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docs: relocate init args to __init__ (#6259)

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Griffe expects parameter documentation to be in the method where
parameters are defined, not in the class docstring.

Class docstrings describe what the class does, while `__init__`
docstrings describe how to instantiate it with specific parameters.

---------

Co-authored-by: Sydney Runkle <54324534+sydney-runkle@users.noreply.github.com>
Co-authored-by: ccurme <chester.curme@gmail.com>
Co-authored-by: Sydney Runkle <sydneymarierunkle@gmail.com>
Co-authored-by: William FH <13333726+hinthornw@users.noreply.github.com>
This commit is contained in:
Mason Daugherty
2025-10-16 07:11:42 -04:00
committed by GitHub
co-authored by Sydney Runkle ccurme Sydney Runkle William FH
parent bce1dcfcd2
commit 6bf9a7a4bc
2 changed files with 42 additions and 46 deletions
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libs/prebuilt/langgraph/prebuilt/tool_node.py
+28 -34
View File
@@ -249,29 +249,6 @@ class ToolNode(RunnableCallable):
Tool calls can also be passed directly as a list of `ToolCall` dicts.
Args:
tools: A sequence of tools that can be invoked by this node. Tools can be
BaseTool instances or plain functions that will be converted to tools.
name: The name identifier for this node in the graph. Used for debugging
and visualization. Defaults to "tools".
tags: Optional metadata tags to associate with the node for filtering
and organization. Defaults to None.
handle_tool_errors: Configuration for error handling during tool execution.
Defaults to True. Supports multiple strategies:
- True: Catch all errors and return a ToolMessage with the default
error template containing the exception details.
- str: Catch all errors and return a ToolMessage with this custom
error message string.
- tuple[type[Exception], ...]: Only catch exceptions of the specified
types and return default error messages for them.
- Callable[..., str]: Catch exceptions matching the callable's signature
and return the string result of calling it with the exception.
- False: Disable error handling entirely, allowing exceptions to propagate.
messages_key: The key in the state dictionary that contains the message list.
This same key will be used for the output ToolMessages. Defaults to "messages".
Example:
Basic usage with simple tools:
@@ -334,11 +311,26 @@ class ToolNode(RunnableCallable):
"""Initialize the ToolNode with the provided tools and configuration.
Args:
tools: Sequence of tools to make available for execution.
name: Node name for graph identification.
tags: Optional metadata tags.
handle_tool_errors: Error handling configuration.
messages_key: State key containing messages.
tools: A sequence of tools that can be invoked by this node. Tools can be
BaseTool instances or plain functions that will be converted to tools.
name: The name identifier for this node in the graph. Used for debugging
and visualization. Defaults to "tools".
tags: Optional metadata tags to associate with the node for filtering
and organization. Defaults to None.
handle_tool_errors: Configuration for error handling during tool execution.
Defaults to True. Supports multiple strategies:
- True: Catch all errors and return a ToolMessage with the default
error template containing the exception details.
- str: Catch all errors and return a ToolMessage with this custom
error message string.
- tuple[type[Exception], ...]: Only catch exceptions of the specified
types and return default error messages for them.
- Callable[..., str]: Catch exceptions matching the callable's signature
and return the string result of calling it with the exception.
- False: Disable error handling entirely, allowing exceptions to propagate.
messages_key: The key in the state dictionary that contains the message list.
This same key will be used for the output ToolMessages. Defaults to "messages".
"""
super().__init__(self._func, self._afunc, name=name, tags=tags, trace=False)
self.tools_by_name: dict[str, BaseTool] = {}
@@ -867,12 +859,6 @@ class InjectedState(InjectedToolArg):
receive state data automatically during execution while remaining invisible
to the model's tool-calling interface.
Args:
field: Optional key to extract from the state dictionary. If None, the entire
state is injected. If specified, only that field's value is injected.
This allows tools to request specific state components rather than
processing the full state structure.
Example:
```python
from typing import List
@@ -930,6 +916,14 @@ class InjectedState(InjectedToolArg):
""" # noqa: E501
def __init__(self, field: Optional[str] = None) -> None:
"""Initialize InjectedState annotation.
Args:
field: Optional key to extract from the state dictionary. If None, the entire
state is injected. If specified, only that field's value is injected.
This allows tools to request specific state components rather than
processing the full state structure.
"""
self.field = field
libs/prebuilt/langgraph/prebuilt/tool_validator.py
+14 -12
View File
@@ -62,18 +62,6 @@ class ValidationNode(RunnableCallable):
structured output that conforms to a complex schema without losing the original
messages and tool IDs (for use in multi-turn conversations).
Args:
schemas: A list of schemas to validate the tool calls with. These can be
any of the following:
- A pydantic BaseModel class
- A BaseTool instance (the args_schema will be used)
- A function (a schema will be created from the function signature)
format_error: A function that takes an exception, a ToolCall, and a schema
and returns a formatted error string. By default, it returns the
exception repr and a message to respond after fixing validation errors.
name: The name of the node.
tags: A list of tags to add to the node.
Returns:
(Union[Dict[str, List[ToolMessage]], Sequence[ToolMessage]]): A list of ToolMessages with the validated content or error messages.
@@ -140,6 +128,20 @@ class ValidationNode(RunnableCallable):
name: str = "validation",
tags: Optional[list[str]] = None,
) -> None:
"""Initialize the ValidationNode.
Args:
schemas: A list of schemas to validate the tool calls with. These can be
any of the following:
- A pydantic BaseModel class
- A BaseTool instance (the args_schema will be used)
- A function (a schema will be created from the function signature)
format_error: A function that takes an exception, a ToolCall, and a schema
and returns a formatted error string. By default, it returns the
exception repr and a message to respond after fixing validation errors.
name: The name of the node.
tags: A list of tags to add to the node.
"""
super().__init__(self._func, None, name=name, tags=tags, trace=False)
self._format_error = format_error or _default_format_error
self.schemas_by_name: Dict[str, Type[BaseModel]] = {}