Source code for langchain.agents.react.agent

from __future__ import annotations

from typing import Sequence

from langchain_core.language_models import BaseLanguageModel
from langchain_core.prompts import BasePromptTemplate
from langchain_core.runnables import Runnable, RunnablePassthrough
from langchain_core.tools import BaseTool

from langchain.agents.format_scratchpad import format_log_to_str
from langchain.agents.output_parsers import ReActSingleInputOutputParser
from langchain.tools.render import render_text_description


def create_react_agent(
    llm: BaseLanguageModel, tools: Sequence[BaseTool], prompt: BasePromptTemplate
) -> Runnable:
    """Create an agent that uses ReAct prompting.

    Examples:

        .. code-block:: python

            from langchain import hub
            from langchain_community.llms import OpenAI
            from langchain.agents import AgentExecutor, create_react_agent

            prompt = hub.pull("hwchase17/react")
            model = OpenAI()
            tools = ...

            agent = create_react_agent(model, tools, prompt)
            agent_executor = AgentExecutor(agent=agent, tools=tools)

            agent_executor.invoke({"input": "hi"})

            # Use with chat history
            from langchain_core.messages import AIMessage, HumanMessage
            agent_executor.invoke(
                {
                    "input": "what's my name?",
                    # Notice that chat_history is a string
                    # since this prompt is aimed at LLMs, not chat models
                    "chat_history": "Human: My name is Bob\nAI: Hello Bob!",
                }
            )

    Args:
        llm: LLM to use as the agent.
        tools: Tools this agent has access to.
        prompt: The prompt to use, must have input keys of
            `tools`, `tool_names`, and `agent_scratchpad`.

    Returns:
        A runnable sequence representing an agent. It takes as input all the same input
        variables as the prompt passed in does. It returns as output either an
        AgentAction or AgentFinish.

    """
    missing_vars = {"tools", "tool_names", "agent_scratchpad"}.difference(
        prompt.input_variables
    )
    if missing_vars:
        raise ValueError(f"Prompt missing required variables: {missing_vars}")

    prompt = prompt.partial(
        tools=render_text_description(list(tools)),
        tool_names=", ".join([t.name for t in tools]),
    )
    llm_with_stop = llm.bind(stop=["\nObservation"])
    agent = (
        RunnablePassthrough.assign(
            agent_scratchpad=lambda x: format_log_to_str(x["intermediate_steps"]),
        )
        | prompt
        | llm_with_stop
        | ReActSingleInputOutputParser()
    )
    return agent