base.py — langchain Source File

Source Code

from collections.abc import Sequence

from langchain_core.language_models import BaseLanguageModel
from langchain_core.prompts.chat import ChatPromptTemplate
from langchain_core.runnables import Runnable, RunnablePassthrough
from langchain_core.tools import BaseTool
from langchain_core.tools.render import ToolsRenderer, render_text_description

from langchain_classic.agents.format_scratchpad import format_log_to_messages
from langchain_classic.agents.json_chat.prompt import TEMPLATE_TOOL_RESPONSE
from langchain_classic.agents.output_parsers import JSONAgentOutputParser


def create_json_chat_agent(
    llm: BaseLanguageModel,
    tools: Sequence[BaseTool],
    prompt: ChatPromptTemplate,
    stop_sequence: bool | list[str] = True,  # noqa: FBT001,FBT002
    tools_renderer: ToolsRenderer = render_text_description,
    template_tool_response: str = TEMPLATE_TOOL_RESPONSE,
) -> Runnable:
    r"""Create an agent that uses JSON to format its logic, build for Chat Models.

    Args:
        llm: LLM to use as the agent.
        tools: Tools this agent has access to.
        prompt: The prompt to use. See Prompt section below for more.
        stop_sequence: bool or list of str.
            If `True`, adds a stop token of "Observation:" to avoid hallucinates.
            If `False`, does not add a stop token.
            If a list of str, uses the provided list as the stop tokens.

            You may to set this to False if the LLM you are using does not support stop
            sequences.
        tools_renderer: This controls how the tools are converted into a string and
            then passed into the LLM.
        template_tool_response: Template prompt that uses the tool response
            (observation) to make the LLM generate the next action to take.

    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.

    Raises:
        ValueError: If the prompt is missing required variables.
        ValueError: If the template_tool_response is missing
            the required variable 'observation'.

    Example:
        ```python
        from langchain_classic import hub
        from langchain_openai import ChatOpenAI
        from langchain_classic.agents import AgentExecutor, create_json_chat_agent

        prompt = hub.pull("hwchase17/react-chat-json")
        model = ChatOpenAI()
        tools = ...

        agent = create_json_chat_agent(model, tools, prompt)
// ... (136 more lines)