From 9ce241ae0ea8919d550aafead99cc2c970197b0e Mon Sep 17 00:00:00 2001 From: Erick Friis Date: Thu, 26 Sep 2024 14:36:00 -0700 Subject: [PATCH] migrate Milvus (#1) --- .github/scripts/check_diff.py | 2 +- .github/workflows/_release.yml | 4 +- README.md | 60 +- libs/milvus/.gitignore | 1 + libs/milvus/LICENSE | 21 + libs/milvus/Makefile | 59 + libs/milvus/README.md | 42 + libs/milvus/langchain_milvus/__init__.py | 12 + libs/milvus/langchain_milvus/py.typed | 0 .../langchain_milvus/retrievers/__init__.py | 8 + .../retrievers/milvus_hybrid_search.py | 161 ++ .../zilliz_cloud_pipeline_retriever.py | 215 ++ .../milvus/langchain_milvus/utils/__init__.py | 0 libs/milvus/langchain_milvus/utils/sparse.py | 55 + .../langchain_milvus/vectorstores/__init__.py | 7 + .../langchain_milvus/vectorstores/milvus.py | 1351 ++++++++++ .../langchain_milvus/vectorstores/zilliz.py | 197 ++ libs/milvus/poetry.lock | 2243 +++++++++++++++++ libs/milvus/pyproject.toml | 85 + libs/milvus/scripts/check_imports.py | 17 + libs/milvus/scripts/lint_imports.sh | 17 + libs/milvus/tests/__init__.py | 0 .../tests/integration_tests/__init__.py | 0 .../integration_tests/retrievers/__init__.py | 0 .../tests/integration_tests/test_compile.py | 7 + libs/milvus/tests/integration_tests/utils.py | 40 + .../vectorstores/__init__.py | 0 .../vectorstores/test_milvus.py | 430 ++++ libs/milvus/tests/unit_tests/__init__.py | 0 .../tests/unit_tests/retrievers/__init__.py | 0 libs/milvus/tests/unit_tests/test_imports.py | 12 + .../tests/unit_tests/vectorstores/__init__.py | 0 .../unit_tests/vectorstores/test_milvus.py | 17 + 33 files changed, 5002 insertions(+), 61 deletions(-) create mode 100644 libs/milvus/.gitignore create mode 100644 libs/milvus/LICENSE create mode 100644 libs/milvus/Makefile create mode 100644 libs/milvus/README.md create mode 100644 libs/milvus/langchain_milvus/__init__.py create mode 100644 libs/milvus/langchain_milvus/py.typed create mode 100644 libs/milvus/langchain_milvus/retrievers/__init__.py create mode 100644 libs/milvus/langchain_milvus/retrievers/milvus_hybrid_search.py create mode 100644 libs/milvus/langchain_milvus/retrievers/zilliz_cloud_pipeline_retriever.py create mode 100644 libs/milvus/langchain_milvus/utils/__init__.py create mode 100644 libs/milvus/langchain_milvus/utils/sparse.py create mode 100644 libs/milvus/langchain_milvus/vectorstores/__init__.py create mode 100644 libs/milvus/langchain_milvus/vectorstores/milvus.py create mode 100644 libs/milvus/langchain_milvus/vectorstores/zilliz.py create mode 100644 libs/milvus/poetry.lock create mode 100644 libs/milvus/pyproject.toml create mode 100644 libs/milvus/scripts/check_imports.py create mode 100755 libs/milvus/scripts/lint_imports.sh create mode 100644 libs/milvus/tests/__init__.py create mode 100644 libs/milvus/tests/integration_tests/__init__.py create mode 100644 libs/milvus/tests/integration_tests/retrievers/__init__.py create mode 100644 libs/milvus/tests/integration_tests/test_compile.py create mode 100644 libs/milvus/tests/integration_tests/utils.py create mode 100644 libs/milvus/tests/integration_tests/vectorstores/__init__.py create mode 100644 libs/milvus/tests/integration_tests/vectorstores/test_milvus.py create mode 100644 libs/milvus/tests/unit_tests/__init__.py create mode 100644 libs/milvus/tests/unit_tests/retrievers/__init__.py create mode 100644 libs/milvus/tests/unit_tests/test_imports.py create mode 100644 libs/milvus/tests/unit_tests/vectorstores/__init__.py create mode 100644 libs/milvus/tests/unit_tests/vectorstores/test_milvus.py diff --git a/.github/scripts/check_diff.py b/.github/scripts/check_diff.py index 0fcb163..a278914 100644 --- a/.github/scripts/check_diff.py +++ b/.github/scripts/check_diff.py @@ -2,7 +2,7 @@ import sys from typing import Dict -LIB_DIRS = ["libs/{lib}"] +LIB_DIRS = ["libs/milvus"] if __name__ == "__main__": files = sys.argv[1:] diff --git a/.github/workflows/_release.yml b/.github/workflows/_release.yml index 8a7a6c6..f840f50 100644 --- a/.github/workflows/_release.yml +++ b/.github/workflows/_release.yml @@ -12,7 +12,7 @@ on: working-directory: required: true type: string - default: 'libs/{lib}' + default: 'libs/milvus' env: PYTHON_VERSION: "3.11" @@ -158,8 +158,6 @@ jobs: working-directory: ${{ inputs.working-directory }} - name: Run integration tests - env: - PARTNER_API_KEY: ${{ secrets.PARTNER_API_KEY }} run: make integration_tests working-directory: ${{ inputs.working-directory }} diff --git a/README.md b/README.md index 3b485db..e252d22 100644 --- a/README.md +++ b/README.md @@ -1,59 +1,5 @@ -# 🦜️🔗 LangChain {partner} +# 🦜️🔗 LangChain Milvus -This repository contains 1 package with {partner} integrations with LangChain: +This repository contains 1 package with Milvus integrations with LangChain: -- [langchain-{package_lower}](https://pypi.org/project/langchain-{package_lower}/) - -## Initial Repo Checklist (Remove this section after completing) - -This setup assumes that the partner package is already split. For those instructions, -see [these docs](https://python.langchain.com/docs/contributing/integrations#partner-packages). - -Code (auto ecli) - -- [ ] Fill out the readme above (for folks that follow pypi link) -- [ ] Copy package into /libs folder -- [ ] Update these fields in /libs/*/pyproject.toml - - - `tool.poetry.repository` - - `tool.poetry.urls["Source Code"]` - -Workflow code (auto ecli) - -- [ ] Populate .github/workflows/_release.yml with `on.workflow_dispatch.inputs.working-directory.default` -- [ ] Configure `LIB_DIRS` in .github/scripts/check_diff.py - -Workflow code (manual) - -- [ ] Add secrets as env vars in .github/workflows/_release.yml - -In github (manual) - -- [ ] Add integration testing secrets in Github (ask Erick for help) -- [ ] Add partner collaborators in Github (ask Erick for help) -- [ ] "Allow auto-merge" in General Settings -- [ ] Only "Allow squash merging" in General Settings -- [ ] Set up ruleset matching CI build (ask Erick for help) - - name: ci build - - enforcement: active - - bypass: write - - target: default branch - - rules: restrict deletions, require status checks ("CI Success"), block force pushes -- [ ] Set up ruleset - - name: require prs - - enforcement: active - - bypass: none - - target: default branch - - rules: restrict deletions, require a pull request before merging (0 approvals, no boxes), block force pushes - -Pypi (manual) - -- [ ] Add new repo to test-pypi and pypi trusted publishing (ask Erick for help) - -Slack - -- [ ] Set up release alerting in Slack (ask Erick for help) - -release: -/github subscribe langchain-ai/langchain-{partner_lower} releases workflows:{name:"release"} -/github unsubscribe langchain-ai/langchain-{partner_lower} issues pulls commits deployments +- [langchain-milvus](https://pypi.org/project/langchain-milvus/) diff --git a/libs/milvus/.gitignore b/libs/milvus/.gitignore new file mode 100644 index 0000000..bee8a64 --- /dev/null +++ b/libs/milvus/.gitignore @@ -0,0 +1 @@ +__pycache__ diff --git a/libs/milvus/LICENSE b/libs/milvus/LICENSE new file mode 100644 index 0000000..426b650 --- /dev/null +++ b/libs/milvus/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 LangChain, Inc. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/libs/milvus/Makefile b/libs/milvus/Makefile new file mode 100644 index 0000000..9234f58 --- /dev/null +++ b/libs/milvus/Makefile @@ -0,0 +1,59 @@ +.PHONY: all format lint test tests integration_tests docker_tests help extended_tests + +# Default target executed when no arguments are given to make. +all: help + +# Define a variable for the test file path. +TEST_FILE ?= tests/unit_tests/ +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 $(TEST_FILE) + + +###################### +# LINTING AND FORMATTING +###################### + +# Define a variable for Python and notebook files. +PYTHON_FILES=. +MYPY_CACHE=.mypy_cache +lint format: PYTHON_FILES=. +lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/partners/milvus --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$') +lint_package: PYTHON_FILES=langchain_milvus +lint_tests: PYTHON_FILES=tests +lint_tests: MYPY_CACHE=.mypy_cache_test + +lint lint_diff lint_package lint_tests: + [ "$(PYTHON_FILES)" = "" ] || poetry run ruff $(PYTHON_FILES) + [ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) --diff + [ "$(PYTHON_FILES)" = "" ] || mkdir -p $(MYPY_CACHE) && poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE) + +format format_diff: + [ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) + [ "$(PYTHON_FILES)" = "" ] || poetry run ruff --select I --fix $(PYTHON_FILES) + +spell_check: + poetry run codespell --toml pyproject.toml + +spell_fix: + poetry run codespell --toml pyproject.toml -w + +check_imports: $(shell find langchain_milvus -name '*.py') + poetry run python ./scripts/check_imports.py $^ + +###################### +# HELP +###################### + +help: + @echo '----' + @echo 'check_imports - check imports' + @echo 'format - run code formatters' + @echo 'lint - run linters' + @echo 'test - run unit tests' + @echo 'tests - run unit tests' + @echo 'test TEST_FILE= - run all tests in file' diff --git a/libs/milvus/README.md b/libs/milvus/README.md new file mode 100644 index 0000000..80820f3 --- /dev/null +++ b/libs/milvus/README.md @@ -0,0 +1,42 @@ +# langchain-milvus + +This is a library integration with [Milvus](https://milvus.io/) and [Zilliz Cloud](https://zilliz.com/cloud). + +## Installation + +```bash +pip install -U langchain-milvus +``` + +## Milvus vector database + +See a [usage example](https://python.langchain.com/v0.2/docs/integrations/vectorstores/milvus/) + +```python +from langchain_milvus import Milvus +``` + +## Milvus hybrid search + +See a [usage example](https://python.langchain.com/v0.2/docs/integrations/retrievers/milvus_hybrid_search/). + +```python +from langchain_milvus import MilvusCollectionHybridSearchRetriever +``` + + +## Zilliz Cloud vector database + +See a [usage example](https://python.langchain.com/v0.2/docs/integrations/vectorstores/zilliz/). + +```python +from langchain_milvus import Zilliz +``` + +## Zilliz Cloud Pipeline Retriever + +See a [usage example](https://python.langchain.com/v0.2/docs/integrations/retrievers/zilliz_cloud_pipeline/). + +```python +from langchain_milvus import ZillizCloudPipelineRetriever +``` \ No newline at end of file diff --git a/libs/milvus/langchain_milvus/__init__.py b/libs/milvus/langchain_milvus/__init__.py new file mode 100644 index 0000000..b19bc1d --- /dev/null +++ b/libs/milvus/langchain_milvus/__init__.py @@ -0,0 +1,12 @@ +from langchain_milvus.retrievers import ( + MilvusCollectionHybridSearchRetriever, + ZillizCloudPipelineRetriever, +) +from langchain_milvus.vectorstores import Milvus, Zilliz + +__all__ = [ + "Milvus", + "Zilliz", + "ZillizCloudPipelineRetriever", + "MilvusCollectionHybridSearchRetriever", +] diff --git a/libs/milvus/langchain_milvus/py.typed b/libs/milvus/langchain_milvus/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/libs/milvus/langchain_milvus/retrievers/__init__.py b/libs/milvus/langchain_milvus/retrievers/__init__.py new file mode 100644 index 0000000..1edac3c --- /dev/null +++ b/libs/milvus/langchain_milvus/retrievers/__init__.py @@ -0,0 +1,8 @@ +from langchain_milvus.retrievers.milvus_hybrid_search import ( + MilvusCollectionHybridSearchRetriever, +) +from langchain_milvus.retrievers.zilliz_cloud_pipeline_retriever import ( + ZillizCloudPipelineRetriever, +) + +__all__ = ["ZillizCloudPipelineRetriever", "MilvusCollectionHybridSearchRetriever"] diff --git a/libs/milvus/langchain_milvus/retrievers/milvus_hybrid_search.py b/libs/milvus/langchain_milvus/retrievers/milvus_hybrid_search.py new file mode 100644 index 0000000..b6e309c --- /dev/null +++ b/libs/milvus/langchain_milvus/retrievers/milvus_hybrid_search.py @@ -0,0 +1,161 @@ +from typing import Any, Dict, List, Optional, Union + +from langchain_core.callbacks import CallbackManagerForRetrieverRun +from langchain_core.documents import Document +from langchain_core.embeddings import Embeddings +from langchain_core.retrievers import BaseRetriever +from pymilvus import AnnSearchRequest, Collection +from pymilvus.client.abstract import BaseRanker, SearchResult # type: ignore + +from langchain_milvus.utils.sparse import BaseSparseEmbedding + + +class MilvusCollectionHybridSearchRetriever(BaseRetriever): + """Hybrid search retriever + that uses Milvus Collection to retrieve documents based on multiple fields. + + For more information, please refer to: + https://milvus.io/docs/release_notes.md#Multi-Embedding---Hybrid-Search + """ + + collection: Collection + """Milvus Collection object.""" + rerank: BaseRanker + """Milvus ranker object. Such as WeightedRanker or RRFRanker.""" + anns_fields: List[str] + """The names of vector fields that are used for ANNS search.""" + field_embeddings: List[Union[Embeddings, BaseSparseEmbedding]] + """The embedding functions of each vector fields, + which can be either Embeddings or BaseSparseEmbedding.""" + field_search_params: Optional[List[Dict]] = None + """The search parameters of each vector fields. + If not specified, the default search parameters will be used.""" + field_limits: Optional[List[int]] = None + """Limit number of results for each ANNS field. + If not specified, the default top_k will be used.""" + field_exprs: Optional[List[Optional[str]]] = None + """The boolean expression for filtering the search results.""" + top_k: int = 4 + """Final top-K number of documents to retrieve.""" + text_field: str = "text" + """The text field name, + which will be used as the `page_content` of a `Document` object.""" + output_fields: Optional[List[str]] = None + """Final output fields of the documents. + If not specified, all fields except the vector fields will be used as output fields, + which will be the `metadata` of a `Document` object.""" + + def __init__(self, **kwargs: Any): + super().__init__(**kwargs) + + # If some parameters are not specified, set default values + if self.field_search_params is None: + default_search_params = { + "metric_type": "L2", + "params": {"nprobe": 10}, + } + self.field_search_params = [default_search_params] * len(self.anns_fields) + if self.field_limits is None: + self.field_limits = [self.top_k] * len(self.anns_fields) + if self.field_exprs is None: + self.field_exprs = [None] * len(self.anns_fields) + + # Check the fields + self._validate_fields_num() + self.output_fields = self._get_output_fields() + self._validate_fields_name() + + # Load collection + self.collection.load() + + def _validate_fields_num(self) -> None: + assert ( + len(self.anns_fields) >= 2 + ), "At least two fields are required for hybrid search." + lengths = [len(self.anns_fields)] + if self.field_limits is not None: + lengths.append(len(self.field_limits)) + if self.field_exprs is not None: + lengths.append(len(self.field_exprs)) + + if not all(length == lengths[0] for length in lengths): + raise ValueError("All field-related lists must have the same length.") + + if len(self.field_search_params) != len(self.anns_fields): # type: ignore[arg-type] + raise ValueError( + "field_search_params must have the same length as anns_fields." + ) + + def _validate_fields_name(self) -> None: + collection_fields = [x.name for x in self.collection.schema.fields] + for field in self.anns_fields: + assert ( + field in collection_fields + ), f"{field} is not a valid field in the collection." + assert ( + self.text_field in collection_fields + ), f"{self.text_field} is not a valid field in the collection." + for field in self.output_fields: # type: ignore[union-attr] + assert ( + field in collection_fields + ), f"{field} is not a valid field in the collection." + + def _get_output_fields(self) -> List[str]: + if self.output_fields: + return self.output_fields + output_fields = [x.name for x in self.collection.schema.fields] + for field in self.anns_fields: + if field in output_fields: + output_fields.remove(field) + if self.text_field not in output_fields: + output_fields.append(self.text_field) + return output_fields + + def _build_ann_search_requests(self, query: str) -> List[AnnSearchRequest]: + search_requests = [] + for ann_field, embedding, param, limit, expr in zip( + self.anns_fields, + self.field_embeddings, + self.field_search_params, # type: ignore[arg-type] + self.field_limits, # type: ignore[arg-type] + self.field_exprs, # type: ignore[arg-type] + ): + request = AnnSearchRequest( + data=[embedding.embed_query(query)], + anns_field=ann_field, + param=param, + limit=limit, + expr=expr, + ) + search_requests.append(request) + return search_requests + + def _parse_document(self, data: dict) -> Document: + return Document( + page_content=data.pop(self.text_field), + metadata=data, + ) + + def _process_search_result( + self, search_results: List[SearchResult] + ) -> List[Document]: + documents = [] + for result in search_results[0]: + data = {x: result.entity.get(x) for x in self.output_fields} # type: ignore[union-attr] + doc = self._parse_document(data) + documents.append(doc) + return documents + + def _get_relevant_documents( + self, + query: str, + *, + run_manager: CallbackManagerForRetrieverRun, + **kwargs: Any, + ) -> List[Document]: + requests = self._build_ann_search_requests(query) + search_result = self.collection.hybrid_search( + requests, self.rerank, limit=self.top_k, output_fields=self.output_fields + ) + documents = self._process_search_result(search_result) + return documents diff --git a/libs/milvus/langchain_milvus/retrievers/zilliz_cloud_pipeline_retriever.py b/libs/milvus/langchain_milvus/retrievers/zilliz_cloud_pipeline_retriever.py new file mode 100644 index 0000000..88c6a55 --- /dev/null +++ b/libs/milvus/langchain_milvus/retrievers/zilliz_cloud_pipeline_retriever.py @@ -0,0 +1,215 @@ +from typing import Any, Dict, List, Optional + +import requests +from langchain_core.callbacks.manager import CallbackManagerForRetrieverRun +from langchain_core.documents import Document +from langchain_core.retrievers import BaseRetriever + + +class ZillizCloudPipelineRetriever(BaseRetriever): + """`Zilliz Cloud Pipeline` retriever. + + Parameters: + pipeline_ids: A dictionary of pipeline ids. + Valid keys: "ingestion", "search", "deletion". + token: Zilliz Cloud's token. Defaults to "". + cloud_region: The region of Zilliz Cloud's cluster. + Defaults to 'gcp-us-west1'. + """ + + pipeline_ids: Dict + token: str = "" + cloud_region: str = "gcp-us-west1" + + def _get_relevant_documents( + self, + query: str, + top_k: int = 10, + offset: int = 0, + output_fields: List = [], + filter: str = "", + *, + run_manager: CallbackManagerForRetrieverRun, + ) -> List[Document]: + """ + Get documents relevant to a query. + + Args: + query: String to find relevant documents for + top_k: The number of results. Defaults to 10. + offset: The number of records to skip in the search result. + Defaults to 0. + output_fields: The extra fields to present in output. + filter: The Milvus expression to filter search results. + Defaults to "". + run_manager: The callbacks handler to use. + + Returns: + List of relevant documents + """ + if "search" in self.pipeline_ids: + search_pipe_id = self.pipeline_ids.get("search") + else: + raise Exception( + "A search pipeline id must be provided in pipeline_ids to " + "get relevant documents." + ) + domain = ( + f"https://controller.api.{self.cloud_region}.zillizcloud.com/v1/pipelines" + ) + headers = { + "Authorization": f"Bearer {self.token}", + "Accept": "application/json", + "Content-Type": "application/json", + } + url = f"{domain}/{search_pipe_id}/run" + + params = { + "data": {"query_text": query}, + "params": { + "limit": top_k, + "offset": offset, + "outputFields": output_fields, + "filter": filter, + }, + } + + response = requests.post(url, headers=headers, json=params) + if response.status_code != 200: + raise RuntimeError(response.text) + response_dict = response.json() + if response_dict["code"] != 200: + raise RuntimeError(response_dict) + response_data = response_dict["data"] + search_results = response_data["result"] + return [ + Document( + page_content=result.pop("text") + if "text" in result + else result.pop("chunk_text"), + metadata=result, + ) + for result in search_results + ] + + def add_texts( + self, texts: List[str], metadata: Optional[Dict[str, Any]] = None + ) -> Dict: + """ + Add documents to store. + Only supported by a text ingestion pipeline in Zilliz Cloud. + + Args: + texts: A list of text strings. + metadata: A key-value dictionary of metadata will + be inserted as preserved fields required by ingestion pipeline. + Defaults to None. + """ + if "ingestion" in self.pipeline_ids: + ingeset_pipe_id = self.pipeline_ids.get("ingestion") + else: + raise Exception( + "An ingestion pipeline id must be provided in pipeline_ids to" + " add documents." + ) + domain = ( + f"https://controller.api.{self.cloud_region}.zillizcloud.com/v1/pipelines" + ) + headers = { + "Authorization": f"Bearer {self.token}", + "Accept": "application/json", + "Content-Type": "application/json", + } + url = f"{domain}/{ingeset_pipe_id}/run" + + metadata = {} if metadata is None else metadata + params = {"data": {"text_list": texts}} + params["data"].update(metadata) + + response = requests.post(url, headers=headers, json=params) + if response.status_code != 200: + raise Exception(response.text) + response_dict = response.json() + if response_dict["code"] != 200: + raise Exception(response_dict) + response_data = response_dict["data"] + return response_data + + def add_doc_url( + self, doc_url: str, metadata: Optional[Dict[str, Any]] = None + ) -> Dict: + """ + Add a document from url. + Only supported by a document ingestion pipeline in Zilliz Cloud. + + Args: + doc_url: A document url. + metadata: A key-value dictionary of metadata will + be inserted as preserved fields required by ingestion pipeline. + Defaults to None. + """ + if "ingestion" in self.pipeline_ids: + ingest_pipe_id = self.pipeline_ids.get("ingestion") + else: + raise Exception( + "An ingestion pipeline id must be provided in pipeline_ids to " + "add documents." + ) + domain = ( + f"https://controller.api.{self.cloud_region}.zillizcloud.com/v1/pipelines" + ) + headers = { + "Authorization": f"Bearer {self.token}", + "Accept": "application/json", + "Content-Type": "application/json", + } + url = f"{domain}/{ingest_pipe_id}/run" + + params = {"data": {"doc_url": doc_url}} + metadata = {} if metadata is None else metadata + params["data"].update(metadata) + + response = requests.post(url, headers=headers, json=params) + if response.status_code != 200: + raise Exception(response.text) + response_dict = response.json() + if response_dict["code"] != 200: + raise Exception(response_dict) + response_data = response_dict["data"] + return response_data + + def delete(self, key: str, value: Any) -> Dict: + """ + Delete documents. Only supported by a deletion pipeline in Zilliz Cloud. + + Args: + key: input name to run the deletion pipeline + value: input value to run deletion pipeline + """ + if "deletion" in self.pipeline_ids: + deletion_pipe_id = self.pipeline_ids.get("deletion") + else: + raise Exception( + "A deletion pipeline id must be provided in pipeline_ids to " + "add documents." + ) + domain = ( + f"https://controller.api.{self.cloud_region}.zillizcloud.com/v1/pipelines" + ) + headers = { + "Authorization": f"Bearer {self.token}", + "Accept": "application/json", + "Content-Type": "application/json", + } + url = f"{domain}/{deletion_pipe_id}/run" + + params = {"data": {key: value}} + + response = requests.post(url, headers=headers, json=params) + if response.status_code != 200: + raise Exception(response.text) + response_dict = response.json() + if response_dict["code"] != 200: + raise Exception(response_dict) + response_data = response_dict["data"] + return response_data diff --git a/libs/milvus/langchain_milvus/utils/__init__.py b/libs/milvus/langchain_milvus/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/libs/milvus/langchain_milvus/utils/sparse.py b/libs/milvus/langchain_milvus/utils/sparse.py new file mode 100644 index 0000000..47c1956 --- /dev/null +++ b/libs/milvus/langchain_milvus/utils/sparse.py @@ -0,0 +1,55 @@ +from abc import ABC, abstractmethod +from typing import Dict, List + +from scipy.sparse import csr_array # type: ignore + + +class BaseSparseEmbedding(ABC): + """Interface for Sparse embedding models. + + You can inherit from it and implement your custom sparse embedding model. + """ + + @abstractmethod + def embed_query(self, query: str) -> Dict[int, float]: + """Embed query text.""" + + @abstractmethod + def embed_documents(self, texts: List[str]) -> List[Dict[int, float]]: + """Embed search docs.""" + + +class BM25SparseEmbedding(BaseSparseEmbedding): + """Sparse embedding model based on BM25. + + This class uses the BM25 model in Milvus model to implement sparse vector embedding. + This model requires pymilvus[model] to be installed. + `pip install pymilvus[model]` + For more information please refer to: + https://milvus.io/docs/embed-with-bm25.md + """ + + def __init__(self, corpus: List[str], language: str = "en"): + from pymilvus.model.sparse import BM25EmbeddingFunction # type: ignore + from pymilvus.model.sparse.bm25.tokenizers import ( # type: ignore + build_default_analyzer, + ) + + self.analyzer = build_default_analyzer(language=language) + self.bm25_ef = BM25EmbeddingFunction(self.analyzer, num_workers=1) + self.bm25_ef.fit(corpus) + + def embed_query(self, text: str) -> Dict[int, float]: + return self._sparse_to_dict(self.bm25_ef.encode_queries([text])) + + def embed_documents(self, texts: List[str]) -> List[Dict[int, float]]: + sparse_arrays = self.bm25_ef.encode_documents(texts) + return [self._sparse_to_dict(sparse_array) for sparse_array in sparse_arrays] + + def _sparse_to_dict(self, sparse_array: csr_array) -> Dict[int, float]: + row_indices, col_indices = sparse_array.nonzero() + non_zero_values = sparse_array.data + result_dict = {} + for col_index, value in zip(col_indices, non_zero_values): + result_dict[col_index] = value + return result_dict diff --git a/libs/milvus/langchain_milvus/vectorstores/__init__.py b/libs/milvus/langchain_milvus/vectorstores/__init__.py new file mode 100644 index 0000000..5c6f304 --- /dev/null +++ b/libs/milvus/langchain_milvus/vectorstores/__init__.py @@ -0,0 +1,7 @@ +from langchain_milvus.vectorstores.milvus import Milvus +from langchain_milvus.vectorstores.zilliz import Zilliz + +__all__ = [ + "Milvus", + "Zilliz", +] diff --git a/libs/milvus/langchain_milvus/vectorstores/milvus.py b/libs/milvus/langchain_milvus/vectorstores/milvus.py new file mode 100644 index 0000000..279f963 --- /dev/null +++ b/libs/milvus/langchain_milvus/vectorstores/milvus.py @@ -0,0 +1,1351 @@ +from __future__ import annotations + +import logging +from typing import Any, Dict, Iterable, List, Optional, Tuple, Union +from uuid import uuid4 + +import numpy as np +from langchain_core.documents import Document +from langchain_core.embeddings import Embeddings +from langchain_core.vectorstores import VectorStore + +from langchain_milvus.utils.sparse import BaseSparseEmbedding + +logger = logging.getLogger(__name__) + +DEFAULT_MILVUS_CONNECTION = { + "uri": "http://localhost:19530", +} + +Matrix = Union[List[List[float]], List[np.ndarray], np.ndarray] + + +def cosine_similarity(X: Matrix, Y: Matrix) -> np.ndarray: + """Row-wise cosine similarity between two equal-width matrices.""" + if len(X) == 0 or len(Y) == 0: + return np.array([]) + + X = np.array(X) + Y = np.array(Y) + if X.shape[1] != Y.shape[1]: + raise ValueError( + f"Number of columns in X and Y must be the same. X has shape {X.shape} " + f"and Y has shape {Y.shape}." + ) + try: + import simsimd as simd + + X = np.array(X, dtype=np.float32) + Y = np.array(Y, dtype=np.float32) + Z = 1 - np.array(simd.cdist(X, Y, metric="cosine")) + return Z + except ImportError: + logger.debug( + "Unable to import simsimd, defaulting to NumPy implementation. If you want " + "to use simsimd please install with `pip install simsimd`." + ) + X_norm = np.linalg.norm(X, axis=1) + Y_norm = np.linalg.norm(Y, axis=1) + # Ignore divide by zero errors run time warnings as those are handled below. + with np.errstate(divide="ignore", invalid="ignore"): + similarity = np.dot(X, Y.T) / np.outer(X_norm, Y_norm) + similarity[np.isnan(similarity) | np.isinf(similarity)] = 0.0 + return similarity + + +def maximal_marginal_relevance( + query_embedding: np.ndarray, + embedding_list: list, + lambda_mult: float = 0.5, + k: int = 4, +) -> List[int]: + """Calculate maximal marginal relevance. + + Args: + query_embedding: The query embedding. + embedding_list: The list of embeddings. + lambda_mult: The lambda multiplier. Defaults to 0.5. + k: The number of results to return. Defaults to 4. + + Returns: + List[int]: The list of indices. + """ + if min(k, len(embedding_list)) <= 0: + return [] + if query_embedding.ndim == 1: + query_embedding = np.expand_dims(query_embedding, axis=0) + similarity_to_query = cosine_similarity(query_embedding, embedding_list)[0] + most_similar = int(np.argmax(similarity_to_query)) + idxs = [most_similar] + selected = np.array([embedding_list[most_similar]]) + while len(idxs) < min(k, len(embedding_list)): + best_score = -np.inf + idx_to_add = -1 + similarity_to_selected = cosine_similarity(embedding_list, selected) + for i, query_score in enumerate(similarity_to_query): + if i in idxs: + continue + redundant_score = max(similarity_to_selected[i]) + equation_score = ( + lambda_mult * query_score - (1 - lambda_mult) * redundant_score + ) + if equation_score > best_score: + best_score = equation_score + idx_to_add = i + idxs.append(idx_to_add) + selected = np.append(selected, [embedding_list[idx_to_add]], axis=0) + return idxs + + +class Milvus(VectorStore): + """Milvus vector store integration. + + Setup: + Install ``langchain_milvus`` package: + + .. code-block:: bash + + pip install -qU langchain_milvus + + Key init args — indexing params: + collection_name: str + Name of the collection. + collection_description: str + Description of the collection. + embedding_function: Union[Embeddings, BaseSparseEmbedding] + Embedding function to use. + + Key init args — client params: + connection_args: Optional[dict] + Connection arguments. + + Instantiate: + .. code-block:: python + + from langchain_milvus import Milvus + from langchain_openai import OpenAIEmbeddings + + URI = "./milvus_example.db" + + vector_store = Milvus( + embedding_function=OpenAIEmbeddings(), + connection_args={"uri": URI}, + ) + + Add Documents: + .. code-block:: python + + from langchain_core.documents import Document + + document_1 = Document(page_content="foo", metadata={"baz": "bar"}) + document_2 = Document(page_content="thud", metadata={"baz": "baz"}) + document_3 = Document(page_content="i will be deleted :(", metadata={"baz": "qux"}) + + documents = [document_1, document_2, document_3] + ids = ["1", "2", "3"] + vector_store.add_documents(documents=documents, ids=ids) + + Delete Documents: + .. code-block:: python + + vector_store.delete(ids=["3"]) + + Search: + .. code-block:: python + + results = vector_store.similarity_search(query="thud",k=1) + for doc in results: + print(f"* {doc.page_content} [{doc.metadata}]") + + .. code-block:: python + + * thud [{'baz': 'baz', 'pk': '2'}] + + Search with filter: + .. code-block:: python + + results = vector_store.similarity_search(query="thud",k=1,filter={"bar": "baz"}) + for doc in results: + print(f"* {doc.page_content} [{doc.metadata}]") + + .. code-block:: python + + * thud [{'baz': 'baz', 'pk': '2'}] + + Search with score: + .. code-block:: python + + results = vector_store.similarity_search_with_score(query="qux",k=1) + for doc, score in results: + print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]") + + .. code-block:: python + + * [SIM=0.335463] foo [{'baz': 'bar', 'pk': '1'}] + + Async: + .. code-block:: python + + # add documents + # await vector_store.aadd_documents(documents=documents, ids=ids) + + # delete documents + # await vector_store.adelete(ids=["3"]) + + # search + # results = vector_store.asimilarity_search(query="thud",k=1) + + # search with score + results = await vector_store.asimilarity_search_with_score(query="qux",k=1) + for doc,score in results: + print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]") + + .. code-block:: python + + * [SIM=0.335463] foo [{'baz': 'bar', 'pk': '1'}] + + Use as Retriever: + .. code-block:: python + + retriever = vector_store.as_retriever( + search_type="mmr", + search_kwargs={"k": 1, "fetch_k": 2, "lambda_mult": 0.5}, + ) + retriever.invoke("thud") + + .. code-block:: python + + [Document(metadata={'baz': 'baz', 'pk': '2'}, page_content='thud')] + + """ # noqa: E501 + + def __init__( + self, + embedding_function: Union[Embeddings, BaseSparseEmbedding], # type: ignore + collection_name: str = "LangChainCollection", + collection_description: str = "", + collection_properties: Optional[dict[str, Any]] = None, + connection_args: Optional[dict[str, Any]] = None, + consistency_level: str = "Session", + index_params: Optional[dict] = None, + search_params: Optional[dict] = None, + drop_old: Optional[bool] = False, + auto_id: bool = False, + *, + primary_field: str = "pk", + text_field: str = "text", + vector_field: str = "vector", + enable_dynamic_field: bool = False, + metadata_field: Optional[str] = None, + partition_key_field: Optional[str] = None, + partition_names: Optional[list] = None, + replica_number: int = 1, + timeout: Optional[float] = None, + num_shards: Optional[int] = None, + metadata_schema: Optional[dict[str, Any]] = None, + ): + """Initialize the Milvus vector store.""" + try: + from pymilvus import Collection, utility + except ImportError: + raise ValueError( + "Could not import pymilvus python package. " + "Please install it with `pip install pymilvus`." + ) + + # Default search params when one is not provided. + self.default_search_params = { + "IVF_FLAT": {"metric_type": "L2", "params": {"nprobe": 10}}, + "IVF_SQ8": {"metric_type": "L2", "params": {"nprobe": 10}}, + "IVF_PQ": {"metric_type": "L2", "params": {"nprobe": 10}}, + "HNSW": {"metric_type": "L2", "params": {"ef": 10}}, + "RHNSW_FLAT": {"metric_type": "L2", "params": {"ef": 10}}, + "RHNSW_SQ": {"metric_type": "L2", "params": {"ef": 10}}, + "RHNSW_PQ": {"metric_type": "L2", "params": {"ef": 10}}, + "IVF_HNSW": {"metric_type": "L2", "params": {"nprobe": 10, "ef": 10}}, + "ANNOY": {"metric_type": "L2", "params": {"search_k": 10}}, + "SCANN": {"metric_type": "L2", "params": {"search_k": 10}}, + "AUTOINDEX": {"metric_type": "L2", "params": {}}, + "GPU_CAGRA": { + "metric_type": "L2", + "params": { + "itopk_size": 128, + "search_width": 4, + "min_iterations": 0, + "max_iterations": 0, + "team_size": 0, + }, + }, + "GPU_IVF_FLAT": {"metric_type": "L2", "params": {"nprobe": 10}}, + "GPU_IVF_PQ": {"metric_type": "L2", "params": {"nprobe": 10}}, + "SPARSE_INVERTED_INDEX": { + "metric_type": "IP", + "params": {"drop_ratio_build": 0.2}, + }, + "SPARSE_WAND": {"metric_type": "IP", "params": {"drop_ratio_build": 0.2}}, + } + + self.embedding_func = embedding_function + self.collection_name = collection_name + self.collection_description = collection_description + self.collection_properties = collection_properties + self.index_params = index_params + self.search_params = search_params + self.consistency_level = consistency_level + self.auto_id = auto_id + + # In order for a collection to be compatible, pk needs to be varchar + self._primary_field = primary_field + # In order for compatibility, the text field will need to be called "text" + self._text_field = text_field + # In order for compatibility, the vector field needs to be called "vector" + self._vector_field = vector_field + if metadata_field: + logger.warning( + "DeprecationWarning: `metadata_field` is about to be deprecated, " + "please set `enable_dynamic_field`=True instead." + ) + if enable_dynamic_field and metadata_field: + metadata_field = None + logger.warning( + "When `enable_dynamic_field` is True, `metadata_field` is ignored." + ) + self.enable_dynamic_field = enable_dynamic_field + self._metadata_field = metadata_field + self._partition_key_field = partition_key_field + self.fields: list[str] = [] + self.partition_names = partition_names + self.replica_number = replica_number + self.timeout = timeout + self.num_shards = num_shards + self.metadata_schema = metadata_schema + + # Create the connection to the server + if connection_args is None: + connection_args = DEFAULT_MILVUS_CONNECTION + self.alias = self._create_connection_alias(connection_args) + self.col: Optional[Collection] = None + + # Grab the existing collection if it exists + if utility.has_collection(self.collection_name, using=self.alias): + self.col = Collection( + self.collection_name, + using=self.alias, + ) + if self.collection_properties is not None: + self.col.set_properties(self.collection_properties) + # If need to drop old, drop it + if drop_old and isinstance(self.col, Collection): + self.col.drop() + self.col = None + + # Initialize the vector store + self._init( + partition_names=partition_names, + replica_number=replica_number, + timeout=timeout, + ) + + @property + def embeddings(self) -> Union[Embeddings, BaseSparseEmbedding]: # type: ignore + return self.embedding_func + + def _create_connection_alias(self, connection_args: dict) -> str: + """Create the connection to the Milvus server.""" + from pymilvus import MilvusException, connections + + # Grab the connection arguments that are used for checking existing connection + host: str = connection_args.get("host", None) + port: Union[str, int] = connection_args.get("port", None) + address: str = connection_args.get("address", None) + uri: str = connection_args.get("uri", None) + user = connection_args.get("user", None) + db_name = connection_args.get("db_name", "default") + + # Order of use is host/port, uri, address + if host is not None and port is not None: + given_address = str(host) + ":" + str(port) + elif uri is not None: + if uri.startswith("https://"): + given_address = uri.split("https://")[1] + elif uri.startswith("http://"): + given_address = uri.split("http://")[1] + else: + given_address = uri # Milvus lite + elif address is not None: + given_address = address + else: + given_address = None + logger.debug("Missing standard address type for reuse attempt") + + # User defaults to empty string when getting connection info + if user is not None: + tmp_user = user + else: + tmp_user = "" + + # If a valid address was given, then check if a connection exists + if given_address is not None: + for con in connections.list_connections(): + addr = connections.get_connection_addr(con[0]) + if ( + con[1] + and ("address" in addr) + and (addr["address"] == given_address) + and ("user" in addr) + and (addr["user"] == tmp_user) + and (addr.get("db_name", "default") == db_name) + ): + logger.debug("Using previous connection: %s", con[0]) + return con[0] + + # Generate a new connection if one doesn't exist + alias = uuid4().hex + try: + connections.connect(alias=alias, **connection_args) + logger.debug("Created new connection using: %s", alias) + return alias + except MilvusException as e: + logger.error("Failed to create new connection using: %s", alias) + raise e + + @property + def _is_sparse_embedding(self) -> bool: + return isinstance(self.embedding_func, BaseSparseEmbedding) + + def _init( + self, + embeddings: Optional[list] = None, + metadatas: Optional[list[dict]] = None, + partition_names: Optional[list] = None, + replica_number: int = 1, + timeout: Optional[float] = None, + ) -> None: + if embeddings is not None: + self._create_collection(embeddings, metadatas) + self._extract_fields() + self._create_index() + self._create_search_params() + self._load( + partition_names=partition_names, + replica_number=replica_number, + timeout=timeout, + ) + + def _create_collection( + self, embeddings: list, metadatas: Optional[list[dict]] = None + ) -> None: + from pymilvus import ( + Collection, + CollectionSchema, + DataType, + FieldSchema, + MilvusException, + ) + from pymilvus.orm.types import infer_dtype_bydata # type: ignore + + # Determine embedding dim + dim = len(embeddings[0]) + fields = [] + # If enable_dynamic_field, we don't need to create fields, and just pass it. + # In the future, when metadata_field is deprecated, + # This logical structure will be simplified like this: + # ``` + # if not self.enable_dynamic_field and metadatas: + # for key, value in metadatas[0].items(): + # ... + # ``` + if self.enable_dynamic_field: + # If both dynamic fields and partition key field are enabled + if self._partition_key_field is not None: + # create the partition field + fields.append( + FieldSchema( + self._partition_key_field, DataType.VARCHAR, max_length=65_535 + ) + ) + elif self._metadata_field is not None: + fields.append(FieldSchema(self._metadata_field, DataType.JSON)) + else: + # Determine metadata schema + if metadatas: + # Create FieldSchema for each entry in metadata. + for key, value in metadatas[0].items(): + if key in [ + self._vector_field, + self._primary_field, + self._text_field, + ]: + logger.error( + ( + "Failure to create collection, " + "metadata key: %s is reserved." + ), + key, + ) + raise ValueError(f"Metadata key {key} is reserved.") + # Infer the corresponding datatype of the metadata + if ( + self.metadata_schema + and key in self.metadata_schema # type: ignore + and "dtype" in self.metadata_schema[key] # type: ignore + ): + kwargs = self.metadata_schema[key].get("kwargs", {}) # type: ignore + fields.append( + FieldSchema( + name=key, + dtype=self.metadata_schema[key]["dtype"], # type: ignore + **kwargs, + ) + ) + else: + dtype = infer_dtype_bydata(value) + # Datatype isn't compatible + if dtype == DataType.UNKNOWN or dtype == DataType.NONE: + logger.error( + ( + "Failure to create collection, " + "unrecognized dtype for key: %s" + ), + key, + ) + raise ValueError(f"Unrecognized datatype for {key}.") + # Datatype is a string/varchar equivalent + elif dtype == DataType.VARCHAR: + fields.append( + FieldSchema(key, DataType.VARCHAR, max_length=65_535) + ) + # infer_dtype_bydata currently can't recognize array type, + # so this line can not be accessed. + # This line may need to be modified in the future when + # infer_dtype_bydata can recognize array type. + # https://github.com/milvus-io/pymilvus/issues/2165 + elif dtype == DataType.ARRAY: + kwargs = self.metadata_schema[key]["kwargs"] # type: ignore + fields.append( + FieldSchema(name=key, dtype=DataType.ARRAY, **kwargs) + ) + else: + fields.append(FieldSchema(key, dtype)) + + # Create the text field + fields.append( + FieldSchema(self._text_field, DataType.VARCHAR, max_length=65_535) + ) + # Create the primary key field + if self.auto_id: + fields.append( + FieldSchema( + self._primary_field, DataType.INT64, is_primary=True, auto_id=True + ) + ) + else: + fields.append( + FieldSchema( + self._primary_field, + DataType.VARCHAR, + is_primary=True, + auto_id=False, + max_length=65_535, + ) + ) + # Create the vector field, supports binary or float vectors + if self._is_sparse_embedding: + fields.append(FieldSchema(self._vector_field, DataType.SPARSE_FLOAT_VECTOR)) + else: + fields.append( + FieldSchema( + self._vector_field, infer_dtype_bydata(embeddings[0]), dim=dim + ) + ) + + # Create the schema for the collection + schema = CollectionSchema( + fields, + description=self.collection_description, + partition_key_field=self._partition_key_field, + enable_dynamic_field=self.enable_dynamic_field, + ) + + # Create the collection + try: + if self.num_shards is not None: + # Issue with defaults: + # https://github.com/milvus-io/pymilvus/blob/59bf5e811ad56e20946559317fed855330758d9c/pymilvus/client/prepare.py#L82-L85 + self.col = Collection( + name=self.collection_name, + schema=schema, + consistency_level=self.consistency_level, + using=self.alias, + num_shards=self.num_shards, + ) + else: + self.col = Collection( + name=self.collection_name, + schema=schema, + consistency_level=self.consistency_level, + using=self.alias, + ) + # Set the collection properties if they exist + if self.collection_properties is not None: + self.col.set_properties(self.collection_properties) + except MilvusException as e: + logger.error( + "Failed to create collection: %s error: %s", self.collection_name, e + ) + raise e + + def _extract_fields(self) -> None: + """Grab the existing fields from the Collection""" + from pymilvus import Collection + + if isinstance(self.col, Collection): + schema = self.col.schema + for x in schema.fields: + self.fields.append(x.name) + + def _get_index(self) -> Optional[dict[str, Any]]: + """Return the vector index information if it exists""" + from pymilvus import Collection + + if isinstance(self.col, Collection): + for x in self.col.indexes: + if x.field_name == self._vector_field: + return x.to_dict() + return None + + def _create_index(self) -> None: + """Create a index on the collection""" + from pymilvus import Collection, MilvusException + + if isinstance(self.col, Collection) and self._get_index() is None: + try: + # If no index params, use a default HNSW based one + if self.index_params is None: + if self._is_sparse_embedding: + self.index_params = { + "metric_type": "IP", + "index_type": "SPARSE_INVERTED_INDEX", + "params": {"drop_ratio_build": 0.2}, + } + else: + self.index_params = { + "metric_type": "L2", + "index_type": "HNSW", + "params": {"M": 8, "efConstruction": 64}, + } + + try: + self.col.create_index( + self._vector_field, + index_params=self.index_params, + using=self.alias, + ) + + # If default did not work, most likely on Zilliz Cloud + except MilvusException: + # Use AUTOINDEX based index + self.index_params = { + "metric_type": "L2", + "index_type": "AUTOINDEX", + "params": {}, + } + self.col.create_index( + self._vector_field, + index_params=self.index_params, + using=self.alias, + ) + logger.debug( + "Successfully created an index on collection: %s", + self.collection_name, + ) + + except MilvusException as e: + logger.error( + "Failed to create an index on collection: %s", self.collection_name + ) + raise e + + def _create_search_params(self) -> None: + """Generate search params based on the current index type""" + from pymilvus import Collection + + if isinstance(self.col, Collection) and self.search_params is None: + index = self._get_index() + if index is not None: + index_type: str = index["index_param"]["index_type"] + metric_type: str = index["index_param"]["metric_type"] + self.search_params = self.default_search_params[index_type] + self.search_params["metric_type"] = metric_type + + def _load( + self, + partition_names: Optional[list] = None, + replica_number: int = 1, + timeout: Optional[float] = None, + ) -> None: + """Load the collection if available.""" + from pymilvus import Collection, utility + from pymilvus.client.types import LoadState # type: ignore + + timeout = self.timeout or timeout + if ( + isinstance(self.col, Collection) + and self._get_index() is not None + and utility.load_state(self.collection_name, using=self.alias) + == LoadState.NotLoad + ): + self.col.load( + partition_names=partition_names, + replica_number=replica_number, + timeout=timeout, + ) + + def add_texts( + self, + texts: Iterable[str], + metadatas: Optional[List[dict]] = None, + timeout: Optional[float] = None, + batch_size: int = 1000, + *, + ids: Optional[List[str]] = None, + **kwargs: Any, + ) -> List[str]: + """Insert text data into Milvus. + + Inserting data when the collection has not be made yet will result + in creating a new Collection. The data of the first entity decides + the schema of the new collection, the dim is extracted from the first + embedding and the columns are decided by the first metadata dict. + Metadata keys will need to be present for all inserted values. At + the moment there is no None equivalent in Milvus. + + Args: + texts (Iterable[str]): The texts to embed, it is assumed + that they all fit in memory. + metadatas (Optional[List[dict]]): Metadata dicts attached to each of + the texts. Defaults to None. + should be less than 65535 bytes. Required and work when auto_id is False. + timeout (Optional[float]): Timeout for each batch insert. Defaults + to None. + batch_size (int, optional): Batch size to use for insertion. + Defaults to 1000. + ids (Optional[List[str]]): List of text ids. The length of each item + + Raises: + MilvusException: Failure to add texts + + Returns: + List[str]: The resulting keys for each inserted element. + """ + from pymilvus import Collection, MilvusException + + texts = list(texts) + if not self.auto_id: + assert isinstance(ids, list), ( + "A list of valid ids are required when auto_id is False. " + "You can set `auto_id` to True in this Milvus instance to generate " + "ids automatically, or specify string-type ids for each text." + ) + assert len(set(ids)) == len( + texts + ), "Different lengths of texts and unique ids are provided." + assert all(isinstance(x, str) for x in ids), "All ids should be strings." + assert all( + len(x.encode()) <= 65_535 for x in ids + ), "Each id should be a string less than 65535 bytes." + + else: + if ids is not None: + logger.warning( + "The ids parameter is ignored when auto_id is True. " + "The ids will be generated automatically." + ) + + try: + embeddings: list = self.embedding_func.embed_documents(texts) + except NotImplementedError: + embeddings = [self.embedding_func.embed_query(x) for x in texts] + + if len(embeddings) == 0: + logger.debug("Nothing to insert, skipping.") + return [] + + # If the collection hasn't been initialized yet, perform all steps to do so + if not isinstance(self.col, Collection): + kwargs = {"embeddings": embeddings, "metadatas": metadatas} + if self.partition_names: + kwargs["partition_names"] = self.partition_names + if self.replica_number: + kwargs["replica_number"] = self.replica_number + if self.timeout: + kwargs["timeout"] = self.timeout + self._init(**kwargs) + + insert_list: list[dict] = [] + + assert len(texts) == len( + embeddings + ), "Mismatched lengths of texts and embeddings." + if metadatas is not None: + assert len(texts) == len( + metadatas + ), "Mismatched lengths of texts and metadatas." + + for i, text, embedding in zip(range(len(texts)), texts, embeddings): + entity_dict = {} + metadata = metadatas[i] if metadatas else {} + if not self.auto_id: + entity_dict[self._primary_field] = ids[i] # type: ignore[index] + + entity_dict[self._text_field] = text + entity_dict[self._vector_field] = embedding + + if self._metadata_field and not self.enable_dynamic_field: + entity_dict[self._metadata_field] = metadata + else: + for key, value in metadata.items(): + # if not enable_dynamic_field, skip fields not in the collection. + if not self.enable_dynamic_field and key not in self.fields: + continue + # If enable_dynamic_field, all fields are allowed. + entity_dict[key] = value + + insert_list.append(entity_dict) + + # Total insert count + total_count = len(insert_list) + + pks: list[str] = [] + + assert isinstance(self.col, Collection) + for i in range(0, total_count, batch_size): + # Grab end index + end = min(i + batch_size, total_count) + batch_insert_list = insert_list[i:end] + # Insert into the collection. + try: + res: Collection + timeout = self.timeout or timeout + res = self.col.insert(batch_insert_list, timeout=timeout, **kwargs) + pks.extend(res.primary_keys) + except MilvusException as e: + logger.error( + "Failed to insert batch starting at entity: %s/%s", i, total_count + ) + raise e + return pks + + def _collection_search( + self, + embedding: List[float] | Dict[int, float], + k: int = 4, + param: Optional[dict] = None, + expr: Optional[str] = None, + timeout: Optional[float] = None, + **kwargs: Any, + ) -> "pymilvus.client.abstract.SearchResult | None": # type: ignore[name-defined] # noqa: F821 + """Perform a search on an embedding and return milvus search results. + + For more information about the search parameters, take a look at the pymilvus + documentation found here: + https://milvus.io/api-reference/pymilvus/v2.4.x/ORM/Collection/search.md + + Args: + embedding (List[float] | Dict[int, float]): The embedding vector being + searched. + k (int, optional): The amount of results to return. Defaults to 4. + param (dict): The search params for the specified index. + Defaults to None. + expr (str, optional): Filtering expression. Defaults to None. + timeout (float, optional): How long to wait before timeout error. + Defaults to None. + kwargs: Collection.search() keyword arguments. + + Returns: + pymilvus.client.abstract.SearchResult: Milvus search result. + """ + if self.col is None: + logger.debug("No existing collection to search.") + return None + + if param is None: + param = self.search_params + + # Determine result metadata fields with PK. + if self.enable_dynamic_field: + output_fields = ["*"] + else: + output_fields = self.fields[:] + output_fields.remove(self._vector_field) + timeout = self.timeout or timeout + # Perform the search. + res = self.col.search( + data=[embedding], + anns_field=self._vector_field, + param=param, + limit=k, + expr=expr, + output_fields=output_fields, + timeout=timeout, + **kwargs, + ) + return res + + def similarity_search( + self, + query: str, + k: int = 4, + param: Optional[dict] = None, + expr: Optional[str] = None, + timeout: Optional[float] = None, + **kwargs: Any, + ) -> List[Document]: + """Perform a similarity search against the query string. + + Args: + query (str): The text to search. + k (int, optional): How many results to return. Defaults to 4. + param (dict, optional): The search params for the index type. + Defaults to None. + expr (str, optional): Filtering expression. Defaults to None. + timeout (int, optional): How long to wait before timeout error. + Defaults to None. + kwargs: Collection.search() keyword arguments. + + Returns: + List[Document]: Document results for search. + """ + if self.col is None: + logger.debug("No existing collection to search.") + return [] + timeout = self.timeout or timeout + res = self.similarity_search_with_score( + query=query, k=k, param=param, expr=expr, timeout=timeout, **kwargs + ) + return [doc for doc, _ in res] + + def similarity_search_by_vector( + self, + embedding: List[float], + k: int = 4, + param: Optional[dict] = None, + expr: Optional[str] = None, + timeout: Optional[float] = None, + **kwargs: Any, + ) -> List[Document]: + """Perform a similarity search against the query string. + + Args: + embedding (List[float]): The embedding vector to search. + k (int, optional): How many results to return. Defaults to 4. + param (dict, optional): The search params for the index type. + Defaults to None. + expr (str, optional): Filtering expression. Defaults to None. + timeout (int, optional): How long to wait before timeout error. + Defaults to None. + kwargs: Collection.search() keyword arguments. + + Returns: + List[Document]: Document results for search. + """ + if self.col is None: + logger.debug("No existing collection to search.") + return [] + timeout = self.timeout or timeout + res = self.similarity_search_with_score_by_vector( + embedding=embedding, k=k, param=param, expr=expr, timeout=timeout, **kwargs + ) + return [doc for doc, _ in res] + + def similarity_search_with_score( + self, + query: str, + k: int = 4, + param: Optional[dict] = None, + expr: Optional[str] = None, + timeout: Optional[float] = None, + **kwargs: Any, + ) -> List[Tuple[Document, float]]: + """Perform a search on a query string and return results with score. + + For more information about the search parameters, take a look at the pymilvus + documentation found here: + https://milvus.io/api-reference/pymilvus/v2.4.x/ORM/Collection/search.md + + Args: + query (str): The text being searched. + k (int, optional): The amount of results to return. Defaults to 4. + param (dict): The search params for the specified index. + Defaults to None. + expr (str, optional): Filtering expression. Defaults to None. + timeout (float, optional): How long to wait before timeout error. + Defaults to None. + kwargs: Collection.search() keyword arguments. + + Returns: + List[float], List[Tuple[Document, any, any]]: + """ + if self.col is None: + logger.debug("No existing collection to search.") + return [] + + # Embed the query text. + embedding = self.embedding_func.embed_query(query) + timeout = self.timeout or timeout + res = self.similarity_search_with_score_by_vector( + embedding=embedding, k=k, param=param, expr=expr, timeout=timeout, **kwargs + ) + return res + + def similarity_search_with_score_by_vector( + self, + embedding: List[float] | Dict[int, float], + k: int = 4, + param: Optional[dict] = None, + expr: Optional[str] = None, + timeout: Optional[float] = None, + **kwargs: Any, + ) -> List[Tuple[Document, float]]: + """Perform a search on an embedding and return results with score. + + For more information about the search parameters, take a look at the pymilvus + documentation found here: + https://milvus.io/api-reference/pymilvus/v2.4.x/ORM/Collection/search.md + + Args: + embedding (List[float] | Dict[int, float]): The embedding vector being + searched. + k (int, optional): The amount of results to return. Defaults to 4. + param (dict): The search params for the specified index. + Defaults to None. + expr (str, optional): Filtering expression. Defaults to None. + timeout (float, optional): How long to wait before timeout error. + Defaults to None. + kwargs: Collection.search() keyword arguments. + + Returns: + List[Tuple[Document, float]]: Result doc and score. + """ + col_search_res = self._collection_search( + embedding=embedding, k=k, param=param, expr=expr, timeout=timeout, **kwargs + ) + if col_search_res is None: + return [] + ret = [] + for result in col_search_res[0]: + data = {x: result.entity.get(x) for x in result.entity.fields} + doc = self._parse_document(data) + pair = (doc, result.score) + ret.append(pair) + + return ret + + def max_marginal_relevance_search( + self, + query: str, + k: int = 4, + fetch_k: int = 20, + lambda_mult: float = 0.5, + param: Optional[dict] = None, + expr: Optional[str] = None, + timeout: Optional[float] = None, + **kwargs: Any, + ) -> List[Document]: + """Perform a search and return results that are reordered by MMR. + + Args: + query (str): The text being searched. + k (int, optional): How many results to give. Defaults to 4. + fetch_k (int, optional): Total results to select k from. + Defaults to 20. + lambda_mult: Number between 0 and 1 that determines the degree + of diversity among the results with 0 corresponding + to maximum diversity and 1 to minimum diversity. + Defaults to 0.5 + param (dict, optional): The search params for the specified index. + Defaults to None. + expr (str, optional): Filtering expression. Defaults to None. + timeout (float, optional): How long to wait before timeout error. + Defaults to None. + kwargs: Collection.search() keyword arguments. + + + Returns: + List[Document]: Document results for search. + """ + if self.col is None: + logger.debug("No existing collection to search.") + return [] + + embedding = self.embedding_func.embed_query(query) + timeout = self.timeout or timeout + return self.max_marginal_relevance_search_by_vector( + embedding=embedding, + k=k, + fetch_k=fetch_k, + lambda_mult=lambda_mult, + param=param, + expr=expr, + timeout=timeout, + **kwargs, + ) + + def max_marginal_relevance_search_by_vector( + self, + embedding: list[float] | dict[int, float], + k: int = 4, + fetch_k: int = 20, + lambda_mult: float = 0.5, + param: Optional[dict] = None, + expr: Optional[str] = None, + timeout: Optional[float] = None, + **kwargs: Any, + ) -> List[Document]: + """Perform a search and return results that are reordered by MMR. + + Args: + embedding (list[float] | dict[int, float]): The embedding vector being + searched. + k (int, optional): How many results to give. Defaults to 4. + fetch_k (int, optional): Total results to select k from. + Defaults to 20. + lambda_mult: Number between 0 and 1 that determines the degree + of diversity among the results with 0 corresponding + to maximum diversity and 1 to minimum diversity. + Defaults to 0.5 + param (dict, optional): The search params for the specified index. + Defaults to None. + expr (str, optional): Filtering expression. Defaults to None. + timeout (float, optional): How long to wait before timeout error. + Defaults to None. + kwargs: Collection.search() keyword arguments. + + Returns: + List[Document]: Document results for search. + """ + col_search_res = self._collection_search( + embedding=embedding, + k=fetch_k, + param=param, + expr=expr, + timeout=timeout, + **kwargs, + ) + if col_search_res is None: + return [] + ids = [] + documents = [] + scores = [] + for result in col_search_res[0]: + data = {x: result.entity.get(x) for x in result.entity.fields} + doc = self._parse_document(data) + documents.append(doc) + scores.append(result.score) + ids.append(result.id) + + vectors = self.col.query( # type: ignore[union-attr] + expr=f"{self._primary_field} in {ids}", + output_fields=[self._primary_field, self._vector_field], + timeout=timeout, + ) + # Reorganize the results from query to match search order. + vectors = {x[self._primary_field]: x[self._vector_field] for x in vectors} + + ordered_result_embeddings = [vectors[x] for x in ids] + + # Get the new order of results. + new_ordering = maximal_marginal_relevance( + np.array(embedding), ordered_result_embeddings, k=k, lambda_mult=lambda_mult + ) + + # Reorder the values and return. + ret = [] + for x in new_ordering: + # Function can return -1 index + if x == -1: + break + else: + ret.append(documents[x]) + return ret + + def delete( # type: ignore[no-untyped-def] + self, ids: Optional[List[str]] = None, expr: Optional[str] = None, **kwargs: str + ): + """Delete by vector ID or boolean expression. + Refer to [Milvus documentation](https://milvus.io/docs/delete_data.md) + for notes and examples of expressions. + + Args: + ids: List of ids to delete. + expr: Boolean expression that specifies the entities to delete. + kwargs: Other parameters in Milvus delete api. + """ + if isinstance(ids, list) and len(ids) > 0: + if expr is not None: + logger.warning( + "Both ids and expr are provided. " "Ignore expr and delete by ids." + ) + expr = f"{self._primary_field} in {ids}" + else: + assert isinstance( + expr, str + ), "Either ids list or expr string must be provided." + return self.col.delete(expr=expr, **kwargs) # type: ignore[union-attr] + + @classmethod + def from_texts( + cls, + texts: List[str], + embedding: Union[Embeddings, BaseSparseEmbedding], # type: ignore + metadatas: Optional[List[dict]] = None, + collection_name: str = "LangChainCollection", + connection_args: dict[str, Any] = DEFAULT_MILVUS_CONNECTION, + consistency_level: str = "Session", + index_params: Optional[dict] = None, + search_params: Optional[dict] = None, + drop_old: bool = False, + *, + ids: Optional[List[str]] = None, + **kwargs: Any, + ) -> Milvus: + """Create a Milvus collection, indexes it with HNSW, and insert data. + + Args: + texts (List[str]): Text data. + embedding (Union[Embeddings, BaseSparseEmbedding]): Embedding function. + metadatas (Optional[List[dict]]): Metadata for each text if it exists. + Defaults to None. + collection_name (str, optional): Collection name to use. Defaults to + "LangChainCollection". + connection_args (dict[str, Any], optional): Connection args to use. Defaults + to DEFAULT_MILVUS_CONNECTION. + consistency_level (str, optional): Which consistency level to use. Defaults + to "Session". + index_params (Optional[dict], optional): Which index_params to use. Defaults + to None. + search_params (Optional[dict], optional): Which search params to use. + Defaults to None. + drop_old (Optional[bool], optional): Whether to drop the collection with + that name if it exists. Defaults to False. + ids (Optional[List[str]]): List of text ids. Defaults to None. + + Returns: + Milvus: Milvus Vector Store + """ + if isinstance(ids, list) and len(ids) > 0: + auto_id = False + else: + auto_id = True + + vector_db = cls( + embedding_function=embedding, + collection_name=collection_name, + connection_args=connection_args, + consistency_level=consistency_level, + index_params=index_params, + search_params=search_params, + drop_old=drop_old, + auto_id=auto_id, + **kwargs, + ) + vector_db.add_texts(texts=texts, metadatas=metadatas, ids=ids) + return vector_db + + def _parse_document(self, data: dict) -> Document: + if self._vector_field in data: + data.pop(self._vector_field) + return Document( + page_content=data.pop(self._text_field), + metadata=data.pop(self._metadata_field) if self._metadata_field else data, + ) + + def add_documents(self, documents: List[Document], **kwargs: Any) -> List[str]: + """Run more documents through the embeddings and add to the vectorstore. + + Args: + documents: Documents to add to the vectorstore. + + Returns: + List of IDs of the added texts. + """ + # TODO: Handle the case where the user doesn't provide ids on the Collection + texts = [doc.page_content for doc in documents] + metadatas = [doc.metadata for doc in documents] + return self.add_texts(texts, metadatas, **kwargs) + + async def aadd_documents( + self, documents: List[Document], **kwargs: Any + ) -> List[str]: + """Run more documents through the embeddings and add to the vectorstore. + + Args: + documents: Documents to add to the vectorstore. + + Returns: + List of IDs of the added texts. + """ + texts = [doc.page_content for doc in documents] + metadatas = [doc.metadata for doc in documents] + return await self.aadd_texts(texts, metadatas, **kwargs) + + def get_pks(self, expr: str, **kwargs: Any) -> List[int] | None: + """Get primary keys with expression + + Args: + expr: Expression - E.g: "id in [1, 2]", or "title LIKE 'Abc%'" + + Returns: + List[int]: List of IDs (Primary Keys) + """ + + from pymilvus import MilvusException + + if self.col is None: + logger.debug("No existing collection to get pk.") + return None + + try: + query_result = self.col.query( + expr=expr, output_fields=[self._primary_field] + ) + except MilvusException as exc: + logger.error("Failed to get ids: %s error: %s", self.collection_name, exc) + raise exc + pks = [item.get(self._primary_field) for item in query_result] + return pks + + def upsert( # type: ignore + self, + ids: Optional[List[str]] = None, + documents: List[Document] | None = None, + **kwargs: Any, + ) -> List[str] | None: + """Update/Insert documents to the vectorstore. + + Args: + ids: IDs to update - Let's call get_pks to get ids with expression \n + documents (List[Document]): Documents to add to the vectorstore. + + Returns: + List[str]: IDs of the added texts. + """ + + from pymilvus import MilvusException + + if documents is None or len(documents) == 0: + logger.debug("No documents to upsert.") + return None + + if ids is not None and len(ids): + try: + self.delete(ids=ids) + except MilvusException: + pass + try: + return self.add_documents(documents=documents, **kwargs) + except MilvusException as exc: + logger.error( + "Failed to upsert entities: %s error: %s", self.collection_name, exc + ) + raise exc diff --git a/libs/milvus/langchain_milvus/vectorstores/zilliz.py b/libs/milvus/langchain_milvus/vectorstores/zilliz.py new file mode 100644 index 0000000..976651d --- /dev/null +++ b/libs/milvus/langchain_milvus/vectorstores/zilliz.py @@ -0,0 +1,197 @@ +from __future__ import annotations + +import logging +from typing import Any, Dict, List, Optional, Union + +from langchain_core.embeddings import Embeddings + +from langchain_milvus.utils.sparse import BaseSparseEmbedding +from langchain_milvus.vectorstores.milvus import Milvus + +logger = logging.getLogger(__name__) + + +class Zilliz(Milvus): + """`Zilliz` vector store. + + You need to have `pymilvus` installed and a + running Zilliz database. + + See the following documentation for how to run a Zilliz instance: + https://docs.zilliz.com/docs/create-cluster + + + IF USING L2/IP metric IT IS HIGHLY SUGGESTED TO NORMALIZE YOUR DATA. + + Args: + embedding_function (Embeddings): Function used to embed the text. + collection_name (str): Which Zilliz collection to use. Defaults to + "LangChainCollection". + connection_args (Optional[dict[str, any]]): The connection args used for + this class comes in the form of a dict. + consistency_level (str): The consistency level to use for a collection. + Defaults to "Session". + index_params (Optional[dict]): Which index params to use. Defaults to + HNSW/AUTOINDEX depending on service. + search_params (Optional[dict]): Which search params to use. Defaults to + default of index. + drop_old (Optional[bool]): Whether to drop the current collection. Defaults + to False. + auto_id (bool): Whether to enable auto id for primary key. Defaults to False. + If False, you needs to provide text ids (string less than 65535 bytes). + If True, Milvus will generate unique integers as primary keys. + + The connection args used for this class comes in the form of a dict, + here are a few of the options: + address (str): The actual address of Zilliz + instance. Example address: "localhost:19530" + uri (str): The uri of Zilliz instance. Example uri: + "https://in03-ba4234asae.api.gcp-us-west1.zillizcloud.com", + host (str): The host of Zilliz instance. Default at "localhost", + PyMilvus will fill in the default host if only port is provided. + port (str/int): The port of Zilliz instance. Default at 19530, PyMilvus + will fill in the default port if only host is provided. + user (str): Use which user to connect to Zilliz instance. If user and + password are provided, we will add related header in every RPC call. + password (str): Required when user is provided. The password + corresponding to the user. + token (str): API key, for serverless clusters which can be used as + replacements for user and password. + secure (bool): Default is false. If set to true, tls will be enabled. + client_key_path (str): If use tls two-way authentication, need to + write the client.key path. + client_pem_path (str): If use tls two-way authentication, need to + write the client.pem path. + ca_pem_path (str): If use tls two-way authentication, need to write + the ca.pem path. + server_pem_path (str): If use tls one-way authentication, need to + write the server.pem path. + server_name (str): If use tls, need to write the common name. + + Example: + .. code-block:: python + + from langchain_community.vectorstores import Zilliz + from langchain_community.embeddings import OpenAIEmbeddings + + embedding = OpenAIEmbeddings() + # Connect to a Zilliz instance + milvus_store = Milvus( + embedding_function = embedding, + collection_name = "LangChainCollection", + connection_args = { + "uri": "https://in03-ba4234asae.api.gcp-us-west1.zillizcloud.com", + "user": "temp", + "password": "temp", + "token": "temp", # API key as replacements for user and password + "secure": True + } + drop_old: True, + ) + + Raises: + ValueError: If the pymilvus python package is not installed. + """ + + def _create_index(self) -> None: + """Create a index on the collection""" + from pymilvus import Collection, MilvusException + + if isinstance(self.col, Collection) and self._get_index() is None: + try: + # If no index params, use a default AutoIndex based one + if self.index_params is None: + self.index_params = { + "metric_type": "L2", + "index_type": "AUTOINDEX", + "params": {}, + } + + try: + self.col.create_index( + self._vector_field, + index_params=self.index_params, + using=self.alias, + ) + + # If default did not work, most likely Milvus self-hosted + except MilvusException: + # Use HNSW based index + self.index_params = { + "metric_type": "L2", + "index_type": "HNSW", + "params": {"M": 8, "efConstruction": 64}, + } + self.col.create_index( + self._vector_field, + index_params=self.index_params, + using=self.alias, + ) + logger.debug( + "Successfully created an index on collection: %s", + self.collection_name, + ) + + except MilvusException as e: + logger.error( + "Failed to create an index on collection: %s", self.collection_name + ) + raise e + + @classmethod + def from_texts( + cls, + texts: List[str], + embedding: Union[Embeddings, BaseSparseEmbedding], + metadatas: Optional[List[dict]] = None, + collection_name: str = "LangChainCollection", + connection_args: Optional[Dict[str, Any]] = None, + consistency_level: str = "Session", + index_params: Optional[dict] = None, + search_params: Optional[dict] = None, + drop_old: bool = False, + *, + ids: Optional[List[str]] = None, + auto_id: bool = False, + **kwargs: Any, + ) -> Zilliz: + """Create a Zilliz collection, indexes it with HNSW, and insert data. + + Args: + texts (List[str]): Text data. + embedding (Embeddings): Embedding function. + metadatas (Optional[List[dict]]): Metadata for each text if it exists. + Defaults to None. + collection_name (str, optional): Collection name to use. Defaults to + "LangChainCollection". + connection_args (dict[str, Any], optional): Connection args to use. Defaults + to DEFAULT_MILVUS_CONNECTION. + consistency_level (str, optional): Which consistency level to use. Defaults + to "Session". + index_params (Optional[dict], optional): Which index_params to use. + Defaults to None. + search_params (Optional[dict], optional): Which search params to use. + Defaults to None. + drop_old (Optional[bool], optional): Whether to drop the collection with + that name if it exists. Defaults to False. + ids (Optional[List[str]]): List of text ids. + auto_id (bool): Whether to enable auto id for primary key. Defaults to + False. If False, you needs to provide text ids (string less than 65535 + bytes). If True, Milvus will generate unique integers as primary keys. + + Returns: + Zilliz: Zilliz Vector Store + """ + vector_db = cls( + embedding_function=embedding, + collection_name=collection_name, + connection_args=connection_args or {}, + consistency_level=consistency_level, + index_params=index_params, + search_params=search_params, + drop_old=drop_old, + auto_id=auto_id, + **kwargs, + ) + vector_db.add_texts(texts=texts, metadatas=metadatas, ids=ids) + return vector_db diff --git a/libs/milvus/poetry.lock b/libs/milvus/poetry.lock new file mode 100644 index 0000000..4388426 --- /dev/null +++ b/libs/milvus/poetry.lock @@ -0,0 +1,2243 @@ +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. + +[[package]] +name = "annotated-types" +version = "0.7.0" +description = "Reusable constraint types to use with typing.Annotated" +optional = false +python-versions = ">=3.8" +files = [ + {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]] +name = "anyio" +version = "4.6.0" +description = "High level compatibility layer for multiple asynchronous event loop implementations" +optional = false +python-versions = ">=3.9" +files = [ + {file = "anyio-4.6.0-py3-none-any.whl", hash = "sha256:c7d2e9d63e31599eeb636c8c5c03a7e108d73b345f064f1c19fdc87b79036a9a"}, + {file = "anyio-4.6.0.tar.gz", hash = "sha256:137b4559cbb034c477165047febb6ff83f390fc3b20bf181c1fc0a728cb8beeb"}, +] + +[package.dependencies] +exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""} +idna = ">=2.8" +sniffio = ">=1.1" +typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""} + +[package.extras] +doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"] +test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.21.0b1)"] +trio = ["trio (>=0.26.1)"] + +[[package]] +name = "certifi" +version = "2024.8.30" +description = "Python package for providing Mozilla's CA Bundle." +optional = false +python-versions = ">=3.6" +files = [ + {file = "certifi-2024.8.30-py3-none-any.whl", hash = "sha256:922820b53db7a7257ffbda3f597266d435245903d80737e34f8a45ff3e3230d8"}, + {file = "certifi-2024.8.30.tar.gz", hash = "sha256:bec941d2aa8195e248a60b31ff9f0558284cf01a52591ceda73ea9afffd69fd9"}, +] + +[[package]] +name = "charset-normalizer" +version = "3.3.2" +description = "The Real First Universal Charset Detector. 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b/libs/milvus/pyproject.toml new file mode 100644 index 0000000..cfac2e0 --- /dev/null +++ b/libs/milvus/pyproject.toml @@ -0,0 +1,85 @@ +[build-system] +requires = ["poetry-core>=1.0.0"] +build-backend = "poetry.core.masonry.api" + +[tool.poetry] +name = "langchain-milvus" +version = "0.1.5" +description = "An integration package connecting Milvus and LangChain" +authors = [] +readme = "README.md" +repository = "https://github.com/langchain-ai/langchain-milvus" +license = "MIT" + +[tool.ruff] +select = ["E", "F", "I", "T201"] + +[tool.mypy] +disallow_untyped_defs = "True" +[[tool.mypy.overrides]] +module = ["pymilvus"] +ignore_missing_imports = "True" + +[tool.poetry.urls] +"Source Code" = "https://github.com/langchain-ai/langchain-milvus/tree/main/libs/milvus" +"Release Notes" = "https://github.com/langchain-ai/langchain-milvus/releases" + +[tool.poetry.dependencies] +python = ">=3.9,<4.0" +pymilvus = "^2.4.3" +langchain-core = { version = ">=0.2.38,<0.4", python = ">=3.9" } + +[tool.coverage.run] +omit = ["tests/*"] + +[tool.pytest.ini_options] +addopts = "--snapshot-warn-unused --strict-markers --strict-config --durations=5" +markers = [ + "requires: mark tests as requiring a specific library", + "asyncio: mark tests as requiring asyncio", + "compile: mark placeholder test used to compile integration tests without running them", +] +asyncio_mode = "auto" + +[tool.poetry.group.test] +optional = true + +[tool.poetry.group.codespell] +optional = true + +[tool.poetry.group.test_integration] +optional = true + +[tool.poetry.group.lint] +optional = true + +[tool.poetry.group.dev] +optional = true + +[tool.poetry.group.test.dependencies] +pytest = "^7.3.0" +freezegun = "^1.2.2" +pytest-mock = "^3.10.0" +syrupy = "^4.0.2" +pytest-watcher = "^0.3.4" +pytest-asyncio = "^0.21.1" +milvus_model = "^0.2.0" +langchain-core = {git = "https://github.com/langchain-ai/langchain.git", subdirectory = "libs/core"} + +[tool.poetry.group.codespell.dependencies] +codespell = "^2.2.0" + +[tool.poetry.group.test_integration.dependencies] +milvus_model = "^0.2.0" + +[tool.poetry.group.lint.dependencies] +ruff = "^0.1.5" + +[tool.poetry.group.typing.dependencies] +mypy = "^0.991" +types-requests = "^2" +simsimd = "^5.0.0" +langchain-core = {git = "https://github.com/langchain-ai/langchain.git", subdirectory = "libs/core"} + +[tool.poetry.group.dev.dependencies] +langchain-core = {git = "https://github.com/langchain-ai/langchain.git", subdirectory = "libs/core"} diff --git a/libs/milvus/scripts/check_imports.py b/libs/milvus/scripts/check_imports.py new file mode 100644 index 0000000..58a460c --- /dev/null +++ b/libs/milvus/scripts/check_imports.py @@ -0,0 +1,17 @@ +import sys +import traceback +from importlib.machinery import SourceFileLoader + +if __name__ == "__main__": + files = sys.argv[1:] + has_failure = False + for file in files: + try: + SourceFileLoader("x", file).load_module() + except Exception: + has_failure = True + print(file) # noqa: T201 + traceback.print_exc() + print() # noqa: T201 + + sys.exit(1 if has_failure else 0) diff --git a/libs/milvus/scripts/lint_imports.sh b/libs/milvus/scripts/lint_imports.sh new file mode 100755 index 0000000..695613c --- /dev/null +++ b/libs/milvus/scripts/lint_imports.sh @@ -0,0 +1,17 @@ +#!/bin/bash + +set -eu + +# Initialize a variable to keep track of errors +errors=0 + +# make sure not importing from langchain or langchain_experimental +git --no-pager grep '^from langchain\.' . && errors=$((errors+1)) +git --no-pager grep '^from langchain_experimental\.' . && errors=$((errors+1)) + +# Decide on an exit status based on the errors +if [ "$errors" -gt 0 ]; then + exit 1 +else + exit 0 +fi diff --git a/libs/milvus/tests/__init__.py b/libs/milvus/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/libs/milvus/tests/integration_tests/__init__.py b/libs/milvus/tests/integration_tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/libs/milvus/tests/integration_tests/retrievers/__init__.py b/libs/milvus/tests/integration_tests/retrievers/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/libs/milvus/tests/integration_tests/test_compile.py b/libs/milvus/tests/integration_tests/test_compile.py new file mode 100644 index 0000000..33ecccd --- /dev/null +++ b/libs/milvus/tests/integration_tests/test_compile.py @@ -0,0 +1,7 @@ +import pytest + + +@pytest.mark.compile +def test_placeholder() -> None: + """Used for compiling integration tests without running any real tests.""" + pass diff --git a/libs/milvus/tests/integration_tests/utils.py b/libs/milvus/tests/integration_tests/utils.py new file mode 100644 index 0000000..f3ef87d --- /dev/null +++ b/libs/milvus/tests/integration_tests/utils.py @@ -0,0 +1,40 @@ +from typing import List + +from langchain_core.documents import Document +from langchain_core.embeddings import Embeddings + +fake_texts = ["foo", "bar", "baz"] + + +class FakeEmbeddings(Embeddings): + """Fake embeddings functionality for testing.""" + + def embed_documents(self, texts: List[str]) -> List[List[float]]: + """Return simple embeddings. + Embeddings encode each text as its index.""" + return [[float(1.0)] * 9 + [float(i)] for i in range(len(texts))] + + async def aembed_documents(self, texts: List[str]) -> List[List[float]]: + return self.embed_documents(texts) + + def embed_query(self, text: str) -> List[float]: + """Return constant query embeddings. + Embeddings are identical to embed_documents(texts)[0]. + Distance to each text will be that text's index, + as it was passed to embed_documents.""" + return [float(1.0)] * 9 + [float(0.0)] + + async def aembed_query(self, text: str) -> List[float]: + return self.embed_query(text) + + +def assert_docs_equal_without_pk( + docs1: List[Document], docs2: List[Document], pk_field: str = "pk" +) -> None: + """Assert two lists of Documents are equal, ignoring the primary key field.""" + assert len(docs1) == len(docs2) + for doc1, doc2 in zip(docs1, docs2): + assert doc1.page_content == doc2.page_content + doc1.metadata.pop(pk_field, None) + doc2.metadata.pop(pk_field, None) + assert doc1.metadata == doc2.metadata diff --git a/libs/milvus/tests/integration_tests/vectorstores/__init__.py b/libs/milvus/tests/integration_tests/vectorstores/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/libs/milvus/tests/integration_tests/vectorstores/test_milvus.py b/libs/milvus/tests/integration_tests/vectorstores/test_milvus.py new file mode 100644 index 0000000..fffb0bb --- /dev/null +++ b/libs/milvus/tests/integration_tests/vectorstores/test_milvus.py @@ -0,0 +1,430 @@ +"""Test Milvus functionality.""" + +import tempfile +from typing import Any, List, Optional + +import pytest +from langchain_core.documents import Document + +from langchain_milvus.utils.sparse import BM25SparseEmbedding +from langchain_milvus.vectorstores import Milvus +from tests.integration_tests.utils import ( + FakeEmbeddings, + assert_docs_equal_without_pk, + fake_texts, +) + + +# +# To run this test properly, please start a Milvus server with the following command: +# +# ```shell +# wget https://raw.githubusercontent.com/milvus-io/milvus/master/scripts/standalone_embed.sh +# bash standalone_embed.sh start +# ``` +# +# Here is the reference: +# https://milvus.io/docs/install_standalone-docker.md +# +@pytest.fixture +def temp_milvus_db() -> Any: + with tempfile.NamedTemporaryFile(suffix=".db") as temp_file: + yield temp_file.name + + +def _milvus_from_texts( + metadatas: Optional[List[dict]] = None, + ids: Optional[List[str]] = None, + drop: bool = True, + db_path: str = "./milvus_demo.db", + **kwargs: Any, +) -> Milvus: + return Milvus.from_texts( + fake_texts, + FakeEmbeddings(), + metadatas=metadatas, + ids=ids, + # connection_args={"uri": "http://127.0.0.1:19530"}, + connection_args={"uri": db_path}, + drop_old=drop, + consistency_level="Strong", + **kwargs, + ) + + +def _get_pks(expr: str, docsearch: Milvus) -> List[Any]: + return docsearch.get_pks(expr) # type: ignore[return-value] + + +def test_milvus(temp_milvus_db: Any) -> None: + """Test end to end construction and search.""" + docsearch = _milvus_from_texts(db_path=temp_milvus_db) + output = docsearch.similarity_search("foo", k=1) + assert_docs_equal_without_pk(output, [Document(page_content="foo")]) + + +def test_milvus_vector_search(temp_milvus_db: Any) -> None: + """Test end to end construction and search by vector.""" + docsearch = _milvus_from_texts(db_path=temp_milvus_db) + output = docsearch.similarity_search_by_vector( + FakeEmbeddings().embed_query("foo"), k=1 + ) + assert_docs_equal_without_pk(output, [Document(page_content="foo")]) + + +def test_milvus_with_metadata(temp_milvus_db: Any) -> None: + """Test with metadata""" + docsearch = _milvus_from_texts( + metadatas=[{"label": "test"}] * len(fake_texts), db_path=temp_milvus_db + ) + output = docsearch.similarity_search("foo", k=1) + assert_docs_equal_without_pk( + output, [Document(page_content="foo", metadata={"label": "test"})] + ) + + +def test_milvus_with_id(temp_milvus_db: Any) -> None: + """Test with ids""" + ids = ["id_" + str(i) for i in range(len(fake_texts))] + docsearch = _milvus_from_texts(ids=ids, db_path=temp_milvus_db) + output = docsearch.similarity_search("foo", k=1) + assert_docs_equal_without_pk(output, [Document(page_content="foo")]) + + output = docsearch.delete(ids=ids) + assert output.delete_count == len(fake_texts) # type: ignore[attr-defined] + + try: + ids = ["dup_id" for _ in fake_texts] + _milvus_from_texts(ids=ids, db_path=temp_milvus_db) + except Exception as e: + assert isinstance(e, AssertionError) + + +def test_milvus_with_score(temp_milvus_db: Any) -> None: + """Test end to end construction and search with scores and IDs.""" + texts = ["foo", "bar", "baz"] + metadatas = [{"page": i} for i in range(len(texts))] + docsearch = _milvus_from_texts(metadatas=metadatas, db_path=temp_milvus_db) + output = docsearch.similarity_search_with_score("foo", k=3) + docs = [o[0] for o in output] + scores = [o[1] for o in output] + assert_docs_equal_without_pk( + docs, + [ + Document(page_content="foo", metadata={"page": 0}), + Document(page_content="bar", metadata={"page": 1}), + Document(page_content="baz", metadata={"page": 2}), + ], + ) + assert scores[0] < scores[1] < scores[2] + + +def test_milvus_max_marginal_relevance_search(temp_milvus_db: Any) -> None: + """Test end to end construction and MRR search.""" + texts = ["foo", "bar", "baz"] + metadatas = [{"page": i} for i in range(len(texts))] + docsearch = _milvus_from_texts(metadatas=metadatas, db_path=temp_milvus_db) + output = docsearch.max_marginal_relevance_search("foo", k=2, fetch_k=3) + assert_docs_equal_without_pk( + output, + [ + Document(page_content="foo", metadata={"page": 0}), + Document(page_content="bar", metadata={"page": 1}), + ], + ) + + +def test_milvus_max_marginal_relevance_search_with_dynamic_field( + temp_milvus_db: Any, +) -> None: + """Test end to end construction and MRR search with enabling dynamic field.""" + texts = ["foo", "bar", "baz"] + metadatas = [{"page": i} for i in range(len(texts))] + docsearch = _milvus_from_texts( + metadatas=metadatas, enable_dynamic_field=True, db_path=temp_milvus_db + ) + output = docsearch.max_marginal_relevance_search("foo", k=2, fetch_k=3) + assert_docs_equal_without_pk( + output, + [ + Document(page_content="foo", metadata={"page": 0}), + Document(page_content="bar", metadata={"page": 1}), + ], + ) + + +def test_milvus_add_extra(temp_milvus_db: Any) -> None: + """Test end to end construction and MRR search.""" + texts = ["foo", "bar", "baz"] + metadatas = [{"page": i} for i in range(len(texts))] + docsearch = _milvus_from_texts(metadatas=metadatas, db_path=temp_milvus_db) + + docsearch.add_texts(texts, metadatas) + + output = docsearch.similarity_search("foo", k=10) + assert len(output) == 6 + + +def test_milvus_no_drop(temp_milvus_db: Any) -> None: + """Test construction without dropping old data.""" + texts = ["foo", "bar", "baz"] + metadatas = [{"page": i} for i in range(len(texts))] + docsearch = _milvus_from_texts(metadatas=metadatas, db_path=temp_milvus_db) + del docsearch + + docsearch = _milvus_from_texts( + metadatas=metadatas, drop=False, db_path=temp_milvus_db + ) + + output = docsearch.similarity_search("foo", k=10) + assert len(output) == 6 + + +def test_milvus_get_pks(temp_milvus_db: Any) -> None: + """Test end to end construction and get pks with expr""" + texts = ["foo", "bar", "baz"] + metadatas = [{"id": i} for i in range(len(texts))] + docsearch = _milvus_from_texts(metadatas=metadatas, db_path=temp_milvus_db) + expr = "id in [1,2]" + output = _get_pks(expr, docsearch) + assert len(output) == 2 + + +def test_milvus_delete_entities(temp_milvus_db: Any) -> None: + """Test end to end construction and delete entities""" + texts = ["foo", "bar", "baz"] + metadatas = [{"id": i} for i in range(len(texts))] + docsearch = _milvus_from_texts(metadatas=metadatas, db_path=temp_milvus_db) + expr = "id in [1,2]" + pks = _get_pks(expr, docsearch) + result = docsearch.delete(pks) + assert result.delete_count == 2 # type: ignore[attr-defined] + + +def test_milvus_upsert_entities(temp_milvus_db: Any) -> None: + """Test end to end construction and upsert entities""" + texts = ["foo", "bar", "baz"] + metadatas = [{"id": i} for i in range(len(texts))] + docsearch = _milvus_from_texts(metadatas=metadatas, db_path=temp_milvus_db) + expr = "id in [1,2]" + pks = _get_pks(expr, docsearch) + documents = [ + Document(page_content="test_1", metadata={"id": 1}), + Document(page_content="test_2", metadata={"id": 3}), + ] + ids = docsearch.upsert(pks, documents) + assert len(ids) == 2 # type: ignore[arg-type] + + +def test_milvus_enable_dynamic_field(temp_milvus_db: Any) -> None: + """Test end to end construction and enable dynamic field""" + texts = ["foo", "bar", "baz"] + metadatas = [{"id": i} for i in range(len(texts))] + docsearch = _milvus_from_texts( + metadatas=metadatas, enable_dynamic_field=True, db_path=temp_milvus_db + ) + output = docsearch.similarity_search("foo", k=10) + assert len(output) == 3 + + # When enable dynamic field, any new field data will be added to the collection. + new_metadatas = [{"id_new": i} for i in range(len(texts))] + docsearch.add_texts(texts, new_metadatas) + + output = docsearch.similarity_search("foo", k=10) + assert len(output) == 6 + + assert set(docsearch.fields) == { + docsearch._primary_field, + docsearch._text_field, + docsearch._vector_field, + } + + +def test_milvus_disable_dynamic_field(temp_milvus_db: Any) -> None: + """Test end to end construction and disable dynamic field""" + texts = ["foo", "bar", "baz"] + metadatas = [{"id": i} for i in range(len(texts))] + docsearch = _milvus_from_texts( + metadatas=metadatas, enable_dynamic_field=False, db_path=temp_milvus_db + ) + output = docsearch.similarity_search("foo", k=10) + assert len(output) == 3 + # ["pk", "text", "vector", "id"] + assert set(docsearch.fields) == { + docsearch._primary_field, + docsearch._text_field, + docsearch._vector_field, + "id", + } + + # Try to add new fields "id_new", but since dynamic field is disabled, + # all fields in the collection is specified as ["pk", "text", "vector", "id"], + # new field information "id_new" will not be added. + new_metadatas = [{"id": i, "id_new": i} for i in range(len(texts))] + docsearch.add_texts(texts, new_metadatas) + output = docsearch.similarity_search("foo", k=10) + assert len(output) == 6 + for doc in output: + assert set(doc.metadata.keys()) == {"id", "pk"} # `id_new` is not added. + + # When disable dynamic field, + # missing data of the created fields "id", will raise an exception. + with pytest.raises(Exception): + new_metadatas = [{"id_new": i} for i in range(len(texts))] + docsearch.add_texts(texts, new_metadatas) + + +def test_milvus_metadata_field(temp_milvus_db: Any) -> None: + """Test end to end construction and use metadata field""" + texts = ["foo", "bar", "baz"] + metadatas = [{"id": i} for i in range(len(texts))] + docsearch = _milvus_from_texts( + metadatas=metadatas, metadata_field="metadata", db_path=temp_milvus_db + ) + output = docsearch.similarity_search("foo", k=10) + assert len(output) == 3 + + new_metadatas = [{"id_new": i} for i in range(len(texts))] + docsearch.add_texts(texts, new_metadatas) + + output = docsearch.similarity_search("foo", k=10) + assert len(output) == 6 + + assert set(docsearch.fields) == { + docsearch._primary_field, + docsearch._text_field, + docsearch._vector_field, + docsearch._metadata_field, + } + + +def test_milvus_enable_dynamic_field_with_partition_key(temp_milvus_db: Any) -> None: + """ + Test end to end construction and enable dynamic field + with partition_key_field + """ + texts = ["foo", "bar", "baz"] + metadatas = [{"id": i, "namespace": f"name_{i}"} for i in range(len(texts))] + + docsearch = _milvus_from_texts( + metadatas=metadatas, + enable_dynamic_field=True, + partition_key_field="namespace", + db_path=temp_milvus_db, + ) + + # filter on a single namespace + output = docsearch.similarity_search("foo", k=10, expr="namespace == 'name_2'") + assert len(output) == 1 + + # without namespace filter + output = docsearch.similarity_search("foo", k=10) + assert len(output) == 3 + + assert set(docsearch.fields) == { + docsearch._primary_field, + docsearch._text_field, + docsearch._vector_field, + docsearch._partition_key_field, + } + + +def test_milvus_sparse_embeddings() -> None: + texts = [ + "In 'The Clockwork Kingdom' by Augusta Wynter, a brilliant inventor discovers " + "a hidden world of clockwork machines and ancient magic, where a rebellion is " + "brewing against the tyrannical ruler of the land.", + "In 'The Phantom Pilgrim' by Rowan Welles, a charismatic smuggler is hired by " + "a mysterious organization to transport a valuable artifact across a war-torn " + "continent, but soon finds themselves pursued by assassins and rival factions.", + "In 'The Dreamwalker's Journey' by Lyra Snow, a young dreamwalker discovers " + "she has the ability to enter people's dreams, but soon finds herself trapped " + "in a surreal world of nightmares and illusions, where the boundaries between " + "reality and fantasy blur.", + ] + try: + sparse_embedding_func = BM25SparseEmbedding(corpus=texts) + except LookupError: + import nltk # type: ignore[import] + + nltk.download("punkt_tab") + sparse_embedding_func = BM25SparseEmbedding(corpus=texts) + + with tempfile.NamedTemporaryFile(suffix=".db") as temp_db: + docsearch = Milvus.from_texts( + embedding=sparse_embedding_func, + texts=texts, + connection_args={"uri": temp_db.name}, + drop_old=True, + ) + + output = docsearch.similarity_search("Pilgrim", k=1) + assert "Pilgrim" in output[0].page_content + + +def test_milvus_array_field(temp_milvus_db: Any) -> None: + """Manually specify metadata schema, including an array_field. + For more information about array data type and filtering, please refer to + https://milvus.io/docs/array_data_type.md + """ + from pymilvus import DataType + + texts = ["foo", "bar", "baz"] + metadatas = [{"id": i, "array_field": [i, i + 1, i + 2]} for i in range(len(texts))] + + # Manually specify metadata schema, including an array_field. + # If some fields are not specified, Milvus will automatically infer their schemas. + docsearch = _milvus_from_texts( + metadatas=metadatas, + metadata_schema={ + "array_field": { + "dtype": DataType.ARRAY, + "kwargs": {"element_type": DataType.INT64, "max_capacity": 50}, + }, + # "id": { + # "dtype": DataType.INT64, + # } + }, + db_path=temp_milvus_db, + ) + output = docsearch.similarity_search("foo", k=10, expr="array_field[0] < 2") + assert len(output) == 2 + output = docsearch.similarity_search( + "foo", k=10, expr="ARRAY_CONTAINS(array_field, 3)" + ) + assert len(output) == 2 + + # If we use enable_dynamic_field, + # there is no need to manually specify metadata schema. + docsearch = _milvus_from_texts( + enable_dynamic_field=True, + metadatas=metadatas, + db_path=temp_milvus_db, + ) + output = docsearch.similarity_search("foo", k=10, expr="array_field[0] < 2") + assert len(output) == 2 + output = docsearch.similarity_search( + "foo", k=10, expr="ARRAY_CONTAINS(array_field, 3)" + ) + assert len(output) == 2 + + +# if __name__ == "__main__": +# test_milvus() +# test_milvus_vector_search() +# test_milvus_with_metadata() +# test_milvus_with_id() +# test_milvus_with_score() +# test_milvus_max_marginal_relevance_search() +# test_milvus_max_marginal_relevance_search_with_dynamic_field() +# test_milvus_add_extra() +# test_milvus_no_drop() +# test_milvus_get_pks() +# test_milvus_delete_entities() +# test_milvus_upsert_entities() +# test_milvus_enable_dynamic_field() +# test_milvus_disable_dynamic_field() +# test_milvus_metadata_field() +# test_milvus_enable_dynamic_field_with_partition_key() +# test_milvus_sparse_embeddings() +# test_milvus_array_field() diff --git a/libs/milvus/tests/unit_tests/__init__.py b/libs/milvus/tests/unit_tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/libs/milvus/tests/unit_tests/retrievers/__init__.py b/libs/milvus/tests/unit_tests/retrievers/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/libs/milvus/tests/unit_tests/test_imports.py b/libs/milvus/tests/unit_tests/test_imports.py new file mode 100644 index 0000000..8be170e --- /dev/null +++ b/libs/milvus/tests/unit_tests/test_imports.py @@ -0,0 +1,12 @@ +from langchain_milvus import __all__ + +EXPECTED_ALL = [ + "Milvus", + "MilvusCollectionHybridSearchRetriever", + "Zilliz", + "ZillizCloudPipelineRetriever", +] + + +def test_all_imports() -> None: + assert sorted(EXPECTED_ALL) == sorted(__all__) diff --git a/libs/milvus/tests/unit_tests/vectorstores/__init__.py b/libs/milvus/tests/unit_tests/vectorstores/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/libs/milvus/tests/unit_tests/vectorstores/test_milvus.py b/libs/milvus/tests/unit_tests/vectorstores/test_milvus.py new file mode 100644 index 0000000..1ef2314 --- /dev/null +++ b/libs/milvus/tests/unit_tests/vectorstores/test_milvus.py @@ -0,0 +1,17 @@ +import os +from tempfile import TemporaryDirectory +from unittest.mock import Mock + +from langchain_milvus.vectorstores import Milvus + + +def test_initialization() -> None: + """Test integration milvus initialization.""" + embedding = Mock() + with TemporaryDirectory() as tmp_dir: + Milvus( + embedding_function=embedding, + connection_args={ + "uri": os.path.join(tmp_dir, "milvus.db"), + }, + )