mirror of
https://github.com/hwchase17/langchain.git
synced 2025-06-01 20:49:17 +00:00
_prep_run_args，tool_input copy
This commit is contained in:
xzq.xu
parent
e90abce577
commit
3382b0d8ea
@ -91,11 +91,11 @@ def _get_annotation_description(arg_type: type) -> str | None:
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def _get_filtered_args(
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inferred_model: type[BaseModel],
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func: Callable,
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*,
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filter_args: Sequence[str],
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include_injected: bool = True,
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inferred_model: type[BaseModel],
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func: Callable,
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*,
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filter_args: Sequence[str],
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include_injected: bool = True,
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) -> dict:
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"""Get the arguments from a function's signature."""
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schema = inferred_model.model_json_schema()["properties"]
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@ -104,13 +104,13 @@ def _get_filtered_args(
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k: schema[k]
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for i, (k, param) in enumerate(valid_keys.items())
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if k not in filter_args
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and (i > 0 or param.name not in ("self", "cls"))
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and (include_injected or not _is_injected_arg_type(param.annotation))
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and (i > 0 or param.name not in ("self", "cls"))
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and (include_injected or not _is_injected_arg_type(param.annotation))
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}
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def _parse_python_function_docstring(
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function: Callable, annotations: dict, error_on_invalid_docstring: bool = False
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function: Callable, annotations: dict, error_on_invalid_docstring: bool = False
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) -> tuple[str, dict]:
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"""Parse the function and argument descriptions from the docstring of a function.
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@ -125,7 +125,7 @@ def _parse_python_function_docstring(
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def _validate_docstring_args_against_annotations(
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arg_descriptions: dict, annotations: dict
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arg_descriptions: dict, annotations: dict
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) -> None:
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"""Raise error if docstring arg is not in type annotations."""
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for docstring_arg in arg_descriptions:
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@ -135,10 +135,10 @@ def _validate_docstring_args_against_annotations(
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def _infer_arg_descriptions(
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fn: Callable,
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*,
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parse_docstring: bool = False,
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error_on_invalid_docstring: bool = False,
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fn: Callable,
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*,
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parse_docstring: bool = False,
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error_on_invalid_docstring: bool = False,
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) -> tuple[str, dict]:
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"""Infer argument descriptions from a function's docstring."""
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if hasattr(inspect, "get_annotations"):
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@ -173,7 +173,7 @@ def _is_pydantic_annotation(annotation: Any, pydantic_version: str = "v2") -> bo
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def _function_annotations_are_pydantic_v1(
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signature: inspect.Signature, func: Callable
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signature: inspect.Signature, func: Callable
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) -> bool:
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"""Determine if all Pydantic annotations in a function signature are from V1."""
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any_v1_annotations = any(
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@ -210,13 +210,13 @@ class _SchemaConfig:
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def create_schema_from_function(
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model_name: str,
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func: Callable,
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*,
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filter_args: Optional[Sequence[str]] = None,
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parse_docstring: bool = False,
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error_on_invalid_docstring: bool = False,
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include_injected: bool = True,
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model_name: str,
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func: Callable,
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*,
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filter_args: Optional[Sequence[str]] = None,
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parse_docstring: bool = False,
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error_on_invalid_docstring: bool = False,
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include_injected: bool = True,
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) -> type[BaseModel]:
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"""Create a pydantic schema from a function's signature.
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@ -277,7 +277,7 @@ def create_schema_from_function(
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for existing_param in existing_params:
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if not include_injected and _is_injected_arg_type(
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sig.parameters[existing_param].annotation
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sig.parameters[existing_param].annotation
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):
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filter_args_.append(existing_param)
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@ -427,10 +427,10 @@ class ChildTool(BaseTool):
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def __init__(self, **kwargs: Any) -> None:
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"""Initialize the tool."""
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if (
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"args_schema" in kwargs
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and kwargs["args_schema"] is not None
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and not is_basemodel_subclass(kwargs["args_schema"])
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and not isinstance(kwargs["args_schema"], dict)
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"args_schema" in kwargs
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and kwargs["args_schema"] is not None
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and not is_basemodel_subclass(kwargs["args_schema"])
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and not isinstance(kwargs["args_schema"], dict)
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):
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msg = (
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"args_schema must be a subclass of pydantic BaseModel or "
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@ -481,7 +481,7 @@ class ChildTool(BaseTool):
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# --- Runnable ---
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def get_input_schema(
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self, config: Optional[RunnableConfig] = None
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self, config: Optional[RunnableConfig] = None
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) -> type[BaseModel]:
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"""The tool's input schema.
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@ -499,19 +499,19 @@ class ChildTool(BaseTool):
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return create_schema_from_function(self.name, self._run)
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def invoke(
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self,
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input: Union[str, dict, ToolCall],
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config: Optional[RunnableConfig] = None,
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**kwargs: Any,
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self,
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input: Union[str, dict, ToolCall],
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config: Optional[RunnableConfig] = None,
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**kwargs: Any,
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) -> Any:
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tool_input, kwargs = _prep_run_args(input, config, **kwargs)
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return self.run(tool_input, **kwargs)
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async def ainvoke(
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self,
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input: Union[str, dict, ToolCall],
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config: Optional[RunnableConfig] = None,
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**kwargs: Any,
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self,
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input: Union[str, dict, ToolCall],
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config: Optional[RunnableConfig] = None,
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**kwargs: Any,
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) -> Any:
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tool_input, kwargs = _prep_run_args(input, config, **kwargs)
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return await self.arun(tool_input, **kwargs)
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@ -519,7 +519,7 @@ class ChildTool(BaseTool):
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# --- Tool ---
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def _parse_input(
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self, tool_input: Union[str, dict], tool_call_id: Optional[str]
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self, tool_input: Union[str, dict], tool_call_id: Optional[str]
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) -> Union[str, dict[str, Any]]:
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"""Convert tool input to a pydantic model.
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@ -548,8 +548,8 @@ class ChildTool(BaseTool):
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elif issubclass(input_args, BaseModel):
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for k, v in get_all_basemodel_annotations(input_args).items():
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if (
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_is_injected_arg_type(v, injected_type=InjectedToolCallId)
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and k not in tool_input
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_is_injected_arg_type(v, injected_type=InjectedToolCallId)
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and k not in tool_input
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):
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if tool_call_id is None:
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msg = (
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@ -566,8 +566,8 @@ class ChildTool(BaseTool):
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elif issubclass(input_args, BaseModelV1):
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for k, v in get_all_basemodel_annotations(input_args).items():
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if (
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_is_injected_arg_type(v, injected_type=InjectedToolCallId)
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and k not in tool_input
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_is_injected_arg_type(v, injected_type=InjectedToolCallId)
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and k not in tool_input
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):
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if tool_call_id is None:
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msg = (
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@ -629,19 +629,19 @@ class ChildTool(BaseTool):
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to child implementations to enable tracing.
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"""
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if kwargs.get("run_manager") and signature(self._run).parameters.get(
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"run_manager"
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"run_manager"
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):
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kwargs["run_manager"] = kwargs["run_manager"].get_sync()
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return await run_in_executor(None, self._run, *args, **kwargs)
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def _to_args_and_kwargs(
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self, tool_input: Union[str, dict], tool_call_id: Optional[str]
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self, tool_input: Union[str, dict], tool_call_id: Optional[str]
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) -> tuple[tuple, dict]:
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if (
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self.args_schema is not None
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and isinstance(self.args_schema, type)
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and is_basemodel_subclass(self.args_schema)
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and not get_fields(self.args_schema)
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self.args_schema is not None
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and isinstance(self.args_schema, type)
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and is_basemodel_subclass(self.args_schema)
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and not get_fields(self.args_schema)
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):
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# StructuredTool with no args
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return (), {}
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@ -654,20 +654,20 @@ class ChildTool(BaseTool):
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return (), tool_input
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def run(
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self,
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tool_input: Union[str, dict[str, Any]],
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verbose: Optional[bool] = None,
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start_color: Optional[str] = "green",
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color: Optional[str] = "green",
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callbacks: Callbacks = None,
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*,
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tags: Optional[list[str]] = None,
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metadata: Optional[dict[str, Any]] = None,
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run_name: Optional[str] = None,
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run_id: Optional[uuid.UUID] = None,
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config: Optional[RunnableConfig] = None,
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tool_call_id: Optional[str] = None,
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**kwargs: Any,
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self,
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tool_input: Union[str, dict[str, Any]],
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verbose: Optional[bool] = None,
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start_color: Optional[str] = "green",
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color: Optional[str] = "green",
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callbacks: Callbacks = None,
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*,
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tags: Optional[list[str]] = None,
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metadata: Optional[dict[str, Any]] = None,
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run_name: Optional[str] = None,
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run_id: Optional[uuid.UUID] = None,
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config: Optional[RunnableConfig] = None,
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tool_call_id: Optional[str] = None,
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**kwargs: Any,
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) -> Any:
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"""Run the tool.
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@ -766,20 +766,20 @@ class ChildTool(BaseTool):
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return output
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async def arun(
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self,
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tool_input: Union[str, dict],
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verbose: Optional[bool] = None,
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start_color: Optional[str] = "green",
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color: Optional[str] = "green",
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callbacks: Callbacks = None,
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*,
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tags: Optional[list[str]] = None,
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metadata: Optional[dict[str, Any]] = None,
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run_name: Optional[str] = None,
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run_id: Optional[uuid.UUID] = None,
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config: Optional[RunnableConfig] = None,
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tool_call_id: Optional[str] = None,
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**kwargs: Any,
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self,
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tool_input: Union[str, dict],
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verbose: Optional[bool] = None,
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start_color: Optional[str] = "green",
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color: Optional[str] = "green",
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callbacks: Callbacks = None,
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*,
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tags: Optional[list[str]] = None,
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metadata: Optional[dict[str, Any]] = None,
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run_name: Optional[str] = None,
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run_id: Optional[uuid.UUID] = None,
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config: Optional[RunnableConfig] = None,
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tool_call_id: Optional[str] = None,
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**kwargs: Any,
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) -> Any:
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"""Run the tool asynchronously.
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@ -837,8 +837,6 @@ class ChildTool(BaseTool):
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self._run if self.__class__._arun is BaseTool._arun else self._arun
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)
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if signature(func_to_check).parameters.get("run_manager"):
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import copy
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tool_kwargs = copy.deepcopy(tool_kwargs)
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tool_kwargs["run_manager"] = run_manager
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if config_param := _get_runnable_config_param(func_to_check):
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tool_kwargs[config_param] = config
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@ -895,11 +893,11 @@ def _is_tool_call(x: Any) -> bool:
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def _handle_validation_error(
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e: Union[ValidationError, ValidationErrorV1],
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*,
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flag: Union[
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Literal[True], str, Callable[[Union[ValidationError, ValidationErrorV1]], str]
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],
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e: Union[ValidationError, ValidationErrorV1],
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*,
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flag: Union[
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Literal[True], str, Callable[[Union[ValidationError, ValidationErrorV1]], str]
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],
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) -> str:
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if isinstance(flag, bool):
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content = "Tool input validation error"
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@ -917,9 +915,9 @@ def _handle_validation_error(
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def _handle_tool_error(
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e: ToolException,
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*,
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flag: Optional[Union[Literal[True], str, Callable[[ToolException], str]]],
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e: ToolException,
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*,
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flag: Optional[Union[Literal[True], str, Callable[[ToolException], str]]],
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) -> str:
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if isinstance(flag, bool):
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content = e.args[0] if e.args else "Tool execution error"
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@ -937,9 +935,9 @@ def _handle_tool_error(
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def _prep_run_args(
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input: Union[str, dict, ToolCall],
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config: Optional[RunnableConfig],
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**kwargs: Any,
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input: Union[str, dict, ToolCall],
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config: Optional[RunnableConfig],
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**kwargs: Any,
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) -> tuple[Union[str, dict], dict]:
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config = ensure_config(config)
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if _is_tool_call(input):
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@ -947,7 +945,7 @@ def _prep_run_args(
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tool_input: Union[str, dict] = cast(ToolCall, input)["args"].copy()
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else:
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tool_call_id = None
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tool_input = cast(Union[str, dict], input)
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tool_input = cast(Union[str, dict], input).copy()
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return (
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tool_input,
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dict(
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@ -964,11 +962,11 @@ def _prep_run_args(
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def _format_output(
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content: Any,
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artifact: Any,
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tool_call_id: Optional[str],
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name: str,
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status: str,
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content: Any,
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artifact: Any,
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tool_call_id: Optional[str],
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name: str,
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status: str,
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) -> Union[ToolOutputMixin, Any]:
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if isinstance(content, ToolOutputMixin) or tool_call_id is None:
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return content
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@ -986,9 +984,9 @@ def _format_output(
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def _is_message_content_type(obj: Any) -> bool:
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"""Check for OpenAI or Anthropic format tool message content."""
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return (
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isinstance(obj, str)
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or isinstance(obj, list)
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and all(_is_message_content_block(e) for e in obj)
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isinstance(obj, str)
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or isinstance(obj, list)
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and all(_is_message_content_block(e) for e in obj)
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)
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@ -1051,7 +1049,7 @@ class InjectedToolCallId(InjectedToolArg):
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def _is_injected_arg_type(
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type_: type, injected_type: Optional[type[InjectedToolArg]] = None
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type_: type, injected_type: Optional[type[InjectedToolArg]] = None
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) -> bool:
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injected_type = injected_type or InjectedToolArg
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return any(
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@ -1062,7 +1060,7 @@ def _is_injected_arg_type(
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def get_all_basemodel_annotations(
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cls: Union[TypeBaseModel, Any], *, default_to_bound: bool = True
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cls: Union[TypeBaseModel, Any], *, default_to_bound: bool = True
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) -> dict[str, type]:
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# cls has no subscript: cls = FooBar
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if isinstance(cls, type):
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@ -1071,8 +1069,8 @@ def get_all_basemodel_annotations(
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# Exclude hidden init args added by pydantic Config. For example if
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# BaseModel(extra="allow") then "extra_data" will part of init sig.
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if (
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fields := getattr(cls, "model_fields", {}) # pydantic v2+
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or getattr(cls, "__fields__", {}) # pydantic v1
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fields := getattr(cls, "model_fields", {}) # pydantic v2+
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or getattr(cls, "__fields__", {}) # pydantic v1
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) and name not in fields:
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continue
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annotations[name] = param.annotation
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@ -1122,9 +1120,9 @@ def get_all_basemodel_annotations(
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def _replace_type_vars(
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type_: type,
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generic_map: Optional[dict[TypeVar, type]] = None,
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default_to_bound: bool = True,
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type_: type,
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generic_map: Optional[dict[TypeVar, type]] = None,
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default_to_bound: bool = True,
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) -> type:
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generic_map = generic_map or {}
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if isinstance(type_, TypeVar):
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@ -1148,4 +1146,4 @@ class BaseToolkit(BaseModel, ABC):
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@abstractmethod
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def get_tools(self) -> list[BaseTool]:
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"""Get the tools in the toolkit."""
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"""Get the tools in the toolkit."""
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