如何在异步环境中使用回调
如果您计划使用异步 API,建议使用并扩展 AsyncCallbackHandler
以避免阻塞事件。
warning
如果您在使用异步方法运行您的大型语言模型 / 链 / 工具 / 代理时使用同步 CallbackHandler
,它仍然会工作。然而,在底层,它将通过 run_in_executor
被调用,如果您的 CallbackHandler
不是线程安全的,可能会导致问题。
danger
如果您使用的是 python<=3.10
,您需要记住在从 RunnableLambda
、RunnableGenerator
或 @tool
中调用其他 runnable
时传播 config
或 callbacks
。如果您不这样做,
回调将不会传播到被调用的子可运行对象。
<!--IMPORTS:[{"imported": "ChatAnthropic", "source": "langchain_anthropic", "docs": "https://python.langchain.com/api_reference/anthropic/chat_models/langchain_anthropic.chat_models.ChatAnthropic.html", "title": "How to use callbacks in async environments"}, {"imported": "AsyncCallbackHandler", "source": "langchain_core.callbacks", "docs": "https://python.langchain.com/api_reference/core/callbacks/langchain_core.callbacks.base.AsyncCallbackHandler.html", "title": "How to use callbacks in async environments"}, {"imported": "BaseCallbackHandler", "source": "langchain_core.callbacks", "docs": "https://python.langchain.com/api_reference/core/callbacks/langchain_core.callbacks.base.BaseCallbackHandler.html", "title": "How to use callbacks in async environments"}, {"imported": "HumanMessage", "source": "langchain_core.messages", "docs": "https://python.langchain.com/api_reference/core/messages/langchain_core.messages.human.HumanMessage.html", "title": "How to use callbacks in async environments"}, {"imported": "LLMResult", "source": "langchain_core.outputs", "docs": "https://python.langchain.com/api_reference/core/outputs/langchain_core.outputs.llm_result.LLMResult.html", "title": "How to use callbacks in async environments"}]-->
import asyncio
from typing import Any, Dict, List
from langchain_anthropic import ChatAnthropic
from langchain_core.callbacks import AsyncCallbackHandler, BaseCallbackHandler
from langchain_core.messages import HumanMessage
from langchain_core.outputs import LLMResult
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):
"""Async callback handler that can be used to handle callbacks from langchain."""
async def on_llm_start(
self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
) -> None:
"""Run when chain starts running."""
print("zzzz....")
await asyncio.sleep(0.3)
class_name = serialized["name"]
print("Hi! I just woke up. Your llm is starting")
async def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
"""Run when chain ends running."""
print("zzzz....")
await asyncio.sleep(0.3)
print("Hi! I just woke up. Your llm is ending")
# To enable streaming, we pass in `streaming=True` to the ChatModel constructor
# Additionally, we pass in a list with our custom handler
chat = ChatAnthropic(
model="claude-3-sonnet-20240229",
max_tokens=25,
streaming=True,
callbacks=[MyCustomSyncHandler(), MyCustomAsyncHandler()],
)
await chat.agenerate([[HumanMessage(content="Tell me a joke")]])
zzzz....
Hi! I just woke up. Your llm is starting
Sync handler being called in a `thread_pool_executor`: token: Here
Sync handler being called in a `thread_pool_executor`: token: 's
Sync handler being called in a `thread_pool_executor`: token: a
Sync handler being called in a `thread_pool_executor`: token: little
Sync handler being called in a `thread_pool_executor`: token: joke
Sync handler being called in a `thread_pool_executor`: token: for
Sync handler being called in a `thread_pool_executor`: token: you
Sync handler being called in a `thread_pool_executor`: token: :
Sync handler being called in a `thread_pool_executor`: token:
Why
Sync handler being called in a `thread_pool_executor`: token: can
Sync handler being called in a `thread_pool_executor`: token: 't
Sync handler being called in a `thread_pool_executor`: token: a
Sync handler being called in a `thread_pool_executor`: token: bicycle
Sync handler being called in a `thread_pool_executor`: token: stan
Sync handler being called in a `thread_pool_executor`: token: d up
Sync handler being called in a `thread_pool_executor`: token: by
Sync handler being called in a `thread_pool_executor`: token: itself
Sync handler being called in a `thread_pool_executor`: token: ?
Sync handler being called in a `thread_pool_executor`: token: Because
Sync handler being called in a `thread_pool_executor`: token: it
Sync handler being called in a `thread_pool_executor`: token: 's
Sync handler being called in a `thread_pool_executor`: token: two
Sync handler being called in a `thread_pool_executor`: token: -
Sync handler being called in a `thread_pool_executor`: token: tire
zzzz....
Hi! I just woke up. Your llm is ending
LLMResult(generations=[[ChatGeneration(text="Here's a little joke for you:\n\nWhy can't a bicycle stand up by itself? Because it's two-tire", message=AIMessage(content="Here's a little joke for you:\n\nWhy can't a bicycle stand up by itself? Because it's two-tire", id='run-8afc89e8-02c0-4522-8480-d96977240bd4-0'))]], llm_output={}, run=[RunInfo(run_id=UUID('8afc89e8-02c0-4522-8480-d96977240bd4'))])
下一步
您现在已经学习了如何创建自己的自定义回调处理程序。
接下来,请查看本节中的其他操作指南,例如 如何将回调附加到可运行的。