langchain/langchain/agents/mrkl/base.py

"""Attempt to implement MRKL systems as described in arxiv.org/pdf/2205.00445.pdf."""
from __future__ import annotations

from typing import Any, Callable, List, NamedTuple, Optional, Sequence

from pydantic import Field

from langchain.agents.agent import Agent, AgentExecutor, AgentOutputParser
from langchain.agents.agent_types import AgentType
from langchain.agents.mrkl.output_parser import MRKLOutputParser
from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS, PREFIX, SUFFIX
from langchain.agents.tools import Tool
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.base import BaseCallbackManager
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain.tools.base import BaseTool


class ChainConfig(NamedTuple):
    """Configuration for chain to use in MRKL system.

    Args:
        action_name: Name of the action.
        action: Action function to call.
        action_description: Description of the action.
    """

    action_name: str
    action: Callable
    action_description: str


class ZeroShotAgent(Agent):
    """Agent for the MRKL chain."""

    output_parser: AgentOutputParser = Field(default_factory=MRKLOutputParser)

    @classmethod
    def _get_default_output_parser(cls, **kwargs: Any) -> AgentOutputParser:
        return MRKLOutputParser()

    @property
    def _agent_type(self) -> str:
        """Return Identifier of agent type."""
        return AgentType.ZERO_SHOT_REACT_DESCRIPTION

    @property
    def observation_prefix(self) -> str:
        """Prefix to append the observation with."""
        return "Observation: "

    @property
    def llm_prefix(self) -> str:
        """Prefix to append the llm call with."""
        return "Thought:"

    @classmethod
    def create_prompt(
        cls,
        tools: Sequence[BaseTool],
        prefix: str = PREFIX,
        suffix: str = SUFFIX,
        format_instructions: str = FORMAT_INSTRUCTIONS,
        input_variables: Optional[List[str]] = None,
    ) -> PromptTemplate:
        """Create prompt in the style of the zero shot agent.

        Args:
            tools: List of tools the agent will have access to, used to format the
                prompt.
            prefix: String to put before the list of tools.
            suffix: String to put after the list of tools.
            input_variables: List of input variables the final prompt will expect.

        Returns:
            A PromptTemplate with the template assembled from the pieces here.
        """
        tool_strings = "\n".join([f"{tool.name}: {tool.description}" for tool in tools])
        tool_names = ", ".join([tool.name for tool in tools])
        format_instructions = format_instructions.format(tool_names=tool_names)
        template = "\n\n".join([prefix, tool_strings, format_instructions, suffix])
        if input_variables is None:
            input_variables = ["input", "agent_scratchpad"]
        return PromptTemplate(template=template, input_variables=input_variables)

    @classmethod
    def from_llm_and_tools(
        cls,
        llm: BaseLanguageModel,
        tools: Sequence[BaseTool],
        callback_manager: Optional[BaseCallbackManager] = None,
        output_parser: Optional[AgentOutputParser] = None,
        prefix: str = PREFIX,
        suffix: str = SUFFIX,
        format_instructions: str = FORMAT_INSTRUCTIONS,
        input_variables: Optional[List[str]] = None,
        **kwargs: Any,
    ) -> Agent:
        """Construct an agent from an LLM and tools."""
        cls._validate_tools(tools)
        prompt = cls.create_prompt(
            tools,
            prefix=prefix,
            suffix=suffix,
            format_instructions=format_instructions,
            input_variables=input_variables,
        )
        llm_chain = LLMChain(
            llm=llm,
            prompt=prompt,
            callback_manager=callback_manager,
        )
        tool_names = [tool.name for tool in tools]
        _output_parser = output_parser or cls._get_default_output_parser()
        return cls(
            llm_chain=llm_chain,
            allowed_tools=tool_names,
            output_parser=_output_parser,
            **kwargs,
        )

    @classmethod
    def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:
        super()._validate_tools(tools)
        for tool in tools:
            if tool.description is None:
                raise ValueError(
                    f"Got a tool {tool.name} without a description. For this agent, "
                    f"a description must always be provided."
                )


class MRKLChain(AgentExecutor):
    """Chain that implements the MRKL system.

    Example:
        .. code-block:: python

            from langchain import OpenAI, MRKLChain
            from langchain.chains.mrkl.base import ChainConfig
            llm = OpenAI(temperature=0)
            prompt = PromptTemplate(...)
            chains = [...]
            mrkl = MRKLChain.from_chains(llm=llm, prompt=prompt)
    """

    @classmethod
    def from_chains(
        cls, llm: BaseLanguageModel, chains: List[ChainConfig], **kwargs: Any
    ) -> AgentExecutor:
        """User friendly way to initialize the MRKL chain.

        This is intended to be an easy way to get up and running with the
        MRKL chain.

        Args:
            llm: The LLM to use as the agent LLM.
            chains: The chains the MRKL system has access to.
            **kwargs: parameters to be passed to initialization.

        Returns:
            An initialized MRKL chain.

        Example:
            .. code-block:: python

                from langchain import LLMMathChain, OpenAI, SerpAPIWrapper, MRKLChain
                from langchain.chains.mrkl.base import ChainConfig
                llm = OpenAI(temperature=0)
                search = SerpAPIWrapper()
                llm_math_chain = LLMMathChain(llm=llm)
                chains = [
                    ChainConfig(
                        action_name = "Search",
                        action=search.search,
                        action_description="useful for searching"
                    ),
                    ChainConfig(
                        action_name="Calculator",
                        action=llm_math_chain.run,
                        action_description="useful for doing math"
                    )
                ]
                mrkl = MRKLChain.from_chains(llm, chains)
        """
        tools = [
            Tool(
                name=c.action_name,
                func=c.action,
                description=c.action_description,
            )
            for c in chains
        ]
        agent = ZeroShotAgent.from_llm_and_tools(llm, tools)
        return cls(agent=agent, tools=tools, **kwargs)