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1043 lines
36 KiB
Python
1043 lines
36 KiB
Python
"""Chain that takes in an input and produces an action and action input."""
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from __future__ import annotations
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import asyncio
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import json
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import logging
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import time
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from abc import abstractmethod
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from pathlib import Path
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from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
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import yaml
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from pydantic import BaseModel, root_validator
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from langchain.agents.agent_types import AgentType
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from langchain.agents.tools import InvalidTool
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from langchain.base_language import BaseLanguageModel
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from langchain.callbacks.base import BaseCallbackManager
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from langchain.callbacks.manager import (
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AsyncCallbackManagerForChainRun,
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AsyncCallbackManagerForToolRun,
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CallbackManagerForChainRun,
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CallbackManagerForToolRun,
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Callbacks,
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)
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from langchain.chains.base import Chain
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from langchain.chains.llm import LLMChain
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from langchain.input import get_color_mapping
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from langchain.prompts.base import BasePromptTemplate
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from langchain.prompts.few_shot import FewShotPromptTemplate
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from langchain.prompts.prompt import PromptTemplate
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from langchain.schema import (
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AgentAction,
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AgentFinish,
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BaseMessage,
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BaseOutputParser,
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)
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from langchain.tools.base import BaseTool
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from langchain.utilities.asyncio import asyncio_timeout
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logger = logging.getLogger(__name__)
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class BaseSingleActionAgent(BaseModel):
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"""Base Agent class."""
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@property
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def return_values(self) -> List[str]:
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"""Return values of the agent."""
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return ["output"]
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def get_allowed_tools(self) -> Optional[List[str]]:
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return None
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@abstractmethod
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def plan(
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self,
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intermediate_steps: List[Tuple[AgentAction, str]],
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callbacks: Callbacks = None,
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**kwargs: Any,
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) -> Union[AgentAction, AgentFinish]:
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"""Given input, decided what to do.
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Args:
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intermediate_steps: Steps the LLM has taken to date,
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along with observations
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callbacks: Callbacks to run.
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**kwargs: User inputs.
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Returns:
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Action specifying what tool to use.
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"""
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@abstractmethod
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async def aplan(
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self,
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intermediate_steps: List[Tuple[AgentAction, str]],
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callbacks: Callbacks = None,
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**kwargs: Any,
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) -> Union[AgentAction, AgentFinish]:
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"""Given input, decided what to do.
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Args:
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intermediate_steps: Steps the LLM has taken to date,
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along with observations
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callbacks: Callbacks to run.
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**kwargs: User inputs.
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Returns:
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Action specifying what tool to use.
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"""
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@property
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@abstractmethod
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def input_keys(self) -> List[str]:
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"""Return the input keys.
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:meta private:
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"""
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def return_stopped_response(
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self,
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early_stopping_method: str,
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intermediate_steps: List[Tuple[AgentAction, str]],
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**kwargs: Any,
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) -> AgentFinish:
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"""Return response when agent has been stopped due to max iterations."""
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if early_stopping_method == "force":
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# `force` just returns a constant string
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return AgentFinish(
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{"output": "Agent stopped due to iteration limit or time limit."}, ""
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)
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else:
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raise ValueError(
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f"Got unsupported early_stopping_method `{early_stopping_method}`"
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)
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@classmethod
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def from_llm_and_tools(
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cls,
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llm: BaseLanguageModel,
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tools: Sequence[BaseTool],
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callback_manager: Optional[BaseCallbackManager] = None,
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**kwargs: Any,
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) -> BaseSingleActionAgent:
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raise NotImplementedError
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@property
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def _agent_type(self) -> str:
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"""Return Identifier of agent type."""
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raise NotImplementedError
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def dict(self, **kwargs: Any) -> Dict:
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"""Return dictionary representation of agent."""
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_dict = super().dict()
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_type = self._agent_type
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if isinstance(_type, AgentType):
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_dict["_type"] = str(_type.value)
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else:
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_dict["_type"] = _type
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return _dict
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def save(self, file_path: Union[Path, str]) -> None:
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"""Save the agent.
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Args:
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file_path: Path to file to save the agent to.
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Example:
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.. code-block:: python
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# If working with agent executor
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agent.agent.save(file_path="path/agent.yaml")
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"""
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# Convert file to Path object.
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if isinstance(file_path, str):
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save_path = Path(file_path)
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else:
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save_path = file_path
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directory_path = save_path.parent
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directory_path.mkdir(parents=True, exist_ok=True)
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# Fetch dictionary to save
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agent_dict = self.dict()
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if save_path.suffix == ".json":
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with open(file_path, "w") as f:
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json.dump(agent_dict, f, indent=4)
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elif save_path.suffix == ".yaml":
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with open(file_path, "w") as f:
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yaml.dump(agent_dict, f, default_flow_style=False)
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else:
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raise ValueError(f"{save_path} must be json or yaml")
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def tool_run_logging_kwargs(self) -> Dict:
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return {}
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class BaseMultiActionAgent(BaseModel):
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"""Base Agent class."""
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@property
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def return_values(self) -> List[str]:
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"""Return values of the agent."""
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return ["output"]
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def get_allowed_tools(self) -> Optional[List[str]]:
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return None
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@abstractmethod
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def plan(
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self,
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intermediate_steps: List[Tuple[AgentAction, str]],
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callbacks: Callbacks = None,
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**kwargs: Any,
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) -> Union[List[AgentAction], AgentFinish]:
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"""Given input, decided what to do.
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Args:
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intermediate_steps: Steps the LLM has taken to date,
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along with observations
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callbacks: Callbacks to run.
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**kwargs: User inputs.
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Returns:
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Actions specifying what tool to use.
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"""
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@abstractmethod
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async def aplan(
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self,
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intermediate_steps: List[Tuple[AgentAction, str]],
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callbacks: Callbacks = None,
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**kwargs: Any,
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) -> Union[List[AgentAction], AgentFinish]:
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"""Given input, decided what to do.
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Args:
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intermediate_steps: Steps the LLM has taken to date,
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along with observations
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callbacks: Callbacks to run.
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**kwargs: User inputs.
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Returns:
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Actions specifying what tool to use.
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"""
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@property
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@abstractmethod
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def input_keys(self) -> List[str]:
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"""Return the input keys.
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:meta private:
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"""
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def return_stopped_response(
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self,
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early_stopping_method: str,
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intermediate_steps: List[Tuple[AgentAction, str]],
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**kwargs: Any,
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) -> AgentFinish:
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"""Return response when agent has been stopped due to max iterations."""
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if early_stopping_method == "force":
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# `force` just returns a constant string
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return AgentFinish({"output": "Agent stopped due to max iterations."}, "")
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else:
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raise ValueError(
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f"Got unsupported early_stopping_method `{early_stopping_method}`"
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)
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@property
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def _agent_type(self) -> str:
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"""Return Identifier of agent type."""
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raise NotImplementedError
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def dict(self, **kwargs: Any) -> Dict:
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"""Return dictionary representation of agent."""
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_dict = super().dict()
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_dict["_type"] = str(self._agent_type)
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return _dict
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def save(self, file_path: Union[Path, str]) -> None:
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"""Save the agent.
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Args:
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file_path: Path to file to save the agent to.
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Example:
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.. code-block:: python
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# If working with agent executor
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agent.agent.save(file_path="path/agent.yaml")
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"""
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# Convert file to Path object.
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if isinstance(file_path, str):
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save_path = Path(file_path)
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else:
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save_path = file_path
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directory_path = save_path.parent
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directory_path.mkdir(parents=True, exist_ok=True)
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# Fetch dictionary to save
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agent_dict = self.dict()
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if save_path.suffix == ".json":
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with open(file_path, "w") as f:
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json.dump(agent_dict, f, indent=4)
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elif save_path.suffix == ".yaml":
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with open(file_path, "w") as f:
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yaml.dump(agent_dict, f, default_flow_style=False)
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else:
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raise ValueError(f"{save_path} must be json or yaml")
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def tool_run_logging_kwargs(self) -> Dict:
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return {}
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class AgentOutputParser(BaseOutputParser):
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@abstractmethod
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def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
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"""Parse text into agent action/finish."""
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class LLMSingleActionAgent(BaseSingleActionAgent):
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llm_chain: LLMChain
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output_parser: AgentOutputParser
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stop: List[str]
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@property
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def input_keys(self) -> List[str]:
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return list(set(self.llm_chain.input_keys) - {"intermediate_steps"})
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def dict(self, **kwargs: Any) -> Dict:
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"""Return dictionary representation of agent."""
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_dict = super().dict()
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del _dict["output_parser"]
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return _dict
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def plan(
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self,
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intermediate_steps: List[Tuple[AgentAction, str]],
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callbacks: Callbacks = None,
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**kwargs: Any,
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) -> Union[AgentAction, AgentFinish]:
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"""Given input, decided what to do.
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Args:
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intermediate_steps: Steps the LLM has taken to date,
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along with observations
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callbacks: Callbacks to run.
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**kwargs: User inputs.
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Returns:
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Action specifying what tool to use.
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"""
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output = self.llm_chain.run(
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intermediate_steps=intermediate_steps,
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stop=self.stop,
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callbacks=callbacks,
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**kwargs,
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)
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return self.output_parser.parse(output)
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async def aplan(
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self,
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intermediate_steps: List[Tuple[AgentAction, str]],
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callbacks: Callbacks = None,
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**kwargs: Any,
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) -> Union[AgentAction, AgentFinish]:
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"""Given input, decided what to do.
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Args:
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intermediate_steps: Steps the LLM has taken to date,
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along with observations
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callbacks: Callbacks to run.
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**kwargs: User inputs.
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Returns:
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Action specifying what tool to use.
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"""
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output = await self.llm_chain.arun(
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intermediate_steps=intermediate_steps,
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stop=self.stop,
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callbacks=callbacks,
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**kwargs,
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)
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return self.output_parser.parse(output)
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def tool_run_logging_kwargs(self) -> Dict:
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return {
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"llm_prefix": "",
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"observation_prefix": "" if len(self.stop) == 0 else self.stop[0],
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}
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class Agent(BaseSingleActionAgent):
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"""Class responsible for calling the language model and deciding the action.
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This is driven by an LLMChain. The prompt in the LLMChain MUST include
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a variable called "agent_scratchpad" where the agent can put its
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intermediary work.
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"""
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llm_chain: LLMChain
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output_parser: AgentOutputParser
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allowed_tools: Optional[List[str]] = None
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def dict(self, **kwargs: Any) -> Dict:
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"""Return dictionary representation of agent."""
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_dict = super().dict()
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del _dict["output_parser"]
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return _dict
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def get_allowed_tools(self) -> Optional[List[str]]:
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return self.allowed_tools
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@property
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def return_values(self) -> List[str]:
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return ["output"]
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def _fix_text(self, text: str) -> str:
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"""Fix the text."""
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raise ValueError("fix_text not implemented for this agent.")
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@property
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def _stop(self) -> List[str]:
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return [
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f"\n{self.observation_prefix.rstrip()}",
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f"\n\t{self.observation_prefix.rstrip()}",
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]
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def _construct_scratchpad(
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self, intermediate_steps: List[Tuple[AgentAction, str]]
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) -> Union[str, List[BaseMessage]]:
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"""Construct the scratchpad that lets the agent continue its thought process."""
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thoughts = ""
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for action, observation in intermediate_steps:
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thoughts += action.log
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thoughts += f"\n{self.observation_prefix}{observation}\n{self.llm_prefix}"
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return thoughts
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def plan(
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self,
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intermediate_steps: List[Tuple[AgentAction, str]],
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callbacks: Callbacks = None,
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**kwargs: Any,
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) -> Union[AgentAction, AgentFinish]:
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"""Given input, decided what to do.
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Args:
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intermediate_steps: Steps the LLM has taken to date,
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|
along with observations
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|
callbacks: Callbacks to run.
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**kwargs: User inputs.
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Returns:
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Action specifying what tool to use.
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"""
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full_inputs = self.get_full_inputs(intermediate_steps, **kwargs)
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full_output = self.llm_chain.predict(callbacks=callbacks, **full_inputs)
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return self.output_parser.parse(full_output)
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async def aplan(
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self,
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intermediate_steps: List[Tuple[AgentAction, str]],
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callbacks: Callbacks = None,
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**kwargs: Any,
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) -> Union[AgentAction, AgentFinish]:
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"""Given input, decided what to do.
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|
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|
Args:
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intermediate_steps: Steps the LLM has taken to date,
|
|
along with observations
|
|
callbacks: Callbacks to run.
|
|
**kwargs: User inputs.
|
|
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Returns:
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Action specifying what tool to use.
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"""
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full_inputs = self.get_full_inputs(intermediate_steps, **kwargs)
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full_output = await self.llm_chain.apredict(callbacks=callbacks, **full_inputs)
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return self.output_parser.parse(full_output)
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def get_full_inputs(
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self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any
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) -> Dict[str, Any]:
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"""Create the full inputs for the LLMChain from intermediate steps."""
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thoughts = self._construct_scratchpad(intermediate_steps)
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new_inputs = {"agent_scratchpad": thoughts, "stop": self._stop}
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full_inputs = {**kwargs, **new_inputs}
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return full_inputs
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@property
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def input_keys(self) -> List[str]:
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"""Return the input keys.
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|
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|
:meta private:
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"""
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return list(set(self.llm_chain.input_keys) - {"agent_scratchpad"})
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@root_validator()
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def validate_prompt(cls, values: Dict) -> Dict:
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"""Validate that prompt matches format."""
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prompt = values["llm_chain"].prompt
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if "agent_scratchpad" not in prompt.input_variables:
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logger.warning(
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"`agent_scratchpad` should be a variable in prompt.input_variables."
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" Did not find it, so adding it at the end."
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)
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prompt.input_variables.append("agent_scratchpad")
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if isinstance(prompt, PromptTemplate):
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prompt.template += "\n{agent_scratchpad}"
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elif isinstance(prompt, FewShotPromptTemplate):
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prompt.suffix += "\n{agent_scratchpad}"
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else:
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raise ValueError(f"Got unexpected prompt type {type(prompt)}")
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return values
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|
|
@property
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|
@abstractmethod
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|
def observation_prefix(self) -> str:
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"""Prefix to append the observation with."""
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|
@property
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@abstractmethod
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|
def llm_prefix(self) -> str:
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"""Prefix to append the LLM call with."""
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|
|
|
@classmethod
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|
@abstractmethod
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|
def create_prompt(cls, tools: Sequence[BaseTool]) -> BasePromptTemplate:
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|
"""Create a prompt for this class."""
|
|
|
|
@classmethod
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|
def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:
|
|
"""Validate that appropriate tools are passed in."""
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pass
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|
|
|
@classmethod
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|
@abstractmethod
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|
def _get_default_output_parser(cls, **kwargs: Any) -> AgentOutputParser:
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|
"""Get default output parser for this class."""
|
|
|
|
@classmethod
|
|
def from_llm_and_tools(
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cls,
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|
llm: BaseLanguageModel,
|
|
tools: Sequence[BaseTool],
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|
callback_manager: Optional[BaseCallbackManager] = None,
|
|
output_parser: Optional[AgentOutputParser] = None,
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**kwargs: Any,
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) -> Agent:
|
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"""Construct an agent from an LLM and tools."""
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cls._validate_tools(tools)
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llm_chain = LLMChain(
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llm=llm,
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prompt=cls.create_prompt(tools),
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callback_manager=callback_manager,
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)
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tool_names = [tool.name for tool in tools]
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_output_parser = output_parser or cls._get_default_output_parser()
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return cls(
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llm_chain=llm_chain,
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allowed_tools=tool_names,
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output_parser=_output_parser,
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**kwargs,
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)
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|
|
def return_stopped_response(
|
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self,
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early_stopping_method: str,
|
|
intermediate_steps: List[Tuple[AgentAction, str]],
|
|
**kwargs: Any,
|
|
) -> AgentFinish:
|
|
"""Return response when agent has been stopped due to max iterations."""
|
|
if early_stopping_method == "force":
|
|
# `force` just returns a constant string
|
|
return AgentFinish(
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|
{"output": "Agent stopped due to iteration limit or time limit."}, ""
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)
|
|
elif early_stopping_method == "generate":
|
|
# Generate does one final forward pass
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|
thoughts = ""
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for action, observation in intermediate_steps:
|
|
thoughts += action.log
|
|
thoughts += (
|
|
f"\n{self.observation_prefix}{observation}\n{self.llm_prefix}"
|
|
)
|
|
# Adding to the previous steps, we now tell the LLM to make a final pred
|
|
thoughts += (
|
|
"\n\nI now need to return a final answer based on the previous steps:"
|
|
)
|
|
new_inputs = {"agent_scratchpad": thoughts, "stop": self._stop}
|
|
full_inputs = {**kwargs, **new_inputs}
|
|
full_output = self.llm_chain.predict(**full_inputs)
|
|
# We try to extract a final answer
|
|
parsed_output = self.output_parser.parse(full_output)
|
|
if isinstance(parsed_output, AgentFinish):
|
|
# If we can extract, we send the correct stuff
|
|
return parsed_output
|
|
else:
|
|
# If we can extract, but the tool is not the final tool,
|
|
# we just return the full output
|
|
return AgentFinish({"output": full_output}, full_output)
|
|
else:
|
|
raise ValueError(
|
|
"early_stopping_method should be one of `force` or `generate`, "
|
|
f"got {early_stopping_method}"
|
|
)
|
|
|
|
def tool_run_logging_kwargs(self) -> Dict:
|
|
return {
|
|
"llm_prefix": self.llm_prefix,
|
|
"observation_prefix": self.observation_prefix,
|
|
}
|
|
|
|
|
|
class ExceptionTool(BaseTool):
|
|
name = "_Exception"
|
|
description = "Exception tool"
|
|
|
|
def _run(
|
|
self,
|
|
query: str,
|
|
run_manager: Optional[CallbackManagerForToolRun] = None,
|
|
) -> str:
|
|
return query
|
|
|
|
async def _arun(
|
|
self,
|
|
query: str,
|
|
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
|
|
) -> str:
|
|
return query
|
|
|
|
|
|
class AgentExecutor(Chain):
|
|
"""Consists of an agent using tools."""
|
|
|
|
agent: Union[Agent, BaseSingleActionAgent, BaseMultiActionAgent]
|
|
tools: Sequence[BaseTool]
|
|
return_intermediate_steps: bool = False
|
|
max_iterations: Optional[int] = 15
|
|
max_execution_time: Optional[float] = None
|
|
early_stopping_method: str = "force"
|
|
handle_parsing_errors: bool = False
|
|
|
|
@classmethod
|
|
def from_agent_and_tools(
|
|
cls,
|
|
agent: Union[BaseSingleActionAgent, BaseMultiActionAgent],
|
|
tools: Sequence[BaseTool],
|
|
callback_manager: Optional[BaseCallbackManager] = None,
|
|
**kwargs: Any,
|
|
) -> AgentExecutor:
|
|
"""Create from agent and tools."""
|
|
return cls(
|
|
agent=agent, tools=tools, callback_manager=callback_manager, **kwargs
|
|
)
|
|
|
|
@root_validator()
|
|
def validate_tools(cls, values: Dict) -> Dict:
|
|
"""Validate that tools are compatible with agent."""
|
|
agent = values["agent"]
|
|
tools = values["tools"]
|
|
allowed_tools = agent.get_allowed_tools()
|
|
if allowed_tools is not None:
|
|
if set(allowed_tools) != set([tool.name for tool in tools]):
|
|
raise ValueError(
|
|
f"Allowed tools ({allowed_tools}) different than "
|
|
f"provided tools ({[tool.name for tool in tools]})"
|
|
)
|
|
return values
|
|
|
|
@root_validator()
|
|
def validate_return_direct_tool(cls, values: Dict) -> Dict:
|
|
"""Validate that tools are compatible with agent."""
|
|
agent = values["agent"]
|
|
tools = values["tools"]
|
|
if isinstance(agent, BaseMultiActionAgent):
|
|
for tool in tools:
|
|
if tool.return_direct:
|
|
raise ValueError(
|
|
"Tools that have `return_direct=True` are not allowed "
|
|
"in multi-action agents"
|
|
)
|
|
return values
|
|
|
|
def save(self, file_path: Union[Path, str]) -> None:
|
|
"""Raise error - saving not supported for Agent Executors."""
|
|
raise ValueError(
|
|
"Saving not supported for agent executors. "
|
|
"If you are trying to save the agent, please use the "
|
|
"`.save_agent(...)`"
|
|
)
|
|
|
|
def save_agent(self, file_path: Union[Path, str]) -> None:
|
|
"""Save the underlying agent."""
|
|
return self.agent.save(file_path)
|
|
|
|
@property
|
|
def input_keys(self) -> List[str]:
|
|
"""Return the input keys.
|
|
|
|
:meta private:
|
|
"""
|
|
return self.agent.input_keys
|
|
|
|
@property
|
|
def output_keys(self) -> List[str]:
|
|
"""Return the singular output key.
|
|
|
|
:meta private:
|
|
"""
|
|
if self.return_intermediate_steps:
|
|
return self.agent.return_values + ["intermediate_steps"]
|
|
else:
|
|
return self.agent.return_values
|
|
|
|
def lookup_tool(self, name: str) -> BaseTool:
|
|
"""Lookup tool by name."""
|
|
return {tool.name: tool for tool in self.tools}[name]
|
|
|
|
def add_tools(self, tools: Sequence[BaseTool]) -> None:
|
|
"""Add extra tools to an active agent instance."""
|
|
self.agent._validate_tools(tools)
|
|
|
|
#HACK: should not cast to list
|
|
self.tools = list(self.tools) + list(tools)
|
|
|
|
# update allowed_tools
|
|
new_tool_names = [tool.name for tool in tools]
|
|
self.agent._allowed_tools = self.agent.get_allowed_tools().extend(new_tool_names)
|
|
|
|
# how to update the agent prompt
|
|
# update the agent's llm_chain prompt
|
|
self.agent.llm_chain.prompt.update
|
|
|
|
|
|
|
|
def _should_continue(self, iterations: int, time_elapsed: float) -> bool:
|
|
if self.max_iterations is not None and iterations >= self.max_iterations:
|
|
return False
|
|
if (
|
|
self.max_execution_time is not None
|
|
and time_elapsed >= self.max_execution_time
|
|
):
|
|
return False
|
|
|
|
return True
|
|
|
|
def _return(
|
|
self,
|
|
output: AgentFinish,
|
|
intermediate_steps: list,
|
|
run_manager: Optional[CallbackManagerForChainRun] = None,
|
|
) -> Dict[str, Any]:
|
|
if run_manager:
|
|
run_manager.on_agent_finish(output, color="green", verbose=self.verbose)
|
|
final_output = output.return_values
|
|
if self.return_intermediate_steps:
|
|
final_output["intermediate_steps"] = intermediate_steps
|
|
return final_output
|
|
|
|
async def _areturn(
|
|
self,
|
|
output: AgentFinish,
|
|
intermediate_steps: list,
|
|
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
|
|
) -> Dict[str, Any]:
|
|
if run_manager:
|
|
await run_manager.on_agent_finish(
|
|
output, color="green", verbose=self.verbose
|
|
)
|
|
final_output = output.return_values
|
|
if self.return_intermediate_steps:
|
|
final_output["intermediate_steps"] = intermediate_steps
|
|
return final_output
|
|
|
|
def _take_next_step(
|
|
self,
|
|
name_to_tool_map: Dict[str, BaseTool],
|
|
color_mapping: Dict[str, str],
|
|
inputs: Dict[str, str],
|
|
intermediate_steps: List[Tuple[AgentAction, str]],
|
|
run_manager: Optional[CallbackManagerForChainRun] = None,
|
|
) -> Union[AgentFinish, List[Tuple[AgentAction, str]]]:
|
|
"""Take a single step in the thought-action-observation loop.
|
|
|
|
Override this to take control of how the agent makes and acts on choices.
|
|
"""
|
|
try:
|
|
# Call the LLM to see what to do.
|
|
output = self.agent.plan(
|
|
intermediate_steps,
|
|
callbacks=run_manager.get_child() if run_manager else None,
|
|
**inputs,
|
|
)
|
|
except Exception as e:
|
|
if not self.handle_parsing_errors:
|
|
raise e
|
|
text = str(e).split("`")[1]
|
|
observation = "Invalid or incomplete response"
|
|
output = AgentAction("_Exception", observation, text)
|
|
tool_run_kwargs = self.agent.tool_run_logging_kwargs()
|
|
observation = ExceptionTool().run(
|
|
output.tool,
|
|
verbose=self.verbose,
|
|
color=None,
|
|
callbacks=run_manager.get_child() if run_manager else None,
|
|
**tool_run_kwargs,
|
|
)
|
|
return [(output, observation)]
|
|
# If the tool chosen is the finishing tool, then we end and return.
|
|
if isinstance(output, AgentFinish):
|
|
return output
|
|
actions: List[AgentAction]
|
|
if isinstance(output, AgentAction):
|
|
actions = [output]
|
|
else:
|
|
actions = output
|
|
result = []
|
|
for agent_action in actions:
|
|
if run_manager:
|
|
run_manager.on_agent_action(agent_action, color="green")
|
|
# Otherwise we lookup the tool
|
|
if agent_action.tool in name_to_tool_map:
|
|
tool = name_to_tool_map[agent_action.tool]
|
|
return_direct = tool.return_direct
|
|
color = color_mapping[agent_action.tool]
|
|
tool_run_kwargs = self.agent.tool_run_logging_kwargs()
|
|
if return_direct:
|
|
tool_run_kwargs["llm_prefix"] = ""
|
|
# We then call the tool on the tool input to get an observation
|
|
observation = tool.run(
|
|
agent_action.tool_input,
|
|
verbose=self.verbose,
|
|
color=color,
|
|
callbacks=run_manager.get_child() if run_manager else None,
|
|
**tool_run_kwargs,
|
|
)
|
|
else:
|
|
tool_run_kwargs = self.agent.tool_run_logging_kwargs()
|
|
observation = InvalidTool().run(
|
|
agent_action.tool,
|
|
verbose=self.verbose,
|
|
color=None,
|
|
callbacks=run_manager.get_child() if run_manager else None,
|
|
**tool_run_kwargs,
|
|
)
|
|
result.append((agent_action, observation))
|
|
return result
|
|
|
|
async def _atake_next_step(
|
|
self,
|
|
name_to_tool_map: Dict[str, BaseTool],
|
|
color_mapping: Dict[str, str],
|
|
inputs: Dict[str, str],
|
|
intermediate_steps: List[Tuple[AgentAction, str]],
|
|
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
|
|
) -> Union[AgentFinish, List[Tuple[AgentAction, str]]]:
|
|
"""Take a single step in the thought-action-observation loop.
|
|
|
|
Override this to take control of how the agent makes and acts on choices.
|
|
"""
|
|
try:
|
|
# Call the LLM to see what to do.
|
|
output = await self.agent.aplan(
|
|
intermediate_steps,
|
|
callbacks=run_manager.get_child() if run_manager else None,
|
|
**inputs,
|
|
)
|
|
except Exception as e:
|
|
if not self.handle_parsing_errors:
|
|
raise e
|
|
text = str(e).split("`")[1]
|
|
observation = "Invalid or incomplete response"
|
|
output = AgentAction("_Exception", observation, text)
|
|
tool_run_kwargs = self.agent.tool_run_logging_kwargs()
|
|
observation = await ExceptionTool().arun(
|
|
output.tool,
|
|
verbose=self.verbose,
|
|
color=None,
|
|
callbacks=run_manager.get_child() if run_manager else None,
|
|
**tool_run_kwargs,
|
|
)
|
|
return [(output, observation)]
|
|
# If the tool chosen is the finishing tool, then we end and return.
|
|
if isinstance(output, AgentFinish):
|
|
return output
|
|
actions: List[AgentAction]
|
|
if isinstance(output, AgentAction):
|
|
actions = [output]
|
|
else:
|
|
actions = output
|
|
|
|
async def _aperform_agent_action(
|
|
agent_action: AgentAction,
|
|
) -> Tuple[AgentAction, str]:
|
|
if run_manager:
|
|
await run_manager.on_agent_action(
|
|
agent_action, verbose=self.verbose, color="green"
|
|
)
|
|
# Otherwise we lookup the tool
|
|
if agent_action.tool in name_to_tool_map:
|
|
tool = name_to_tool_map[agent_action.tool]
|
|
return_direct = tool.return_direct
|
|
color = color_mapping[agent_action.tool]
|
|
tool_run_kwargs = self.agent.tool_run_logging_kwargs()
|
|
if return_direct:
|
|
tool_run_kwargs["llm_prefix"] = ""
|
|
# We then call the tool on the tool input to get an observation
|
|
observation = await tool.arun(
|
|
agent_action.tool_input,
|
|
verbose=self.verbose,
|
|
color=color,
|
|
callbacks=run_manager.get_child() if run_manager else None,
|
|
**tool_run_kwargs,
|
|
)
|
|
else:
|
|
tool_run_kwargs = self.agent.tool_run_logging_kwargs()
|
|
observation = await InvalidTool().arun(
|
|
agent_action.tool,
|
|
verbose=self.verbose,
|
|
color=None,
|
|
callbacks=run_manager.get_child() if run_manager else None,
|
|
**tool_run_kwargs,
|
|
)
|
|
return agent_action, observation
|
|
|
|
# Use asyncio.gather to run multiple tool.arun() calls concurrently
|
|
result = await asyncio.gather(
|
|
*[_aperform_agent_action(agent_action) for agent_action in actions]
|
|
)
|
|
|
|
return list(result)
|
|
|
|
def _call(
|
|
self,
|
|
inputs: Dict[str, str],
|
|
run_manager: Optional[CallbackManagerForChainRun] = None,
|
|
) -> Dict[str, Any]:
|
|
"""Run text through and get agent response."""
|
|
# Construct a mapping of tool name to tool for easy lookup
|
|
name_to_tool_map = {tool.name: tool for tool in self.tools}
|
|
# We construct a mapping from each tool to a color, used for logging.
|
|
color_mapping = get_color_mapping(
|
|
[tool.name for tool in self.tools], excluded_colors=["green"]
|
|
)
|
|
intermediate_steps: List[Tuple[AgentAction, str]] = []
|
|
# Let's start tracking the number of iterations and time elapsed
|
|
iterations = 0
|
|
time_elapsed = 0.0
|
|
start_time = time.time()
|
|
# We now enter the agent loop (until it returns something).
|
|
while self._should_continue(iterations, time_elapsed):
|
|
next_step_output = self._take_next_step(
|
|
name_to_tool_map,
|
|
color_mapping,
|
|
inputs,
|
|
intermediate_steps,
|
|
run_manager=run_manager,
|
|
)
|
|
if isinstance(next_step_output, AgentFinish):
|
|
return self._return(
|
|
next_step_output, intermediate_steps, run_manager=run_manager
|
|
)
|
|
|
|
intermediate_steps.extend(next_step_output)
|
|
if len(next_step_output) == 1:
|
|
next_step_action = next_step_output[0]
|
|
# See if tool should return directly
|
|
tool_return = self._get_tool_return(next_step_action)
|
|
if tool_return is not None:
|
|
return self._return(
|
|
tool_return, intermediate_steps, run_manager=run_manager
|
|
)
|
|
iterations += 1
|
|
time_elapsed = time.time() - start_time
|
|
output = self.agent.return_stopped_response(
|
|
self.early_stopping_method, intermediate_steps, **inputs
|
|
)
|
|
return self._return(output, intermediate_steps, run_manager=run_manager)
|
|
|
|
async def _acall(
|
|
self,
|
|
inputs: Dict[str, str],
|
|
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
|
|
) -> Dict[str, str]:
|
|
"""Run text through and get agent response."""
|
|
# Construct a mapping of tool name to tool for easy lookup
|
|
name_to_tool_map = {tool.name: tool for tool in self.tools}
|
|
# We construct a mapping from each tool to a color, used for logging.
|
|
color_mapping = get_color_mapping(
|
|
[tool.name for tool in self.tools], excluded_colors=["green"]
|
|
)
|
|
intermediate_steps: List[Tuple[AgentAction, str]] = []
|
|
# Let's start tracking the number of iterations and time elapsed
|
|
iterations = 0
|
|
time_elapsed = 0.0
|
|
start_time = time.time()
|
|
# We now enter the agent loop (until it returns something).
|
|
async with asyncio_timeout(self.max_execution_time):
|
|
try:
|
|
while self._should_continue(iterations, time_elapsed):
|
|
next_step_output = await self._atake_next_step(
|
|
name_to_tool_map,
|
|
color_mapping,
|
|
inputs,
|
|
intermediate_steps,
|
|
run_manager=run_manager,
|
|
)
|
|
if isinstance(next_step_output, AgentFinish):
|
|
return await self._areturn(
|
|
next_step_output,
|
|
intermediate_steps,
|
|
run_manager=run_manager,
|
|
)
|
|
|
|
intermediate_steps.extend(next_step_output)
|
|
if len(next_step_output) == 1:
|
|
next_step_action = next_step_output[0]
|
|
# See if tool should return directly
|
|
tool_return = self._get_tool_return(next_step_action)
|
|
if tool_return is not None:
|
|
return await self._areturn(
|
|
tool_return, intermediate_steps, run_manager=run_manager
|
|
)
|
|
|
|
iterations += 1
|
|
time_elapsed = time.time() - start_time
|
|
output = self.agent.return_stopped_response(
|
|
self.early_stopping_method, intermediate_steps, **inputs
|
|
)
|
|
return await self._areturn(
|
|
output, intermediate_steps, run_manager=run_manager
|
|
)
|
|
except TimeoutError:
|
|
# stop early when interrupted by the async timeout
|
|
output = self.agent.return_stopped_response(
|
|
self.early_stopping_method, intermediate_steps, **inputs
|
|
)
|
|
return await self._areturn(
|
|
output, intermediate_steps, run_manager=run_manager
|
|
)
|
|
|
|
def _get_tool_return(
|
|
self, next_step_output: Tuple[AgentAction, str]
|
|
) -> Optional[AgentFinish]:
|
|
"""Check if the tool is a returning tool."""
|
|
agent_action, observation = next_step_output
|
|
name_to_tool_map = {tool.name: tool for tool in self.tools}
|
|
# Invalid tools won't be in the map, so we return False.
|
|
if agent_action.tool in name_to_tool_map:
|
|
if name_to_tool_map[agent_action.tool].return_direct:
|
|
return AgentFinish(
|
|
{self.agent.return_values[0]: observation},
|
|
"",
|
|
)
|
|
return None
|