community[patch]: fix errors introduced by pydantic 2.10 (#28297)

libs/community/langchain_community/agent_toolkits/sql/toolkit.py

@@ -2,6 +2,8 @@
 
 from typing import List
 
+from langchain_core.caches import BaseCache as BaseCache
+from langchain_core.callbacks import Callbacks as Callbacks
 from langchain_core.language_models import BaseLanguageModel
 from langchain_core.tools import BaseTool
 from langchain_core.tools.base import BaseToolkit
@@ -129,3 +131,6 @@ def get_tools(self) -> List[BaseTool]:
     def get_context(self) -> dict:
         """Return db context that you may want in agent prompt."""
         return self.db.get_context()
+
+
+SQLDatabaseToolkit.model_rebuild()

libs/community/langchain_community/chat_models/anyscale.py

@@ -72,7 +72,7 @@ def lc_secrets(self) -> Dict[str, str]:
     def is_lc_serializable(cls) -> bool:
         return False
 
-    anyscale_api_key: SecretStr = Field(default=None)
+    anyscale_api_key: SecretStr = Field(default=SecretStr(""))
     """AnyScale Endpoints API keys."""
     model_name: str = Field(default=DEFAULT_MODEL, alias="model")
     """Model name to use."""

libs/community/langchain_community/chat_models/gpt_router.py

@@ -152,7 +152,7 @@ class GPTRouter(BaseChatModel):
 
     client: Any = Field(default=None, exclude=True)  #: :meta private:
     models_priority_list: List[GPTRouterModel] = Field(min_length=1)
-    gpt_router_api_base: str = Field(default=None)
+    gpt_router_api_base: str = Field(default="")
     """WriteSonic GPTRouter custom endpoint"""
     gpt_router_api_key: Optional[SecretStr] = None
     """WriteSonic GPTRouter API Key"""

libs/community/langchain_community/chat_models/kinetica.py

@@ -38,7 +38,7 @@ class _KdtSuggestContext(BaseModel):
 
     table: Optional[str] = Field(default=None, title="Name of table")
     description: Optional[str] = Field(default=None, title="Table description")
-    columns: List[str] = Field(default=None, title="Table columns list")
+    columns: List[str] = Field(default=[], title="Table columns list")
     rules: Optional[List[str]] = Field(
         default=None, title="Rules that apply to the table."
     )
@@ -121,15 +121,15 @@ class _KdtoSuggestRequest(BaseModel):
 class _KdtMessage(BaseModel):
     """pydantic API response type"""
 
-    role: str = Field(default=None, title="One of [user|assistant|system]")
+    role: str = Field(default="", title="One of [user|assistant|system]")
     content: str
 
 
 class _KdtChoice(BaseModel):
     """pydantic API response type"""
 
     index: int
-    message: _KdtMessage = Field(default=None, title="The generated SQL")
+    message: Optional[_KdtMessage] = Field(default=None, title="The generated SQL")
     finish_reason: str
 
 
@@ -150,7 +150,7 @@ class _KdtSqlResponse(BaseModel):
     model: str
     choices: List[_KdtChoice]
     usage: _KdtUsage
-    prompt: str = Field(default=None, title="The input question")
+    prompt: str = Field(default="", title="The input question")
 
 
 class _KdtCompletionResponse(BaseModel):
@@ -376,9 +376,8 @@ def _generate(
         dict_messages = [self._convert_message_to_dict(m) for m in messages]
         sql_response = self._submit_completion(dict_messages)
 
-        response_message = sql_response.choices[0].message
-        # generated_dict = response_message.model_dump() # pydantic v2
-        generated_dict = response_message.dict()
+        response_message = cast(_KdtMessage, sql_response.choices[0].message)
+        generated_dict = response_message.model_dump()
 
         generated_message = self._convert_message_from_dict(generated_dict)
 
@@ -539,7 +538,7 @@ class KineticaSqlResponse(BaseModel):
     the generated SQL and related Pandas Dataframe fetched from the database.
     """
 
-    sql: str = Field(default=None)
+    sql: str = Field(default="")
     """The generated SQL."""
 
     # dataframe: "pd.DataFrame" = Field(default=None)

libs/community/langchain_community/chat_models/naver.py

@@ -175,8 +175,8 @@ class ChatClovaX(BaseChatModel):
             model.invoke([HumanMessage(content="Come up with 10 names for a song about parrots.")])
     """  # noqa: E501
 
-    client: httpx.Client = Field(default=None)  #: :meta private:
-    async_client: httpx.AsyncClient = Field(default=None)  #: :meta private:
+    client: Optional[httpx.Client] = Field(default=None)  #: :meta private:
+    async_client: Optional[httpx.AsyncClient] = Field(default=None)  #: :meta private:
 
     model_name: str = Field(
         default="HCX-003",
@@ -197,7 +197,7 @@ class ChatClovaX(BaseChatModel):
     ncp_apigw_api_key: Optional[SecretStr] = Field(default=None, alias="apigw_api_key")
     """Automatically inferred from env are `NCP_APIGW_API_KEY` if not provided."""
 
-    base_url: str = Field(default=None, alias="base_url")
+    base_url: str = Field(default="", alias="base_url")
     """
     Automatically inferred from env are `NCP_CLOVASTUDIO_API_BASE_URL` if not provided.
     """
@@ -356,11 +356,12 @@ def _completion_with_retry(self, **kwargs: Any) -> Any:
             kwargs["stream"] = False
 
         stream = kwargs["stream"]
+        client = cast(httpx.Client, self.client)
         if stream:
 
             def iter_sse() -> Iterator[ServerSentEvent]:
                 with connect_sse(
-                    self.client, "POST", self._api_url, json=kwargs
+                    client, "POST", self._api_url, json=kwargs
                 ) as event_source:
                     _raise_on_error(event_source.response)
                     for sse in event_source.iter_sse():
@@ -376,7 +377,7 @@ def iter_sse() -> Iterator[ServerSentEvent]:
 
             return iter_sse()
         else:
-            response = self.client.post(url=self._api_url, json=kwargs)
+            response = client.post(url=self._api_url, json=kwargs)
             _raise_on_error(response)
             return response.json()
 
@@ -395,13 +396,14 @@ async def _completion_with_retry(**kwargs: Any) -> Any:
             if "stream" not in kwargs:
                 kwargs["stream"] = False
             stream = kwargs["stream"]
+            async_client = cast(httpx.AsyncClient, self.async_client)
             if stream:
                 event_source = aconnect_sse(
-                    self.async_client, "POST", self._api_url, json=kwargs
+                    async_client, "POST", self._api_url, json=kwargs
                 )
                 return _aiter_sse(event_source)
             else:
-                response = await self.async_client.post(url=self._api_url, json=kwargs)
+                response = await async_client.post(url=self._api_url, json=kwargs)
                 await _araise_on_error(response)
                 return response.json()
 

libs/community/langchain_community/chat_models/octoai.py

@@ -46,7 +46,7 @@ class ChatOctoAI(ChatOpenAI):
     """
 
     octoai_api_base: str = Field(default=DEFAULT_API_BASE)
-    octoai_api_token: SecretStr = Field(default=None, alias="api_key")
+    octoai_api_token: SecretStr = Field(default=SecretStr(""), alias="api_key")
     model_name: str = Field(default=DEFAULT_MODEL, alias="model")
 
     @property

libs/community/langchain_community/chat_models/sambanova.py

@@ -256,7 +256,7 @@ class Joke(BaseModel):
     sambanova_url: str = Field(default="")
     """SambaNova Cloud Url"""
 
-    sambanova_api_key: SecretStr = Field(default="")
+    sambanova_api_key: SecretStr = Field(default=SecretStr(""))
     """SambaNova Cloud api key"""
 
     model: str = Field(default="Meta-Llama-3.1-8B-Instruct")
@@ -1072,7 +1072,7 @@ class ChatSambaStudio(BaseChatModel):
     sambastudio_url: str = Field(default="")
     """SambaStudio Url"""
 
-    sambastudio_api_key: SecretStr = Field(default="")
+    sambastudio_api_key: SecretStr = Field(default=SecretStr(""))
     """SambaStudio api key"""
 
     base_url: str = Field(default="", exclude=True)

libs/community/langchain_community/embeddings/anyscale.py

@@ -2,7 +2,7 @@
 
 from __future__ import annotations
 
-from typing import Dict
+from typing import Dict, Optional
 
 from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init
 from pydantic import Field, SecretStr
@@ -17,7 +17,7 @@
 class AnyscaleEmbeddings(OpenAIEmbeddings):
     """`Anyscale` Embeddings API."""
 
-    anyscale_api_key: SecretStr = Field(default=None)
+    anyscale_api_key: Optional[SecretStr] = Field(default=None)
     """AnyScale Endpoints API keys."""
     model: str = Field(default=DEFAULT_MODEL)
     """Model name to use."""

libs/community/langchain_community/embeddings/naver.py

@@ -1,5 +1,5 @@
 import logging
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, List, Optional, cast
 
 import httpx
 from langchain_core.embeddings import Embeddings
@@ -60,16 +60,16 @@ class ClovaXEmbeddings(BaseModel, Embeddings):
             output = embedding.embed_documents(documents)
     """  # noqa: E501
 
-    client: httpx.Client = Field(default=None)  #: :meta private:
-    async_client: httpx.AsyncClient = Field(default=None)  #: :meta private:
+    client: Optional[httpx.Client] = Field(default=None)  #: :meta private:
+    async_client: Optional[httpx.AsyncClient] = Field(default=None)  #: :meta private:
 
     ncp_clovastudio_api_key: Optional[SecretStr] = Field(default=None, alias="api_key")
     """Automatically inferred from env are `NCP_CLOVASTUDIO_API_KEY` if not provided."""
 
     ncp_apigw_api_key: Optional[SecretStr] = Field(default=None, alias="apigw_api_key")
     """Automatically inferred from env are `NCP_APIGW_API_KEY` if not provided."""
 
-    base_url: str = Field(default=None, alias="base_url")
+    base_url: Optional[str] = Field(default=None, alias="base_url")
     """
     Automatically inferred from env are  `NCP_CLOVASTUDIO_API_BASE_URL` if not provided.
     """
@@ -168,13 +168,15 @@ def default_headers(self) -> Dict[str, Any]:
 
     def _embed_text(self, text: str) -> List[float]:
         payload = {"text": text}
-        response = self.client.post(url=self._api_url, json=payload)
+        client = cast(httpx.Client, self.client)
+        response = client.post(url=self._api_url, json=payload)
         _raise_on_error(response)
         return response.json()["result"]["embedding"]
 
     async def _aembed_text(self, text: str) -> List[float]:
         payload = {"text": text}
-        response = await self.async_client.post(url=self._api_url, json=payload)
+        async_client = cast(httpx.AsyncClient, self.client)
+        response = await async_client.post(url=self._api_url, json=payload)
         await _araise_on_error(response)
         return response.json()["result"]["embedding"]
 

libs/community/langchain_community/embeddings/octoai_embeddings.py

@@ -1,4 +1,4 @@
-from typing import Dict
+from typing import Dict, Optional
 
 from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env, pre_init
 from pydantic import Field, SecretStr
@@ -20,7 +20,7 @@ class OctoAIEmbeddings(OpenAIEmbeddings):
     Alternatively, you can use the octoai_api_token keyword argument.
     """
 
-    octoai_api_token: SecretStr = Field(default=None)
+    octoai_api_token: Optional[SecretStr] = Field(default=None)
     """OctoAI Endpoints API keys."""
     endpoint_url: str = Field(default=DEFAULT_API_BASE)
     """Base URL path for API requests."""

libs/community/langchain_community/llms/anyscale.py

@@ -84,7 +84,7 @@ def send_query(llm, text):
 
     """Key word arguments to pass to the model."""
     anyscale_api_base: str = Field(default=DEFAULT_BASE_URL)
-    anyscale_api_key: SecretStr = Field(default=None)
+    anyscale_api_key: SecretStr = Field(default=SecretStr(""))
     model_name: str = Field(default=DEFAULT_MODEL)
 
     prefix_messages: List = Field(default_factory=list)

libs/community/langchain_community/llms/exllamav2.py

@@ -43,7 +43,7 @@ class ExLlamaV2(LLM):
     # Langchain parameters
     logfunc: Callable = print
 
-    stop_sequences: List[str] = Field("")
+    stop_sequences: List[str] = Field([])
     """Sequences that immediately will stop the generator."""
 
     max_new_tokens: int = Field(150)
@@ -56,7 +56,7 @@ class ExLlamaV2(LLM):
     """Whether to print debug information."""
 
     # Generator parameters
-    disallowed_tokens: List[int] = Field(None)
+    disallowed_tokens: Optional[List[int]] = Field(None)
     """List of tokens to disallow during generation."""
 
     @pre_init

libs/community/langchain_community/llms/gooseai.py

@@ -61,7 +61,7 @@ class GooseAI(LLM):
     model_kwargs: Dict[str, Any] = Field(default_factory=dict)
     """Holds any model parameters valid for `create` call not explicitly specified."""
 
-    logit_bias: Optional[Dict[str, float]] = Field(default_factory=dict)
+    logit_bias: Optional[Dict[str, float]] = Field(default_factory=dict)  # type: ignore[arg-type]
     """Adjust the probability of specific tokens being generated."""
 
     gooseai_api_key: Optional[SecretStr] = None

libs/community/langchain_community/llms/octoai_endpoint.py

@@ -36,7 +36,7 @@ class OctoAIEndpoint(BaseOpenAI):  # type: ignore[override]
 
     """Key word arguments to pass to the model."""
     octoai_api_base: str = Field(default=DEFAULT_BASE_URL)
-    octoai_api_token: SecretStr = Field(default=None)
+    octoai_api_token: SecretStr = Field(default=SecretStr(""))
     model_name: str = Field(default=DEFAULT_MODEL)
 
     @classmethod

libs/community/langchain_community/llms/openai.py

@@ -219,7 +219,7 @@ def is_lc_serializable(cls) -> bool:
     )
     """Timeout for requests to OpenAI completion API. Can be float, httpx.Timeout or 
         None."""
-    logit_bias: Optional[Dict[str, float]] = Field(default_factory=dict)
+    logit_bias: Optional[Dict[str, float]] = Field(default_factory=dict)  # type: ignore[arg-type]
     """Adjust the probability of specific tokens being generated."""
     max_retries: int = 2
     """Maximum number of retries to make when generating."""

libs/community/langchain_community/llms/sambanova.py

@@ -105,7 +105,7 @@ class SambaStudio(LLM):
     sambastudio_url: str = Field(default="")
     """SambaStudio Url"""
 
-    sambastudio_api_key: SecretStr = Field(default="")
+    sambastudio_api_key: SecretStr = Field(default=SecretStr(""))
     """SambaStudio api key"""
 
     base_url: str = Field(default="", exclude=True)
@@ -607,7 +607,7 @@ class SambaNovaCloud(LLM):
     sambanova_url: str = Field(default="")
     """SambaNova Cloud Url"""
 
-    sambanova_api_key: SecretStr = Field(default="")
+    sambanova_api_key: SecretStr = Field(default=SecretStr(""))
     """SambaNova Cloud api key"""
 
     model: str = Field(default="Meta-Llama-3.1-8B-Instruct")

libs/community/langchain_community/tools/edenai/edenai_base_tool.py

@@ -23,7 +23,7 @@ class EdenaiTool(BaseTool):  # type: ignore[override]
 
     feature: str
     subfeature: str
-    edenai_api_key: SecretStr = Field(
+    edenai_api_key: Optional[SecretStr] = Field(
         default_factory=secret_from_env("EDENAI_API_KEY", default=None)
     )
     is_async: bool = False
@@ -48,8 +48,9 @@ def _call_eden_ai(self, query_params: Dict[str, Any]) -> str:
             requests.Response: The response from the EdenAI API call.
 
         """
+        api_key = self.edenai_api_key.get_secret_value() if self.edenai_api_key else ""
         headers = {
-            "Authorization": f"Bearer {self.edenai_api_key.get_secret_value()}",
+            "Authorization": f"Bearer {api_key}",
             "User-Agent": self.get_user_agent(),
         }
 

libs/community/langchain_community/tools/office365/messages_search.py

@@ -19,7 +19,7 @@ class SearchEmailsInput(BaseModel):
     """From https://learn.microsoft.com/en-us/graph/search-query-parameter"""
 
     folder: str = Field(
-        default=None,
+        default="",
         description=(
             " If the user wants to search in only one folder, the name of the folder. "
             'Default folders are "inbox", "drafts", "sent items", "deleted ttems", but '

libs/community/langchain_community/utilities/dalle_image_generator.py

@@ -31,7 +31,7 @@ class DallEAPIWrapper(BaseModel):
     async_client: Any = Field(default=None, exclude=True)  #: :meta private:
     model_name: str = Field(default="dall-e-2", alias="model")
     model_kwargs: Dict[str, Any] = Field(default_factory=dict)
-    openai_api_key: SecretStr = Field(
+    openai_api_key: Optional[SecretStr] = Field(
         alias="api_key",
         default_factory=secret_from_env(
             "OPENAI_API_KEY",

libs/community/langchain_community/utilities/scenexplain.py

@@ -23,7 +23,7 @@ class SceneXplainAPIWrapper(BaseModel):
       and create a new API key.
     """
 
-    scenex_api_key: str = Field(..., default_factory=from_env("SCENEX_API_KEY"))
+    scenex_api_key: str = Field(..., default_factory=from_env("SCENEX_API_KEY"))  # type: ignore[call-overload]
     scenex_api_url: str = "https://api.scenex.jina.ai/v1/describe"
 
     def _describe_image(self, image: str) -> str:

libs/community/langchain_community/vectorstores/redis/schema.py

@@ -97,7 +97,7 @@ class RedisVectorField(RedisField):
     dims: int = Field(...)
     algorithm: object = Field(...)
     datatype: str = Field(default="FLOAT32")
-    distance_metric: RedisDistanceMetric = Field(default="COSINE")
+    distance_metric: RedisDistanceMetric = Field(default="COSINE")  # type: ignore[assignment]
     initial_cap: Optional[int] = None
 
     @field_validator("algorithm", "datatype", "distance_metric", mode="before")