langchain 的异步回调函数

异步回调 

多个回调处理程序 | 🦜️🔗 Langchain

import asyncio
from typing import Any, Dict, List

from langchain.chat_models import ChatOpenAI
from langchain.schema import LLMResult, HumanMessage
from langchain.callbacks.base import AsyncCallbackHandler, BaseCallbackHandler


class MyCustomSyncHandler(BaseCallbackHandler):
    def on_llm_new_token(self, token: str, **kwargs) -> None:
        print(f"Sync handler being called in a `thread_pool_executor`: token: {token}")


class MyCustomAsyncHandler(AsyncCallbackHandler):
    """用于处理来自langchain的回调的异步回调处理程序。"""

    async def on_llm_start(
        self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
    ) -> None:
        """当链条开始运行时运行。"""
        print("zzzz....")
        await asyncio.sleep(0.3)
        class_name = serialized["name"]
        print("嗨！我刚醒来。您的llm正在启动")

    async def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
        """当链条结束运行时运行。"""
        print("zzzz....")
        await asyncio.sleep(0.3)
        print("嗨！我刚醒来。您的llm正在结束")


# 为了启用流式传输，我们在ChatModel构造函数中传入`streaming=True`$# 此外，我们还传入一个包含自定义处理程序的列表
chat = ChatOpenAI(
    max_tokens=25,
    streaming=True,
    callbacks=[MyCustomSyncHandler(), MyCustomAsyncHandler()],
)

await chat.agenerate([[HumanMessage(content="给我讲个笑话")]])