mirror of
https://github.com/Mintplex-Labs/langchain-python.git
synced 2026-07-25 04:26:41 -04:00
Ankush Gola
82baecc892
This PR adds * `ZeroShotAgent.as_sql_agent`, which returns an agent for interacting with a sql database. This builds off of `SQLDatabaseChain`. The main advantages are 1) answering general questions about the db, 2) access to a tool for double checking queries, and 3) recovering from errors * `ZeroShotAgent.as_json_agent` which returns an agent for interacting with json blobs. * Several examples in notebooks --------- Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
122 lines
3.9 KiB
Python
122 lines
3.9 KiB
Python
"""Base implementation for tools or skills."""
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from abc import abstractmethod
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from typing import Any, Optional
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from pydantic import BaseModel, Extra, Field, validator
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from langchain.callbacks import get_callback_manager
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from langchain.callbacks.base import BaseCallbackManager
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class BaseTool(BaseModel):
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"""Class responsible for defining a tool or skill for an LLM."""
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name: str
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description: str
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return_direct: bool = False
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verbose: bool = False
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callback_manager: BaseCallbackManager = Field(default_factory=get_callback_manager)
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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.forbid
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arbitrary_types_allowed = True
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@validator("callback_manager", pre=True, always=True)
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def set_callback_manager(
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cls, callback_manager: Optional[BaseCallbackManager]
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) -> BaseCallbackManager:
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"""If callback manager is None, set it.
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This allows users to pass in None as callback manager, which is a nice UX.
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"""
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return callback_manager or get_callback_manager()
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@abstractmethod
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def _run(self, tool_input: str) -> str:
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"""Use the tool."""
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@abstractmethod
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async def _arun(self, tool_input: str) -> str:
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"""Use the tool asynchronously."""
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def __call__(self, tool_input: str) -> str:
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"""Make tools callable with str input."""
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return self.run(tool_input)
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def run(
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self,
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tool_input: str,
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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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**kwargs: Any
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) -> str:
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"""Run the tool."""
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if verbose is None:
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verbose = self.verbose
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self.callback_manager.on_tool_start(
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{"name": self.name, "description": self.description},
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tool_input,
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verbose=verbose,
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color=start_color,
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**kwargs,
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)
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try:
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observation = self._run(tool_input)
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except (Exception, KeyboardInterrupt) as e:
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self.callback_manager.on_tool_error(e, verbose=verbose)
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raise e
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self.callback_manager.on_tool_end(
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observation, verbose=verbose, color=color, **kwargs
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)
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return observation
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async def arun(
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self,
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tool_input: str,
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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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**kwargs: Any
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) -> str:
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"""Run the tool asynchronously."""
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if verbose is None:
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verbose = self.verbose
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if self.callback_manager.is_async:
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await self.callback_manager.on_tool_start(
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{"name": self.name, "description": self.description},
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tool_input,
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verbose=verbose,
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color=start_color,
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**kwargs,
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)
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else:
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self.callback_manager.on_tool_start(
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{"name": self.name, "description": self.description},
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tool_input,
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verbose=verbose,
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color=start_color,
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**kwargs,
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)
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try:
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# We then call the tool on the tool input to get an observation
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observation = await self._arun(tool_input)
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except (Exception, KeyboardInterrupt) as e:
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if self.callback_manager.is_async:
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await self.callback_manager.on_tool_error(e, verbose=verbose)
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else:
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self.callback_manager.on_tool_error(e, verbose=verbose)
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raise e
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if self.callback_manager.is_async:
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await self.callback_manager.on_tool_end(
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observation, verbose=verbose, color=color, **kwargs
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)
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else:
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self.callback_manager.on_tool_end(
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observation, verbose=verbose, color=color, **kwargs
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)
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return observation
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