create_json_chat_agent() — langchain Function Reference

Source Code

libs/langchain/langchain_classic/agents/json_chat/base.py lines 14–195

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)
        agent_executor = AgentExecutor(agent=agent, tools=tools)

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

        # Using with chat history
        from langchain_core.messages import AIMessage, HumanMessage

        agent_executor.invoke(
            {
                "input": "what's my name?",
                "chat_history": [
                    HumanMessage(content="hi! my name is bob"),
                    AIMessage(content="Hello Bob! How can I assist you today?"),
                ],
            }
        )
        ```

    Prompt:

        The prompt must have input keys:
            * `tools`: contains descriptions and arguments for each tool.
            * `tool_names`: contains all tool names.
            * `agent_scratchpad`: must be a MessagesPlaceholder. Contains previous
                agent actions and tool outputs as messages.

        Here's an example:

        ```python
        from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder

        system = '''Assistant is a large language model trained by OpenAI.

        Assistant is designed to be able to assist with a wide range of tasks, from answering