如何在运行时传递回调

前置条件

本指南假设您熟悉以下概念：

在许多情况下，在运行对象时传入处理器是有益的。当我们在使用callbacks关键字参数执行运行时通过CallbackHandlers传递时，所有参与执行的嵌套对象都会发出这些回调。例如，当处理器被传递给Agent时，它将用于与该Agent相关的所有回调，以及该Agent执行过程中涉及的所有对象，在本例中即为Tools和LLM。

这防止了我们必须手动将处理程序附加到每个单独的嵌套对象。这是一个示例：

from typing import Any, Dict, List

from langchain_anthropic import ChatAnthropic
from langchain_core.callbacks import BaseCallbackHandler
from langchain_core.messages import BaseMessage
from langchain_core.outputs import LLMResult
from langchain_core.prompts import ChatPromptTemplate


class LoggingHandler(BaseCallbackHandler):
    def on_chat_model_start(
        self, serialized: Dict[str, Any], messages: List[List[BaseMessage]], **kwargs
    ) -> None:
        print("Chat model started")

    def on_llm_end(self, response: LLMResult, **kwargs) -> None:
        print(f"Chat model ended, response: {response}")

    def on_chain_start(
        self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs
    ) -> None:
        print(f"Chain {serialized.get('name')} started")

    def on_chain_end(self, outputs: Dict[str, Any], **kwargs) -> None:
        print(f"Chain ended, outputs: {outputs}")


callbacks = [LoggingHandler()]
llm = ChatAnthropic(model="claude-3-sonnet-20240229")
prompt = ChatPromptTemplate.from_template("What is 1 + {number}?")

chain = prompt | llm

chain.invoke({"number": "2"}, config={"callbacks": callbacks})

API 参考:ChatAnthropic |基础回调处理器 |BaseMessage |LLM 结果 |ChatPromptTemplate

Chain RunnableSequence started
Chain ChatPromptTemplate started
Chain ended, outputs: messages=[HumanMessage(content='What is 1 + 2?')]
Chat model started
Chat model ended, response: generations=[[ChatGeneration(text='1 + 2 = 3', message=AIMessage(content='1 + 2 = 3', response_metadata={'id': 'msg_01D8Tt5FdtBk5gLTfBPm2tac', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 16, 'output_tokens': 13}}, id='run-bb0dddd8-85f3-4e6b-8553-eaa79f859ef8-0'))]] llm_output={'id': 'msg_01D8Tt5FdtBk5gLTfBPm2tac', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 16, 'output_tokens': 13}} run=None
Chain ended, outputs: content='1 + 2 = 3' response_metadata={'id': 'msg_01D8Tt5FdtBk5gLTfBPm2tac', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 16, 'output_tokens': 13}} id='run-bb0dddd8-85f3-4e6b-8553-eaa79f859ef8-0'

AIMessage(content='1 + 2 = 3', response_metadata={'id': 'msg_01D8Tt5FdtBk5gLTfBPm2tac', 'model': 'claude-3-sonnet-20240229', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 16, 'output_tokens': 13}}, id='run-bb0dddd8-85f3-4e6b-8553-eaa79f859ef8-0')

如果模块已经存在关联的回调，这些回调将在运行时传入的任何回调之外运行。

下一步

您现在已了解如何在运行时传递回调。

接下来，查看本节中的其他操操作指南，例如如何将回调函数传递到模块构造函数中pass callbacks into a module constructor。

下一步​

下一步