mirror of
https://github.com/hwchase17/langchain
synced 2024-11-10 01:10:59 +00:00
6ed0aa3239
Do not prefix function signature --- * Reason for this is that information is already present with tool calling models. * This will save on tokens for those models, and makes it more obvious what the description is! * The @tool can get more parameters to allow a user to re-introduce the the signature if we want
1100 lines
38 KiB
Python
1100 lines
38 KiB
Python
"""**Tools** are classes that an Agent uses to interact with the world.
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Each tool has a **description**. Agent uses the description to choose the right
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tool for the job.
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**Class hierarchy:**
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.. code-block::
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RunnableSerializable --> BaseTool --> <name>Tool # Examples: AIPluginTool, BaseGraphQLTool
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<name> # Examples: BraveSearch, HumanInputRun
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**Main helpers:**
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.. code-block::
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CallbackManagerForToolRun, AsyncCallbackManagerForToolRun
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""" # noqa: E501
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from __future__ import annotations
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import asyncio
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import inspect
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import textwrap
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import uuid
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import warnings
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from abc import ABC, abstractmethod
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from contextvars import copy_context
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from functools import partial
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from inspect import signature
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from typing import Any, Awaitable, Callable, Dict, List, Optional, Tuple, Type, Union
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from langchain_core._api import deprecated
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from langchain_core.callbacks import (
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AsyncCallbackManager,
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AsyncCallbackManagerForToolRun,
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BaseCallbackManager,
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CallbackManager,
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CallbackManagerForToolRun,
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)
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from langchain_core.callbacks.manager import (
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Callbacks,
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)
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from langchain_core.load.serializable import Serializable
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from langchain_core.prompts import (
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BasePromptTemplate,
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PromptTemplate,
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aformat_document,
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format_document,
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)
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from langchain_core.pydantic_v1 import (
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BaseModel,
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Extra,
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Field,
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ValidationError,
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create_model,
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root_validator,
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validate_arguments,
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)
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from langchain_core.retrievers import BaseRetriever
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from langchain_core.runnables import (
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Runnable,
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RunnableConfig,
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RunnableSerializable,
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ensure_config,
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)
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from langchain_core.runnables.config import (
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patch_config,
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run_in_executor,
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var_child_runnable_config,
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)
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from langchain_core.runnables.utils import accepts_context
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class SchemaAnnotationError(TypeError):
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"""Raised when 'args_schema' is missing or has an incorrect type annotation."""
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def _create_subset_model(
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name: str, model: Type[BaseModel], field_names: list
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) -> Type[BaseModel]:
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"""Create a pydantic model with only a subset of model's fields."""
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fields = {}
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for field_name in field_names:
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field = model.__fields__[field_name]
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t = (
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# this isn't perfect but should work for most functions
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field.outer_type_
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if field.required and not field.allow_none
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else Optional[field.outer_type_]
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)
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fields[field_name] = (t, field.field_info)
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rtn = create_model(name, **fields) # type: ignore
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return rtn
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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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) -> dict:
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"""Get the arguments from a function's signature."""
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schema = inferred_model.schema()["properties"]
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valid_keys = signature(func).parameters
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return {k: schema[k] for k in valid_keys if k not in ("run_manager", "callbacks")}
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class _SchemaConfig:
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"""Configuration for the pydantic model."""
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extra: Any = Extra.forbid
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arbitrary_types_allowed: bool = True
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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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) -> Type[BaseModel]:
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"""Create a pydantic schema from a function's signature.
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Args:
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model_name: Name to assign to the generated pydandic schema
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func: Function to generate the schema from
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Returns:
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A pydantic model with the same arguments as the function
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"""
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# https://docs.pydantic.dev/latest/usage/validation_decorator/
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validated = validate_arguments(func, config=_SchemaConfig) # type: ignore
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inferred_model = validated.model # type: ignore
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if "run_manager" in inferred_model.__fields__:
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del inferred_model.__fields__["run_manager"]
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if "callbacks" in inferred_model.__fields__:
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del inferred_model.__fields__["callbacks"]
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# Pydantic adds placeholder virtual fields we need to strip
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valid_properties = _get_filtered_args(inferred_model, func)
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return _create_subset_model(
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f"{model_name}Schema", inferred_model, list(valid_properties)
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)
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class ToolException(Exception):
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"""Optional exception that tool throws when execution error occurs.
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When this exception is thrown, the agent will not stop working,
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but it will handle the exception according to the handle_tool_error
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variable of the tool, and the processing result will be returned
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to the agent as observation, and printed in red on the console.
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"""
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pass
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class BaseTool(RunnableSerializable[Union[str, Dict], Any]):
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"""Interface LangChain tools must implement."""
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def __init_subclass__(cls, **kwargs: Any) -> None:
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"""Create the definition of the new tool class."""
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super().__init_subclass__(**kwargs)
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args_schema_type = cls.__annotations__.get("args_schema", None)
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if args_schema_type is not None and args_schema_type == BaseModel:
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# Throw errors for common mis-annotations.
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# TODO: Use get_args / get_origin and fully
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# specify valid annotations.
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typehint_mandate = """
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class ChildTool(BaseTool):
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...
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args_schema: Type[BaseModel] = SchemaClass
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..."""
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name = cls.__name__
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raise SchemaAnnotationError(
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f"Tool definition for {name} must include valid type annotations"
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f" for argument 'args_schema' to behave as expected.\n"
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f"Expected annotation of 'Type[BaseModel]'"
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f" but got '{args_schema_type}'.\n"
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f"Expected class looks like:\n"
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f"{typehint_mandate}"
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)
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name: str
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"""The unique name of the tool that clearly communicates its purpose."""
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description: str
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"""Used to tell the model how/when/why to use the tool.
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You can provide few-shot examples as a part of the description.
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"""
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args_schema: Optional[Type[BaseModel]] = None
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"""Pydantic model class to validate and parse the tool's input arguments."""
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return_direct: bool = False
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"""Whether to return the tool's output directly. Setting this to True means
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that after the tool is called, the AgentExecutor will stop looping.
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"""
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verbose: bool = False
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"""Whether to log the tool's progress."""
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callbacks: Callbacks = Field(default=None, exclude=True)
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"""Callbacks to be called during tool execution."""
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callback_manager: Optional[BaseCallbackManager] = Field(default=None, exclude=True)
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"""Deprecated. Please use callbacks instead."""
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tags: Optional[List[str]] = None
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"""Optional list of tags associated with the tool. Defaults to None
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These tags will be associated with each call to this tool,
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and passed as arguments to the handlers defined in `callbacks`.
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You can use these to eg identify a specific instance of a tool with its use case.
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"""
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metadata: Optional[Dict[str, Any]] = None
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"""Optional metadata associated with the tool. Defaults to None
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This metadata will be associated with each call to this tool,
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and passed as arguments to the handlers defined in `callbacks`.
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You can use these to eg identify a specific instance of a tool with its use case.
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"""
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handle_tool_error: Optional[
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Union[bool, str, Callable[[ToolException], str]]
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] = False
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"""Handle the content of the ToolException thrown."""
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handle_validation_error: Optional[
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Union[bool, str, Callable[[ValidationError], str]]
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] = False
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"""Handle the content of the ValidationError thrown."""
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class Config(Serializable.Config):
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"""Configuration for this pydantic object."""
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arbitrary_types_allowed = True
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@property
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def is_single_input(self) -> bool:
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"""Whether the tool only accepts a single input."""
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keys = {k for k in self.args if k != "kwargs"}
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return len(keys) == 1
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@property
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def args(self) -> dict:
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if self.args_schema is not None:
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return self.args_schema.schema()["properties"]
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else:
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schema = create_schema_from_function(self.name, self._run)
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return schema.schema()["properties"]
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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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) -> Type[BaseModel]:
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"""The tool's input schema."""
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if self.args_schema is not None:
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return self.args_schema
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else:
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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],
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config: Optional[RunnableConfig] = None,
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**kwargs: Any,
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) -> Any:
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config = ensure_config(config)
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return self.run(
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input,
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callbacks=config.get("callbacks"),
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tags=config.get("tags"),
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metadata=config.get("metadata"),
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run_name=config.get("run_name"),
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run_id=config.pop("run_id", None),
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config=config,
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**kwargs,
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)
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async def ainvoke(
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self,
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input: Union[str, Dict],
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config: Optional[RunnableConfig] = None,
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**kwargs: Any,
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) -> Any:
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config = ensure_config(config)
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return await self.arun(
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input,
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callbacks=config.get("callbacks"),
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tags=config.get("tags"),
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metadata=config.get("metadata"),
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run_name=config.get("run_name"),
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run_id=config.pop("run_id", None),
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config=config,
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**kwargs,
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)
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# --- Tool ---
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def _parse_input(
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self,
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tool_input: Union[str, Dict],
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) -> Union[str, Dict[str, Any]]:
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"""Convert tool input to pydantic model."""
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input_args = self.args_schema
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if isinstance(tool_input, str):
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if input_args is not None:
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key_ = next(iter(input_args.__fields__.keys()))
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input_args.validate({key_: tool_input})
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return tool_input
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else:
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if input_args is not None:
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result = input_args.parse_obj(tool_input)
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return {
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k: getattr(result, k)
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for k, v in result.dict().items()
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if k in tool_input
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}
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return tool_input
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@root_validator()
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def raise_deprecation(cls, values: Dict) -> Dict:
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"""Raise deprecation warning if callback_manager is used."""
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if values.get("callback_manager") is not None:
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warnings.warn(
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"callback_manager is deprecated. Please use callbacks instead.",
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DeprecationWarning,
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)
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values["callbacks"] = values.pop("callback_manager", None)
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return values
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@abstractmethod
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def _run(
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self,
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*args: Any,
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**kwargs: Any,
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) -> Any:
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"""Use the tool.
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Add run_manager: Optional[CallbackManagerForToolRun] = None
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to child implementations to enable tracing,
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"""
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async def _arun(
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self,
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*args: Any,
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**kwargs: Any,
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) -> Any:
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"""Use the tool asynchronously.
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Add run_manager: Optional[AsyncCallbackManagerForToolRun] = None
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to child implementations to enable tracing,
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"""
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return await run_in_executor(None, self._run, *args, **kwargs)
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def _to_args_and_kwargs(self, tool_input: Union[str, Dict]) -> Tuple[Tuple, Dict]:
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# For backwards compatibility, if run_input is a string,
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# pass as a positional argument.
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if isinstance(tool_input, str):
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return (tool_input,), {}
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else:
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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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**kwargs: Any,
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) -> Any:
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"""Run the tool."""
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if not self.verbose and verbose is not None:
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verbose_ = verbose
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else:
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verbose_ = self.verbose
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callback_manager = CallbackManager.configure(
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callbacks,
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self.callbacks,
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verbose_,
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tags,
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self.tags,
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metadata,
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self.metadata,
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)
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# TODO: maybe also pass through run_manager is _run supports kwargs
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new_arg_supported = signature(self._run).parameters.get("run_manager")
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run_manager = callback_manager.on_tool_start(
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{"name": self.name, "description": self.description},
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tool_input if isinstance(tool_input, str) else str(tool_input),
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color=start_color,
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name=run_name,
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run_id=run_id,
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# Inputs by definition should always be dicts.
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# For now, it's unclear whether this assumption is ever violated,
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# but if it is we will send a `None` value to the callback instead
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# And will need to address issue via a patch.
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inputs=None if isinstance(tool_input, str) else tool_input,
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**kwargs,
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)
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try:
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child_config = patch_config(
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config,
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callbacks=run_manager.get_child(),
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)
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context = copy_context()
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context.run(var_child_runnable_config.set, child_config)
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parsed_input = self._parse_input(tool_input)
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tool_args, tool_kwargs = self._to_args_and_kwargs(parsed_input)
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observation = (
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context.run(
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self._run, *tool_args, run_manager=run_manager, **tool_kwargs
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)
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if new_arg_supported
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else context.run(self._run, *tool_args, **tool_kwargs)
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)
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except ValidationError as e:
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if not self.handle_validation_error:
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raise e
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elif isinstance(self.handle_validation_error, bool):
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observation = "Tool input validation error"
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elif isinstance(self.handle_validation_error, str):
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observation = self.handle_validation_error
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elif callable(self.handle_validation_error):
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observation = self.handle_validation_error(e)
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else:
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raise ValueError(
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f"Got unexpected type of `handle_validation_error`. Expected bool, "
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f"str or callable. Received: {self.handle_validation_error}"
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)
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return observation
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except ToolException as e:
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if not self.handle_tool_error:
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run_manager.on_tool_error(e)
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raise e
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elif isinstance(self.handle_tool_error, bool):
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if e.args:
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observation = e.args[0]
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else:
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observation = "Tool execution error"
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elif isinstance(self.handle_tool_error, str):
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observation = self.handle_tool_error
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elif callable(self.handle_tool_error):
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observation = self.handle_tool_error(e)
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else:
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raise ValueError(
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f"Got unexpected type of `handle_tool_error`. Expected bool, str "
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f"or callable. Received: {self.handle_tool_error}"
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)
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run_manager.on_tool_end(observation, color="red", name=self.name, **kwargs)
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return observation
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except (Exception, KeyboardInterrupt) as e:
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run_manager.on_tool_error(e)
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raise e
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else:
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run_manager.on_tool_end(observation, color=color, name=self.name, **kwargs)
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return observation
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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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**kwargs: Any,
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) -> Any:
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"""Run the tool asynchronously."""
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if not self.verbose and verbose is not None:
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verbose_ = verbose
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else:
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verbose_ = self.verbose
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callback_manager = AsyncCallbackManager.configure(
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callbacks,
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self.callbacks,
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verbose_,
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tags,
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self.tags,
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metadata,
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self.metadata,
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)
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new_arg_supported = signature(self._arun).parameters.get("run_manager")
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run_manager = await callback_manager.on_tool_start(
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{"name": self.name, "description": self.description},
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tool_input if isinstance(tool_input, str) else str(tool_input),
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color=start_color,
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name=run_name,
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inputs=tool_input,
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run_id=run_id,
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**kwargs,
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)
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try:
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parsed_input = self._parse_input(tool_input)
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# We then call the tool on the tool input to get an observation
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tool_args, tool_kwargs = self._to_args_and_kwargs(parsed_input)
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child_config = patch_config(
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config,
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callbacks=run_manager.get_child(),
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)
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context = copy_context()
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context.run(var_child_runnable_config.set, child_config)
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coro = (
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context.run(
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self._arun, *tool_args, run_manager=run_manager, **tool_kwargs
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)
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if new_arg_supported
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else context.run(self._arun, *tool_args, **tool_kwargs)
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)
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if accepts_context(asyncio.create_task):
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observation = await asyncio.create_task(coro, context=context) # type: ignore
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else:
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observation = await coro
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except ValidationError as e:
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if not self.handle_validation_error:
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raise e
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elif isinstance(self.handle_validation_error, bool):
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observation = "Tool input validation error"
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elif isinstance(self.handle_validation_error, str):
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observation = self.handle_validation_error
|
|
elif callable(self.handle_validation_error):
|
|
observation = self.handle_validation_error(e)
|
|
else:
|
|
raise ValueError(
|
|
f"Got unexpected type of `handle_validation_error`. Expected bool, "
|
|
f"str or callable. Received: {self.handle_validation_error}"
|
|
)
|
|
return observation
|
|
except ToolException as e:
|
|
if not self.handle_tool_error:
|
|
await run_manager.on_tool_error(e)
|
|
raise e
|
|
elif isinstance(self.handle_tool_error, bool):
|
|
if e.args:
|
|
observation = e.args[0]
|
|
else:
|
|
observation = "Tool execution error"
|
|
elif isinstance(self.handle_tool_error, str):
|
|
observation = self.handle_tool_error
|
|
elif callable(self.handle_tool_error):
|
|
observation = self.handle_tool_error(e)
|
|
else:
|
|
raise ValueError(
|
|
f"Got unexpected type of `handle_tool_error`. Expected bool, str "
|
|
f"or callable. Received: {self.handle_tool_error}"
|
|
)
|
|
await run_manager.on_tool_end(
|
|
observation, color="red", name=self.name, **kwargs
|
|
)
|
|
return observation
|
|
except (Exception, KeyboardInterrupt) as e:
|
|
await run_manager.on_tool_error(e)
|
|
raise e
|
|
else:
|
|
await run_manager.on_tool_end(
|
|
observation, color=color, name=self.name, **kwargs
|
|
)
|
|
return observation
|
|
|
|
@deprecated("0.1.47", alternative="invoke", removal="0.3.0")
|
|
def __call__(self, tool_input: str, callbacks: Callbacks = None) -> str:
|
|
"""Make tool callable."""
|
|
return self.run(tool_input, callbacks=callbacks)
|
|
|
|
|
|
class Tool(BaseTool):
|
|
"""Tool that takes in function or coroutine directly."""
|
|
|
|
description: str = ""
|
|
func: Optional[Callable[..., str]]
|
|
"""The function to run when the tool is called."""
|
|
coroutine: Optional[Callable[..., Awaitable[str]]] = None
|
|
"""The asynchronous version of the function."""
|
|
|
|
# --- Runnable ---
|
|
|
|
async def ainvoke(
|
|
self,
|
|
input: Union[str, Dict],
|
|
config: Optional[RunnableConfig] = None,
|
|
**kwargs: Any,
|
|
) -> Any:
|
|
if not self.coroutine:
|
|
# If the tool does not implement async, fall back to default implementation
|
|
return await run_in_executor(config, self.invoke, input, config, **kwargs)
|
|
|
|
return await super().ainvoke(input, config, **kwargs)
|
|
|
|
# --- Tool ---
|
|
|
|
@property
|
|
def args(self) -> dict:
|
|
"""The tool's input arguments."""
|
|
if self.args_schema is not None:
|
|
return self.args_schema.schema()["properties"]
|
|
# For backwards compatibility, if the function signature is ambiguous,
|
|
# assume it takes a single string input.
|
|
return {"tool_input": {"type": "string"}}
|
|
|
|
def _to_args_and_kwargs(self, tool_input: Union[str, Dict]) -> Tuple[Tuple, Dict]:
|
|
"""Convert tool input to pydantic model."""
|
|
args, kwargs = super()._to_args_and_kwargs(tool_input)
|
|
# For backwards compatibility. The tool must be run with a single input
|
|
all_args = list(args) + list(kwargs.values())
|
|
if len(all_args) != 1:
|
|
raise ToolException(
|
|
f"""Too many arguments to single-input tool {self.name}.
|
|
Consider using StructuredTool instead."""
|
|
f" Args: {all_args}"
|
|
)
|
|
return tuple(all_args), {}
|
|
|
|
def _run(
|
|
self,
|
|
*args: Any,
|
|
run_manager: Optional[CallbackManagerForToolRun] = None,
|
|
**kwargs: Any,
|
|
) -> Any:
|
|
"""Use the tool."""
|
|
if self.func:
|
|
new_argument_supported = signature(self.func).parameters.get("callbacks")
|
|
return (
|
|
self.func(
|
|
*args,
|
|
callbacks=run_manager.get_child() if run_manager else None,
|
|
**kwargs,
|
|
)
|
|
if new_argument_supported
|
|
else self.func(*args, **kwargs)
|
|
)
|
|
raise NotImplementedError("Tool does not support sync")
|
|
|
|
async def _arun(
|
|
self,
|
|
*args: Any,
|
|
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
|
|
**kwargs: Any,
|
|
) -> Any:
|
|
"""Use the tool asynchronously."""
|
|
if self.coroutine:
|
|
new_argument_supported = signature(self.coroutine).parameters.get(
|
|
"callbacks"
|
|
)
|
|
return (
|
|
await self.coroutine(
|
|
*args,
|
|
callbacks=run_manager.get_child() if run_manager else None,
|
|
**kwargs,
|
|
)
|
|
if new_argument_supported
|
|
else await self.coroutine(*args, **kwargs)
|
|
)
|
|
else:
|
|
return await run_in_executor(
|
|
None,
|
|
self._run,
|
|
run_manager=run_manager.get_sync() if run_manager else None,
|
|
*args,
|
|
**kwargs,
|
|
)
|
|
|
|
# TODO: this is for backwards compatibility, remove in future
|
|
def __init__(
|
|
self, name: str, func: Optional[Callable], description: str, **kwargs: Any
|
|
) -> None:
|
|
"""Initialize tool."""
|
|
super(Tool, self).__init__( # type: ignore[call-arg]
|
|
name=name, func=func, description=description, **kwargs
|
|
)
|
|
|
|
@classmethod
|
|
def from_function(
|
|
cls,
|
|
func: Optional[Callable],
|
|
name: str, # We keep these required to support backwards compatibility
|
|
description: str,
|
|
return_direct: bool = False,
|
|
args_schema: Optional[Type[BaseModel]] = None,
|
|
coroutine: Optional[
|
|
Callable[..., Awaitable[Any]]
|
|
] = None, # This is last for compatibility, but should be after func
|
|
**kwargs: Any,
|
|
) -> Tool:
|
|
"""Initialize tool from a function."""
|
|
if func is None and coroutine is None:
|
|
raise ValueError("Function and/or coroutine must be provided")
|
|
return cls(
|
|
name=name,
|
|
func=func,
|
|
coroutine=coroutine,
|
|
description=description,
|
|
return_direct=return_direct,
|
|
args_schema=args_schema,
|
|
**kwargs,
|
|
)
|
|
|
|
|
|
class StructuredTool(BaseTool):
|
|
"""Tool that can operate on any number of inputs."""
|
|
|
|
description: str = ""
|
|
args_schema: Type[BaseModel] = Field(..., description="The tool schema.")
|
|
"""The input arguments' schema."""
|
|
func: Optional[Callable[..., Any]]
|
|
"""The function to run when the tool is called."""
|
|
coroutine: Optional[Callable[..., Awaitable[Any]]] = None
|
|
"""The asynchronous version of the function."""
|
|
|
|
# --- Runnable ---
|
|
|
|
async def ainvoke(
|
|
self,
|
|
input: Union[str, Dict],
|
|
config: Optional[RunnableConfig] = None,
|
|
**kwargs: Any,
|
|
) -> Any:
|
|
if not self.coroutine:
|
|
# If the tool does not implement async, fall back to default implementation
|
|
return await run_in_executor(config, self.invoke, input, config, **kwargs)
|
|
|
|
return await super().ainvoke(input, config, **kwargs)
|
|
|
|
# --- Tool ---
|
|
|
|
@property
|
|
def args(self) -> dict:
|
|
"""The tool's input arguments."""
|
|
return self.args_schema.schema()["properties"]
|
|
|
|
def _run(
|
|
self,
|
|
*args: Any,
|
|
run_manager: Optional[CallbackManagerForToolRun] = None,
|
|
**kwargs: Any,
|
|
) -> Any:
|
|
"""Use the tool."""
|
|
if self.func:
|
|
new_argument_supported = signature(self.func).parameters.get("callbacks")
|
|
return (
|
|
self.func(
|
|
*args,
|
|
callbacks=run_manager.get_child() if run_manager else None,
|
|
**kwargs,
|
|
)
|
|
if new_argument_supported
|
|
else self.func(*args, **kwargs)
|
|
)
|
|
raise NotImplementedError("Tool does not support sync")
|
|
|
|
async def _arun(
|
|
self,
|
|
*args: Any,
|
|
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
|
|
**kwargs: Any,
|
|
) -> str:
|
|
"""Use the tool asynchronously."""
|
|
if self.coroutine:
|
|
new_argument_supported = signature(self.coroutine).parameters.get(
|
|
"callbacks"
|
|
)
|
|
return (
|
|
await self.coroutine(
|
|
*args,
|
|
callbacks=run_manager.get_child() if run_manager else None,
|
|
**kwargs,
|
|
)
|
|
if new_argument_supported
|
|
else await self.coroutine(*args, **kwargs)
|
|
)
|
|
return await run_in_executor(
|
|
None,
|
|
self._run,
|
|
run_manager=run_manager.get_sync() if run_manager else None,
|
|
*args,
|
|
**kwargs,
|
|
)
|
|
|
|
@classmethod
|
|
def from_function(
|
|
cls,
|
|
func: Optional[Callable] = None,
|
|
coroutine: Optional[Callable[..., Awaitable[Any]]] = None,
|
|
name: Optional[str] = None,
|
|
description: Optional[str] = None,
|
|
return_direct: bool = False,
|
|
args_schema: Optional[Type[BaseModel]] = None,
|
|
infer_schema: bool = True,
|
|
**kwargs: Any,
|
|
) -> StructuredTool:
|
|
"""Create tool from a given function.
|
|
|
|
A classmethod that helps to create a tool from a function.
|
|
|
|
Args:
|
|
func: The function from which to create a tool
|
|
coroutine: The async function from which to create a tool
|
|
name: The name of the tool. Defaults to the function name
|
|
description: The description of the tool. Defaults to the function docstring
|
|
return_direct: Whether to return the result directly or as a callback
|
|
args_schema: The schema of the tool's input arguments
|
|
infer_schema: Whether to infer the schema from the function's signature
|
|
**kwargs: Additional arguments to pass to the tool
|
|
|
|
Returns:
|
|
The tool
|
|
|
|
Examples:
|
|
|
|
.. code-block:: python
|
|
|
|
def add(a: int, b: int) -> int:
|
|
\"\"\"Add two numbers\"\"\"
|
|
return a + b
|
|
tool = StructuredTool.from_function(add)
|
|
tool.run(1, 2) # 3
|
|
"""
|
|
|
|
if func is not None:
|
|
source_function = func
|
|
elif coroutine is not None:
|
|
source_function = coroutine
|
|
else:
|
|
raise ValueError("Function and/or coroutine must be provided")
|
|
name = name or source_function.__name__
|
|
description_ = description or source_function.__doc__
|
|
if description_ is None:
|
|
raise ValueError(
|
|
"Function must have a docstring if description not provided."
|
|
)
|
|
if description is None:
|
|
# Only apply if using the function's docstring
|
|
description_ = textwrap.dedent(description_).strip()
|
|
|
|
# Description example:
|
|
# search_api(query: str) - Searches the API for the query.
|
|
description_ = f"{description_.strip()}"
|
|
_args_schema = args_schema
|
|
if _args_schema is None and infer_schema:
|
|
# schema name is appended within function
|
|
_args_schema = create_schema_from_function(name, source_function)
|
|
return cls(
|
|
name=name,
|
|
func=func,
|
|
coroutine=coroutine,
|
|
args_schema=_args_schema, # type: ignore[arg-type]
|
|
description=description_,
|
|
return_direct=return_direct,
|
|
**kwargs,
|
|
)
|
|
|
|
|
|
def tool(
|
|
*args: Union[str, Callable, Runnable],
|
|
return_direct: bool = False,
|
|
args_schema: Optional[Type[BaseModel]] = None,
|
|
infer_schema: bool = True,
|
|
) -> Callable:
|
|
"""Make tools out of functions, can be used with or without arguments.
|
|
|
|
Args:
|
|
*args: The arguments to the tool.
|
|
return_direct: Whether to return directly from the tool rather
|
|
than continuing the agent loop.
|
|
args_schema: optional argument schema for user to specify
|
|
infer_schema: Whether to infer the schema of the arguments from
|
|
the function's signature. This also makes the resultant tool
|
|
accept a dictionary input to its `run()` function.
|
|
|
|
Requires:
|
|
- Function must be of type (str) -> str
|
|
- Function must have a docstring
|
|
|
|
Examples:
|
|
.. code-block:: python
|
|
|
|
@tool
|
|
def search_api(query: str) -> str:
|
|
# Searches the API for the query.
|
|
return
|
|
|
|
@tool("search", return_direct=True)
|
|
def search_api(query: str) -> str:
|
|
# Searches the API for the query.
|
|
return
|
|
"""
|
|
|
|
def _make_with_name(tool_name: str) -> Callable:
|
|
def _make_tool(dec_func: Union[Callable, Runnable]) -> BaseTool:
|
|
if isinstance(dec_func, Runnable):
|
|
runnable = dec_func
|
|
|
|
if runnable.input_schema.schema().get("type") != "object":
|
|
raise ValueError("Runnable must have an object schema.")
|
|
|
|
async def ainvoke_wrapper(
|
|
callbacks: Optional[Callbacks] = None, **kwargs: Any
|
|
) -> Any:
|
|
return await runnable.ainvoke(kwargs, {"callbacks": callbacks})
|
|
|
|
def invoke_wrapper(
|
|
callbacks: Optional[Callbacks] = None, **kwargs: Any
|
|
) -> Any:
|
|
return runnable.invoke(kwargs, {"callbacks": callbacks})
|
|
|
|
coroutine = ainvoke_wrapper
|
|
func = invoke_wrapper
|
|
schema: Optional[Type[BaseModel]] = runnable.input_schema
|
|
description = repr(runnable)
|
|
elif inspect.iscoroutinefunction(dec_func):
|
|
coroutine = dec_func
|
|
func = None
|
|
schema = args_schema
|
|
description = None
|
|
else:
|
|
coroutine = None
|
|
func = dec_func
|
|
schema = args_schema
|
|
description = None
|
|
|
|
if infer_schema or args_schema is not None:
|
|
return StructuredTool.from_function(
|
|
func,
|
|
coroutine,
|
|
name=tool_name,
|
|
description=description,
|
|
return_direct=return_direct,
|
|
args_schema=schema,
|
|
infer_schema=infer_schema,
|
|
)
|
|
# If someone doesn't want a schema applied, we must treat it as
|
|
# a simple string->string function
|
|
if func.__doc__ is None:
|
|
raise ValueError(
|
|
"Function must have a docstring if "
|
|
"description not provided and infer_schema is False."
|
|
)
|
|
return Tool(
|
|
name=tool_name,
|
|
func=func,
|
|
description=f"{tool_name} tool",
|
|
return_direct=return_direct,
|
|
coroutine=coroutine,
|
|
)
|
|
|
|
return _make_tool
|
|
|
|
if len(args) == 2 and isinstance(args[0], str) and isinstance(args[1], Runnable):
|
|
return _make_with_name(args[0])(args[1])
|
|
elif len(args) == 1 and isinstance(args[0], str):
|
|
# if the argument is a string, then we use the string as the tool name
|
|
# Example usage: @tool("search", return_direct=True)
|
|
return _make_with_name(args[0])
|
|
elif len(args) == 1 and callable(args[0]):
|
|
# if the argument is a function, then we use the function name as the tool name
|
|
# Example usage: @tool
|
|
return _make_with_name(args[0].__name__)(args[0])
|
|
elif len(args) == 0:
|
|
# if there are no arguments, then we use the function name as the tool name
|
|
# Example usage: @tool(return_direct=True)
|
|
def _partial(func: Callable[[str], str]) -> BaseTool:
|
|
return _make_with_name(func.__name__)(func)
|
|
|
|
return _partial
|
|
else:
|
|
raise ValueError("Too many arguments for tool decorator")
|
|
|
|
|
|
class RetrieverInput(BaseModel):
|
|
"""Input to the retriever."""
|
|
|
|
query: str = Field(description="query to look up in retriever")
|
|
|
|
|
|
def _get_relevant_documents(
|
|
query: str,
|
|
retriever: BaseRetriever,
|
|
document_prompt: BasePromptTemplate,
|
|
document_separator: str,
|
|
callbacks: Callbacks = None,
|
|
) -> str:
|
|
docs = retriever.invoke(query, config={"callbacks": callbacks})
|
|
return document_separator.join(
|
|
format_document(doc, document_prompt) for doc in docs
|
|
)
|
|
|
|
|
|
async def _aget_relevant_documents(
|
|
query: str,
|
|
retriever: BaseRetriever,
|
|
document_prompt: BasePromptTemplate,
|
|
document_separator: str,
|
|
callbacks: Callbacks = None,
|
|
) -> str:
|
|
docs = await retriever.ainvoke(query, config={"callbacks": callbacks})
|
|
return document_separator.join(
|
|
[await aformat_document(doc, document_prompt) for doc in docs]
|
|
)
|
|
|
|
|
|
def create_retriever_tool(
|
|
retriever: BaseRetriever,
|
|
name: str,
|
|
description: str,
|
|
*,
|
|
document_prompt: Optional[BasePromptTemplate] = None,
|
|
document_separator: str = "\n\n",
|
|
) -> Tool:
|
|
"""Create a tool to do retrieval of documents.
|
|
|
|
Args:
|
|
retriever: The retriever to use for the retrieval
|
|
name: The name for the tool. This will be passed to the language model,
|
|
so should be unique and somewhat descriptive.
|
|
description: The description for the tool. This will be passed to the language
|
|
model, so should be descriptive.
|
|
|
|
Returns:
|
|
Tool class to pass to an agent
|
|
"""
|
|
document_prompt = document_prompt or PromptTemplate.from_template("{page_content}")
|
|
func = partial(
|
|
_get_relevant_documents,
|
|
retriever=retriever,
|
|
document_prompt=document_prompt,
|
|
document_separator=document_separator,
|
|
)
|
|
afunc = partial(
|
|
_aget_relevant_documents,
|
|
retriever=retriever,
|
|
document_prompt=document_prompt,
|
|
document_separator=document_separator,
|
|
)
|
|
return Tool(
|
|
name=name,
|
|
description=description,
|
|
func=func,
|
|
coroutine=afunc,
|
|
args_schema=RetrieverInput,
|
|
)
|
|
|
|
|
|
ToolsRenderer = Callable[[List[BaseTool]], str]
|
|
|
|
|
|
def render_text_description(tools: List[BaseTool]) -> str:
|
|
"""Render the tool name and description in plain text.
|
|
|
|
Output will be in the format of:
|
|
|
|
.. code-block:: markdown
|
|
|
|
search: This tool is used for search
|
|
calculator: This tool is used for math
|
|
"""
|
|
descriptions = []
|
|
for tool in tools:
|
|
if hasattr(tool, "func") and tool.func:
|
|
sig = signature(tool.func)
|
|
description = f"{tool.name}{sig} - {tool.description}"
|
|
else:
|
|
description = f"{tool.name} - {tool.description}"
|
|
|
|
descriptions.append(description)
|
|
return "\n".join(descriptions)
|
|
|
|
|
|
def render_text_description_and_args(tools: List[BaseTool]) -> str:
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"""Render the tool name, description, and args in plain text.
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Output will be in the format of:
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.. code-block:: markdown
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search: This tool is used for search, args: {"query": {"type": "string"}}
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|
calculator: This tool is used for math, \
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args: {"expression": {"type": "string"}}
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"""
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|
tool_strings = []
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for tool in tools:
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args_schema = str(tool.args)
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if hasattr(tool, "func") and tool.func:
|
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sig = signature(tool.func)
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description = f"{tool.name}{sig} - {tool.description}"
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else:
|
|
description = f"{tool.name} - {tool.description}"
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tool_strings.append(f"{description}, args: {args_schema}")
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return "\n".join(tool_strings)
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|
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class BaseToolkit(BaseModel, ABC):
|
|
"""Base Toolkit representing a collection of related tools."""
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|
|
@abstractmethod
|
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def get_tools(self) -> List[BaseTool]:
|
|
"""Get the tools in the toolkit."""
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