style: some cleanup (#33857)

libs/partners/mistralai/langchain_mistralai/chat_models.py

@@ -452,27 +452,41 @@ class ChatMistralAI(BaseChatModel):
     client: httpx.Client = Field(  # type: ignore[assignment] # : meta private:
         default=None, exclude=True
     )
+
     async_client: httpx.AsyncClient = Field(  # type: ignore[assignment] # : meta private:
         default=None, exclude=True
-    )  #: :meta private:
+    )
+
     mistral_api_key: SecretStr | None = Field(
         alias="api_key",
         default_factory=secret_from_env("MISTRAL_API_KEY", default=None),
     )
+
     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: 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: 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."""
 
@@ -803,10 +817,10 @@ class ChatMistralAI(BaseChatModel):
         Args:
             schema: The output schema. Can be passed in as:
 
-                - an OpenAI function/tool schema,
-                - a JSON Schema,
-                - a `TypedDict` class,
-                - or a Pydantic class.
+                - An OpenAI function/tool schema,
+                - A JSON Schema,
+                - A `TypedDict` class,
+                - Or a Pydantic class.
 
                 If `schema` is a Pydantic class then the model output will be a
                 Pydantic instance of that class, and the model-generated fields will be
@@ -836,11 +850,15 @@ class ChatMistralAI(BaseChatModel):
                     Added method="json_schema"
 
             include_raw:
-                If `False` then only the parsed structured output is returned. If
-                an error occurs during model output parsing it will be raised. If `True`
-                then both the raw model response (a `BaseMessage`) and the parsed model
-                response will be returned. If an error occurs during output parsing it
-                will be caught and returned as well.
+                If `False` then only the parsed structured output is returned.
+
+                If an error occurs during model output parsing it will be raised.
+
+                If `True` then both the raw model response (a `BaseMessage`) and the
+                parsed model response will be returned.
+
+                If an error occurs during output parsing it will be caught and returned
+                as well.
 
                 The final output is always a `dict` with keys `'raw'`, `'parsed'`, and
                 `'parsing_error'`.

libs/partners/mistralai/langchain_mistralai/embeddings.py

@@ -124,20 +124,27 @@ class MistralAIEmbeddings(BaseModel, Embeddings):
     # The type for client and async_client is ignored because the type is not
     # an Optional after the model is initialized and the model_validator
     # is run.
-    client: httpx.Client = Field(default=None)  # type: ignore[assignment] # :meta private:
+    client: httpx.Client = Field(default=None)  # type: ignore[assignment]
 
-    async_client: httpx.AsyncClient = Field(  # type: ignore[assignment] # :meta private:
+    async_client: httpx.AsyncClient = Field(  # type: ignore[assignment]
         default=None
     )
+
     mistral_api_key: SecretStr = Field(
         alias="api_key",
         default_factory=secret_from_env("MISTRAL_API_KEY", default=""),
     )
+
     endpoint: str = "https://api.mistral.ai/v1/"
+
     max_retries: int | None = 5
+
     timeout: int = 120
+
     wait_time: int | None = 30
+
     max_concurrent_requests: int = 64
+
     tokenizer: Tokenizer = Field(default=None)
 
     model: str = "mistral-embed"