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613 lines
19 KiB
613 lines
19 KiB
"""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 json
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import logging
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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 langchain.agents.agent_types import AgentType
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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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AsyncCallbackManagerForToolRun,
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CallbackManagerForToolRun,
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Callbacks,
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)
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from langchain.chains.llm import LLMChain
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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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BaseOutputParser,
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BasePromptTemplate,
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)
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from langchain.schema.messages import BaseMessage
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from langchain.tools.base import BaseTool
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from pydantic import BaseModel, root_validator
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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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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 = 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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: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."""
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|
|
@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."""
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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,
|
|
tools: Sequence[BaseTool],
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|
callback_manager: Optional[BaseCallbackManager] = None,
|
|
output_parser: Optional[AgentOutputParser] = None,
|
|
**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,
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intermediate_steps: List[Tuple[AgentAction, str]],
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|
**kwargs: Any,
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|
) -> AgentFinish:
|
|
"""Return response when agent has been stopped due to max iterations."""
|
|
if early_stopping_method == "force":
|
|
# `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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elif early_stopping_method == "generate":
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|
# Generate does one final forward pass
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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 += (
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f"\n{self.observation_prefix}{observation}\n{self.llm_prefix}"
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)
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# Adding to the previous steps, we now tell the LLM to make a final pred
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thoughts += (
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"\n\nI now need to return a final answer based on the previous steps:"
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)
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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
|
|
|
|
|
|
|
|
|
|
|