langchain.agents.agent_toolkits.conversational_retrieval.openai_functions

from typing import Any, List, Optional  # noqa: E501

from langchain_core.language_models import BaseLanguageModel
from langchain_core.memory import BaseMemory
from langchain_core.messages import SystemMessage
from langchain_core.prompts.chat import MessagesPlaceholder
from langchain_core.tools import BaseTool

from langchain.agents.agent import AgentExecutor
from langchain.agents.openai_functions_agent.agent_token_buffer_memory import (
    AgentTokenBufferMemory,
)
from langchain.agents.openai_functions_agent.base import OpenAIFunctionsAgent
from langchain.memory.token_buffer import ConversationTokenBufferMemory


def _get_default_system_message() -> SystemMessage:
    return SystemMessage(
        content=(
            "Do your best to answer the questions. "
            "Feel free to use any tools available to look up "
            "relevant information, only if necessary"
        )
    )


[docs]def create_conversational_retrieval_agent(
    llm: BaseLanguageModel,
    tools: List[BaseTool],
    remember_intermediate_steps: bool = True,
    memory_key: str = "chat_history",
    system_message: Optional[SystemMessage] = None,
    verbose: bool = False,
    max_token_limit: int = 2000,
    **kwargs: Any,
) -> AgentExecutor:
    """用于创建对话式检索代理的便利方法。

参数：
    llm: 要使用的语言模型，应该是ChatOpenAI
    tools: 代理可以访问的工具列表
    remember_intermediate_steps: 代理是否应记住中间步骤。中间步骤是指来自先前问题的先前操作/观察对。记住这些的好处是，如果其中有相关信息，代理可以使用它来回答后续问题。缺点是它将占用更多的令牌。
    memory_key: 提示中记忆键的名称。
    system_message: 要使用的系统消息。默认情况下，将使用基本消息。
    verbose: 最终的AgentExecutor是否应该冗长，默认为False。
    max_token_limit: 在内存中保留的最大令牌数。默认为2000。

返回：
    适当初始化的代理执行器
"""

    if remember_intermediate_steps:
        memory: BaseMemory = AgentTokenBufferMemory(
            memory_key=memory_key, llm=llm, max_token_limit=max_token_limit
        )
    else:
        memory = ConversationTokenBufferMemory(
            memory_key=memory_key,
            return_messages=True,
            output_key="output",
            llm=llm,
            max_token_limit=max_token_limit,
        )

    _system_message = system_message or _get_default_system_message()
    prompt = OpenAIFunctionsAgent.create_prompt(
        system_message=_system_message,
        extra_prompt_messages=[MessagesPlaceholder(variable_name=memory_key)],
    )
    agent = OpenAIFunctionsAgent(llm=llm, tools=tools, prompt=prompt)
    return AgentExecutor(
        agent=agent,
        tools=tools,
        memory=memory,
        verbose=verbose,
        return_intermediate_steps=remember_intermediate_steps,
        **kwargs,
    )
Source code for langchain.agents.agent_toolkits.conversational_retrieval.openai_functions
