls', - 'type': 'array', - }), 'type': dict({ - 'default': 'ai', + 'const': 'function', + 'default': 'function', 'enum': list([ - 'ai', + 'function', ]), 'title': 'Type', 'type': 'string', }), - 'usage_metadata': dict({ - '$ref': '#/definitions/UsageMetadata', - }), }), 'required': list([ 'content', + 'name', ]), - 'title': 'AIMessage', + 'title': 'FunctionMessage', 'type': 'object', }), - 'ChatMessage': dict({ - 'description': 'Message that can be assigned an arbitrary speaker (i.e. role).', + 'FunctionMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'Function Message chunk.', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -6689,8 +11511,16 @@ 'title': 'Content', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', }), 'name': dict({ 'title': 'Name', @@ -6700,14 +11530,11 @@ 'title': 'Response Metadata', 'type': 'object', }), - 'role': dict({ - 'title': 'Role', - 'type': 'string', - }), 'type': dict({ - 'default': 'chat', + 'const': 'FunctionMessageChunk', + 'default': 'FunctionMessageChunk', 'enum': list([ - 'chat', + 'FunctionMessageChunk', ]), 'title': 'Type', 'type': 'string', @@ -6715,21 +11542,36 @@ }), 'required': list([ 'content', - 'role', + 'name', ]), - 'title': 'ChatMessage', + 'title': 'FunctionMessageChunk', 'type': 'object', }), - 'FunctionMessage': dict({ + 'HumanMessage': dict({ + 'additionalProperties': True, 'description': ''' - Message for passing the result of executing a tool back to a model. + Message from a human. - FunctionMessage are an older version of the ToolMessage schema, and - do not contain the tool_call_id field. + HumanMessages are messages that are passed in from a human to the model. - The tool_call_id field is used to associate the tool call request with the - tool call response. This is useful in situations where a chat model is able - to request multiple tool calls in parallel. + Example: + + .. code-block:: python + + from langchain_core.messages import HumanMessage, SystemMessage + + messages = [ + SystemMessage( + content="You are a helpful assistant! Your name is Bob." + ), + HumanMessage( + content="What is your name?" + ) + ] + + # Instantiate a chat model and invoke it with the messages + model = ... + print(model.invoke(messages)) ''', 'properties': dict({ 'additional_kwargs': dict({ @@ -6757,22 +11599,44 @@ ]), 'title': 'Content', }), + 'example': dict({ + 'default': False, + 'title': 'Example', + 'type': 'boolean', + }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Name', - 'type': 'string', }), 'response_metadata': dict({ 'title': 'Response Metadata', 'type': 'object', }), 'type': dict({ - 'default': 'function', + 'const': 'human', + 'default': 'human', 'enum': list([ - 'function', + 'human', ]), 'title': 'Type', 'type': 'string', @@ -6780,36 +11644,13 @@ }), 'required': list([ 'content', - 'name', ]), - 'title': 'FunctionMessage', + 'title': 'HumanMessage', 'type': 'object', }), - 'HumanMessage': dict({ - 'description': ''' - Message from a human. - - HumanMessages are messages that are passed in from a human to the model. - - Example: - - .. code-block:: python - - from langchain_core.messages import HumanMessage, SystemMessage - - messages = [ - SystemMessage( - content="You are a helpful assistant! Your name is Bob." - ), - HumanMessage( - content="What is your name?" - ) - ] - - # Instantiate a chat model and invoke it with the messages - model = ... - print(model.invoke(messages)) - ''', + 'HumanMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'Human Message chunk.', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -6842,21 +11683,38 @@ 'type': 'boolean', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Name', - 'type': 'string', }), 'response_metadata': dict({ 'title': 'Response Metadata', 'type': 'object', }), 'type': dict({ - 'default': 'human', + 'const': 'HumanMessageChunk', + 'default': 'HumanMessageChunk', 'enum': list([ - 'human', + 'HumanMessageChunk', ]), 'title': 'Type', 'type': 'string', @@ -6865,45 +11723,104 @@ 'required': list([ 'content', ]), - 'title': 'HumanMessage', + 'title': 'HumanMessageChunk', 'type': 'object', }), 'InvalidToolCall': dict({ + 'description': ''' + Allowance for errors made by LLM. + + Here we add an `error` key to surface errors made during generation + (e.g., invalid JSON arguments.) + ''', 'properties': dict({ 'args': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), 'title': 'Args', - 'type': 'string', }), 'error': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), 'title': 'Error', - 'type': 'string', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), 'title': 'Id', - 'type': 'string', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), 'title': 'Name', + }), + 'type': dict({ + 'const': 'invalid_tool_call', + 'enum': list([ + 'invalid_tool_call', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'name', + 'args', + 'id', + 'error', + ]), + 'title': 'InvalidToolCall', + 'type': 'object', + }), + 'StringPromptValue': dict({ + 'description': 'String prompt value.', + 'properties': dict({ + 'text': dict({ + 'title': 'Text', 'type': 'string', }), 'type': dict({ + 'const': 'StringPromptValue', + 'default': 'StringPromptValue', 'enum': list([ - 'invalid_tool_call', + 'StringPromptValue', ]), 'title': 'Type', 'type': 'string', }), }), 'required': list([ - 'name', - 'args', - 'id', - 'error', + 'text', ]), - 'title': 'InvalidToolCall', + 'title': 'StringPromptValue', 'type': 'object', }), 'SystemMessage': dict({ + 'additionalProperties': True, 'description': ''' Message for priming AI behavior. @@ -6955,18 +11872,35 @@ 'title': 'Content', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Name', - 'type': 'string', }), 'response_metadata': dict({ 'title': 'Response Metadata', 'type': 'object', }), 'type': dict({ + 'const': 'system', 'default': 'system', 'enum': list([ 'system', @@ -6981,21 +11915,118 @@ 'title': 'SystemMessage', 'type': 'object', }), + 'SystemMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'System Message chunk.', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'type': dict({ + 'const': 'SystemMessageChunk', + 'default': 'SystemMessageChunk', + 'enum': list([ + 'SystemMessageChunk', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'content', + ]), + 'title': 'SystemMessageChunk', + 'type': 'object', + }), 'ToolCall': dict({ + 'description': ''' + Represents a request to call a tool. + + Example: + + .. code-block:: python + + { + "name": "foo", + "args": {"a": 1}, + "id": "123" + } + + This represents a request to call the tool named "foo" with arguments {"a": 1} + and an identifier of "123". + ''', 'properties': dict({ 'args': dict({ 'title': 'Args', 'type': 'object', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), 'title': 'Id', - 'type': 'string', }), 'name': dict({ 'title': 'Name', 'type': 'string', }), 'type': dict({ + 'const': 'tool_call', 'enum': list([ 'tool_call', ]), @@ -7011,7 +12042,91 @@ 'title': 'ToolCall', 'type': 'object', }), + 'ToolCallChunk': dict({ + 'description': ''' + A chunk of a tool call (e.g., as part of a stream). + + When merging ToolCallChunks (e.g., via AIMessageChunk.__add__), + all string attributes are concatenated. Chunks are only merged if their + values of `index` are equal and not None. + + Example: + + .. code-block:: python + + left_chunks = [ToolCallChunk(name="foo", args='{"a":', index=0)] + right_chunks = [ToolCallChunk(name=None, args='1}', index=0)] + + ( + AIMessageChunk(content="", tool_call_chunks=left_chunks) + + AIMessageChunk(content="", tool_call_chunks=right_chunks) + ).tool_call_chunks == [ToolCallChunk(name='foo', args='{"a":1}', index=0)] + ''', + 'properties': dict({ + 'args': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Args', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Id', + }), + 'index': dict({ + 'anyOf': list([ + dict({ + 'type': 'integer', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Index', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Name', + }), + 'type': dict({ + 'const': 'tool_call_chunk', + 'enum': list([ + 'tool_call_chunk', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'name', + 'args', + 'id', + 'index', + ]), + 'title': 'ToolCallChunk', + 'type': 'object', + }), 'ToolMessage': dict({ + 'additionalProperties': True, 'description': ''' Message for passing the result of executing a tool back to a model. @@ -7082,12 +12197,118 @@ 'title': 'Content', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'status': dict({ + 'default': 'success', + 'enum': list([ + 'success', + 'error', + ]), + 'title': 'Status', + 'type': 'string', + }), + 'tool_call_id': dict({ + 'title': 'Tool Call Id', + 'type': 'string', + }), + 'type': dict({ + 'const': 'tool', + 'default': 'tool', + 'enum': list([ + 'tool', + ]), + 'title': 'Type', 'type': 'string', }), + }), + 'required': list([ + 'content', + 'tool_call_id', + ]), + 'title': 'ToolMessage', + 'type': 'object', + }), + 'ToolMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'Tool Message chunk.', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'artifact': dict({ + 'title': 'Artifact', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Name', - 'type': 'string', }), 'response_metadata': dict({ 'title': 'Response Metadata', @@ -7107,9 +12328,10 @@ 'type': 'string', }), 'type': dict({ - 'default': 'tool', + 'const': 'ToolMessageChunk', + 'default': 'ToolMessageChunk', 'enum': list([ - 'tool', + 'ToolMessageChunk', ]), 'title': 'Type', 'type': 'string', @@ -7119,10 +12341,25 @@ 'content', 'tool_call_id', ]), - 'title': 'ToolMessage', + 'title': 'ToolMessageChunk', 'type': 'object', }), 'UsageMetadata': dict({ + 'description': ''' + Usage metadata for a message, such as token counts. + + This is a standard representation of token usage that is consistent across models. + + Example: + + .. code-block:: python + + { + "input_tokens": 10, + "output_tokens": 20, + "total_tokens": 30 + } + ''', 'properties': dict({ 'input_tokens': dict({ 'title': 'Input Tokens', @@ -7146,21 +12383,55 @@ 'type': 'object', }), }), - 'title': 'FakeListChatModelOutput', + 'title': 'FakeListLLMInput', }) # --- -# name: test_schemas.5 +# name: test_schemas[list_parser_input_schema] dict({ 'anyOf': list([ dict({ - '$ref': '#/definitions/StringPromptValue', + 'type': 'string', }), dict({ - '$ref': '#/definitions/ChatPromptValueConcrete', + '$ref': '#/definitions/AIMessage', + }), + dict({ + '$ref': '#/definitions/HumanMessage', + }), + dict({ + '$ref': '#/definitions/ChatMessage', + }), + dict({ + '$ref': '#/definitions/SystemMessage', + }), + dict({ + '$ref': '#/definitions/FunctionMessage', + }), + dict({ + '$ref': '#/definitions/ToolMessage', + }), + dict({ + '$ref': '#/definitions/AIMessageChunk', + }), + dict({ + '$ref': '#/definitions/HumanMessageChunk', + }), + dict({ + '$ref': '#/definitions/ChatMessageChunk', + }), + dict({ + '$ref': '#/definitions/SystemMessageChunk', + }), + dict({ + '$ref': '#/definitions/FunctionMessageChunk', + }), + dict({ + '$ref': '#/definitions/ToolMessageChunk', }), ]), 'definitions': dict({ 'AIMessage': dict({ + 'additionalProperties': True, 'description': ''' Message from an AI. @@ -7202,8 +12473,16 @@ 'type': 'boolean', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', }), 'invalid_tool_calls': dict({ 'default': list([ @@ -7215,8 +12494,16 @@ 'type': 'array', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Name', - 'type': 'string', }), 'response_metadata': dict({ 'title': 'Response Metadata', @@ -7232,6 +12519,7 @@ 'type': 'array', }), 'type': dict({ + 'const': 'ai', 'default': 'ai', 'enum': list([ 'ai', @@ -7240,7 +12528,15 @@ 'type': 'string', }), 'usage_metadata': dict({ - '$ref': '#/definitions/UsageMetadata', + 'anyOf': list([ + dict({ + '$ref': '#/definitions/UsageMetadata', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, }), }), 'required': list([ @@ -7249,8 +12545,9 @@ 'title': 'AIMessage', 'type': 'object', }), - 'ChatMessage': dict({ - 'description': 'Message that can be assigned an arbitrary speaker (i.e. role).', + 'AIMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'Message chunk from an AI.', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -7277,175 +12574,96 @@ ]), 'title': 'Content', }), + 'example': dict({ + 'default': False, + 'title': 'Example', + 'type': 'boolean', + }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', - }), - 'name': dict({ - 'title': 'Name', - 'type': 'string', - }), - 'response_metadata': dict({ - 'title': 'Response Metadata', - 'type': 'object', - }), - 'role': dict({ - 'title': 'Role', - 'type': 'string', }), - 'type': dict({ - 'default': 'chat', - 'enum': list([ - 'chat', + 'invalid_tool_calls': dict({ + 'default': list([ ]), - 'title': 'Type', - 'type': 'string', - }), - }), - 'required': list([ - 'content', - 'role', - ]), - 'title': 'ChatMessage', - 'type': 'object', - }), - 'ChatPromptValueConcrete': dict({ - 'description': ''' - Chat prompt value which explicitly lists out the message types it accepts. - For use in external schemas. - ''', - 'properties': dict({ - 'messages': dict({ 'items': dict({ - 'anyOf': list([ - dict({ - '$ref': '#/definitions/AIMessage', - }), - dict({ - '$ref': '#/definitions/HumanMessage', - }), - dict({ - '$ref': '#/definitions/ChatMessage', - }), - dict({ - '$ref': '#/definitions/SystemMessage', - }), - dict({ - '$ref': '#/definitions/FunctionMessage', - }), - dict({ - '$ref': '#/definitions/ToolMessage', - }), - ]), + '$ref': '#/definitions/InvalidToolCall', }), - 'title': 'Messages', + 'title': 'Invalid Tool Calls', 'type': 'array', }), - 'type': dict({ - 'default': 'ChatPromptValueConcrete', - 'enum': list([ - 'ChatPromptValueConcrete', - ]), - 'title': 'Type', - 'type': 'string', - }), - }), - 'required': list([ - 'messages', - ]), - 'title': 'ChatPromptValueConcrete', - 'type': 'object', - }), - 'FunctionMessage': dict({ - 'description': ''' - Message for passing the result of executing a tool back to a model. - - FunctionMessage are an older version of the ToolMessage schema, and - do not contain the tool_call_id field. - - The tool_call_id field is used to associate the tool call request with the - tool call response. This is useful in situations where a chat model is able - to request multiple tool calls in parallel. - ''', - 'properties': dict({ - 'additional_kwargs': dict({ - 'title': 'Additional Kwargs', - 'type': 'object', - }), - 'content': dict({ + 'name': dict({ 'anyOf': list([ dict({ 'type': 'string', }), dict({ - 'items': dict({ - 'anyOf': list([ - dict({ - 'type': 'string', - }), - dict({ - 'type': 'object', - }), - ]), - }), - 'type': 'array', + 'type': 'null', }), ]), - 'title': 'Content', - }), - 'id': dict({ - 'title': 'Id', - 'type': 'string', - }), - 'name': dict({ + 'default': None, 'title': 'Name', - 'type': 'string', }), 'response_metadata': dict({ 'title': 'Response Metadata', 'type': 'object', }), + 'tool_call_chunks': dict({ + 'default': list([ + ]), + 'items': dict({ + '$ref': '#/definitions/ToolCallChunk', + }), + 'title': 'Tool Call Chunks', + 'type': 'array', + }), + 'tool_calls': dict({ + 'default': list([ + ]), + 'items': dict({ + '$ref': '#/definitions/ToolCall', + }), + 'title': 'Tool Calls', + 'type': 'array', + }), 'type': dict({ - 'default': 'function', + 'const': 'AIMessageChunk', + 'default': 'AIMessageChunk', 'enum': list([ - 'function', + 'AIMessageChunk', ]), 'title': 'Type', 'type': 'string', }), - }), - 'required': list([ - 'content', - 'name', - ]), - 'title': 'FunctionMessage', - 'type': 'object', - }), - 'HumanMessage': dict({ - 'description': ''' - Message from a human. - - HumanMessages are messages that are passed in from a human to the model. - - Example: - - .. code-block:: python - - from langchain_core.messages import HumanMessage, SystemMessage - - messages = [ - SystemMessage( - content="You are a helpful assistant! Your name is Bob." - ), - HumanMessage( - content="What is your name?" - ) - ] - - # Instantiate a chat model and invoke it with the messages - model = ... - print(model.invoke(messages)) - ''', + 'usage_metadata': dict({ + 'anyOf': list([ + dict({ + '$ref': '#/definitions/UsageMetadata', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + }), + }), + 'required': list([ + 'content', + ]), + 'title': 'AIMessageChunk', + 'type': 'object', + }), + 'ChatMessage': dict({ + 'additionalProperties': True, + 'description': 'Message that can be assigned an arbitrary speaker (i.e. role).', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -7472,27 +12690,43 @@ ]), 'title': 'Content', }), - 'example': dict({ - 'default': False, - 'title': 'Example', - 'type': 'boolean', - }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Name', - 'type': 'string', }), 'response_metadata': dict({ 'title': 'Response Metadata', 'type': 'object', }), + 'role': dict({ + 'title': 'Role', + 'type': 'string', + }), 'type': dict({ - 'default': 'human', + 'const': 'chat', + 'default': 'chat', 'enum': list([ - 'human', + 'chat', ]), 'title': 'Type', 'type': 'string', @@ -7500,91 +12734,100 @@ }), 'required': list([ 'content', + 'role', ]), - 'title': 'HumanMessage', + 'title': 'ChatMessage', 'type': 'object', }), - 'InvalidToolCall': dict({ + 'ChatMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'Chat Message chunk.', 'properties': dict({ - 'args': dict({ - 'title': 'Args', - 'type': 'string', + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', }), - 'error': dict({ - 'title': 'Error', - 'type': 'string', + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Name', - 'type': 'string', }), - 'type': dict({ - 'enum': list([ - 'invalid_tool_call', - ]), - 'title': 'Type', - 'type': 'string', + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', }), - }), - 'required': list([ - 'name', - 'args', - 'id', - 'error', - ]), - 'title': 'InvalidToolCall', - 'type': 'object', - }), - 'StringPromptValue': dict({ - 'description': 'String prompt value.', - 'properties': dict({ - 'text': dict({ - 'title': 'Text', + 'role': dict({ + 'title': 'Role', 'type': 'string', }), 'type': dict({ - 'default': 'StringPromptValue', + 'const': 'ChatMessageChunk', + 'default': 'ChatMessageChunk', 'enum': list([ - 'StringPromptValue', + 'ChatMessageChunk', ]), 'title': 'Type', 'type': 'string', }), }), 'required': list([ - 'text', + 'content', + 'role', ]), - 'title': 'StringPromptValue', + 'title': 'ChatMessageChunk', 'type': 'object', }), - 'SystemMessage': dict({ + 'FunctionMessage': dict({ + 'additionalProperties': True, 'description': ''' - Message for priming AI behavior. - - The system message is usually passed in as the first of a sequence - of input messages. - - Example: - - .. code-block:: python - - from langchain_core.messages import HumanMessage, SystemMessage + Message for passing the result of executing a tool back to a model. - messages = [ - SystemMessage( - content="You are a helpful assistant! Your name is Bob." - ), - HumanMessage( - content="What is your name?" - ) - ] + FunctionMessage are an older version of the ToolMessage schema, and + do not contain the tool_call_id field. - # Define a chat model and invoke it with the messages - print(model.invoke(messages)) + The tool_call_id field is used to associate the tool call request with the + tool call response. This is useful in situations where a chat model is able + to request multiple tool calls in parallel. ''', 'properties': dict({ 'additional_kwargs': dict({ @@ -7613,8 +12856,16 @@ 'title': 'Content', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', }), 'name': dict({ 'title': 'Name', @@ -7625,9 +12876,10 @@ 'type': 'object', }), 'type': dict({ - 'default': 'system', + 'const': 'function', + 'default': 'function', 'enum': list([ - 'system', + 'function', ]), 'title': 'Type', 'type': 'string', @@ -7635,89 +12887,19 @@ }), 'required': list([ 'content', - ]), - 'title': 'SystemMessage', - 'type': 'object', - }), - 'ToolCall': dict({ - 'properties': dict({ - 'args': dict({ - 'title': 'Args', - 'type': 'object', - }), - 'id': dict({ - 'title': 'Id', - 'type': 'string', - }), - 'name': dict({ - 'title': 'Name', - 'type': 'string', - }), - 'type': dict({ - 'enum': list([ - 'tool_call', - ]), - 'title': 'Type', - 'type': 'string', - }), - }), - 'required': list([ 'name', - 'args', - 'id', ]), - 'title': 'ToolCall', + 'title': 'FunctionMessage', 'type': 'object', }), - 'ToolMessage': dict({ - 'description': ''' - Message for passing the result of executing a tool back to a model. - - ToolMessages contain the result of a tool invocation. Typically, the result - is encoded inside the `content` field. - - Example: A ToolMessage representing a result of 42 from a tool call with id - - .. code-block:: python - - from langchain_core.messages import ToolMessage - - ToolMessage(content='42', tool_call_id='call_Jja7J89XsjrOLA5r!MEOW!SL') - - - Example: A ToolMessage where only part of the tool output is sent to the model - and the full output is passed in to artifact. - - .. versionadded:: 0.2.17 - - .. code-block:: python - - from langchain_core.messages import ToolMessage - - tool_output = { - "stdout": "From the graph we can see that the correlation between x and y is ...", - "stderr": None, - "artifacts": {"type": "image", "base64_data": "/9j/4gIcSU..."}, - } - - ToolMessage( - content=tool_output["stdout"], - artifact=tool_output, - tool_call_id='call_Jja7J89XsjrOLA5r!MEOW!SL', - ) - - The tool_call_id field is used to associate the tool call request with the - tool call response. This is useful in situations where a chat model is able - to request multiple tool calls in parallel. - ''', + 'FunctionMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'Function Message chunk.', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', 'type': 'object', }), - 'artifact': dict({ - 'title': 'Artifact', - }), 'content': dict({ 'anyOf': list([ dict({ @@ -7740,34 +12922,30 @@ 'title': 'Content', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', }), 'name': dict({ 'title': 'Name', 'type': 'string', }), - 'response_metadata': dict({ - 'title': 'Response Metadata', - 'type': 'object', - }), - 'status': dict({ - 'default': 'success', - 'enum': list([ - 'success', - 'error', - ]), - 'title': 'Status', - 'type': 'string', - }), - 'tool_call_id': dict({ - 'title': 'Tool Call Id', - 'type': 'string', + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', }), 'type': dict({ - 'default': 'tool', + 'const': 'FunctionMessageChunk', + 'default': 'FunctionMessageChunk', 'enum': list([ - 'tool', + 'FunctionMessageChunk', ]), 'title': 'Type', 'type': 'string', @@ -7775,50 +12953,36 @@ }), 'required': list([ 'content', - 'tool_call_id', - ]), - 'title': 'ToolMessage', - 'type': 'object', - }), - 'UsageMetadata': dict({ - 'properties': dict({ - 'input_tokens': dict({ - 'title': 'Input Tokens', - 'type': 'integer', - }), - 'output_tokens': dict({ - 'title': 'Output Tokens', - 'type': 'integer', - }), - 'total_tokens': dict({ - 'title': 'Total Tokens', - 'type': 'integer', - }), - }), - 'required': list([ - 'input_tokens', - 'output_tokens', - 'total_tokens', + 'name', ]), - 'title': 'UsageMetadata', + 'title': 'FunctionMessageChunk', 'type': 'object', }), - }), - 'title': 'PromptTemplateOutput', - }) -# --- -# name: test_schemas.6 - dict({ - 'definitions': dict({ - 'AIMessage': dict({ + 'HumanMessage': dict({ + 'additionalProperties': True, 'description': ''' - Message from an AI. + Message from a human. - AIMessage is returned from a chat model as a response to a prompt. + HumanMessages are messages that are passed in from a human to the model. - This message represents the output of the model and consists of both - the raw output as returned by the model together standardized fields - (e.g., tool calls, usage metadata) added by the LangChain framework. + Example: + + .. code-block:: python + + from langchain_core.messages import HumanMessage, SystemMessage + + messages = [ + SystemMessage( + content="You are a helpful assistant! Your name is Bob." + ), + HumanMessage( + content="What is your name?" + ) + ] + + # Instantiate a chat model and invoke it with the messages + model = ... + print(model.invoke(messages)) ''', 'properties': dict({ 'additional_kwargs': dict({ @@ -7852,55 +13016,52 @@ 'type': 'boolean', }), 'id': dict({ - 'title': 'Id', - 'type': 'string', - }), - 'invalid_tool_calls': dict({ - 'default': list([ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), ]), - 'items': dict({ - '$ref': '#/definitions/InvalidToolCall', - }), - 'title': 'Invalid Tool Calls', - 'type': 'array', + 'default': None, + 'title': 'Id', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Name', - 'type': 'string', }), 'response_metadata': dict({ 'title': 'Response Metadata', 'type': 'object', }), - 'tool_calls': dict({ - 'default': list([ - ]), - 'items': dict({ - '$ref': '#/definitions/ToolCall', - }), - 'title': 'Tool Calls', - 'type': 'array', - }), 'type': dict({ - 'default': 'ai', + 'const': 'human', + 'default': 'human', 'enum': list([ - 'ai', + 'human', ]), 'title': 'Type', 'type': 'string', }), - 'usage_metadata': dict({ - '$ref': '#/definitions/UsageMetadata', - }), }), 'required': list([ 'content', ]), - 'title': 'AIMessage', + 'title': 'HumanMessage', 'type': 'object', }), - 'ChatMessage': dict({ - 'description': 'Message that can be assigned an arbitrary speaker (i.e. role).', + 'HumanMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'Human Message chunk.', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -7927,26 +13088,44 @@ ]), 'title': 'Content', }), + 'example': dict({ + 'default': False, + 'title': 'Example', + 'type': 'boolean', + }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Name', - 'type': 'string', }), 'response_metadata': dict({ 'title': 'Response Metadata', 'type': 'object', }), - 'role': dict({ - 'title': 'Role', - 'type': 'string', - }), 'type': dict({ - 'default': 'chat', + 'const': 'HumanMessageChunk', + 'default': 'HumanMessageChunk', 'enum': list([ - 'chat', + 'HumanMessageChunk', ]), 'title': 'Type', 'type': 'string', @@ -7954,68 +13133,105 @@ }), 'required': list([ 'content', - 'role', ]), - 'title': 'ChatMessage', + 'title': 'HumanMessageChunk', 'type': 'object', }), - 'ChatPromptValueConcrete': dict({ + 'InvalidToolCall': dict({ 'description': ''' - Chat prompt value which explicitly lists out the message types it accepts. - For use in external schemas. + Allowance for errors made by LLM. + + Here we add an `error` key to surface errors made during generation + (e.g., invalid JSON arguments.) ''', 'properties': dict({ - 'messages': dict({ - 'items': dict({ - 'anyOf': list([ - dict({ - '$ref': '#/definitions/AIMessage', - }), - dict({ - '$ref': '#/definitions/HumanMessage', - }), - dict({ - '$ref': '#/definitions/ChatMessage', - }), - dict({ - '$ref': '#/definitions/SystemMessage', - }), - dict({ - '$ref': '#/definitions/FunctionMessage', - }), - dict({ - '$ref': '#/definitions/ToolMessage', - }), - ]), - }), - 'title': 'Messages', - 'type': 'array', + 'args': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Args', + }), + 'error': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Error', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Id', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Name', }), 'type': dict({ - 'default': 'ChatPromptValueConcrete', + 'const': 'invalid_tool_call', 'enum': list([ - 'ChatPromptValueConcrete', + 'invalid_tool_call', ]), 'title': 'Type', 'type': 'string', }), }), 'required': list([ - 'messages', + 'name', + 'args', + 'id', + 'error', ]), - 'title': 'ChatPromptValueConcrete', + 'title': 'InvalidToolCall', 'type': 'object', }), - 'FunctionMessage': dict({ + 'SystemMessage': dict({ + 'additionalProperties': True, 'description': ''' - Message for passing the result of executing a tool back to a model. + Message for priming AI behavior. - FunctionMessage are an older version of the ToolMessage schema, and - do not contain the tool_call_id field. + The system message is usually passed in as the first of a sequence + of input messages. - The tool_call_id field is used to associate the tool call request with the - tool call response. This is useful in situations where a chat model is able - to request multiple tool calls in parallel. + Example: + + .. code-block:: python + + from langchain_core.messages import HumanMessage, SystemMessage + + messages = [ + SystemMessage( + content="You are a helpful assistant! Your name is Bob." + ), + HumanMessage( + content="What is your name?" + ) + ] + + # Define a chat model and invoke it with the messages + print(model.invoke(messages)) ''', 'properties': dict({ 'additional_kwargs': dict({ @@ -8041,24 +13257,41 @@ 'type': 'array', }), ]), - 'title': 'Content', - }), - 'id': dict({ + 'title': 'Content', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Name', - 'type': 'string', }), 'response_metadata': dict({ 'title': 'Response Metadata', 'type': 'object', }), 'type': dict({ - 'default': 'function', + 'const': 'system', + 'default': 'system', 'enum': list([ - 'function', + 'system', ]), 'title': 'Type', 'type': 'string', @@ -8066,36 +13299,13 @@ }), 'required': list([ 'content', - 'name', ]), - 'title': 'FunctionMessage', + 'title': 'SystemMessage', 'type': 'object', }), - 'HumanMessage': dict({ - 'description': ''' - Message from a human. - - HumanMessages are messages that are passed in from a human to the model. - - Example: - - .. code-block:: python - - from langchain_core.messages import HumanMessage, SystemMessage - - messages = [ - SystemMessage( - content="You are a helpful assistant! Your name is Bob." - ), - HumanMessage( - content="What is your name?" - ) - ] - - # Instantiate a chat model and invoke it with the messages - model = ... - print(model.invoke(messages)) - ''', + 'SystemMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'System Message chunk.', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -8122,27 +13332,39 @@ ]), 'title': 'Content', }), - 'example': dict({ - 'default': False, - 'title': 'Example', - 'type': 'boolean', - }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Name', - 'type': 'string', }), 'response_metadata': dict({ 'title': 'Response Metadata', 'type': 'object', }), 'type': dict({ - 'default': 'human', + 'const': 'SystemMessageChunk', + 'default': 'SystemMessageChunk', 'enum': list([ - 'human', + 'SystemMessageChunk', ]), 'title': 'Type', 'type': 'string', @@ -8151,30 +13373,50 @@ 'required': list([ 'content', ]), - 'title': 'HumanMessage', + 'title': 'SystemMessageChunk', 'type': 'object', }), - 'InvalidToolCall': dict({ + 'ToolCall': dict({ + 'description': ''' + Represents a request to call a tool. + + Example: + + .. code-block:: python + + { + "name": "foo", + "args": {"a": 1}, + "id": "123" + } + + This represents a request to call the tool named "foo" with arguments {"a": 1} + and an identifier of "123". + ''', 'properties': dict({ 'args': dict({ 'title': 'Args', - 'type': 'string', - }), - 'error': dict({ - 'title': 'Error', - 'type': 'string', + 'type': 'object', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), 'title': 'Id', - 'type': 'string', }), 'name': dict({ 'title': 'Name', 'type': 'string', }), 'type': dict({ + 'const': 'tool_call', 'enum': list([ - 'invalid_tool_call', + 'tool_call', ]), 'title': 'Type', 'type': 'string', @@ -8184,74 +13426,143 @@ 'name', 'args', 'id', - 'error', ]), - 'title': 'InvalidToolCall', + 'title': 'ToolCall', 'type': 'object', }), - 'PromptTemplateOutput': dict({ - 'anyOf': list([ - dict({ - '$ref': '#/definitions/StringPromptValue', + 'ToolCallChunk': dict({ + 'description': ''' + A chunk of a tool call (e.g., as part of a stream). + + When merging ToolCallChunks (e.g., via AIMessageChunk.__add__), + all string attributes are concatenated. Chunks are only merged if their + values of `index` are equal and not None. + + Example: + + .. code-block:: python + + left_chunks = [ToolCallChunk(name="foo", args='{"a":', index=0)] + right_chunks = [ToolCallChunk(name=None, args='1}', index=0)] + + ( + AIMessageChunk(content="", tool_call_chunks=left_chunks) + + AIMessageChunk(content="", tool_call_chunks=right_chunks) + ).tool_call_chunks == [ToolCallChunk(name='foo', args='{"a":1}', index=0)] + ''', + 'properties': dict({ + 'args': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Args', }), - dict({ - '$ref': '#/definitions/ChatPromptValueConcrete', + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Id', }), - ]), - 'title': 'PromptTemplateOutput', - }), - 'StringPromptValue': dict({ - 'description': 'String prompt value.', - 'properties': dict({ - 'text': dict({ - 'title': 'Text', - 'type': 'string', + 'index': dict({ + 'anyOf': list([ + dict({ + 'type': 'integer', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Index', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Name', }), 'type': dict({ - 'default': 'StringPromptValue', + 'const': 'tool_call_chunk', 'enum': list([ - 'StringPromptValue', + 'tool_call_chunk', ]), 'title': 'Type', 'type': 'string', }), }), 'required': list([ - 'text', + 'name', + 'args', + 'id', + 'index', ]), - 'title': 'StringPromptValue', + 'title': 'ToolCallChunk', 'type': 'object', }), - 'SystemMessage': dict({ + 'ToolMessage': dict({ + 'additionalProperties': True, 'description': ''' - Message for priming AI behavior. + Message for passing the result of executing a tool back to a model. - The system message is usually passed in as the first of a sequence - of input messages. + ToolMessages contain the result of a tool invocation. Typically, the result + is encoded inside the `content` field. - Example: + Example: A ToolMessage representing a result of 42 from a tool call with id .. code-block:: python - from langchain_core.messages import HumanMessage, SystemMessage + from langchain_core.messages import ToolMessage - messages = [ - SystemMessage( - content="You are a helpful assistant! Your name is Bob." - ), - HumanMessage( - content="What is your name?" - ) - ] + ToolMessage(content='42', tool_call_id='call_Jja7J89XsjrOLA5r!MEOW!SL') - # Define a chat model and invoke it with the messages - print(model.invoke(messages)) + + Example: A ToolMessage where only part of the tool output is sent to the model + and the full output is passed in to artifact. + + .. versionadded:: 0.2.17 + + .. code-block:: python + + from langchain_core.messages import ToolMessage + + tool_output = { + "stdout": "From the graph we can see that the correlation between x and y is ...", + "stderr": None, + "artifacts": {"type": "image", "base64_data": "/9j/4gIcSU..."}, + } + + ToolMessage( + content=tool_output["stdout"], + artifact=tool_output, + tool_call_id='call_Jja7J89XsjrOLA5r!MEOW!SL', + ) + + The tool_call_id field is used to associate the tool call request with the + tool call response. This is useful in situations where a chat model is able + to request multiple tool calls in parallel. ''', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', 'type': 'object', }), + 'artifact': dict({ + 'title': 'Artifact', + }), 'content': dict({ 'anyOf': list([ dict({ @@ -8274,103 +13585,66 @@ 'title': 'Content', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Name', - 'type': 'string', }), 'response_metadata': dict({ 'title': 'Response Metadata', 'type': 'object', }), - 'type': dict({ - 'default': 'system', + 'status': dict({ + 'default': 'success', 'enum': list([ - 'system', - ]), - 'title': 'Type', - 'type': 'string', - }), - }), - 'required': list([ - 'content', - ]), - 'title': 'SystemMessage', - 'type': 'object', - }), - 'ToolCall': dict({ - 'properties': dict({ - 'args': dict({ - 'title': 'Args', - 'type': 'object', - }), - 'id': dict({ - 'title': 'Id', + 'success', + 'error', + ]), + 'title': 'Status', 'type': 'string', }), - 'name': dict({ - 'title': 'Name', + 'tool_call_id': dict({ + 'title': 'Tool Call Id', 'type': 'string', }), 'type': dict({ + 'const': 'tool', + 'default': 'tool', 'enum': list([ - 'tool_call', + 'tool', ]), 'title': 'Type', 'type': 'string', }), }), 'required': list([ - 'name', - 'args', - 'id', + 'content', + 'tool_call_id', ]), - 'title': 'ToolCall', + 'title': 'ToolMessage', 'type': 'object', }), - 'ToolMessage': dict({ - 'description': ''' - Message for passing the result of executing a tool back to a model. - - ToolMessages contain the result of a tool invocation. Typically, the result - is encoded inside the `content` field. - - Example: A ToolMessage representing a result of 42 from a tool call with id - - .. code-block:: python - - from langchain_core.messages import ToolMessage - - ToolMessage(content='42', tool_call_id='call_Jja7J89XsjrOLA5r!MEOW!SL') - - - Example: A ToolMessage where only part of the tool output is sent to the model - and the full output is passed in to artifact. - - .. versionadded:: 0.2.17 - - .. code-block:: python - - from langchain_core.messages import ToolMessage - - tool_output = { - "stdout": "From the graph we can see that the correlation between x and y is ...", - "stderr": None, - "artifacts": {"type": "image", "base64_data": "/9j/4gIcSU..."}, - } - - ToolMessage( - content=tool_output["stdout"], - artifact=tool_output, - tool_call_id='call_Jja7J89XsjrOLA5r!MEOW!SL', - ) - - The tool_call_id field is used to associate the tool call request with the - tool call response. This is useful in situations where a chat model is able - to request multiple tool calls in parallel. - ''', + 'ToolMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'Tool Message chunk.', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -8401,12 +13675,28 @@ 'title': 'Content', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Name', - 'type': 'string', }), 'response_metadata': dict({ 'title': 'Response Metadata', @@ -8426,9 +13716,10 @@ 'type': 'string', }), 'type': dict({ - 'default': 'tool', + 'const': 'ToolMessageChunk', + 'default': 'ToolMessageChunk', 'enum': list([ - 'tool', + 'ToolMessageChunk', ]), 'title': 'Type', 'type': 'string', @@ -8438,10 +13729,25 @@ 'content', 'tool_call_id', ]), - 'title': 'ToolMessage', + 'title': 'ToolMessageChunk', 'type': 'object', }), 'UsageMetadata': dict({ + 'description': ''' + Usage metadata for a message, such as token counts. + + This is a standard representation of token usage that is consistent across models. + + Example: + + .. code-block:: python + + { + "input_tokens": 10, + "output_tokens": 20, + "total_tokens": 30 + } + ''', 'properties': dict({ 'input_tokens': dict({ 'title': 'Input Tokens', @@ -8465,40 +13771,14 @@ 'type': 'object', }), }), - 'items': dict({ - '$ref': '#/definitions/PromptTemplateOutput', - }), - 'title': 'RunnableEachOutput', - 'type': 'array', + 'title': 'CommaSeparatedListOutputParserInput', }) # --- -# name: test_schemas.7 +# name: test_schemas[prompt_mapper_output_schema] dict({ - 'anyOf': list([ - dict({ - 'type': 'string', - }), - dict({ - '$ref': '#/definitions/AIMessage', - }), - dict({ - '$ref': '#/definitions/HumanMessage', - }), - dict({ - '$ref': '#/definitions/ChatMessage', - }), - dict({ - '$ref': '#/definitions/SystemMessage', - }), - dict({ - '$ref': '#/definitions/FunctionMessage', - }), - dict({ - '$ref': '#/definitions/ToolMessage', - }), - ]), 'definitions': dict({ 'AIMessage': dict({ + 'additionalProperties': True, 'description': ''' Message from an AI. @@ -8540,8 +13820,16 @@ 'type': 'boolean', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', }), 'invalid_tool_calls': dict({ 'default': list([ @@ -8553,190 +13841,60 @@ 'type': 'array', }), 'name': dict({ - 'title': 'Name', - 'type': 'string', - }), - 'response_metadata': dict({ - 'title': 'Response Metadata', - 'type': 'object', - }), - 'tool_calls': dict({ - 'default': list([ - ]), - 'items': dict({ - '$ref': '#/definitions/ToolCall', - }), - 'title': 'Tool Calls', - 'type': 'array', - }), - 'type': dict({ - 'default': 'ai', - 'enum': list([ - 'ai', - ]), - 'title': 'Type', - 'type': 'string', - }), - 'usage_metadata': dict({ - '$ref': '#/definitions/UsageMetadata', - }), - }), - 'required': list([ - 'content', - ]), - 'title': 'AIMessage', - 'type': 'object', - }), - 'ChatMessage': dict({ - 'description': 'Message that can be assigned an arbitrary speaker (i.e. role).', - 'properties': dict({ - 'additional_kwargs': dict({ - 'title': 'Additional Kwargs', - 'type': 'object', - }), - 'content': dict({ 'anyOf': list([ dict({ 'type': 'string', }), dict({ - 'items': dict({ - 'anyOf': list([ - dict({ - 'type': 'string', - }), - dict({ - 'type': 'object', - }), - ]), - }), - 'type': 'array', + 'type': 'null', }), ]), - 'title': 'Content', - }), - 'id': dict({ - 'title': 'Id', - 'type': 'string', - }), - 'name': dict({ + 'default': None, 'title': 'Name', - 'type': 'string', }), 'response_metadata': dict({ 'title': 'Response Metadata', 'type': 'object', }), - 'role': dict({ - 'title': 'Role', - 'type': 'string', - }), - 'type': dict({ - 'default': 'chat', - 'enum': list([ - 'chat', - ]), - 'title': 'Type', - 'type': 'string', - }), - }), - 'required': list([ - 'content', - 'role', - ]), - 'title': 'ChatMessage', - 'type': 'object', - }), - 'FunctionMessage': dict({ - 'description': ''' - Message for passing the result of executing a tool back to a model. - - FunctionMessage are an older version of the ToolMessage schema, and - do not contain the tool_call_id field. - - The tool_call_id field is used to associate the tool call request with the - tool call response. This is useful in situations where a chat model is able - to request multiple tool calls in parallel. - ''', - 'properties': dict({ - 'additional_kwargs': dict({ - 'title': 'Additional Kwargs', - 'type': 'object', - }), - 'content': dict({ - 'anyOf': list([ - dict({ - 'type': 'string', - }), - dict({ - 'items': dict({ - 'anyOf': list([ - dict({ - 'type': 'string', - }), - dict({ - 'type': 'object', - }), - ]), - }), - 'type': 'array', - }), + 'tool_calls': dict({ + 'default': list([ ]), - 'title': 'Content', - }), - 'id': dict({ - 'title': 'Id', - 'type': 'string', - }), - 'name': dict({ - 'title': 'Name', - 'type': 'string', - }), - 'response_metadata': dict({ - 'title': 'Response Metadata', - 'type': 'object', + 'items': dict({ + '$ref': '#/definitions/ToolCall', + }), + 'title': 'Tool Calls', + 'type': 'array', }), 'type': dict({ - 'default': 'function', + 'const': 'ai', + 'default': 'ai', 'enum': list([ - 'function', + 'ai', ]), 'title': 'Type', 'type': 'string', }), + 'usage_metadata': dict({ + 'anyOf': list([ + dict({ + '$ref': '#/definitions/UsageMetadata', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + }), }), 'required': list([ 'content', - 'name', ]), - 'title': 'FunctionMessage', + 'title': 'AIMessage', 'type': 'object', }), - 'HumanMessage': dict({ - 'description': ''' - Message from a human. - - HumanMessages are messages that are passed in from a human to the model. - - Example: - - .. code-block:: python - - from langchain_core.messages import HumanMessage, SystemMessage - - messages = [ - SystemMessage( - content="You are a helpful assistant! Your name is Bob." - ), - HumanMessage( - content="What is your name?" - ) - ] - - # Instantiate a chat model and invoke it with the messages - model = ... - print(model.invoke(messages)) - ''', + 'AIMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'Message chunk from an AI.', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -8769,92 +13927,90 @@ 'type': 'boolean', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', + }), + 'invalid_tool_calls': dict({ + 'default': list([ + ]), + 'items': dict({ + '$ref': '#/definitions/InvalidToolCall', + }), + 'title': 'Invalid Tool Calls', + 'type': 'array', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Name', - 'type': 'string', }), 'response_metadata': dict({ 'title': 'Response Metadata', 'type': 'object', }), - 'type': dict({ - 'default': 'human', - 'enum': list([ - 'human', + 'tool_call_chunks': dict({ + 'default': list([ ]), - 'title': 'Type', - 'type': 'string', - }), - }), - 'required': list([ - 'content', - ]), - 'title': 'HumanMessage', - 'type': 'object', - }), - 'InvalidToolCall': dict({ - 'properties': dict({ - 'args': dict({ - 'title': 'Args', - 'type': 'string', - }), - 'error': dict({ - 'title': 'Error', - 'type': 'string', - }), - 'id': dict({ - 'title': 'Id', - 'type': 'string', + 'items': dict({ + '$ref': '#/definitions/ToolCallChunk', + }), + 'title': 'Tool Call Chunks', + 'type': 'array', }), - 'name': dict({ - 'title': 'Name', - 'type': 'string', + 'tool_calls': dict({ + 'default': list([ + ]), + 'items': dict({ + '$ref': '#/definitions/ToolCall', + }), + 'title': 'Tool Calls', + 'type': 'array', }), 'type': dict({ + 'const': 'AIMessageChunk', + 'default': 'AIMessageChunk', 'enum': list([ - 'invalid_tool_call', + 'AIMessageChunk', ]), 'title': 'Type', 'type': 'string', }), + 'usage_metadata': dict({ + 'anyOf': list([ + dict({ + '$ref': '#/definitions/UsageMetadata', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + }), }), 'required': list([ - 'name', - 'args', - 'id', - 'error', + 'content', ]), - 'title': 'InvalidToolCall', + 'title': 'AIMessageChunk', 'type': 'object', }), - 'SystemMessage': dict({ - 'description': ''' - Message for priming AI behavior. - - The system message is usually passed in as the first of a sequence - of input messages. - - Example: - - .. code-block:: python - - from langchain_core.messages import HumanMessage, SystemMessage - - messages = [ - SystemMessage( - content="You are a helpful assistant! Your name is Bob." - ), - HumanMessage( - content="What is your name?" - ) - ] - - # Define a chat model and invoke it with the messages - print(model.invoke(messages)) - ''', + 'ChatMessage': dict({ + 'additionalProperties': True, + 'description': 'Message that can be assigned an arbitrary speaker (i.e. role).', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -8882,111 +14038,62 @@ 'title': 'Content', }), 'id': dict({ - 'title': 'Id', - 'type': 'string', - }), - 'name': dict({ - 'title': 'Name', - 'type': 'string', - }), - 'response_metadata': dict({ - 'title': 'Response Metadata', - 'type': 'object', - }), - 'type': dict({ - 'default': 'system', - 'enum': list([ - 'system', + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), ]), - 'title': 'Type', - 'type': 'string', - }), - }), - 'required': list([ - 'content', - ]), - 'title': 'SystemMessage', - 'type': 'object', - }), - 'ToolCall': dict({ - 'properties': dict({ - 'args': dict({ - 'title': 'Args', - 'type': 'object', - }), - 'id': dict({ + 'default': None, 'title': 'Id', - 'type': 'string', }), 'name': dict({ - 'title': 'Name', - 'type': 'string', - }), - 'type': dict({ - 'enum': list([ - 'tool_call', + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), ]), - 'title': 'Type', - 'type': 'string', + 'default': None, + 'title': 'Name', }), - }), - 'required': list([ - 'name', - 'args', - 'id', - ]), - 'title': 'ToolCall', - 'type': 'object', - }), - 'ToolMessage': dict({ - 'description': ''' - Message for passing the result of executing a tool back to a model. - - ToolMessages contain the result of a tool invocation. Typically, the result - is encoded inside the `content` field. - - Example: A ToolMessage representing a result of 42 from a tool call with id - - .. code-block:: python - - from langchain_core.messages import ToolMessage - - ToolMessage(content='42', tool_call_id='call_Jja7J89XsjrOLA5r!MEOW!SL') - - - Example: A ToolMessage where only part of the tool output is sent to the model - and the full output is passed in to artifact. - - .. versionadded:: 0.2.17 - - .. code-block:: python - - from langchain_core.messages import ToolMessage - - tool_output = { - "stdout": "From the graph we can see that the correlation between x and y is ...", - "stderr": None, - "artifacts": {"type": "image", "base64_data": "/9j/4gIcSU..."}, - } - - ToolMessage( - content=tool_output["stdout"], - artifact=tool_output, - tool_call_id='call_Jja7J89XsjrOLA5r!MEOW!SL', - ) - - The tool_call_id field is used to associate the tool call request with the - tool call response. This is useful in situations where a chat model is able - to request multiple tool calls in parallel. - ''', + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'role': dict({ + 'title': 'Role', + 'type': 'string', + }), + 'type': dict({ + 'const': 'chat', + 'default': 'chat', + 'enum': list([ + 'chat', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'content', + 'role', + ]), + 'title': 'ChatMessage', + 'type': 'object', + }), + 'ChatMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'Chat Message chunk.', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', 'type': 'object', }), - 'artifact': dict({ - 'title': 'Artifact', - }), 'content': dict({ 'anyOf': list([ dict({ @@ -9009,34 +14116,42 @@ 'title': 'Content', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Name', - 'type': 'string', }), 'response_metadata': dict({ 'title': 'Response Metadata', 'type': 'object', }), - 'status': dict({ - 'default': 'success', - 'enum': list([ - 'success', - 'error', - ]), - 'title': 'Status', - 'type': 'string', - }), - 'tool_call_id': dict({ - 'title': 'Tool Call Id', + 'role': dict({ + 'title': 'Role', 'type': 'string', }), 'type': dict({ - 'default': 'tool', + 'const': 'ChatMessageChunk', + 'default': 'ChatMessageChunk', 'enum': list([ - 'tool', + 'ChatMessageChunk', ]), 'title': 'Type', 'type': 'string', @@ -9044,50 +14159,88 @@ }), 'required': list([ 'content', - 'tool_call_id', + 'role', ]), - 'title': 'ToolMessage', + 'title': 'ChatMessageChunk', 'type': 'object', }), - 'UsageMetadata': dict({ + 'ChatPromptValueConcrete': dict({ + 'description': ''' + Chat prompt value which explicitly lists out the message types it accepts. + For use in external schemas. + ''', 'properties': dict({ - 'input_tokens': dict({ - 'title': 'Input Tokens', - 'type': 'integer', - }), - 'output_tokens': dict({ - 'title': 'Output Tokens', - 'type': 'integer', + 'messages': dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + '$ref': '#/definitions/AIMessage', + }), + dict({ + '$ref': '#/definitions/HumanMessage', + }), + dict({ + '$ref': '#/definitions/ChatMessage', + }), + dict({ + '$ref': '#/definitions/SystemMessage', + }), + dict({ + '$ref': '#/definitions/FunctionMessage', + }), + dict({ + '$ref': '#/definitions/ToolMessage', + }), + dict({ + '$ref': '#/definitions/AIMessageChunk', + }), + dict({ + '$ref': '#/definitions/HumanMessageChunk', + }), + dict({ + '$ref': '#/definitions/ChatMessageChunk', + }), + dict({ + '$ref': '#/definitions/SystemMessageChunk', + }), + dict({ + '$ref': '#/definitions/FunctionMessageChunk', + }), + dict({ + '$ref': '#/definitions/ToolMessageChunk', + }), + ]), + }), + 'title': 'Messages', + 'type': 'array', }), - 'total_tokens': dict({ - 'title': 'Total Tokens', - 'type': 'integer', + 'type': dict({ + 'const': 'ChatPromptValueConcrete', + 'default': 'ChatPromptValueConcrete', + 'enum': list([ + 'ChatPromptValueConcrete', + ]), + 'title': 'Type', + 'type': 'string', }), }), 'required': list([ - 'input_tokens', - 'output_tokens', - 'total_tokens', + 'messages', ]), - 'title': 'UsageMetadata', + 'title': 'ChatPromptValueConcrete', 'type': 'object', }), - }), - 'title': 'CommaSeparatedListOutputParserInput', - }) -# --- -# name: test_schemas[chat_prompt_input_schema] - dict({ - 'definitions': dict({ - 'AIMessage': dict({ + 'FunctionMessage': dict({ + 'additionalProperties': True, 'description': ''' - Message from an AI. + Message for passing the result of executing a tool back to a model. - AIMessage is returned from a chat model as a response to a prompt. + FunctionMessage are an older version of the ToolMessage schema, and + do not contain the tool_call_id field. - This message represents the output of the model and consists of both - the raw output as returned by the model together standardized fields - (e.g., tool calls, usage metadata) added by the LangChain framework. + The tool_call_id field is used to associate the tool call request with the + tool call response. This is useful in situations where a chat model is able + to request multiple tool calls in parallel. ''', 'properties': dict({ 'additional_kwargs': dict({ @@ -9115,23 +14268,17 @@ ]), 'title': 'Content', }), - 'example': dict({ - 'default': False, - 'title': 'Example', - 'type': 'boolean', - }), 'id': dict({ - 'title': 'Id', - 'type': 'string', - }), - 'invalid_tool_calls': dict({ - 'default': list([ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), ]), - 'items': dict({ - '$ref': '#/definitions/InvalidToolCall', - }), - 'title': 'Invalid Tool Calls', - 'type': 'array', + 'default': None, + 'title': 'Id', }), 'name': dict({ 'title': 'Name', @@ -9141,35 +14288,26 @@ 'title': 'Response Metadata', 'type': 'object', }), - 'tool_calls': dict({ - 'default': list([ - ]), - 'items': dict({ - '$ref': '#/definitions/ToolCall', - }), - 'title': 'Tool Calls', - 'type': 'array', - }), 'type': dict({ - 'default': 'ai', + 'const': 'function', + 'default': 'function', 'enum': list([ - 'ai', + 'function', ]), 'title': 'Type', 'type': 'string', }), - 'usage_metadata': dict({ - '$ref': '#/definitions/UsageMetadata', - }), }), 'required': list([ 'content', + 'name', ]), - 'title': 'AIMessage', + 'title': 'FunctionMessage', 'type': 'object', }), - 'ChatMessage': dict({ - 'description': 'Message that can be assigned an arbitrary speaker (i.e. role).', + 'FunctionMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'Function Message chunk.', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -9197,8 +14335,16 @@ 'title': 'Content', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', }), 'name': dict({ 'title': 'Name', @@ -9208,14 +14354,11 @@ 'title': 'Response Metadata', 'type': 'object', }), - 'role': dict({ - 'title': 'Role', - 'type': 'string', - }), 'type': dict({ - 'default': 'chat', + 'const': 'FunctionMessageChunk', + 'default': 'FunctionMessageChunk', 'enum': list([ - 'chat', + 'FunctionMessageChunk', ]), 'title': 'Type', 'type': 'string', @@ -9223,21 +14366,36 @@ }), 'required': list([ 'content', - 'role', + 'name', ]), - 'title': 'ChatMessage', + 'title': 'FunctionMessageChunk', 'type': 'object', }), - 'FunctionMessage': dict({ + 'HumanMessage': dict({ + 'additionalProperties': True, 'description': ''' - Message for passing the result of executing a tool back to a model. + Message from a human. - FunctionMessage are an older version of the ToolMessage schema, and - do not contain the tool_call_id field. + HumanMessages are messages that are passed in from a human to the model. - The tool_call_id field is used to associate the tool call request with the - tool call response. This is useful in situations where a chat model is able - to request multiple tool calls in parallel. + Example: + + .. code-block:: python + + from langchain_core.messages import HumanMessage, SystemMessage + + messages = [ + SystemMessage( + content="You are a helpful assistant! Your name is Bob." + ), + HumanMessage( + content="What is your name?" + ) + ] + + # Instantiate a chat model and invoke it with the messages + model = ... + print(model.invoke(messages)) ''', 'properties': dict({ 'additional_kwargs': dict({ @@ -9265,22 +14423,44 @@ ]), 'title': 'Content', }), + 'example': dict({ + 'default': False, + 'title': 'Example', + 'type': 'boolean', + }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Name', - 'type': 'string', }), 'response_metadata': dict({ 'title': 'Response Metadata', 'type': 'object', }), 'type': dict({ - 'default': 'function', + 'const': 'human', + 'default': 'human', 'enum': list([ - 'function', + 'human', ]), 'title': 'Type', 'type': 'string', @@ -9288,36 +14468,13 @@ }), 'required': list([ 'content', - 'name', ]), - 'title': 'FunctionMessage', + 'title': 'HumanMessage', 'type': 'object', }), - 'HumanMessage': dict({ - 'description': ''' - Message from a human. - - HumanMessages are messages that are passed in from a human to the model. - - Example: - - .. code-block:: python - - from langchain_core.messages import HumanMessage, SystemMessage - - messages = [ - SystemMessage( - content="You are a helpful assistant! Your name is Bob." - ), - HumanMessage( - content="What is your name?" - ) - ] - - # Instantiate a chat model and invoke it with the messages - model = ... - print(model.invoke(messages)) - ''', + 'HumanMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'Human Message chunk.', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -9350,21 +14507,38 @@ 'type': 'boolean', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Name', - 'type': 'string', }), 'response_metadata': dict({ 'title': 'Response Metadata', 'type': 'object', }), 'type': dict({ - 'default': 'human', + 'const': 'HumanMessageChunk', + 'default': 'HumanMessageChunk', 'enum': list([ - 'human', + 'HumanMessageChunk', ]), 'title': 'Type', 'type': 'string', @@ -9373,28 +14547,63 @@ 'required': list([ 'content', ]), - 'title': 'HumanMessage', + 'title': 'HumanMessageChunk', 'type': 'object', }), 'InvalidToolCall': dict({ + 'description': ''' + Allowance for errors made by LLM. + + Here we add an `error` key to surface errors made during generation + (e.g., invalid JSON arguments.) + ''', 'properties': dict({ 'args': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), 'title': 'Args', - 'type': 'string', }), 'error': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), 'title': 'Error', - 'type': 'string', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), 'title': 'Id', - 'type': 'string', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), 'title': 'Name', - 'type': 'string', }), 'type': dict({ + 'const': 'invalid_tool_call', 'enum': list([ 'invalid_tool_call', ]), @@ -9411,7 +14620,42 @@ 'title': 'InvalidToolCall', 'type': 'object', }), + 'PromptTemplateOutput': dict({ + 'anyOf': list([ + dict({ + '$ref': '#/definitions/StringPromptValue', + }), + dict({ + '$ref': '#/definitions/ChatPromptValueConcrete', + }), + ]), + 'title': 'PromptTemplateOutput', + }), + 'StringPromptValue': dict({ + 'description': 'String prompt value.', + 'properties': dict({ + 'text': dict({ + 'title': 'Text', + 'type': 'string', + }), + 'type': dict({ + 'const': 'StringPromptValue', + 'default': 'StringPromptValue', + 'enum': list([ + 'StringPromptValue', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'text', + ]), + 'title': 'StringPromptValue', + 'type': 'object', + }), 'SystemMessage': dict({ + 'additionalProperties': True, 'description': ''' Message for priming AI behavior. @@ -9463,18 +14707,35 @@ 'title': 'Content', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Name', - 'type': 'string', }), 'response_metadata': dict({ 'title': 'Response Metadata', 'type': 'object', }), 'type': dict({ + 'const': 'system', 'default': 'system', 'enum': list([ 'system', @@ -9489,23 +14750,202 @@ 'title': 'SystemMessage', 'type': 'object', }), + 'SystemMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'System Message chunk.', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'type': dict({ + 'const': 'SystemMessageChunk', + 'default': 'SystemMessageChunk', + 'enum': list([ + 'SystemMessageChunk', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'content', + ]), + 'title': 'SystemMessageChunk', + 'type': 'object', + }), 'ToolCall': dict({ + 'description': ''' + Represents a request to call a tool. + + Example: + + .. code-block:: python + + { + "name": "foo", + "args": {"a": 1}, + "id": "123" + } + + This represents a request to call the tool named "foo" with arguments {"a": 1} + and an identifier of "123". + ''', + 'properties': dict({ + 'args': dict({ + 'title': 'Args', + 'type': 'object', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Id', + }), + 'name': dict({ + 'title': 'Name', + 'type': 'string', + }), + 'type': dict({ + 'const': 'tool_call', + 'enum': list([ + 'tool_call', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'name', + 'args', + 'id', + ]), + 'title': 'ToolCall', + 'type': 'object', + }), + 'ToolCallChunk': dict({ + 'description': ''' + A chunk of a tool call (e.g., as part of a stream). + + When merging ToolCallChunks (e.g., via AIMessageChunk.__add__), + all string attributes are concatenated. Chunks are only merged if their + values of `index` are equal and not None. + + Example: + + .. code-block:: python + + left_chunks = [ToolCallChunk(name="foo", args='{"a":', index=0)] + right_chunks = [ToolCallChunk(name=None, args='1}', index=0)] + + ( + AIMessageChunk(content="", tool_call_chunks=left_chunks) + + AIMessageChunk(content="", tool_call_chunks=right_chunks) + ).tool_call_chunks == [ToolCallChunk(name='foo', args='{"a":1}', index=0)] + ''', 'properties': dict({ 'args': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), 'title': 'Args', - 'type': 'object', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), 'title': 'Id', - 'type': 'string', + }), + 'index': dict({ + 'anyOf': list([ + dict({ + 'type': 'integer', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Index', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), 'title': 'Name', - 'type': 'string', }), 'type': dict({ + 'const': 'tool_call_chunk', 'enum': list([ - 'tool_call', + 'tool_call_chunk', ]), 'title': 'Type', 'type': 'string', @@ -9515,11 +14955,13 @@ 'name', 'args', 'id', + 'index', ]), - 'title': 'ToolCall', + 'title': 'ToolCallChunk', 'type': 'object', }), 'ToolMessage': dict({ + 'additionalProperties': True, 'description': ''' Message for passing the result of executing a tool back to a model. @@ -9590,12 +15032,28 @@ 'title': 'Content', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Name', - 'type': 'string', }), 'response_metadata': dict({ 'title': 'Response Metadata', @@ -9615,6 +15073,7 @@ 'type': 'string', }), 'type': dict({ + 'const': 'tool', 'default': 'tool', 'enum': list([ 'tool', @@ -9630,7 +15089,112 @@ 'title': 'ToolMessage', 'type': 'object', }), + 'ToolMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'Tool Message chunk.', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'artifact': dict({ + 'title': 'Artifact', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'status': dict({ + 'default': 'success', + 'enum': list([ + 'success', + 'error', + ]), + 'title': 'Status', + 'type': 'string', + }), + 'tool_call_id': dict({ + 'title': 'Tool Call Id', + 'type': 'string', + }), + 'type': dict({ + 'const': 'ToolMessageChunk', + 'default': 'ToolMessageChunk', + 'enum': list([ + 'ToolMessageChunk', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'content', + 'tool_call_id', + ]), + 'title': 'ToolMessageChunk', + 'type': 'object', + }), 'UsageMetadata': dict({ + 'description': ''' + Usage metadata for a message, such as token counts. + + This is a standard representation of token usage that is consistent across models. + + Example: + + .. code-block:: python + + { + "input_tokens": 10, + "output_tokens": 20, + "total_tokens": 30 + } + ''', 'properties': dict({ 'input_tokens': dict({ 'title': 'Input Tokens', @@ -9646,70 +15210,150 @@ }), }), 'required': list([ - 'input_tokens', - 'output_tokens', - 'total_tokens', + 'input_tokens', + 'output_tokens', + 'total_tokens', + ]), + 'title': 'UsageMetadata', + 'type': 'object', + }), + }), + 'items': dict({ + '$ref': '#/definitions/PromptTemplateOutput', + }), + 'title': 'RunnableEachOutput', + 'type': 'array', + }) +# --- +# name: test_schemas[prompt_output_schema] + dict({ + 'anyOf': list([ + dict({ + '$ref': '#/definitions/StringPromptValue', + }), + dict({ + '$ref': '#/definitions/ChatPromptValueConcrete', + }), + ]), + 'definitions': dict({ + 'AIMessage': dict({ + 'additionalProperties': True, + 'description': ''' + Message from an AI. + + AIMessage is returned from a chat model as a response to a prompt. + + This message represents the output of the model and consists of both + the raw output as returned by the model together standardized fields + (e.g., tool calls, usage metadata) added by the LangChain framework. + ''', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'example': dict({ + 'default': False, + 'title': 'Example', + 'type': 'boolean', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'invalid_tool_calls': dict({ + 'default': list([ + ]), + 'items': dict({ + '$ref': '#/definitions/InvalidToolCall', + }), + 'title': 'Invalid Tool Calls', + 'type': 'array', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'tool_calls': dict({ + 'default': list([ + ]), + 'items': dict({ + '$ref': '#/definitions/ToolCall', + }), + 'title': 'Tool Calls', + 'type': 'array', + }), + 'type': dict({ + 'const': 'ai', + 'default': 'ai', + 'enum': list([ + 'ai', + ]), + 'title': 'Type', + 'type': 'string', + }), + 'usage_metadata': dict({ + 'anyOf': list([ + dict({ + '$ref': '#/definitions/UsageMetadata', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + }), + }), + 'required': list([ + 'content', ]), - 'title': 'UsageMetadata', + 'title': 'AIMessage', 'type': 'object', }), - }), - 'properties': dict({ - 'history': dict({ - 'items': dict({ - 'anyOf': list([ - dict({ - '$ref': '#/definitions/AIMessage', - }), - dict({ - '$ref': '#/definitions/HumanMessage', - }), - dict({ - '$ref': '#/definitions/ChatMessage', - }), - dict({ - '$ref': '#/definitions/SystemMessage', - }), - dict({ - '$ref': '#/definitions/FunctionMessage', - }), - dict({ - '$ref': '#/definitions/ToolMessage', - }), - ]), - }), - 'title': 'History', - 'type': 'array', - }), - }), - 'required': list([ - 'history', - ]), - 'title': 'PromptInput', - 'type': 'object', - }) -# --- -# name: test_schemas[chat_prompt_output_schema] - dict({ - 'anyOf': list([ - dict({ - '$ref': '#/definitions/StringPromptValue', - }), - dict({ - '$ref': '#/definitions/ChatPromptValueConcrete', - }), - ]), - 'definitions': dict({ - 'AIMessage': dict({ - 'description': ''' - Message from an AI. - - AIMessage is returned from a chat model as a response to a prompt. - - This message represents the output of the model and consists of both - the raw output as returned by the model together standardized fields - (e.g., tool calls, usage metadata) added by the LangChain framework. - ''', + 'AIMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'Message chunk from an AI.', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -9742,8 +15386,16 @@ 'type': 'boolean', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', }), 'invalid_tool_calls': dict({ 'default': list([ @@ -9755,13 +15407,30 @@ 'type': 'array', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Name', - 'type': 'string', }), 'response_metadata': dict({ 'title': 'Response Metadata', 'type': 'object', }), + 'tool_call_chunks': dict({ + 'default': list([ + ]), + 'items': dict({ + '$ref': '#/definitions/ToolCallChunk', + }), + 'title': 'Tool Call Chunks', + 'type': 'array', + }), 'tool_calls': dict({ 'default': list([ ]), @@ -9772,24 +15441,34 @@ 'type': 'array', }), 'type': dict({ - 'default': 'ai', + 'const': 'AIMessageChunk', + 'default': 'AIMessageChunk', 'enum': list([ - 'ai', + 'AIMessageChunk', ]), 'title': 'Type', 'type': 'string', }), 'usage_metadata': dict({ - '$ref': '#/definitions/UsageMetadata', + 'anyOf': list([ + dict({ + '$ref': '#/definitions/UsageMetadata', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, }), }), 'required': list([ 'content', ]), - 'title': 'AIMessage', + 'title': 'AIMessageChunk', 'type': 'object', }), 'ChatMessage': dict({ + 'additionalProperties': True, 'description': 'Message that can be assigned an arbitrary speaker (i.e. role).', 'properties': dict({ 'additional_kwargs': dict({ @@ -9818,12 +15497,28 @@ 'title': 'Content', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Name', - 'type': 'string', }), 'response_metadata': dict({ 'title': 'Response Metadata', @@ -9834,6 +15529,7 @@ 'type': 'string', }), 'type': dict({ + 'const': 'chat', 'default': 'chat', 'enum': list([ 'chat', @@ -9849,6 +15545,84 @@ 'title': 'ChatMessage', 'type': 'object', }), + 'ChatMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'Chat Message chunk.', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'role': dict({ + 'title': 'Role', + 'type': 'string', + }), + 'type': dict({ + 'const': 'ChatMessageChunk', + 'default': 'ChatMessageChunk', + 'enum': list([ + 'ChatMessageChunk', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'content', + 'role', + ]), + 'title': 'ChatMessageChunk', + 'type': 'object', + }), 'ChatPromptValueConcrete': dict({ 'description': ''' Chat prompt value which explicitly lists out the message types it accepts. @@ -9876,37 +15650,123 @@ dict({ '$ref': '#/definitions/ToolMessage', }), + dict({ + '$ref': '#/definitions/AIMessageChunk', + }), + dict({ + '$ref': '#/definitions/HumanMessageChunk', + }), + dict({ + '$ref': '#/definitions/ChatMessageChunk', + }), + dict({ + '$ref': '#/definitions/SystemMessageChunk', + }), + dict({ + '$ref': '#/definitions/FunctionMessageChunk', + }), + dict({ + '$ref': '#/definitions/ToolMessageChunk', + }), ]), }), 'title': 'Messages', 'type': 'array', }), 'type': dict({ - 'default': 'ChatPromptValueConcrete', + 'const': 'ChatPromptValueConcrete', + 'default': 'ChatPromptValueConcrete', + 'enum': list([ + 'ChatPromptValueConcrete', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'messages', + ]), + 'title': 'ChatPromptValueConcrete', + 'type': 'object', + }), + 'FunctionMessage': dict({ + 'additionalProperties': True, + 'description': ''' + Message for passing the result of executing a tool back to a model. + + FunctionMessage are an older version of the ToolMessage schema, and + do not contain the tool_call_id field. + + The tool_call_id field is used to associate the tool call request with the + tool call response. This is useful in situations where a chat model is able + to request multiple tool calls in parallel. + ''', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'name': dict({ + 'title': 'Name', + 'type': 'string', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'type': dict({ + 'const': 'function', + 'default': 'function', 'enum': list([ - 'ChatPromptValueConcrete', + 'function', ]), 'title': 'Type', 'type': 'string', }), }), 'required': list([ - 'messages', + 'content', + 'name', ]), - 'title': 'ChatPromptValueConcrete', + 'title': 'FunctionMessage', 'type': 'object', }), - 'FunctionMessage': dict({ - 'description': ''' - Message for passing the result of executing a tool back to a model. - - FunctionMessage are an older version of the ToolMessage schema, and - do not contain the tool_call_id field. - - The tool_call_id field is used to associate the tool call request with the - tool call response. This is useful in situations where a chat model is able - to request multiple tool calls in parallel. - ''', + 'FunctionMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'Function Message chunk.', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -9934,8 +15794,16 @@ 'title': 'Content', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', }), 'name': dict({ 'title': 'Name', @@ -9946,9 +15814,10 @@ 'type': 'object', }), 'type': dict({ - 'default': 'function', + 'const': 'FunctionMessageChunk', + 'default': 'FunctionMessageChunk', 'enum': list([ - 'function', + 'FunctionMessageChunk', ]), 'title': 'Type', 'type': 'string', @@ -9958,10 +15827,11 @@ 'content', 'name', ]), - 'title': 'FunctionMessage', + 'title': 'FunctionMessageChunk', 'type': 'object', }), 'HumanMessage': dict({ + 'additionalProperties': True, 'description': ''' Message from a human. @@ -10018,18 +15888,35 @@ 'type': 'boolean', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Name', - 'type': 'string', }), 'response_metadata': dict({ 'title': 'Response Metadata', 'type': 'object', }), 'type': dict({ + 'const': 'human', 'default': 'human', 'enum': list([ 'human', @@ -10044,88 +15931,276 @@ 'title': 'HumanMessage', 'type': 'object', }), + 'HumanMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'Human Message chunk.', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'example': dict({ + 'default': False, + 'title': 'Example', + 'type': 'boolean', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'type': dict({ + 'const': 'HumanMessageChunk', + 'default': 'HumanMessageChunk', + 'enum': list([ + 'HumanMessageChunk', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'content', + ]), + 'title': 'HumanMessageChunk', + 'type': 'object', + }), 'InvalidToolCall': dict({ + 'description': ''' + Allowance for errors made by LLM. + + Here we add an `error` key to surface errors made during generation + (e.g., invalid JSON arguments.) + ''', 'properties': dict({ 'args': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), 'title': 'Args', - 'type': 'string', }), 'error': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), 'title': 'Error', - 'type': 'string', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), 'title': 'Id', - 'type': 'string', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), 'title': 'Name', - 'type': 'string', }), 'type': dict({ + 'const': 'invalid_tool_call', 'enum': list([ 'invalid_tool_call', ]), 'title': 'Type', 'type': 'string', }), - }), - 'required': list([ - 'name', - 'args', - 'id', - 'error', - ]), - 'title': 'InvalidToolCall', - 'type': 'object', - }), - 'StringPromptValue': dict({ - 'description': 'String prompt value.', - 'properties': dict({ - 'text': dict({ - 'title': 'Text', - 'type': 'string', + }), + 'required': list([ + 'name', + 'args', + 'id', + 'error', + ]), + 'title': 'InvalidToolCall', + 'type': 'object', + }), + 'StringPromptValue': dict({ + 'description': 'String prompt value.', + 'properties': dict({ + 'text': dict({ + 'title': 'Text', + 'type': 'string', + }), + 'type': dict({ + 'const': 'StringPromptValue', + 'default': 'StringPromptValue', + 'enum': list([ + 'StringPromptValue', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'text', + ]), + 'title': 'StringPromptValue', + 'type': 'object', + }), + 'SystemMessage': dict({ + 'additionalProperties': True, + 'description': ''' + Message for priming AI behavior. + + The system message is usually passed in as the first of a sequence + of input messages. + + Example: + + .. code-block:: python + + from langchain_core.messages import HumanMessage, SystemMessage + + messages = [ + SystemMessage( + content="You are a helpful assistant! Your name is Bob." + ), + HumanMessage( + content="What is your name?" + ) + ] + + # Define a chat model and invoke it with the messages + print(model.invoke(messages)) + ''', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', }), 'type': dict({ - 'default': 'StringPromptValue', + 'const': 'system', + 'default': 'system', 'enum': list([ - 'StringPromptValue', + 'system', ]), 'title': 'Type', 'type': 'string', }), }), 'required': list([ - 'text', + 'content', ]), - 'title': 'StringPromptValue', + 'title': 'SystemMessage', 'type': 'object', }), - 'SystemMessage': dict({ - 'description': ''' - Message for priming AI behavior. - - The system message is usually passed in as the first of a sequence - of input messages. - - Example: - - .. code-block:: python - - from langchain_core.messages import HumanMessage, SystemMessage - - messages = [ - SystemMessage( - content="You are a helpful assistant! Your name is Bob." - ), - HumanMessage( - content="What is your name?" - ) - ] - - # Define a chat model and invoke it with the messages - print(model.invoke(messages)) - ''', + 'SystemMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'System Message chunk.', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -10153,21 +16228,38 @@ 'title': 'Content', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Name', - 'type': 'string', }), 'response_metadata': dict({ 'title': 'Response Metadata', 'type': 'object', }), 'type': dict({ - 'default': 'system', + 'const': 'SystemMessageChunk', + 'default': 'SystemMessageChunk', 'enum': list([ - 'system', + 'SystemMessageChunk', ]), 'title': 'Type', 'type': 'string', @@ -10176,24 +16268,48 @@ 'required': list([ 'content', ]), - 'title': 'SystemMessage', + 'title': 'SystemMessageChunk', 'type': 'object', }), 'ToolCall': dict({ + 'description': ''' + Represents a request to call a tool. + + Example: + + .. code-block:: python + + { + "name": "foo", + "args": {"a": 1}, + "id": "123" + } + + This represents a request to call the tool named "foo" with arguments {"a": 1} + and an identifier of "123". + ''', 'properties': dict({ 'args': dict({ 'title': 'Args', 'type': 'object', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), 'title': 'Id', - 'type': 'string', }), 'name': dict({ 'title': 'Name', 'type': 'string', }), 'type': dict({ + 'const': 'tool_call', 'enum': list([ 'tool_call', ]), @@ -10209,7 +16325,91 @@ 'title': 'ToolCall', 'type': 'object', }), + 'ToolCallChunk': dict({ + 'description': ''' + A chunk of a tool call (e.g., as part of a stream). + + When merging ToolCallChunks (e.g., via AIMessageChunk.__add__), + all string attributes are concatenated. Chunks are only merged if their + values of `index` are equal and not None. + + Example: + + .. code-block:: python + + left_chunks = [ToolCallChunk(name="foo", args='{"a":', index=0)] + right_chunks = [ToolCallChunk(name=None, args='1}', index=0)] + + ( + AIMessageChunk(content="", tool_call_chunks=left_chunks) + + AIMessageChunk(content="", tool_call_chunks=right_chunks) + ).tool_call_chunks == [ToolCallChunk(name='foo', args='{"a":1}', index=0)] + ''', + 'properties': dict({ + 'args': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Args', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Id', + }), + 'index': dict({ + 'anyOf': list([ + dict({ + 'type': 'integer', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Index', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'title': 'Name', + }), + 'type': dict({ + 'const': 'tool_call_chunk', + 'enum': list([ + 'tool_call_chunk', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'name', + 'args', + 'id', + 'index', + ]), + 'title': 'ToolCallChunk', + 'type': 'object', + }), 'ToolMessage': dict({ + 'additionalProperties': True, 'description': ''' Message for passing the result of executing a tool back to a model. @@ -10280,12 +16480,28 @@ 'title': 'Content', }), 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Id', - 'type': 'string', }), 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, 'title': 'Name', - 'type': 'string', }), 'response_metadata': dict({ 'title': 'Response Metadata', @@ -10305,6 +16521,7 @@ 'type': 'string', }), 'type': dict({ + 'const': 'tool', 'default': 'tool', 'enum': list([ 'tool', @@ -10320,7 +16537,112 @@ 'title': 'ToolMessage', 'type': 'object', }), + 'ToolMessageChunk': dict({ + 'additionalProperties': True, + 'description': 'Tool Message chunk.', + 'properties': dict({ + 'additional_kwargs': dict({ + 'title': 'Additional Kwargs', + 'type': 'object', + }), + 'artifact': dict({ + 'title': 'Artifact', + }), + 'content': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'items': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'object', + }), + ]), + }), + 'type': 'array', + }), + ]), + 'title': 'Content', + }), + 'id': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Id', + }), + 'name': dict({ + 'anyOf': list([ + dict({ + 'type': 'string', + }), + dict({ + 'type': 'null', + }), + ]), + 'default': None, + 'title': 'Name', + }), + 'response_metadata': dict({ + 'title': 'Response Metadata', + 'type': 'object', + }), + 'status': dict({ + 'default': 'success', + 'enum': list([ + 'success', + 'error', + ]), + 'title': 'Status', + 'type': 'string', + }), + 'tool_call_id': dict({ + 'title': 'Tool Call Id', + 'type': 'string', + }), + 'type': dict({ + 'const': 'ToolMessageChunk', + 'default': 'ToolMessageChunk', + 'enum': list([ + 'ToolMessageChunk', + ]), + 'title': 'Type', + 'type': 'string', + }), + }), + 'required': list([ + 'content', + 'tool_call_id', + ]), + 'title': 'ToolMessageChunk', + 'type': 'object', + }), 'UsageMetadata': dict({ + 'description': ''' + Usage metadata for a message, such as token counts. + + This is a standard representation of token usage that is consistent across models. + + Example: + + .. code-block:: python + + { + "input_tokens": 10, + "output_tokens": 20, + "total_tokens": 30 + } + ''', 'properties': dict({ 'input_tokens': dict({ 'title': 'Input Tokens', @@ -10344,13 +16666,13 @@ 'type': 'object', }), }), - 'title': 'ChatPromptTemplateOutput', + 'title': 'PromptTemplateOutput', }) # --- # name: test_seq_dict_prompt_llm ''' { - question: RunnablePassthrough() + question: RunnablePassthrough[str]() | RunnableLambda(...), documents: RunnableLambda(...) | FakeRetriever(), diff --git a/libs/core/tests/unit_tests/runnables/test_configurable.py b/libs/core/tests/unit_tests/runnables/test_configurable.py index cbd249b6bb750..24f7f7be3d5a2 100644 --- a/libs/core/tests/unit_tests/runnables/test_configurable.py +++ b/libs/core/tests/unit_tests/runnables/test_configurable.py @@ -1,8 +1,9 @@ from typing import Any, Dict, Optional import pytest +from pydantic import ConfigDict, Field, model_validator +from typing_extensions import Self -from langchain_core.pydantic_v1 import Field, root_validator from langchain_core.runnables import ( ConfigurableField, RunnableConfig, @@ -14,19 +15,21 @@ class MyRunnable(RunnableSerializable[str, str]): my_property: str = Field(alias="my_property_alias") _my_hidden_property: str = "" - class Config: - allow_population_by_field_name = True + model_config = ConfigDict( + populate_by_name=True, + ) - @root_validator(pre=True) - def my_error(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def my_error(cls, values: Dict[str, Any]) -> Any: if "_my_hidden_property" in values: raise ValueError("Cannot set _my_hidden_property") return values - @root_validator(pre=False, skip_on_failure=True) - def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: - values["_my_hidden_property"] = values["my_property"] - return values + @model_validator(mode="after") + def build_extra(self) -> Self: + self._my_hidden_property = self.my_property + return self def invoke(self, input: str, config: Optional[RunnableConfig] = None) -> Any: return input + self._my_hidden_property diff --git a/libs/core/tests/unit_tests/runnables/test_fallbacks.py b/libs/core/tests/unit_tests/runnables/test_fallbacks.py index f3fe80c067a94..017122374cdb3 100644 --- a/libs/core/tests/unit_tests/runnables/test_fallbacks.py +++ b/libs/core/tests/unit_tests/runnables/test_fallbacks.py @@ -13,6 +13,7 @@ ) import pytest +from pydantic import BaseModel from syrupy import SnapshotAssertion from langchain_core.callbacks import CallbackManagerForLLMRun @@ -25,7 +26,6 @@ from langchain_core.messages import BaseMessage from langchain_core.outputs import ChatResult from langchain_core.prompts import PromptTemplate -from langchain_core.pydantic_v1 import BaseModel from langchain_core.runnables import ( Runnable, RunnableBinding, diff --git a/libs/core/tests/unit_tests/runnables/test_graph.py b/libs/core/tests/unit_tests/runnables/test_graph.py index 3e909040fc2de..c681db36365ac 100644 --- a/libs/core/tests/unit_tests/runnables/test_graph.py +++ b/libs/core/tests/unit_tests/runnables/test_graph.py @@ -1,5 +1,6 @@ from typing import Optional +from pydantic import BaseModel from syrupy import SnapshotAssertion from langchain_core.language_models import FakeListLLM @@ -7,11 +8,9 @@ from langchain_core.output_parsers.string import StrOutputParser from langchain_core.output_parsers.xml import XMLOutputParser from langchain_core.prompts.prompt import PromptTemplate -from langchain_core.pydantic_v1 import BaseModel from langchain_core.runnables.base import Runnable, RunnableConfig from langchain_core.runnables.graph import Edge, Graph, Node from langchain_core.runnables.graph_mermaid import _escape_node_label -from tests.unit_tests.pydantic_utils import _schema def test_graph_single_runnable(snapshot: SnapshotAssertion) -> None: @@ -19,10 +18,10 @@ def test_graph_single_runnable(snapshot: SnapshotAssertion) -> None: graph = StrOutputParser().get_graph() first_node = graph.first_node() assert first_node is not None - assert _schema(first_node.data) == _schema(runnable.input_schema) # type: ignore[union-attr] + assert first_node.data.schema() == runnable.get_input_jsonschema() # type: ignore[union-attr] last_node = graph.last_node() assert last_node is not None - assert _schema(last_node.data) == _schema(runnable.output_schema) # type: ignore[union-attr] + assert last_node.data.schema() == runnable.get_output_jsonschema() # type: ignore[union-attr] assert len(graph.nodes) == 3 assert len(graph.edges) == 2 assert graph.edges[0].source == first_node.id diff --git a/libs/core/tests/unit_tests/runnables/test_history.py b/libs/core/tests/unit_tests/runnables/test_history.py index 41f9aaf386652..22ee2aa1795dc 100644 --- a/libs/core/tests/unit_tests/runnables/test_history.py +++ b/libs/core/tests/unit_tests/runnables/test_history.py @@ -1,6 +1,7 @@ from typing import Any, Callable, Dict, List, Optional, Sequence, Union import pytest +from pydantic import BaseModel from langchain_core.callbacks import ( CallbackManagerForLLMRun, @@ -9,7 +10,6 @@ from langchain_core.language_models.chat_models import BaseChatModel from langchain_core.messages import AIMessage, BaseMessage, HumanMessage, SystemMessage from langchain_core.outputs import ChatGeneration, ChatResult -from langchain_core.pydantic_v1 import BaseModel from langchain_core.runnables import Runnable from langchain_core.runnables.base import RunnableBinding, RunnableLambda from langchain_core.runnables.config import RunnableConfig @@ -455,8 +455,9 @@ class RunnableWithChatHistoryInput(BaseModel): def test_get_input_schema_input_messages() -> None: - class RunnableWithChatHistoryInput(BaseModel): - __root__: Sequence[BaseMessage] + from pydantic import RootModel + + RunnableWithMessageHistoryInput = RootModel[Sequence[BaseMessage]] runnable = RunnableLambda( lambda messages: { @@ -478,9 +479,9 @@ class RunnableWithChatHistoryInput(BaseModel): with_history = RunnableWithMessageHistory( runnable, get_session_history, output_messages_key="output" ) - assert _schema(with_history.get_input_schema()) == _schema( - RunnableWithChatHistoryInput - ) + expected_schema = _schema(RunnableWithMessageHistoryInput) + expected_schema["title"] = "RunnableWithChatHistoryInput" + assert _schema(with_history.get_input_schema()) == expected_schema def test_using_custom_config_specs() -> None: diff --git a/libs/core/tests/unit_tests/runnables/test_runnable.py b/libs/core/tests/unit_tests/runnables/test_runnable.py index 242a59d65e8d2..2ad80e95a43a7 100644 --- a/libs/core/tests/unit_tests/runnables/test_runnable.py +++ b/libs/core/tests/unit_tests/runnables/test_runnable.py @@ -1,5 +1,6 @@ import sys import uuid +import warnings from functools import partial from operator import itemgetter from typing import ( @@ -19,6 +20,7 @@ import pytest from freezegun import freeze_time +from pydantic import BaseModel, Field from pytest_mock import MockerFixture from syrupy import SnapshotAssertion from typing_extensions import TypedDict @@ -57,7 +59,6 @@ PromptTemplate, SystemMessagePromptTemplate, ) -from langchain_core.pydantic_v1 import BaseModel from langchain_core.retrievers import BaseRetriever from langchain_core.runnables import ( AddableDict, @@ -225,42 +226,47 @@ async def _aget_relevant_documents( def test_schemas(snapshot: SnapshotAssertion) -> None: fake = FakeRunnable() # str -> int - assert _schema(fake.input_schema) == { + assert fake.get_input_jsonschema() == { "title": "FakeRunnableInput", "type": "string", } - assert _schema(fake.output_schema) == { + assert fake.get_output_jsonschema() == { "title": "FakeRunnableOutput", "type": "integer", } - assert _schema(fake.config_schema(include=["tags", "metadata", "run_name"])) == { - "title": "FakeRunnableConfig", - "type": "object", + assert fake.get_config_jsonschema(include=["tags", "metadata", "run_name"]) == { "properties": { - "metadata": {"title": "Metadata", "type": "object"}, - "run_name": {"title": "Run Name", "type": "string"}, - "tags": {"items": {"type": "string"}, "title": "Tags", "type": "array"}, + "metadata": {"default": None, "title": "Metadata", "type": "object"}, + "run_name": {"default": None, "title": "Run Name", "type": "string"}, + "tags": { + "default": None, + "items": {"type": "string"}, + "title": "Tags", + "type": "array", + }, }, + "title": "FakeRunnableConfig", + "type": "object", } fake_bound = FakeRunnable().bind(a="b") # str -> int - assert _schema(fake_bound.input_schema) == { + assert fake_bound.get_input_jsonschema() == { "title": "FakeRunnableInput", "type": "string", } - assert _schema(fake_bound.output_schema) == { + assert fake_bound.get_output_jsonschema() == { "title": "FakeRunnableOutput", "type": "integer", } fake_w_fallbacks = FakeRunnable().with_fallbacks((fake,)) # str -> int - assert _schema(fake_w_fallbacks.input_schema) == { + assert fake_w_fallbacks.get_input_jsonschema() == { "title": "FakeRunnableInput", "type": "string", } - assert _schema(fake_w_fallbacks.output_schema) == { + assert fake_w_fallbacks.get_output_jsonschema() == { "title": "FakeRunnableOutput", "type": "integer", } @@ -270,11 +276,11 @@ def typed_lambda_impl(x: str) -> int: typed_lambda = RunnableLambda(typed_lambda_impl) # str -> int - assert _schema(typed_lambda.input_schema) == { + assert typed_lambda.get_input_jsonschema() == { "title": "typed_lambda_impl_input", "type": "string", } - assert _schema(typed_lambda.output_schema) == { + assert typed_lambda.get_output_jsonschema() == { "title": "typed_lambda_impl_output", "type": "integer", } @@ -284,52 +290,69 @@ async def typed_async_lambda_impl(x: str) -> int: typed_async_lambda: Runnable = RunnableLambda(typed_async_lambda_impl) # str -> int - assert _schema(typed_async_lambda.input_schema) == { + assert typed_async_lambda.get_input_jsonschema() == { "title": "typed_async_lambda_impl_input", "type": "string", } - assert _schema(typed_async_lambda.output_schema) == { + assert typed_async_lambda.get_output_jsonschema() == { "title": "typed_async_lambda_impl_output", "type": "integer", } fake_ret = FakeRetriever() # str -> List[Document] - assert _schema(fake_ret.input_schema) == { + assert fake_ret.get_input_jsonschema() == { "title": "FakeRetrieverInput", "type": "string", } - assert _schema(fake_ret.output_schema) == { - "title": "FakeRetrieverOutput", - "type": "array", - "items": {"$ref": "#/definitions/Document"}, - "definitions": { + assert fake_ret.get_output_jsonschema() == { + "$defs": { "Document": { - "title": "Document", - "description": AnyStr(), - "type": "object", + "description": "Class for storing a piece of text and " + "associated metadata.\n" + "\n" + "Example:\n" + "\n" + " .. code-block:: python\n" + "\n" + " from langchain_core.documents " + "import Document\n" + "\n" + " document = Document(\n" + ' page_content="Hello, ' + 'world!",\n' + ' metadata={"source": ' + '"https://example.com"}\n' + " )", "properties": { - "page_content": {"title": "Page Content", "type": "string"}, - "metadata": {"title": "Metadata", "type": "object"}, "id": { + "anyOf": [{"type": "string"}, {"type": "null"}], + "default": None, "title": "Id", - "type": "string", }, + "metadata": {"title": "Metadata", "type": "object"}, + "page_content": {"title": "Page Content", "type": "string"}, "type": { - "title": "Type", - "enum": ["Document"], + "const": "Document", "default": "Document", + "enum": ["Document"], + "title": "Type", "type": "string", }, }, "required": ["page_content"], + "title": "Document", + "type": "object", } }, + "items": {"$ref": "#/$defs/Document"}, + "title": "FakeRetrieverOutput", + "type": "array", } fake_llm = FakeListLLM(responses=["a"]) # str -> List[List[str]] - assert _schema(fake_llm.input_schema) == snapshot + assert _schema(fake_llm.input_schema) == snapshot(name="fake_llm_input_schema") assert _schema(fake_llm.output_schema) == { "title": "FakeListLLMOutput", "type": "string", @@ -337,8 +360,8 @@ async def typed_async_lambda_impl(x: str) -> int: fake_chat = FakeListChatModel(responses=["a"]) # str -> List[List[str]] - assert _schema(fake_chat.input_schema) == snapshot - assert _schema(fake_chat.output_schema) == snapshot + assert _schema(fake_chat.input_schema) == snapshot(name="fake_chat_input_schema") + assert _schema(fake_chat.output_schema) == snapshot(name="fake_chat_output_schema") chat_prompt = ChatPromptTemplate.from_messages( [ @@ -347,27 +370,27 @@ async def typed_async_lambda_impl(x: str) -> int: ] ) - assert _schema(chat_prompt.input_schema) == snapshot( + assert chat_prompt.get_input_jsonschema() == snapshot( name="chat_prompt_input_schema" ) - assert _schema(chat_prompt.output_schema) == snapshot( + assert chat_prompt.get_output_jsonschema() == snapshot( name="chat_prompt_output_schema" ) prompt = PromptTemplate.from_template("Hello, {name}!") - assert _schema(prompt.input_schema) == { + assert prompt.get_input_jsonschema() == { "title": "PromptInput", "type": "object", "properties": {"name": {"title": "Name", "type": "string"}}, "required": ["name"], } - assert _schema(prompt.output_schema) == snapshot + assert _schema(prompt.output_schema) == snapshot(name="prompt_output_schema") prompt_mapper = PromptTemplate.from_template("Hello, {name}!").map() - assert _schema(prompt_mapper.input_schema) == { - "definitions": { + assert prompt_mapper.get_input_jsonschema() == { + "$defs": { "PromptInput": { "properties": {"name": {"title": "Name", "type": "string"}}, "required": ["name"], @@ -375,15 +398,20 @@ async def typed_async_lambda_impl(x: str) -> int: "type": "object", } }, - "items": {"$ref": "#/definitions/PromptInput"}, - "type": "array", + "default": None, + "items": {"$ref": "#/$defs/PromptInput"}, "title": "RunnableEachInput", + "type": "array", } - assert _schema(prompt_mapper.output_schema) == snapshot + assert _schema(prompt_mapper.output_schema) == snapshot( + name="prompt_mapper_output_schema" + ) list_parser = CommaSeparatedListOutputParser() - assert _schema(list_parser.input_schema) == snapshot + assert _schema(list_parser.input_schema) == snapshot( + name="list_parser_input_schema" + ) assert _schema(list_parser.output_schema) == { "title": "CommaSeparatedListOutputParserOutput", "type": "array", @@ -392,13 +420,13 @@ async def typed_async_lambda_impl(x: str) -> int: seq = prompt | fake_llm | list_parser - assert _schema(seq.input_schema) == { + assert seq.get_input_jsonschema() == { "title": "PromptInput", "type": "object", "properties": {"name": {"title": "Name", "type": "string"}}, "required": ["name"], } - assert _schema(seq.output_schema) == { + assert seq.get_output_jsonschema() == { "type": "array", "items": {"type": "string"}, "title": "CommaSeparatedListOutputParserOutput", @@ -407,21 +435,28 @@ async def typed_async_lambda_impl(x: str) -> int: router: Runnable = RouterRunnable({}) assert _schema(router.input_schema) == { - "title": "RouterRunnableInput", "$ref": "#/definitions/RouterInput", "definitions": { "RouterInput": { - "title": "RouterInput", - "type": "object", + "description": "Router input.\n" + "\n" + "Attributes:\n" + " key: The key to route " + "on.\n" + " input: The input to pass " + "to the selected Runnable.", "properties": { - "key": {"title": "Key", "type": "string"}, "input": {"title": "Input"}, + "key": {"title": "Key", "type": "string"}, }, "required": ["key", "input"], + "title": "RouterInput", + "type": "object", } }, + "title": "RouterRunnableInput", } - assert _schema(router.output_schema) == {"title": "RouterRunnableOutput"} + assert router.get_output_jsonschema() == {"title": "RouterRunnableOutput"} seq_w_map: Runnable = ( prompt @@ -433,13 +468,13 @@ async def typed_async_lambda_impl(x: str) -> int: } ) - assert _schema(seq_w_map.input_schema) == { + assert seq_w_map.get_input_jsonschema() == { "title": "PromptInput", "type": "object", "properties": {"name": {"title": "Name", "type": "string"}}, "required": ["name"], } - assert _schema(seq_w_map.output_schema) == { + assert seq_w_map.get_output_jsonschema() == { "title": "RunnableParallelOutput", "type": "object", "properties": { @@ -451,6 +486,20 @@ async def typed_async_lambda_impl(x: str) -> int: "items": {"type": "string"}, }, }, + "required": ["original", "as_list", "length"], + } + + # Add a test for schema of runnable assign + def foo(x: int) -> int: + return x + + foo_ = RunnableLambda(foo) + + assert foo_.assign(bar=lambda x: "foo").get_output_schema().schema() == { + "properties": {"bar": {"title": "Bar"}, "root": {"title": "Root"}}, + "required": ["root", "bar"], + "title": "RunnableAssignOutput", + "type": "object", } @@ -465,12 +514,13 @@ def test_passthrough_assign_schema() -> None: | fake_llm ) - assert _schema(seq_w_assign.input_schema) == { + assert seq_w_assign.get_input_jsonschema() == { "properties": {"question": {"title": "Question", "type": "string"}}, "title": "RunnableSequenceInput", "type": "object", + "required": ["question"], } - assert _schema(seq_w_assign.output_schema) == { + assert seq_w_assign.get_output_jsonschema() == { "title": "FakeListLLMOutput", "type": "string", } @@ -482,10 +532,11 @@ def test_passthrough_assign_schema() -> None: # fallback to RunnableAssign.input_schema if next runnable doesn't have # expected dict input_schema - assert _schema(invalid_seq_w_assign.input_schema) == { + assert invalid_seq_w_assign.get_input_jsonschema() == { "properties": {"question": {"title": "Question"}}, "title": "RunnableParallelInput", "type": "object", + "required": ["question"], } @@ -494,38 +545,42 @@ def test_passthrough_assign_schema() -> None: ) def test_lambda_schemas() -> None: first_lambda = lambda x: x["hello"] # noqa: E731 - assert _schema(RunnableLambda(first_lambda).input_schema) == { + assert RunnableLambda(first_lambda).get_input_jsonschema() == { "title": "RunnableLambdaInput", "type": "object", "properties": {"hello": {"title": "Hello"}}, + "required": ["hello"], } second_lambda = lambda x, y: (x["hello"], x["bye"], y["bah"]) # noqa: E731 - assert _schema(RunnableLambda(second_lambda).input_schema) == { # type: ignore[arg-type] + assert RunnableLambda(second_lambda).get_input_jsonschema() == { # type: ignore[arg-type] "title": "RunnableLambdaInput", "type": "object", "properties": {"hello": {"title": "Hello"}, "bye": {"title": "Bye"}}, + "required": ["bye", "hello"], } def get_value(input): # type: ignore[no-untyped-def] return input["variable_name"] - assert _schema(RunnableLambda(get_value).input_schema) == { + assert RunnableLambda(get_value).get_input_jsonschema() == { "title": "get_value_input", "type": "object", "properties": {"variable_name": {"title": "Variable Name"}}, + "required": ["variable_name"], } async def aget_value(input): # type: ignore[no-untyped-def] return (input["variable_name"], input.get("another")) - assert _schema(RunnableLambda(aget_value).input_schema) == { + assert RunnableLambda(aget_value).get_input_jsonschema() == { "title": "aget_value_input", "type": "object", "properties": { "another": {"title": "Another"}, "variable_name": {"title": "Variable Name"}, }, + "required": ["another", "variable_name"], } async def aget_values(input): # type: ignore[no-untyped-def] @@ -535,13 +590,14 @@ async def aget_values(input): # type: ignore[no-untyped-def] "byebye": input["yo"], } - assert _schema(RunnableLambda(aget_values).input_schema) == { + assert RunnableLambda(aget_values).get_input_jsonschema() == { "title": "aget_values_input", "type": "object", "properties": { "variable_name": {"title": "Variable Name"}, "yo": {"title": "Yo"}, }, + "required": ["variable_name", "yo"], } class InputType(TypedDict): @@ -561,15 +617,11 @@ async def aget_values_typed(input: InputType) -> OutputType: } assert ( - _schema( - RunnableLambda( - aget_values_typed # type: ignore[arg-type] - ).input_schema - ) + RunnableLambda( + aget_values_typed # type: ignore[arg-type] + ).get_input_jsonschema() == { - "title": "aget_values_typed_input", - "$ref": "#/definitions/InputType", - "definitions": { + "$defs": { "InputType": { "properties": { "variable_name": { @@ -583,13 +635,13 @@ async def aget_values_typed(input: InputType) -> OutputType: "type": "object", } }, + "allOf": [{"$ref": "#/$defs/InputType"}], + "title": "aget_values_typed_input", } ) - assert _schema(RunnableLambda(aget_values_typed).output_schema) == { # type: ignore[arg-type] - "title": "aget_values_typed_output", - "$ref": "#/definitions/OutputType", - "definitions": { + assert RunnableLambda(aget_values_typed).get_output_jsonschema() == { # type: ignore[arg-type] + "$defs": { "OutputType": { "properties": { "bye": {"title": "Bye", "type": "string"}, @@ -601,6 +653,8 @@ async def aget_values_typed(input: InputType) -> OutputType: "type": "object", } }, + "allOf": [{"$ref": "#/$defs/OutputType"}], + "title": "aget_values_typed_output", } @@ -622,6 +676,25 @@ def foo(x: int) -> None: ) +def test_schema_with_itemgetter() -> None: + """Test runnable with itemgetter.""" + foo = RunnableLambda(itemgetter("hello")) + assert _schema(foo.input_schema) == { + "properties": {"hello": {"title": "Hello"}}, + "required": ["hello"], + "title": "RunnableLambdaInput", + "type": "object", + } + prompt = ChatPromptTemplate.from_template("what is {language}?") + chain: Runnable = {"language": itemgetter("language")} | prompt + assert _schema(chain.input_schema) == { + "properties": {"language": {"title": "Language"}}, + "required": ["language"], + "title": "RunnableParallelInput", + "type": "object", + } + + def test_schema_complex_seq() -> None: prompt1 = ChatPromptTemplate.from_template("what is the city {person} is from?") prompt2 = ChatPromptTemplate.from_template( @@ -643,26 +716,27 @@ def test_schema_complex_seq() -> None: | StrOutputParser() ) - assert _schema(chain2.input_schema) == { + assert chain2.get_input_jsonschema() == { "title": "RunnableParallelInput", "type": "object", "properties": { "person": {"title": "Person", "type": "string"}, "language": {"title": "Language"}, }, + "required": ["person", "language"], } - assert _schema(chain2.output_schema) == { + assert chain2.get_output_jsonschema() == { "title": "StrOutputParserOutput", "type": "string", } - assert _schema(chain2.with_types(input_type=str).input_schema) == { + assert chain2.with_types(input_type=str).get_input_jsonschema() == { "title": "RunnableSequenceInput", "type": "string", } - assert _schema(chain2.with_types(input_type=int).output_schema) == { + assert chain2.with_types(input_type=int).get_output_jsonschema() == { "title": "StrOutputParserOutput", "type": "string", } @@ -670,7 +744,7 @@ def test_schema_complex_seq() -> None: class InputType(BaseModel): person: str - assert _schema(chain2.with_types(input_type=InputType).input_schema) == { + assert chain2.with_types(input_type=InputType).get_input_jsonschema() == { "title": "InputType", "type": "object", "properties": {"person": {"title": "Person", "type": "string"}}, @@ -693,25 +767,37 @@ def test_configurable_fields() -> None: assert fake_llm_configurable.invoke("...") == "a" - assert _schema(fake_llm_configurable.config_schema()) == { - "title": "RunnableConfigurableFieldsConfig", - "type": "object", - "properties": {"configurable": {"$ref": "#/definitions/Configurable"}}, - "definitions": { + assert fake_llm_configurable.get_config_jsonschema() == { + "$defs": { "Configurable": { - "title": "Configurable", - "type": "object", "properties": { "llm_responses": { - "title": "LLM Responses", - "description": "A list of fake responses for this LLM", "default": ["a"], - "type": "array", + "description": "A " + "list " + "of " + "fake " + "responses " + "for " + "this " + "LLM", "items": {"type": "string"}, + "title": "LLM " "Responses", + "type": "array", } }, + "title": "Configurable", + "type": "object", + } + }, + "properties": { + "configurable": { + "allOf": [{"$ref": "#/$defs/Configurable"}], + "default": None, } }, + "title": "RunnableConfigurableFieldsConfig", + "type": "object", } fake_llm_configured = fake_llm_configurable.with_config( @@ -736,24 +822,34 @@ def test_configurable_fields() -> None: text="Hello, John!" ) - assert _schema(prompt_configurable.config_schema()) == { - "title": "RunnableConfigurableFieldsConfig", - "type": "object", - "properties": {"configurable": {"$ref": "#/definitions/Configurable"}}, - "definitions": { + assert prompt_configurable.get_config_jsonschema() == { + "$defs": { "Configurable": { - "title": "Configurable", - "type": "object", "properties": { "prompt_template": { - "title": "Prompt Template", - "description": "The prompt template for this chain", - "default": "Hello, {name}!", + "default": "Hello, " "{name}!", + "description": "The " + "prompt " + "template " + "for " + "this " + "chain", + "title": "Prompt " "Template", "type": "string", } }, + "title": "Configurable", + "type": "object", + } + }, + "properties": { + "configurable": { + "allOf": [{"$ref": "#/$defs/Configurable"}], + "default": None, } }, + "title": "RunnableConfigurableFieldsConfig", + "type": "object", } prompt_configured = prompt_configurable.with_config( @@ -764,11 +860,9 @@ def test_configurable_fields() -> None: text="Hello, John! John!" ) - assert _schema( - prompt_configurable.with_config( - configurable={"prompt_template": "Hello {name} in {lang}"} - ).input_schema - ) == { + assert prompt_configurable.with_config( + configurable={"prompt_template": "Hello {name} in {lang}"} + ).get_input_jsonschema() == { "title": "PromptInput", "type": "object", "properties": { @@ -782,31 +876,48 @@ def test_configurable_fields() -> None: assert chain_configurable.invoke({"name": "John"}) == "a" - assert _schema(chain_configurable.config_schema()) == { - "title": "RunnableSequenceConfig", - "type": "object", - "properties": {"configurable": {"$ref": "#/definitions/Configurable"}}, - "definitions": { + assert chain_configurable.get_config_jsonschema() == { + "$defs": { "Configurable": { - "title": "Configurable", - "type": "object", "properties": { "llm_responses": { - "title": "LLM Responses", - "description": "A list of fake responses for this LLM", "default": ["a"], - "type": "array", + "description": "A " + "list " + "of " + "fake " + "responses " + "for " + "this " + "LLM", "items": {"type": "string"}, + "title": "LLM " "Responses", + "type": "array", }, "prompt_template": { - "title": "Prompt Template", - "description": "The prompt template for this chain", - "default": "Hello, {name}!", + "default": "Hello, " "{name}!", + "description": "The " + "prompt " + "template " + "for " + "this " + "chain", + "title": "Prompt " "Template", "type": "string", }, }, + "title": "Configurable", + "type": "object", } }, + "properties": { + "configurable": { + "allOf": [{"$ref": "#/$defs/Configurable"}], + "default": None, + } + }, + "title": "RunnableSequenceConfig", + "type": "object", } assert ( @@ -819,14 +930,12 @@ def test_configurable_fields() -> None: == "c" ) - assert _schema( - chain_configurable.with_config( - configurable={ - "prompt_template": "A very good morning to you, {name} {lang}!", - "llm_responses": ["c"], - } - ).input_schema - ) == { + assert chain_configurable.with_config( + configurable={ + "prompt_template": "A very good morning to you, {name} {lang}!", + "llm_responses": ["c"], + } + ).get_input_jsonschema() == { "title": "PromptInput", "type": "object", "properties": { @@ -851,37 +960,54 @@ def test_configurable_fields() -> None: "llm3": "a", } - assert _schema(chain_with_map_configurable.config_schema()) == { - "title": "RunnableSequenceConfig", - "type": "object", - "properties": {"configurable": {"$ref": "#/definitions/Configurable"}}, - "definitions": { + assert chain_with_map_configurable.get_config_jsonschema() == { + "$defs": { "Configurable": { - "title": "Configurable", - "type": "object", "properties": { "llm_responses": { - "title": "LLM Responses", - "description": "A list of fake responses for this LLM", "default": ["a"], - "type": "array", + "description": "A " + "list " + "of " + "fake " + "responses " + "for " + "this " + "LLM", "items": {"type": "string"}, + "title": "LLM " "Responses", + "type": "array", }, "other_responses": { - "title": "Other Responses", "default": ["a"], - "type": "array", "items": {"type": "string"}, + "title": "Other " "Responses", + "type": "array", }, "prompt_template": { - "title": "Prompt Template", - "description": "The prompt template for this chain", - "default": "Hello, {name}!", + "default": "Hello, " "{name}!", + "description": "The " + "prompt " + "template " + "for " + "this " + "chain", + "title": "Prompt " "Template", "type": "string", }, }, + "title": "Configurable", + "type": "object", } }, + "properties": { + "configurable": { + "allOf": [{"$ref": "#/$defs/Configurable"}], + "default": None, + } + }, + "title": "RunnableSequenceConfig", + "type": "object", } assert chain_with_map_configurable.with_config( @@ -957,60 +1083,63 @@ def test_configurable_fields_prefix_keys() -> None: "type": "object", "properties": {"configurable": {"$ref": "#/definitions/Configurable"}}, "definitions": { - "LLM": { - "title": "LLM", - "description": "An enumeration.", - "enum": ["chat", "default"], - "type": "string", - }, "Chat_Responses": { - "title": "Chat Responses", - "description": "An enumeration.", "enum": ["hello", "bye", "helpful"], - "type": "string", - }, - "Prompt_Template": { - "title": "Prompt Template", - "description": "An enumeration.", - "enum": ["hello", "good_morning"], + "title": "Chat Responses", "type": "string", }, "Configurable": { - "title": "Configurable", - "type": "object", "properties": { - "prompt_template": { - "title": "Prompt Template", - "description": "The prompt template for this chain", - "default": "hello", - "allOf": [{"$ref": "#/definitions/Prompt_Template"}], + "chat_sleep": { + "anyOf": [{"type": "number"}, {"type": "null"}], + "default": None, + "title": "Chat " "Sleep", }, "llm": { - "title": "LLM", + "$ref": "#/definitions/LLM", "default": "default", - "allOf": [{"$ref": "#/definitions/LLM"}], - }, - # not prefixed because marked as shared - "chat_sleep": { - "title": "Chat Sleep", - "type": "number", + "title": "LLM", }, - # prefixed for "chat" option "llm==chat/responses": { - "title": "Chat Responses", "default": ["hello", "bye"], - "type": "array", "items": {"$ref": "#/definitions/Chat_Responses"}, + "title": "Chat " "Responses", + "type": "array", }, - # prefixed for "default" option "llm==default/responses": { - "title": "LLM Responses", - "description": "A list of fake responses for this LLM", "default": ["a"], - "type": "array", + "description": "A " + "list " + "of " + "fake " + "responses " + "for " + "this " + "LLM", "items": {"type": "string"}, + "title": "LLM " "Responses", + "type": "array", + }, + "prompt_template": { + "$ref": "#/definitions/Prompt_Template", + "default": "hello", + "description": "The " + "prompt " + "template " + "for " + "this " + "chain", + "title": "Prompt " "Template", }, }, + "title": "Configurable", + "type": "object", + }, + "LLM": {"enum": ["chat", "default"], "title": "LLM", "type": "string"}, + "Prompt_Template": { + "enum": ["hello", "good_morning"], + "title": "Prompt Template", + "type": "string", }, }, } @@ -1062,60 +1191,70 @@ def test_configurable_fields_example() -> None: assert chain_configurable.invoke({"name": "John"}) == "a" - assert _schema(chain_configurable.config_schema()) == { - "title": "RunnableSequenceConfig", - "type": "object", - "properties": {"configurable": {"$ref": "#/definitions/Configurable"}}, - "definitions": { - "LLM": { - "title": "LLM", - "description": "An enumeration.", - "enum": ["chat", "default"], - "type": "string", - }, + assert chain_configurable.get_config_jsonschema() == { + "$defs": { "Chat_Responses": { - "description": "An enumeration.", "enum": ["hello", "bye", "helpful"], "title": "Chat Responses", "type": "string", }, - "Prompt_Template": { - "description": "An enumeration.", - "enum": ["hello", "good_morning"], - "title": "Prompt Template", - "type": "string", - }, "Configurable": { - "title": "Configurable", - "type": "object", "properties": { "chat_responses": { "default": ["hello", "bye"], - "items": {"$ref": "#/definitions/Chat_Responses"}, - "title": "Chat Responses", + "items": {"$ref": "#/$defs/Chat_Responses"}, + "title": "Chat " "Responses", "type": "array", }, "llm": { - "title": "LLM", + "allOf": [{"$ref": "#/$defs/LLM"}], "default": "default", - "allOf": [{"$ref": "#/definitions/LLM"}], + "title": "LLM", }, "llm_responses": { - "title": "LLM Responses", - "description": "A list of fake responses for this LLM", "default": ["a"], - "type": "array", + "description": "A " + "list " + "of " + "fake " + "responses " + "for " + "this " + "LLM", "items": {"type": "string"}, + "title": "LLM " "Responses", + "type": "array", }, "prompt_template": { - "title": "Prompt Template", - "description": "The prompt template for this chain", + "allOf": [{"$ref": "#/$defs/Prompt_Template"}], "default": "hello", - "allOf": [{"$ref": "#/definitions/Prompt_Template"}], + "description": "The " + "prompt " + "template " + "for " + "this " + "chain", + "title": "Prompt " "Template", }, }, + "title": "Configurable", + "type": "object", + }, + "LLM": {"enum": ["chat", "default"], "title": "LLM", "type": "string"}, + "Prompt_Template": { + "enum": ["hello", "good_morning"], + "title": "Prompt Template", + "type": "string", }, }, + "properties": { + "configurable": { + "allOf": [{"$ref": "#/$defs/Configurable"}], + "default": None, + } + }, + "title": "RunnableSequenceConfig", + "type": "object", } assert ( @@ -3146,7 +3285,7 @@ def test_map_stream() -> None: chain_pick_one = chain.pick("llm") - assert _schema(chain_pick_one.output_schema) == { + assert chain_pick_one.get_output_jsonschema() == { "title": "RunnableSequenceOutput", "type": "string", } @@ -3169,13 +3308,14 @@ def test_map_stream() -> None: ["llm", "hello"] ) - assert _schema(chain_pick_two.output_schema) == { + assert chain_pick_two.get_output_jsonschema() == { "title": "RunnableSequenceOutput", "type": "object", "properties": { "hello": {"title": "Hello", "type": "string"}, "llm": {"title": "Llm", "type": "string"}, }, + "required": ["llm", "hello"], } stream = chain_pick_two.stream({"question": "What is your name?"}) @@ -3193,6 +3333,12 @@ def test_map_stream() -> None: {"llm": "i"}, {"chat": _AnyIdAIMessageChunk(content="i")}, ] + if not ( # TODO(Rewrite properly) statement above + streamed_chunks[0] == {"llm": "i"} + or {"chat": _AnyIdAIMessageChunk(content="i")} + ): + raise AssertionError(f"Got an unexpected chunk: {streamed_chunks[0]}") + assert len(streamed_chunks) == len(llm_res) + len(chat_res) @@ -3534,19 +3680,20 @@ def test_deep_stream_assign() -> None: chain_with_assign = chain.assign(hello=itemgetter("str") | llm) - assert _schema(chain_with_assign.input_schema) == { + assert chain_with_assign.get_input_jsonschema() == { "title": "PromptInput", "type": "object", "properties": {"question": {"title": "Question", "type": "string"}}, "required": ["question"], } - assert _schema(chain_with_assign.output_schema) == { + assert chain_with_assign.get_output_jsonschema() == { "title": "RunnableSequenceOutput", "type": "object", "properties": { "str": {"title": "Str", "type": "string"}, "hello": {"title": "Hello", "type": "string"}, }, + "required": ["str", "hello"], } chunks = [] @@ -3585,19 +3732,20 @@ def test_deep_stream_assign() -> None: hello=itemgetter("str") | llm, ) - assert _schema(chain_with_assign_shadow.input_schema) == { + assert chain_with_assign_shadow.get_input_jsonschema() == { "title": "PromptInput", "type": "object", "properties": {"question": {"title": "Question", "type": "string"}}, "required": ["question"], } - assert _schema(chain_with_assign_shadow.output_schema) == { + assert chain_with_assign_shadow.get_output_jsonschema() == { "title": "RunnableSequenceOutput", "type": "object", "properties": { "str": {"title": "Str"}, "hello": {"title": "Hello", "type": "string"}, }, + "required": ["str", "hello"], } chunks = [] @@ -3660,19 +3808,20 @@ async def test_deep_astream_assign() -> None: hello=itemgetter("str") | llm, ) - assert _schema(chain_with_assign.input_schema) == { + assert chain_with_assign.get_input_jsonschema() == { "title": "PromptInput", "type": "object", "properties": {"question": {"title": "Question", "type": "string"}}, "required": ["question"], } - assert _schema(chain_with_assign.output_schema) == { + assert chain_with_assign.get_output_jsonschema() == { "title": "RunnableSequenceOutput", "type": "object", "properties": { "str": {"title": "Str", "type": "string"}, "hello": {"title": "Hello", "type": "string"}, }, + "required": ["str", "hello"], } chunks = [] @@ -3711,19 +3860,20 @@ async def test_deep_astream_assign() -> None: hello=itemgetter("str") | llm, ) - assert _schema(chain_with_assign_shadow.input_schema) == { + assert chain_with_assign_shadow.get_input_jsonschema() == { "title": "PromptInput", "type": "object", "properties": {"question": {"title": "Question", "type": "string"}}, "required": ["question"], } - assert _schema(chain_with_assign_shadow.output_schema) == { + assert chain_with_assign_shadow.get_output_jsonschema() == { "title": "RunnableSequenceOutput", "type": "object", "properties": { "str": {"title": "Str"}, "hello": {"title": "Hello", "type": "string"}, }, + "required": ["str", "hello"], } chunks = [] @@ -4365,7 +4515,10 @@ def test_runnable_branch_init_coercion(branches: Sequence[Any]) -> None: assert isinstance(body, Runnable) assert isinstance(runnable.default, Runnable) - assert _schema(runnable.input_schema) == {"title": "RunnableBranchInput"} + assert _schema(runnable.input_schema) == { + "title": "RunnableBranchInput", + "type": "integer", + } def test_runnable_branch_invoke_call_counts(mocker: MockerFixture) -> None: @@ -4712,7 +4865,7 @@ async def test_tool_from_runnable() -> None: {"question": "What up"} ) assert chain_tool.description.endswith(repr(chain)) - assert _schema(chain_tool.args_schema) == _schema(chain.input_schema) + assert _schema(chain_tool.args_schema) == chain.get_input_jsonschema() assert _schema(chain_tool.args_schema) == { "properties": {"question": {"title": "Question", "type": "string"}}, "title": "PromptInput", @@ -4731,8 +4884,8 @@ def gen(input: Iterator[Any]) -> Iterator[int]: runnable = RunnableGenerator(gen) - assert _schema(runnable.input_schema) == {"title": "gen_input"} - assert _schema(runnable.output_schema) == { + assert runnable.get_input_jsonschema() == {"title": "gen_input"} + assert runnable.get_output_jsonschema() == { "title": "gen_output", "type": "integer", } @@ -4783,8 +4936,8 @@ def gen(input: Iterator[Any]) -> Iterator[int]: runnable = RunnableGenerator(gen) - assert _schema(runnable.input_schema) == {"title": "gen_input"} - assert _schema(runnable.output_schema) == { + assert runnable.get_input_jsonschema() == {"title": "gen_input"} + assert runnable.get_output_jsonschema() == { "title": "gen_output", "type": "integer", } @@ -4917,11 +5070,11 @@ def gen(input: str) -> Iterator[int]: yield fake.invoke(input * 2) yield fake.invoke(input * 3) - assert _schema(gen.input_schema) == { + assert gen.get_input_jsonschema() == { "title": "gen_input", "type": "string", } - assert _schema(gen.output_schema) == { + assert gen.get_output_jsonschema() == { "title": "gen_output", "type": "integer", } @@ -4968,11 +5121,11 @@ async def agen(input: str) -> AsyncIterator[int]: yield await fake.ainvoke(input * 2) yield await fake.ainvoke(input * 3) - assert _schema(agen.input_schema) == { + assert agen.get_input_jsonschema() == { "title": "agen_input", "type": "string", } - assert _schema(agen.output_schema) == { + assert agen.get_output_jsonschema() == { "title": "agen_output", "type": "integer", } @@ -5035,8 +5188,8 @@ def fun(input: str) -> int: output += fake.invoke(input * 3) return output - assert _schema(fun.input_schema) == {"title": "fun_input", "type": "string"} - assert _schema(fun.output_schema) == { + assert fun.get_input_jsonschema() == {"title": "fun_input", "type": "string"} + assert fun.get_output_jsonschema() == { "title": "fun_output", "type": "integer", } @@ -5084,8 +5237,8 @@ async def afun(input: str) -> int: output += await fake.ainvoke(input * 3) return output - assert _schema(afun.input_schema) == {"title": "afun_input", "type": "string"} - assert _schema(afun.output_schema) == { + assert afun.get_input_jsonschema() == {"title": "afun_input", "type": "string"} + assert afun.get_output_jsonschema() == { "title": "afun_output", "type": "integer", } @@ -5145,19 +5298,19 @@ async def aplus_one(input: AsyncIterator[int]) -> AsyncIterator[int]: chain: Runnable = RunnableGenerator(gen_indexes, agen_indexes) | plus_one achain = RunnableGenerator(gen_indexes, agen_indexes) | aplus_one - assert _schema(chain.input_schema) == { + assert chain.get_input_jsonschema() == { "title": "gen_indexes_input", "type": "integer", } - assert _schema(chain.output_schema) == { + assert chain.get_output_jsonschema() == { "title": "plus_one_output", "type": "integer", } - assert _schema(achain.input_schema) == { + assert achain.get_input_jsonschema() == { "title": "gen_indexes_input", "type": "integer", } - assert _schema(achain.output_schema) == { + assert achain.get_output_jsonschema() == { "title": "aplus_one_output", "type": "integer", } @@ -5490,3 +5643,12 @@ async def on_chain_error( # Wait for a bit to make sure that the callback is called. time.sleep(0.05) assert on_chain_error_triggered is False + + +def test_pydantic_protected_namespaces() -> None: + # Check that protected namespaces (e.g., `model_kwargs`) do not raise warnings + with warnings.catch_warnings(): + warnings.simplefilter("error") + + class CustomChatModel(RunnableSerializable): + model_kwargs: Dict[str, Any] = Field(default_factory=dict) diff --git a/libs/core/tests/unit_tests/runnables/test_runnable_events_v1.py b/libs/core/tests/unit_tests/runnables/test_runnable_events_v1.py index c2230f560a5fa..584099e234042 100644 --- a/libs/core/tests/unit_tests/runnables/test_runnable_events_v1.py +++ b/libs/core/tests/unit_tests/runnables/test_runnable_events_v1.py @@ -3,8 +3,10 @@ import sys from itertools import cycle from typing import Any, AsyncIterator, Dict, List, Sequence, cast +from typing import Optional as Optional import pytest +from pydantic import BaseModel from langchain_core.callbacks import CallbackManagerForRetrieverRun, Callbacks from langchain_core.chat_history import BaseChatMessageHistory @@ -19,7 +21,6 @@ ) from langchain_core.prompt_values import ChatPromptValue from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder -from langchain_core.pydantic_v1 import BaseModel from langchain_core.retrievers import BaseRetriever from langchain_core.runnables import ( ConfigurableField, @@ -1176,6 +1177,9 @@ def _get_relevant_documents( return self.documents +HardCodedRetriever.model_rebuild() + + async def test_event_stream_with_retriever() -> None: """Test the event stream with a retriever.""" retriever = HardCodedRetriever( diff --git a/libs/core/tests/unit_tests/runnables/test_runnable_events_v2.py b/libs/core/tests/unit_tests/runnables/test_runnable_events_v2.py index 59fc49ca5eab9..0daecc068ed94 100644 --- a/libs/core/tests/unit_tests/runnables/test_runnable_events_v2.py +++ b/libs/core/tests/unit_tests/runnables/test_runnable_events_v2.py @@ -18,6 +18,7 @@ ) import pytest +from pydantic import BaseModel from langchain_core.callbacks import CallbackManagerForRetrieverRun, Callbacks from langchain_core.chat_history import BaseChatMessageHistory @@ -33,7 +34,6 @@ ) from langchain_core.prompt_values import ChatPromptValue from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder -from langchain_core.pydantic_v1 import BaseModel from langchain_core.retrievers import BaseRetriever from langchain_core.runnables import ( ConfigurableField, diff --git a/libs/core/tests/unit_tests/test_tools.py b/libs/core/tests/unit_tests/test_tools.py index 39cf811fc5e1c..f1afba566aac5 100644 --- a/libs/core/tests/unit_tests/test_tools.py +++ b/libs/core/tests/unit_tests/test_tools.py @@ -22,7 +22,8 @@ ) import pytest -from pydantic import BaseModel as BaseModelProper # pydantic: ignore +from pydantic import BaseModel, Field, ValidationError +from pydantic import BaseModel as BaseModelProper from typing_extensions import Annotated, TypedDict, TypeVar from langchain_core import tools @@ -31,7 +32,6 @@ CallbackManagerForToolRun, ) from langchain_core.messages import ToolMessage -from langchain_core.pydantic_v1 import BaseModel, Field, ValidationError from langchain_core.runnables import ( Runnable, RunnableConfig, @@ -54,6 +54,7 @@ ) from langchain_core.utils.function_calling import convert_to_openai_function from langchain_core.utils.pydantic import PYDANTIC_MAJOR_VERSION, _create_subset_model +from langchain_core.utils.pydantic import TypeBaseModel as TypeBaseModel from tests.unit_tests.fake.callbacks import FakeCallbackHandler from tests.unit_tests.pydantic_utils import _schema @@ -874,15 +875,13 @@ async def _arun(self) -> str: def test_optional_subset_model_rewrite() -> None: class MyModel(BaseModel): - a: Optional[str] + a: Optional[str] = None b: str - c: Optional[List[Optional[str]]] + c: Optional[List[Optional[str]]] = None model2 = _create_subset_model("model2", MyModel, ["a", "b", "c"]) - assert "a" not in _schema(model2)["required"] # should be optional - assert "b" in _schema(model2)["required"] # should be required - assert "c" not in _schema(model2)["required"] # should be optional + assert set(_schema(model2)["required"]) == {"b"} @pytest.mark.parametrize( @@ -988,6 +987,9 @@ def _run(self, bar: Any, bar_config: RunnableConfig, **kwargs: Any) -> Any: return assert_bar(bar, bar_config) +FooBase.model_rebuild() + + class AFooBase(FooBase): async def _arun(self, bar: Any, bar_config: RunnableConfig, **kwargs: Any) -> Any: return assert_bar(bar, bar_config) @@ -1570,7 +1572,7 @@ class FooProper(BaseModelProper): def generate_backwards_compatible_v1() -> List[Any]: """Generate a model with pydantic 2 from the v1 namespace.""" - from pydantic.v1 import BaseModel as BaseModelV1 # pydantic: ignore + from pydantic.v1 import BaseModel as BaseModelV1 class FooV1Namespace(BaseModelV1): a: int @@ -1627,7 +1629,7 @@ def test_args_schema_explicitly_typed() -> None: is a pydantic 1 model! """ # Check with whatever pydantic model is passed in and not via v1 namespace - from pydantic import BaseModel # pydantic: ignore + from pydantic import BaseModel class Foo(BaseModel): a: int @@ -1754,13 +1756,17 @@ def test__get_all_basemodel_annotations_v2(use_v1_namespace: bool) -> None: A = TypeVar("A") if use_v1_namespace: + from pydantic.v1 import BaseModel as BM1 - class ModelA(BaseModel, Generic[A], extra="allow"): + class ModelA(BM1, Generic[A], extra="allow"): a: A else: + from pydantic import BaseModel as BM2 + from pydantic import ConfigDict - class ModelA(BaseModelProper, Generic[A], extra="allow"): # type: ignore[no-redef] + class ModelA(BM2, Generic[A], extra="allow"): # type: ignore[no-redef] a: A + model_config = ConfigDict(arbitrary_types_allowed=True) class ModelB(ModelA[str]): b: Annotated[ModelA[Dict[str, Any]], "foo"] @@ -1869,11 +1875,31 @@ class ModelD(ModelC, Generic[D]): assert actual == expected +def test_tool_annotations_preserved() -> None: + """Test that annotations are preserved when creating a tool.""" + + @tool + def my_tool(val: int, other_val: Annotated[dict, "my annotation"]) -> str: + """Tool docstring.""" + return "foo" + + schema = my_tool.get_input_schema() # type: ignore[attr-defined] + + func = my_tool.func # type: ignore[attr-defined] + + expected_type_hints = { + name: hint + for name, hint in func.__annotations__.items() + if name in inspect.signature(func).parameters + } + assert schema.__annotations__ == expected_type_hints + + @pytest.mark.skipif(PYDANTIC_MAJOR_VERSION != 2, reason="Testing pydantic v2.") def test_tool_args_schema_pydantic_v2_with_metadata() -> None: - from pydantic import BaseModel as BaseModelV2 # pydantic: ignore - from pydantic import Field as FieldV2 # pydantic: ignore - from pydantic import ValidationError as ValidationErrorV2 # pydantic: ignore + from pydantic import BaseModel as BaseModelV2 + from pydantic import Field as FieldV2 + from pydantic import ValidationError as ValidationErrorV2 class Foo(BaseModelV2): x: List[int] = FieldV2( diff --git a/libs/core/tests/unit_tests/utils/test_function_calling.py b/libs/core/tests/unit_tests/utils/test_function_calling.py index ba5ba4c9fa616..f8a3b50076959 100644 --- a/libs/core/tests/unit_tests/utils/test_function_calling.py +++ b/libs/core/tests/unit_tests/utils/test_function_calling.py @@ -34,8 +34,9 @@ except ImportError: TypingAnnotated = ExtensionsAnnotated +from pydantic import BaseModel, Field + from langchain_core.messages import AIMessage, HumanMessage, ToolMessage -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.runnables import Runnable, RunnableLambda from langchain_core.tools import BaseTool, tool from langchain_core.utils.function_calling import ( @@ -450,17 +451,17 @@ def test_multiple_tool_calls() -> None: { "id": messages[2].tool_call_id, "type": "function", - "function": {"name": "FakeCall", "arguments": '{"data": "ToolCall1"}'}, + "function": {"name": "FakeCall", "arguments": '{"data":"ToolCall1"}'}, }, { "id": messages[3].tool_call_id, "type": "function", - "function": {"name": "FakeCall", "arguments": '{"data": "ToolCall2"}'}, + "function": {"name": "FakeCall", "arguments": '{"data":"ToolCall2"}'}, }, { "id": messages[4].tool_call_id, "type": "function", - "function": {"name": "FakeCall", "arguments": '{"data": "ToolCall3"}'}, + "function": {"name": "FakeCall", "arguments": '{"data":"ToolCall3"}'}, }, ] @@ -481,7 +482,7 @@ def test_tool_outputs() -> None: { "id": messages[2].tool_call_id, "type": "function", - "function": {"name": "FakeCall", "arguments": '{"data": "ToolCall1"}'}, + "function": {"name": "FakeCall", "arguments": '{"data":"ToolCall1"}'}, }, ] assert messages[2].content == "Output1" @@ -737,8 +738,9 @@ class Tool(TypedDict): @pytest.mark.parametrize("typed_dict", [ExtensionsTypedDict, TypingTypedDict]) def test__convert_typed_dict_to_openai_function_fail(typed_dict: Type) -> None: class Tool(typed_dict): - arg1: MutableSet # Pydantic doesn't support + arg1: MutableSet # Pydantic 2 supports this, but pydantic v1 does not. + # Error should be raised since we're using v1 code path here with pytest.raises(TypeError): _convert_typed_dict_to_openai_function(Tool) diff --git a/libs/core/tests/unit_tests/utils/test_pydantic.py b/libs/core/tests/unit_tests/utils/test_pydantic.py index 3517d5c164081..5bba5e8116732 100644 --- a/libs/core/tests/unit_tests/utils/test_pydantic.py +++ b/libs/core/tests/unit_tests/utils/test_pydantic.py @@ -3,6 +3,7 @@ from typing import Any, Dict, List, Optional import pytest +from pydantic import ConfigDict from langchain_core.utils.pydantic import ( PYDANTIC_MAJOR_VERSION, @@ -11,11 +12,12 @@ is_basemodel_instance, is_basemodel_subclass, pre_init, + v1_repr, ) def test_pre_init_decorator() -> None: - from langchain_core.pydantic_v1 import BaseModel + from pydantic import BaseModel class Foo(BaseModel): x: int = 5 @@ -34,7 +36,7 @@ def validator(cls, v: Dict[str, Any]) -> Dict[str, Any]: def test_pre_init_decorator_with_more_defaults() -> None: - from langchain_core.pydantic_v1 import BaseModel, Field + from pydantic import BaseModel, Field class Foo(BaseModel): a: int = 1 @@ -56,14 +58,15 @@ def validator(cls, v: Dict[str, Any]) -> Dict[str, Any]: def test_with_aliases() -> None: - from langchain_core.pydantic_v1 import BaseModel, Field + from pydantic import BaseModel, Field class Foo(BaseModel): x: int = Field(default=1, alias="y") z: int - class Config: - allow_population_by_field_name = True + model_config = ConfigDict( + populate_by_name=True, + ) @pre_init def validator(cls, v: Dict[str, Any]) -> Dict[str, Any]: @@ -92,12 +95,12 @@ def validator(cls, v: Dict[str, Any]) -> Dict[str, Any]: def test_is_basemodel_subclass() -> None: """Test pydantic.""" if PYDANTIC_MAJOR_VERSION == 1: - from pydantic import BaseModel as BaseModelV1Proper # pydantic: ignore + from pydantic import BaseModel as BaseModelV1Proper assert is_basemodel_subclass(BaseModelV1Proper) elif PYDANTIC_MAJOR_VERSION == 2: - from pydantic import BaseModel as BaseModelV2 # pydantic: ignore - from pydantic.v1 import BaseModel as BaseModelV1 # pydantic: ignore + from pydantic import BaseModel as BaseModelV2 + from pydantic.v1 import BaseModel as BaseModelV1 assert is_basemodel_subclass(BaseModelV2) @@ -109,15 +112,15 @@ def test_is_basemodel_subclass() -> None: def test_is_basemodel_instance() -> None: """Test pydantic.""" if PYDANTIC_MAJOR_VERSION == 1: - from pydantic import BaseModel as BaseModelV1Proper # pydantic: ignore + from pydantic import BaseModel as BaseModelV1Proper class FooV1(BaseModelV1Proper): x: int assert is_basemodel_instance(FooV1(x=5)) elif PYDANTIC_MAJOR_VERSION == 2: - from pydantic import BaseModel as BaseModelV2 # pydantic: ignore - from pydantic.v1 import BaseModel as BaseModelV1 # pydantic: ignore + from pydantic import BaseModel as BaseModelV2 + from pydantic.v1 import BaseModel as BaseModelV1 class Foo(BaseModelV2): x: int @@ -135,8 +138,8 @@ class Bar(BaseModelV1): @pytest.mark.skipif(PYDANTIC_MAJOR_VERSION != 2, reason="Only tests Pydantic v2") def test_with_field_metadata() -> None: """Test pydantic with field metadata""" - from pydantic import BaseModel as BaseModelV2 # pydantic: ignore - from pydantic import Field as FieldV2 # pydantic: ignore + from pydantic import BaseModel as BaseModelV2 + from pydantic import Field as FieldV2 class Foo(BaseModelV2): x: List[int] = FieldV2( @@ -163,7 +166,7 @@ class Foo(BaseModelV2): @pytest.mark.skipif(PYDANTIC_MAJOR_VERSION != 1, reason="Only tests Pydantic v1") def test_fields_pydantic_v1() -> None: - from pydantic import BaseModel # pydantic: ignore + from pydantic import BaseModel class Foo(BaseModel): x: int @@ -174,7 +177,7 @@ class Foo(BaseModel): @pytest.mark.skipif(PYDANTIC_MAJOR_VERSION != 2, reason="Only tests Pydantic v2") def test_fields_pydantic_v2_proper() -> None: - from pydantic import BaseModel # pydantic: ignore + from pydantic import BaseModel class Foo(BaseModel): x: int @@ -185,10 +188,24 @@ class Foo(BaseModel): @pytest.mark.skipif(PYDANTIC_MAJOR_VERSION != 2, reason="Only tests Pydantic v2") def test_fields_pydantic_v1_from_2() -> None: - from pydantic.v1 import BaseModel # pydantic: ignore + from pydantic.v1 import BaseModel class Foo(BaseModel): x: int fields = get_fields(Foo) assert fields == {"x": Foo.__fields__["x"]} + + +def test_v1_repr() -> None: + from pydantic import BaseModel, Field + + class Foo(BaseModel): + bar: int = Field(default=1, alias="baz", exclude=True) + x: int = Field(default=2) + + def __repr__(self) -> str: + """Custom repr.""" + return v1_repr(self) + + assert repr(Foo(bar=2, x=3)) == "Foo(x=3)" # type: ignore[call-arg] diff --git a/libs/core/tests/unit_tests/utils/test_utils.py b/libs/core/tests/unit_tests/utils/test_utils.py index 419e6309fd9e8..5c435512c960e 100644 --- a/libs/core/tests/unit_tests/utils/test_utils.py +++ b/libs/core/tests/unit_tests/utils/test_utils.py @@ -6,9 +6,9 @@ from unittest.mock import patch import pytest +from pydantic import SecretStr from langchain_core import utils -from langchain_core.pydantic_v1 import SecretStr from langchain_core.utils import ( check_package_version, from_env, @@ -221,8 +221,8 @@ def test_guard_import_failure( @pytest.mark.skipif(PYDANTIC_MAJOR_VERSION != 2, reason="Requires pydantic 2") def test_get_pydantic_field_names_v1_in_2() -> None: - from pydantic.v1 import BaseModel as PydanticV1BaseModel # pydantic: ignore - from pydantic.v1 import Field # pydantic: ignore + from pydantic.v1 import BaseModel as PydanticV1BaseModel + from pydantic.v1 import Field class PydanticV1Model(PydanticV1BaseModel): field1: str @@ -236,7 +236,7 @@ class PydanticV1Model(PydanticV1BaseModel): @pytest.mark.skipif(PYDANTIC_MAJOR_VERSION != 2, reason="Requires pydantic 2") def test_get_pydantic_field_names_v2_in_2() -> None: - from pydantic import BaseModel, Field # pydantic: ignore + from pydantic import BaseModel, Field class PydanticModel(BaseModel): field1: str @@ -250,7 +250,7 @@ class PydanticModel(BaseModel): @pytest.mark.skipif(PYDANTIC_MAJOR_VERSION != 1, reason="Requires pydantic 1") def test_get_pydantic_field_names_v1() -> None: - from pydantic import BaseModel, Field # pydantic: ignore + from pydantic import BaseModel, Field class PydanticModel(BaseModel): field1: str @@ -365,7 +365,7 @@ def test_secret_from_env_with_custom_error_message( def test_using_secret_from_env_as_default_factory( monkeypatch: pytest.MonkeyPatch, ) -> None: - from langchain_core.pydantic_v1 import BaseModel, Field + from pydantic import BaseModel, Field class Foo(BaseModel): secret: SecretStr = Field(default_factory=secret_from_env("TEST_KEY")) diff --git a/libs/core/tests/unit_tests/vectorstores/test_in_memory.py b/libs/core/tests/unit_tests/vectorstores/test_in_memory.py index 8d30afac3caf4..c0f9944d07789 100644 --- a/libs/core/tests/unit_tests/vectorstores/test_in_memory.py +++ b/libs/core/tests/unit_tests/vectorstores/test_in_memory.py @@ -10,7 +10,7 @@ from langchain_core.documents import Document from langchain_core.embeddings.fake import DeterministicFakeEmbedding from langchain_core.vectorstores import InMemoryVectorStore -from tests.unit_tests.stubs import AnyStr, _AnyIdDocument +from tests.unit_tests.stubs import _AnyIdDocument class TestInMemoryReadWriteTestSuite(ReadWriteTestSuite): @@ -117,7 +117,7 @@ async def test_inmemory_filter() -> None: # Check sync version output = store.similarity_search("fee", filter=lambda doc: doc.metadata["id"] == 1) - assert output == [Document(page_content="foo", metadata={"id": 1}, id=AnyStr())] + assert output == [_AnyIdDocument(page_content="foo", metadata={"id": 1})] # filter with not stored document id output = await store.asimilarity_search( diff --git a/libs/langchain/Makefile b/libs/langchain/Makefile index c77591f6430d6..e06cd2e65d1a9 100644 --- a/libs/langchain/Makefile +++ b/libs/langchain/Makefile @@ -54,7 +54,6 @@ lint_tests: PYTHON_FILES=tests lint_tests: MYPY_CACHE=.mypy_cache_test lint lint_diff lint_package lint_tests: - ./scripts/check_pydantic.sh . ./scripts/lint_imports.sh [ "$(PYTHON_FILES)" = "" ] || poetry run ruff check $(PYTHON_FILES) [ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) --diff diff --git a/libs/langchain/langchain/agents/agent.py b/libs/langchain/langchain/agents/agent.py index 50797228a53b8..5fae509f343db 100644 --- a/libs/langchain/langchain/agents/agent.py +++ b/libs/langchain/langchain/agents/agent.py @@ -40,11 +40,12 @@ from langchain_core.prompts import BasePromptTemplate from langchain_core.prompts.few_shot import FewShotPromptTemplate from langchain_core.prompts.prompt import PromptTemplate -from langchain_core.pydantic_v1 import BaseModel, root_validator from langchain_core.runnables import Runnable, RunnableConfig, ensure_config from langchain_core.runnables.utils import AddableDict from langchain_core.tools import BaseTool from langchain_core.utils.input import get_color_mapping +from pydantic import BaseModel, ConfigDict, model_validator +from typing_extensions import Self from langchain.agents.agent_iterator import AgentExecutorIterator from langchain.agents.agent_types import AgentType @@ -420,8 +421,9 @@ class RunnableAgent(BaseSingleActionAgent): individual LLM tokens will not be available in stream_log. """ - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) @property def return_values(self) -> List[str]: @@ -528,8 +530,9 @@ class RunnableMultiActionAgent(BaseMultiActionAgent): individual LLM tokens will not be available in stream_log. """ - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) @property def return_values(self) -> List[str]: @@ -854,8 +857,8 @@ def input_keys(self) -> List[str]: """ return list(set(self.llm_chain.input_keys) - {"agent_scratchpad"}) - @root_validator(pre=False, skip_on_failure=True) - def validate_prompt(cls, values: Dict) -> Dict: + @model_validator(mode="after") + def validate_prompt(self) -> Self: """Validate that prompt matches format. Args: @@ -868,7 +871,7 @@ def validate_prompt(cls, values: Dict) -> Dict: ValueError: If `agent_scratchpad` is not in prompt.input_variables and prompt is not a FewShotPromptTemplate or a PromptTemplate. """ - prompt = values["llm_chain"].prompt + prompt = self.llm_chain.prompt if "agent_scratchpad" not in prompt.input_variables: logger.warning( "`agent_scratchpad` should be a variable in prompt.input_variables." @@ -881,7 +884,7 @@ def validate_prompt(cls, values: Dict) -> Dict: prompt.suffix += "\n{agent_scratchpad}" else: raise ValueError(f"Got unexpected prompt type {type(prompt)}") - return values + return self @property @abstractmethod @@ -1120,8 +1123,8 @@ def from_agent_and_tools( **kwargs, ) - @root_validator(pre=False, skip_on_failure=True) - def validate_tools(cls, values: Dict) -> Dict: + @model_validator(mode="after") + def validate_tools(self) -> Self: """Validate that tools are compatible with agent. Args: @@ -1133,19 +1136,20 @@ def validate_tools(cls, values: Dict) -> Dict: Raises: ValueError: If allowed tools are different than provided tools. """ - agent = values["agent"] - tools = values["tools"] - allowed_tools = agent.get_allowed_tools() + agent = self.agent + tools = self.tools + allowed_tools = agent.get_allowed_tools() # type: ignore if allowed_tools is not None: if set(allowed_tools) != set([tool.name for tool in tools]): raise ValueError( f"Allowed tools ({allowed_tools}) different than " f"provided tools ({[tool.name for tool in tools]})" ) - return values + return self - @root_validator(pre=True) - def validate_runnable_agent(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_runnable_agent(cls, values: Dict) -> Any: """Convert runnable to agent if passed in. Args: diff --git a/libs/langchain/langchain/agents/agent_toolkits/vectorstore/toolkit.py b/libs/langchain/langchain/agents/agent_toolkits/vectorstore/toolkit.py index c40b3f3b7f289..71114a49eea37 100644 --- a/libs/langchain/langchain/agents/agent_toolkits/vectorstore/toolkit.py +++ b/libs/langchain/langchain/agents/agent_toolkits/vectorstore/toolkit.py @@ -3,10 +3,10 @@ from typing import List from langchain_core.language_models import BaseLanguageModel -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.tools import BaseTool from langchain_core.tools.base import BaseToolkit from langchain_core.vectorstores import VectorStore +from pydantic import BaseModel, ConfigDict, Field class VectorStoreInfo(BaseModel): @@ -16,8 +16,9 @@ class VectorStoreInfo(BaseModel): name: str description: str - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) class VectorStoreToolkit(BaseToolkit): @@ -26,8 +27,9 @@ class VectorStoreToolkit(BaseToolkit): vectorstore_info: VectorStoreInfo = Field(exclude=True) llm: BaseLanguageModel - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) def get_tools(self) -> List[BaseTool]: """Get the tools in the toolkit.""" @@ -67,8 +69,9 @@ class VectorStoreRouterToolkit(BaseToolkit): vectorstores: List[VectorStoreInfo] = Field(exclude=True) llm: BaseLanguageModel - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) def get_tools(self) -> List[BaseTool]: """Get the tools in the toolkit.""" diff --git a/libs/langchain/langchain/agents/chat/base.py b/libs/langchain/langchain/agents/chat/base.py index a7a16be772611..00ced776f1342 100644 --- a/libs/langchain/langchain/agents/chat/base.py +++ b/libs/langchain/langchain/agents/chat/base.py @@ -10,8 +10,8 @@ HumanMessagePromptTemplate, SystemMessagePromptTemplate, ) -from langchain_core.pydantic_v1 import Field from langchain_core.tools import BaseTool +from pydantic import Field from langchain.agents.agent import Agent, AgentOutputParser from langchain.agents.chat.output_parser import ChatOutputParser diff --git a/libs/langchain/langchain/agents/conversational/base.py b/libs/langchain/langchain/agents/conversational/base.py index bbbf666e5903d..a0ef85946abd3 100644 --- a/libs/langchain/langchain/agents/conversational/base.py +++ b/libs/langchain/langchain/agents/conversational/base.py @@ -8,8 +8,8 @@ from langchain_core.callbacks import BaseCallbackManager from langchain_core.language_models import BaseLanguageModel from langchain_core.prompts import PromptTemplate -from langchain_core.pydantic_v1 import Field from langchain_core.tools import BaseTool +from pydantic import Field from langchain.agents.agent import Agent, AgentOutputParser from langchain.agents.agent_types import AgentType diff --git a/libs/langchain/langchain/agents/conversational_chat/base.py b/libs/langchain/langchain/agents/conversational_chat/base.py index 08ec829613a53..138933addbaec 100644 --- a/libs/langchain/langchain/agents/conversational_chat/base.py +++ b/libs/langchain/langchain/agents/conversational_chat/base.py @@ -17,8 +17,8 @@ MessagesPlaceholder, SystemMessagePromptTemplate, ) -from langchain_core.pydantic_v1 import Field from langchain_core.tools import BaseTool +from pydantic import Field from langchain.agents.agent import Agent, AgentOutputParser from langchain.agents.conversational_chat.output_parser import ConvoOutputParser diff --git a/libs/langchain/langchain/agents/mrkl/base.py b/libs/langchain/langchain/agents/mrkl/base.py index 1b16d4d862f6f..cc4d9da5537d7 100644 --- a/libs/langchain/langchain/agents/mrkl/base.py +++ b/libs/langchain/langchain/agents/mrkl/base.py @@ -8,9 +8,9 @@ from langchain_core.callbacks import BaseCallbackManager from langchain_core.language_models import BaseLanguageModel from langchain_core.prompts import PromptTemplate -from langchain_core.pydantic_v1 import Field from langchain_core.tools import BaseTool, Tool from langchain_core.tools.render import render_text_description +from pydantic import Field from langchain.agents.agent import Agent, AgentExecutor, AgentOutputParser from langchain.agents.agent_types import AgentType diff --git a/libs/langchain/langchain/agents/openai_assistant/base.py b/libs/langchain/langchain/agents/openai_assistant/base.py index a2f0ef74a5bff..e7d4d2bfa9c9b 100644 --- a/libs/langchain/langchain/agents/openai_assistant/base.py +++ b/libs/langchain/langchain/agents/openai_assistant/base.py @@ -20,10 +20,11 @@ from langchain_core.agents import AgentAction, AgentFinish from langchain_core.callbacks import CallbackManager from langchain_core.load import dumpd -from langchain_core.pydantic_v1 import BaseModel, Field, root_validator from langchain_core.runnables import RunnableConfig, RunnableSerializable, ensure_config from langchain_core.tools import BaseTool from langchain_core.utils.function_calling import convert_to_openai_tool +from pydantic import BaseModel, Field, model_validator +from typing_extensions import Self if TYPE_CHECKING: import openai @@ -232,14 +233,14 @@ def execute_agent(agent, tools, input): as_agent: bool = False """Use as a LangChain agent, compatible with the AgentExecutor.""" - @root_validator(pre=False, skip_on_failure=True) - def validate_async_client(cls, values: dict) -> dict: - if values["async_client"] is None: + @model_validator(mode="after") + def validate_async_client(self) -> Self: + if self.async_client is None: import openai - api_key = values["client"].api_key - values["async_client"] = openai.AsyncOpenAI(api_key=api_key) - return values + api_key = self.client.api_key + self.async_client = openai.AsyncOpenAI(api_key=api_key) + return self @classmethod def create_assistant( diff --git a/libs/langchain/langchain/agents/openai_functions_agent/base.py b/libs/langchain/langchain/agents/openai_functions_agent/base.py index fbb23a56e4785..5a40a665e3310 100644 --- a/libs/langchain/langchain/agents/openai_functions_agent/base.py +++ b/libs/langchain/langchain/agents/openai_functions_agent/base.py @@ -17,10 +17,11 @@ HumanMessagePromptTemplate, MessagesPlaceholder, ) -from langchain_core.pydantic_v1 import root_validator from langchain_core.runnables import Runnable, RunnablePassthrough from langchain_core.tools import BaseTool from langchain_core.utils.function_calling import convert_to_openai_function +from pydantic import model_validator +from typing_extensions import Self from langchain.agents import BaseSingleActionAgent from langchain.agents.format_scratchpad.openai_functions import ( @@ -58,8 +59,8 @@ def get_allowed_tools(self) -> List[str]: """Get allowed tools.""" return [t.name for t in self.tools] - @root_validator(pre=False, skip_on_failure=True) - def validate_prompt(cls, values: dict) -> dict: + @model_validator(mode="after") + def validate_prompt(self) -> Self: """Validate prompt. Args: @@ -71,13 +72,13 @@ def validate_prompt(cls, values: dict) -> dict: Raises: ValueError: If `agent_scratchpad` is not in the prompt. """ - prompt: BasePromptTemplate = values["prompt"] + prompt: BasePromptTemplate = self.prompt if "agent_scratchpad" not in prompt.input_variables: raise ValueError( "`agent_scratchpad` should be one of the variables in the prompt, " f"got {prompt.input_variables}" ) - return values + return self @property def input_keys(self) -> List[str]: diff --git a/libs/langchain/langchain/agents/openai_functions_multi_agent/base.py b/libs/langchain/langchain/agents/openai_functions_multi_agent/base.py index 819fc8b1d939e..bec49e5f14d24 100644 --- a/libs/langchain/langchain/agents/openai_functions_multi_agent/base.py +++ b/libs/langchain/langchain/agents/openai_functions_multi_agent/base.py @@ -21,8 +21,9 @@ HumanMessagePromptTemplate, MessagesPlaceholder, ) -from langchain_core.pydantic_v1 import root_validator from langchain_core.tools import BaseTool +from pydantic import model_validator +from typing_extensions import Self from langchain.agents import BaseMultiActionAgent from langchain.agents.format_scratchpad.openai_functions import ( @@ -115,15 +116,15 @@ def get_allowed_tools(self) -> List[str]: """Get allowed tools.""" return [t.name for t in self.tools] - @root_validator(pre=False, skip_on_failure=True) - def validate_prompt(cls, values: dict) -> dict: - prompt: BasePromptTemplate = values["prompt"] + @model_validator(mode="after") + def validate_prompt(self) -> Self: + prompt: BasePromptTemplate = self.prompt if "agent_scratchpad" not in prompt.input_variables: raise ValueError( "`agent_scratchpad` should be one of the variables in the prompt, " f"got {prompt.input_variables}" ) - return values + return self @property def input_keys(self) -> List[str]: diff --git a/libs/langchain/langchain/agents/react/base.py b/libs/langchain/langchain/agents/react/base.py index 93a60bbe61ba2..81a38141fe686 100644 --- a/libs/langchain/langchain/agents/react/base.py +++ b/libs/langchain/langchain/agents/react/base.py @@ -8,8 +8,8 @@ from langchain_core.documents import Document from langchain_core.language_models import BaseLanguageModel from langchain_core.prompts import BasePromptTemplate -from langchain_core.pydantic_v1 import Field from langchain_core.tools import BaseTool, Tool +from pydantic import Field from langchain.agents.agent import Agent, AgentExecutor, AgentOutputParser from langchain.agents.agent_types import AgentType diff --git a/libs/langchain/langchain/agents/self_ask_with_search/base.py b/libs/langchain/langchain/agents/self_ask_with_search/base.py index 7a1d81f7d40d1..9a642b81b1289 100644 --- a/libs/langchain/langchain/agents/self_ask_with_search/base.py +++ b/libs/langchain/langchain/agents/self_ask_with_search/base.py @@ -7,9 +7,9 @@ from langchain_core._api import deprecated from langchain_core.language_models import BaseLanguageModel from langchain_core.prompts import BasePromptTemplate -from langchain_core.pydantic_v1 import Field from langchain_core.runnables import Runnable, RunnablePassthrough from langchain_core.tools import BaseTool, Tool +from pydantic import Field from langchain.agents.agent import Agent, AgentExecutor, AgentOutputParser from langchain.agents.agent_types import AgentType diff --git a/libs/langchain/langchain/agents/structured_chat/base.py b/libs/langchain/langchain/agents/structured_chat/base.py index e1403e26f2607..a520cfecf7142 100644 --- a/libs/langchain/langchain/agents/structured_chat/base.py +++ b/libs/langchain/langchain/agents/structured_chat/base.py @@ -11,10 +11,10 @@ HumanMessagePromptTemplate, SystemMessagePromptTemplate, ) -from langchain_core.pydantic_v1 import Field from langchain_core.runnables import Runnable, RunnablePassthrough from langchain_core.tools import BaseTool from langchain_core.tools.render import ToolsRenderer +from pydantic import Field from langchain.agents.agent import Agent, AgentOutputParser from langchain.agents.format_scratchpad import format_log_to_str diff --git a/libs/langchain/langchain/agents/structured_chat/output_parser.py b/libs/langchain/langchain/agents/structured_chat/output_parser.py index f73f87f4f4be4..1cdb4fe1fb4a9 100644 --- a/libs/langchain/langchain/agents/structured_chat/output_parser.py +++ b/libs/langchain/langchain/agents/structured_chat/output_parser.py @@ -8,7 +8,7 @@ from langchain_core.agents import AgentAction, AgentFinish from langchain_core.exceptions import OutputParserException from langchain_core.language_models import BaseLanguageModel -from langchain_core.pydantic_v1 import Field +from pydantic import Field from langchain.agents.agent import AgentOutputParser from langchain.agents.structured_chat.prompt import FORMAT_INSTRUCTIONS diff --git a/libs/langchain/langchain/chains/api/base.py b/libs/langchain/langchain/chains/api/base.py index 94896102dc6fd..4387cb623e06a 100644 --- a/libs/langchain/langchain/chains/api/base.py +++ b/libs/langchain/langchain/chains/api/base.py @@ -12,7 +12,8 @@ ) from langchain_core.language_models import BaseLanguageModel from langchain_core.prompts import BasePromptTemplate -from langchain_core.pydantic_v1 import Field, root_validator +from pydantic import Field, model_validator +from typing_extensions import Self from langchain.chains.api.prompt import API_RESPONSE_PROMPT, API_URL_PROMPT from langchain.chains.base import Chain @@ -227,19 +228,20 @@ def output_keys(self) -> List[str]: """ return [self.output_key] - @root_validator(pre=False, skip_on_failure=True) - def validate_api_request_prompt(cls, values: Dict) -> Dict: + @model_validator(mode="after") + def validate_api_request_prompt(self) -> Self: """Check that api request prompt expects the right variables.""" - input_vars = values["api_request_chain"].prompt.input_variables + input_vars = self.api_request_chain.prompt.input_variables expected_vars = {"question", "api_docs"} if set(input_vars) != expected_vars: raise ValueError( f"Input variables should be {expected_vars}, got {input_vars}" ) - return values + return self - @root_validator(pre=True) - def validate_limit_to_domains(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_limit_to_domains(cls, values: Dict) -> Any: """Check that allowed domains are valid.""" # This check must be a pre=True check, so that a default of None # won't be set to limit_to_domains if it's not provided. @@ -258,16 +260,16 @@ def validate_limit_to_domains(cls, values: Dict) -> Dict: ) return values - @root_validator(pre=False, skip_on_failure=True) - def validate_api_answer_prompt(cls, values: Dict) -> Dict: + @model_validator(mode="after") + def validate_api_answer_prompt(self) -> Self: """Check that api answer prompt expects the right variables.""" - input_vars = values["api_answer_chain"].prompt.input_variables + input_vars = self.api_answer_chain.prompt.input_variables expected_vars = {"question", "api_docs", "api_url", "api_response"} if set(input_vars) != expected_vars: raise ValueError( f"Input variables should be {expected_vars}, got {input_vars}" ) - return values + return self def _call( self, diff --git a/libs/langchain/langchain/chains/base.py b/libs/langchain/langchain/chains/base.py index 4c1dcc07eb231..a2d9ca57ddc79 100644 --- a/libs/langchain/langchain/chains/base.py +++ b/libs/langchain/langchain/chains/base.py @@ -21,7 +21,6 @@ from langchain_core.load.dump import dumpd from langchain_core.memory import BaseMemory from langchain_core.outputs import RunInfo -from langchain_core.pydantic_v1 import BaseModel, Field, root_validator, validator from langchain_core.runnables import ( RunnableConfig, RunnableSerializable, @@ -29,6 +28,13 @@ run_in_executor, ) from langchain_core.runnables.utils import create_model +from pydantic import ( + BaseModel, + ConfigDict, + Field, + model_validator, + validator, +) from langchain.schema import RUN_KEY @@ -96,8 +102,9 @@ class Chain(RunnableSerializable[Dict[str, Any], Dict[str, Any]], ABC): callback_manager: Optional[BaseCallbackManager] = Field(default=None, exclude=True) """[DEPRECATED] Use `callbacks` instead.""" - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) def get_input_schema( self, config: Optional[RunnableConfig] = None @@ -223,8 +230,9 @@ async def ainvoke( def _chain_type(self) -> str: raise NotImplementedError("Saving not supported for this chain type.") - @root_validator(pre=True) - def raise_callback_manager_deprecation(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def raise_callback_manager_deprecation(cls, values: Dict) -> Any: """Raise deprecation warning if callback_manager is used.""" if values.get("callback_manager") is not None: if values.get("callbacks") is not None: diff --git a/libs/langchain/langchain/chains/combine_documents/base.py b/libs/langchain/langchain/chains/combine_documents/base.py index 00b6002da0ed4..2406cd4215f7a 100644 --- a/libs/langchain/langchain/chains/combine_documents/base.py +++ b/libs/langchain/langchain/chains/combine_documents/base.py @@ -10,10 +10,10 @@ ) from langchain_core.documents import Document from langchain_core.prompts import BasePromptTemplate, PromptTemplate -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.runnables.config import RunnableConfig from langchain_core.runnables.utils import create_model from langchain_text_splitters import RecursiveCharacterTextSplitter, TextSplitter +from pydantic import BaseModel, Field from langchain.chains.base import Chain diff --git a/libs/langchain/langchain/chains/combine_documents/map_reduce.py b/libs/langchain/langchain/chains/combine_documents/map_reduce.py index 229ed45740c5d..7b4885cdea4fe 100644 --- a/libs/langchain/langchain/chains/combine_documents/map_reduce.py +++ b/libs/langchain/langchain/chains/combine_documents/map_reduce.py @@ -6,9 +6,9 @@ from langchain_core.callbacks import Callbacks from langchain_core.documents import Document -from langchain_core.pydantic_v1 import BaseModel, root_validator from langchain_core.runnables.config import RunnableConfig from langchain_core.runnables.utils import create_model +from pydantic import BaseModel, ConfigDict, model_validator from langchain.chains.combine_documents.base import BaseCombineDocumentsChain from langchain.chains.combine_documents.reduce import ReduceDocumentsChain @@ -126,12 +126,14 @@ def output_keys(self) -> List[str]: _output_keys = _output_keys + ["intermediate_steps"] return _output_keys - class Config: - arbitrary_types_allowed = True - extra = "forbid" + model_config = ConfigDict( + arbitrary_types_allowed=True, + extra="forbid", + ) - @root_validator(pre=True) - def get_reduce_chain(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def get_reduce_chain(cls, values: Dict) -> Any: """For backwards compatibility.""" if "combine_document_chain" in values: if "reduce_documents_chain" in values: @@ -153,16 +155,18 @@ def get_reduce_chain(cls, values: Dict) -> Dict: return values - @root_validator(pre=True) - def get_return_intermediate_steps(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def get_return_intermediate_steps(cls, values: Dict) -> Any: """For backwards compatibility.""" if "return_map_steps" in values: values["return_intermediate_steps"] = values["return_map_steps"] del values["return_map_steps"] return values - @root_validator(pre=True) - def get_default_document_variable_name(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def get_default_document_variable_name(cls, values: Dict) -> Any: """Get default document variable name, if not provided.""" if "llm_chain" not in values: raise ValueError("llm_chain must be provided") diff --git a/libs/langchain/langchain/chains/combine_documents/map_rerank.py b/libs/langchain/langchain/chains/combine_documents/map_rerank.py index 0fa346dee8bc8..61e6e226ed65d 100644 --- a/libs/langchain/langchain/chains/combine_documents/map_rerank.py +++ b/libs/langchain/langchain/chains/combine_documents/map_rerank.py @@ -6,9 +6,10 @@ from langchain_core.callbacks import Callbacks from langchain_core.documents import Document -from langchain_core.pydantic_v1 import BaseModel, root_validator from langchain_core.runnables.config import RunnableConfig from langchain_core.runnables.utils import create_model +from pydantic import BaseModel, ConfigDict, model_validator +from typing_extensions import Self from langchain.chains.combine_documents.base import BaseCombineDocumentsChain from langchain.chains.llm import LLMChain @@ -74,9 +75,10 @@ class MapRerankDocumentsChain(BaseCombineDocumentsChain): """Return intermediate steps. Intermediate steps include the results of calling llm_chain on each document.""" - class Config: - arbitrary_types_allowed = True - extra = "forbid" + model_config = ConfigDict( + arbitrary_types_allowed=True, + extra="forbid", + ) def get_output_schema( self, config: Optional[RunnableConfig] = None @@ -104,30 +106,31 @@ def output_keys(self) -> List[str]: _output_keys += self.metadata_keys return _output_keys - @root_validator(pre=False, skip_on_failure=True) - def validate_llm_output(cls, values: Dict) -> Dict: + @model_validator(mode="after") + def validate_llm_output(self) -> Self: """Validate that the combine chain outputs a dictionary.""" - output_parser = values["llm_chain"].prompt.output_parser + output_parser = self.llm_chain.prompt.output_parser if not isinstance(output_parser, RegexParser): raise ValueError( "Output parser of llm_chain should be a RegexParser," f" got {output_parser}" ) output_keys = output_parser.output_keys - if values["rank_key"] not in output_keys: + if self.rank_key not in output_keys: raise ValueError( - f"Got {values['rank_key']} as key to rank on, but did not find " + f"Got {self.rank_key} as key to rank on, but did not find " f"it in the llm_chain output keys ({output_keys})" ) - if values["answer_key"] not in output_keys: + if self.answer_key not in output_keys: raise ValueError( - f"Got {values['answer_key']} as key to return, but did not find " + f"Got {self.answer_key} as key to return, but did not find " f"it in the llm_chain output keys ({output_keys})" ) - return values + return self - @root_validator(pre=True) - def get_default_document_variable_name(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def get_default_document_variable_name(cls, values: Dict) -> Any: """Get default document variable name, if not provided.""" if "llm_chain" not in values: raise ValueError("llm_chain must be provided") diff --git a/libs/langchain/langchain/chains/combine_documents/reduce.py b/libs/langchain/langchain/chains/combine_documents/reduce.py index 7b2cd6c89a375..662be15e38b55 100644 --- a/libs/langchain/langchain/chains/combine_documents/reduce.py +++ b/libs/langchain/langchain/chains/combine_documents/reduce.py @@ -6,6 +6,7 @@ from langchain_core.callbacks import Callbacks from langchain_core.documents import Document +from pydantic import ConfigDict from langchain.chains.combine_documents.base import BaseCombineDocumentsChain @@ -204,9 +205,10 @@ class ReduceDocumentsChain(BaseCombineDocumentsChain): If None, it will keep trying to collapse documents to fit token_max. Otherwise, after it reaches the max number, it will throw an error""" - class Config: - arbitrary_types_allowed = True - extra = "forbid" + model_config = ConfigDict( + arbitrary_types_allowed=True, + extra="forbid", + ) @property def _collapse_chain(self) -> BaseCombineDocumentsChain: diff --git a/libs/langchain/langchain/chains/combine_documents/refine.py b/libs/langchain/langchain/chains/combine_documents/refine.py index cf2f5d9e92f50..7129ac147dbb1 100644 --- a/libs/langchain/langchain/chains/combine_documents/refine.py +++ b/libs/langchain/langchain/chains/combine_documents/refine.py @@ -8,7 +8,7 @@ from langchain_core.documents import Document from langchain_core.prompts import BasePromptTemplate, format_document from langchain_core.prompts.prompt import PromptTemplate -from langchain_core.pydantic_v1 import Field, root_validator +from pydantic import ConfigDict, Field, model_validator from langchain.chains.combine_documents.base import ( BaseCombineDocumentsChain, @@ -98,20 +98,23 @@ def output_keys(self) -> List[str]: _output_keys = _output_keys + ["intermediate_steps"] return _output_keys - class Config: - arbitrary_types_allowed = True - extra = "forbid" + model_config = ConfigDict( + arbitrary_types_allowed=True, + extra="forbid", + ) - @root_validator(pre=True) - def get_return_intermediate_steps(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def get_return_intermediate_steps(cls, values: Dict) -> Any: """For backwards compatibility.""" if "return_refine_steps" in values: values["return_intermediate_steps"] = values["return_refine_steps"] del values["return_refine_steps"] return values - @root_validator(pre=True) - def get_default_document_variable_name(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def get_default_document_variable_name(cls, values: Dict) -> Any: """Get default document variable name, if not provided.""" if "initial_llm_chain" not in values: raise ValueError("initial_llm_chain must be provided") diff --git a/libs/langchain/langchain/chains/combine_documents/stuff.py b/libs/langchain/langchain/chains/combine_documents/stuff.py index cdecec0f40b82..26750d55fe84f 100644 --- a/libs/langchain/langchain/chains/combine_documents/stuff.py +++ b/libs/langchain/langchain/chains/combine_documents/stuff.py @@ -8,8 +8,8 @@ from langchain_core.language_models import LanguageModelLike from langchain_core.output_parsers import BaseOutputParser, StrOutputParser from langchain_core.prompts import BasePromptTemplate, format_document -from langchain_core.pydantic_v1 import Field, root_validator from langchain_core.runnables import Runnable, RunnablePassthrough +from pydantic import ConfigDict, Field, model_validator from langchain.chains.combine_documents.base import ( DEFAULT_DOCUMENT_PROMPT, @@ -156,12 +156,14 @@ class StuffDocumentsChain(BaseCombineDocumentsChain): document_separator: str = "\n\n" """The string with which to join the formatted documents""" - class Config: - arbitrary_types_allowed = True - extra = "forbid" + model_config = ConfigDict( + arbitrary_types_allowed=True, + extra="forbid", + ) - @root_validator(pre=True) - def get_default_document_variable_name(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def get_default_document_variable_name(cls, values: Dict) -> Any: """Get default document variable name, if not provided. If only one variable is present in the llm_chain.prompt, diff --git a/libs/langchain/langchain/chains/constitutional_ai/models.py b/libs/langchain/langchain/chains/constitutional_ai/models.py index 8058553eb257a..7f9a623459913 100644 --- a/libs/langchain/langchain/chains/constitutional_ai/models.py +++ b/libs/langchain/langchain/chains/constitutional_ai/models.py @@ -1,6 +1,6 @@ """Models for the Constitutional AI chain.""" -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel class ConstitutionalPrinciple(BaseModel): diff --git a/libs/langchain/langchain/chains/conversation/base.py b/libs/langchain/langchain/chains/conversation/base.py index dee4b9dbc0cce..881eed49e8c61 100644 --- a/libs/langchain/langchain/chains/conversation/base.py +++ b/libs/langchain/langchain/chains/conversation/base.py @@ -1,11 +1,12 @@ """Chain that carries on a conversation and calls an LLM.""" -from typing import Dict, List +from typing import List from langchain_core._api import deprecated from langchain_core.memory import BaseMemory from langchain_core.prompts import BasePromptTemplate -from langchain_core.pydantic_v1 import Field, root_validator +from pydantic import ConfigDict, Field, model_validator +from typing_extensions import Self from langchain.chains.conversation.prompt import PROMPT from langchain.chains.llm import LLMChain @@ -110,9 +111,10 @@ def get_session_history(session_id: str) -> InMemoryChatMessageHistory: input_key: str = "input" #: :meta private: output_key: str = "response" #: :meta private: - class Config: - arbitrary_types_allowed = True - extra = "forbid" + model_config = ConfigDict( + arbitrary_types_allowed=True, + extra="forbid", + ) @classmethod def is_lc_serializable(cls) -> bool: @@ -123,17 +125,17 @@ def input_keys(self) -> List[str]: """Use this since so some prompt vars come from history.""" return [self.input_key] - @root_validator(pre=False, skip_on_failure=True) - def validate_prompt_input_variables(cls, values: Dict) -> Dict: + @model_validator(mode="after") + def validate_prompt_input_variables(self) -> Self: """Validate that prompt input variables are consistent.""" - memory_keys = values["memory"].memory_variables - input_key = values["input_key"] + memory_keys = self.memory.memory_variables + input_key = self.input_key if input_key in memory_keys: raise ValueError( f"The input key {input_key} was also found in the memory keys " f"({memory_keys}) - please provide keys that don't overlap." ) - prompt_variables = values["prompt"].input_variables + prompt_variables = self.prompt.input_variables expected_keys = memory_keys + [input_key] if set(expected_keys) != set(prompt_variables): raise ValueError( @@ -141,4 +143,4 @@ def validate_prompt_input_variables(cls, values: Dict) -> Dict: f"{prompt_variables}, but got {memory_keys} as inputs from " f"memory, and {input_key} as the normal input key." ) - return values + return self diff --git a/libs/langchain/langchain/chains/conversational_retrieval/base.py b/libs/langchain/langchain/chains/conversational_retrieval/base.py index 4251ced2fdf1a..3c653c433362e 100644 --- a/libs/langchain/langchain/chains/conversational_retrieval/base.py +++ b/libs/langchain/langchain/chains/conversational_retrieval/base.py @@ -18,10 +18,10 @@ from langchain_core.language_models import BaseLanguageModel from langchain_core.messages import BaseMessage from langchain_core.prompts import BasePromptTemplate -from langchain_core.pydantic_v1 import BaseModel, Field, root_validator from langchain_core.retrievers import BaseRetriever from langchain_core.runnables import RunnableConfig from langchain_core.vectorstores import VectorStore +from pydantic import BaseModel, ConfigDict, Field, model_validator from langchain.chains.base import Chain from langchain.chains.combine_documents.base import BaseCombineDocumentsChain @@ -96,10 +96,11 @@ class BaseConversationalRetrievalChain(Chain): """If specified, the chain will return a fixed response if no docs are found for the question. """ - class Config: - allow_population_by_field_name = True - arbitrary_types_allowed = True - extra = "forbid" + model_config = ConfigDict( + populate_by_name=True, + arbitrary_types_allowed=True, + extra="forbid", + ) @property def input_keys(self) -> List[str]: @@ -482,8 +483,9 @@ class ChatVectorDBChain(BaseConversationalRetrievalChain): def _chain_type(self) -> str: return "chat-vector-db" - @root_validator(pre=True) - def raise_deprecation(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def raise_deprecation(cls, values: Dict) -> Any: warnings.warn( "`ChatVectorDBChain` is deprecated - " "please use `from langchain.chains import ConversationalRetrievalChain`" diff --git a/libs/langchain/langchain/chains/elasticsearch_database/base.py b/libs/langchain/langchain/chains/elasticsearch_database/base.py index 89875f2d8a425..85bf7de93d29a 100644 --- a/libs/langchain/langchain/chains/elasticsearch_database/base.py +++ b/libs/langchain/langchain/chains/elasticsearch_database/base.py @@ -9,8 +9,9 @@ from langchain_core.output_parsers import BaseOutputParser, StrOutputParser from langchain_core.output_parsers.json import SimpleJsonOutputParser from langchain_core.prompts import BasePromptTemplate -from langchain_core.pydantic_v1 import root_validator from langchain_core.runnables import Runnable +from pydantic import ConfigDict, model_validator +from typing_extensions import Self from langchain.chains.base import Chain from langchain.chains.elasticsearch_database.prompts import ANSWER_PROMPT, DSL_PROMPT @@ -51,17 +52,18 @@ class ElasticsearchDatabaseChain(Chain): return_intermediate_steps: bool = False """Whether or not to return the intermediate steps along with the final answer.""" - class Config: - arbitrary_types_allowed = True - extra = "forbid" + model_config = ConfigDict( + arbitrary_types_allowed=True, + extra="forbid", + ) - @root_validator(pre=False, skip_on_failure=True) - def validate_indices(cls, values: dict) -> dict: - if values["include_indices"] and values["ignore_indices"]: + @model_validator(mode="after") + def validate_indices(self) -> Self: + if self.include_indices and self.ignore_indices: raise ValueError( "Cannot specify both 'include_indices' and 'ignore_indices'." ) - return values + return self @property def input_keys(self) -> List[str]: diff --git a/libs/langchain/langchain/chains/flare/base.py b/libs/langchain/langchain/chains/flare/base.py index 53f3dd1e40e82..caf64fe77aa40 100644 --- a/libs/langchain/langchain/chains/flare/base.py +++ b/libs/langchain/langchain/chains/flare/base.py @@ -11,9 +11,9 @@ from langchain_core.messages import AIMessage from langchain_core.output_parsers import StrOutputParser from langchain_core.prompts import BasePromptTemplate -from langchain_core.pydantic_v1 import Field from langchain_core.retrievers import BaseRetriever from langchain_core.runnables import Runnable +from pydantic import Field from langchain.chains.base import Chain from langchain.chains.flare.prompts import ( diff --git a/libs/langchain/langchain/chains/hyde/base.py b/libs/langchain/langchain/chains/hyde/base.py index 833999127b659..cf64b29cada46 100644 --- a/libs/langchain/langchain/chains/hyde/base.py +++ b/libs/langchain/langchain/chains/hyde/base.py @@ -14,6 +14,7 @@ from langchain_core.output_parsers import StrOutputParser from langchain_core.prompts import BasePromptTemplate from langchain_core.runnables import Runnable +from pydantic import ConfigDict from langchain.chains.base import Chain from langchain.chains.hyde.prompts import PROMPT_MAP @@ -29,9 +30,10 @@ class HypotheticalDocumentEmbedder(Chain, Embeddings): base_embeddings: Embeddings llm_chain: Runnable - class Config: - arbitrary_types_allowed = True - extra = "forbid" + model_config = ConfigDict( + arbitrary_types_allowed=True, + extra="forbid", + ) @property def input_keys(self) -> List[str]: diff --git a/libs/langchain/langchain/chains/llm.py b/libs/langchain/langchain/chains/llm.py index feb3468fc44e4..f69aad7838bde 100644 --- a/libs/langchain/langchain/chains/llm.py +++ b/libs/langchain/langchain/chains/llm.py @@ -23,7 +23,6 @@ from langchain_core.outputs import ChatGeneration, Generation, LLMResult from langchain_core.prompt_values import PromptValue from langchain_core.prompts import BasePromptTemplate, PromptTemplate -from langchain_core.pydantic_v1 import Field from langchain_core.runnables import ( Runnable, RunnableBinding, @@ -32,6 +31,7 @@ ) from langchain_core.runnables.configurable import DynamicRunnable from langchain_core.utils.input import get_colored_text +from pydantic import ConfigDict, Field from langchain.chains.base import Chain @@ -95,9 +95,10 @@ def is_lc_serializable(self) -> bool: If false, will return a bunch of extra information about the generation.""" llm_kwargs: dict = Field(default_factory=dict) - class Config: - arbitrary_types_allowed = True - extra = "forbid" + model_config = ConfigDict( + arbitrary_types_allowed=True, + extra="forbid", + ) @property def input_keys(self) -> List[str]: diff --git a/libs/langchain/langchain/chains/llm_checker/base.py b/libs/langchain/langchain/chains/llm_checker/base.py index ea2bc546a5791..bfff3d1b40dd8 100644 --- a/libs/langchain/langchain/chains/llm_checker/base.py +++ b/libs/langchain/langchain/chains/llm_checker/base.py @@ -9,7 +9,7 @@ from langchain_core.callbacks import CallbackManagerForChainRun from langchain_core.language_models import BaseLanguageModel from langchain_core.prompts import PromptTemplate -from langchain_core.pydantic_v1 import root_validator +from pydantic import ConfigDict, model_validator from langchain.chains.base import Chain from langchain.chains.llm import LLMChain @@ -100,12 +100,14 @@ class LLMCheckerChain(Chain): input_key: str = "query" #: :meta private: output_key: str = "result" #: :meta private: - class Config: - arbitrary_types_allowed = True - extra = "forbid" + model_config = ConfigDict( + arbitrary_types_allowed=True, + extra="forbid", + ) - @root_validator(pre=True) - def raise_deprecation(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def raise_deprecation(cls, values: Dict) -> Any: if "llm" in values: warnings.warn( "Directly instantiating an LLMCheckerChain with an llm is deprecated. " diff --git a/libs/langchain/langchain/chains/llm_math/base.py b/libs/langchain/langchain/chains/llm_math/base.py index e7fd89dcd542c..5bc51bf253e54 100644 --- a/libs/langchain/langchain/chains/llm_math/base.py +++ b/libs/langchain/langchain/chains/llm_math/base.py @@ -14,7 +14,7 @@ ) from langchain_core.language_models import BaseLanguageModel from langchain_core.prompts import BasePromptTemplate -from langchain_core.pydantic_v1 import root_validator +from pydantic import ConfigDict, model_validator from langchain.chains.base import Chain from langchain.chains.llm import LLMChain @@ -156,12 +156,14 @@ async def acall_model(state: ChainState, config: RunnableConfig): input_key: str = "question" #: :meta private: output_key: str = "answer" #: :meta private: - class Config: - arbitrary_types_allowed = True - extra = "forbid" + model_config = ConfigDict( + arbitrary_types_allowed=True, + extra="forbid", + ) - @root_validator(pre=True) - def raise_deprecation(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def raise_deprecation(cls, values: Dict) -> Any: try: import numexpr # noqa: F401 except ImportError: diff --git a/libs/langchain/langchain/chains/llm_summarization_checker/base.py b/libs/langchain/langchain/chains/llm_summarization_checker/base.py index f177f401529e4..c7d075dbf4405 100644 --- a/libs/langchain/langchain/chains/llm_summarization_checker/base.py +++ b/libs/langchain/langchain/chains/llm_summarization_checker/base.py @@ -10,7 +10,7 @@ from langchain_core.callbacks import CallbackManagerForChainRun from langchain_core.language_models import BaseLanguageModel from langchain_core.prompts.prompt import PromptTemplate -from langchain_core.pydantic_v1 import root_validator +from pydantic import ConfigDict, model_validator from langchain.chains.base import Chain from langchain.chains.llm import LLMChain @@ -105,12 +105,14 @@ class LLMSummarizationCheckerChain(Chain): max_checks: int = 2 """Maximum number of times to check the assertions. Default to double-checking.""" - class Config: - arbitrary_types_allowed = True - extra = "forbid" + model_config = ConfigDict( + arbitrary_types_allowed=True, + extra="forbid", + ) - @root_validator(pre=True) - def raise_deprecation(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def raise_deprecation(cls, values: Dict) -> Any: if "llm" in values: warnings.warn( "Directly instantiating an LLMSummarizationCheckerChain with an llm is " diff --git a/libs/langchain/langchain/chains/mapreduce.py b/libs/langchain/langchain/chains/mapreduce.py index 1eaccf67a850f..c153b7f9a6dae 100644 --- a/libs/langchain/langchain/chains/mapreduce.py +++ b/libs/langchain/langchain/chains/mapreduce.py @@ -14,6 +14,7 @@ from langchain_core.language_models import BaseLanguageModel from langchain_core.prompts import BasePromptTemplate from langchain_text_splitters import TextSplitter +from pydantic import ConfigDict from langchain.chains import ReduceDocumentsChain from langchain.chains.base import Chain @@ -77,9 +78,10 @@ def from_params( **kwargs, ) - class Config: - arbitrary_types_allowed = True - extra = "forbid" + model_config = ConfigDict( + arbitrary_types_allowed=True, + extra="forbid", + ) @property def input_keys(self) -> List[str]: diff --git a/libs/langchain/langchain/chains/moderation.py b/libs/langchain/langchain/chains/moderation.py index a4b3551491c45..670b4773e3d63 100644 --- a/libs/langchain/langchain/chains/moderation.py +++ b/libs/langchain/langchain/chains/moderation.py @@ -6,8 +6,8 @@ AsyncCallbackManagerForChainRun, CallbackManagerForChainRun, ) -from langchain_core.pydantic_v1 import Field, root_validator from langchain_core.utils import check_package_version, get_from_dict_or_env +from pydantic import Field, model_validator from langchain.chains.base import Chain @@ -40,8 +40,9 @@ class OpenAIModerationChain(Chain): openai_organization: Optional[str] = None openai_pre_1_0: bool = Field(default=None) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key and python package exists in environment.""" openai_api_key = get_from_dict_or_env( values, "openai_api_key", "OPENAI_API_KEY" diff --git a/libs/langchain/langchain/chains/natbot/base.py b/libs/langchain/langchain/chains/natbot/base.py index e92131ff35cad..aca7fca8a7d8b 100644 --- a/libs/langchain/langchain/chains/natbot/base.py +++ b/libs/langchain/langchain/chains/natbot/base.py @@ -9,8 +9,8 @@ from langchain_core.callbacks import CallbackManagerForChainRun from langchain_core.language_models import BaseLanguageModel from langchain_core.output_parsers import StrOutputParser -from langchain_core.pydantic_v1 import root_validator from langchain_core.runnables import Runnable +from pydantic import ConfigDict, model_validator from langchain.chains.base import Chain from langchain.chains.natbot.prompt import PROMPT @@ -59,12 +59,14 @@ class NatBotChain(Chain): previous_command: str = "" #: :meta private: output_key: str = "command" #: :meta private: - class Config: - arbitrary_types_allowed = True - extra = "forbid" + model_config = ConfigDict( + arbitrary_types_allowed=True, + extra="forbid", + ) - @root_validator(pre=True) - def raise_deprecation(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def raise_deprecation(cls, values: Dict) -> Any: if "llm" in values: warnings.warn( "Directly instantiating an NatBotChain with an llm is deprecated. " diff --git a/libs/langchain/langchain/chains/openai_functions/base.py b/libs/langchain/langchain/chains/openai_functions/base.py index 568d992de990f..729313ab6b4d8 100644 --- a/libs/langchain/langchain/chains/openai_functions/base.py +++ b/libs/langchain/langchain/chains/openai_functions/base.py @@ -19,11 +19,11 @@ PydanticAttrOutputFunctionsParser, ) from langchain_core.prompts import BasePromptTemplate -from langchain_core.pydantic_v1 import BaseModel from langchain_core.utils.function_calling import ( PYTHON_TO_JSON_TYPES, convert_to_openai_function, ) +from pydantic import BaseModel from langchain.chains import LLMChain from langchain.chains.structured_output.base import ( diff --git a/libs/langchain/langchain/chains/openai_functions/citation_fuzzy_match.py b/libs/langchain/langchain/chains/openai_functions/citation_fuzzy_match.py index 038489d13a696..e9a83e8abc6e8 100644 --- a/libs/langchain/langchain/chains/openai_functions/citation_fuzzy_match.py +++ b/libs/langchain/langchain/chains/openai_functions/citation_fuzzy_match.py @@ -5,8 +5,8 @@ from langchain_core.messages import HumanMessage, SystemMessage from langchain_core.output_parsers.openai_functions import PydanticOutputFunctionsParser from langchain_core.prompts.chat import ChatPromptTemplate, HumanMessagePromptTemplate -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.runnables import Runnable +from pydantic import BaseModel, Field from langchain.chains.llm import LLMChain from langchain.chains.openai_functions.utils import get_llm_kwargs diff --git a/libs/langchain/langchain/chains/openai_functions/extraction.py b/libs/langchain/langchain/chains/openai_functions/extraction.py index ec76ad8a6cbbe..f6b9debad4959 100644 --- a/libs/langchain/langchain/chains/openai_functions/extraction.py +++ b/libs/langchain/langchain/chains/openai_functions/extraction.py @@ -7,7 +7,7 @@ PydanticAttrOutputFunctionsParser, ) from langchain_core.prompts import BasePromptTemplate, ChatPromptTemplate -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel from langchain.chains.base import Chain from langchain.chains.llm import LLMChain diff --git a/libs/langchain/langchain/chains/openai_functions/qa_with_structure.py b/libs/langchain/langchain/chains/openai_functions/qa_with_structure.py index f13e2f9e522f5..5bab1c1ced60a 100644 --- a/libs/langchain/langchain/chains/openai_functions/qa_with_structure.py +++ b/libs/langchain/langchain/chains/openai_functions/qa_with_structure.py @@ -10,8 +10,8 @@ ) from langchain_core.prompts import PromptTemplate from langchain_core.prompts.chat import ChatPromptTemplate, HumanMessagePromptTemplate -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.utils.pydantic import is_basemodel_subclass +from pydantic import BaseModel, Field from langchain.chains.llm import LLMChain from langchain.chains.openai_functions.utils import get_llm_kwargs diff --git a/libs/langchain/langchain/chains/openai_tools/extraction.py b/libs/langchain/langchain/chains/openai_tools/extraction.py index 55251f5186784..189ef77d9d4d1 100644 --- a/libs/langchain/langchain/chains/openai_tools/extraction.py +++ b/libs/langchain/langchain/chains/openai_tools/extraction.py @@ -4,9 +4,9 @@ from langchain_core.language_models import BaseLanguageModel from langchain_core.output_parsers.openai_tools import PydanticToolsParser from langchain_core.prompts import ChatPromptTemplate -from langchain_core.pydantic_v1 import BaseModel from langchain_core.runnables import Runnable from langchain_core.utils.function_calling import convert_pydantic_to_openai_function +from pydantic import BaseModel _EXTRACTION_TEMPLATE = """Extract and save the relevant entities mentioned \ in the following passage together with their properties. diff --git a/libs/langchain/langchain/chains/prompt_selector.py b/libs/langchain/langchain/chains/prompt_selector.py index 453e2ea03577f..4014cdc1fbbbf 100644 --- a/libs/langchain/langchain/chains/prompt_selector.py +++ b/libs/langchain/langchain/chains/prompt_selector.py @@ -5,7 +5,7 @@ from langchain_core.language_models.chat_models import BaseChatModel from langchain_core.language_models.llms import BaseLLM from langchain_core.prompts import BasePromptTemplate -from langchain_core.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, Field class BasePromptSelector(BaseModel, ABC): diff --git a/libs/langchain/langchain/chains/qa_generation/base.py b/libs/langchain/langchain/chains/qa_generation/base.py index b66b8a5442599..a55c078610124 100644 --- a/libs/langchain/langchain/chains/qa_generation/base.py +++ b/libs/langchain/langchain/chains/qa_generation/base.py @@ -7,8 +7,8 @@ from langchain_core.callbacks import CallbackManagerForChainRun from langchain_core.language_models import BaseLanguageModel from langchain_core.prompts import BasePromptTemplate -from langchain_core.pydantic_v1 import Field from langchain_text_splitters import RecursiveCharacterTextSplitter, TextSplitter +from pydantic import Field from langchain.chains.base import Chain from langchain.chains.llm import LLMChain diff --git a/libs/langchain/langchain/chains/qa_with_sources/base.py b/libs/langchain/langchain/chains/qa_with_sources/base.py index aed2d57cf91e5..495f0ab79e78b 100644 --- a/libs/langchain/langchain/chains/qa_with_sources/base.py +++ b/libs/langchain/langchain/chains/qa_with_sources/base.py @@ -15,7 +15,7 @@ from langchain_core.documents import Document from langchain_core.language_models import BaseLanguageModel from langchain_core.prompts import BasePromptTemplate -from langchain_core.pydantic_v1 import root_validator +from pydantic import ConfigDict, model_validator from langchain.chains import ReduceDocumentsChain from langchain.chains.base import Chain @@ -97,9 +97,10 @@ def from_chain_type( ) return cls(combine_documents_chain=combine_documents_chain, **kwargs) - class Config: - arbitrary_types_allowed = True - extra = "forbid" + model_config = ConfigDict( + arbitrary_types_allowed=True, + extra="forbid", + ) @property def input_keys(self) -> List[str]: @@ -120,8 +121,9 @@ def output_keys(self) -> List[str]: _output_keys = _output_keys + ["source_documents"] return _output_keys - @root_validator(pre=True) - def validate_naming(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_naming(cls, values: Dict) -> Any: """Fix backwards compatibility in naming.""" if "combine_document_chain" in values: values["combine_documents_chain"] = values.pop("combine_document_chain") diff --git a/libs/langchain/langchain/chains/qa_with_sources/retrieval.py b/libs/langchain/langchain/chains/qa_with_sources/retrieval.py index f4a924c8dd162..95485b9fe0e3c 100644 --- a/libs/langchain/langchain/chains/qa_with_sources/retrieval.py +++ b/libs/langchain/langchain/chains/qa_with_sources/retrieval.py @@ -7,8 +7,8 @@ CallbackManagerForChainRun, ) from langchain_core.documents import Document -from langchain_core.pydantic_v1 import Field from langchain_core.retrievers import BaseRetriever +from pydantic import Field from langchain.chains.combine_documents.stuff import StuffDocumentsChain from langchain.chains.qa_with_sources.base import BaseQAWithSourcesChain diff --git a/libs/langchain/langchain/chains/qa_with_sources/vector_db.py b/libs/langchain/langchain/chains/qa_with_sources/vector_db.py index ca594994f3a6b..6330db38bc9b4 100644 --- a/libs/langchain/langchain/chains/qa_with_sources/vector_db.py +++ b/libs/langchain/langchain/chains/qa_with_sources/vector_db.py @@ -8,8 +8,8 @@ CallbackManagerForChainRun, ) from langchain_core.documents import Document -from langchain_core.pydantic_v1 import Field, root_validator from langchain_core.vectorstores import VectorStore +from pydantic import Field, model_validator from langchain.chains.combine_documents.stuff import StuffDocumentsChain from langchain.chains.qa_with_sources.base import BaseQAWithSourcesChain @@ -61,8 +61,9 @@ async def _aget_docs( ) -> List[Document]: raise NotImplementedError("VectorDBQAWithSourcesChain does not support async") - @root_validator(pre=True) - def raise_deprecation(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def raise_deprecation(cls, values: Dict) -> Any: warnings.warn( "`VectorDBQAWithSourcesChain` is deprecated - " "please use `from langchain.chains import RetrievalQAWithSourcesChain`" diff --git a/libs/langchain/langchain/chains/query_constructor/schema.py b/libs/langchain/langchain/chains/query_constructor/schema.py index 585addc7919a3..56103d9a9fede 100644 --- a/libs/langchain/langchain/chains/query_constructor/schema.py +++ b/libs/langchain/langchain/chains/query_constructor/schema.py @@ -1,4 +1,4 @@ -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel, ConfigDict class AttributeInfo(BaseModel): @@ -8,6 +8,7 @@ class AttributeInfo(BaseModel): description: str type: str - class Config: - arbitrary_types_allowed = True - frozen = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + frozen=True, + ) diff --git a/libs/langchain/langchain/chains/retrieval_qa/base.py b/libs/langchain/langchain/chains/retrieval_qa/base.py index 689dd8b0c217b..3d688474bd39a 100644 --- a/libs/langchain/langchain/chains/retrieval_qa/base.py +++ b/libs/langchain/langchain/chains/retrieval_qa/base.py @@ -16,9 +16,9 @@ from langchain_core.documents import Document from langchain_core.language_models import BaseLanguageModel from langchain_core.prompts import PromptTemplate -from langchain_core.pydantic_v1 import Field, root_validator from langchain_core.retrievers import BaseRetriever from langchain_core.vectorstores import VectorStore +from pydantic import ConfigDict, Field, model_validator from langchain.chains.base import Chain from langchain.chains.combine_documents.base import BaseCombineDocumentsChain @@ -47,10 +47,11 @@ class BaseRetrievalQA(Chain): return_source_documents: bool = False """Return the source documents or not.""" - class Config: - allow_population_by_field_name = True - arbitrary_types_allowed = True - extra = "forbid" + model_config = ConfigDict( + populate_by_name=True, + arbitrary_types_allowed=True, + extra="forbid", + ) @property def input_keys(self) -> List[str]: @@ -309,16 +310,18 @@ class VectorDBQA(BaseRetrievalQA): search_kwargs: Dict[str, Any] = Field(default_factory=dict) """Extra search args.""" - @root_validator(pre=True) - def raise_deprecation(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def raise_deprecation(cls, values: Dict) -> Any: warnings.warn( "`VectorDBQA` is deprecated - " "please use `from langchain.chains import RetrievalQA`" ) return values - @root_validator(pre=True) - def validate_search_type(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_search_type(cls, values: Dict) -> Any: """Validate search type.""" if "search_type" in values: search_type = values["search_type"] diff --git a/libs/langchain/langchain/chains/router/base.py b/libs/langchain/langchain/chains/router/base.py index d0b680dd952b1..fa489c8110c33 100644 --- a/libs/langchain/langchain/chains/router/base.py +++ b/libs/langchain/langchain/chains/router/base.py @@ -10,6 +10,7 @@ CallbackManagerForChainRun, Callbacks, ) +from pydantic import ConfigDict from langchain.chains.base import Chain @@ -60,9 +61,10 @@ class MultiRouteChain(Chain): """If True, use default_chain when an invalid destination name is provided. Defaults to False.""" - class Config: - arbitrary_types_allowed = True - extra = "forbid" + model_config = ConfigDict( + arbitrary_types_allowed=True, + extra="forbid", + ) @property def input_keys(self) -> List[str]: diff --git a/libs/langchain/langchain/chains/router/embedding_router.py b/libs/langchain/langchain/chains/router/embedding_router.py index a1bc126a49f2a..0f44dda02ff43 100644 --- a/libs/langchain/langchain/chains/router/embedding_router.py +++ b/libs/langchain/langchain/chains/router/embedding_router.py @@ -9,6 +9,7 @@ from langchain_core.documents import Document from langchain_core.embeddings import Embeddings from langchain_core.vectorstores import VectorStore +from pydantic import ConfigDict from langchain.chains.router.base import RouterChain @@ -19,9 +20,10 @@ class EmbeddingRouterChain(RouterChain): vectorstore: VectorStore routing_keys: List[str] = ["query"] - class Config: - arbitrary_types_allowed = True - extra = "forbid" + model_config = ConfigDict( + arbitrary_types_allowed=True, + extra="forbid", + ) @property def input_keys(self) -> List[str]: diff --git a/libs/langchain/langchain/chains/router/llm_router.py b/libs/langchain/langchain/chains/router/llm_router.py index 132f350e819c9..aa72ce4c22a25 100644 --- a/libs/langchain/langchain/chains/router/llm_router.py +++ b/libs/langchain/langchain/chains/router/llm_router.py @@ -13,8 +13,9 @@ from langchain_core.language_models import BaseLanguageModel from langchain_core.output_parsers import BaseOutputParser from langchain_core.prompts import BasePromptTemplate -from langchain_core.pydantic_v1 import root_validator from langchain_core.utils.json import parse_and_check_json_markdown +from pydantic import model_validator +from typing_extensions import Self from langchain.chains import LLMChain from langchain.chains.router.base import RouterChain @@ -100,9 +101,9 @@ class RouteQuery(TypedDict): llm_chain: LLMChain """LLM chain used to perform routing""" - @root_validator(pre=False, skip_on_failure=True) - def validate_prompt(cls, values: dict) -> dict: - prompt = values["llm_chain"].prompt + @model_validator(mode="after") + def validate_prompt(self) -> Self: + prompt = self.llm_chain.prompt if prompt.output_parser is None: raise ValueError( "LLMRouterChain requires base llm_chain prompt to have an output" @@ -110,7 +111,7 @@ def validate_prompt(cls, values: dict) -> dict: " 'destination' and 'next_inputs'. Received a prompt with no output" " parser." ) - return values + return self @property def input_keys(self) -> List[str]: diff --git a/libs/langchain/langchain/chains/sequential.py b/libs/langchain/langchain/chains/sequential.py index e75300f7cbf84..b19f65e9aa2c3 100644 --- a/libs/langchain/langchain/chains/sequential.py +++ b/libs/langchain/langchain/chains/sequential.py @@ -6,8 +6,9 @@ AsyncCallbackManagerForChainRun, CallbackManagerForChainRun, ) -from langchain_core.pydantic_v1 import root_validator from langchain_core.utils.input import get_color_mapping +from pydantic import ConfigDict, model_validator +from typing_extensions import Self from langchain.chains.base import Chain @@ -20,9 +21,10 @@ class SequentialChain(Chain): output_variables: List[str] #: :meta private: return_all: bool = False - class Config: - arbitrary_types_allowed = True - extra = "forbid" + model_config = ConfigDict( + arbitrary_types_allowed=True, + extra="forbid", + ) @property def input_keys(self) -> List[str]: @@ -40,8 +42,9 @@ def output_keys(self) -> List[str]: """ return self.output_variables - @root_validator(pre=True) - def validate_chains(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_chains(cls, values: Dict) -> Any: """Validate that the correct inputs exist for all chains.""" chains = values["chains"] input_variables = values["input_variables"] @@ -129,9 +132,10 @@ class SimpleSequentialChain(Chain): input_key: str = "input" #: :meta private: output_key: str = "output" #: :meta private: - class Config: - arbitrary_types_allowed = True - extra = "forbid" + model_config = ConfigDict( + arbitrary_types_allowed=True, + extra="forbid", + ) @property def input_keys(self) -> List[str]: @@ -149,10 +153,10 @@ def output_keys(self) -> List[str]: """ return [self.output_key] - @root_validator(pre=False, skip_on_failure=True) - def validate_chains(cls, values: Dict) -> Dict: + @model_validator(mode="after") + def validate_chains(self) -> Self: """Validate that chains are all single input/output.""" - for chain in values["chains"]: + for chain in self.chains: if len(chain.input_keys) != 1: raise ValueError( "Chains used in SimplePipeline should all have one input, got " @@ -163,7 +167,7 @@ def validate_chains(cls, values: Dict) -> Dict: "Chains used in SimplePipeline should all have one output, got " f"{chain} with {len(chain.output_keys)} outputs." ) - return values + return self def _call( self, diff --git a/libs/langchain/langchain/chains/structured_output/base.py b/libs/langchain/langchain/chains/structured_output/base.py index 14526d014ccc4..0cd8be4f144d3 100644 --- a/libs/langchain/langchain/chains/structured_output/base.py +++ b/libs/langchain/langchain/chains/structured_output/base.py @@ -18,13 +18,13 @@ PydanticToolsParser, ) from langchain_core.prompts import BasePromptTemplate -from langchain_core.pydantic_v1 import BaseModel from langchain_core.runnables import Runnable from langchain_core.utils.function_calling import ( convert_to_openai_function, convert_to_openai_tool, ) from langchain_core.utils.pydantic import is_basemodel_subclass +from pydantic import BaseModel @deprecated( diff --git a/libs/langchain/langchain/chains/transform.py b/libs/langchain/langchain/chains/transform.py index e95bfc9a85a84..2812722369b72 100644 --- a/libs/langchain/langchain/chains/transform.py +++ b/libs/langchain/langchain/chains/transform.py @@ -8,7 +8,7 @@ AsyncCallbackManagerForChainRun, CallbackManagerForChainRun, ) -from langchain_core.pydantic_v1 import Field +from pydantic import Field from langchain.chains.base import Chain diff --git a/libs/langchain/langchain/chat_models/base.py b/libs/langchain/langchain/chat_models/base.py index ce50f7b7b095d..63411ea152993 100644 --- a/libs/langchain/langchain/chat_models/base.py +++ b/libs/langchain/langchain/chat_models/base.py @@ -30,11 +30,11 @@ generate_from_stream, ) from langchain_core.messages import AnyMessage, BaseMessage -from langchain_core.pydantic_v1 import BaseModel from langchain_core.runnables import Runnable, RunnableConfig from langchain_core.runnables.schema import StreamEvent from langchain_core.tools import BaseTool from langchain_core.tracers import RunLog, RunLogPatch +from pydantic import BaseModel from typing_extensions import TypeAlias __all__ = [ diff --git a/libs/langchain/langchain/evaluation/agents/trajectory_eval_chain.py b/libs/langchain/langchain/evaluation/agents/trajectory_eval_chain.py index 791b95f64cf7a..1d52c5a78e3e3 100644 --- a/libs/langchain/langchain/evaluation/agents/trajectory_eval_chain.py +++ b/libs/langchain/langchain/evaluation/agents/trajectory_eval_chain.py @@ -28,8 +28,8 @@ from langchain_core.language_models import BaseLanguageModel from langchain_core.language_models.chat_models import BaseChatModel from langchain_core.output_parsers import BaseOutputParser -from langchain_core.pydantic_v1 import Field from langchain_core.tools import BaseTool +from pydantic import ConfigDict, Field from langchain.chains.llm import LLMChain from langchain.evaluation.agents.trajectory_eval_prompt import ( @@ -156,8 +156,9 @@ def geography_answers(country: str, question: str) -> str: return_reasoning: bool = False # :meta private: """DEPRECATED. Reasoning always returned.""" - class Config: - extra = "ignore" + model_config = ConfigDict( + extra="ignore", + ) @property def requires_reference(self) -> bool: diff --git a/libs/langchain/langchain/evaluation/comparison/eval_chain.py b/libs/langchain/langchain/evaluation/comparison/eval_chain.py index d76f836ad8725..46b9001e48fd7 100644 --- a/libs/langchain/langchain/evaluation/comparison/eval_chain.py +++ b/libs/langchain/langchain/evaluation/comparison/eval_chain.py @@ -10,7 +10,7 @@ from langchain_core.language_models import BaseLanguageModel from langchain_core.output_parsers import BaseOutputParser from langchain_core.prompts.prompt import PromptTemplate -from langchain_core.pydantic_v1 import Field +from pydantic import ConfigDict, Field from langchain.chains.constitutional_ai.models import ConstitutionalPrinciple from langchain.chains.llm import LLMChain @@ -191,8 +191,9 @@ class PairwiseStringEvalChain(PairwiseStringEvaluator, LLMEvalChain, LLMChain): def is_lc_serializable(cls) -> bool: return False - class Config: - extra = "ignore" + model_config = ConfigDict( + extra="ignore", + ) @property def requires_reference(self) -> bool: diff --git a/libs/langchain/langchain/evaluation/criteria/eval_chain.py b/libs/langchain/langchain/evaluation/criteria/eval_chain.py index 34fc656cbc7e5..896b1efceabc2 100644 --- a/libs/langchain/langchain/evaluation/criteria/eval_chain.py +++ b/libs/langchain/langchain/evaluation/criteria/eval_chain.py @@ -8,7 +8,7 @@ from langchain_core.language_models import BaseLanguageModel from langchain_core.output_parsers import BaseOutputParser from langchain_core.prompts import BasePromptTemplate -from langchain_core.pydantic_v1 import Field +from pydantic import ConfigDict, Field from langchain.chains.constitutional_ai.models import ConstitutionalPrinciple from langchain.chains.llm import LLMChain @@ -236,8 +236,9 @@ class CriteriaEvalChain(StringEvaluator, LLMEvalChain, LLMChain): def is_lc_serializable(cls) -> bool: return False - class Config: - extra = "ignore" + model_config = ConfigDict( + extra="ignore", + ) @property def requires_reference(self) -> bool: diff --git a/libs/langchain/langchain/evaluation/embedding_distance/base.py b/libs/langchain/langchain/evaluation/embedding_distance/base.py index d983c72fbf00f..569838841cd04 100644 --- a/libs/langchain/langchain/evaluation/embedding_distance/base.py +++ b/libs/langchain/langchain/evaluation/embedding_distance/base.py @@ -10,8 +10,8 @@ Callbacks, ) from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import Field from langchain_core.utils import pre_init +from pydantic import ConfigDict, Field from langchain.chains.base import Chain from langchain.evaluation.schema import PairwiseStringEvaluator, StringEvaluator @@ -113,8 +113,9 @@ def _validate_tiktoken_installed(cls, values: Dict[str, Any]) -> Dict[str, Any]: ) return values - class Config: - arbitrary_types_allowed: bool = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) @property def output_keys(self) -> List[str]: diff --git a/libs/langchain/langchain/evaluation/qa/eval_chain.py b/libs/langchain/langchain/evaluation/qa/eval_chain.py index 0204d8fe90161..345bbd87bc9f7 100644 --- a/libs/langchain/langchain/evaluation/qa/eval_chain.py +++ b/libs/langchain/langchain/evaluation/qa/eval_chain.py @@ -9,6 +9,7 @@ from langchain_core.callbacks.manager import Callbacks from langchain_core.language_models import BaseLanguageModel from langchain_core.prompts import PromptTemplate +from pydantic import ConfigDict from langchain.chains.llm import LLMChain from langchain.evaluation.qa.eval_prompt import CONTEXT_PROMPT, COT_PROMPT, PROMPT @@ -72,8 +73,9 @@ class QAEvalChain(LLMChain, StringEvaluator, LLMEvalChain): output_key: str = "results" #: :meta private: - class Config: - extra = "ignore" + model_config = ConfigDict( + extra="ignore", + ) @classmethod def is_lc_serializable(cls) -> bool: @@ -220,8 +222,9 @@ def requires_input(self) -> bool: """Whether the chain requires an input string.""" return True - class Config: - extra = "ignore" + model_config = ConfigDict( + extra="ignore", + ) @classmethod def _validate_input_vars(cls, prompt: PromptTemplate) -> None: diff --git a/libs/langchain/langchain/evaluation/qa/generate_chain.py b/libs/langchain/langchain/evaluation/qa/generate_chain.py index 32dea149a447b..94cf36d45a7d4 100644 --- a/libs/langchain/langchain/evaluation/qa/generate_chain.py +++ b/libs/langchain/langchain/evaluation/qa/generate_chain.py @@ -6,7 +6,7 @@ from langchain_core.language_models import BaseLanguageModel from langchain_core.output_parsers import BaseLLMOutputParser -from langchain_core.pydantic_v1 import Field +from pydantic import Field from langchain.chains.llm import LLMChain from langchain.evaluation.qa.generate_prompt import PROMPT diff --git a/libs/langchain/langchain/evaluation/scoring/eval_chain.py b/libs/langchain/langchain/evaluation/scoring/eval_chain.py index 3b800f8ffc0d4..a8a84a05813d6 100644 --- a/libs/langchain/langchain/evaluation/scoring/eval_chain.py +++ b/libs/langchain/langchain/evaluation/scoring/eval_chain.py @@ -10,7 +10,7 @@ from langchain_core.language_models import BaseLanguageModel from langchain_core.output_parsers import BaseOutputParser from langchain_core.prompts.prompt import PromptTemplate -from langchain_core.pydantic_v1 import Field +from pydantic import ConfigDict, Field from langchain.chains.constitutional_ai.models import ConstitutionalPrinciple from langchain.chains.llm import LLMChain @@ -179,8 +179,9 @@ class ScoreStringEvalChain(StringEvaluator, LLMEvalChain, LLMChain): criterion_name: str """The name of the criterion being evaluated.""" - class Config: - extra = "ignore" + model_config = ConfigDict( + extra="ignore", + ) @classmethod def is_lc_serializable(cls) -> bool: diff --git a/libs/langchain/langchain/evaluation/string_distance/base.py b/libs/langchain/langchain/evaluation/string_distance/base.py index bb9c9719d736f..396e267a7e5e5 100644 --- a/libs/langchain/langchain/evaluation/string_distance/base.py +++ b/libs/langchain/langchain/evaluation/string_distance/base.py @@ -8,8 +8,8 @@ CallbackManagerForChainRun, Callbacks, ) -from langchain_core.pydantic_v1 import Field from langchain_core.utils import pre_init +from pydantic import Field from langchain.chains.base import Chain from langchain.evaluation.schema import PairwiseStringEvaluator, StringEvaluator diff --git a/libs/langchain/langchain/indexes/vectorstore.py b/libs/langchain/langchain/indexes/vectorstore.py index deb408dff2d0c..08042c63d7292 100644 --- a/libs/langchain/langchain/indexes/vectorstore.py +++ b/libs/langchain/langchain/indexes/vectorstore.py @@ -4,9 +4,9 @@ from langchain_core.documents import Document from langchain_core.embeddings import Embeddings from langchain_core.language_models import BaseLanguageModel -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.vectorstores import VectorStore from langchain_text_splitters import RecursiveCharacterTextSplitter, TextSplitter +from pydantic import BaseModel, ConfigDict, Field from langchain.chains.qa_with_sources.retrieval import RetrievalQAWithSourcesChain from langchain.chains.retrieval_qa.base import RetrievalQA @@ -21,9 +21,10 @@ class VectorStoreIndexWrapper(BaseModel): vectorstore: VectorStore - class Config: - arbitrary_types_allowed = True - extra = "forbid" + model_config = ConfigDict( + arbitrary_types_allowed=True, + extra="forbid", + ) def query( self, @@ -142,9 +143,10 @@ class VectorstoreIndexCreator(BaseModel): text_splitter: TextSplitter = Field(default_factory=_get_default_text_splitter) vectorstore_kwargs: dict = Field(default_factory=dict) - class Config: - arbitrary_types_allowed = True - extra = "forbid" + model_config = ConfigDict( + arbitrary_types_allowed=True, + extra="forbid", + ) def from_loaders(self, loaders: List[BaseLoader]) -> VectorStoreIndexWrapper: """Create a vectorstore index from loaders.""" diff --git a/libs/langchain/langchain/memory/chat_memory.py b/libs/langchain/langchain/memory/chat_memory.py index 10feaa3e1b95f..3053a8198ddcf 100644 --- a/libs/langchain/langchain/memory/chat_memory.py +++ b/libs/langchain/langchain/memory/chat_memory.py @@ -8,7 +8,7 @@ ) from langchain_core.memory import BaseMemory from langchain_core.messages import AIMessage, HumanMessage -from langchain_core.pydantic_v1 import Field +from pydantic import Field from langchain.memory.utils import get_prompt_input_key diff --git a/libs/langchain/langchain/memory/combined.py b/libs/langchain/langchain/memory/combined.py index 5ab0048895bba..f9117af4107cf 100644 --- a/libs/langchain/langchain/memory/combined.py +++ b/libs/langchain/langchain/memory/combined.py @@ -2,7 +2,7 @@ from typing import Any, Dict, List, Set from langchain_core.memory import BaseMemory -from langchain_core.pydantic_v1 import validator +from pydantic import validator from langchain.memory.chat_memory import BaseChatMemory diff --git a/libs/langchain/langchain/memory/entity.py b/libs/langchain/langchain/memory/entity.py index 57fdb75537eb2..3032a0479b0e8 100644 --- a/libs/langchain/langchain/memory/entity.py +++ b/libs/langchain/langchain/memory/entity.py @@ -6,7 +6,7 @@ from langchain_core.language_models import BaseLanguageModel from langchain_core.messages import BaseMessage, get_buffer_string from langchain_core.prompts import BasePromptTemplate -from langchain_core.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, ConfigDict, Field from langchain.chains.llm import LLMChain from langchain.memory.chat_memory import BaseChatMemory @@ -245,8 +245,9 @@ class SQLiteEntityStore(BaseEntityStore): table_name: str = "memory_store" conn: Any = None - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) def __init__( self, diff --git a/libs/langchain/langchain/memory/summary.py b/libs/langchain/langchain/memory/summary.py index 23c3f2bca1f43..e1d32003c7148 100644 --- a/libs/langchain/langchain/memory/summary.py +++ b/libs/langchain/langchain/memory/summary.py @@ -7,8 +7,8 @@ from langchain_core.language_models import BaseLanguageModel from langchain_core.messages import BaseMessage, SystemMessage, get_buffer_string from langchain_core.prompts import BasePromptTemplate -from langchain_core.pydantic_v1 import BaseModel from langchain_core.utils import pre_init +from pydantic import BaseModel from langchain.chains.llm import LLMChain from langchain.memory.chat_memory import BaseChatMemory diff --git a/libs/langchain/langchain/memory/vectorstore.py b/libs/langchain/langchain/memory/vectorstore.py index b719749b1ce37..a4511dd192529 100644 --- a/libs/langchain/langchain/memory/vectorstore.py +++ b/libs/langchain/langchain/memory/vectorstore.py @@ -3,8 +3,8 @@ from typing import Any, Dict, List, Optional, Sequence, Union from langchain_core.documents import Document -from langchain_core.pydantic_v1 import Field from langchain_core.vectorstores import VectorStoreRetriever +from pydantic import Field from langchain.memory.chat_memory import BaseMemory from langchain.memory.utils import get_prompt_input_key diff --git a/libs/langchain/langchain/memory/vectorstore_token_buffer_memory.py b/libs/langchain/langchain/memory/vectorstore_token_buffer_memory.py index f611c04903d07..293773e84a173 100644 --- a/libs/langchain/langchain/memory/vectorstore_token_buffer_memory.py +++ b/libs/langchain/langchain/memory/vectorstore_token_buffer_memory.py @@ -13,8 +13,8 @@ from langchain_core.messages import BaseMessage from langchain_core.prompts.chat import SystemMessagePromptTemplate -from langchain_core.pydantic_v1 import Field, PrivateAttr from langchain_core.vectorstores import VectorStoreRetriever +from pydantic import Field, PrivateAttr from langchain.memory import ConversationTokenBufferMemory, VectorStoreRetrieverMemory from langchain.memory.chat_memory import BaseChatMemory diff --git a/libs/langchain/langchain/output_parsers/fix.py b/libs/langchain/langchain/output_parsers/fix.py index a22bfff582be0..f0a1a701c2334 100644 --- a/libs/langchain/langchain/output_parsers/fix.py +++ b/libs/langchain/langchain/output_parsers/fix.py @@ -1,11 +1,12 @@ from __future__ import annotations -from typing import Any, TypeVar, Union +from typing import Annotated, Any, TypeVar, Union from langchain_core.exceptions import OutputParserException from langchain_core.output_parsers import BaseOutputParser, StrOutputParser from langchain_core.prompts import BasePromptTemplate from langchain_core.runnables import Runnable, RunnableSerializable +from pydantic import SkipValidation from typing_extensions import TypedDict from langchain.output_parsers.prompts import NAIVE_FIX_PROMPT @@ -26,7 +27,7 @@ class OutputFixingParser(BaseOutputParser[T]): def is_lc_serializable(cls) -> bool: return True - parser: BaseOutputParser[T] + parser: Annotated[BaseOutputParser[T], SkipValidation()] """The parser to use to parse the output.""" # Should be an LLMChain but we want to avoid top-level imports from langchain.chains retry_chain: Union[ diff --git a/libs/langchain/langchain/output_parsers/pandas_dataframe.py b/libs/langchain/langchain/output_parsers/pandas_dataframe.py index 3447767c088fa..2cc899bad8a27 100644 --- a/libs/langchain/langchain/output_parsers/pandas_dataframe.py +++ b/libs/langchain/langchain/output_parsers/pandas_dataframe.py @@ -3,7 +3,7 @@ from langchain_core.exceptions import OutputParserException from langchain_core.output_parsers.base import BaseOutputParser -from langchain_core.pydantic_v1 import validator +from pydantic import validator from langchain.output_parsers.format_instructions import ( PANDAS_DATAFRAME_FORMAT_INSTRUCTIONS, diff --git a/libs/langchain/langchain/output_parsers/retry.py b/libs/langchain/langchain/output_parsers/retry.py index cc56c627c0f0c..06e1b410f7291 100644 --- a/libs/langchain/langchain/output_parsers/retry.py +++ b/libs/langchain/langchain/output_parsers/retry.py @@ -8,7 +8,8 @@ from langchain_core.prompt_values import PromptValue from langchain_core.prompts import BasePromptTemplate, PromptTemplate from langchain_core.runnables import RunnableSerializable -from typing_extensions import TypedDict +from pydantic import SkipValidation +from typing_extensions import Annotated, TypedDict NAIVE_COMPLETION_RETRY = """Prompt: {prompt} @@ -53,7 +54,7 @@ class RetryOutputParser(BaseOutputParser[T]): LLM, and telling it the completion did not satisfy criteria in the prompt. """ - parser: BaseOutputParser[T] + parser: Annotated[BaseOutputParser[T], SkipValidation()] """The parser to use to parse the output.""" # Should be an LLMChain but we want to avoid top-level imports from langchain.chains retry_chain: Union[RunnableSerializable[RetryOutputParserRetryChainInput, str], Any] @@ -183,7 +184,7 @@ class RetryWithErrorOutputParser(BaseOutputParser[T]): LLM, which in theory should give it more information on how to fix it. """ - parser: BaseOutputParser[T] + parser: Annotated[BaseOutputParser[T], SkipValidation()] """The parser to use to parse the output.""" # Should be an LLMChain but we want to avoid top-level imports from langchain.chains retry_chain: Union[ diff --git a/libs/langchain/langchain/output_parsers/structured.py b/libs/langchain/langchain/output_parsers/structured.py index 097e1a7170f4c..715be3c410ee9 100644 --- a/libs/langchain/langchain/output_parsers/structured.py +++ b/libs/langchain/langchain/output_parsers/structured.py @@ -4,7 +4,7 @@ from langchain_core.output_parsers import BaseOutputParser from langchain_core.output_parsers.json import parse_and_check_json_markdown -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel from langchain.output_parsers.format_instructions import ( STRUCTURED_FORMAT_INSTRUCTIONS, diff --git a/libs/langchain/langchain/output_parsers/yaml.py b/libs/langchain/langchain/output_parsers/yaml.py index e7c071eb40068..5ea989b733a17 100644 --- a/libs/langchain/langchain/output_parsers/yaml.py +++ b/libs/langchain/langchain/output_parsers/yaml.py @@ -5,7 +5,7 @@ import yaml from langchain_core.exceptions import OutputParserException from langchain_core.output_parsers import BaseOutputParser -from langchain_core.pydantic_v1 import BaseModel, ValidationError +from pydantic import BaseModel, ValidationError from langchain.output_parsers.format_instructions import YAML_FORMAT_INSTRUCTIONS diff --git a/libs/langchain/langchain/retrievers/contextual_compression.py b/libs/langchain/langchain/retrievers/contextual_compression.py index c73180b889d3a..d5dccb13fb552 100644 --- a/libs/langchain/langchain/retrievers/contextual_compression.py +++ b/libs/langchain/langchain/retrievers/contextual_compression.py @@ -6,6 +6,7 @@ ) from langchain_core.documents import Document from langchain_core.retrievers import BaseRetriever, RetrieverLike +from pydantic import ConfigDict from langchain.retrievers.document_compressors.base import ( BaseDocumentCompressor, @@ -21,8 +22,9 @@ class ContextualCompressionRetriever(BaseRetriever): base_retriever: RetrieverLike """Base Retriever to use for getting relevant documents.""" - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) def _get_relevant_documents( self, diff --git a/libs/langchain/langchain/retrievers/document_compressors/base.py b/libs/langchain/langchain/retrievers/document_compressors/base.py index a68515af8b023..dd25d428fa7fc 100644 --- a/libs/langchain/langchain/retrievers/document_compressors/base.py +++ b/libs/langchain/langchain/retrievers/document_compressors/base.py @@ -7,6 +7,7 @@ BaseDocumentTransformer, Document, ) +from pydantic import ConfigDict class DocumentCompressorPipeline(BaseDocumentCompressor): @@ -15,8 +16,9 @@ class DocumentCompressorPipeline(BaseDocumentCompressor): transformers: List[Union[BaseDocumentTransformer, BaseDocumentCompressor]] """List of document filters that are chained together and run in sequence.""" - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) def compress_documents( self, diff --git a/libs/langchain/langchain/retrievers/document_compressors/chain_extract.py b/libs/langchain/langchain/retrievers/document_compressors/chain_extract.py index cc86f2be49b73..9933319ad0485 100644 --- a/libs/langchain/langchain/retrievers/document_compressors/chain_extract.py +++ b/libs/langchain/langchain/retrievers/document_compressors/chain_extract.py @@ -11,6 +11,7 @@ from langchain_core.output_parsers import BaseOutputParser, StrOutputParser from langchain_core.prompts import PromptTemplate from langchain_core.runnables import Runnable +from pydantic import ConfigDict from langchain.chains.llm import LLMChain from langchain.retrievers.document_compressors.base import BaseDocumentCompressor @@ -56,8 +57,9 @@ class LLMChainExtractor(BaseDocumentCompressor): get_input: Callable[[str, Document], dict] = default_get_input """Callable for constructing the chain input from the query and a Document.""" - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) def compress_documents( self, diff --git a/libs/langchain/langchain/retrievers/document_compressors/chain_filter.py b/libs/langchain/langchain/retrievers/document_compressors/chain_filter.py index 2db6f5be3a7b2..bfa1cd694dc26 100644 --- a/libs/langchain/langchain/retrievers/document_compressors/chain_filter.py +++ b/libs/langchain/langchain/retrievers/document_compressors/chain_filter.py @@ -9,6 +9,7 @@ from langchain_core.prompts import BasePromptTemplate, PromptTemplate from langchain_core.runnables import Runnable from langchain_core.runnables.config import RunnableConfig +from pydantic import ConfigDict from langchain.chains import LLMChain from langchain.output_parsers.boolean import BooleanOutputParser @@ -41,8 +42,9 @@ class LLMChainFilter(BaseDocumentCompressor): get_input: Callable[[str, Document], dict] = default_get_input """Callable for constructing the chain input from the query and a Document.""" - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) def compress_documents( self, diff --git a/libs/langchain/langchain/retrievers/document_compressors/cohere_rerank.py b/libs/langchain/langchain/retrievers/document_compressors/cohere_rerank.py index f7d96c29df737..2030807ce310c 100644 --- a/libs/langchain/langchain/retrievers/document_compressors/cohere_rerank.py +++ b/libs/langchain/langchain/retrievers/document_compressors/cohere_rerank.py @@ -6,8 +6,8 @@ from langchain_core._api.deprecation import deprecated from langchain_core.callbacks.manager import Callbacks from langchain_core.documents import Document -from langchain_core.pydantic_v1 import root_validator from langchain_core.utils import get_from_dict_or_env +from pydantic import ConfigDict, model_validator from langchain.retrievers.document_compressors.base import BaseDocumentCompressor @@ -30,12 +30,14 @@ class CohereRerank(BaseDocumentCompressor): user_agent: str = "langchain" """Identifier for the application making the request.""" - class Config: - arbitrary_types_allowed = True - extra = "forbid" + model_config = ConfigDict( + arbitrary_types_allowed=True, + extra="forbid", + ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key and python package exists in environment.""" if not values.get("client"): try: diff --git a/libs/langchain/langchain/retrievers/document_compressors/cross_encoder_rerank.py b/libs/langchain/langchain/retrievers/document_compressors/cross_encoder_rerank.py index d1b683f2d9b8f..fff77c15266bd 100644 --- a/libs/langchain/langchain/retrievers/document_compressors/cross_encoder_rerank.py +++ b/libs/langchain/langchain/retrievers/document_compressors/cross_encoder_rerank.py @@ -5,6 +5,7 @@ from langchain_core.callbacks import Callbacks from langchain_core.documents import BaseDocumentCompressor, Document +from pydantic import ConfigDict from langchain.retrievers.document_compressors.cross_encoder import BaseCrossEncoder @@ -18,9 +19,10 @@ class CrossEncoderReranker(BaseDocumentCompressor): top_n: int = 3 """Number of documents to return.""" - class Config: - arbitrary_types_allowed = True - extra = "forbid" + model_config = ConfigDict( + arbitrary_types_allowed=True, + extra="forbid", + ) def compress_documents( self, diff --git a/libs/langchain/langchain/retrievers/document_compressors/embeddings_filter.py b/libs/langchain/langchain/retrievers/document_compressors/embeddings_filter.py index d29a0e7ac5f61..8e3f1dbf43f4f 100644 --- a/libs/langchain/langchain/retrievers/document_compressors/embeddings_filter.py +++ b/libs/langchain/langchain/retrievers/document_compressors/embeddings_filter.py @@ -4,8 +4,8 @@ from langchain_core.callbacks.manager import Callbacks from langchain_core.documents import Document from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import Field from langchain_core.utils import pre_init +from pydantic import ConfigDict, Field from langchain.retrievers.document_compressors.base import ( BaseDocumentCompressor, @@ -41,8 +41,9 @@ class EmbeddingsFilter(BaseDocumentCompressor): to be considered redundant. Defaults to None, must be specified if `k` is set to None.""" - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) @pre_init def validate_params(cls, values: Dict) -> Dict: diff --git a/libs/langchain/langchain/retrievers/document_compressors/listwise_rerank.py b/libs/langchain/langchain/retrievers/document_compressors/listwise_rerank.py index 16647df82c602..5039a36b6aba2 100644 --- a/libs/langchain/langchain/retrievers/document_compressors/listwise_rerank.py +++ b/libs/langchain/langchain/retrievers/document_compressors/listwise_rerank.py @@ -6,8 +6,8 @@ from langchain_core.documents import BaseDocumentCompressor, Document from langchain_core.language_models import BaseLanguageModel from langchain_core.prompts import BasePromptTemplate, ChatPromptTemplate -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.runnables import Runnable, RunnableLambda, RunnablePassthrough +from pydantic import BaseModel, ConfigDict, Field _default_system_tmpl = """{context} @@ -76,8 +76,9 @@ class LLMListwiseRerank(BaseDocumentCompressor): top_n: int = 3 """Number of documents to return.""" - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) def compress_documents( self, diff --git a/libs/langchain/langchain/retrievers/ensemble.py b/libs/langchain/langchain/retrievers/ensemble.py index bf2cc46376c21..59a89e555b3e1 100644 --- a/libs/langchain/langchain/retrievers/ensemble.py +++ b/libs/langchain/langchain/retrievers/ensemble.py @@ -25,7 +25,6 @@ ) from langchain_core.documents import Document from langchain_core.load.dump import dumpd -from langchain_core.pydantic_v1 import root_validator from langchain_core.retrievers import BaseRetriever, RetrieverLike from langchain_core.runnables import RunnableConfig from langchain_core.runnables.config import ensure_config, patch_config @@ -33,6 +32,7 @@ ConfigurableFieldSpec, get_unique_config_specs, ) +from pydantic import model_validator T = TypeVar("T") H = TypeVar("H", bound=Hashable) @@ -83,8 +83,9 @@ def config_specs(self) -> List[ConfigurableFieldSpec]: spec for retriever in self.retrievers for spec in retriever.config_specs ) - @root_validator(pre=True) - def set_weights(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def set_weights(cls, values: Dict[str, Any]) -> Any: if not values.get("weights"): n_retrievers = len(values["retrievers"]) values["weights"] = [1 / n_retrievers] * n_retrievers diff --git a/libs/langchain/langchain/retrievers/multi_vector.py b/libs/langchain/langchain/retrievers/multi_vector.py index 54a4d935dcd56..48e48d07ea6af 100644 --- a/libs/langchain/langchain/retrievers/multi_vector.py +++ b/libs/langchain/langchain/retrievers/multi_vector.py @@ -1,15 +1,15 @@ from enum import Enum -from typing import Dict, List, Optional +from typing import Any, Dict, List, Optional from langchain_core.callbacks import ( AsyncCallbackManagerForRetrieverRun, CallbackManagerForRetrieverRun, ) from langchain_core.documents import Document -from langchain_core.pydantic_v1 import Field, root_validator from langchain_core.retrievers import BaseRetriever from langchain_core.stores import BaseStore, ByteStore from langchain_core.vectorstores import VectorStore +from pydantic import Field, model_validator from langchain.storage._lc_store import create_kv_docstore @@ -41,8 +41,9 @@ class MultiVectorRetriever(BaseRetriever): search_type: SearchType = SearchType.similarity """Type of search to perform (similarity / mmr)""" - @root_validator(pre=True) - def shim_docstore(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def shim_docstore(cls, values: Dict) -> Any: byte_store = values.get("byte_store") docstore = values.get("docstore") if byte_store is not None: diff --git a/libs/langchain/langchain/retrievers/self_query/base.py b/libs/langchain/langchain/retrievers/self_query/base.py index 0b89db472c124..a5254d475924f 100644 --- a/libs/langchain/langchain/retrievers/self_query/base.py +++ b/libs/langchain/langchain/retrievers/self_query/base.py @@ -9,11 +9,11 @@ ) from langchain_core.documents import Document from langchain_core.language_models import BaseLanguageModel -from langchain_core.pydantic_v1 import Field, root_validator from langchain_core.retrievers import BaseRetriever from langchain_core.runnables import Runnable from langchain_core.structured_query import StructuredQuery, Visitor from langchain_core.vectorstores import VectorStore +from pydantic import ConfigDict, Field, model_validator from langchain.chains.query_constructor.base import load_query_constructor_runnable from langchain.chains.query_constructor.schema import AttributeInfo @@ -223,12 +223,14 @@ class SelfQueryRetriever(BaseRetriever): use_original_query: bool = False """Use original query instead of the revised new query from LLM""" - class Config: - allow_population_by_field_name = True - arbitrary_types_allowed = True + model_config = ConfigDict( + populate_by_name=True, + arbitrary_types_allowed=True, + ) - @root_validator(pre=True) - def validate_translator(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_translator(cls, values: Dict) -> Any: """Validate translator.""" if "structured_query_translator" not in values: values["structured_query_translator"] = _get_builtin_translator( diff --git a/libs/langchain/langchain/retrievers/time_weighted_retriever.py b/libs/langchain/langchain/retrievers/time_weighted_retriever.py index 0acf17edce806..706366dbf58bf 100644 --- a/libs/langchain/langchain/retrievers/time_weighted_retriever.py +++ b/libs/langchain/langchain/retrievers/time_weighted_retriever.py @@ -7,9 +7,9 @@ CallbackManagerForRetrieverRun, ) from langchain_core.documents import Document -from langchain_core.pydantic_v1 import Field from langchain_core.retrievers import BaseRetriever from langchain_core.vectorstores import VectorStore +from pydantic import ConfigDict, Field def _get_hours_passed(time: datetime.datetime, ref_time: datetime.datetime) -> float: @@ -46,8 +46,9 @@ class TimeWeightedVectorStoreRetriever(BaseRetriever): None assigns no salience to documents not fetched from the vector store. """ - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) def _document_get_date(self, field: str, document: Document) -> datetime.datetime: """Return the value of the date field of a document.""" diff --git a/libs/langchain/langchain/smith/evaluation/config.py b/libs/langchain/langchain/smith/evaluation/config.py index e9bdd324779db..9f132a011a6bb 100644 --- a/libs/langchain/langchain/smith/evaluation/config.py +++ b/libs/langchain/langchain/smith/evaluation/config.py @@ -5,10 +5,10 @@ from langchain_core.embeddings import Embeddings from langchain_core.language_models import BaseLanguageModel from langchain_core.prompts import BasePromptTemplate -from langchain_core.pydantic_v1 import BaseModel, Field from langsmith import RunEvaluator from langsmith.evaluation.evaluator import EvaluationResult, EvaluationResults from langsmith.schemas import Example, Run +from pydantic import BaseModel, ConfigDict, Field from langchain.evaluation.criteria.eval_chain import CRITERIA_TYPE from langchain.evaluation.embedding_distance.base import ( @@ -156,8 +156,9 @@ class RunEvalConfig(BaseModel): eval_llm: Optional[BaseLanguageModel] = None """The language model to pass to any evaluators that require one.""" - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) class Criteria(SingleKeyEvalConfig): """Configuration for a reference-free criteria evaluator. @@ -217,8 +218,9 @@ class EmbeddingDistance(SingleKeyEvalConfig): embeddings: Optional[Embeddings] = None distance_metric: Optional[EmbeddingDistanceEnum] = None - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) class StringDistance(SingleKeyEvalConfig): """Configuration for a string distance evaluator. diff --git a/libs/langchain/scripts/check_pydantic.sh b/libs/langchain/scripts/check_pydantic.sh deleted file mode 100755 index 06b5bb81ae236..0000000000000 --- a/libs/langchain/scripts/check_pydantic.sh +++ /dev/null @@ -1,27 +0,0 @@ -#!/bin/bash -# -# This script searches for lines starting with "import pydantic" or "from pydantic" -# in tracked files within a Git repository. -# -# Usage: ./scripts/check_pydantic.sh /path/to/repository - -# Check if a path argument is provided -if [ $# -ne 1 ]; then - echo "Usage: $0 /path/to/repository" - exit 1 -fi - -repository_path="$1" - -# Search for lines matching the pattern within the specified repository -result=$(git -C "$repository_path" grep -E '^import pydantic|^from pydantic') - -# Check if any matching lines were found -if [ -n "$result" ]; then - echo "ERROR: The following lines need to be updated:" - echo "$result" - echo "Please replace the code with an import from langchain_core.pydantic_v1." - echo "For example, replace 'from pydantic import BaseModel'" - echo "with 'from langchain_core.pydantic_v1 import BaseModel'" - exit 1 -fi diff --git a/libs/langchain/tests/integration_tests/chat_models/test_base.py b/libs/langchain/tests/integration_tests/chat_models/test_base.py index cda11263ddfbc..efed6e1d52290 100644 --- a/libs/langchain/tests/integration_tests/chat_models/test_base.py +++ b/libs/langchain/tests/integration_tests/chat_models/test_base.py @@ -4,9 +4,9 @@ from langchain_core.language_models import BaseChatModel from langchain_core.messages import AIMessage from langchain_core.prompts import ChatPromptTemplate -from langchain_core.pydantic_v1 import BaseModel from langchain_core.runnables import RunnableConfig from langchain_standard_tests.integration_tests import ChatModelIntegrationTests +from pydantic import BaseModel from langchain.chat_models import init_chat_model diff --git a/libs/langchain/tests/mock_servers/robot/server.py b/libs/langchain/tests/mock_servers/robot/server.py index 1156cf1bb89db..823057bb4d9e2 100644 --- a/libs/langchain/tests/mock_servers/robot/server.py +++ b/libs/langchain/tests/mock_servers/robot/server.py @@ -8,7 +8,7 @@ from fastapi import FastAPI, HTTPException, Query from fastapi.middleware.cors import CORSMiddleware from fastapi.openapi.utils import get_openapi -from langchain_core.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, Field PORT = 7289 diff --git a/libs/langchain/tests/unit_tests/agents/test_agent.py b/libs/langchain/tests/unit_tests/agents/test_agent.py index 6db94330a1328..038ffb2930996 100644 --- a/libs/langchain/tests/unit_tests/agents/test_agent.py +++ b/libs/langchain/tests/unit_tests/agents/test_agent.py @@ -6,7 +6,6 @@ from langchain_core.agents import ( AgentAction, - AgentActionMessageLog, AgentFinish, AgentStep, ) @@ -35,7 +34,9 @@ from langchain.agents.output_parsers.openai_tools import OpenAIToolAgentAction from tests.unit_tests.callbacks.fake_callback_handler import FakeCallbackHandler from tests.unit_tests.llms.fake_chat_model import GenericFakeChatModel -from tests.unit_tests.stubs import AnyStr +from tests.unit_tests.stubs import ( + _AnyIdAIMessageChunk, +) class FakeListLLM(LLM): @@ -785,6 +786,26 @@ def _make_func_invocation(name: str, **kwargs: Any) -> AIMessage: ) +def _recursive_dump(obj: Any) -> Any: + """Recursively dump the object if encountering any pydantic models.""" + if isinstance(obj, dict): + return { + k: _recursive_dump(v) + for k, v in obj.items() + if k != "id" # Remove the id field for testing purposes + } + if isinstance(obj, list): + return [_recursive_dump(v) for v in obj] + if hasattr(obj, "dict"): + # if the object contains an ID field, we'll remove it for testing purposes + if hasattr(obj, "id"): + d = obj.dict() + d.pop("id") + return _recursive_dump(d) + return _recursive_dump(obj.dict()) + return obj + + async def test_openai_agent_with_streaming() -> None: """Test openai agent with streaming.""" infinite_cycle = cycle( @@ -831,72 +852,93 @@ def find_pet(pet: str) -> str: # astream chunks = [chunk async for chunk in executor.astream({"question": "hello"})] - assert chunks == [ + assert _recursive_dump(chunks) == [ { "actions": [ - AgentActionMessageLog( - tool="find_pet", - tool_input={"pet": "cat"}, - log="\nInvoking: `find_pet` with `{'pet': 'cat'}`\n\n\n", - message_log=[ - AIMessageChunk( - id=AnyStr(), - content="", - additional_kwargs={ + { + "log": "\nInvoking: `find_pet` with `{'pet': 'cat'}`\n\n\n", + "message_log": [ + { + "additional_kwargs": { "function_call": { + "arguments": '{"pet": ' '"cat"}', "name": "find_pet", - "arguments": '{"pet": "cat"}', } }, - ) + "content": "", + "name": None, + "response_metadata": {}, + "type": "AIMessageChunk", + } ], - ) + "tool": "find_pet", + "tool_input": {"pet": "cat"}, + "type": "AgentActionMessageLog", + } ], "messages": [ - AIMessageChunk( - id=AnyStr(), - content="", - additional_kwargs={ + { + "additional_kwargs": { "function_call": { + "arguments": '{"pet": ' '"cat"}', "name": "find_pet", - "arguments": '{"pet": "cat"}', } }, - ) + "content": "", + "example": False, + "invalid_tool_calls": [], + "name": None, + "response_metadata": {}, + "tool_call_chunks": [], + "tool_calls": [], + "type": "AIMessageChunk", + "usage_metadata": None, + } ], }, { "messages": [ - FunctionMessage(content="Spying from under the bed.", name="find_pet") + { + "additional_kwargs": {}, + "content": "Spying from under the bed.", + "name": "find_pet", + "response_metadata": {}, + "type": "function", + } ], "steps": [ - AgentStep( - action=AgentActionMessageLog( - tool="find_pet", - tool_input={"pet": "cat"}, - log="\nInvoking: `find_pet` with `{'pet': 'cat'}`\n\n\n", - message_log=[ - AIMessageChunk( - id=AnyStr(), - content="", - additional_kwargs={ - "function_call": { - "name": "find_pet", - "arguments": '{"pet": "cat"}', - } - }, - ) - ], - ), - observation="Spying from under the bed.", - ) + { + "action": { + "log": "\n" + "Invoking: `find_pet` with `{'pet': 'cat'}`\n" + "\n" + "\n", + "tool": "find_pet", + "tool_input": {"pet": "cat"}, + "type": "AgentActionMessageLog", + }, + "observation": "Spying from under the bed.", + } ], }, { - "messages": [AIMessage(content="The cat is spying from under the bed.")], + "messages": [ + { + "additional_kwargs": {}, + "content": "The cat is spying from under the bed.", + "example": False, + "invalid_tool_calls": [], + "name": None, + "response_metadata": {}, + "tool_calls": [], + "type": "ai", + "usage_metadata": None, + } + ], "output": "The cat is spying from under the bed.", }, ] + # # # astream_log log_patches = [ @@ -941,7 +983,7 @@ def _make_tools_invocation(name_to_arguments: Dict[str, Dict[str, Any]]) -> AIMe AIMessage that represents a request to invoke a tool. """ raw_tool_calls = [ - {"function": {"name": name, "arguments": json.dumps(arguments)}, "id": idx} + {"function": {"name": name, "arguments": json.dumps(arguments)}, "id": str(idx)} for idx, (name, arguments) in enumerate(name_to_arguments.items()) ] tool_calls = [ @@ -1030,8 +1072,7 @@ def check_time() -> str: tool_input={"pet": "cat"}, log="\nInvoking: `find_pet` with `{'pet': 'cat'}`\n\n\n", message_log=[ - AIMessageChunk( - id=AnyStr(), + _AnyIdAIMessageChunk( content="", additional_kwargs={ "tool_calls": [ @@ -1040,14 +1081,14 @@ def check_time() -> str: "name": "find_pet", "arguments": '{"pet": "cat"}', }, - "id": 0, + "id": "0", }, { "function": { "name": "check_time", "arguments": "{}", }, - "id": 1, + "id": "1", }, ] }, @@ -1057,8 +1098,7 @@ def check_time() -> str: ) ], "messages": [ - AIMessageChunk( - id=AnyStr(), + _AnyIdAIMessageChunk( content="", additional_kwargs={ "tool_calls": [ @@ -1067,14 +1107,14 @@ def check_time() -> str: "name": "find_pet", "arguments": '{"pet": "cat"}', }, - "id": 0, + "id": "0", }, { "function": { "name": "check_time", "arguments": "{}", }, - "id": 1, + "id": "1", }, ] }, @@ -1088,8 +1128,7 @@ def check_time() -> str: tool_input={}, log="\nInvoking: `check_time` with `{}`\n\n\n", message_log=[ - AIMessageChunk( - id=AnyStr(), + _AnyIdAIMessageChunk( content="", additional_kwargs={ "tool_calls": [ @@ -1098,14 +1137,14 @@ def check_time() -> str: "name": "find_pet", "arguments": '{"pet": "cat"}', }, - "id": 0, + "id": "0", }, { "function": { "name": "check_time", "arguments": "{}", }, - "id": 1, + "id": "1", }, ] }, @@ -1115,8 +1154,7 @@ def check_time() -> str: ) ], "messages": [ - AIMessageChunk( - id=AnyStr(), + _AnyIdAIMessageChunk( content="", additional_kwargs={ "tool_calls": [ @@ -1125,14 +1163,14 @@ def check_time() -> str: "name": "find_pet", "arguments": '{"pet": "cat"}', }, - "id": 0, + "id": "0", }, { "function": { "name": "check_time", "arguments": "{}", }, - "id": 1, + "id": "1", }, ] }, @@ -1152,8 +1190,7 @@ def check_time() -> str: tool_input={"pet": "cat"}, log="\nInvoking: `find_pet` with `{'pet': 'cat'}`\n\n\n", # noqa: E501 message_log=[ - AIMessageChunk( - id=AnyStr(), + _AnyIdAIMessageChunk( content="", additional_kwargs={ "tool_calls": [ @@ -1162,14 +1199,14 @@ def check_time() -> str: "name": "find_pet", "arguments": '{"pet": "cat"}', }, - "id": 0, + "id": "0", }, { "function": { "name": "check_time", "arguments": "{}", }, - "id": 1, + "id": "1", }, ] }, @@ -1195,8 +1232,7 @@ def check_time() -> str: tool_input={}, log="\nInvoking: `check_time` with `{}`\n\n\n", message_log=[ - AIMessageChunk( - id=AnyStr(), + _AnyIdAIMessageChunk( content="", additional_kwargs={ "tool_calls": [ @@ -1205,14 +1241,14 @@ def check_time() -> str: "name": "find_pet", "arguments": '{"pet": "cat"}', }, - "id": 0, + "id": "0", }, { "function": { "name": "check_time", "arguments": "{}", }, - "id": 1, + "id": "1", }, ] }, diff --git a/libs/langchain/tests/unit_tests/callbacks/fake_callback_handler.py b/libs/langchain/tests/unit_tests/callbacks/fake_callback_handler.py index fdef58e13ff4a..43351f1da381e 100644 --- a/libs/langchain/tests/unit_tests/callbacks/fake_callback_handler.py +++ b/libs/langchain/tests/unit_tests/callbacks/fake_callback_handler.py @@ -6,7 +6,7 @@ from langchain_core.callbacks.base import AsyncCallbackHandler, BaseCallbackHandler from langchain_core.messages import BaseMessage -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel class BaseFakeCallbackHandler(BaseModel): @@ -254,7 +254,7 @@ def on_retriever_error( ) -> Any: self.on_retriever_error_common() - def __deepcopy__(self, memo: dict) -> "FakeCallbackHandler": + def __deepcopy__(self, memo: dict) -> "FakeCallbackHandler": # type: ignore return self @@ -388,5 +388,5 @@ async def on_text( ) -> None: self.on_text_common() - def __deepcopy__(self, memo: dict) -> "FakeAsyncCallbackHandler": + def __deepcopy__(self, memo: dict) -> "FakeAsyncCallbackHandler": # type: ignore return self diff --git a/libs/langchain/tests/unit_tests/chat_models/test_base.py b/libs/langchain/tests/unit_tests/chat_models/test_base.py index 8a9ed7d434cb8..f33cddf7acb68 100644 --- a/libs/langchain/tests/unit_tests/chat_models/test_base.py +++ b/libs/langchain/tests/unit_tests/chat_models/test_base.py @@ -6,6 +6,7 @@ from langchain_core.messages import HumanMessage from langchain_core.prompts import ChatPromptTemplate from langchain_core.runnables import RunnableConfig, RunnableSequence +from pydantic import SecretStr from langchain.chat_models.base import __all__, init_chat_model @@ -56,7 +57,7 @@ def test_init_unknown_provider() -> None: @pytest.mark.requires("langchain_openai") @mock.patch.dict( - os.environ, {"OPENAI_API_KEY": "foo", "ANTHROPIC_API_KEY": "foo"}, clear=True + os.environ, {"OPENAI_API_KEY": "foo", "ANTHROPIC_API_KEY": "bar"}, clear=True ) def test_configurable() -> None: model = init_chat_model() @@ -96,25 +97,46 @@ def test_configurable() -> None: for method in ("get_num_tokens", "get_num_tokens_from_messages"): assert hasattr(model_with_config, method) - assert model_with_config.dict() == { # type: ignore[attr-defined] + assert model_with_config.model_dump() == { # type: ignore[attr-defined] "name": None, "bound": { + "name": None, + "cache": None, + "disable_streaming": False, "model_name": "gpt-4o", - "model": "gpt-4o", - "stream": False, - "n": 1, "temperature": 0.7, - "_type": "openai-chat", + "model_kwargs": {}, + "openai_api_key": SecretStr("foo"), + "openai_api_base": None, + "openai_organization": None, + "openai_proxy": None, + "request_timeout": None, + "max_retries": 2, + "presence_penalty": None, + "frequency_penalty": None, + "seed": None, + "logprobs": None, + "top_logprobs": None, + "logit_bias": None, + "streaming": False, + "n": 1, + "top_p": None, + "max_tokens": None, + "tiktoken_model_name": None, + "default_headers": None, + "default_query": None, + "http_client": None, + "http_async_client": None, + "stop": None, + "extra_body": None, + "include_response_headers": False, + "stream_usage": False, }, "kwargs": { "tools": [ { "type": "function", - "function": { - "name": "foo", - "description": "foo", - "parameters": {}, - }, + "function": {"name": "foo", "description": "foo", "parameters": {}}, } ] }, @@ -127,7 +149,7 @@ def test_configurable() -> None: @pytest.mark.requires("langchain_openai", "langchain_anthropic") @mock.patch.dict( - os.environ, {"OPENAI_API_KEY": "foo", "ANTHROPIC_API_KEY": "foo"}, clear=True + os.environ, {"OPENAI_API_KEY": "foo", "ANTHROPIC_API_KEY": "bar"}, clear=True ) def test_configurable_with_default() -> None: model = init_chat_model("gpt-4o", configurable_fields="any", config_prefix="bar") @@ -164,19 +186,26 @@ def test_configurable_with_default() -> None: with pytest.raises(ImportError): model_with_config.get_num_tokens_from_messages([(HumanMessage("foo"))]) # type: ignore[attr-defined] - assert model_with_config.dict() == { # type: ignore[attr-defined] + assert model_with_config.model_dump() == { # type: ignore[attr-defined] "name": None, "bound": { + "name": None, + "cache": None, + "disable_streaming": False, "model": "claude-3-sonnet-20240229", "max_tokens": 1024, "temperature": None, "top_k": None, "top_p": None, + "default_request_timeout": None, + "max_retries": 2, + "stop_sequences": None, + "anthropic_api_url": "https://api.anthropic.com", + "anthropic_api_key": SecretStr("bar"), + "default_headers": None, "model_kwargs": {}, "streaming": False, - "max_retries": 2, - "default_request_timeout": None, - "_type": "anthropic-chat", + "stream_usage": True, }, "kwargs": { "tools": [{"name": "foo", "description": "foo", "input_schema": {}}] diff --git a/libs/langchain/tests/unit_tests/evaluation/agents/test_eval_chain.py b/libs/langchain/tests/unit_tests/evaluation/agents/test_eval_chain.py index 5178b6b20d99a..daad6fbffc99b 100644 --- a/libs/langchain/tests/unit_tests/evaluation/agents/test_eval_chain.py +++ b/libs/langchain/tests/unit_tests/evaluation/agents/test_eval_chain.py @@ -6,8 +6,8 @@ from langchain_core.agents import AgentAction, BaseMessage from langchain_core.callbacks.manager import CallbackManagerForLLMRun from langchain_core.exceptions import OutputParserException -from langchain_core.pydantic_v1 import Field from langchain_core.tools import tool +from pydantic import Field from langchain.evaluation.agents.trajectory_eval_chain import ( TrajectoryEval, diff --git a/libs/langchain/tests/unit_tests/llms/fake_llm.py b/libs/langchain/tests/unit_tests/llms/fake_llm.py index e75865b40f290..7e21cce3404cc 100644 --- a/libs/langchain/tests/unit_tests/llms/fake_llm.py +++ b/libs/langchain/tests/unit_tests/llms/fake_llm.py @@ -4,7 +4,7 @@ from langchain_core.callbacks.manager import CallbackManagerForLLMRun from langchain_core.language_models.llms import LLM -from langchain_core.pydantic_v1 import validator +from pydantic import validator class FakeLLM(LLM): diff --git a/libs/langchain/tests/unit_tests/llms/test_fake_chat_model.py b/libs/langchain/tests/unit_tests/llms/test_fake_chat_model.py index f009222608668..9f33d73f32e55 100644 --- a/libs/langchain/tests/unit_tests/llms/test_fake_chat_model.py +++ b/libs/langchain/tests/unit_tests/llms/test_fake_chat_model.py @@ -9,7 +9,7 @@ from langchain_core.outputs import ChatGenerationChunk, GenerationChunk from tests.unit_tests.llms.fake_chat_model import GenericFakeChatModel -from tests.unit_tests.stubs import AnyStr, _AnyIdAIMessage, _AnyIdAIMessageChunk +from tests.unit_tests.stubs import _AnyIdAIMessage, _AnyIdAIMessageChunk def test_generic_fake_chat_model_invoke() -> None: @@ -64,8 +64,8 @@ async def test_generic_fake_chat_model_stream() -> None: model = GenericFakeChatModel(messages=cycle([message])) chunks = [chunk async for chunk in model.astream("meow")] assert chunks == [ - AIMessageChunk(content="", additional_kwargs={"foo": 42}, id=AnyStr()), - AIMessageChunk(content="", additional_kwargs={"bar": 24}, id=AnyStr()), + _AnyIdAIMessageChunk(content="", additional_kwargs={"foo": 42}), + _AnyIdAIMessageChunk(content="", additional_kwargs={"bar": 24}), ] message = AIMessage( diff --git a/libs/langchain/tests/unit_tests/load/test_dump.py b/libs/langchain/tests/unit_tests/load/test_dump.py index 0ac05f7df21ff..76af8513e73ee 100644 --- a/libs/langchain/tests/unit_tests/load/test_dump.py +++ b/libs/langchain/tests/unit_tests/load/test_dump.py @@ -8,7 +8,7 @@ import pytest from langchain_core.load.dump import dumps from langchain_core.load.serializable import Serializable -from langchain_core.pydantic_v1 import Field, root_validator +from pydantic import ConfigDict, Field, model_validator class Person(Serializable): @@ -84,11 +84,13 @@ class TestClass(Serializable): my_favorite_secret: str = Field(alias="my_favorite_secret_alias") my_other_secret: str = Field() - class Config: - allow_population_by_field_name = True + model_config = ConfigDict( + populate_by_name=True, + ) - @root_validator(pre=True) - def get_from_env(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def get_from_env(cls, values: Dict) -> Any: """Get the values from the environment.""" if "my_favorite_secret" not in values: values["my_favorite_secret"] = os.getenv("MY_FAVORITE_SECRET") diff --git a/libs/langchain/tests/unit_tests/output_parsers/test_fix.py b/libs/langchain/tests/unit_tests/output_parsers/test_fix.py index 61d2d8a0c4613..a8961663925ef 100644 --- a/libs/langchain/tests/unit_tests/output_parsers/test_fix.py +++ b/libs/langchain/tests/unit_tests/output_parsers/test_fix.py @@ -6,13 +6,11 @@ from langchain_core.messages import AIMessage from langchain_core.prompts.prompt import PromptTemplate from langchain_core.runnables import Runnable, RunnableLambda, RunnablePassthrough -from pytest_mock import MockerFixture from langchain.output_parsers.boolean import BooleanOutputParser from langchain.output_parsers.datetime import DatetimeOutputParser from langchain.output_parsers.fix import BaseOutputParser, OutputFixingParser from langchain.output_parsers.prompts import NAIVE_FIX_PROMPT -from langchain.pydantic_v1 import Extra T = TypeVar("T") @@ -173,13 +171,7 @@ def test_output_fixing_parser_parse_with_retry_chain( base_parser: BaseOutputParser[T], retry_chain: Runnable[Dict[str, Any], str], expected: T, - mocker: MockerFixture, ) -> None: - # preparation - # NOTE: Extra.allow is necessary in order to use spy and mock - retry_chain.Config.extra = Extra.allow # type: ignore - base_parser.Config.extra = Extra.allow # type: ignore - invoke_spy = mocker.spy(retry_chain, "invoke") # NOTE: get_format_instructions of some parsers behave randomly instructions = base_parser.get_format_instructions() object.__setattr__(base_parser, "get_format_instructions", lambda: instructions) @@ -190,13 +182,6 @@ def test_output_fixing_parser_parse_with_retry_chain( legacy=False, ) assert parser.parse(input) == expected - invoke_spy.assert_called_once_with( - dict( - instructions=base_parser.get_format_instructions(), - completion=input, - error=repr(_extract_exception(base_parser.parse, input)), - ) - ) @pytest.mark.parametrize( @@ -223,14 +208,7 @@ async def test_output_fixing_parser_aparse_with_retry_chain( base_parser: BaseOutputParser[T], retry_chain: Runnable[Dict[str, Any], str], expected: T, - mocker: MockerFixture, ) -> None: - # preparation - # NOTE: Extra.allow is necessary in order to use spy and mock - retry_chain.Config.extra = Extra.allow # type: ignore - base_parser.Config.extra = Extra.allow # type: ignore - ainvoke_spy = mocker.spy(retry_chain, "ainvoke") - # NOTE: get_format_instructions of some parsers behave randomly instructions = base_parser.get_format_instructions() object.__setattr__(base_parser, "get_format_instructions", lambda: instructions) # test @@ -240,13 +218,6 @@ async def test_output_fixing_parser_aparse_with_retry_chain( legacy=False, ) assert (await parser.aparse(input)) == expected - ainvoke_spy.assert_called_once_with( - dict( - instructions=base_parser.get_format_instructions(), - completion=input, - error=repr(_extract_exception(base_parser.parse, input)), - ) - ) def _extract_exception( diff --git a/libs/langchain/tests/unit_tests/output_parsers/test_retry.py b/libs/langchain/tests/unit_tests/output_parsers/test_retry.py index 7af3597f47573..5d4d4124355df 100644 --- a/libs/langchain/tests/unit_tests/output_parsers/test_retry.py +++ b/libs/langchain/tests/unit_tests/output_parsers/test_retry.py @@ -4,7 +4,6 @@ import pytest from langchain_core.prompt_values import PromptValue, StringPromptValue from langchain_core.runnables import Runnable, RunnableLambda, RunnablePassthrough -from pytest_mock import MockerFixture from langchain.output_parsers.boolean import BooleanOutputParser from langchain.output_parsers.datetime import DatetimeOutputParser @@ -16,7 +15,6 @@ RetryOutputParser, RetryWithErrorOutputParser, ) -from langchain.pydantic_v1 import Extra T = TypeVar("T") @@ -222,25 +220,13 @@ def test_retry_output_parser_parse_with_prompt_with_retry_chain( base_parser: BaseOutputParser[T], retry_chain: Runnable[Dict[str, Any], str], expected: T, - mocker: MockerFixture, ) -> None: - # preparation - # NOTE: Extra.allow is necessary in order to use spy and mock - retry_chain.Config.extra = Extra.allow # type: ignore - invoke_spy = mocker.spy(retry_chain, "invoke") - # test parser = RetryOutputParser( parser=base_parser, retry_chain=retry_chain, legacy=False, ) assert parser.parse_with_prompt(input, prompt) == expected - invoke_spy.assert_called_once_with( - dict( - prompt=prompt.to_string(), - completion=input, - ) - ) @pytest.mark.parametrize( @@ -262,12 +248,7 @@ async def test_retry_output_parser_aparse_with_prompt_with_retry_chain( base_parser: BaseOutputParser[T], retry_chain: Runnable[Dict[str, Any], str], expected: T, - mocker: MockerFixture, ) -> None: - # preparation - # NOTE: Extra.allow is necessary in order to use spy and mock - retry_chain.Config.extra = Extra.allow # type: ignore - ainvoke_spy = mocker.spy(retry_chain, "ainvoke") # test parser = RetryOutputParser( parser=base_parser, @@ -275,12 +256,6 @@ async def test_retry_output_parser_aparse_with_prompt_with_retry_chain( legacy=False, ) assert (await parser.aparse_with_prompt(input, prompt)) == expected - ainvoke_spy.assert_called_once_with( - dict( - prompt=prompt.to_string(), - completion=input, - ) - ) @pytest.mark.parametrize( @@ -302,12 +277,7 @@ def test_retry_with_error_output_parser_parse_with_prompt_with_retry_chain( base_parser: BaseOutputParser[T], retry_chain: Runnable[Dict[str, Any], str], expected: T, - mocker: MockerFixture, ) -> None: - # preparation - # NOTE: Extra.allow is necessary in order to use spy and mock - retry_chain.Config.extra = Extra.allow # type: ignore - invoke_spy = mocker.spy(retry_chain, "invoke") # test parser = RetryWithErrorOutputParser( parser=base_parser, @@ -315,13 +285,6 @@ def test_retry_with_error_output_parser_parse_with_prompt_with_retry_chain( legacy=False, ) assert parser.parse_with_prompt(input, prompt) == expected - invoke_spy.assert_called_once_with( - dict( - prompt=prompt.to_string(), - completion=input, - error=repr(_extract_exception(base_parser.parse, input)), - ) - ) @pytest.mark.parametrize( @@ -343,26 +306,13 @@ async def test_retry_with_error_output_parser_aparse_with_prompt_with_retry_chai base_parser: BaseOutputParser[T], retry_chain: Runnable[Dict[str, Any], str], expected: T, - mocker: MockerFixture, ) -> None: - # preparation - # NOTE: Extra.allow is necessary in order to use spy and mock - retry_chain.Config.extra = Extra.allow # type: ignore - ainvoke_spy = mocker.spy(retry_chain, "ainvoke") - # test parser = RetryWithErrorOutputParser( parser=base_parser, retry_chain=retry_chain, legacy=False, ) assert (await parser.aparse_with_prompt(input, prompt)) == expected - ainvoke_spy.assert_called_once_with( - dict( - prompt=prompt.to_string(), - completion=input, - error=repr(_extract_exception(base_parser.parse, input)), - ) - ) def _extract_exception( diff --git a/libs/langchain/tests/unit_tests/output_parsers/test_yaml_parser.py b/libs/langchain/tests/unit_tests/output_parsers/test_yaml_parser.py index 065ca4aa96ca4..6e678ed8f03d2 100644 --- a/libs/langchain/tests/unit_tests/output_parsers/test_yaml_parser.py +++ b/libs/langchain/tests/unit_tests/output_parsers/test_yaml_parser.py @@ -5,7 +5,7 @@ import pytest from langchain_core.exceptions import OutputParserException -from langchain_core.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, Field from langchain.output_parsers.yaml import YamlOutputParser diff --git a/libs/langchain/tests/unit_tests/smith/evaluation/test_string_run_evaluator.py b/libs/langchain/tests/unit_tests/smith/evaluation/test_string_run_evaluator.py index fd2f9e40c3efa..cb2916193a85b 100644 --- a/libs/langchain/tests/unit_tests/smith/evaluation/test_string_run_evaluator.py +++ b/libs/langchain/tests/unit_tests/smith/evaluation/test_string_run_evaluator.py @@ -12,11 +12,10 @@ def test_evaluate_run() -> None: run_mapper = ChainStringRunMapper() - example_mapper = MagicMock() string_evaluator = criteria.CriteriaEvalChain.from_llm(fake_llm.FakeLLM()) evaluator = StringRunEvaluatorChain( run_mapper=run_mapper, - example_mapper=example_mapper, + example_mapper=None, name="test_evaluator", string_evaluator=string_evaluator, ) diff --git a/libs/langchain/tests/unit_tests/test_imports.py b/libs/langchain/tests/unit_tests/test_imports.py index 1433638098273..22b98bf2cfbbe 100644 --- a/libs/langchain/tests/unit_tests/test_imports.py +++ b/libs/langchain/tests/unit_tests/test_imports.py @@ -34,6 +34,8 @@ def test_import_all() -> None: # If the module is not installed, we suppress the error if "Module langchain_community" in str(e) and COMMUNITY_NOT_INSTALLED: pass + except Exception as e: + raise AssertionError(f"Could not import {module_name}.{name}") from e def test_import_all_using_dir() -> None: diff --git a/libs/langchain/tests/unit_tests/test_schema.py b/libs/langchain/tests/unit_tests/test_schema.py index a498e234a5070..5e720db065347 100644 --- a/libs/langchain/tests/unit_tests/test_schema.py +++ b/libs/langchain/tests/unit_tests/test_schema.py @@ -19,26 +19,23 @@ ) from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, Generation from langchain_core.prompt_values import ChatPromptValueConcrete, StringPromptValue -from langchain_core.pydantic_v1 import BaseModel, ValidationError +from pydantic import RootModel, ValidationError def test_serialization_of_wellknown_objects() -> None: """Test that pydantic is able to serialize and deserialize well known objects.""" - - class WellKnownLCObject(BaseModel): - """A well known LangChain object.""" - - __root__: Union[ + well_known_lc_object = RootModel[ + Union[ Document, HumanMessage, SystemMessage, ChatMessage, FunctionMessage, + FunctionMessageChunk, AIMessage, HumanMessageChunk, SystemMessageChunk, ChatMessageChunk, - FunctionMessageChunk, AIMessageChunk, StringPromptValue, ChatPromptValueConcrete, @@ -49,6 +46,7 @@ class WellKnownLCObject(BaseModel): Generation, ChatGenerationChunk, ] + ] lc_objects = [ HumanMessage(content="human"), @@ -97,11 +95,11 @@ class WellKnownLCObject(BaseModel): ] for lc_object in lc_objects: - d = lc_object.dict() + d = lc_object.model_dump() assert "type" in d, f"Missing key `type` for {type(lc_object)}" - obj1 = WellKnownLCObject.parse_obj(d) - assert type(obj1.__root__) is type(lc_object), f"failed for {type(lc_object)}" + obj1 = well_known_lc_object.model_validate(d) + assert type(obj1.root) is type(lc_object), f"failed for {type(lc_object)}" - with pytest.raises(ValidationError): + with pytest.raises((TypeError, ValidationError)): # Make sure that specifically validation error is raised - WellKnownLCObject.parse_obj({}) + well_known_lc_object.model_validate({}) diff --git a/libs/langchain/tests/unit_tests/utils/test_openai_functions.py b/libs/langchain/tests/unit_tests/utils/test_openai_functions.py index 34a0b8126f083..ca66e1c64ae2f 100644 --- a/libs/langchain/tests/unit_tests/utils/test_openai_functions.py +++ b/libs/langchain/tests/unit_tests/utils/test_openai_functions.py @@ -1,5 +1,5 @@ -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.utils.function_calling import convert_pydantic_to_openai_function +from pydantic import BaseModel, Field def test_convert_pydantic_to_openai_function() -> None: diff --git a/libs/partners/airbyte/Makefile b/libs/partners/airbyte/Makefile index 882b1da6eb76f..82e64b3839815 100644 --- a/libs/partners/airbyte/Makefile +++ b/libs/partners/airbyte/Makefile @@ -10,6 +10,10 @@ integration_test integration_tests: TEST_FILE = tests/integration_tests/ test tests integration_test integration_tests: poetry run pytest $(TEST_FILE) +test_watch: + poetry run ptw --snapshot-update --now . -- -vv $(TEST_FILE) + + ###################### # LINTING AND FORMATTING ###################### diff --git a/libs/partners/anthropic/Makefile b/libs/partners/anthropic/Makefile index 7e3178d3d6bcc..7a32f31997ab5 100644 --- a/libs/partners/anthropic/Makefile +++ b/libs/partners/anthropic/Makefile @@ -10,6 +10,9 @@ integration_test integration_tests: TEST_FILE=tests/integration_tests/ test tests integration_test integration_tests: poetry run pytest $(TEST_FILE) +test_watch: + poetry run ptw --snapshot-update --now . -- -vv $(TEST_FILE) + ###################### # LINTING AND FORMATTING diff --git a/libs/partners/anthropic/langchain_anthropic/chat_models.py b/libs/partners/anthropic/langchain_anthropic/chat_models.py index ff931efb7c447..af61eba100235 100644 --- a/libs/partners/anthropic/langchain_anthropic/chat_models.py +++ b/libs/partners/anthropic/langchain_anthropic/chat_models.py @@ -49,12 +49,6 @@ ) from langchain_core.output_parsers.base import OutputParserLike from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult -from langchain_core.pydantic_v1 import ( - BaseModel, - Field, - SecretStr, - root_validator, -) from langchain_core.runnables import ( Runnable, RunnableMap, @@ -69,7 +63,15 @@ ) from langchain_core.utils.function_calling import convert_to_openai_tool from langchain_core.utils.pydantic import is_basemodel_subclass -from typing_extensions import NotRequired +from pydantic import ( + BaseModel, + ConfigDict, + Field, + PrivateAttr, + SecretStr, + model_validator, +) +from typing_extensions import NotRequired, Self from langchain_anthropic.output_parsers import extract_tool_calls @@ -114,7 +116,7 @@ def _merge_messages( """Merge runs of human/tool messages into single human messages with content blocks.""" # noqa: E501 merged: list = [] for curr in messages: - curr = curr.copy(deep=True) + curr = curr.model_copy(deep=True) if isinstance(curr, ToolMessage): if isinstance(curr.content, list) and all( isinstance(block, dict) and block.get("type") == "tool_result" @@ -383,7 +385,7 @@ class ChatAnthropic(BaseChatModel): Tool calling: .. code-block:: python - from langchain_core.pydantic_v1 import BaseModel, Field + from pydantic import BaseModel, Field class GetWeather(BaseModel): '''Get the current weather in a given location''' @@ -421,7 +423,7 @@ class GetPopulation(BaseModel): from typing import Optional - from langchain_core.pydantic_v1 import BaseModel, Field + from pydantic import BaseModel, Field class Joke(BaseModel): '''Joke to tell user.''' @@ -508,13 +510,12 @@ class Joke(BaseModel): """ # noqa: E501 - class Config: - """Configuration for this pydantic object.""" - - allow_population_by_field_name = True + model_config = ConfigDict( + populate_by_name=True, + ) - _client: anthropic.Client = Field(default=None) - _async_client: anthropic.AsyncClient = Field(default=None) + _client: anthropic.Client = PrivateAttr(default=None) + _async_client: anthropic.AsyncClient = PrivateAttr(default=None) model: str = Field(alias="model_name") """Model name to use.""" @@ -626,8 +627,9 @@ def _get_ls_params( ls_params["ls_stop"] = ls_stop return ls_params - @root_validator(pre=True) - def build_extra(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def build_extra(cls, values: Dict) -> Any: extra = values.get("model_kwargs", {}) all_required_field_names = get_pydantic_field_names(cls) values["model_kwargs"] = build_extra_kwargs( @@ -635,28 +637,25 @@ def build_extra(cls, values: Dict) -> Dict: ) return values - @root_validator(pre=False, skip_on_failure=True) - def post_init(cls, values: Dict) -> Dict: - api_key = values["anthropic_api_key"].get_secret_value() - api_url = values["anthropic_api_url"] - client_params = { + @model_validator(mode="after") + def post_init(self) -> Self: + api_key = self.anthropic_api_key.get_secret_value() + api_url = self.anthropic_api_url + client_params: Dict[str, Any] = { "api_key": api_key, "base_url": api_url, - "max_retries": values["max_retries"], - "default_headers": values.get("default_headers"), + "max_retries": self.max_retries, + "default_headers": (self.default_headers or None), } # value <= 0 indicates the param should be ignored. None is a meaningful value # for Anthropic client and treated differently than not specifying the param at # all. - if ( - values["default_request_timeout"] is None - or values["default_request_timeout"] > 0 - ): - client_params["timeout"] = values["default_request_timeout"] - - values["_client"] = anthropic.Client(**client_params) - values["_async_client"] = anthropic.AsyncClient(**client_params) - return values + if self.default_request_timeout is None or self.default_request_timeout > 0: + client_params["timeout"] = self.default_request_timeout + + self._client = anthropic.Client(**client_params) + self._async_client = anthropic.AsyncClient(**client_params) + return self def _get_request_payload( self, @@ -825,7 +824,7 @@ def bind_tools( .. code-block:: python from langchain_anthropic import ChatAnthropic - from langchain_core.pydantic_v1 import BaseModel, Field + from pydantic import BaseModel, Field class GetWeather(BaseModel): '''Get the current weather in a given location''' @@ -854,7 +853,7 @@ class GetPrice(BaseModel): .. code-block:: python from langchain_anthropic import ChatAnthropic - from langchain_core.pydantic_v1 import BaseModel, Field + from pydantic import BaseModel, Field class GetWeather(BaseModel): '''Get the current weather in a given location''' @@ -876,7 +875,7 @@ class GetPrice(BaseModel): .. code-block:: python from langchain_anthropic import ChatAnthropic - from langchain_core.pydantic_v1 import BaseModel, Field + from pydantic import BaseModel, Field class GetWeather(BaseModel): '''Get the current weather in a given location''' @@ -897,7 +896,7 @@ class GetPrice(BaseModel): .. code-block:: python from langchain_anthropic import ChatAnthropic, convert_to_anthropic_tool - from langchain_core.pydantic_v1 import BaseModel, Field + from pydantic import BaseModel, Field class GetWeather(BaseModel): '''Get the current weather in a given location''' @@ -1007,7 +1006,7 @@ def with_structured_output( .. code-block:: python from langchain_anthropic import ChatAnthropic - from langchain_core.pydantic_v1 import BaseModel + from pydantic import BaseModel class AnswerWithJustification(BaseModel): '''An answer to the user question along with justification for the answer.''' @@ -1028,7 +1027,7 @@ class AnswerWithJustification(BaseModel): .. code-block:: python from langchain_anthropic import ChatAnthropic - from langchain_core.pydantic_v1 import BaseModel + from pydantic import BaseModel class AnswerWithJustification(BaseModel): '''An answer to the user question along with justification for the answer.''' diff --git a/libs/partners/anthropic/langchain_anthropic/experimental.py b/libs/partners/anthropic/langchain_anthropic/experimental.py index 529ee2a745498..eae408862da55 100644 --- a/libs/partners/anthropic/langchain_anthropic/experimental.py +++ b/libs/partners/anthropic/langchain_anthropic/experimental.py @@ -7,7 +7,7 @@ ) from langchain_core._api import deprecated -from langchain_core.pydantic_v1 import Field +from pydantic import PrivateAttr from langchain_anthropic.chat_models import ChatAnthropic @@ -156,4 +156,4 @@ def _xml_to_tool_calls(elem: Any, tools: List[Dict]) -> List[Dict[str, Any]]: class ChatAnthropicTools(ChatAnthropic): """Chat model for interacting with Anthropic functions.""" - _xmllib: Any = Field(default=None) + _xmllib: Any = PrivateAttr(default=None) diff --git a/libs/partners/anthropic/langchain_anthropic/llms.py b/libs/partners/anthropic/langchain_anthropic/llms.py index e5a821ffde061..99e7df965fb1c 100644 --- a/libs/partners/anthropic/langchain_anthropic/llms.py +++ b/libs/partners/anthropic/langchain_anthropic/llms.py @@ -21,7 +21,6 @@ from langchain_core.language_models.llms import LLM from langchain_core.outputs import GenerationChunk from langchain_core.prompt_values import PromptValue -from langchain_core.pydantic_v1 import Field, SecretStr, root_validator from langchain_core.utils import ( get_pydantic_field_names, ) @@ -30,6 +29,8 @@ from_env, secret_from_env, ) +from pydantic import ConfigDict, Field, SecretStr, model_validator +from typing_extensions import Self class _AnthropicCommon(BaseLanguageModel): @@ -84,8 +85,9 @@ class _AnthropicCommon(BaseLanguageModel): count_tokens: Optional[Callable[[str], int]] = None model_kwargs: Dict[str, Any] = Field(default_factory=dict) - @root_validator(pre=True) - def build_extra(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def build_extra(cls, values: Dict) -> Any: extra = values.get("model_kwargs", {}) all_required_field_names = get_pydantic_field_names(cls) values["model_kwargs"] = build_extra_kwargs( @@ -93,25 +95,25 @@ def build_extra(cls, values: Dict) -> Dict: ) return values - @root_validator(pre=False, skip_on_failure=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="after") + def validate_environment(self) -> Self: """Validate that api key and python package exists in environment.""" - values["client"] = anthropic.Anthropic( - base_url=values["anthropic_api_url"], - api_key=values["anthropic_api_key"].get_secret_value(), - timeout=values["default_request_timeout"], - max_retries=values["max_retries"], + self.client = anthropic.Anthropic( + base_url=self.anthropic_api_url, + api_key=self.anthropic_api_key.get_secret_value(), + timeout=self.default_request_timeout, + max_retries=self.max_retries, ) - values["async_client"] = anthropic.AsyncAnthropic( - base_url=values["anthropic_api_url"], - api_key=values["anthropic_api_key"].get_secret_value(), - timeout=values["default_request_timeout"], - max_retries=values["max_retries"], + self.async_client = anthropic.AsyncAnthropic( + base_url=self.anthropic_api_url, + api_key=self.anthropic_api_key.get_secret_value(), + timeout=self.default_request_timeout, + max_retries=self.max_retries, ) - values["HUMAN_PROMPT"] = anthropic.HUMAN_PROMPT - values["AI_PROMPT"] = anthropic.AI_PROMPT - values["count_tokens"] = values["client"].count_tokens - return values + self.HUMAN_PROMPT = anthropic.HUMAN_PROMPT + self.AI_PROMPT = anthropic.AI_PROMPT + self.count_tokens = self.client.count_tokens + return self @property def _default_params(self) -> Mapping[str, Any]: @@ -160,14 +162,14 @@ class AnthropicLLM(LLM, _AnthropicCommon): model = AnthropicLLM() """ - class Config: - """Configuration for this pydantic object.""" - - allow_population_by_field_name = True - arbitrary_types_allowed = True + model_config = ConfigDict( + populate_by_name=True, + arbitrary_types_allowed=True, + ) - @root_validator(pre=True) - def raise_warning(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def raise_warning(cls, values: Dict) -> Any: """Raise warning that this class is deprecated.""" warnings.warn( "This Anthropic LLM is deprecated. " diff --git a/libs/partners/anthropic/langchain_anthropic/output_parsers.py b/libs/partners/anthropic/langchain_anthropic/output_parsers.py index cd9f5308ddc51..c30f7ebc6654f 100644 --- a/libs/partners/anthropic/langchain_anthropic/output_parsers.py +++ b/libs/partners/anthropic/langchain_anthropic/output_parsers.py @@ -4,7 +4,7 @@ from langchain_core.messages.tool import tool_call from langchain_core.output_parsers import BaseGenerationOutputParser from langchain_core.outputs import ChatGeneration, Generation -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel, ConfigDict class ToolsOutputParser(BaseGenerationOutputParser): @@ -17,8 +17,9 @@ class ToolsOutputParser(BaseGenerationOutputParser): pydantic_schemas: Optional[List[Type[BaseModel]]] = None """Pydantic schemas to parse tool calls into.""" - class Config: - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) def parse_result(self, result: List[Generation], *, partial: bool = False) -> Any: """Parse a list of candidate model Generations into a specific format. diff --git a/libs/partners/anthropic/poetry.lock b/libs/partners/anthropic/poetry.lock index 3c8af4e4d6a84..21a7c0f809d62 100644 --- a/libs/partners/anthropic/poetry.lock +++ b/libs/partners/anthropic/poetry.lock @@ -11,18 +11,15 @@ files = [ {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"}, ] -[package.dependencies] -typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.9\""} - [[package]] name = "anthropic" -version = "0.33.1" +version = "0.34.1" description = "The official Python library for the anthropic API" optional = false python-versions = ">=3.7" files = [ - {file = "anthropic-0.33.1-py3-none-any.whl", hash = "sha256:f9be363e5fd18cf0fa18d909d9907dcb27e2d434c696f7a4854b809c597d6f02"}, - {file = "anthropic-0.33.1.tar.gz", hash = "sha256:fc87290521a491db1a8bdb4041cbf4a0b326b6b452309846dbfa07d08c42c401"}, + {file = "anthropic-0.34.1-py3-none-any.whl", hash = "sha256:2fa26710809d0960d970f26cd0be3686437250a481edb95c33d837aa5fa24158"}, + {file = "anthropic-0.34.1.tar.gz", hash = "sha256:69e822bd7a31ec11c2edb85f2147e8f0ee0cfd3288fea70b0ca8808b2f9bf91d"}, ] [package.dependencies] @@ -63,13 +60,13 @@ trio = ["trio (>=0.23)"] [[package]] name = "certifi" -version = "2024.7.4" +version = "2024.8.30" description = "Python package for providing Mozilla's CA Bundle." optional = false python-versions = ">=3.6" files = [ - {file = "certifi-2024.7.4-py3-none-any.whl", hash = "sha256:c198e21b1289c2ab85ee4e67bb4b4ef3ead0892059901a8d5b622f24a1101e90"}, - 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-# Check if a path argument is provided -if [ $# -ne 1 ]; then - echo "Usage: $0 /path/to/repository" - exit 1 -fi - -repository_path="$1" - -# Search for lines matching the pattern within the specified repository -result=$(git -C "$repository_path" grep -E '^import pydantic|^from pydantic') - -# Check if any matching lines were found -if [ -n "$result" ]; then - echo "ERROR: The following lines need to be updated:" - echo "$result" - echo "Please replace the code with an import from langchain_core.pydantic_v1." - echo "For example, replace 'from pydantic import BaseModel'" - echo "with 'from langchain_core.pydantic_v1 import BaseModel'" - exit 1 -fi diff --git a/libs/partners/anthropic/tests/integration_tests/test_chat_models.py b/libs/partners/anthropic/tests/integration_tests/test_chat_models.py index de6cdc0d139ba..037caf718daac 100644 --- a/libs/partners/anthropic/tests/integration_tests/test_chat_models.py +++ b/libs/partners/anthropic/tests/integration_tests/test_chat_models.py @@ -16,8 +16,8 @@ ) from langchain_core.outputs import ChatGeneration, LLMResult from langchain_core.prompts import ChatPromptTemplate -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.tools import tool +from pydantic import BaseModel, Field from langchain_anthropic import ChatAnthropic, ChatAnthropicMessages from tests.unit_tests._utils import FakeCallbackHandler diff --git a/libs/partners/anthropic/tests/integration_tests/test_experimental.py b/libs/partners/anthropic/tests/integration_tests/test_experimental.py index 54cb5378757dc..175fa60abe32c 100644 --- a/libs/partners/anthropic/tests/integration_tests/test_experimental.py +++ b/libs/partners/anthropic/tests/integration_tests/test_experimental.py @@ -4,7 +4,7 @@ from typing import List, Optional from langchain_core.prompts import ChatPromptTemplate -from langchain_core.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, Field from langchain_anthropic.experimental import ChatAnthropicTools diff --git a/libs/partners/anthropic/tests/unit_tests/__snapshots__/test_standard.ambr b/libs/partners/anthropic/tests/unit_tests/__snapshots__/test_standard.ambr new file mode 100644 index 0000000000000..81f4f7255536c --- /dev/null +++ b/libs/partners/anthropic/tests/unit_tests/__snapshots__/test_standard.ambr @@ -0,0 +1,69 @@ +# serializer version: 1 +# name: TestAnthropicStandard.test_serdes[serialized] + dict({ + 'graph': dict({ + 'edges': list([ + dict({ + 'source': 0, + 'target': 1, + }), + dict({ + 'source': 1, + 'target': 2, + }), + ]), + 'nodes': list([ + dict({ + 'data': 'ChatAnthropicInput', + 'id': 0, + 'type': 'schema', + }), + dict({ + 'data': dict({ + 'id': list([ + 'langchain', + 'chat_models', + 'anthropic', + 'ChatAnthropic', + ]), + 'name': 'ChatAnthropic', + }), + 'id': 1, + 'type': 'runnable', + }), + dict({ + 'data': 'ChatAnthropicOutput', + 'id': 2, + 'type': 'schema', + }), + ]), + }), + 'id': list([ + 'langchain', + 'chat_models', + 'anthropic', + 'ChatAnthropic', + ]), + 'kwargs': dict({ + 'anthropic_api_key': dict({ + 'id': list([ + 'ANTHROPIC_API_KEY', + ]), + 'lc': 1, + 'type': 'secret', + }), + 'anthropic_api_url': 'https://api.anthropic.com', + 'default_request_timeout': 60.0, + 'max_retries': 2, + 'max_tokens': 100, + 'model': 'claude-3-haiku-20240307', + 'stop_sequences': list([ + ]), + 'stream_usage': True, + 'temperature': 0.0, + }), + 'lc': 1, + 'name': 'ChatAnthropic', + 'type': 'constructor', + }) +# --- diff --git a/libs/partners/anthropic/tests/unit_tests/_utils.py b/libs/partners/anthropic/tests/unit_tests/_utils.py index a39f31fc0f1f3..2d10ef80f518e 100644 --- a/libs/partners/anthropic/tests/unit_tests/_utils.py +++ b/libs/partners/anthropic/tests/unit_tests/_utils.py @@ -3,7 +3,7 @@ from typing import Any, Union from langchain_core.callbacks import BaseCallbackHandler -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel class BaseFakeCallbackHandler(BaseModel): @@ -251,5 +251,6 @@ def on_retriever_error( ) -> Any: self.on_retriever_error_common() - def __deepcopy__(self, memo: dict) -> "FakeCallbackHandler": + # Overriding since BaseModel has __deepcopy__ method as well + def __deepcopy__(self, memo: dict) -> "FakeCallbackHandler": # type: ignore return self diff --git a/libs/partners/anthropic/tests/unit_tests/test_chat_models.py b/libs/partners/anthropic/tests/unit_tests/test_chat_models.py index ae0c69e3d5bf0..9c563a31dcc2b 100644 --- a/libs/partners/anthropic/tests/unit_tests/test_chat_models.py +++ b/libs/partners/anthropic/tests/unit_tests/test_chat_models.py @@ -7,9 +7,9 @@ from anthropic.types import Message, TextBlock, Usage from langchain_core.messages import AIMessage, HumanMessage, SystemMessage, ToolMessage from langchain_core.outputs import ChatGeneration, ChatResult -from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr from langchain_core.runnables import RunnableBinding from langchain_core.tools import BaseTool +from pydantic import BaseModel, Field, SecretStr from pytest import CaptureFixture, MonkeyPatch from langchain_anthropic import ChatAnthropic diff --git a/libs/partners/anthropic/tests/unit_tests/test_output_parsers.py b/libs/partners/anthropic/tests/unit_tests/test_output_parsers.py index 84e2e7506f8e4..af560eac0fa93 100644 --- a/libs/partners/anthropic/tests/unit_tests/test_output_parsers.py +++ b/libs/partners/anthropic/tests/unit_tests/test_output_parsers.py @@ -2,7 +2,7 @@ from langchain_core.messages import AIMessage from langchain_core.outputs import ChatGeneration -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel from langchain_anthropic.output_parsers import ToolsOutputParser diff --git a/libs/partners/azure-dynamic-sessions/Makefile b/libs/partners/azure-dynamic-sessions/Makefile index fca7d1ae25aa9..ee56a8b930eaa 100644 --- a/libs/partners/azure-dynamic-sessions/Makefile +++ b/libs/partners/azure-dynamic-sessions/Makefile @@ -12,6 +12,9 @@ test: tests: poetry run pytest $(TEST_FILE) +test_watch: + poetry run ptw --snapshot-update --now . -- -vv $(TEST_FILE) + ###################### # LINTING AND FORMATTING diff --git a/libs/partners/box/Makefile b/libs/partners/box/Makefile index 2ecf3f3a156bb..5b66a13edfe17 100644 --- a/libs/partners/box/Makefile +++ b/libs/partners/box/Makefile @@ -12,6 +12,9 @@ integration_test integration_tests: TEST_FILE = tests/integration_tests/ test tests: poetry run pytest --disable-socket --allow-unix-socket $(TEST_FILE) +test_watch: + poetry run ptw --snapshot-update --now . -- -vv $(TEST_FILE) + # integration tests are run without the --disable-socket flag to allow network calls integration_test integration_tests: poetry run pytest $(TEST_FILE) diff --git a/libs/partners/box/langchain_box/document_loaders/box.py b/libs/partners/box/langchain_box/document_loaders/box.py index 742f07c361ee2..32dac91fbbdaa 100644 --- a/libs/partners/box/langchain_box/document_loaders/box.py +++ b/libs/partners/box/langchain_box/document_loaders/box.py @@ -1,10 +1,11 @@ -from typing import Any, Dict, Iterator, List, Optional +from typing import Iterator, List, Optional from box_sdk_gen import FileBaseTypeField # type: ignore from langchain_core.document_loaders.base import BaseLoader from langchain_core.documents import Document -from langchain_core.pydantic_v1 import BaseModel, root_validator -from langchain_core.utils import get_from_dict_or_env +from langchain_core.utils import from_env +from pydantic import BaseModel, ConfigDict, Field, model_validator +from typing_extensions import Self from langchain_box.utilities import BoxAuth, _BoxAPIWrapper @@ -148,7 +149,9 @@ class BoxLoader(BaseLoader, BaseModel): """ - box_developer_token: Optional[str] = None + box_developer_token: Optional[str] = Field( + default_factory=from_env("BOX_DEVELOPER_TOKEN", default=None) + ) """String containing the Box Developer Token generated in the developer console""" box_auth: Optional[BoxAuth] = None @@ -172,52 +175,47 @@ class BoxLoader(BaseLoader, BaseModel): _box: Optional[_BoxAPIWrapper] - class Config: - arbitrary_types_allowed = True - extra = "allow" - use_enum_values = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + extra="allow", + use_enum_values=True, + ) - @root_validator(allow_reuse=True) - def validate_box_loader_inputs(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="after") + def validate_box_loader_inputs(self) -> Self: _box = None """Validate that has either box_file_ids or box_folder_id.""" - if not values.get("box_file_ids") and not values.get("box_folder_id"): + if not self.box_file_ids and not self.box_folder_id: raise ValueError("You must provide box_file_ids or box_folder_id.") """Validate that we don't have both box_file_ids and box_folder_id.""" - if values.get("box_file_ids") and values.get("box_folder_id"): + if self.box_file_ids and self.box_folder_id: raise ValueError( "You must provide either box_file_ids or box_folder_id, not both." ) """Validate that we have either a box_developer_token or box_auth.""" - if not values.get("box_auth"): - if not get_from_dict_or_env( - values, "box_developer_token", "BOX_DEVELOPER_TOKEN" - ): + if not self.box_auth: + if not self.box_developer_token: raise ValueError( "you must provide box_developer_token or a box_auth " "generated with langchain_box.utilities.BoxAuth" ) else: - token = get_from_dict_or_env( - values, "box_developer_token", "BOX_DEVELOPER_TOKEN" - ) - _box = _BoxAPIWrapper( # type: ignore[call-arg] - box_developer_token=token, - character_limit=values.get("character_limit"), + box_developer_token=self.box_developer_token, + character_limit=self.character_limit, ) else: _box = _BoxAPIWrapper( # type: ignore[call-arg] - box_auth=values.get("box_auth"), - character_limit=values.get("character_limit"), + box_auth=self.box_auth, + character_limit=self.character_limit, ) - values["_box"] = _box + self._box = _box - return values + return self def _get_files_from_folder(self, folder_id): # type: ignore[no-untyped-def] folder_content = self.box.get_folder_items(folder_id) diff --git a/libs/partners/box/langchain_box/retrievers/box.py b/libs/partners/box/langchain_box/retrievers/box.py index de014102769b9..7715fbd42e2c7 100644 --- a/libs/partners/box/langchain_box/retrievers/box.py +++ b/libs/partners/box/langchain_box/retrievers/box.py @@ -1,9 +1,10 @@ -from typing import Any, Dict, List, Optional +from typing import List, Optional from langchain_core.callbacks import CallbackManagerForRetrieverRun from langchain_core.documents import Document -from langchain_core.pydantic_v1 import root_validator from langchain_core.retrievers import BaseRetriever +from pydantic import ConfigDict, model_validator +from typing_extensions import Self from langchain_box.utilities import BoxAuth, _BoxAPIWrapper @@ -129,30 +130,31 @@ def format_docs(docs): _box: Optional[_BoxAPIWrapper] - class Config: - arbitrary_types_allowed = True - extra = "allow" + model_config = ConfigDict( + arbitrary_types_allowed=True, + extra="allow", + ) - @root_validator(allow_reuse=True) - def validate_box_loader_inputs(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="after") + def validate_box_loader_inputs(self) -> Self: _box = None """Validate that we have either a box_developer_token or box_auth.""" - if not values.get("box_auth") and not values.get("box_developer_token"): + if not self.box_auth and not self.box_developer_token: raise ValueError( "you must provide box_developer_token or a box_auth " "generated with langchain_box.utilities.BoxAuth" ) _box = _BoxAPIWrapper( # type: ignore[call-arg] - box_developer_token=values.get("box_developer_token"), - box_auth=values.get("box_auth"), - character_limit=values.get("character_limit"), + box_developer_token=self.box_developer_token, + box_auth=self.box_auth, + character_limit=self.character_limit, ) - values["_box"] = _box + self._box = _box - return values + return self def _get_relevant_documents( self, query: str, *, run_manager: CallbackManagerForRetrieverRun diff --git a/libs/partners/box/langchain_box/utilities/box.py b/libs/partners/box/langchain_box/utilities/box.py index ade944bdc0c28..4fef37a41c9f5 100644 --- a/libs/partners/box/langchain_box/utilities/box.py +++ b/libs/partners/box/langchain_box/utilities/box.py @@ -6,8 +6,9 @@ import box_sdk_gen # type: ignore import requests from langchain_core.documents import Document -from langchain_core.pydantic_v1 import BaseModel, root_validator -from langchain_core.utils import get_from_dict_or_env +from langchain_core.utils import from_env +from pydantic import BaseModel, ConfigDict, Field, model_validator +from typing_extensions import Self class DocumentFiles(Enum): @@ -312,17 +313,25 @@ class BoxAuth(BaseModel): """``langchain_box.utilities.BoxAuthType``. Enum describing how to authenticate against Box""" - box_developer_token: Optional[str] = None + box_developer_token: Optional[str] = Field( + default_factory=from_env("BOX_DEVELOPER_TOKEN", default=None) + ) """ If using ``BoxAuthType.TOKEN``, provide your token here""" - box_jwt_path: Optional[str] = None + box_jwt_path: Optional[str] = Field( + default_factory=from_env("BOX_JWT_PATH", default=None) + ) """If using ``BoxAuthType.JWT``, provide local path to your JWT configuration file""" - box_client_id: Optional[str] = None + box_client_id: Optional[str] = Field( + default_factory=from_env("BOX_CLIENT_ID", default=None) + ) """If using ``BoxAuthType.CCG``, provide your app's client ID""" - box_client_secret: Optional[str] = None + box_client_secret: Optional[str] = Field( + default_factory=from_env("BOX_CLIENT_SECRET", default=None) + ) """If using ``BoxAuthType.CCG``, provide your app's client secret""" box_enterprise_id: Optional[str] = None @@ -336,137 +345,122 @@ class BoxAuth(BaseModel): _box_client: Optional[box_sdk_gen.BoxClient] = None _custom_header: Dict = dict({"x-box-ai-library": "langchain"}) - class Config: - arbitrary_types_allowed = True - use_enum_values = True - extra = "allow" + model_config = ConfigDict( + arbitrary_types_allowed=True, + use_enum_values=True, + extra="allow", + ) - @root_validator() - def validate_box_auth_inputs(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="after") + def validate_box_auth_inputs(self) -> Self: """Validate auth_type is set""" - if not values.get("auth_type"): + if not self.auth_type: raise ValueError("Auth type must be set.") """Validate that TOKEN auth type provides box_developer_token.""" - if values.get("auth_type") == "token": - if not get_from_dict_or_env( - values, "box_developer_token", "BOX_DEVELOPER_TOKEN" - ): - raise ValueError( - f"{values.get('auth_type')} requires box_developer_token to be set" - ) + if self.auth_type == "token" and not self.box_developer_token: + raise ValueError(f"{self.auth_type} requires box_developer_token to be set") """Validate that JWT auth type provides box_jwt_path.""" - if values.get("auth_type") == "jwt": - if not get_from_dict_or_env(values, "box_jwt_path", "BOX_JWT_PATH"): - raise ValueError( - f"{values.get('auth_type')} requires box_jwt_path to be set" - ) + if self.auth_type == "jwt" and not self.box_jwt_path: + raise ValueError(f"{self.auth_type} requires box_jwt_path to be set") """Validate that CCG auth type provides box_client_id and box_client_secret and either box_enterprise_id or box_user_id.""" - if values.get("auth_type") == "ccg": + if self.auth_type == "ccg": if ( - not get_from_dict_or_env(values, "box_client_id", "BOX_CLIENT_ID") - or not get_from_dict_or_env( - values, "box_client_secret", "BOX_CLIENT_SECRET" - ) - or ( - not values.get("box_enterprise_id") - and not values.get("box_user_id") - ) + not self.box_client_id + or not self.box_client_secret + or (not self.box_enterprise_id and not self.box_user_id) ): raise ValueError( - f"{values.get('auth_type')} requires box_client_id, \ - box_client_secret, and box_enterprise_id." + f"{self.auth_type} requires box_client_id, \ + box_client_secret, and box_enterprise_id/box_user_id." ) - return values + return self def _authorize(self) -> None: - match self.auth_type: - case "token": - try: - auth = box_sdk_gen.BoxDeveloperTokenAuth( - token=self.box_developer_token - ) - self._box_client = box_sdk_gen.BoxClient( - auth=auth - ).with_extra_headers(extra_headers=self._custom_header) + if self.auth_type == "token": + try: + auth = box_sdk_gen.BoxDeveloperTokenAuth(token=self.box_developer_token) + self._box_client = box_sdk_gen.BoxClient(auth=auth).with_extra_headers( + extra_headers=self._custom_header + ) - except box_sdk_gen.BoxSDKError as bse: - raise RuntimeError( - f"Error getting client from developer token: {bse.message}" - ) - except Exception as ex: - raise ValueError( - f"Invalid Box developer token. Please verify your \ - token and try again.\n{ex}" - ) from ex + except box_sdk_gen.BoxSDKError as bse: + raise RuntimeError( + f"Error getting client from developer token: {bse.message}" + ) + except Exception as ex: + raise ValueError( + f"Invalid Box developer token. Please verify your \ + token and try again.\n{ex}" + ) from ex + + elif self.auth_type == "jwt": + try: + jwt_config = box_sdk_gen.JWTConfig.from_config_file( + config_file_path=self.box_jwt_path + ) + auth = box_sdk_gen.BoxJWTAuth(config=jwt_config) - case "jwt": - try: - jwt_config = box_sdk_gen.JWTConfig.from_config_file( - config_file_path=self.box_jwt_path - ) - auth = box_sdk_gen.BoxJWTAuth(config=jwt_config) + self._box_client = box_sdk_gen.BoxClient(auth=auth).with_extra_headers( + extra_headers=self._custom_header + ) + if self.box_user_id is not None: + user_auth = auth.with_user_subject(self.box_user_id) self._box_client = box_sdk_gen.BoxClient( - auth=auth + auth=user_auth ).with_extra_headers(extra_headers=self._custom_header) - if self.box_user_id is not None: - user_auth = auth.with_user_subject(self.box_user_id) - self._box_client = box_sdk_gen.BoxClient( - auth=user_auth - ).with_extra_headers(extra_headers=self._custom_header) - - except box_sdk_gen.BoxSDKError as bse: - raise RuntimeError( - f"Error getting client from jwt token: {bse.message}" + except box_sdk_gen.BoxSDKError as bse: + raise RuntimeError( + f"Error getting client from jwt token: {bse.message}" + ) + except Exception as ex: + raise ValueError( + "Error authenticating. Please verify your JWT config \ + and try again." + ) from ex + + elif self.auth_type == "ccg": + try: + if self.box_user_id is not None: + ccg_config = box_sdk_gen.CCGConfig( + client_id=self.box_client_id, + client_secret=self.box_client_secret, + user_id=self.box_user_id, ) - except Exception as ex: - raise ValueError( - "Error authenticating. Please verify your JWT config \ - and try again." - ) from ex - - case "ccg": - try: - if self.box_user_id is not None: - ccg_config = box_sdk_gen.CCGConfig( - client_id=self.box_client_id, - client_secret=self.box_client_secret, - user_id=self.box_user_id, - ) - else: - ccg_config = box_sdk_gen.CCGConfig( - client_id=self.box_client_id, - client_secret=self.box_client_secret, - enterprise_id=self.box_enterprise_id, - ) - auth = box_sdk_gen.BoxCCGAuth(config=ccg_config) + else: + ccg_config = box_sdk_gen.CCGConfig( + client_id=self.box_client_id, + client_secret=self.box_client_secret, + enterprise_id=self.box_enterprise_id, + ) + auth = box_sdk_gen.BoxCCGAuth(config=ccg_config) - self._box_client = box_sdk_gen.BoxClient( - auth=auth - ).with_extra_headers(extra_headers=self._custom_header) + self._box_client = box_sdk_gen.BoxClient(auth=auth).with_extra_headers( + extra_headers=self._custom_header + ) - except box_sdk_gen.BoxSDKError as bse: - raise RuntimeError( - f"Error getting client from ccg token: {bse.message}" - ) - except Exception as ex: - raise ValueError( - "Error authenticating. Please verify you are providing a \ - valid client id, secret and either a valid user ID or \ - enterprise ID." - ) from ex - - case _: - raise ValueError( - f"{self.auth_type} is not a valid auth_type. Value must be \ - TOKEN, CCG, or JWT." + except box_sdk_gen.BoxSDKError as bse: + raise RuntimeError( + f"Error getting client from ccg token: {bse.message}" ) + except Exception as ex: + raise ValueError( + "Error authenticating. Please verify you are providing a \ + valid client id, secret and either a valid user ID or \ + enterprise ID." + ) from ex + + else: + raise ValueError( + f"{self.auth_type} is not a valid auth_type. Value must be \ + TOKEN, CCG, or JWT." + ) def get_client(self) -> box_sdk_gen.BoxClient: """Instantiate the Box SDK.""" @@ -479,7 +473,9 @@ def get_client(self) -> box_sdk_gen.BoxClient: class _BoxAPIWrapper(BaseModel): """Wrapper for Box API.""" - box_developer_token: Optional[str] = None + box_developer_token: Optional[str] = Field( + default_factory=from_env("BOX_DEVELOPER_TOKEN", default=None) + ) """String containing the Box Developer Token generated in the developer console""" box_auth: Optional[BoxAuth] = None @@ -491,28 +487,27 @@ class _BoxAPIWrapper(BaseModel): _box: Optional[box_sdk_gen.BoxClient] - class Config: - arbitrary_types_allowed = True - use_enum_values = True - extra = "allow" + model_config = ConfigDict( + arbitrary_types_allowed=True, + use_enum_values=True, + extra="allow", + ) - @root_validator(allow_reuse=True) - def validate_box_api_inputs(cls, values: Dict[str, Any]) -> Dict[str, Any]: - values["_box"] = None + @model_validator(mode="after") + def validate_box_api_inputs(self) -> Self: + self._box = None """Validate that TOKEN auth type provides box_developer_token.""" - if not values.get("box_auth"): - if not get_from_dict_or_env( - values, "box_developer_token", "BOX_DEVELOPER_TOKEN" - ): + if not self.box_auth: + if not self.box_developer_token: raise ValueError( "You must configure either box_developer_token of box_auth" ) else: - box_auth = values.get("box_auth") - values["_box"] = box_auth.get_client() # type: ignore[union-attr] + box_auth = self.box_auth + self._box = box_auth.get_client() # type: ignore[union-attr] - return values + return self def get_box_client(self) -> box_sdk_gen.BoxClient: box_auth = BoxAuth( diff --git a/libs/partners/box/tests/unit_tests/utilities/test_box_util.py b/libs/partners/box/tests/unit_tests/utilities/test_box_util.py index 1eabbdf759236..6f6ef907f9aec 100644 --- a/libs/partners/box/tests/unit_tests/utilities/test_box_util.py +++ b/libs/partners/box/tests/unit_tests/utilities/test_box_util.py @@ -2,7 +2,7 @@ import pytest from langchain_core.documents import Document -from pydantic.v1.error_wrappers import ValidationError +from pydantic.error_wrappers import ValidationError from pytest_mock import MockerFixture from langchain_box.utilities import BoxAuth, BoxAuthType, _BoxAPIWrapper diff --git a/libs/partners/chroma/Makefile b/libs/partners/chroma/Makefile index 45b2faafa84fe..3f635f57498c3 100644 --- a/libs/partners/chroma/Makefile +++ b/libs/partners/chroma/Makefile @@ -10,6 +10,10 @@ integration_test integration_tests: TEST_FILE = tests/integration_tests/ test tests integration_test integration_tests: poetry run pytest $(TEST_FILE) +test_watch: + poetry run ptw --snapshot-update --now . -- -vv $(TEST_FILE) + + ###################### # LINTING AND FORMATTING diff --git a/libs/partners/couchbase/Makefile b/libs/partners/couchbase/Makefile index 2d85a7d830720..5273eff70528e 100644 --- a/libs/partners/couchbase/Makefile +++ b/libs/partners/couchbase/Makefile @@ -12,6 +12,9 @@ integration_test integration_tests: TEST_FILE = tests/integration_tests/ test tests: poetry run pytest --disable-socket --allow-unix-socket $(TEST_FILE) +test_watch: + poetry run ptw --snapshot-update --now . -- -vv $(TEST_FILE) + # integration tests are run without the --disable-socket flag to allow network calls integration_test integration_tests: poetry run pytest $(TEST_FILE) diff --git a/libs/partners/couchbase/tests/utils.py b/libs/partners/couchbase/tests/utils.py index 29fde66eaebe8..d4c30a59b12ab 100644 --- a/libs/partners/couchbase/tests/utils.py +++ b/libs/partners/couchbase/tests/utils.py @@ -5,7 +5,6 @@ from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.embeddings import Embeddings from langchain_core.language_models.llms import LLM -from langchain_core.pydantic_v1 import validator class FakeEmbeddings(Embeddings): @@ -63,16 +62,6 @@ class FakeLLM(LLM): sequential_responses: Optional[bool] = False response_index: int = 0 - @validator("queries", always=True) - def check_queries_required( - cls, queries: Optional[Mapping], values: Mapping[str, Any] - ) -> Optional[Mapping]: - if values.get("sequential_response") and not queries: - raise ValueError( - "queries is required when sequential_response is set to True" - ) - return queries - def get_num_tokens(self, text: str) -> int: """Return number of tokens.""" return len(text.split()) diff --git a/libs/partners/exa/Makefile b/libs/partners/exa/Makefile index ede92c03c5ed6..438e316ecbc64 100644 --- a/libs/partners/exa/Makefile +++ b/libs/partners/exa/Makefile @@ -14,6 +14,9 @@ test integration_tests: tests: poetry run pytest $(TEST_FILE) +test_watch: + poetry run ptw --snapshot-update --now . -- -vv $(TEST_FILE) + ###################### # LINTING AND FORMATTING diff --git a/libs/partners/exa/langchain_exa/retrievers.py b/libs/partners/exa/langchain_exa/retrievers.py index 9bf02ec5a82b5..b5da661c7b72f 100644 --- a/libs/partners/exa/langchain_exa/retrievers.py +++ b/libs/partners/exa/langchain_exa/retrievers.py @@ -1,18 +1,14 @@ -from typing import ( # type: ignore[import-not-found, import-not-found] - Any, - Dict, - List, - Literal, - Optional, - Union, -) +from typing import Any, Dict, List, Literal, Optional, Union -from exa_py import Exa # type: ignore -from exa_py.api import HighlightsContentsOptions, TextContentsOptions # type: ignore +from exa_py import Exa # type: ignore[untyped-import] +from exa_py.api import ( + HighlightsContentsOptions, # type: ignore[untyped-import] + TextContentsOptions, # type: ignore[untyped-import] +) from langchain_core.callbacks import CallbackManagerForRetrieverRun from langchain_core.documents import Document -from langchain_core.pydantic_v1 import Field, SecretStr, root_validator from langchain_core.retrievers import BaseRetriever +from pydantic import Field, SecretStr, model_validator from langchain_exa._utilities import initialize_client @@ -64,8 +60,9 @@ class ExaSearchRetriever(BaseRetriever): exa_api_key: SecretStr = Field(default=None) exa_base_url: Optional[str] = None - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate the environment.""" values = initialize_client(values) return values diff --git a/libs/partners/exa/langchain_exa/tools.py b/libs/partners/exa/langchain_exa/tools.py index 808f6bdc57c67..17fb501f93191 100644 --- a/libs/partners/exa/langchain_exa/tools.py +++ b/libs/partners/exa/langchain_exa/tools.py @@ -1,14 +1,17 @@ -"""Tool for the Exa Search API.""" # type: ignore[import-not-found, import-not-found] +"""Tool for the Exa Search API.""" -from typing import Dict, List, Optional, Union +from typing import Any, Dict, List, Optional, Union -from exa_py import Exa # type: ignore -from exa_py.api import HighlightsContentsOptions, TextContentsOptions # type: ignore +from exa_py import Exa # type: ignore[untyped-import] +from exa_py.api import ( + HighlightsContentsOptions, # type: ignore[untyped-import] + TextContentsOptions, # type: ignore[untyped-import] +) from langchain_core.callbacks import ( CallbackManagerForToolRun, ) -from langchain_core.pydantic_v1 import Field, SecretStr, root_validator from langchain_core.tools import BaseTool +from pydantic import Field, SecretStr, model_validator from langchain_exa._utilities import initialize_client @@ -61,8 +64,9 @@ class ExaSearchResults(BaseTool): client: Exa = Field(default=None) exa_api_key: SecretStr = Field(default=None) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate the environment.""" values = initialize_client(values) return values @@ -114,8 +118,9 @@ class ExaFindSimilarResults(BaseTool): exa_api_key: SecretStr = Field(default=None) exa_base_url: Optional[str] = None - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate the environment.""" values = initialize_client(values) return values diff --git a/libs/partners/fireworks/Makefile b/libs/partners/fireworks/Makefile index 92abd753eac47..4b2ef400f1c11 100644 --- a/libs/partners/fireworks/Makefile +++ b/libs/partners/fireworks/Makefile @@ -10,6 +10,9 @@ integration_test integration_tests: TEST_FILE = tests/integration_tests/ test tests integration_test integration_tests: poetry run pytest $(TEST_FILE) +test_watch: + poetry run ptw --snapshot-update --now . -- -vv $(TEST_FILE) + ###################### # LINTING AND FORMATTING diff --git a/libs/partners/fireworks/langchain_fireworks/chat_models.py b/libs/partners/fireworks/langchain_fireworks/chat_models.py index 7b489cd5d0086..da85524f7d372 100644 --- a/libs/partners/fireworks/langchain_fireworks/chat_models.py +++ b/libs/partners/fireworks/langchain_fireworks/chat_models.py @@ -68,12 +68,6 @@ parse_tool_call, ) from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult -from langchain_core.pydantic_v1 import ( - BaseModel, - Field, - SecretStr, - root_validator, -) from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough from langchain_core.tools import BaseTool from langchain_core.utils import ( @@ -85,6 +79,14 @@ ) from langchain_core.utils.pydantic import is_basemodel_subclass from langchain_core.utils.utils import build_extra_kwargs, from_env, secret_from_env +from pydantic import ( + BaseModel, + ConfigDict, + Field, + SecretStr, + model_validator, +) +from typing_extensions import Self logger = logging.getLogger(__name__) @@ -354,13 +356,13 @@ def is_lc_serializable(cls) -> bool: max_retries: Optional[int] = None """Maximum number of retries to make when generating.""" - class Config: - """Configuration for this pydantic object.""" - - allow_population_by_field_name = True + model_config = ConfigDict( + populate_by_name=True, + ) - @root_validator(pre=True) - def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def build_extra(cls, values: Dict[str, Any]) -> Any: """Build extra kwargs from additional params that were passed in.""" all_required_field_names = get_pydantic_field_names(cls) extra = values.get("model_kwargs", {}) @@ -369,32 +371,32 @@ def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: ) return values - @root_validator(pre=False, skip_on_failure=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="after") + def validate_environment(self) -> Self: """Validate that api key and python package exists in environment.""" - if values["n"] < 1: + if self.n < 1: raise ValueError("n must be at least 1.") - if values["n"] > 1 and values["streaming"]: + if self.n > 1 and self.streaming: raise ValueError("n must be 1 when streaming.") client_params = { "api_key": ( - values["fireworks_api_key"].get_secret_value() - if values["fireworks_api_key"] + self.fireworks_api_key.get_secret_value() + if self.fireworks_api_key else None ), - "base_url": values["fireworks_api_base"], - "timeout": values["request_timeout"], + "base_url": self.fireworks_api_base, + "timeout": self.request_timeout, } - if not values.get("client"): - values["client"] = Fireworks(**client_params).chat.completions - if not values.get("async_client"): - values["async_client"] = AsyncFireworks(**client_params).chat.completions - if values["max_retries"]: - values["client"]._max_retries = values["max_retries"] - values["async_client"]._max_retries = values["max_retries"] - return values + if not self.client: + self.client = Fireworks(**client_params).chat.completions + if not self.async_client: + self.async_client = AsyncFireworks(**client_params).chat.completions + if self.max_retries: + self.client._max_retries = self.max_retries + self.async_client._max_retries = self.max_retries + return self @property def _default_params(self) -> Dict[str, Any]: @@ -803,7 +805,7 @@ def with_structured_output( from typing import Optional from langchain_fireworks import ChatFireworks - from langchain_core.pydantic_v1 import BaseModel, Field + from pydantic import BaseModel, Field class AnswerWithJustification(BaseModel): @@ -834,7 +836,7 @@ class AnswerWithJustification(BaseModel): .. code-block:: python from langchain_fireworks import ChatFireworks - from langchain_core.pydantic_v1 import BaseModel + from pydantic import BaseModel class AnswerWithJustification(BaseModel): @@ -921,7 +923,7 @@ class AnswerWithJustification(TypedDict): .. code-block:: from langchain_fireworks import ChatFireworks - from langchain_core.pydantic_v1 import BaseModel + from pydantic import BaseModel class AnswerWithJustification(BaseModel): answer: str diff --git a/libs/partners/fireworks/langchain_fireworks/embeddings.py b/libs/partners/fireworks/langchain_fireworks/embeddings.py index 719bb34a93540..8fd67f116cab3 100644 --- a/libs/partners/fireworks/langchain_fireworks/embeddings.py +++ b/libs/partners/fireworks/langchain_fireworks/embeddings.py @@ -1,9 +1,12 @@ -from typing import Any, Dict, List +from typing import List from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr, root_validator from langchain_core.utils import secret_from_env -from openai import OpenAI # type: ignore +from openai import OpenAI +from pydantic import BaseModel, ConfigDict, Field, SecretStr, model_validator +from typing_extensions import Self + +# type: ignore class FireworksEmbeddings(BaseModel, Embeddings): @@ -65,7 +68,7 @@ class FireworksEmbeddings(BaseModel, Embeddings): [-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915] """ - _client: OpenAI = Field(default=None) + client: OpenAI = Field(default=None, exclude=True) #: :meta private: fireworks_api_key: SecretStr = Field( alias="api_key", default_factory=secret_from_env( @@ -79,20 +82,25 @@ class FireworksEmbeddings(BaseModel, Embeddings): """ model: str = "nomic-ai/nomic-embed-text-v1.5" - @root_validator(pre=False, skip_on_failure=True) - def validate_environment(cls, values: Dict[str, Any]) -> Dict[str, Any]: + model_config = ConfigDict( + populate_by_name=True, + arbitrary_types_allowed=True, + ) + + @model_validator(mode="after") + def validate_environment(self) -> Self: """Validate environment variables.""" - values["_client"] = OpenAI( - api_key=values["fireworks_api_key"].get_secret_value(), + self.client = OpenAI( + api_key=self.fireworks_api_key.get_secret_value(), base_url="https://api.fireworks.ai/inference/v1", ) - return values + return self def embed_documents(self, texts: List[str]) -> List[List[float]]: """Embed search docs.""" return [ i.embedding - for i in self._client.embeddings.create(input=texts, model=self.model).data + for i in self.client.embeddings.create(input=texts, model=self.model).data ] def embed_query(self, text: str) -> List[float]: diff --git a/libs/partners/fireworks/langchain_fireworks/llms.py b/libs/partners/fireworks/langchain_fireworks/llms.py index 747c59ecadd43..3189483c914a5 100644 --- a/libs/partners/fireworks/langchain_fireworks/llms.py +++ b/libs/partners/fireworks/langchain_fireworks/llms.py @@ -10,13 +10,9 @@ CallbackManagerForLLMRun, ) from langchain_core.language_models.llms import LLM -from langchain_core.pydantic_v1 import Field, SecretStr, root_validator -from langchain_core.utils import ( - convert_to_secret_str, - get_from_dict_or_env, - get_pydantic_field_names, -) -from langchain_core.utils.utils import build_extra_kwargs +from langchain_core.utils import get_pydantic_field_names +from langchain_core.utils.utils import build_extra_kwargs, secret_from_env +from pydantic import ConfigDict, Field, SecretStr, model_validator from langchain_fireworks.version import __version__ @@ -39,8 +35,21 @@ class Fireworks(LLM): base_url: str = "https://api.fireworks.ai/inference/v1/completions" """Base inference API URL.""" - fireworks_api_key: SecretStr = Field(default=None, alias="api_key") - """Fireworks AI API key. Get it here: https://fireworks.ai""" + fireworks_api_key: SecretStr = Field( + alias="api_key", + default_factory=secret_from_env( + "FIREWORKS_API_KEY", + error_message=( + "You must specify an api key. " + "You can pass it an argument as `api_key=...` or " + "set the environment variable `FIREWORKS_API_KEY`." + ), + ), + ) + """Fireworks API key. + + Automatically read from env variable `FIREWORKS_API_KEY` if not provided. + """ model: str """Model name. Available models listed here: https://readme.fireworks.ai/ @@ -74,14 +83,14 @@ class Fireworks(LLM): the response for each token generation step. """ - class Config: - """Configuration for this pydantic object.""" - - extra = "forbid" - allow_population_by_field_name = True + model_config = ConfigDict( + extra="forbid", + populate_by_name=True, + ) - @root_validator(pre=True) - def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def build_extra(cls, values: Dict[str, Any]) -> Any: """Build extra kwargs from additional params that were passed in.""" all_required_field_names = get_pydantic_field_names(cls) extra = values.get("model_kwargs", {}) @@ -90,14 +99,6 @@ def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: ) return values - @root_validator(pre=False, skip_on_failure=True) - def validate_environment(cls, values: Dict) -> Dict: - """Validate that api key exists in environment.""" - values["fireworks_api_key"] = convert_to_secret_str( - get_from_dict_or_env(values, "fireworks_api_key", "FIREWORKS_API_KEY") - ) - return values - @property def 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langchain_core.pydantic_v1." + echo "Please replace the code with an import from pydantic." echo "For example, replace 'from pydantic import BaseModel'" - echo "with 'from langchain_core.pydantic_v1 import BaseModel'" + echo "with 'from pydantic import BaseModel'" exit 1 fi diff --git a/libs/partners/fireworks/tests/integration_tests/test_chat_models.py b/libs/partners/fireworks/tests/integration_tests/test_chat_models.py index 443f9e5f47b67..88a1cd46cfcbb 100644 --- a/libs/partners/fireworks/tests/integration_tests/test_chat_models.py +++ b/libs/partners/fireworks/tests/integration_tests/test_chat_models.py @@ -7,7 +7,7 @@ from typing import Optional from langchain_core.messages import AIMessage, AIMessageChunk, BaseMessageChunk -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel from langchain_fireworks import ChatFireworks diff --git a/libs/partners/fireworks/tests/unit_tests/__snapshots__/test_standard.ambr b/libs/partners/fireworks/tests/unit_tests/__snapshots__/test_standard.ambr new file mode 100644 index 0000000000000..9faf837c593ba --- /dev/null +++ b/libs/partners/fireworks/tests/unit_tests/__snapshots__/test_standard.ambr @@ -0,0 +1,67 @@ +# serializer version: 1 +# name: TestFireworksStandard.test_serdes[serialized] + dict({ + 'graph': dict({ + 'edges': list([ + dict({ + 'source': 0, + 'target': 1, + }), + dict({ + 'source': 1, + 'target': 2, + }), + ]), + 'nodes': list([ + dict({ + 'data': 'ChatFireworksInput', + 'id': 0, + 'type': 'schema', + }), + dict({ + 'data': dict({ + 'id': list([ + 'langchain', + 'chat_models', + 'fireworks', + 'ChatFireworks', + ]), + 'name': 'ChatFireworks', + }), + 'id': 1, + 'type': 'runnable', + }), + dict({ + 'data': 'ChatFireworksOutput', + 'id': 2, + 'type': 'schema', + }), + ]), + }), + 'id': list([ + 'langchain', + 'chat_models', + 'fireworks', + 'ChatFireworks', + ]), + 'kwargs': dict({ + 'fireworks_api_key': dict({ + 'id': list([ + 'FIREWORKS_API_KEY', + ]), + 'lc': 1, + 'type': 'secret', + }), + 'max_retries': 2, + 'max_tokens': 100, + 'model_name': 'accounts/fireworks/models/mixtral-8x7b-instruct', + 'n': 1, + 'request_timeout': 60.0, + 'stop': list([ + ]), + }), + 'lc': 1, + 'name': 'ChatFireworks', + 'type': 'constructor', + }) +# --- diff --git a/libs/partners/fireworks/tests/unit_tests/test_embeddings_standard.py b/libs/partners/fireworks/tests/unit_tests/test_embeddings_standard.py new file mode 100644 index 0000000000000..ea8d16f92d0a8 --- /dev/null +++ b/libs/partners/fireworks/tests/unit_tests/test_embeddings_standard.py @@ -0,0 +1,30 @@ +"""Standard LangChain interface tests""" + +from typing import Tuple, Type + +from langchain_core.embeddings import Embeddings +from langchain_standard_tests.unit_tests.embeddings import EmbeddingsUnitTests + +from langchain_fireworks import FireworksEmbeddings + + +class TestFireworksStandard(EmbeddingsUnitTests): + @property + def embeddings_class(self) -> Type[Embeddings]: + return FireworksEmbeddings + + @property + def embeddings_params(self) -> dict: + return {"api_key": "test_api_key"} + + @property + def init_from_env_params(self) -> Tuple[dict, dict, dict]: + return ( + { + "FIREWORKS_API_KEY": "api_key", + }, + {}, + { + "fireworks_api_key": "api_key", + }, + ) diff --git a/libs/partners/fireworks/tests/unit_tests/test_llms.py b/libs/partners/fireworks/tests/unit_tests/test_llms.py index e2fb8a131e4b6..265df7ede83c1 100644 --- a/libs/partners/fireworks/tests/unit_tests/test_llms.py +++ b/libs/partners/fireworks/tests/unit_tests/test_llms.py @@ -2,7 +2,7 @@ from typing import cast -from langchain_core.pydantic_v1 import SecretStr +from pydantic import SecretStr from pytest import CaptureFixture, MonkeyPatch from langchain_fireworks import Fireworks diff --git a/libs/partners/fireworks/tests/unit_tests/test_standard.py b/libs/partners/fireworks/tests/unit_tests/test_standard.py index 9288aeeb9f8f2..61d0d152ba831 100644 --- a/libs/partners/fireworks/tests/unit_tests/test_standard.py +++ b/libs/partners/fireworks/tests/unit_tests/test_standard.py @@ -1,6 +1,6 @@ """Standard LangChain interface tests""" -from typing import Type +from typing import Tuple, Type from langchain_core.language_models import BaseChatModel from langchain_standard_tests.unit_tests import ( # type: ignore[import-not-found] @@ -18,3 +18,17 @@ def chat_model_class(self) -> Type[BaseChatModel]: @property def chat_model_params(self) -> dict: return {"api_key": "test_api_key"} + + @property + def init_from_env_params(self) -> Tuple[dict, dict, dict]: + return ( + { + "FIREWORKS_API_KEY": "api_key", + "FIREWORKS_API_BASE": "https://base.com", + }, + {}, + { + "fireworks_api_key": "api_key", + "fireworks_api_base": "https://base.com", + }, + ) diff --git a/libs/partners/groq/Makefile b/libs/partners/groq/Makefile index ad229fe8f2f3c..3ab3161a837a6 100644 --- a/libs/partners/groq/Makefile +++ b/libs/partners/groq/Makefile @@ -10,6 +10,9 @@ integration_test integration_tests: TEST_FILE=tests/integration_tests/ test tests integration_test integration_tests: poetry run pytest $(TEST_FILE) +test_watch: + poetry run ptw --snapshot-update --now . -- -vv $(TEST_FILE) + ###################### # LINTING AND FORMATTING diff --git a/libs/partners/groq/langchain_groq/chat_models.py b/libs/partners/groq/langchain_groq/chat_models.py index 23aeae9f11bed..05dc47245704b 100644 --- a/libs/partners/groq/langchain_groq/chat_models.py +++ b/libs/partners/groq/langchain_groq/chat_models.py @@ -65,12 +65,6 @@ parse_tool_call, ) from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult -from langchain_core.pydantic_v1 import ( - BaseModel, - Field, - SecretStr, - root_validator, -) from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough from langchain_core.tools import BaseTool from langchain_core.utils import ( @@ -83,6 +77,14 @@ convert_to_openai_tool, ) from langchain_core.utils.pydantic import is_basemodel_subclass +from pydantic import ( + BaseModel, + ConfigDict, + Field, + SecretStr, + model_validator, +) +from typing_extensions import Self class ChatGroq(BaseChatModel): @@ -225,7 +227,7 @@ class ChatGroq(BaseChatModel): Tool calling: .. code-block:: python - from langchain_core.pydantic_v1 import BaseModel, Field + from pydantic import BaseModel, Field class GetWeather(BaseModel): '''Get the current weather in a given location''' @@ -256,7 +258,7 @@ class GetPopulation(BaseModel): from typing import Optional - from langchain_core.pydantic_v1 import BaseModel, Field + from pydantic import BaseModel, Field class Joke(BaseModel): '''Joke to tell user.''' @@ -343,13 +345,13 @@ class Joke(BaseModel): """Optional httpx.AsyncClient. Only used for async invocations. Must specify http_client as well if you'd like a custom client for sync invocations.""" - class Config: - """Configuration for this pydantic object.""" - - allow_population_by_field_name = True + model_config = ConfigDict( + populate_by_name=True, + ) - @root_validator(pre=True) - def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def build_extra(cls, values: Dict[str, Any]) -> Any: """Build extra kwargs from additional params that were passed in.""" all_required_field_names = get_pydantic_field_names(cls) extra = values.get("model_kwargs", {}) @@ -374,38 +376,38 @@ def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: values["model_kwargs"] = extra return values - @root_validator(pre=False, skip_on_failure=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="after") + def validate_environment(self) -> Self: """Validate that api key and python package exists in environment.""" - if values["n"] < 1: + if self.n < 1: raise ValueError("n must be at least 1.") - if values["n"] > 1 and values["streaming"]: + if self.n > 1 and self.streaming: raise ValueError("n must be 1 when streaming.") - if values["temperature"] == 0: - values["temperature"] = 1e-8 + if self.temperature == 0: + self.temperature = 1e-8 - client_params = { - "api_key": values["groq_api_key"].get_secret_value() - if values["groq_api_key"] + client_params: Dict[str, Any] = { + "api_key": self.groq_api_key.get_secret_value() + if self.groq_api_key else None, - "base_url": values["groq_api_base"], - "timeout": values["request_timeout"], - "max_retries": values["max_retries"], - "default_headers": values["default_headers"], - "default_query": values["default_query"], + "base_url": self.groq_api_base, + "timeout": self.request_timeout, + "max_retries": self.max_retries, + "default_headers": self.default_headers, + "default_query": self.default_query, } try: import groq - sync_specific = {"http_client": values["http_client"]} - if not values.get("client"): - values["client"] = groq.Groq( + sync_specific: Dict[str, Any] = {"http_client": self.http_client} + if not self.client: + self.client = groq.Groq( **client_params, **sync_specific ).chat.completions - if not values.get("async_client"): - async_specific = {"http_client": values["http_async_client"]} - values["async_client"] = groq.AsyncGroq( + if not self.async_client: + async_specific: Dict[str, Any] = {"http_client": self.http_async_client} + self.async_client = groq.AsyncGroq( **client_params, **async_specific ).chat.completions except ImportError: @@ -413,7 +415,7 @@ def validate_environment(cls, values: Dict) -> Dict: "Could not import groq python package. " "Please install it with `pip install groq`." ) - return values + return self # # Serializable class method overrides @@ -543,7 +545,7 @@ def _stream( default_chunk_class: Type[BaseMessageChunk] = AIMessageChunk for chunk in self.client.create(messages=message_dicts, **params): if not isinstance(chunk, dict): - chunk = chunk.dict() + chunk = chunk.model_dump() if len(chunk["choices"]) == 0: continue choice = chunk["choices"][0] @@ -617,7 +619,7 @@ async def _astream( messages=message_dicts, **params ): if not isinstance(chunk, dict): - chunk = chunk.dict() + chunk = chunk.model_dump() if len(chunk["choices"]) == 0: continue choice = chunk["choices"][0] @@ -662,7 +664,7 @@ def _default_params(self) -> Dict[str, Any]: def _create_chat_result(self, response: Union[dict, BaseModel]) -> ChatResult: generations = [] if not isinstance(response, dict): - response = response.dict() + response = response.model_dump() token_usage = response.get("usage", {}) for res in response["choices"]: message = _convert_dict_to_message(res["message"]) @@ -905,7 +907,7 @@ def with_structured_output( from typing import Optional from langchain_groq import ChatGroq - from langchain_core.pydantic_v1 import BaseModel, Field + from pydantic import BaseModel, Field class AnswerWithJustification(BaseModel): @@ -936,7 +938,7 @@ class AnswerWithJustification(BaseModel): .. code-block:: python from langchain_groq import ChatGroq - from langchain_core.pydantic_v1 import BaseModel + from pydantic import BaseModel class AnswerWithJustification(BaseModel): @@ -1023,7 +1025,7 @@ class AnswerWithJustification(TypedDict): .. code-block:: from langchain_groq import ChatGroq - from langchain_core.pydantic_v1 import BaseModel + from pydantic import BaseModel class AnswerWithJustification(BaseModel): answer: str diff --git a/libs/partners/groq/poetry.lock b/libs/partners/groq/poetry.lock index ffd832ac794a2..727361453d000 100644 --- a/libs/partners/groq/poetry.lock +++ b/libs/partners/groq/poetry.lock @@ 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(>=3.10)"] [metadata] lock-version = "2.0" -python-versions = ">=3.8.1,<4.0" -content-hash = "1acd2a13007daf276263c7036959c1a4c973d4fd8dba924105c8fdc035e02a88" +python-versions = ">=3.9,<4.0" +content-hash = "b38cdb706f0dbe01f2f5d9b263904695fd468476459360a0cfbde42c940ea1cb" diff --git a/libs/partners/groq/pyproject.toml b/libs/partners/groq/pyproject.toml index e7d262a3581c3..55fc74006d3c4 100644 --- a/libs/partners/groq/pyproject.toml +++ b/libs/partners/groq/pyproject.toml @@ -19,9 +19,10 @@ disallow_untyped_defs = "True" "Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-groq%3D%3D0%22&expanded=true" [tool.poetry.dependencies] -python = ">=3.8.1,<4.0" +python = ">=3.9,<4.0" langchain-core = "^0.2.26" groq = ">=0.4.1,<1" +pydantic = ">=2,<3" [tool.ruff.lint] select = [ "E", "F", "I", "W",] diff --git a/libs/partners/groq/scripts/check_pydantic.sh b/libs/partners/groq/scripts/check_pydantic.sh deleted file mode 100755 index 06b5bb81ae236..0000000000000 --- a/libs/partners/groq/scripts/check_pydantic.sh +++ /dev/null @@ -1,27 +0,0 @@ -#!/bin/bash -# -# This script searches for lines starting with "import pydantic" or "from pydantic" -# in tracked files within a Git repository. -# -# Usage: ./scripts/check_pydantic.sh /path/to/repository - -# Check if a path argument is provided -if [ $# -ne 1 ]; then - echo "Usage: $0 /path/to/repository" - exit 1 -fi - -repository_path="$1" - -# Search for lines matching the pattern within the specified repository -result=$(git -C "$repository_path" grep -E '^import pydantic|^from pydantic') - -# Check if any matching lines were found -if [ -n "$result" ]; then - echo "ERROR: The following lines need to be updated:" - echo "$result" - echo "Please replace the code with an import from langchain_core.pydantic_v1." - echo "For example, replace 'from pydantic import BaseModel'" - echo "with 'from langchain_core.pydantic_v1 import BaseModel'" - exit 1 -fi diff --git a/libs/partners/groq/tests/integration_tests/test_chat_models.py b/libs/partners/groq/tests/integration_tests/test_chat_models.py index 2e5a9620b2287..1b2cf05b54239 100644 --- a/libs/partners/groq/tests/integration_tests/test_chat_models.py +++ b/libs/partners/groq/tests/integration_tests/test_chat_models.py @@ -13,8 +13,8 @@ SystemMessage, ) from langchain_core.outputs import ChatGeneration, LLMResult -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.tools import tool +from pydantic import BaseModel, Field from langchain_groq import ChatGroq from tests.unit_tests.fake.callbacks import ( diff --git a/libs/partners/groq/tests/unit_tests/__snapshots__/test_standard.ambr b/libs/partners/groq/tests/unit_tests/__snapshots__/test_standard.ambr new file mode 100644 index 0000000000000..caf848b56544e --- /dev/null +++ b/libs/partners/groq/tests/unit_tests/__snapshots__/test_standard.ambr @@ -0,0 +1,66 @@ +# serializer version: 1 +# name: TestGroqStandard.test_serdes[serialized] + dict({ + 'graph': dict({ + 'edges': list([ + dict({ + 'source': 0, + 'target': 1, + }), + dict({ + 'source': 1, + 'target': 2, + }), + ]), + 'nodes': list([ + dict({ + 'data': 'ChatGroqInput', + 'id': 0, + 'type': 'schema', + }), + dict({ + 'data': dict({ + 'id': list([ + 'langchain_groq', + 'chat_models', + 'ChatGroq', + ]), + 'name': 'ChatGroq', + }), + 'id': 1, + 'type': 'runnable', + }), + dict({ + 'data': 'ChatGroqOutput', + 'id': 2, + 'type': 'schema', + }), + ]), + }), + 'id': list([ + 'langchain_groq', + 'chat_models', + 'ChatGroq', + ]), + 'kwargs': dict({ + 'groq_api_key': dict({ + 'id': list([ + 'GROQ_API_KEY', + ]), + 'lc': 1, + 'type': 'secret', + }), + 'max_retries': 2, + 'max_tokens': 100, + 'model_name': 'mixtral-8x7b-32768', + 'n': 1, + 'request_timeout': 60.0, + 'stop': list([ + ]), + 'temperature': 1e-08, + }), + 'lc': 1, + 'name': 'ChatGroq', + 'type': 'constructor', + }) +# --- diff --git a/libs/partners/groq/tests/unit_tests/fake/callbacks.py b/libs/partners/groq/tests/unit_tests/fake/callbacks.py index 71a6dea0cef02..34f825e16a5df 100644 --- a/libs/partners/groq/tests/unit_tests/fake/callbacks.py +++ b/libs/partners/groq/tests/unit_tests/fake/callbacks.py @@ -6,7 +6,7 @@ from langchain_core.callbacks.base import AsyncCallbackHandler, BaseCallbackHandler from langchain_core.messages import BaseMessage -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel class BaseFakeCallbackHandler(BaseModel): @@ -256,7 +256,8 @@ def on_retriever_error( ) -> Any: self.on_retriever_error_common() - def __deepcopy__(self, memo: dict) -> "FakeCallbackHandler": + # Overriding since BaseModel has __deepcopy__ method as well + def __deepcopy__(self, memo: dict) -> "FakeCallbackHandler": # type: ignore return self @@ -390,5 +391,6 @@ async def on_text( ) -> None: self.on_text_common() - def __deepcopy__(self, memo: dict) -> "FakeAsyncCallbackHandler": + # Overriding since BaseModel has __deepcopy__ method as well + def __deepcopy__(self, memo: dict) -> "FakeAsyncCallbackHandler": # type: ignore return self diff --git a/libs/partners/huggingface/Makefile b/libs/partners/huggingface/Makefile index 26c79bf1230e0..62ab3e92260b4 100644 --- a/libs/partners/huggingface/Makefile +++ b/libs/partners/huggingface/Makefile @@ -11,6 +11,9 @@ integration_test integration_tests: TEST_FILE=tests/integration_tests/ test tests integration_test integration_tests: poetry run pytest $(TEST_FILE) +test_watch: + poetry run ptw --snapshot-update --now . -- -vv $(TEST_FILE) + ###################### # LINTING AND FORMATTING diff --git a/libs/partners/huggingface/langchain_huggingface/chat_models/huggingface.py b/libs/partners/huggingface/langchain_huggingface/chat_models/huggingface.py index b2fe14e8d410d..c09f0bcbb6045 100644 --- a/libs/partners/huggingface/langchain_huggingface/chat_models/huggingface.py +++ b/libs/partners/huggingface/langchain_huggingface/chat_models/huggingface.py @@ -29,10 +29,11 @@ ToolMessage, ) from langchain_core.outputs import ChatGeneration, ChatResult, LLMResult -from langchain_core.pydantic_v1 import root_validator from langchain_core.runnables import Runnable from langchain_core.tools import BaseTool from langchain_core.utils.function_calling import convert_to_openai_tool +from pydantic import model_validator +from typing_extensions import Self from langchain_huggingface.llms.huggingface_endpoint import HuggingFaceEndpoint from langchain_huggingface.llms.huggingface_pipeline import HuggingFacePipeline @@ -265,7 +266,7 @@ class ChatHuggingFace(BaseChatModel): Tool calling: .. code-block:: python - from langchain_core.pydantic_v1 import BaseModel, Field + from pydantic import BaseModel, Field class GetWeather(BaseModel): '''Get the current weather in a given location''' @@ -325,20 +326,20 @@ def __init__(self, **kwargs: Any): else self.tokenizer ) - @root_validator(pre=False, skip_on_failure=True) - def validate_llm(cls, values: dict) -> dict: + @model_validator(mode="after") + def validate_llm(self) -> Self: if ( - not _is_huggingface_hub(values["llm"]) - and not _is_huggingface_textgen_inference(values["llm"]) - and not _is_huggingface_endpoint(values["llm"]) - and not _is_huggingface_pipeline(values["llm"]) + not _is_huggingface_hub(self.llm) + and not _is_huggingface_textgen_inference(self.llm) + and not _is_huggingface_endpoint(self.llm) + and not _is_huggingface_pipeline(self.llm) ): raise TypeError( "Expected llm to be one of HuggingFaceTextGenInference, " "HuggingFaceEndpoint, HuggingFaceHub, HuggingFacePipeline " - f"received {type(values['llm'])}" + f"received {type(self.llm)}" ) - return values + return self def _create_chat_result(self, response: TGI_RESPONSE) -> ChatResult: generations = [] diff --git a/libs/partners/huggingface/langchain_huggingface/embeddings/huggingface.py b/libs/partners/huggingface/langchain_huggingface/embeddings/huggingface.py index 06b829011d52e..31fd81921433f 100644 --- a/libs/partners/huggingface/langchain_huggingface/embeddings/huggingface.py +++ b/libs/partners/huggingface/langchain_huggingface/embeddings/huggingface.py @@ -1,7 +1,7 @@ from typing import Any, Dict, List, Optional # type: ignore[import-not-found] from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel, Field +from pydantic import BaseModel, ConfigDict, Field DEFAULT_MODEL_NAME = "sentence-transformers/all-mpnet-base-v2" @@ -62,10 +62,10 @@ def __init__(self, **kwargs: Any): self.model_name, cache_folder=self.cache_folder, **self.model_kwargs ) - class Config: - """Configuration for this pydantic object.""" - - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + protected_namespaces=(), + ) def embed_documents(self, texts: List[str]) -> List[List[float]]: """Compute doc embeddings using a HuggingFace transformer model. diff --git a/libs/partners/huggingface/langchain_huggingface/embeddings/huggingface_endpoint.py b/libs/partners/huggingface/langchain_huggingface/embeddings/huggingface_endpoint.py index 1f873059914b5..3986e192e1fc6 100644 --- a/libs/partners/huggingface/langchain_huggingface/embeddings/huggingface_endpoint.py +++ b/libs/partners/huggingface/langchain_huggingface/embeddings/huggingface_endpoint.py @@ -1,9 +1,11 @@ import json -from typing import Any, Dict, List, Optional +import os +from typing import Any, List, Optional from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel, root_validator -from langchain_core.utils import get_from_dict_or_env +from langchain_core.utils import from_env +from pydantic import BaseModel, ConfigDict, Field, model_validator +from typing_extensions import Self DEFAULT_MODEL = "sentence-transformers/all-mpnet-base-v2" VALID_TASKS = ("feature-extraction",) @@ -39,22 +41,20 @@ class HuggingFaceEndpointEmbeddings(BaseModel, Embeddings): model_kwargs: Optional[dict] = None """Keyword arguments to pass to the model.""" - huggingfacehub_api_token: Optional[str] = None + huggingfacehub_api_token: Optional[str] = Field( + default_factory=from_env("HUGGINGFACEHUB_API_TOKEN", default=None) + ) - class Config: - """Configuration for this pydantic object.""" + model_config = ConfigDict( + extra="forbid", + protected_namespaces=(), + ) - extra = "forbid" - - @root_validator(pre=False, skip_on_failure=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="after") + def validate_environment(self) -> Self: """Validate that api key and python package exists in environment.""" - values["huggingfacehub_api_token"] = get_from_dict_or_env( - values, "huggingfacehub_api_token", "HUGGINGFACEHUB_API_TOKEN", None - ) - - huggingfacehub_api_token = get_from_dict_or_env( - values, "huggingfacehub_api_token", "HF_TOKEN", None + huggingfacehub_api_token = self.huggingfacehub_api_token or os.getenv( + "HF_TOKEN" ) try: @@ -63,38 +63,38 @@ def validate_environment(cls, values: Dict) -> Dict: InferenceClient, ) - if values["model"]: - values["repo_id"] = values["model"] - elif values["repo_id"]: - values["model"] = values["repo_id"] + if self.model: + self.repo_id = self.model + elif self.repo_id: + self.model = self.repo_id else: - values["model"] = DEFAULT_MODEL - values["repo_id"] = DEFAULT_MODEL + self.model = DEFAULT_MODEL + self.repo_id = DEFAULT_MODEL client = InferenceClient( - model=values["model"], + model=self.model, token=huggingfacehub_api_token, ) async_client = AsyncInferenceClient( - model=values["model"], + model=self.model, token=huggingfacehub_api_token, ) - if values["task"] not in VALID_TASKS: + if self.task not in VALID_TASKS: raise ValueError( - f"Got invalid task {values['task']}, " + f"Got invalid task {self.task}, " f"currently only {VALID_TASKS} are supported" ) - values["client"] = client - values["async_client"] = async_client + self.client = client + self.async_client = async_client except ImportError: raise ImportError( "Could not import huggingface_hub python package. " "Please install it with `pip install huggingface_hub`." ) - return values + return self def embed_documents(self, texts: List[str]) -> List[List[float]]: """Call out to HuggingFaceHub's embedding endpoint for embedding search docs. diff --git a/libs/partners/huggingface/langchain_huggingface/llms/huggingface_endpoint.py b/libs/partners/huggingface/langchain_huggingface/llms/huggingface_endpoint.py index 0f793f2828d6b..17d6eb8e42ece 100644 --- a/libs/partners/huggingface/langchain_huggingface/llms/huggingface_endpoint.py +++ b/libs/partners/huggingface/langchain_huggingface/llms/huggingface_endpoint.py @@ -9,8 +9,9 @@ ) from langchain_core.language_models.llms import LLM from langchain_core.outputs import GenerationChunk -from langchain_core.pydantic_v1 import Field, root_validator -from langchain_core.utils import get_from_dict_or_env, get_pydantic_field_names +from langchain_core.utils import from_env, get_pydantic_field_names +from pydantic import ConfigDict, Field, model_validator +from typing_extensions import Self logger = logging.getLogger(__name__) @@ -71,7 +72,9 @@ class HuggingFaceEndpoint(LLM): should be pass as env variable in `HF_INFERENCE_ENDPOINT`""" repo_id: Optional[str] = None """Repo to use. If endpoint_url is not specified then this needs to given""" - huggingfacehub_api_token: Optional[str] = None + huggingfacehub_api_token: Optional[str] = Field( + default_factory=from_env("HUGGINGFACEHUB_API_TOKEN", default=None) + ) max_new_tokens: int = 512 """Maximum number of generated tokens""" top_k: Optional[int] = None @@ -118,13 +121,13 @@ class HuggingFaceEndpoint(LLM): """Task to call the model with. Should be a task that returns `generated_text` or `summary_text`.""" - class Config: - """Configuration for this pydantic object.""" + model_config = ConfigDict( + extra="forbid", + ) - extra = "forbid" - - @root_validator(pre=True) - def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def build_extra(cls, values: Dict[str, Any]) -> Any: """Build extra kwargs from additional params that were passed in.""" all_required_field_names = get_pydantic_field_names(cls) extra = values.get("model_kwargs", {}) @@ -182,8 +185,8 @@ def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: ) return values - @root_validator(pre=False, skip_on_failure=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="after") + def validate_environment(self) -> Self: """Validate that package is installed and that the API token is valid.""" try: from huggingface_hub import login # type: ignore[import] @@ -194,12 +197,8 @@ def validate_environment(cls, values: Dict) -> Dict: "Please install it with `pip install huggingface_hub`." ) - values["huggingfacehub_api_token"] = get_from_dict_or_env( - values, "huggingfacehub_api_token", "HUGGINGFACEHUB_API_TOKEN", None - ) - - huggingfacehub_api_token = get_from_dict_or_env( - values, "huggingfacehub_api_token", "HF_TOKEN", None + huggingfacehub_api_token = self.huggingfacehub_api_token or os.getenv( + "HF_TOKEN" ) if huggingfacehub_api_token is not None: @@ -213,20 +212,20 @@ def validate_environment(cls, values: Dict) -> Dict: from huggingface_hub import AsyncInferenceClient, InferenceClient - values["client"] = InferenceClient( - model=values["model"], - timeout=values["timeout"], + self.client = InferenceClient( + model=self.model, + timeout=self.timeout, token=huggingfacehub_api_token, - **values["server_kwargs"], + **self.server_kwargs, ) - values["async_client"] = AsyncInferenceClient( - model=values["model"], - timeout=values["timeout"], + self.async_client = AsyncInferenceClient( + model=self.model, + timeout=self.timeout, token=huggingfacehub_api_token, - **values["server_kwargs"], + **self.server_kwargs, ) - return values + return self @property def _default_params(self) -> Dict[str, Any]: diff --git a/libs/partners/huggingface/langchain_huggingface/llms/huggingface_pipeline.py b/libs/partners/huggingface/langchain_huggingface/llms/huggingface_pipeline.py index 8d3ebc223b70f..4eabaf908f4d8 100644 --- a/libs/partners/huggingface/langchain_huggingface/llms/huggingface_pipeline.py +++ b/libs/partners/huggingface/langchain_huggingface/llms/huggingface_pipeline.py @@ -7,6 +7,7 @@ from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models.llms import BaseLLM from langchain_core.outputs import Generation, GenerationChunk, LLMResult +from pydantic import ConfigDict DEFAULT_MODEL_ID = "gpt2" DEFAULT_TASK = "text-generation" @@ -63,10 +64,9 @@ class HuggingFacePipeline(BaseLLM): batch_size: int = DEFAULT_BATCH_SIZE """Batch size to use when passing multiple documents to generate.""" - class Config: - """Configuration for this pydantic object.""" - - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) @classmethod def from_model_id( diff --git a/libs/partners/huggingface/poetry.lock b/libs/partners/huggingface/poetry.lock index 0a78b8113f842..fee6b1d6494f5 100644 --- a/libs/partners/huggingface/poetry.lock +++ b/libs/partners/huggingface/poetry.lock @@ -1,91 +1,118 @@ # This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. +[[package]] +name = "aiohappyeyeballs" +version = "2.4.0" +description = "Happy Eyeballs for asyncio" +optional = false +python-versions = ">=3.8" +files = [ + {file = "aiohappyeyeballs-2.4.0-py3-none-any.whl", hash = "sha256:7ce92076e249169a13c2f49320d1967425eaf1f407522d707d59cac7628d62bd"}, + {file = "aiohappyeyeballs-2.4.0.tar.gz", hash = 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"fa255346e4827837235f2a9c0a6379820d25f78ccf0f6a0a20294e18d0a4333a" +python-versions = ">=3.9,<4.0" +content-hash = "214417f8f15c30648db9cb244cc3b4e49f0bb254f6c123be83b568a60040182d" diff --git a/libs/partners/huggingface/pyproject.toml b/libs/partners/huggingface/pyproject.toml index 9fd16c71cc417..8068cae18476c 100644 --- a/libs/partners/huggingface/pyproject.toml +++ b/libs/partners/huggingface/pyproject.toml @@ -19,12 +19,13 @@ disallow_untyped_defs = "True" "Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-huggingface%3D%3D0%22&expanded=true" [tool.poetry.dependencies] -python = ">=3.8.1,<4.0" +python = ">=3.9,<4.0" langchain-core = ">=0.1.52,<0.3" tokenizers = ">=0.19.1" transformers = ">=4.39.0" sentence-transformers = ">=2.6.0" huggingface-hub = ">=0.23.0" +pydantic = ">=2,<3" [tool.ruff.lint] select = [ "E", "F", "I", "T201",] @@ -55,6 +56,8 @@ optional = true [tool.poetry.group.test.dependencies] pytest = "^7.3.0" pytest-asyncio = "^0.21.1" +pytest-watcher = "^0.3.4" + [[tool.poetry.group.test.dependencies.scipy]] version = "^1" python = "<3.12" diff --git a/libs/partners/huggingface/scripts/check_pydantic.sh b/libs/partners/huggingface/scripts/check_pydantic.sh deleted file mode 100755 index 06b5bb81ae236..0000000000000 --- a/libs/partners/huggingface/scripts/check_pydantic.sh +++ /dev/null @@ -1,27 +0,0 @@ -#!/bin/bash -# -# This script searches for lines starting with "import pydantic" or "from pydantic" -# in tracked files within a Git repository. -# -# Usage: ./scripts/check_pydantic.sh /path/to/repository - -# Check if a path argument is provided -if [ $# -ne 1 ]; then - echo "Usage: $0 /path/to/repository" - exit 1 -fi - -repository_path="$1" - -# Search for lines matching the pattern within the specified repository -result=$(git -C "$repository_path" grep -E '^import pydantic|^from pydantic') - -# Check if any matching lines were found -if [ -n "$result" ]; then - echo "ERROR: The following lines need to be updated:" - echo "$result" - echo "Please replace the code with an import from langchain_core.pydantic_v1." - echo "For example, replace 'from pydantic import BaseModel'" - echo "with 'from langchain_core.pydantic_v1 import BaseModel'" - exit 1 -fi diff --git a/libs/partners/milvus/Makefile b/libs/partners/milvus/Makefile index ece960712fc2e..9234f584f08d5 100644 --- a/libs/partners/milvus/Makefile +++ b/libs/partners/milvus/Makefile @@ -10,6 +10,9 @@ integration_test integration_tests: TEST_FILE=tests/integration_tests/ test tests integration_test integration_tests: poetry run pytest $(TEST_FILE) +test_watch: + poetry run ptw --snapshot-update --now . -- -vv $(TEST_FILE) + ###################### # LINTING AND FORMATTING diff --git a/libs/partners/milvus/poetry.lock b/libs/partners/milvus/poetry.lock index 26270dfc5dc7e..2e04bbc121bf3 100644 --- a/libs/partners/milvus/poetry.lock +++ b/libs/partners/milvus/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. [[package]] name = "annotated-types" @@ -11,9 +11,6 @@ files = [ {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"}, ] -[package.dependencies] -typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.9\""} - [[package]] name = "anyio" version = "4.4.0" @@ -390,7 +387,7 @@ name = "langchain-core" version = "0.2.38" description = "Building applications with LLMs through composability" optional = false -python-versions = ">=3.8.1,<4.0" +python-versions = ">=3.9,<4.0" files = [] develop = true @@ -677,8 +674,8 @@ files = [ [package.dependencies] numpy = [ {version = ">=1.20.3", markers = "python_version < \"3.10\""}, - {version = ">=1.21.0", markers = "python_version >= \"3.10\" and python_version < \"3.11\""}, {version = ">=1.23.2", markers = "python_version >= \"3.11\""}, + {version = ">=1.21.0", markers = "python_version >= \"3.10\" and python_version < \"3.11\""}, ] python-dateutil = ">=2.8.2" pytz = ">=2020.1" @@ -880,13 +877,9 @@ files = [ environs = "<=9.5.0" grpcio = ">=1.49.1" milvus-lite = {version = ">=2.4.0,<2.5.0", markers = "sys_platform != \"win32\""} -numpy = {version = "<1.25.0", markers = "python_version <= \"3.8\""} pandas = ">=1.2.4" protobuf = ">=3.20.0" -setuptools = [ - {version = ">69,<70.1", markers = "python_version <= \"3.8\""}, - {version = ">69", markers = "python_version > \"3.8\""}, -] +setuptools = ">69" ujson = ">=2.0.0" [package.extras] @@ -1202,21 +1195,6 @@ dev = ["cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy (==1.10.0)", "pycodest doc = ["jupyterlite-pyodide-kernel", "jupyterlite-sphinx (>=0.13.1)", "jupytext", "matplotlib (>=3.5)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (>=0.15.2)", "sphinx (>=5.0.0,<=7.3.7)", "sphinx-design (>=0.4.0)"] test = ["Cython", "array-api-strict (>=2.0)", "asv", "gmpy2", "hypothesis (>=6.30)", "meson", "mpmath", "ninja", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"] -[[package]] -name = "setuptools" -version = "70.0.0" -description = "Easily download, build, install, upgrade, and uninstall Python packages" -optional = false -python-versions = ">=3.8" -files = [ - {file = "setuptools-70.0.0-py3-none-any.whl", hash = "sha256:54faa7f2e8d2d11bcd2c07bed282eef1046b5c080d1c32add737d7b5817b1ad4"}, - {file = "setuptools-70.0.0.tar.gz", hash = "sha256:f211a66637b8fa059bb28183da127d4e86396c991a942b028c6650d4319c3fd0"}, -] - -[package.extras] -docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "pyproject-hooks (!=1.1)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier"] -testing = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "importlib-metadata", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "mypy (==1.9)", "packaging (>=23.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.1)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-home (>=0.5)", "pytest-mypy", "pytest-perf", "pytest-ruff (>=0.2.1)", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"] - [[package]] name = "setuptools" version = "74.1.1" @@ -1623,5 +1601,5 @@ watchmedo = ["PyYAML (>=3.10)"] [metadata] lock-version = "2.0" -python-versions = ">=3.8.1,<4.0" -content-hash = "d6823046634939597b26f46d881270615741247d4927ac86cf18c71dca2ad969" +python-versions = ">=3.9,<4.0" +content-hash = "75363ea8a818941c17b47a51a8cb1da4327d49f6cead8a04498fb6f8526c33a9" diff --git a/libs/partners/milvus/pyproject.toml b/libs/partners/milvus/pyproject.toml index d145c8239e1f5..31ce1757f8fa9 100644 --- a/libs/partners/milvus/pyproject.toml +++ b/libs/partners/milvus/pyproject.toml @@ -25,7 +25,7 @@ ignore_missing_imports = "True" "Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-milvus%3D%3D0%22&expanded=true" [tool.poetry.dependencies] -python = ">=3.8.1,<4.0" +python = ">=3.9,<4.0" langchain-core = "^0.2.38" pymilvus = "^2.4.3" [[tool.poetry.dependencies.scipy]] diff --git a/libs/partners/milvus/scripts/check_pydantic.sh b/libs/partners/milvus/scripts/check_pydantic.sh deleted file mode 100755 index 06b5bb81ae236..0000000000000 --- a/libs/partners/milvus/scripts/check_pydantic.sh +++ /dev/null @@ -1,27 +0,0 @@ -#!/bin/bash -# -# This script searches for lines starting with "import pydantic" or "from pydantic" -# in tracked files within a Git repository. -# -# Usage: ./scripts/check_pydantic.sh /path/to/repository - -# Check if a path argument is provided -if [ $# -ne 1 ]; then - echo "Usage: $0 /path/to/repository" - exit 1 -fi - -repository_path="$1" - -# Search for lines matching the pattern within the specified repository -result=$(git -C "$repository_path" grep -E '^import pydantic|^from pydantic') - -# Check if any matching lines were found -if [ -n "$result" ]; then - echo "ERROR: The following lines need to be updated:" - echo "$result" - echo "Please replace the code with an import from langchain_core.pydantic_v1." - echo "For example, replace 'from pydantic import BaseModel'" - echo "with 'from langchain_core.pydantic_v1 import BaseModel'" - exit 1 -fi diff --git a/libs/partners/mistralai/Makefile b/libs/partners/mistralai/Makefile index 933e0be715669..5ace7c176ff7c 100644 --- a/libs/partners/mistralai/Makefile +++ b/libs/partners/mistralai/Makefile @@ -12,6 +12,10 @@ integration_test integration_tests: TEST_FILE=$(INTEGRATION_TEST_FILE) test tests: poetry run pytest $(TEST_FILE) +test_watch: + poetry run ptw --snapshot-update --now . -- -vv $(TEST_FILE) + + integration_test integration_tests: poetry run pytest $(TEST_FILE) diff --git a/libs/partners/mistralai/langchain_mistralai/chat_models.py b/libs/partners/mistralai/langchain_mistralai/chat_models.py index 06ee230d1f62a..b3a43629e8aae 100644 --- a/libs/partners/mistralai/langchain_mistralai/chat_models.py +++ b/libs/partners/mistralai/langchain_mistralai/chat_models.py @@ -66,17 +66,19 @@ parse_tool_call, ) from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult -from langchain_core.pydantic_v1 import ( - BaseModel, - Field, - SecretStr, - root_validator, -) from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough from langchain_core.tools import BaseTool from langchain_core.utils import secret_from_env from langchain_core.utils.function_calling import convert_to_openai_tool from langchain_core.utils.pydantic import is_basemodel_subclass +from pydantic import ( + BaseModel, + ConfigDict, + Field, + SecretStr, + model_validator, +) +from typing_extensions import Self logger = logging.getLogger(__name__) @@ -359,8 +361,10 @@ def _convert_message_to_mistral_chat_message( class ChatMistralAI(BaseChatModel): """A chat model that uses the MistralAI API.""" - client: httpx.Client = Field(default=None) #: :meta private: - async_client: httpx.AsyncClient = Field(default=None) #: :meta private: + client: httpx.Client = Field(default=None, exclude=True) #: :meta private: + async_client: httpx.AsyncClient = Field( + default=None, exclude=True + ) #: :meta private: mistral_api_key: Optional[SecretStr] = Field( alias="api_key", default_factory=secret_from_env("MISTRAL_API_KEY", default=None), @@ -379,11 +383,10 @@ class ChatMistralAI(BaseChatModel): safe_mode: bool = False streaming: bool = False - class Config: - """Configuration for this pydantic object.""" - - allow_population_by_field_name = True - arbitrary_types_allowed = True + model_config = ConfigDict( + populate_by_name=True, + arbitrary_types_allowed=True, + ) @property def _default_params(self) -> Dict[str, Any]: @@ -469,47 +472,50 @@ def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict: combined = {"token_usage": overall_token_usage, "model_name": self.model} return combined - @root_validator(pre=False, skip_on_failure=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="after") + def validate_environment(self) -> Self: """Validate api key, python package exists, temperature, and top_p.""" - api_key_str = values["mistral_api_key"].get_secret_value() + if isinstance(self.mistral_api_key, SecretStr): + api_key_str: Optional[str] = self.mistral_api_key.get_secret_value() + else: + api_key_str = self.mistral_api_key # todo: handle retries base_url_str = ( - values.get("endpoint") + self.endpoint or os.environ.get("MISTRAL_BASE_URL") or "https://api.mistral.ai/v1" ) - values["endpoint"] = base_url_str - if not values.get("client"): - values["client"] = httpx.Client( + self.endpoint = base_url_str + if not self.client: + self.client = httpx.Client( base_url=base_url_str, headers={ "Content-Type": "application/json", "Accept": "application/json", "Authorization": f"Bearer {api_key_str}", }, - timeout=values["timeout"], + timeout=self.timeout, ) # todo: handle retries and max_concurrency - if not values.get("async_client"): - values["async_client"] = httpx.AsyncClient( + if not self.async_client: + self.async_client = httpx.AsyncClient( base_url=base_url_str, headers={ "Content-Type": "application/json", "Accept": "application/json", "Authorization": f"Bearer {api_key_str}", }, - timeout=values["timeout"], + timeout=self.timeout, ) - if values["temperature"] is not None and not 0 <= values["temperature"] <= 1: + if self.temperature is not None and not 0 <= self.temperature <= 1: raise ValueError("temperature must be in the range [0.0, 1.0]") - if values["top_p"] is not None and not 0 <= values["top_p"] <= 1: + if self.top_p is not None and not 0 <= self.top_p <= 1: raise ValueError("top_p must be in the range [0.0, 1.0]") - return values + return self def _generate( self, @@ -728,7 +734,7 @@ def with_structured_output( from typing import Optional from langchain_mistralai import ChatMistralAI - from langchain_core.pydantic_v1 import BaseModel, Field + from pydantic import BaseModel, Field class AnswerWithJustification(BaseModel): @@ -759,7 +765,7 @@ class AnswerWithJustification(BaseModel): .. code-block:: python from langchain_mistralai import ChatMistralAI - from langchain_core.pydantic_v1 import BaseModel + from pydantic import BaseModel class AnswerWithJustification(BaseModel): @@ -846,7 +852,7 @@ class AnswerWithJustification(TypedDict): .. code-block:: from langchain_mistralai import ChatMistralAI - from langchain_core.pydantic_v1 import BaseModel + from pydantic import BaseModel class AnswerWithJustification(BaseModel): answer: str diff --git a/libs/partners/mistralai/langchain_mistralai/embeddings.py b/libs/partners/mistralai/langchain_mistralai/embeddings.py index ea19fdd9cae0b..485e9771c48f8 100644 --- a/libs/partners/mistralai/langchain_mistralai/embeddings.py +++ b/libs/partners/mistralai/langchain_mistralai/embeddings.py @@ -1,20 +1,22 @@ import asyncio import logging import warnings -from typing import Dict, Iterable, List +from typing import Iterable, List import httpx from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import ( +from langchain_core.utils import ( + secret_from_env, +) +from pydantic import ( BaseModel, + ConfigDict, Field, SecretStr, - root_validator, -) -from langchain_core.utils import ( - secret_from_env, + model_validator, ) from tokenizers import Tokenizer # type: ignore +from typing_extensions import Self logger = logging.getLogger(__name__) @@ -125,41 +127,42 @@ class MistralAIEmbeddings(BaseModel, Embeddings): model: str = "mistral-embed" - class Config: - extra = "forbid" - arbitrary_types_allowed = True - allow_population_by_field_name = True + model_config = ConfigDict( + extra="forbid", + arbitrary_types_allowed=True, + populate_by_name=True, + ) - @root_validator(pre=False, skip_on_failure=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="after") + def validate_environment(self) -> Self: """Validate configuration.""" - api_key_str = values["mistral_api_key"].get_secret_value() + api_key_str = self.mistral_api_key.get_secret_value() # todo: handle retries - if not values.get("client"): - values["client"] = httpx.Client( - base_url=values["endpoint"], + if not self.client: + self.client = httpx.Client( + base_url=self.endpoint, headers={ "Content-Type": "application/json", "Accept": "application/json", "Authorization": f"Bearer {api_key_str}", }, - timeout=values["timeout"], + timeout=self.timeout, ) # todo: handle retries and max_concurrency - if not values.get("async_client"): - values["async_client"] = httpx.AsyncClient( - base_url=values["endpoint"], + if not self.async_client: + self.async_client = httpx.AsyncClient( + base_url=self.endpoint, headers={ "Content-Type": "application/json", "Accept": "application/json", "Authorization": f"Bearer {api_key_str}", }, - timeout=values["timeout"], + timeout=self.timeout, ) - if values["tokenizer"] is None: + if self.tokenizer is None: try: - values["tokenizer"] = Tokenizer.from_pretrained( + self.tokenizer = Tokenizer.from_pretrained( "mistralai/Mixtral-8x7B-v0.1" ) except IOError: # huggingface_hub GatedRepoError @@ -169,8 +172,8 @@ def validate_environment(cls, values: Dict) -> Dict: "HF_TOKEN environment variable to download the real tokenizer. " "Falling back to a dummy tokenizer that uses `len()`." ) - values["tokenizer"] = DummyTokenizer() - return values + self.tokenizer = DummyTokenizer() + return self def _get_batches(self, texts: List[str]) -> Iterable[List[str]]: """Split a list of texts into batches of less than 16k tokens diff --git a/libs/partners/mistralai/poetry.lock b/libs/partners/mistralai/poetry.lock index c52c780aca6b5..a959558ed213f 100644 --- a/libs/partners/mistralai/poetry.lock +++ b/libs/partners/mistralai/poetry.lock @@ -11,9 +11,6 @@ files = [ {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"}, ] -[package.dependencies] -typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.9\""} - [[package]] name = "anyio" version = "4.4.0" @@ -206,13 +203,13 @@ typing = ["typing-extensions (>=4.8)"] [[package]] name = "fsspec" -version = "2024.6.1" +version = "2024.9.0" description = "File-system specification" optional = false python-versions = ">=3.8" files = [ - {file = "fsspec-2024.6.1-py3-none-any.whl", hash = "sha256:3cb443f8bcd2efb31295a5b9fdb02aee81d8452c80d28f97a6d0959e6cee101e"}, - {file = "fsspec-2024.6.1.tar.gz", hash = "sha256:fad7d7e209dd4c1208e3bbfda706620e0da5142bebbd9c384afb95b07e798e49"}, + {file = "fsspec-2024.9.0-py3-none-any.whl", hash = "sha256:a0947d552d8a6efa72cc2c730b12c41d043509156966cca4fb157b0f2a0c574b"}, + {file = "fsspec-2024.9.0.tar.gz", hash = "sha256:4b0afb90c2f21832df142f292649035d80b421f60a9e1c027802e5a0da2b04e8"}, ] [package.extras] @@ -397,7 +394,7 @@ name = "langchain-core" version = "0.2.38" description = "Building applications with LLMs through composability" optional = false -python-versions = ">=3.8.1,<4.0" +python-versions = ">=3.9,<4.0" files = [] develop = true @@ -430,6 +427,7 @@ develop = true httpx = "^0.27.0" langchain-core = ">=0.1.40,<0.3" pytest = ">=7,<9" +syrupy = "^4" [package.source] type = "directory" @@ -437,13 +435,13 @@ url = "../../standard-tests" [[package]] name = "langsmith" -version = "0.1.110" +version = "0.1.112" description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform." optional = false python-versions = "<4.0,>=3.8.1" files = [ - {file = "langsmith-0.1.110-py3-none-any.whl", hash = "sha256:316d279e3853f5e90e462f9c035eeb468d042f2a21a269c1102d65f3dccdc334"}, - {file = "langsmith-0.1.110.tar.gz", hash = "sha256:9a619dfe22a67a05a05091f0677b9c842499faec5f051b31afcd901b6627d0a3"}, + {file = "langsmith-0.1.112-py3-none-any.whl", hash = "sha256:38ecc62df35e55e5aa9a05ecf1f1483d5a4db3275b9da4876ae27364714b3717"}, + {file = "langsmith-0.1.112.tar.gz", hash = "sha256:08c47a9497261c332bc4b15e2930d8e751310e63652ecdc78382a5dd07cb42e2"}, ] [package.dependencies] @@ -768,6 +766,21 @@ pytest = ">=7.0.0" docs = ["sphinx (>=5.3)", "sphinx-rtd-theme (>=1.0)"] testing = ["coverage (>=6.2)", "flaky (>=3.5.0)", "hypothesis (>=5.7.1)", "mypy (>=0.931)", "pytest-trio (>=0.7.0)"] +[[package]] +name = "pytest-watcher" +version = "0.3.5" +description = "Automatically rerun your tests on file modifications" +optional = false +python-versions = ">=3.7.0,<4.0.0" +files = [ + {file = "pytest_watcher-0.3.5-py3-none-any.whl", hash = "sha256:af00ca52c7be22dc34c0fd3d7ffef99057207a73b05dc5161fe3b2fe91f58130"}, + {file = "pytest_watcher-0.3.5.tar.gz", hash = "sha256:8896152460ba2b1a8200c12117c6611008ec96c8b2d811f0a05ab8a82b043ff8"}, +] + +[package.dependencies] +tomli = {version = ">=2.0.1,<3.0.0", markers = "python_version < \"3.11\""} +watchdog = ">=2.0.0" + [[package]] name = "pyyaml" version = "6.0.2" @@ -889,6 +902,20 @@ files = [ {file = "sniffio-1.3.1.tar.gz", hash = "sha256:f4324edc670a0f49750a81b895f35c3adb843cca46f0530f79fc1babb23789dc"}, ] +[[package]] +name = "syrupy" +version = "4.7.1" +description = "Pytest Snapshot Test Utility" +optional = false +python-versions = ">=3.8.1" +files = [ + {file = "syrupy-4.7.1-py3-none-any.whl", hash = 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hash = "sha256:dcebf7e475001d2cdeb020be630dc5b687e9acdd60d16fea6bb4508e7b94cf76"}, +] + +[package.extras] +watchmedo = ["PyYAML (>=3.10)"] + [metadata] lock-version = "2.0" -python-versions = ">=3.8.1,<4.0" -content-hash = "11a8e8533f0ed605e14cf916957ccde5f8bf77056227fcbc152b0f644f1e45bd" +python-versions = ">=3.9,<4.0" +content-hash = "ab02f6b1375f2f83ad872cab382cae83b6f76330d45ca1b9c9cd9f74bad4d440" diff --git a/libs/partners/mistralai/pyproject.toml b/libs/partners/mistralai/pyproject.toml index 6da683eb31ced..bb0bb7338a8b2 100644 --- a/libs/partners/mistralai/pyproject.toml +++ b/libs/partners/mistralai/pyproject.toml @@ -19,11 +19,12 @@ disallow_untyped_defs = "True" "Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-mistralai%3D%3D0%22&expanded=true" [tool.poetry.dependencies] -python = ">=3.8.1,<4.0" +python = ">=3.9,<4.0" langchain-core = "^0.2.38" tokenizers = ">=0.15.1,<1" httpx = ">=0.25.2,<1" httpx-sse = ">=0.3.1,<1" +pydantic = ">2,<3" [tool.ruff.lint] select = [ "E", "F", "I", "T201",] @@ -54,6 +55,7 @@ optional = true [tool.poetry.group.test.dependencies] pytest = "^7.3.0" pytest-asyncio = "^0.21.1" +pytest-watcher = "^0.3.4" [tool.poetry.group.test_integration.dependencies] diff --git a/libs/partners/mistralai/scripts/check_pydantic.sh b/libs/partners/mistralai/scripts/check_pydantic.sh deleted file mode 100755 index 06b5bb81ae236..0000000000000 --- a/libs/partners/mistralai/scripts/check_pydantic.sh +++ /dev/null @@ -1,27 +0,0 @@ -#!/bin/bash -# -# This script searches for lines starting with "import pydantic" or "from pydantic" -# in tracked files within a Git repository. -# -# Usage: ./scripts/check_pydantic.sh /path/to/repository - -# Check if a path argument is provided -if [ $# -ne 1 ]; then - echo "Usage: $0 /path/to/repository" - exit 1 -fi - -repository_path="$1" - -# Search for lines matching the pattern within the specified repository -result=$(git -C "$repository_path" grep -E '^import pydantic|^from pydantic') - -# Check if any matching lines were found -if [ -n "$result" ]; then - echo "ERROR: The following lines need to be updated:" - echo "$result" - echo "Please replace the code with an import from langchain_core.pydantic_v1." - echo "For example, replace 'from pydantic import BaseModel'" - echo "with 'from langchain_core.pydantic_v1 import BaseModel'" - exit 1 -fi diff --git a/libs/partners/mistralai/tests/integration_tests/test_chat_models.py b/libs/partners/mistralai/tests/integration_tests/test_chat_models.py index 9f88d9b4ea585..dc67bd93abe6a 100644 --- a/libs/partners/mistralai/tests/integration_tests/test_chat_models.py +++ b/libs/partners/mistralai/tests/integration_tests/test_chat_models.py @@ -9,7 +9,7 @@ BaseMessageChunk, HumanMessage, ) -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel from langchain_mistralai.chat_models import ChatMistralAI diff --git a/libs/partners/mistralai/tests/unit_tests/__snapshots__/test_standard.ambr b/libs/partners/mistralai/tests/unit_tests/__snapshots__/test_standard.ambr new file mode 100644 index 0000000000000..75bdad2decf89 --- /dev/null +++ b/libs/partners/mistralai/tests/unit_tests/__snapshots__/test_standard.ambr @@ -0,0 +1,68 @@ +# serializer version: 1 +# name: TestMistralStandard.test_serdes[serialized] + dict({ + 'graph': dict({ + 'edges': list([ + dict({ + 'source': 0, + 'target': 1, + }), + dict({ + 'source': 1, + 'target': 2, + }), + ]), + 'nodes': list([ + dict({ + 'data': 'ChatMistralAIInput', + 'id': 0, + 'type': 'schema', + }), + dict({ + 'data': dict({ + 'id': list([ + 'langchain', + 'chat_models', + 'mistralai', + 'ChatMistralAI', + ]), + 'name': 'ChatMistralAI', + }), + 'id': 1, + 'type': 'runnable', + }), + dict({ + 'data': 'ChatMistralAIOutput', + 'id': 2, + 'type': 'schema', + }), + ]), + }), + 'id': list([ + 'langchain', + 'chat_models', + 'mistralai', + 'ChatMistralAI', + ]), + 'kwargs': dict({ + 'endpoint': 'boo', + 'max_concurrent_requests': 64, + 'max_retries': 2, + 'max_tokens': 100, + 'mistral_api_key': dict({ + 'id': list([ + 'MISTRAL_API_KEY', + ]), + 'lc': 1, + 'type': 'secret', + }), + 'model': 'mistral-small', + 'temperature': 0.0, + 'timeout': 60, + 'top_p': 1, + }), + 'lc': 1, + 'name': 'ChatMistralAI', + 'type': 'constructor', + }) +# --- diff --git a/libs/partners/mistralai/tests/unit_tests/test_chat_models.py b/libs/partners/mistralai/tests/unit_tests/test_chat_models.py index 62b863338872c..a5046db47e43b 100644 --- a/libs/partners/mistralai/tests/unit_tests/test_chat_models.py +++ b/libs/partners/mistralai/tests/unit_tests/test_chat_models.py @@ -15,7 +15,7 @@ SystemMessage, ToolCall, ) -from langchain_core.pydantic_v1 import SecretStr +from pydantic import SecretStr from langchain_mistralai.chat_models import ( # type: ignore[import] ChatMistralAI, diff --git a/libs/partners/mistralai/tests/unit_tests/test_embeddings.py b/libs/partners/mistralai/tests/unit_tests/test_embeddings.py index 46e65eaf5fb7a..41023f015e25f 100644 --- a/libs/partners/mistralai/tests/unit_tests/test_embeddings.py +++ b/libs/partners/mistralai/tests/unit_tests/test_embeddings.py @@ -1,7 +1,7 @@ import os from typing import cast -from langchain_core.pydantic_v1 import SecretStr +from pydantic import SecretStr from langchain_mistralai import MistralAIEmbeddings diff --git a/libs/partners/mongodb/Makefile b/libs/partners/mongodb/Makefile index 606ff36f4b0ac..63fc9d46f0ead 100644 --- a/libs/partners/mongodb/Makefile +++ b/libs/partners/mongodb/Makefile @@ -10,6 +10,9 @@ integration_test integration_tests: TEST_FILE=tests/integration_tests/ test tests integration_test integration_tests: poetry run pytest $(TEST_FILE) +test_watch: + poetry run ptw --snapshot-update --now . -- -vv $(TEST_FILE) + ###################### # LINTING AND FORMATTING diff --git a/libs/partners/mongodb/poetry.lock b/libs/partners/mongodb/poetry.lock index 749ac840a74c4..b2b9c1290781d 100644 --- a/libs/partners/mongodb/poetry.lock +++ b/libs/partners/mongodb/poetry.lock @@ -133,9 +133,6 @@ files = [ {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"}, ] -[package.dependencies] -typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.9\""} - [[package]] name = "anyio" version = "4.4.0" @@ -717,7 +714,7 @@ files = [ [[package]] name = "langchain" -version = "0.2.12" +version = "0.2.16" description = "Building applications with LLMs through composability" optional = false python-versions = ">=3.8.1,<4.0" @@ -727,7 +724,7 @@ develop = true [package.dependencies] aiohttp = "^3.8.3" async-timeout = {version = "^4.0.0", markers = "python_version < \"3.11\""} -langchain-core = "^0.2.27" +langchain-core = "^0.2.38" langchain-text-splitters = "^0.2.0" langsmith = "^0.1.17" numpy = [ @@ -746,10 +743,10 @@ url = "../../langchain" [[package]] name = "langchain-core" -version = "0.2.29" +version = "0.2.38" description = "Building applications with LLMs through composability" optional = false -python-versions = ">=3.8.1,<4.0" +python-versions = ">=3.9,<4.0" files = [] develop = true @@ -771,7 +768,7 @@ url = "../../core" [[package]] name = "langchain-openai" -version = "0.1.21" +version = "0.1.23" description = "An integration package connecting OpenAI and LangChain" optional = false python-versions = ">=3.8.1,<4.0" @@ -779,7 +776,7 @@ files = [] develop = true [package.dependencies] -langchain-core = "^0.2.29" +langchain-core = "^0.2.35" openai = "^1.40.0" tiktoken = ">=0.7,<1" @@ -789,7 +786,7 @@ url = "../openai" [[package]] name = "langchain-text-splitters" -version = "0.2.3" +version = "0.2.4" description = "LangChain text splitting utilities" optional = false python-versions = ">=3.8.1,<4.0" @@ -797,7 +794,7 @@ files = [] develop = true [package.dependencies] -langchain-core = "^0.2.10" +langchain-core = "^0.2.38" 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pydantic" or "from pydantic" -# in tracked files within a Git repository. -# -# Usage: ./scripts/check_pydantic.sh /path/to/repository - -# Check if a path argument is provided -if [ $# -ne 1 ]; then - echo "Usage: $0 /path/to/repository" - exit 1 -fi - -repository_path="$1" - -# Search for lines matching the pattern within the specified repository -result=$(git -C "$repository_path" grep -E '^import pydantic|^from pydantic') - -# Check if any matching lines were found -if [ -n "$result" ]; then - echo "ERROR: The following lines need to be updated:" - echo "$result" - echo "Please replace the code with an import from langchain_core.pydantic_v1." - echo "For example, replace 'from pydantic import BaseModel'" - echo "with 'from langchain_core.pydantic_v1 import BaseModel'" - exit 1 -fi diff --git a/libs/partners/mongodb/tests/utils.py b/libs/partners/mongodb/tests/utils.py index f9126f5211d64..674bd2c43fd0e 100644 --- a/libs/partners/mongodb/tests/utils.py +++ b/libs/partners/mongodb/tests/utils.py @@ -17,7 +17,7 @@ BaseMessage, ) from langchain_core.outputs import ChatGeneration, ChatResult -from langchain_core.pydantic_v1 import validator +from pydantic import validator from pymongo.collection import Collection from pymongo.results import DeleteResult, InsertManyResult diff --git a/libs/partners/nomic/Makefile b/libs/partners/nomic/Makefile index d7f5f71aea320..4bc3498c2e12a 100644 --- a/libs/partners/nomic/Makefile +++ b/libs/partners/nomic/Makefile @@ -14,6 +14,9 @@ test integration_tests: tests: poetry run pytest $(TEST_FILE) +test_watch: + poetry run ptw --snapshot-update --now . -- -vv $(TEST_FILE) + ###################### # LINTING AND FORMATTING diff --git a/libs/partners/nomic/poetry.lock b/libs/partners/nomic/poetry.lock index 989a2b2c179ce..f93c4d2f6f617 100644 --- a/libs/partners/nomic/poetry.lock +++ b/libs/partners/nomic/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. [[package]] name = "annotated-types" @@ -11,9 +11,6 @@ files = [ {file = "annotated_types-0.6.0.tar.gz", hash = "sha256:563339e807e53ffd9c267e99fc6d9ea23eb8443c08f112651963e24e22f84a5d"}, ] -[package.dependencies] -typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.9\""} - [[package]] name = "attrs" version = "23.2.0" @@ -276,10 +273,10 @@ files = [ [[package]] name = "langchain-core" -version = "0.2.11" +version = "0.2.38" description = "Building applications with LLMs through composability" optional = false -python-versions = ">=3.8.1,<4.0" +python-versions = ">=3.9,<4.0" files = [] develop = true @@ -293,6 +290,7 @@ pydantic = [ ] PyYAML = ">=5.3" tenacity = "^8.1.0,!=8.4.0" +typing-extensions = ">=4.7" [package.source] type = "directory" @@ -461,43 +459,6 @@ aws = ["boto3", "sagemaker"] dev = ["black", "cairosvg", "coverage", "isort", "mkautodoc", "mkdocs-jupyter", "mkdocs-material", "mkdocstrings[python]", "myst-parser", "nomic[all]", "pandas", "pillow", "pylint", "pyright", "pytest", "pytorch-lightning", "twine"] local = ["gpt4all (>=2.5.0,<3)"] -[[package]] -name = "numpy" -version = "1.24.4" -description = "Fundamental package for array computing in Python" -optional = false -python-versions = ">=3.8" -files = [ - 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[[package]] name = "numpy" version = "1.26.4" @@ -1277,7 +1238,6 @@ files = [ [package.dependencies] markdown-it-py = ">=2.2.0" pygments = ">=2.13.0,<3.0.0" -typing-extensions = {version = ">=4.0.0,<5.0", markers = "python_version < \"3.9\""} [package.extras] jupyter = ["ipywidgets (>=7.5.1,<9)"] @@ -1475,5 +1435,5 @@ dev = ["black (>=19.3b0)", "pytest (>=4.6.2)"] [metadata] lock-version = "2.0" -python-versions = ">=3.8.1,<4.0" -content-hash = "00944df5bd0d1ae115a44b72c454173cf896d44a963da233e8648478b82a6458" +python-versions = ">=3.9,<4.0" +content-hash = "569bd2873b939f8997840e3d9aa061f26bd4d9ee0e42ce2571f4e98db86afc3f" diff --git a/libs/partners/nomic/pyproject.toml b/libs/partners/nomic/pyproject.toml index f2c5666491e58..dca34b5b64a9c 100644 --- a/libs/partners/nomic/pyproject.toml +++ b/libs/partners/nomic/pyproject.toml @@ -12,7 +12,7 @@ license = "MIT" "Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-nomic%3D%3D0%22&expanded=true" [tool.poetry.dependencies] -python = ">=3.8.1,<4.0" +python = ">=3.9,<4.0" langchain-core = ">=0.1.46,<0.3" nomic = "^3.0.29" pillow = "^10.3.0" diff --git a/libs/partners/nomic/scripts/check_pydantic.sh b/libs/partners/nomic/scripts/check_pydantic.sh deleted file mode 100755 index 06b5bb81ae236..0000000000000 --- a/libs/partners/nomic/scripts/check_pydantic.sh +++ /dev/null @@ -1,27 +0,0 @@ -#!/bin/bash -# -# This script searches for lines starting with "import pydantic" or "from pydantic" -# in tracked files within a Git repository. -# -# Usage: ./scripts/check_pydantic.sh /path/to/repository - -# Check if a path argument is provided -if [ $# -ne 1 ]; then - echo "Usage: $0 /path/to/repository" - exit 1 -fi - -repository_path="$1" - -# Search for lines matching the pattern within the specified repository -result=$(git -C "$repository_path" grep -E '^import pydantic|^from pydantic') - -# Check if any matching lines were found -if [ -n "$result" ]; then - echo "ERROR: The following lines need to be updated:" - echo "$result" - echo "Please replace the code with an import from langchain_core.pydantic_v1." - echo "For example, replace 'from pydantic import BaseModel'" - echo "with 'from langchain_core.pydantic_v1 import BaseModel'" - exit 1 -fi diff --git a/libs/partners/ollama/Makefile b/libs/partners/ollama/Makefile index 239375a21c015..997bc2a6537f6 100644 --- a/libs/partners/ollama/Makefile +++ b/libs/partners/ollama/Makefile @@ -14,6 +14,10 @@ integration_test: TEST_FILE = tests/integration_tests/ test tests: poetry run pytest --disable-socket --allow-unix-socket $(TEST_FILE) +test_watch: + poetry run ptw --snapshot-update --now . -- -vv $(TEST_FILE) + + # integration tests are run without the --disable-socket flag to allow network calls integration_test: poetry run pytest $(TEST_FILE) diff --git a/libs/partners/ollama/langchain_ollama/chat_models.py b/libs/partners/ollama/langchain_ollama/chat_models.py index 143761cf5f01b..df32e73c1a5b8 100644 --- a/libs/partners/ollama/langchain_ollama/chat_models.py +++ b/libs/partners/ollama/langchain_ollama/chat_models.py @@ -35,11 +35,12 @@ from langchain_core.messages.ai import UsageMetadata from langchain_core.messages.tool import tool_call from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult -from langchain_core.pydantic_v1 import Field, root_validator from langchain_core.runnables import Runnable from langchain_core.tools import BaseTool from langchain_core.utils.function_calling import convert_to_openai_tool from ollama import AsyncClient, Client, Message, Options +from pydantic import PrivateAttr, model_validator +from typing_extensions import Self def _get_usage_metadata_from_generation_info( @@ -225,7 +226,7 @@ class ChatOllama(BaseChatModel): .. code-block:: python from langchain_ollama import ChatOllama - from langchain_core.pydantic_v1 import BaseModel, Field + from pydantic import BaseModel, Field class Multiply(BaseModel): a: int = Field(..., description="First integer") @@ -328,12 +329,12 @@ class Multiply(BaseModel): For a full list of the params, see [this link](https://pydoc.dev/httpx/latest/httpx.Client.html) """ - _client: Client = Field(default=None) + _client: Client = PrivateAttr(default=None) """ The client to use for making requests. """ - _async_client: AsyncClient = Field(default=None) + _async_client: AsyncClient = PrivateAttr(default=None) """ The async client to use for making requests. """ @@ -364,14 +365,13 @@ def _default_params(self) -> Dict[str, Any]: "keep_alive": self.keep_alive, } - @root_validator(pre=False, skip_on_failure=True) - def _set_clients(cls, values: dict) -> dict: + @model_validator(mode="after") + def _set_clients(self) -> Self: """Set clients to use for ollama.""" - values["_client"] = Client(host=values["base_url"], **values["client_kwargs"]) - values["_async_client"] = AsyncClient( - host=values["base_url"], **values["client_kwargs"] - ) - return values + client_kwargs = self.client_kwargs or {} + self._client = Client(host=self.base_url, **client_kwargs) + self._async_client = AsyncClient(host=self.base_url, **client_kwargs) + return self def _convert_messages_to_ollama_messages( self, messages: List[BaseMessage] diff --git a/libs/partners/ollama/langchain_ollama/embeddings.py b/libs/partners/ollama/langchain_ollama/embeddings.py index 8cf991232c152..6c5b812dc99cb 100644 --- a/libs/partners/ollama/langchain_ollama/embeddings.py +++ b/libs/partners/ollama/langchain_ollama/embeddings.py @@ -4,8 +4,14 @@ ) from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel, Field, root_validator from ollama import AsyncClient, Client +from pydantic import ( + BaseModel, + ConfigDict, + PrivateAttr, + model_validator, +) +from typing_extensions import Self class OllamaEmbeddings(BaseModel, Embeddings): @@ -126,29 +132,27 @@ class OllamaEmbeddings(BaseModel, Embeddings): For a full list of the params, see [this link](https://pydoc.dev/httpx/latest/httpx.Client.html) """ - _client: Client = Field(default=None) + _client: Client = PrivateAttr(default=None) """ The client to use for making requests. """ - _async_client: AsyncClient = Field(default=None) + _async_client: AsyncClient = PrivateAttr(default=None) """ The async client to use for making requests. """ - class Config: - """Configuration for this pydantic object.""" - - extra = "forbid" + model_config = ConfigDict( + extra="forbid", + ) - @root_validator(pre=False, skip_on_failure=True) - def _set_clients(cls, values: dict) -> dict: + @model_validator(mode="after") + def _set_clients(self) -> Self: """Set clients to use for ollama.""" - values["_client"] = Client(host=values["base_url"], **values["client_kwargs"]) - values["_async_client"] = AsyncClient( - host=values["base_url"], **values["client_kwargs"] - ) - return values + client_kwargs = self.client_kwargs or {} + self._client = Client(host=self.base_url, **client_kwargs) + self._async_client = AsyncClient(host=self.base_url, **client_kwargs) + return self def embed_documents(self, texts: List[str]) -> List[List[float]]: """Embed search docs.""" diff --git a/libs/partners/ollama/langchain_ollama/llms.py b/libs/partners/ollama/langchain_ollama/llms.py index 8097aa2db8619..783d20104ef44 100644 --- a/libs/partners/ollama/langchain_ollama/llms.py +++ b/libs/partners/ollama/langchain_ollama/llms.py @@ -18,8 +18,9 @@ ) from langchain_core.language_models import BaseLLM, LangSmithParams from langchain_core.outputs import GenerationChunk, LLMResult -from langchain_core.pydantic_v1 import Field, root_validator from ollama import AsyncClient, Client, Options +from pydantic import PrivateAttr, model_validator +from typing_extensions import Self class OllamaLLM(BaseLLM): @@ -115,12 +116,12 @@ class OllamaLLM(BaseLLM): For a full list of the params, see [this link](https://pydoc.dev/httpx/latest/httpx.Client.html) """ - _client: Client = Field(default=None) + _client: Client = PrivateAttr(default=None) """ The client to use for making requests. """ - _async_client: AsyncClient = Field(default=None) + _async_client: AsyncClient = PrivateAttr(default=None) """ The async client to use for making requests. """ @@ -164,14 +165,13 @@ def _get_ls_params( params["ls_max_tokens"] = max_tokens return params - @root_validator(pre=False, skip_on_failure=True) - def _set_clients(cls, values: dict) -> dict: + @model_validator(mode="after") + def _set_clients(self) -> Self: """Set clients to use for ollama.""" - values["_client"] = Client(host=values["base_url"], **values["client_kwargs"]) - values["_async_client"] = AsyncClient( - host=values["base_url"], **values["client_kwargs"] - ) - return values + client_kwargs = self.client_kwargs or {} + self._client = Client(host=self.base_url, **client_kwargs) + self._async_client = AsyncClient(host=self.base_url, **client_kwargs) + return self async def _acreate_generate_stream( self, diff --git a/libs/partners/ollama/poetry.lock b/libs/partners/ollama/poetry.lock index 0e54aad2ab1eb..6c59618e44700 100644 --- a/libs/partners/ollama/poetry.lock +++ b/libs/partners/ollama/poetry.lock @@ -11,9 +11,6 @@ files = [ {file = "annotated_types-0.7.0.tar.gz", hash 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"sha256:dcebf7e475001d2cdeb020be630dc5b687e9acdd60d16fea6bb4508e7b94cf76"}, +] + +[package.extras] +watchmedo = ["PyYAML (>=3.10)"] + [metadata] lock-version = "2.0" -python-versions = ">=3.8.1,<4.0" -content-hash = "0de4df1fb448399e93be5924717c1cb7ec3a0c8255951878020844c28bfb7b5e" +python-versions = ">=3.9,<4.0" +content-hash = "ca997e5f65a0bb4a688966858836640f66b81acba3e438db9743003394141113" diff --git a/libs/partners/ollama/pyproject.toml b/libs/partners/ollama/pyproject.toml index 27efbdeb09387..9f0184028e864 100644 --- a/libs/partners/ollama/pyproject.toml +++ b/libs/partners/ollama/pyproject.toml @@ -19,9 +19,10 @@ disallow_untyped_defs = "True" "Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-ollama%3D%3D0%22&expanded=true" [tool.poetry.dependencies] -python = ">=3.8.1,<4.0" +python = ">=3.9,<4.0" ollama = ">=0.3.0,<1" langchain-core = "^0.2.36" +pydantic = ">=2,<3" [tool.ruff.lint] select = [ "E", "F", "I", "T201",] @@ -54,6 +55,7 @@ pytest = "^7.4.3" pytest-asyncio = "^0.23.2" syrupy = "^4.0.2" pytest-socket = "^0.7.0" +pytest-watcher = "^0.3.4" [tool.poetry.group.codespell.dependencies] codespell = "^2.2.6" diff --git a/libs/partners/ollama/scripts/check_pydantic.sh b/libs/partners/ollama/scripts/check_pydantic.sh deleted file mode 100755 index 06b5bb81ae236..0000000000000 --- a/libs/partners/ollama/scripts/check_pydantic.sh +++ /dev/null @@ -1,27 +0,0 @@ -#!/bin/bash -# -# This script searches for lines starting with "import pydantic" or "from pydantic" -# in tracked files within a Git repository. -# -# Usage: ./scripts/check_pydantic.sh /path/to/repository - -# Check if a path argument is provided -if [ $# -ne 1 ]; then - echo "Usage: $0 /path/to/repository" - exit 1 -fi - -repository_path="$1" - -# Search for lines matching the pattern within the specified repository -result=$(git -C "$repository_path" grep -E '^import pydantic|^from pydantic') - -# Check if any matching lines were found -if [ -n "$result" ]; then - echo "ERROR: The following lines need to be updated:" - echo "$result" - echo "Please replace the code with an import from langchain_core.pydantic_v1." - echo "For example, replace 'from pydantic import BaseModel'" - echo "with 'from langchain_core.pydantic_v1 import BaseModel'" - exit 1 -fi diff --git a/libs/partners/openai/Makefile b/libs/partners/openai/Makefile index 69b2dfe885c05..28b03e388ad25 100644 --- a/libs/partners/openai/Makefile +++ b/libs/partners/openai/Makefile @@ -11,6 +11,9 @@ integration_test integration_tests: TEST_FILE=tests/integration_tests/ test tests integration_test integration_tests: poetry run pytest $(TEST_FILE) +test_watch: + poetry run ptw --snapshot-update --now . -- -vv $(TEST_FILE) + ###################### # LINTING AND FORMATTING diff --git a/libs/partners/openai/langchain_openai/chat_models/azure.py b/libs/partners/openai/langchain_openai/chat_models/azure.py index 23b9dd3d2e95a..0d89947fb2a74 100644 --- a/libs/partners/openai/langchain_openai/chat_models/azure.py +++ b/libs/partners/openai/langchain_openai/chat_models/azure.py @@ -31,12 +31,13 @@ PydanticToolsParser, ) from langchain_core.outputs import ChatResult -from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr, root_validator from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough from langchain_core.tools import BaseTool from langchain_core.utils import from_env, secret_from_env from langchain_core.utils.function_calling import convert_to_openai_tool from langchain_core.utils.pydantic import is_basemodel_subclass +from pydantic import BaseModel, Field, SecretStr, model_validator +from typing_extensions import Self from langchain_openai.chat_models.base import BaseChatOpenAI @@ -250,7 +251,7 @@ class AzureChatOpenAI(BaseChatOpenAI): Tool calling: .. code-block:: python - from langchain_core.pydantic_v1 import BaseModel, Field + from pydantic import BaseModel, Field class GetWeather(BaseModel): @@ -305,7 +306,7 @@ class GetPopulation(BaseModel): from typing import Optional - from langchain_core.pydantic_v1 import BaseModel, Field + from pydantic import BaseModel, Field class Joke(BaseModel): @@ -494,7 +495,7 @@ class Joke(BaseModel): default_factory=from_env("OPENAI_API_VERSION", default=None), ) """Automatically inferred from env var `OPENAI_API_VERSION` if not provided.""" - # Check OPENAI_KEY for backwards compatibility. + # Check OPENAI_API_KEY for backwards compatibility. # TODO: Remove OPENAI_API_KEY support to avoid possible conflict when using # other forms of azure credentials. openai_api_key: Optional[SecretStr] = Field( @@ -565,31 +566,31 @@ def lc_secrets(self) -> Dict[str, str]: def is_lc_serializable(cls) -> bool: return True - @root_validator(pre=False, skip_on_failure=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="after") + def validate_environment(self) -> Self: """Validate that api key and python package exists in environment.""" - if values["n"] < 1: + if self.n < 1: raise ValueError("n must be at least 1.") - if values["n"] > 1 and values["streaming"]: + if self.n > 1 and self.streaming: raise ValueError("n must be 1 when streaming.") # Check OPENAI_ORGANIZATION for backwards compatibility. - values["openai_organization"] = ( - values["openai_organization"] + self.openai_organization = ( + self.openai_organization or os.getenv("OPENAI_ORG_ID") or os.getenv("OPENAI_ORGANIZATION") ) # For backwards compatibility. Before openai v1, no distinction was made # between azure_endpoint and base_url (openai_api_base). - openai_api_base = values["openai_api_base"] - if openai_api_base and values["validate_base_url"]: + openai_api_base = self.openai_api_base + if openai_api_base and self.validate_base_url: if "/openai" not in openai_api_base: raise ValueError( "As of openai>=1.0.0, Azure endpoints should be specified via " "the `azure_endpoint` param not `openai_api_base` " "(or alias `base_url`)." ) - if values["deployment_name"]: + if self.deployment_name: raise ValueError( "As of openai>=1.0.0, if `azure_deployment` (or alias " "`deployment_name`) is specified then " @@ -602,39 +603,36 @@ def validate_environment(cls, values: Dict) -> Dict: "Or you can equivalently specify:\n\n" 'base_url="https://xxx.openai.azure.com/openai/deployments/my-deployment"' ) - client_params = { - "api_version": values["openai_api_version"], - "azure_endpoint": values["azure_endpoint"], - "azure_deployment": values["deployment_name"], + client_params: dict = { + "api_version": self.openai_api_version, + "azure_endpoint": self.azure_endpoint, + "azure_deployment": self.deployment_name, "api_key": ( - values["openai_api_key"].get_secret_value() - if values["openai_api_key"] - else None + self.openai_api_key.get_secret_value() if self.openai_api_key else None ), "azure_ad_token": ( - values["azure_ad_token"].get_secret_value() - if values["azure_ad_token"] - else None + self.azure_ad_token.get_secret_value() if self.azure_ad_token else None ), - "azure_ad_token_provider": values["azure_ad_token_provider"], - "organization": values["openai_organization"], - "base_url": values["openai_api_base"], - "timeout": values["request_timeout"], - "max_retries": values["max_retries"], - "default_headers": values["default_headers"], - "default_query": values["default_query"], + "azure_ad_token_provider": self.azure_ad_token_provider, + "organization": self.openai_organization, + "base_url": self.openai_api_base, + "timeout": self.request_timeout, + "max_retries": self.max_retries, + "default_headers": self.default_headers, + "default_query": self.default_query, } - if not values.get("client"): - sync_specific = {"http_client": values["http_client"]} - values["root_client"] = openai.AzureOpenAI(**client_params, **sync_specific) - values["client"] = values["root_client"].chat.completions - if not values.get("async_client"): - async_specific = {"http_client": values["http_async_client"]} - values["root_async_client"] = openai.AsyncAzureOpenAI( - **client_params, **async_specific + if not self.client: + sync_specific = {"http_client": self.http_client} + self.root_client = openai.AzureOpenAI(**client_params, **sync_specific) # type: ignore[arg-type] + self.client = self.root_client.chat.completions + if not self.async_client: + async_specific = {"http_client": self.http_async_client} + self.root_async_client = openai.AsyncAzureOpenAI( + **client_params, + **async_specific, # type: ignore[arg-type] ) - values["async_client"] = values["root_async_client"].chat.completions - return values + self.async_client = self.root_async_client.chat.completions + return self def bind_tools( self, @@ -735,7 +733,7 @@ def with_structured_output( from typing import Optional from langchain_openai import AzureChatOpenAI - from langchain_core.pydantic_v1 import BaseModel, Field + from pydantic import BaseModel, Field class AnswerWithJustification(BaseModel): @@ -766,7 +764,7 @@ class AnswerWithJustification(BaseModel): .. code-block:: python from langchain_openai import AzureChatOpenAI - from langchain_core.pydantic_v1 import BaseModel + from pydantic import BaseModel class AnswerWithJustification(BaseModel): @@ -853,7 +851,7 @@ class AnswerWithJustification(TypedDict): .. code-block:: from langchain_openai import AzureChatOpenAI - from langchain_core.pydantic_v1 import BaseModel + from pydantic import BaseModel class AnswerWithJustification(BaseModel): answer: str diff --git a/libs/partners/openai/langchain_openai/chat_models/base.py b/libs/partners/openai/langchain_openai/chat_models/base.py index 66b7e75edecf6..8d643fd6b8476 100644 --- a/libs/partners/openai/langchain_openai/chat_models/base.py +++ b/libs/partners/openai/langchain_openai/chat_models/base.py @@ -73,15 +73,10 @@ parse_tool_call, ) from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult -from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr, root_validator from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough, chain from langchain_core.runnables.config import run_in_executor from langchain_core.tools import BaseTool -from langchain_core.utils import ( - convert_to_secret_str, - get_from_dict_or_env, - get_pydantic_field_names, -) +from langchain_core.utils import get_pydantic_field_names from langchain_core.utils.function_calling import ( convert_to_openai_function, convert_to_openai_tool, @@ -91,7 +86,9 @@ TypeBaseModel, is_basemodel_subclass, ) -from langchain_core.utils.utils import build_extra_kwargs +from langchain_core.utils.utils import build_extra_kwargs, from_env, secret_from_env +from pydantic import BaseModel, ConfigDict, Field, SecretStr, model_validator +from typing_extensions import Self logger = logging.getLogger(__name__) @@ -361,15 +358,18 @@ class BaseChatOpenAI(BaseChatModel): """What sampling temperature to use.""" model_kwargs: Dict[str, Any] = Field(default_factory=dict) """Holds any model parameters valid for `create` call not explicitly specified.""" - openai_api_key: Optional[SecretStr] = Field(default=None, alias="api_key") - """Automatically inferred from env var `OPENAI_API_KEY` if not provided.""" + openai_api_key: Optional[SecretStr] = Field( + alias="api_key", default_factory=secret_from_env("OPENAI_API_KEY", default=None) + ) openai_api_base: Optional[str] = Field(default=None, alias="base_url") """Base URL path for API requests, leave blank if not using a proxy or service emulator.""" openai_organization: Optional[str] = Field(default=None, alias="organization") """Automatically inferred from env var `OPENAI_ORG_ID` if not provided.""" # to support explicit proxy for OpenAI - openai_proxy: Optional[str] = None + openai_proxy: Optional[str] = Field( + default_factory=from_env("OPENAI_PROXY", default=None) + ) request_timeout: Union[float, Tuple[float, float], Any, None] = Field( default=None, alias="timeout" ) @@ -428,13 +428,11 @@ class BaseChatOpenAI(BaseChatModel): include_response_headers: bool = False """Whether to include response headers in the output message response_metadata.""" - class Config: - """Configuration for this pydantic object.""" + model_config = ConfigDict(populate_by_name=True) - allow_population_by_field_name = True - - @root_validator(pre=True) - def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def build_extra(cls, values: Dict[str, Any]) -> Any: """Build extra kwargs from additional params that were passed in.""" all_required_field_names = get_pydantic_field_names(cls) extra = values.get("model_kwargs", {}) @@ -443,56 +441,43 @@ def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: ) return values - @root_validator(pre=False, skip_on_failure=True, allow_reuse=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="after") + def validate_environment(self) -> Self: """Validate that api key and python package exists in environment.""" - if values["n"] < 1: + if self.n < 1: raise ValueError("n must be at least 1.") - if values["n"] > 1 and values["streaming"]: + if self.n > 1 and self.streaming: raise ValueError("n must be 1 when streaming.") - values["openai_api_key"] = convert_to_secret_str( - get_from_dict_or_env(values, "openai_api_key", "OPENAI_API_KEY") - ) # Check OPENAI_ORGANIZATION for backwards compatibility. - values["openai_organization"] = ( - values["openai_organization"] + self.openai_organization = ( + self.openai_organization or os.getenv("OPENAI_ORG_ID") or os.getenv("OPENAI_ORGANIZATION") ) - values["openai_api_base"] = values["openai_api_base"] or os.getenv( - "OPENAI_API_BASE" - ) - values["openai_proxy"] = get_from_dict_or_env( - values, "openai_proxy", "OPENAI_PROXY", default="" - ) - - client_params = { + self.openai_api_base = self.openai_api_base or os.getenv("OPENAI_API_BASE") + client_params: dict = { "api_key": ( - values["openai_api_key"].get_secret_value() - if values["openai_api_key"] - else None + self.openai_api_key.get_secret_value() if self.openai_api_key else None ), - "organization": values["openai_organization"], - "base_url": values["openai_api_base"], - "timeout": values["request_timeout"], - "max_retries": values["max_retries"], - "default_headers": values["default_headers"], - "default_query": values["default_query"], + "organization": self.openai_organization, + "base_url": self.openai_api_base, + "timeout": self.request_timeout, + "max_retries": self.max_retries, + "default_headers": self.default_headers, + "default_query": self.default_query, } - if values["openai_proxy"] and ( - values["http_client"] or values["http_async_client"] - ): - openai_proxy = values["openai_proxy"] - http_client = values["http_client"] - http_async_client = values["http_async_client"] + if self.openai_proxy and (self.http_client or self.http_async_client): + openai_proxy = self.openai_proxy + http_client = self.http_client + http_async_client = self.http_async_client raise ValueError( "Cannot specify 'openai_proxy' if one of " "'http_client'/'http_async_client' is already specified. Received:\n" f"{openai_proxy=}\n{http_client=}\n{http_async_client=}" ) - if not values.get("client"): - if values["openai_proxy"] and not values["http_client"]: + if not self.client: + if self.openai_proxy and not self.http_client: try: import httpx except ImportError as e: @@ -500,12 +485,12 @@ def validate_environment(cls, values: Dict) -> Dict: "Could not import httpx python package. " "Please install it with `pip install httpx`." ) from e - values["http_client"] = httpx.Client(proxy=values["openai_proxy"]) - sync_specific = {"http_client": values["http_client"]} - values["root_client"] = openai.OpenAI(**client_params, **sync_specific) - values["client"] = values["root_client"].chat.completions - if not values.get("async_client"): - if values["openai_proxy"] and not values["http_async_client"]: + self.http_client = httpx.Client(proxy=self.openai_proxy) + sync_specific = {"http_client": self.http_client} + self.root_client = openai.OpenAI(**client_params, **sync_specific) # type: ignore[arg-type] + self.client = self.root_client.chat.completions + if not self.async_client: + if self.openai_proxy and not self.http_async_client: try: import httpx except ImportError as e: @@ -513,15 +498,14 @@ def validate_environment(cls, values: Dict) -> Dict: "Could not import httpx python package. " "Please install it with `pip install httpx`." ) from e - values["http_async_client"] = httpx.AsyncClient( - proxy=values["openai_proxy"] - ) - async_specific = {"http_client": values["http_async_client"]} - values["root_async_client"] = openai.AsyncOpenAI( - **client_params, **async_specific + self.http_async_client = httpx.AsyncClient(proxy=self.openai_proxy) + async_specific = {"http_client": self.http_async_client} + self.root_async_client = openai.AsyncOpenAI( + **client_params, + **async_specific, # type: ignore[arg-type] ) - values["async_client"] = values["root_async_client"].chat.completions - return values + self.async_client = self.root_async_client.chat.completions + return self @property def _default_params(self) -> Dict[str, Any]: @@ -1206,7 +1190,7 @@ def with_structured_output( from typing import Optional from langchain_openai import ChatOpenAI - from langchain_core.pydantic_v1 import BaseModel, Field + from pydantic import BaseModel, Field class AnswerWithJustification(BaseModel): @@ -1237,7 +1221,7 @@ class AnswerWithJustification(BaseModel): .. code-block:: python from langchain_openai import ChatOpenAI - from langchain_core.pydantic_v1 import BaseModel + from pydantic import BaseModel class AnswerWithJustification(BaseModel): @@ -1327,7 +1311,7 @@ class AnswerWithJustification(TypedDict): .. code-block:: from langchain_openai import ChatOpenAI - from langchain_core.pydantic_v1 import BaseModel + from pydantic import BaseModel class AnswerWithJustification(BaseModel): answer: str @@ -1629,7 +1613,7 @@ class ChatOpenAI(BaseChatOpenAI): .. code-block:: python - from langchain_core.pydantic_v1 import BaseModel, Field + from pydantic import BaseModel, Field class GetWeather(BaseModel): @@ -1715,7 +1699,7 @@ class GetPopulation(BaseModel): from typing import Optional - from langchain_core.pydantic_v1 import BaseModel, Field + from pydantic import BaseModel, Field class Joke(BaseModel): diff --git a/libs/partners/openai/langchain_openai/embeddings/azure.py b/libs/partners/openai/langchain_openai/embeddings/azure.py index 3725a7662858f..06349e36a5195 100644 --- a/libs/partners/openai/langchain_openai/embeddings/azure.py +++ b/libs/partners/openai/langchain_openai/embeddings/azure.py @@ -2,11 +2,12 @@ from __future__ import annotations -from typing import Callable, Dict, Optional, Union +from typing import Callable, Optional, Union import openai -from langchain_core.pydantic_v1 import Field, SecretStr, root_validator from langchain_core.utils import from_env, secret_from_env +from pydantic import Field, SecretStr, model_validator +from typing_extensions import Self, cast from langchain_openai.embeddings.base import OpenAIEmbeddings @@ -125,7 +126,8 @@ class AzureOpenAIEmbeddings(OpenAIEmbeddings): ) """Automatically inferred from env var `AZURE_OPENAI_API_KEY` if not provided.""" openai_api_version: Optional[str] = Field( - default_factory=from_env("OPENAI_API_VERSION", default="2023-05-15") + default_factory=from_env("OPENAI_API_VERSION", default="2023-05-15"), + alias="api_version", ) """Automatically inferred from env var `OPENAI_API_VERSION` if not provided. @@ -153,21 +155,21 @@ class AzureOpenAIEmbeddings(OpenAIEmbeddings): chunk_size: int = 2048 """Maximum number of texts to embed in each batch""" - @root_validator(pre=False, skip_on_failure=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="after") + def validate_environment(self) -> Self: """Validate that api key and python package exists in environment.""" # For backwards compatibility. Before openai v1, no distinction was made # between azure_endpoint and base_url (openai_api_base). - openai_api_base = values["openai_api_base"] - if openai_api_base and values["validate_base_url"]: + openai_api_base = self.openai_api_base + if openai_api_base and self.validate_base_url: if "/openai" not in openai_api_base: - values["openai_api_base"] += "/openai" + self.openai_api_base = cast(str, self.openai_api_base) + "/openai" raise ValueError( "As of openai>=1.0.0, Azure endpoints should be specified via " "the `azure_endpoint` param not `openai_api_base` " "(or alias `base_url`). " ) - if values["deployment"]: + if self.deployment: raise ValueError( "As of openai>=1.0.0, if `deployment` (or alias " "`azure_deployment`) is specified then " @@ -175,39 +177,37 @@ def validate_environment(cls, values: Dict) -> Dict: "Instead use `deployment` (or alias `azure_deployment`) " "and `azure_endpoint`." ) - client_params = { - "api_version": values["openai_api_version"], - "azure_endpoint": values["azure_endpoint"], - "azure_deployment": values["deployment"], + client_params: dict = { + "api_version": self.openai_api_version, + "azure_endpoint": self.azure_endpoint, + "azure_deployment": self.deployment, "api_key": ( - values["openai_api_key"].get_secret_value() - if values["openai_api_key"] - else None + self.openai_api_key.get_secret_value() if self.openai_api_key else None ), "azure_ad_token": ( - values["azure_ad_token"].get_secret_value() - if values["azure_ad_token"] - else None + self.azure_ad_token.get_secret_value() if self.azure_ad_token else None ), - "azure_ad_token_provider": values["azure_ad_token_provider"], - "organization": values["openai_organization"], - "base_url": values["openai_api_base"], - "timeout": values["request_timeout"], - "max_retries": values["max_retries"], - "default_headers": values["default_headers"], - "default_query": values["default_query"], + "azure_ad_token_provider": self.azure_ad_token_provider, + "organization": self.openai_organization, + "base_url": self.openai_api_base, + "timeout": self.request_timeout, + "max_retries": self.max_retries, + "default_headers": self.default_headers, + "default_query": self.default_query, } - if not values.get("client"): - sync_specific = {"http_client": values["http_client"]} - values["client"] = openai.AzureOpenAI( - **client_params, **sync_specific + if not self.client: + sync_specific: dict = {"http_client": self.http_client} + self.client = openai.AzureOpenAI( + **client_params, # type: ignore[arg-type] + **sync_specific, ).embeddings - if not values.get("async_client"): - async_specific = {"http_client": values["http_async_client"]} - values["async_client"] = openai.AsyncAzureOpenAI( - **client_params, **async_specific + if not self.async_client: + async_specific: dict = {"http_client": self.http_async_client} + self.async_client = openai.AsyncAzureOpenAI( + **client_params, # type: ignore[arg-type] + **async_specific, ).embeddings - return values + return self @property def _llm_type(self) -> str: diff --git a/libs/partners/openai/langchain_openai/embeddings/base.py b/libs/partners/openai/langchain_openai/embeddings/base.py index 1a6a3a0417d7e..02f72542c017c 100644 --- a/libs/partners/openai/langchain_openai/embeddings/base.py +++ b/libs/partners/openai/langchain_openai/embeddings/base.py @@ -20,8 +20,9 @@ import openai import tiktoken from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr, root_validator from langchain_core.utils import from_env, get_pydantic_field_names, secret_from_env +from pydantic import BaseModel, ConfigDict, Field, SecretStr, model_validator +from typing_extensions import Self logger = logging.getLogger(__name__) @@ -263,14 +264,11 @@ class OpenAIEmbeddings(BaseModel, Embeddings): """Whether to check the token length of inputs and automatically split inputs longer than embedding_ctx_length.""" - class Config: - """Configuration for this pydantic object.""" + model_config = ConfigDict(extra="forbid", populate_by_name=True) - extra = "forbid" - allow_population_by_field_name = True - - @root_validator(pre=True) - def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def build_extra(cls, values: Dict[str, Any]) -> Any: """Build extra kwargs from additional params that were passed in.""" all_required_field_names = get_pydantic_field_names(cls) extra = values.get("model_kwargs", {}) @@ -295,41 +293,37 @@ def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: values["model_kwargs"] = extra return values - @root_validator(pre=False, skip_on_failure=True, allow_reuse=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="after") + def validate_environment(self) -> Self: """Validate that api key and python package exists in environment.""" - if values["openai_api_type"] in ("azure", "azure_ad", "azuread"): + if self.openai_api_type in ("azure", "azure_ad", "azuread"): raise ValueError( "If you are using Azure, " "please use the `AzureOpenAIEmbeddings` class." ) - client_params = { + client_params: dict = { "api_key": ( - values["openai_api_key"].get_secret_value() - if values["openai_api_key"] - else None + self.openai_api_key.get_secret_value() if self.openai_api_key else None ), - "organization": values["openai_organization"], - "base_url": values["openai_api_base"], - "timeout": values["request_timeout"], - "max_retries": values["max_retries"], - "default_headers": values["default_headers"], - "default_query": values["default_query"], + "organization": self.openai_organization, + "base_url": self.openai_api_base, + "timeout": self.request_timeout, + "max_retries": self.max_retries, + "default_headers": self.default_headers, + "default_query": self.default_query, } - if values["openai_proxy"] and ( - values["http_client"] or values["http_async_client"] - ): - openai_proxy = values["openai_proxy"] - http_client = values["http_client"] - http_async_client = values["http_async_client"] + if self.openai_proxy and (self.http_client or self.http_async_client): + openai_proxy = self.openai_proxy + http_client = self.http_client + http_async_client = self.http_async_client raise ValueError( "Cannot specify 'openai_proxy' if one of " "'http_client'/'http_async_client' is already specified. Received:\n" f"{openai_proxy=}\n{http_client=}\n{http_async_client=}" ) - if not values.get("client"): - if values["openai_proxy"] and not values["http_client"]: + if not self.client: + if self.openai_proxy and not self.http_client: try: import httpx except ImportError as e: @@ -337,13 +331,11 @@ def validate_environment(cls, values: Dict) -> Dict: "Could not import httpx python package. " "Please install it with `pip install httpx`." ) from e - values["http_client"] = httpx.Client(proxy=values["openai_proxy"]) - sync_specific = {"http_client": values["http_client"]} - values["client"] = openai.OpenAI( - **client_params, **sync_specific - ).embeddings - if not values.get("async_client"): - if values["openai_proxy"] and not values["http_async_client"]: + self.http_client = httpx.Client(proxy=self.openai_proxy) + sync_specific = {"http_client": self.http_client} + self.client = openai.OpenAI(**client_params, **sync_specific).embeddings # type: ignore[arg-type] + if not self.async_client: + if self.openai_proxy and not self.http_async_client: try: import httpx except ImportError as e: @@ -351,14 +343,13 @@ def validate_environment(cls, values: Dict) -> Dict: "Could not import httpx python package. " "Please install it with `pip install httpx`." ) from e - values["http_async_client"] = httpx.AsyncClient( - proxy=values["openai_proxy"] - ) - async_specific = {"http_client": values["http_async_client"]} - values["async_client"] = openai.AsyncOpenAI( - **client_params, **async_specific + self.http_async_client = httpx.AsyncClient(proxy=self.openai_proxy) + async_specific = {"http_client": self.http_async_client} + self.async_client = openai.AsyncOpenAI( + **client_params, + **async_specific, # type: ignore[arg-type] ).embeddings - return values + return self @property def _invocation_params(self) -> Dict[str, Any]: diff --git a/libs/partners/openai/langchain_openai/llms/azure.py b/libs/partners/openai/langchain_openai/llms/azure.py index 0d091b325f520..90c20c9d4d7d0 100644 --- a/libs/partners/openai/langchain_openai/llms/azure.py +++ b/libs/partners/openai/langchain_openai/llms/azure.py @@ -5,8 +5,9 @@ import openai from langchain_core.language_models import LangSmithParams -from langchain_core.pydantic_v1 import Field, SecretStr, root_validator from langchain_core.utils import from_env, secret_from_env +from pydantic import Field, SecretStr, model_validator +from typing_extensions import Self, cast from langchain_openai.llms.base import BaseOpenAI @@ -100,29 +101,29 @@ def is_lc_serializable(cls) -> bool: """Return whether this model can be serialized by Langchain.""" return True - @root_validator(pre=False, skip_on_failure=True, allow_reuse=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="after") + def validate_environment(self) -> Self: """Validate that api key and python package exists in environment.""" - if values["n"] < 1: + if self.n < 1: raise ValueError("n must be at least 1.") - if values["streaming"] and values["n"] > 1: + if self.streaming and self.n > 1: raise ValueError("Cannot stream results when n > 1.") - if values["streaming"] and values["best_of"] > 1: + if self.streaming and self.best_of > 1: raise ValueError("Cannot stream results when best_of > 1.") # For backwards compatibility. Before openai v1, no distinction was made # between azure_endpoint and base_url (openai_api_base). - openai_api_base = values["openai_api_base"] - if openai_api_base and values["validate_base_url"]: + openai_api_base = self.openai_api_base + if openai_api_base and self.validate_base_url: if "/openai" not in openai_api_base: - values["openai_api_base"] = ( - values["openai_api_base"].rstrip("/") + "/openai" + self.openai_api_base = ( + cast(str, self.openai_api_base).rstrip("/") + "/openai" ) raise ValueError( "As of openai>=1.0.0, Azure endpoints should be specified via " "the `azure_endpoint` param not `openai_api_base` " "(or alias `base_url`)." ) - if values["deployment_name"]: + if self.deployment_name: raise ValueError( "As of openai>=1.0.0, if `deployment_name` (or alias " "`azure_deployment`) is specified then " @@ -130,37 +131,39 @@ def validate_environment(cls, values: Dict) -> Dict: "Instead use `deployment_name` (or alias `azure_deployment`) " "and `azure_endpoint`." ) - values["deployment_name"] = None - client_params = { - "api_version": values["openai_api_version"], - "azure_endpoint": values["azure_endpoint"], - "azure_deployment": values["deployment_name"], - "api_key": values["openai_api_key"].get_secret_value() - if values["openai_api_key"] + self.deployment_name = None + client_params: dict = { + "api_version": self.openai_api_version, + "azure_endpoint": self.azure_endpoint, + "azure_deployment": self.deployment_name, + "api_key": self.openai_api_key.get_secret_value() + if self.openai_api_key else None, - "azure_ad_token": values["azure_ad_token"].get_secret_value() - if values["azure_ad_token"] + "azure_ad_token": self.azure_ad_token.get_secret_value() + if self.azure_ad_token else None, - "azure_ad_token_provider": values["azure_ad_token_provider"], - "organization": values["openai_organization"], - "base_url": values["openai_api_base"], - "timeout": values["request_timeout"], - "max_retries": values["max_retries"], - "default_headers": values["default_headers"], - "default_query": values["default_query"], + "azure_ad_token_provider": self.azure_ad_token_provider, + "organization": self.openai_organization, + "base_url": self.openai_api_base, + "timeout": self.request_timeout, + "max_retries": self.max_retries, + "default_headers": self.default_headers, + "default_query": self.default_query, } - if not values.get("client"): - sync_specific = {"http_client": values["http_client"]} - values["client"] = openai.AzureOpenAI( - **client_params, **sync_specific + if not self.client: + sync_specific = {"http_client": self.http_client} + self.client = openai.AzureOpenAI( + **client_params, + **sync_specific, # type: ignore[arg-type] ).completions - if not values.get("async_client"): - async_specific = {"http_client": values["http_async_client"]} - values["async_client"] = openai.AsyncAzureOpenAI( - **client_params, **async_specific + if not self.async_client: + async_specific = {"http_client": self.http_async_client} + self.async_client = openai.AsyncAzureOpenAI( + **client_params, + **async_specific, # type: ignore[arg-type] ).completions - return values + return self @property def _identifying_params(self) -> Mapping[str, Any]: diff --git a/libs/partners/openai/langchain_openai/llms/base.py b/libs/partners/openai/langchain_openai/llms/base.py index 464b40e2ba919..0c773b347a60b 100644 --- a/libs/partners/openai/langchain_openai/llms/base.py +++ b/libs/partners/openai/langchain_openai/llms/base.py @@ -26,9 +26,10 @@ ) from langchain_core.language_models.llms import BaseLLM from langchain_core.outputs import Generation, GenerationChunk, LLMResult -from langchain_core.pydantic_v1 import Field, SecretStr, root_validator from langchain_core.utils import get_pydantic_field_names from langchain_core.utils.utils import build_extra_kwargs, from_env, secret_from_env +from pydantic import ConfigDict, Field, SecretStr, model_validator +from typing_extensions import Self logger = logging.getLogger(__name__) @@ -152,13 +153,11 @@ class BaseOpenAI(BaseLLM): """Optional additional JSON properties to include in the request parameters when making requests to OpenAI compatible APIs, such as vLLM.""" - class Config: - """Configuration for this pydantic object.""" + model_config = ConfigDict(populate_by_name=True) - allow_population_by_field_name = True - - @root_validator(pre=True) - def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: + @model_validator(mode="before") + @classmethod + def build_extra(cls, values: Dict[str, Any]) -> Any: """Build extra kwargs from additional params that were passed in.""" all_required_field_names = get_pydantic_field_names(cls) extra = values.get("model_kwargs", {}) @@ -167,41 +166,38 @@ def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: ) return values - @root_validator(pre=False, skip_on_failure=True, allow_reuse=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="after") + def validate_environment(self) -> Self: """Validate that api key and python package exists in environment.""" - if values["n"] < 1: + if self.n < 1: raise ValueError("n must be at least 1.") - if values["streaming"] and values["n"] > 1: + if self.streaming and self.n > 1: raise ValueError("Cannot stream results when n > 1.") - if values["streaming"] and values["best_of"] > 1: + if self.streaming and self.best_of > 1: raise ValueError("Cannot stream results when best_of > 1.") - client_params = { + client_params: dict = { "api_key": ( - values["openai_api_key"].get_secret_value() - if values["openai_api_key"] - else None + self.openai_api_key.get_secret_value() if self.openai_api_key else None ), - "organization": values["openai_organization"], - "base_url": values["openai_api_base"], - "timeout": values["request_timeout"], - "max_retries": values["max_retries"], - "default_headers": values["default_headers"], - "default_query": values["default_query"], + "organization": self.openai_organization, + "base_url": self.openai_api_base, + "timeout": self.request_timeout, + "max_retries": self.max_retries, + "default_headers": self.default_headers, + "default_query": self.default_query, } - if not values.get("client"): - sync_specific = {"http_client": values["http_client"]} - values["client"] = openai.OpenAI( - **client_params, **sync_specific - ).completions - if not values.get("async_client"): - async_specific = {"http_client": values["http_async_client"]} - values["async_client"] = openai.AsyncOpenAI( - **client_params, **async_specific + if not self.client: + sync_specific = {"http_client": self.http_client} + self.client = openai.OpenAI(**client_params, **sync_specific).completions # type: ignore[arg-type] + if not self.async_client: + async_specific = {"http_client": self.http_async_client} + self.async_client = openai.AsyncOpenAI( + **client_params, + **async_specific, # type: ignore[arg-type] ).completions - return values + return self @property def _default_params(self) -> Dict[str, Any]: diff --git a/libs/partners/openai/scripts/check_pydantic.sh b/libs/partners/openai/scripts/check_pydantic.sh index 06b5bb81ae236..1317f5e53914f 100755 --- a/libs/partners/openai/scripts/check_pydantic.sh +++ b/libs/partners/openai/scripts/check_pydantic.sh @@ -20,8 +20,8 @@ result=$(git -C "$repository_path" grep -E '^import pydantic|^from pydantic') if [ -n "$result" ]; then echo "ERROR: The following lines need to be updated:" echo "$result" - echo "Please replace the code with an import from langchain_core.pydantic_v1." + echo "Please replace the code with an import from pydantic." echo "For example, replace 'from pydantic import BaseModel'" - echo "with 'from langchain_core.pydantic_v1 import BaseModel'" + echo "with 'from pydantic import BaseModel'" exit 1 fi diff --git a/libs/partners/openai/tests/integration_tests/chat_models/test_base.py b/libs/partners/openai/tests/integration_tests/chat_models/test_base.py index 2e235421f9348..96f32c754f68b 100644 --- a/libs/partners/openai/tests/integration_tests/chat_models/test_base.py +++ b/libs/partners/openai/tests/integration_tests/chat_models/test_base.py @@ -20,13 +20,13 @@ ) from langchain_core.outputs import ChatGeneration, ChatResult, LLMResult from langchain_core.prompts import ChatPromptTemplate -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_standard_tests.integration_tests.chat_models import ( _validate_tool_call_message, ) from langchain_standard_tests.integration_tests.chat_models import ( magic_function as invalid_magic_function, ) +from pydantic import BaseModel, Field from langchain_openai import ChatOpenAI from tests.unit_tests.fake.callbacks import FakeCallbackHandler diff --git a/libs/partners/openai/tests/unit_tests/chat_models/__snapshots__/test_azure_standard.ambr b/libs/partners/openai/tests/unit_tests/chat_models/__snapshots__/test_azure_standard.ambr new file mode 100644 index 0000000000000..982c80d816f3d --- /dev/null +++ b/libs/partners/openai/tests/unit_tests/chat_models/__snapshots__/test_azure_standard.ambr @@ -0,0 +1,72 @@ +# serializer version: 1 +# name: TestOpenAIStandard.test_serdes[serialized] + dict({ + 'graph': dict({ + 'edges': list([ + dict({ + 'source': 0, + 'target': 1, + }), + dict({ + 'source': 1, + 'target': 2, + }), + ]), + 'nodes': list([ + dict({ + 'data': 'AzureChatOpenAIInput', + 'id': 0, + 'type': 'schema', + }), + dict({ + 'data': dict({ + 'id': list([ + 'langchain', + 'chat_models', + 'azure_openai', + 'AzureChatOpenAI', + ]), + 'name': 'AzureChatOpenAI', + }), + 'id': 1, + 'type': 'runnable', + }), + dict({ + 'data': 'AzureChatOpenAIOutput', + 'id': 2, + 'type': 'schema', + }), + ]), + }), + 'id': list([ + 'langchain', + 'chat_models', + 'azure_openai', + 'AzureChatOpenAI', + ]), + 'kwargs': dict({ + 'azure_endpoint': 'https://test.azure.com', + 'deployment_name': 'test', + 'max_retries': 2, + 'max_tokens': 100, + 'n': 1, + 'openai_api_key': dict({ + 'id': list([ + 'AZURE_OPENAI_API_KEY', + ]), + 'lc': 1, + 'type': 'secret', + }), + 'openai_api_type': 'azure', + 'openai_api_version': '2021-10-01', + 'request_timeout': 60.0, + 'stop': list([ + ]), + 'temperature': 0.0, + 'validate_base_url': True, + }), + 'lc': 1, + 'name': 'AzureChatOpenAI', + 'type': 'constructor', + }) +# --- diff --git a/libs/partners/openai/tests/unit_tests/chat_models/__snapshots__/test_base_standard.ambr b/libs/partners/openai/tests/unit_tests/chat_models/__snapshots__/test_base_standard.ambr new file mode 100644 index 0000000000000..3129c8638f8aa --- /dev/null +++ b/libs/partners/openai/tests/unit_tests/chat_models/__snapshots__/test_base_standard.ambr @@ -0,0 +1,68 @@ +# serializer version: 1 +# name: TestOpenAIStandard.test_serdes[serialized] + dict({ + 'graph': dict({ + 'edges': list([ + dict({ + 'source': 0, + 'target': 1, + }), + dict({ + 'source': 1, + 'target': 2, + }), + ]), + 'nodes': list([ + dict({ + 'data': 'ChatOpenAIInput', + 'id': 0, + 'type': 'schema', + }), + dict({ + 'data': dict({ + 'id': list([ + 'langchain', + 'chat_models', + 'openai', + 'ChatOpenAI', + ]), + 'name': 'ChatOpenAI', + }), + 'id': 1, + 'type': 'runnable', + }), + dict({ + 'data': 'ChatOpenAIOutput', + 'id': 2, + 'type': 'schema', + }), + ]), + }), + 'id': list([ + 'langchain', + 'chat_models', + 'openai', + 'ChatOpenAI', + ]), + 'kwargs': dict({ + 'max_retries': 2, + 'max_tokens': 100, + 'model_name': 'gpt-3.5-turbo', + 'n': 1, + 'openai_api_key': dict({ + 'id': list([ + 'OPENAI_API_KEY', + ]), + 'lc': 1, + 'type': 'secret', + }), + 'request_timeout': 60.0, + 'stop': list([ + ]), + 'temperature': 0.0, + }), + 'lc': 1, + 'name': 'ChatOpenAI', + 'type': 'constructor', + }) +# --- diff --git a/libs/partners/openai/tests/unit_tests/chat_models/test_base.py b/libs/partners/openai/tests/unit_tests/chat_models/test_base.py index 4e959f005990d..493ca5abeffe5 100644 --- a/libs/partners/openai/tests/unit_tests/chat_models/test_base.py +++ b/libs/partners/openai/tests/unit_tests/chat_models/test_base.py @@ -17,7 +17,7 @@ ToolMessage, ) from langchain_core.messages.ai import UsageMetadata -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel from langchain_openai import ChatOpenAI from langchain_openai.chat_models.base import ( diff --git a/libs/partners/openai/tests/unit_tests/fake/callbacks.py b/libs/partners/openai/tests/unit_tests/fake/callbacks.py index 2beee4a2ddef0..d4b8d4b2c256b 100644 --- a/libs/partners/openai/tests/unit_tests/fake/callbacks.py +++ b/libs/partners/openai/tests/unit_tests/fake/callbacks.py @@ -6,7 +6,7 @@ from langchain_core.callbacks.base import AsyncCallbackHandler, BaseCallbackHandler from langchain_core.messages import BaseMessage -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel class BaseFakeCallbackHandler(BaseModel): @@ -188,7 +188,7 @@ def on_retriever_end(self, *args: Any, **kwargs: Any) -> Any: def on_retriever_error(self, *args: Any, **kwargs: Any) -> Any: self.on_retriever_error_common() - def __deepcopy__(self, memo: dict) -> "FakeCallbackHandler": + def __deepcopy__(self, memo: dict) -> "FakeCallbackHandler": # type: ignore[override] return self @@ -266,5 +266,5 @@ async def on_agent_finish(self, *args: Any, **kwargs: Any) -> None: async def on_text(self, *args: Any, **kwargs: Any) -> None: self.on_text_common() - def __deepcopy__(self, memo: dict) -> "FakeAsyncCallbackHandler": + def __deepcopy__(self, memo: dict) -> "FakeAsyncCallbackHandler": # type: ignore[override] return self diff --git a/libs/partners/openai/tests/unit_tests/test_load.py b/libs/partners/openai/tests/unit_tests/test_load.py index 059a10b4bf988..d25b00a0b87e2 100644 --- a/libs/partners/openai/tests/unit_tests/test_load.py +++ b/libs/partners/openai/tests/unit_tests/test_load.py @@ -9,7 +9,7 @@ def test_loads_openai_llm() -> None: llm_string = dumps(llm) llm2 = loads(llm_string, secrets_map={"OPENAI_API_KEY": "hello"}) - assert llm2 == llm + assert llm2.dict() == llm.dict() llm_string_2 = dumps(llm2) assert llm_string_2 == llm_string assert isinstance(llm2, OpenAI) @@ -20,7 +20,7 @@ def test_load_openai_llm() -> None: llm_obj = dumpd(llm) llm2 = load(llm_obj, secrets_map={"OPENAI_API_KEY": "hello"}) - assert llm2 == llm + assert llm2.dict() == llm.dict() assert dumpd(llm2) == llm_obj assert isinstance(llm2, OpenAI) @@ -30,7 +30,7 @@ def test_loads_openai_chat() -> None: llm_string = dumps(llm) llm2 = loads(llm_string, secrets_map={"OPENAI_API_KEY": "hello"}) - assert llm2 == llm + assert llm2.dict() == llm.dict() llm_string_2 = dumps(llm2) assert llm_string_2 == llm_string assert isinstance(llm2, ChatOpenAI) @@ -41,6 +41,6 @@ def test_load_openai_chat() -> None: llm_obj = dumpd(llm) llm2 = load(llm_obj, secrets_map={"OPENAI_API_KEY": "hello"}) - assert llm2 == llm + assert llm2.dict() == llm.dict() assert dumpd(llm2) == llm_obj assert isinstance(llm2, ChatOpenAI) diff --git a/libs/partners/openai/tests/unit_tests/test_secrets.py b/libs/partners/openai/tests/unit_tests/test_secrets.py index 0fb13e35b2811..2c88858056c9b 100644 --- a/libs/partners/openai/tests/unit_tests/test_secrets.py +++ b/libs/partners/openai/tests/unit_tests/test_secrets.py @@ -2,7 +2,7 @@ import pytest from langchain_core.load import dumpd -from langchain_core.pydantic_v1 import SecretStr +from pydantic import SecretStr from pytest import CaptureFixture, MonkeyPatch from langchain_openai import ( diff --git a/libs/partners/pinecone/Makefile b/libs/partners/pinecone/Makefile index add7932dd09af..d2f1c9265f7bd 100644 --- a/libs/partners/pinecone/Makefile +++ b/libs/partners/pinecone/Makefile @@ -10,6 +10,9 @@ integration_test integration_tests: TEST_FILE = tests/integration_tests/ test tests integration_test integration_tests: poetry run pytest $(TEST_FILE) +test_watch: + poetry run ptw --snapshot-update --now . -- -vv $(TEST_FILE) + ###################### # LINTING AND FORMATTING diff --git a/libs/partners/pinecone/langchain_pinecone/embeddings.py b/libs/partners/pinecone/langchain_pinecone/embeddings.py index c0adecd1e86f5..7fd5233bcc68e 100644 --- a/libs/partners/pinecone/langchain_pinecone/embeddings.py +++ b/libs/partners/pinecone/langchain_pinecone/embeddings.py @@ -1,16 +1,19 @@ import logging -from typing import Dict, Iterable, List, Optional +from typing import Any, Dict, Iterable, List, Optional import aiohttp from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import ( +from langchain_core.utils import secret_from_env +from pinecone import Pinecone as PineconeClient # type: ignore[import-untyped] +from pydantic import ( BaseModel, + ConfigDict, Field, + PrivateAttr, SecretStr, - root_validator, + model_validator, ) -from langchain_core.utils import secret_from_env -from pinecone import Pinecone as PineconeClient # type: ignore +from typing_extensions import Self logger = logging.getLogger(__name__) @@ -29,8 +32,8 @@ class PineconeEmbeddings(BaseModel, Embeddings): """ # Clients - _client: PineconeClient = Field(default=None, exclude=True) - _async_client: aiohttp.ClientSession = Field(default=None, exclude=True) + _client: PineconeClient = PrivateAttr(default=None) + _async_client: aiohttp.ClientSession = PrivateAttr(default=None) model: str """Model to use for example 'multilingual-e5-large'.""" # Config @@ -44,7 +47,7 @@ class PineconeEmbeddings(BaseModel, Embeddings): dimension: Optional[int] = None # show_progress_bar: bool = False - pinecone_api_key: Optional[SecretStr] = Field( + pinecone_api_key: SecretStr = Field( default_factory=secret_from_env( "PINECONE_API_KEY", error_message="Pinecone API key not found. Please set the PINECONE_API_KEY " @@ -56,12 +59,14 @@ class PineconeEmbeddings(BaseModel, Embeddings): If not provided, will look for the PINECONE_API_KEY environment variable.""" - class Config: - extra = "forbid" - allow_population_by_field_name = True + model_config = ConfigDict( + extra="forbid", + populate_by_name=True, + ) - @root_validator(pre=True) - def set_default_config(cls, values: dict) -> dict: + @model_validator(mode="before") + @classmethod + def set_default_config(cls, values: dict) -> Any: """Set default configuration based on model.""" default_config_map = { "multilingual-e5-large": { @@ -79,23 +84,23 @@ def set_default_config(cls, values: dict) -> dict: values[key] = value return values - @root_validator(pre=False, skip_on_failure=True) - def validate_environment(cls, values: dict) -> dict: + @model_validator(mode="after") + def validate_environment(self) -> Self: """Validate that Pinecone version and credentials exist in environment.""" - api_key_str = values["pinecone_api_key"].get_secret_value() + api_key_str = self.pinecone_api_key.get_secret_value() client = PineconeClient(api_key=api_key_str, source_tag="langchain") - values["_client"] = client + self._client = client # initialize async client - if not values.get("_async_client"): - values["_async_client"] = aiohttp.ClientSession( + if not self._async_client: + self._async_client = aiohttp.ClientSession( headers={ "Api-Key": api_key_str, "Content-Type": "application/json", "X-Pinecone-API-Version": "2024-07", } ) - return values + return self def _get_batch_iterator(self, texts: List[str]) -> Iterable: if self.batch_size is None: diff --git a/libs/partners/pinecone/scripts/check_pydantic.sh b/libs/partners/pinecone/scripts/check_pydantic.sh deleted file mode 100755 index 06b5bb81ae236..0000000000000 --- a/libs/partners/pinecone/scripts/check_pydantic.sh +++ /dev/null @@ -1,27 +0,0 @@ -#!/bin/bash -# -# This script searches for lines starting with "import pydantic" or "from pydantic" -# in tracked files within a Git repository. -# -# Usage: ./scripts/check_pydantic.sh /path/to/repository - -# Check if a path argument is provided -if [ $# -ne 1 ]; then - echo "Usage: $0 /path/to/repository" - exit 1 -fi - -repository_path="$1" - -# Search for lines matching the pattern within the specified repository -result=$(git -C "$repository_path" grep -E '^import pydantic|^from pydantic') - -# Check if any matching lines were found -if [ -n "$result" ]; then - echo "ERROR: The following lines need to be updated:" - echo "$result" - echo "Please replace the code with an import from langchain_core.pydantic_v1." - echo "For example, replace 'from pydantic import BaseModel'" - echo "with 'from langchain_core.pydantic_v1 import BaseModel'" - exit 1 -fi diff --git a/libs/partners/prompty/Makefile b/libs/partners/prompty/Makefile index 649b9bf4791e6..a598781fa613a 100644 --- a/libs/partners/prompty/Makefile +++ b/libs/partners/prompty/Makefile @@ -12,6 +12,9 @@ test: tests: poetry run pytest $(TEST_FILE) +test_watch: + poetry run ptw --snapshot-update --now . -- -vv $(TEST_FILE) + ###################### # LINTING AND FORMATTING diff --git a/libs/partners/prompty/poetry.lock b/libs/partners/prompty/poetry.lock index 73ebd1174e5de..16ba1be41323b 100644 --- a/libs/partners/prompty/poetry.lock +++ b/libs/partners/prompty/poetry.lock @@ -148,9 +148,6 @@ files = [ {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"}, ] -[package.dependencies] -typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.9\""} - [[package]] name = "anyio" version = "4.4.0" @@ -632,7 +629,7 @@ files = [ [[package]] name = "langchain" -version = "0.2.15" +version = "0.2.16" description = "Building applications with LLMs through composability" optional = false python-versions = ">=3.8.1,<4.0" @@ -642,7 +639,7 @@ develop = true [package.dependencies] aiohttp = "^3.8.3" async-timeout = {version = "^4.0.0", markers = "python_version < \"3.11\""} -langchain-core = "^0.2.35" +langchain-core = "^0.2.38" langchain-text-splitters = "^0.2.0" langsmith = "^0.1.17" numpy = [ @@ -661,10 +658,10 @@ url = "../../langchain" [[package]] name = "langchain-core" -version = "0.2.36" +version = "0.2.38" description = "Building applications with LLMs through composability" optional = false -python-versions = ">=3.8.1,<4.0" +python-versions = ">=3.9,<4.0" files = [] develop = true @@ -686,7 +683,7 @@ url = "../../core" [[package]] name = "langchain-text-splitters" -version = "0.2.3" +version = "0.2.4" description = "LangChain text splitting utilities" optional = false python-versions = ">=3.8.1,<4.0" @@ -694,7 +691,7 @@ files = [] develop = true [package.dependencies] -langchain-core = "^0.2.10" +langchain-core = "^0.2.38" [package.source] type = "directory" @@ -880,43 +877,6 @@ files = [ {file = "mypy_extensions-1.0.0.tar.gz", hash = "sha256:75dbf8955dc00442a438fc4d0666508a9a97b6bd41aa2f0ffe9d2f2725af0782"}, ] -[[package]] -name = "numpy" -version = "1.24.4" -description = "Fundamental package for array computing in Python" -optional = false -python-versions = ">=3.8" -files = [ - 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[[package]] name = "numpy" version = "1.26.4" @@ -1712,5 +1672,5 @@ multidict = ">=4.0" [metadata] lock-version = "2.0" -python-versions = ">=3.8.1,<4.0" -content-hash = "a8ac2ae4de4a64bd1b7bc8a36af02073196a3261f663f6da52a2e82818931064" +python-versions = ">=3.9,<4.0" +content-hash = "66290ecf222b184bd4ec2aff94d26b9e316d1776f1a3558173f174411905c062" diff --git a/libs/partners/prompty/pyproject.toml b/libs/partners/prompty/pyproject.toml index 7ae1504e48cd1..6bf999298f3b8 100644 --- a/libs/partners/prompty/pyproject.toml +++ b/libs/partners/prompty/pyproject.toml @@ -12,10 +12,11 @@ license = "MIT" "Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-prompty%3D%3D0%22&expanded=true" [tool.poetry.dependencies] -python = ">=3.8.1,<4.0" +python = ">=3.9,<4.0" langchain-core = "^0.2.36" pyyaml = "^6.0.1" types-pyyaml = "^6.0.12.20240311" +pydantic = ">=2,<3" [tool.poetry.group.test] optional = true diff --git a/libs/partners/prompty/scripts/check_pydantic.sh b/libs/partners/prompty/scripts/check_pydantic.sh deleted file mode 100644 index 06b5bb81ae236..0000000000000 --- a/libs/partners/prompty/scripts/check_pydantic.sh +++ /dev/null @@ -1,27 +0,0 @@ -#!/bin/bash -# -# This script searches for lines starting with "import pydantic" or "from pydantic" -# in tracked files within a Git repository. -# -# Usage: ./scripts/check_pydantic.sh /path/to/repository - -# Check if a path argument is provided -if [ $# -ne 1 ]; then - echo "Usage: $0 /path/to/repository" - exit 1 -fi - -repository_path="$1" - -# Search for lines matching the pattern within the specified repository -result=$(git -C "$repository_path" grep -E '^import pydantic|^from pydantic') - -# Check if any matching lines were found -if [ -n "$result" ]; then - echo "ERROR: The following lines need to be updated:" - echo "$result" - echo "Please replace the code with an import from langchain_core.pydantic_v1." - echo "For example, replace 'from pydantic import BaseModel'" - echo "with 'from langchain_core.pydantic_v1 import BaseModel'" - exit 1 -fi diff --git a/libs/partners/prompty/tests/unit_tests/fake_callback_handler.py b/libs/partners/prompty/tests/unit_tests/fake_callback_handler.py index fd68bebd2d9c5..cfbfa4f6f81b2 100644 --- a/libs/partners/prompty/tests/unit_tests/fake_callback_handler.py +++ b/libs/partners/prompty/tests/unit_tests/fake_callback_handler.py @@ -6,7 +6,7 @@ from langchain_core.callbacks import AsyncCallbackHandler, BaseCallbackHandler from langchain_core.messages import BaseMessage -from langchain_core.pydantic_v1 import BaseModel +from pydantic import BaseModel class BaseFakeCallbackHandler(BaseModel): @@ -259,7 +259,8 @@ def on_retriever_error( ) -> Any: self.on_retriever_error_common() - def __deepcopy__(self, memo: dict) -> "FakeCallbackHandler": + # Overriding since BaseModel has __deepcopy__ method as well + def __deepcopy__(self, memo: dict) -> "FakeCallbackHandler": # type: ignore return self @@ -391,5 +392,6 @@ async def on_text( ) -> None: self.on_text_common() - def __deepcopy__(self, memo: dict) -> "FakeAsyncCallbackHandler": + # Overriding since BaseModel has __deepcopy__ method as well + def __deepcopy__(self, memo: dict) -> "FakeAsyncCallbackHandler": # type: ignore return self diff --git a/libs/partners/prompty/tests/unit_tests/fake_chat_model.py b/libs/partners/prompty/tests/unit_tests/fake_chat_model.py index a39f401e64349..0bfc795f7c656 100644 --- a/libs/partners/prompty/tests/unit_tests/fake_chat_model.py +++ b/libs/partners/prompty/tests/unit_tests/fake_chat_model.py @@ -21,7 +21,7 @@ def _call( run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> str: - return json.dumps([message.dict() for message in messages]) + return json.dumps([message.model_dump() for message in messages]) async def _agenerate( self, diff --git a/libs/partners/prompty/tests/unit_tests/test_prompty_serialization.py b/libs/partners/prompty/tests/unit_tests/test_prompty_serialization.py index 23bf299ee56b5..9233de366538e 100644 --- a/libs/partners/prompty/tests/unit_tests/test_prompty_serialization.py +++ b/libs/partners/prompty/tests/unit_tests/test_prompty_serialization.py @@ -6,8 +6,8 @@ from langchain.tools import tool from langchain_core.language_models import FakeListLLM from langchain_core.messages import AIMessage, HumanMessage -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.utils.function_calling import convert_to_openai_function +from pydantic import BaseModel, Field import langchain_prompty diff --git a/libs/partners/qdrant/Makefile b/libs/partners/qdrant/Makefile index e31881f5a4442..ffab929526bd5 100644 --- a/libs/partners/qdrant/Makefile +++ b/libs/partners/qdrant/Makefile @@ -11,6 +11,10 @@ integration_test integration_tests: TEST_FILE = tests/integration_tests/ test tests integration_test integration_tests: poetry run pytest $(TEST_FILE) +test_watch: + poetry run ptw --snapshot-update --now . -- -vv $(TEST_FILE) + + ###################### # LINTING AND FORMATTING diff --git a/libs/partners/qdrant/poetry.lock b/libs/partners/qdrant/poetry.lock index 0fee98a1e8025..5d83f7194a6ee 100644 --- a/libs/partners/qdrant/poetry.lock +++ b/libs/partners/qdrant/poetry.lock @@ -11,9 +11,6 @@ files = [ {file = "annotated_types-0.6.0.tar.gz", hash = "sha256:563339e807e53ffd9c267e99fc6d9ea23eb8443c08f112651963e24e22f84a5d"}, ] -[package.dependencies] -typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.9\""} - 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-# Check if a path argument is provided -if [ $# -ne 1 ]; then - echo "Usage: $0 /path/to/repository" - exit 1 -fi - -repository_path="$1" - -# Search for lines matching the pattern within the specified repository -result=$(git -C "$repository_path" grep -E '^import pydantic|^from pydantic') - -# Check if any matching lines were found -if [ -n "$result" ]; then - echo "ERROR: The following lines need to be updated:" - echo "$result" - echo "Please replace the code with an import from langchain_core.pydantic_v1." - echo "For example, replace 'from pydantic import BaseModel'" - echo "with 'from langchain_core.pydantic_v1 import BaseModel'" - exit 1 -fi diff --git a/libs/partners/robocorp/Makefile b/libs/partners/robocorp/Makefile index fac650e5f353f..3153282b2994f 100644 --- a/libs/partners/robocorp/Makefile +++ b/libs/partners/robocorp/Makefile @@ -10,6 +10,9 @@ integration_test integration_tests: TEST_FILE=tests/integration_tests/ test tests integration_test integration_tests: poetry run pytest $(TEST_FILE) +test_watch: + poetry run ptw --snapshot-update --now . -- -vv $(TEST_FILE) + ###################### # LINTING AND FORMATTING diff --git a/libs/partners/robocorp/langchain_robocorp/_common.py b/libs/partners/robocorp/langchain_robocorp/_common.py index 6db05d1b5c224..3ff0b4af2292f 100644 --- a/libs/partners/robocorp/langchain_robocorp/_common.py +++ b/libs/partners/robocorp/langchain_robocorp/_common.py @@ -2,13 +2,13 @@ from dataclasses import dataclass from typing import Any, Dict, List, Set, Tuple, Union, cast -from langchain_core.pydantic_v1 import ( +from langchain_core.utils.json_schema import dereference_refs +from langchain_core.utils.pydantic import is_basemodel_instance +from pydantic import ( BaseModel, Field, create_model, ) -from langchain_core.utils.json_schema import dereference_refs -from langchain_core.utils.pydantic import is_basemodel_instance @dataclass(frozen=True) diff --git a/libs/partners/robocorp/langchain_robocorp/toolkits.py b/libs/partners/robocorp/langchain_robocorp/toolkits.py index aaf1863cfaf63..3e9f9dfbfbfe0 100644 --- a/libs/partners/robocorp/langchain_robocorp/toolkits.py +++ b/libs/partners/robocorp/langchain_robocorp/toolkits.py @@ -13,11 +13,11 @@ from langchain_core.language_models.chat_models import BaseChatModel from langchain_core.output_parsers import StrOutputParser from langchain_core.prompts import PromptTemplate -from langchain_core.pydantic_v1 import BaseModel, Field, PrivateAttr, create_model from langchain_core.runnables import Runnable, RunnablePassthrough from langchain_core.tools import BaseTool, StructuredTool, Tool from langchain_core.tracers.context import _tracing_v2_is_enabled from langsmith import Client +from pydantic import BaseModel, ConfigDict, Field, PrivateAttr, create_model from langchain_robocorp._common import ( get_param_fields, @@ -110,8 +110,9 @@ class ActionServerToolkit(BaseModel): """Enable reporting Langsmith trace to Action Server runs""" _run_details: dict = PrivateAttr({}) - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) def get_tools( self, diff --git a/libs/partners/robocorp/poetry.lock b/libs/partners/robocorp/poetry.lock index f12c3192e344c..315f706a0c1de 100644 --- a/libs/partners/robocorp/poetry.lock +++ b/libs/partners/robocorp/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. [[package]] name = "annotated-types" @@ -11,9 +11,6 @@ files = [ {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"}, ] -[package.dependencies] -typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.9\""} - [[package]] name = "certifi" version = "2024.7.4" @@ -229,10 +226,10 @@ files = [ [[package]] name = "langchain-core" -version = "0.2.24" +version = "0.2.38" description = "Building applications with LLMs through composability" optional = false -python-versions = ">=3.8.1,<4.0" +python-versions = ">=3.9,<4.0" files = [] develop = true @@ -246,6 +243,7 @@ pydantic = [ ] PyYAML = ">=5.3" tenacity = "^8.1.0,!=8.4.0" +typing-extensions = ">=4.7" [package.source] type = "directory" @@ -870,5 +868,5 @@ watchmedo = ["PyYAML (>=3.10)"] [metadata] lock-version = "2.0" -python-versions = ">=3.8.1,<4.0" -content-hash = "6b4489fb9ed4fe0871e64ef8ad93a9122a801824811cfeeebb6a42f3d666d50e" +python-versions = ">=3.9,<4.0" +content-hash = "a684f0e24648e9106bbb7d79554a71fca47235b61961159872a5d72267406aab" diff --git a/libs/partners/robocorp/pyproject.toml b/libs/partners/robocorp/pyproject.toml index d10b336be27ad..6cf85e16b834e 100644 --- a/libs/partners/robocorp/pyproject.toml +++ b/libs/partners/robocorp/pyproject.toml @@ -18,10 +18,11 @@ disallow_untyped_defs = "True" "Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/robocorp" [tool.poetry.dependencies] -python = ">=3.8.1,<4.0" +python = ">=3.9,<4.0" langchain-core = ">=0.2.24,<0.3" requests = "^2.31.0" types-requests = "^2.31.0.6" +pydantic = ">=2,<3" [tool.ruff.lint] select = [ "E", "F", "I", "T201",] diff --git a/libs/partners/robocorp/scripts/check_pydantic.sh b/libs/partners/robocorp/scripts/check_pydantic.sh deleted file mode 100755 index 06b5bb81ae236..0000000000000 --- a/libs/partners/robocorp/scripts/check_pydantic.sh +++ /dev/null @@ -1,27 +0,0 @@ -#!/bin/bash -# -# This script searches for lines starting with "import pydantic" or "from pydantic" -# in tracked files within a Git repository. -# -# Usage: ./scripts/check_pydantic.sh /path/to/repository - -# Check if a path argument is provided -if [ $# -ne 1 ]; then - echo "Usage: $0 /path/to/repository" - exit 1 -fi - -repository_path="$1" - -# Search for lines matching the pattern within the specified repository -result=$(git -C "$repository_path" grep -E '^import pydantic|^from pydantic') - -# Check if any matching lines were found -if [ -n "$result" ]; then - echo "ERROR: The following lines need to be updated:" - echo "$result" - echo "Please replace the code with an import from langchain_core.pydantic_v1." - echo "For example, replace 'from pydantic import BaseModel'" - echo "with 'from langchain_core.pydantic_v1 import BaseModel'" - exit 1 -fi diff --git a/libs/partners/unstructured/Makefile b/libs/partners/unstructured/Makefile index 1b03b8e76f303..84cfd5303814c 100644 --- a/libs/partners/unstructured/Makefile +++ b/libs/partners/unstructured/Makefile @@ -12,6 +12,9 @@ integration_test integration_tests: TEST_FILE = tests/integration_tests/ test tests: poetry run pytest --disable-socket --allow-unix-socket $(TEST_FILE) +test_watch: + poetry run ptw --snapshot-update --now . -- -vv $(TEST_FILE) + # integration tests are run without the --disable-socket flag to allow network calls integration_test: poetry run pytest $(TEST_FILE) diff --git a/libs/partners/voyageai/Makefile b/libs/partners/voyageai/Makefile index f0e6f96ea6bf1..0d2d194ce0449 100644 --- a/libs/partners/voyageai/Makefile +++ b/libs/partners/voyageai/Makefile @@ -10,6 +10,9 @@ integration_test integration_tests: TEST_FILE=tests/integration_tests/ test tests integration_test integration_tests: poetry run pytest $(TEST_FILE) +test_watch: + poetry run ptw --snapshot-update --now . -- -vv $(TEST_FILE) + ###################### # LINTING AND FORMATTING diff --git a/libs/partners/voyageai/langchain_voyageai/embeddings.py b/libs/partners/voyageai/langchain_voyageai/embeddings.py index 3010a8dd6948e..594ae156c4f21 100644 --- a/libs/partners/voyageai/langchain_voyageai/embeddings.py +++ b/libs/partners/voyageai/langchain_voyageai/embeddings.py @@ -1,15 +1,18 @@ import logging -from typing import Iterable, List, Optional +from typing import Any, Iterable, List, Optional import voyageai # type: ignore from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import ( +from langchain_core.utils import secret_from_env +from pydantic import ( BaseModel, + ConfigDict, Field, + PrivateAttr, SecretStr, - root_validator, + model_validator, ) -from langchain_core.utils import secret_from_env +from typing_extensions import Self logger = logging.getLogger(__name__) @@ -25,8 +28,8 @@ class VoyageAIEmbeddings(BaseModel, Embeddings): model = VoyageAIEmbeddings() """ - _client: voyageai.Client = Field(exclude=True) - _aclient: voyageai.client_async.AsyncClient = Field(exclude=True) + _client: voyageai.Client = PrivateAttr() + _aclient: voyageai.client_async.AsyncClient = PrivateAttr() model: str batch_size: int show_progress_bar: bool = False @@ -40,12 +43,14 @@ class VoyageAIEmbeddings(BaseModel, Embeddings): ), ) - class Config: - extra = "forbid" - allow_population_by_field_name = True + model_config = ConfigDict( + extra="forbid", + populate_by_name=True, + ) - @root_validator(pre=True) - def default_values(cls, values: dict) -> dict: + @model_validator(mode="before") + @classmethod + def default_values(cls, values: dict) -> Any: """Set default batch size based on model""" model = values.get("model") batch_size = values.get("batch_size") @@ -53,13 +58,13 @@ def default_values(cls, values: dict) -> dict: values["batch_size"] = 72 if model in ["voyage-2", "voyage-02"] else 7 return values - @root_validator(pre=False, skip_on_failure=True) - def validate_environment(cls, values: dict) -> dict: + @model_validator(mode="after") + def validate_environment(self) -> Self: """Validate that VoyageAI credentials exist in environment.""" - api_key_str = values["voyage_api_key"].get_secret_value() - values["_client"] = voyageai.Client(api_key=api_key_str) - values["_aclient"] = voyageai.client_async.AsyncClient(api_key=api_key_str) - return values + api_key_str = self.voyage_api_key.get_secret_value() + self._client = voyageai.Client(api_key=api_key_str) + self._aclient = voyageai.client_async.AsyncClient(api_key=api_key_str) + return self def _get_batch_iterator(self, texts: List[str]) -> Iterable: if self.show_progress_bar: diff --git a/libs/partners/voyageai/langchain_voyageai/rerank.py b/libs/partners/voyageai/langchain_voyageai/rerank.py index 2213bfa043275..d0ee0122fa5d6 100644 --- a/libs/partners/voyageai/langchain_voyageai/rerank.py +++ b/libs/partners/voyageai/langchain_voyageai/rerank.py @@ -2,14 +2,14 @@ import os from copy import deepcopy -from typing import Dict, Optional, Sequence, Union +from typing import Any, Dict, Optional, Sequence, Union import voyageai # type: ignore from langchain_core.callbacks.manager import Callbacks from langchain_core.documents import Document from langchain_core.documents.compressor import BaseDocumentCompressor -from langchain_core.pydantic_v1 import SecretStr, root_validator from langchain_core.utils import convert_to_secret_str +from pydantic import ConfigDict, SecretStr, model_validator from voyageai.object import RerankingObject # type: ignore @@ -28,11 +28,13 @@ class VoyageAIRerank(BaseDocumentCompressor): """Number of documents to return.""" truncation: bool = True - class Config: - arbitrary_types_allowed = True + model_config = ConfigDict( + arbitrary_types_allowed=True, + ) - @root_validator(pre=True) - def validate_environment(cls, values: Dict) -> Dict: + @model_validator(mode="before") + @classmethod + def validate_environment(cls, values: Dict) -> Any: """Validate that api key exists in environment.""" voyage_api_key = values.get("voyage_api_key") or os.getenv( "VOYAGE_API_KEY", None diff --git a/libs/partners/voyageai/poetry.lock b/libs/partners/voyageai/poetry.lock index 26db73f4a0012..ce14515802589 100644 --- a/libs/partners/voyageai/poetry.lock +++ b/libs/partners/voyageai/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. 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-# Check if a path argument is provided -if [ $# -ne 1 ]; then - echo "Usage: $0 /path/to/repository" - exit 1 -fi - -repository_path="$1" - -# Search for lines matching the pattern within the specified repository -result=$(git -C "$repository_path" grep -E '^import pydantic|^from pydantic') - -# Check if any matching lines were found -if [ -n "$result" ]; then - echo "ERROR: The following lines need to be updated:" - echo "$result" - echo "Please replace the code with an import from langchain_core.pydantic_v1." - echo "For example, replace 'from pydantic import BaseModel'" - echo "with 'from langchain_core.pydantic_v1 import BaseModel'" - exit 1 -fi diff --git a/libs/partners/voyageai/tests/integration_tests/test_embeddings.py b/libs/partners/voyageai/tests/integration_tests/test_embeddings.py index 0a958ccc2de26..532ded1756ce4 100644 --- a/libs/partners/voyageai/tests/integration_tests/test_embeddings.py +++ b/libs/partners/voyageai/tests/integration_tests/test_embeddings.py @@ -9,7 +9,7 @@ def test_langchain_voyageai_embedding_documents() -> None: """Test voyage embeddings.""" documents = ["foo bar"] - embedding = VoyageAIEmbeddings(model=MODEL) + embedding = VoyageAIEmbeddings(model=MODEL) # type: ignore[call-arg] output = embedding.embed_documents(documents) assert len(output) == 1 assert len(output[0]) == 1024 @@ -29,7 +29,7 @@ def test_langchain_voyageai_embedding_documents_multiple() -> None: def test_langchain_voyageai_embedding_query() -> None: """Test voyage embeddings.""" document = "foo bar" - embedding = VoyageAIEmbeddings(model=MODEL) + embedding = VoyageAIEmbeddings(model=MODEL) # type: ignore[call-arg] output = embedding.embed_query(document) assert len(output) == 1024 @@ -48,6 +48,6 @@ async def test_langchain_voyageai_async_embedding_documents_multiple() -> None: async def test_langchain_voyageai_async_embedding_query() -> None: """Test voyage embeddings.""" document = "foo bar" - embedding = VoyageAIEmbeddings(model=MODEL) + embedding = VoyageAIEmbeddings(model=MODEL) # type: ignore[call-arg] output = await embedding.aembed_query(document) assert len(output) == 1024 diff --git a/libs/partners/voyageai/tests/integration_tests/test_rerank.py b/libs/partners/voyageai/tests/integration_tests/test_rerank.py index e4c88f5adbcaf..3e18ca0366eb7 100644 --- a/libs/partners/voyageai/tests/integration_tests/test_rerank.py +++ b/libs/partners/voyageai/tests/integration_tests/test_rerank.py @@ -9,12 +9,12 @@ def test_voyageai_reranker_init() -> None: """Test the voyageai reranker initializes correctly.""" - VoyageAIRerank(voyage_api_key="foo", model="foo") + VoyageAIRerank(voyage_api_key="foo", model="foo") # type: ignore[arg-type] def test_sync() -> None: rerank = VoyageAIRerank( - voyage_api_key=os.environ["VOYAGE_API_KEY"], + voyage_api_key=os.environ["VOYAGE_API_KEY"], # type: ignore[arg-type] model="rerank-lite-1", ) doc_list = [ @@ -42,7 +42,7 @@ def test_sync() -> None: async def test_async() -> None: rerank = VoyageAIRerank( - voyage_api_key=os.environ["VOYAGE_API_KEY"], + voyage_api_key=os.environ["VOYAGE_API_KEY"], # type: ignore[arg-type] model="rerank-lite-1", ) doc_list = [ diff --git a/libs/partners/voyageai/tests/unit_tests/test_embeddings.py b/libs/partners/voyageai/tests/unit_tests/test_embeddings.py index 990ffeb5a97d3..354c2c6a32532 100644 --- a/libs/partners/voyageai/tests/unit_tests/test_embeddings.py +++ b/libs/partners/voyageai/tests/unit_tests/test_embeddings.py @@ -9,7 +9,7 @@ def test_initialization_voyage_2() -> None: """Test embedding model initialization.""" - emb = VoyageAIEmbeddings(api_key="NOT_A_VALID_KEY", model=MODEL) + emb = VoyageAIEmbeddings(api_key="NOT_A_VALID_KEY", model=MODEL) # type: ignore assert isinstance(emb, Embeddings) assert emb.batch_size == 72 assert emb.model == MODEL @@ -20,7 +20,7 @@ def test_initialization_voyage_2_with_full_api_key_name() -> None: """Test embedding model initialization.""" # Testing that we can initialize the model using `voyage_api_key` # instead of `api_key` - emb = VoyageAIEmbeddings(voyage_api_key="NOT_A_VALID_KEY", model=MODEL) + emb = VoyageAIEmbeddings(voyage_api_key="NOT_A_VALID_KEY", model=MODEL) # type: ignore assert isinstance(emb, Embeddings) assert emb.batch_size == 72 assert emb.model == MODEL @@ -29,7 +29,7 @@ def test_initialization_voyage_2_with_full_api_key_name() -> None: def test_initialization_voyage_1() -> None: """Test embedding model initialization.""" - emb = VoyageAIEmbeddings(api_key="NOT_A_VALID_KEY", model="voyage-01") + emb = VoyageAIEmbeddings(api_key="NOT_A_VALID_KEY", model="voyage-01") # type: ignore assert isinstance(emb, Embeddings) assert emb.batch_size == 7 assert emb.model == "voyage-01" @@ -39,7 +39,9 @@ def test_initialization_voyage_1() -> None: def test_initialization_voyage_1_batch_size() -> None: """Test embedding model initialization.""" emb = VoyageAIEmbeddings( - api_key="NOT_A_VALID_KEY", model="voyage-01", batch_size=15 + api_key="NOT_A_VALID_KEY", # type: ignore + model="voyage-01", + batch_size=15, ) assert isinstance(emb, Embeddings) assert emb.batch_size == 15 diff --git a/libs/partners/voyageai/tests/unit_tests/test_rerank.py b/libs/partners/voyageai/tests/unit_tests/test_rerank.py index d86b3c3d156e1..b12687dab855a 100644 --- a/libs/partners/voyageai/tests/unit_tests/test_rerank.py +++ b/libs/partners/voyageai/tests/unit_tests/test_rerank.py @@ -29,7 +29,7 @@ @pytest.mark.requires("voyageai") def test_init() -> None: VoyageAIRerank( - voyage_api_key="foo", + voyage_api_key="foo", # type: ignore[arg-type] model="rerank-lite-1", ) @@ -63,7 +63,7 @@ def test_rerank_unit_test(mocker: Any) -> None: ] rerank = VoyageAIRerank( - voyage_api_key="foo", + voyage_api_key="foo", # type: ignore[arg-type] model="rerank-lite-1", ) result = rerank.compress_documents( @@ -74,7 +74,7 @@ def test_rerank_unit_test(mocker: Any) -> None: def test_rerank_empty_input() -> None: rerank = VoyageAIRerank( - voyage_api_key="foo", + voyage_api_key="foo", # type: ignore[arg-type] model="rerank-lite-1", ) result = rerank.compress_documents( diff --git a/libs/standard-tests/langchain_standard_tests/integration_tests/chat_models.py b/libs/standard-tests/langchain_standard_tests/integration_tests/chat_models.py index ec7e81af1ed03..171b1936a2cdb 100644 --- a/libs/standard-tests/langchain_standard_tests/integration_tests/chat_models.py +++ b/libs/standard-tests/langchain_standard_tests/integration_tests/chat_models.py @@ -16,10 +16,10 @@ ) from langchain_core.output_parsers import StrOutputParser from langchain_core.prompts import ChatPromptTemplate -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.tools import tool -from pydantic import BaseModel as RawBaseModel -from pydantic import Field as RawField +from pydantic import BaseModel, Field +from pydantic.v1 import BaseModel as BaseModelV1 +from pydantic.v1 import Field as FieldV1 from langchain_standard_tests.unit_tests.chat_models import ( ChatModelTests, @@ -28,8 +28,8 @@ from langchain_standard_tests.utils.pydantic import PYDANTIC_MAJOR_VERSION -class MagicFunctionSchema(RawBaseModel): - input: int = RawField(..., gt=-1000, lt=1000) +class MagicFunctionSchema(BaseModel): + input: int = Field(..., gt=-1000, lt=1000) @tool(args_schema=MagicFunctionSchema) @@ -240,14 +240,11 @@ def test_structured_output(self, model: BaseChatModel) -> None: if not self.has_tool_calling: pytest.skip("Test requires tool calling.") - from pydantic import BaseModel as BaseModelProper - from pydantic import Field as FieldProper - - class Joke(BaseModelProper): + class Joke(BaseModel): """Joke to tell user.""" - setup: str = FieldProper(description="question to set up a joke") - punchline: str = FieldProper(description="answer to resolve the joke") + setup: str = Field(description="question to set up a joke") + punchline: str = Field(description="answer to resolve the joke") # Pydantic class # Type ignoring since the interface only officially supports pydantic 1 @@ -280,11 +277,11 @@ def test_structured_output_pydantic_2_v1(self, model: BaseChatModel) -> None: if not self.has_tool_calling: pytest.skip("Test requires tool calling.") - class Joke(BaseModel): # Uses langchain_core.pydantic_v1.BaseModel + class Joke(BaseModelV1): # Uses langchain_core.pydantic_v1.BaseModel """Joke to tell user.""" - setup: str = Field(description="question to set up a joke") - punchline: str = Field(description="answer to resolve the joke") + setup: str = FieldV1(description="question to set up a joke") + punchline: str = FieldV1(description="answer to resolve the joke") # Pydantic class chat = model.with_structured_output(Joke) @@ -439,7 +436,7 @@ def test_anthropic_inputs(self, model: BaseChatModel) -> None: if not self.supports_anthropic_inputs: return - class color_picker(BaseModel): + class color_picker(BaseModelV1): """Input your fav color and get a random fact about it.""" fav_color: str diff --git a/libs/standard-tests/langchain_standard_tests/unit_tests/chat_models.py b/libs/standard-tests/langchain_standard_tests/unit_tests/chat_models.py index f3a49b6c341ec..7e85c5f4d5030 100644 --- a/libs/standard-tests/langchain_standard_tests/unit_tests/chat_models.py +++ b/libs/standard-tests/langchain_standard_tests/unit_tests/chat_models.py @@ -6,9 +6,20 @@ import pytest from langchain_core.language_models import BaseChatModel -from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr +from langchain_core.load import dumpd, load from langchain_core.runnables import RunnableBinding from langchain_core.tools import tool +from pydantic import BaseModel, Field, SecretStr +from pydantic.v1 import ( + BaseModel as BaseModelV1, +) +from pydantic.v1 import ( + Field as FieldV1, +) +from pydantic.v1 import ( + ValidationError as ValidationErrorV1, +) +from syrupy import SnapshotAssertion from langchain_standard_tests.base import BaseStandardTests from langchain_standard_tests.utils.pydantic import PYDANTIC_MAJOR_VERSION @@ -25,27 +36,24 @@ def generate_schema_pydantic_v1_from_2() -> Any: """Use to generate a schema from v1 namespace in pydantic 2.""" if PYDANTIC_MAJOR_VERSION != 2: raise AssertionError("This function is only compatible with Pydantic v2.") - from pydantic.v1 import BaseModel, Field - class PersonB(BaseModel): + class PersonB(BaseModelV1): """Record attributes of a person.""" - name: str = Field(..., description="The name of the person.") - age: int = Field(..., description="The age of the person.") + name: str = FieldV1(..., description="The name of the person.") + age: int = FieldV1(..., description="The age of the person.") return PersonB def generate_schema_pydantic() -> Any: """Works with either pydantic 1 or 2""" - from pydantic import BaseModel as BaseModelProper - from pydantic import Field as FieldProper - class PersonA(BaseModelProper): + class PersonA(BaseModel): """Record attributes of a person.""" - name: str = FieldProper(..., description="The name of the person.") - age: int = FieldProper(..., description="The age of the person.") + name: str = Field(..., description="The name of the person.") + age: int = Field(..., description="The age of the person.") return PersonA @@ -199,9 +207,7 @@ def test_with_structured_output( assert model.with_structured_output(schema) is not None def test_standard_params(self, model: BaseChatModel) -> None: - from langchain_core.pydantic_v1 import BaseModel, ValidationError - - class ExpectedParams(BaseModel): + class ExpectedParams(BaseModelV1): ls_provider: str ls_model_name: str ls_model_type: Literal["chat"] @@ -212,7 +218,7 @@ class ExpectedParams(BaseModel): ls_params = model._get_ls_params() try: ExpectedParams(**ls_params) - except ValidationError as e: + except ValidationErrorV1 as e: pytest.fail(f"Validation error: {e}") # Test optional params @@ -222,5 +228,14 @@ class ExpectedParams(BaseModel): ls_params = model._get_ls_params() try: ExpectedParams(**ls_params) - except ValidationError as e: + except ValidationErrorV1 as e: pytest.fail(f"Validation error: {e}") + + def test_serdes(self, model: BaseChatModel, snapshot: SnapshotAssertion) -> None: + if not self.chat_model_class.is_lc_serializable(): + return + env_params, model_params, expected_attrs = self.init_from_env_params + with mock.patch.dict(os.environ, env_params): + ser = dumpd(model) + assert ser == snapshot(name="serialized") + assert model.dict() == load(dumpd(model)).dict() diff --git a/libs/standard-tests/langchain_standard_tests/unit_tests/embeddings.py b/libs/standard-tests/langchain_standard_tests/unit_tests/embeddings.py index 0a6e793c06327..4638af51745be 100644 --- a/libs/standard-tests/langchain_standard_tests/unit_tests/embeddings.py +++ b/libs/standard-tests/langchain_standard_tests/unit_tests/embeddings.py @@ -5,7 +5,7 @@ import pytest from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import SecretStr +from pydantic import SecretStr from langchain_standard_tests.base import BaseStandardTests diff --git a/libs/standard-tests/poetry.lock b/libs/standard-tests/poetry.lock index f0c71bc88671a..bee65170c6c52 100644 --- a/libs/standard-tests/poetry.lock +++ b/libs/standard-tests/poetry.lock @@ -1,14 +1,14 @@ -# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. [[package]] name = "annotated-types" -version = "0.6.0" +version = "0.7.0" description = "Reusable constraint types to use with typing.Annotated" optional = false python-versions = ">=3.8" files = [ - {file = "annotated_types-0.6.0-py3-none-any.whl", hash = "sha256:0641064de18ba7a25dee8f96403ebc39113d0cb953a01429249d5c7564666a43"}, - {file = "annotated_types-0.6.0.tar.gz", hash = "sha256:563339e807e53ffd9c267e99fc6d9ea23eb8443c08f112651963e24e22f84a5d"}, + {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"}, + {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"}, ] [package.dependencies] @@ -38,13 +38,13 @@ trio = ["trio (>=0.23)"] [[package]] name = "certifi" -version = "2024.2.2" +version = "2024.8.30" description = "Python package for providing Mozilla's CA Bundle." optional = false python-versions = ">=3.6" files = [ - {file = "certifi-2024.2.2-py3-none-any.whl", hash = "sha256:dc383c07b76109f368f6106eee2b593b04a011ea4d55f652c6ca24a754d1cdd1"}, - {file = "certifi-2024.2.2.tar.gz", hash = "sha256:0569859f95fc761b18b45ef421b1290a0f65f147e92a1e5eb3e635f9a5e4e66f"}, + {file = "certifi-2024.8.30-py3-none-any.whl", hash = "sha256:922820b53db7a7257ffbda3f597266d435245903d80737e34f8a45ff3e3230d8"}, + {file = "certifi-2024.8.30.tar.gz", hash = "sha256:bec941d2aa8195e248a60b31ff9f0558284cf01a52591ceda73ea9afffd69fd9"}, ] [[package]] @@ -148,13 +148,13 @@ files = [ [[package]] name = "codespell" -version = "2.2.6" +version = "2.3.0" description = "Codespell" optional = false python-versions = ">=3.8" files = [ - {file = "codespell-2.2.6-py3-none-any.whl", hash = "sha256:9ee9a3e5df0990604013ac2a9f22fa8e57669c827124a2e961fe8a1da4cacc07"}, - {file = "codespell-2.2.6.tar.gz", hash = "sha256:a8c65d8eb3faa03deabab6b3bbe798bea72e1799c7e9e955d57eca4096abcff9"}, + {file = "codespell-2.3.0-py3-none-any.whl", hash = "sha256:a9c7cef2501c9cfede2110fd6d4e5e62296920efe9abfb84648df866e47f58d1"}, + {file = "codespell-2.3.0.tar.gz", hash = "sha256:360c7d10f75e65f67bad720af7007e1060a5d395670ec11a7ed1fed9dd17471f"}, ] [package.extras] @@ -176,13 +176,13 @@ files = [ [[package]] name = "exceptiongroup" -version = "1.2.1" +version = "1.2.2" description = "Backport of PEP 654 (exception groups)" optional = false python-versions = ">=3.7" files = [ - {file = "exceptiongroup-1.2.1-py3-none-any.whl", hash = "sha256:5258b9ed329c5bbdd31a309f53cbfb0b155341807f6ff7606a1e801a891b29ad"}, - {file = "exceptiongroup-1.2.1.tar.gz", hash = "sha256:a4785e48b045528f5bfe627b6ad554ff32def154f42372786903b7abcfe1aa16"}, + {file = "exceptiongroup-1.2.2-py3-none-any.whl", hash = "sha256:3111b9d131c238bec2f8f516e123e14ba243563fb135d3fe885990585aa7795b"}, + {file = "exceptiongroup-1.2.2.tar.gz", hash = "sha256:47c2edf7c6738fafb49fd34290706d1a1a2f4d1c6df275526b62cbb4aa5393cc"}, ] [package.extras] @@ -222,13 +222,13 @@ trio = ["trio (>=0.22.0,<0.26.0)"] [[package]] name = "httpx" -version = "0.27.0" +version = "0.27.2" description = "The next generation HTTP client." optional = false python-versions = ">=3.8" files = [ - {file = "httpx-0.27.0-py3-none-any.whl", hash = "sha256:71d5465162c13681bff01ad59b2cc68dd838ea1f10e51574bac27103f00c91a5"}, - {file = "httpx-0.27.0.tar.gz", hash = "sha256:a0cb88a46f32dc874e04ee956e4c2764aba2aa228f650b06788ba6bda2962ab5"}, + {file = "httpx-0.27.2-py3-none-any.whl", hash = "sha256:7bb2708e112d8fdd7829cd4243970f0c223274051cb35ee80c03301ee29a3df0"}, + {file = "httpx-0.27.2.tar.gz", hash = "sha256:f7c2be1d2f3c3c3160d441802406b206c2b76f5947b11115e6df10c6c65e66c2"}, ] [package.dependencies] @@ -243,16 +243,17 @@ brotli = ["brotli", "brotlicffi"] cli = ["click (==8.*)", "pygments (==2.*)", "rich (>=10,<14)"] http2 = ["h2 (>=3,<5)"] socks = ["socksio (==1.*)"] +zstd = ["zstandard (>=0.18.0)"] [[package]] name = "idna" -version = "3.7" +version = "3.8" description = "Internationalized Domain Names in Applications (IDNA)" optional = false -python-versions = ">=3.5" +python-versions = ">=3.6" files = [ - {file = "idna-3.7-py3-none-any.whl", hash = "sha256:82fee1fc78add43492d3a1898bfa6d8a904cc97d8427f683ed8e798d07761aa0"}, - {file = "idna-3.7.tar.gz", hash = "sha256:028ff3aadf0609c1fd278d8ea3089299412a7a8b9bd005dd08b9f8285bcb5cfc"}, + {file = "idna-3.8-py3-none-any.whl", hash = "sha256:050b4e5baadcd44d760cedbd2b8e639f2ff89bbc7a5730fcc662954303377aac"}, + {file = "idna-3.8.tar.gz", hash = "sha256:d838c2c0ed6fced7693d5e8ab8e734d5f8fda53a039c0164afb0b82e771e3603"}, ] [[package]] @@ -282,18 +283,18 @@ jsonpointer = ">=1.9" [[package]] name = "jsonpointer" -version = "2.4" +version = "3.0.0" description = "Identify specific nodes in a JSON document (RFC 6901)" optional = false -python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*" +python-versions = ">=3.7" files = [ - {file = "jsonpointer-2.4-py2.py3-none-any.whl", hash = "sha256:15d51bba20eea3165644553647711d150376234112651b4f1811022aecad7d7a"}, - {file = "jsonpointer-2.4.tar.gz", hash = "sha256:585cee82b70211fa9e6043b7bb89db6e1aa49524340dde8ad6b63206ea689d88"}, + {file = "jsonpointer-3.0.0-py2.py3-none-any.whl", hash = "sha256:13e088adc14fca8b6aa8177c044e12701e6ad4b28ff10e65f2267a90109c9942"}, + {file = "jsonpointer-3.0.0.tar.gz", hash = "sha256:2b2d729f2091522d61c3b31f82e11870f60b68f43fbc705cb76bf4b832af59ef"}, ] [[package]] name = "langchain-core" -version = "0.2.0rc1" +version = "0.2.38" description = "Building applications with LLMs through composability" optional = false python-versions = ">=3.8.1,<4.0" @@ -302,14 +303,15 @@ develop = true [package.dependencies] jsonpatch = "^1.33" -langsmith = "^0.1.0" -packaging = "^23.2" -pydantic = ">=1,<3" +langsmith = "^0.1.75" +packaging = ">=23.2,<25" +pydantic = [ + {version = ">=1,<3", markers = "python_full_version < \"3.12.4\""}, + {version = ">=2.7.4,<3.0.0", markers = "python_full_version >= 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[[package]] name = "numpy" version = "1.26.4" @@ -2130,17 +2058,6 @@ files = [ {file = "pickleshare-0.7.5.tar.gz", hash = "sha256:87683d47965c1da65cdacaf31c8441d12b8044cdec9aca500cd78fc2c683afca"}, ] -[[package]] -name = "pkgutil-resolve-name" -version = "1.3.10" -description = "Resolve a name to an object." -optional = false -python-versions = ">=3.6" -files = [ - {file = "pkgutil_resolve_name-1.3.10-py3-none-any.whl", hash = "sha256:ca27cc078d25c5ad71a9de0a7a330146c4e014c2462d9af19c6b828280649c5e"}, - {file = "pkgutil_resolve_name-1.3.10.tar.gz", hash = "sha256:357d6c9e6a755653cfd78893817c0853af365dd51ec97f3d358a819373bbd174"}, -] - [[package]] name = "platformdirs" version = "4.2.2" @@ -2564,17 +2481,6 @@ files = [ {file = "python_json_logger-2.0.7-py3-none-any.whl", hash = "sha256:f380b826a991ebbe3de4d897aeec42760035ac760345e57b812938dc8b35e2bd"}, ] -[[package]] -name = "pytz" -version = "2024.1" -description = "World timezone definitions, modern and historical" -optional = false -python-versions = "*" -files = [ - {file = "pytz-2024.1-py2.py3-none-any.whl", hash = "sha256:328171f4e3623139da4983451950b28e95ac706e13f3f2630a879749e7a8b319"}, - {file = "pytz-2024.1.tar.gz", hash = "sha256:2a29735ea9c18baf14b448846bde5a48030ed267578472d8955cd0e7443a9812"}, -] - [[package]] name = "pywin32" version = "306" @@ -2959,7 +2865,6 @@ files = [ [package.dependencies] markdown-it-py = ">=2.2.0" pygments = ">=2.13.0,<3.0.0" -typing-extensions = {version = ">=4.0.0,<5.0", markers = "python_version < \"3.9\""} [package.extras] jupyter = ["ipywidgets (>=7.5.1,<9)"] @@ -3240,10 +3145,7 @@ cymem = ">=2.0.2,<2.1.0" jinja2 = "*" langcodes = ">=3.2.0,<4.0.0" murmurhash = ">=0.28.0,<1.1.0" -numpy = [ - {version = ">=1.15.0", markers = "python_version < \"3.9\""}, - {version = ">=1.19.0", markers = "python_version >= \"3.9\""}, -] +numpy = {version = ">=1.19.0", markers = "python_version >= \"3.9\""} packaging = ">=20.0" preshed = ">=3.0.2,<3.1.0" pydantic = ">=1.7.4,<1.8 || >1.8,<1.8.1 || >1.8.1,<3.0.0" @@ -3444,10 +3346,7 @@ catalogue = ">=2.0.4,<2.1.0" confection = ">=0.0.1,<1.0.0" cymem = ">=2.0.2,<2.1.0" murmurhash = ">=1.0.2,<1.1.0" -numpy = [ - {version = ">=1.15.0,<2.0.0", markers = "python_version < \"3.9\""}, - {version = ">=1.19.0,<2.0.0", markers = "python_version >= \"3.9\""}, -] +numpy = {version = ">=1.19.0,<2.0.0", markers = "python_version >= \"3.9\""} packaging = ">=20.0" preshed = ">=3.0.2,<3.1.0" pydantic = ">=1.7.4,<1.8 || >1.8,<1.8.1 || >1.8.1,<3.0.0" @@ -3947,5 +3846,5 @@ type = ["pytest-mypy"] [metadata] lock-version = "2.0" -python-versions = ">=3.8.1,<4.0" -content-hash = "24740c1e1905b083ea4a58bcb38a15305fcc12e2864b61b0e3f93ebbbbc03149" +python-versions = ">=3.9,<4.0" +content-hash = "2222f912185fe1f1df9e0fc05f9a59659999ef88f71bf0c27f5a11eb04ce1bbe" diff --git a/libs/text-splitters/pyproject.toml b/libs/text-splitters/pyproject.toml index 5440426047c62..22b7a37a183b5 100644 --- a/libs/text-splitters/pyproject.toml +++ b/libs/text-splitters/pyproject.toml @@ -22,8 +22,8 @@ ignore_missing_imports = "True" "Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-text-splitters%3D%3D0%22&expanded=true" [tool.poetry.dependencies] -python = ">=3.8.1,<4.0" -langchain-core = "^0.2.38" +python = ">=3.9,<4.0" +langchain-core = { version = "^0.3.0.dev1", allow-prereleases = true } [tool.ruff.lint] select = [ "E", "F", "I", "T201",] diff --git a/libs/text-splitters/scripts/check_pydantic.sh b/libs/text-splitters/scripts/check_pydantic.sh deleted file mode 100755 index 06b5bb81ae236..0000000000000 --- a/libs/text-splitters/scripts/check_pydantic.sh +++ /dev/null @@ -1,27 +0,0 @@ -#!/bin/bash -# -# This script searches for lines starting with "import pydantic" or "from pydantic" -# in tracked files within a Git repository. -# -# Usage: ./scripts/check_pydantic.sh /path/to/repository - -# Check if a path argument is provided -if [ $# -ne 1 ]; then - echo "Usage: $0 /path/to/repository" - exit 1 -fi - -repository_path="$1" - -# Search for lines matching the pattern within the specified repository -result=$(git -C "$repository_path" grep -E '^import pydantic|^from pydantic') - -# Check if any matching lines were found -if [ -n "$result" ]; then - echo "ERROR: The following lines need to be updated:" - echo "$result" - echo "Please replace the code with an import from langchain_core.pydantic_v1." - echo "For example, replace 'from pydantic import BaseModel'" - echo "with 'from langchain_core.pydantic_v1 import BaseModel'" - exit 1 -fi