chore: drop UP045 (#33362)

Python 3.9 EOL

libs/partners/mistralai/langchain_mistralai/chat_models.py

@@ -13,8 +13,6 @@ from typing import (
     TYPE_CHECKING,
     Any,
     Literal,
-    Optional,
-    Union,
     cast,
 )
 
@@ -93,9 +91,7 @@ global_ssl_context = ssl.create_default_context(cafile=certifi.where())
 
 def _create_retry_decorator(
     llm: ChatMistralAI,
-    run_manager: Optional[
-        Union[AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun]
-    ] = None,
+    run_manager: AsyncCallbackManagerForLLMRun | CallbackManagerForLLMRun | None = None,
 ) -> Callable[[Any], Any]:
     """Return a tenacity retry decorator, preconfigured to handle exceptions."""
     errors = [httpx.RequestError, httpx.StreamError]
@@ -211,7 +207,7 @@ async def _aiter_sse(
 
 async def acompletion_with_retry(
     llm: ChatMistralAI,
-    run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+    run_manager: AsyncCallbackManagerForLLMRun | None = None,
     **kwargs: Any,
 ) -> Any:
     """Use tenacity to retry the async completion call."""
@@ -397,23 +393,23 @@ class ChatMistralAI(BaseChatModel):
     async_client: httpx.AsyncClient = Field(  # type: ignore[assignment] # : meta private:
         default=None, exclude=True
     )  #: :meta private:
-    mistral_api_key: Optional[SecretStr] = Field(
+    mistral_api_key: SecretStr | None = Field(
         alias="api_key",
         default_factory=secret_from_env("MISTRAL_API_KEY", default=None),
     )
-    endpoint: Optional[str] = Field(default=None, alias="base_url")
+    endpoint: str | None = Field(default=None, alias="base_url")
     max_retries: int = 5
     timeout: int = 120
     max_concurrent_requests: int = 64
     model: str = Field(default="mistral-small", alias="model_name")
     temperature: float = 0.7
-    max_tokens: Optional[int] = None
+    max_tokens: int | None = None
     top_p: float = 1
     """Decode using nucleus sampling: consider the smallest set of tokens whose
     probability sum is at least ``top_p``. Must be in the closed interval
     ``[0.0, 1.0]``."""
-    random_seed: Optional[int] = None
-    safe_mode: Optional[bool] = None
+    random_seed: int | None = None
+    safe_mode: bool | None = None
     streaming: bool = False
     model_kwargs: dict[str, Any] = Field(default_factory=dict)
     """Holds any invocation parameters not explicitly specified."""
@@ -445,7 +441,7 @@ class ChatMistralAI(BaseChatModel):
         return {k: v for k, v in defaults.items() if v is not None}
 
     def _get_ls_params(
-        self, stop: Optional[list[str]] = None, **kwargs: Any
+        self, stop: list[str] | None = None, **kwargs: Any
     ) -> LangSmithParams:
         """Get standard params for tracing."""
         params = self._get_invocation_params(stop=stop, **kwargs)
@@ -467,7 +463,7 @@ class ChatMistralAI(BaseChatModel):
         return self._default_params
 
     def completion_with_retry(
-        self, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any
+        self, run_manager: CallbackManagerForLLMRun | None = None, **kwargs: Any
     ) -> Any:
         """Use tenacity to retry the completion call."""
         retry_decorator = _create_retry_decorator(self, run_manager=run_manager)
@@ -496,7 +492,7 @@ class ChatMistralAI(BaseChatModel):
 
         return _completion_with_retry(**kwargs)
 
-    def _combine_llm_outputs(self, llm_outputs: list[Optional[dict]]) -> dict:
+    def _combine_llm_outputs(self, llm_outputs: list[dict | None]) -> dict:
         overall_token_usage: dict = {}
         for output in llm_outputs:
             if output is None:
@@ -515,7 +511,7 @@ class ChatMistralAI(BaseChatModel):
     def validate_environment(self) -> Self:
         """Validate api key, python package exists, temperature, and top_p."""
         if isinstance(self.mistral_api_key, SecretStr):
-            api_key_str: Optional[str] = self.mistral_api_key.get_secret_value()
+            api_key_str: str | None = self.mistral_api_key.get_secret_value()
         else:
             api_key_str = self.mistral_api_key
 
@@ -563,9 +559,9 @@ class ChatMistralAI(BaseChatModel):
     def _generate(
         self,
         messages: list[BaseMessage],
-        stop: Optional[list[str]] = None,
-        run_manager: Optional[CallbackManagerForLLMRun] = None,
-        stream: Optional[bool] = None,  # noqa: FBT001
+        stop: list[str] | None = None,
+        run_manager: CallbackManagerForLLMRun | None = None,
+        stream: bool | None = None,  # noqa: FBT001
         **kwargs: Any,
     ) -> ChatResult:
         should_stream = stream if stream is not None else self.streaming
@@ -608,7 +604,7 @@ class ChatMistralAI(BaseChatModel):
         return ChatResult(generations=generations, llm_output=llm_output)
 
     def _create_message_dicts(
-        self, messages: list[BaseMessage], stop: Optional[list[str]]
+        self, messages: list[BaseMessage], stop: list[str] | None
     ) -> tuple[list[dict], dict[str, Any]]:
         params = self._client_params
         if stop is not None or "stop" in params:
@@ -623,8 +619,8 @@ class ChatMistralAI(BaseChatModel):
     def _stream(
         self,
         messages: list[BaseMessage],
-        stop: Optional[list[str]] = None,
-        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        stop: list[str] | None = None,
+        run_manager: CallbackManagerForLLMRun | None = None,
         **kwargs: Any,
     ) -> Iterator[ChatGenerationChunk]:
         message_dicts, params = self._create_message_dicts(messages, stop)
@@ -649,8 +645,8 @@ class ChatMistralAI(BaseChatModel):
     async def _astream(
         self,
         messages: list[BaseMessage],
-        stop: Optional[list[str]] = None,
-        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+        stop: list[str] | None = None,
+        run_manager: AsyncCallbackManagerForLLMRun | None = None,
         **kwargs: Any,
     ) -> AsyncIterator[ChatGenerationChunk]:
         message_dicts, params = self._create_message_dicts(messages, stop)
@@ -675,9 +671,9 @@ class ChatMistralAI(BaseChatModel):
     async def _agenerate(
         self,
         messages: list[BaseMessage],
-        stop: Optional[list[str]] = None,
-        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
-        stream: Optional[bool] = None,  # noqa: FBT001
+        stop: list[str] | None = None,
+        run_manager: AsyncCallbackManagerForLLMRun | None = None,
+        stream: bool | None = None,  # noqa: FBT001
         **kwargs: Any,
     ) -> ChatResult:
         should_stream = stream if stream is not None else self.streaming
@@ -696,8 +692,8 @@ class ChatMistralAI(BaseChatModel):
 
     def bind_tools(
         self,
-        tools: Sequence[Union[dict[str, Any], type, Callable, BaseTool]],
-        tool_choice: Optional[Union[dict, str, Literal["auto", "any"]]] = None,  # noqa: PYI051
+        tools: Sequence[dict[str, Any] | type | Callable | BaseTool],
+        tool_choice: dict | str | Literal["auto", "any"] | None = None,  # noqa: PYI051
         **kwargs: Any,
     ) -> Runnable[LanguageModelInput, AIMessage]:
         """Bind tool-like objects to this chat model.
@@ -710,7 +706,7 @@ class ChatMistralAI(BaseChatModel):
                 `langchain_core.utils.function_calling.convert_to_openai_tool`.
             tool_choice: Which tool to require the model to call.
                 Must be the name of the single provided function or
-                ``'auto'`` to automatically determine which function to call
+                `'auto'` to automatically determine which function to call
                 (if any), or a dict of the form:
                 {"type": "function", "function": {"name": <<tool_name>>}}.
             kwargs: Any additional parameters are passed directly to
@@ -738,14 +734,14 @@ class ChatMistralAI(BaseChatModel):
 
     def with_structured_output(
         self,
-        schema: Optional[Union[dict, type]] = None,
+        schema: dict | type | None = None,
         *,
         method: Literal[
             "function_calling", "json_mode", "json_schema"
         ] = "function_calling",
         include_raw: bool = False,
         **kwargs: Any,
-    ) -> Runnable[LanguageModelInput, Union[dict, BaseModel]]:
+    ) -> Runnable[LanguageModelInput, dict | BaseModel]:
         r"""Model wrapper that returns outputs formatted to match the given schema.
 
         Args:
@@ -1085,7 +1081,7 @@ class ChatMistralAI(BaseChatModel):
 
 
 def _convert_to_openai_response_format(
-    schema: Union[dict[str, Any], type], *, strict: Optional[bool] = None
+    schema: dict[str, Any] | type, *, strict: bool | None = None
 ) -> dict:
     """Perform same op as in ChatOpenAI, but do not pass through Pydantic BaseModels."""
     if (