style(core,langchain-classic,openai): fix griffe warnings (#34074)

Author: mdrxy

libs/core/langchain_core/prompts/chat.py

@@ -903,23 +903,28 @@ def __init__(
                 5. A string which is shorthand for `("human", template)`; e.g.,
                     `"{user_input}"`
             template_format: Format of the template.
-            input_variables: A list of the names of the variables whose values are
-                required as inputs to the prompt.
-            optional_variables: A list of the names of the variables for placeholder
-                or MessagePlaceholder that are optional.
-
-                These variables are auto inferred from the prompt and user need not
-                provide them.
-            partial_variables: A dictionary of the partial variables the prompt
-                template carries.
-
-                Partial variables populate the template so that you don't need to pass
-                them in every time you call the prompt.
-            validate_template: Whether to validate the template.
-            input_types: A dictionary of the types of the variables the prompt template
-                expects.
-
-                If not provided, all variables are assumed to be strings.
+            **kwargs: Additional keyword arguments passed to `BasePromptTemplate`,
+                including (but not limited to):
+
+                - `input_variables`: A list of the names of the variables whose values
+                    are required as inputs to the prompt.
+                - `optional_variables`: A list of the names of the variables for
+                    placeholder or `MessagePlaceholder` that are optional.
+
+                    These variables are auto inferred from the prompt and user need not
+                    provide them.
+
+                - `partial_variables`: A dictionary of the partial variables the prompt
+                    template carries.
+
+                    Partial variables populate the template so that you don't need to
+                    pass them in every time you call the prompt.
+
+                - `validate_template`: Whether to validate the template.
+                - `input_types`: A dictionary of the types of the variables the prompt
+                    template expects.
+
+                    If not provided, all variables are assumed to be strings.
 
         Examples:
             Instantiation from a list of message templates:

libs/core/uv.lock

@@ -26,7 +26,6 @@ def __init__(
             total: The total number of items to be processed.
             ncols: The character width of the progress bar.
             end_with: Last string to print after progress bar reaches end.
-            **kwargs: Additional keyword arguments.
         """
         self.total = total
         self.ncols = ncols

libs/langchain/langchain_classic/smith/evaluation/progress.py

@@ -295,11 +295,7 @@ def _get_prompt(inputs: dict[str, Any]) -> str:
 
 
 class ChatModelInput(TypedDict):
-    """Input for a chat model.
-
-    Args:
-        messages: List of chat messages.
-    """
+    """Input for a chat model."""
 
     messages: list[BaseMessage]
 

libs/langchain/langchain_classic/smith/evaluation/runner_utils.py

@@ -108,8 +108,8 @@ def serialize_outputs(self, outputs: dict) -> str:
             The serialized output text from the first generation.
 
         Raises:
-            ValueError: If no generations are found in the outputs,
-            or if the generations are empty.
+            ValueError: If no generations are found in the outputs or if the generations
+                are empty.
         """
         if not outputs.get("generations"):
             msg = "Cannot evaluate LLM Run without generations."
@@ -436,8 +436,8 @@ def from_run_and_data_type(
             The instantiated evaluation chain.
 
         Raises:
-            If the run type is not supported, or if the evaluator requires a
-            reference from the dataset but the reference key is not provided.
+            ValueError: If the run type is not supported, or if the evaluator requires a
+                reference from the dataset but the reference key is not provided.
 
         """
         # Configure how run inputs/predictions are passed to the evaluator

libs/langchain/langchain_classic/smith/evaluation/string_run_evaluator.py

@@ -905,51 +905,6 @@ def with_structured_output(
                 !!! note
                     `strict` can only be non-null if `method` is `'json_schema'`
                     or `'function_calling'`.
-            tools:
-                A list of tool-like objects to bind to the chat model. Requires that:
-
-                - `method` is `'json_schema'` (default).
-                - `strict=True`
-                - `include_raw=True`
-
-                If a model elects to call a
-                tool, the resulting `AIMessage` in `'raw'` will include tool calls.
-
-                ??? example
-
-                    ```python
-                    from langchain.chat_models import init_chat_model
-                    from pydantic import BaseModel
-
-
-                    class ResponseSchema(BaseModel):
-                        response: str
-
-
-                    def get_weather(location: str) -> str:
-                        \"\"\"Get weather at a location.\"\"\"
-                        pass
-
-                    model = init_chat_model("openai:gpt-4o-mini")
-
-                    structured_model = model.with_structured_output(
-                        ResponseSchema,
-                        tools=[get_weather],
-                        strict=True,
-                        include_raw=True,
-                    )
-
-                    structured_model.invoke("What's the weather in Boston?")
-                    ```
-
-                    ```python
-                    {
-                        "raw": AIMessage(content="", tool_calls=[...], ...),
-                        "parsing_error": None,
-                        "parsed": None,
-                    }
-                    ```
-
             kwargs: Additional keyword args are passed through to the model.
 
         Returns: