如何在运行时传递回调

Prerequisites

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程序是有利的。当我们使用callbacks关键字参数在执行运行时传递CallbackHandlers时，这些回调将由执行过程中涉及的所有嵌套对象发出。例如，当处理程序传递给代理时，它将用于与代理相关的所有回调以及代理执行过程中涉及的所有对象，在这种情况下，包括工具和LLM。

这使我们不必手动将处理程序附加到每个单独的嵌套对象。以下是一个示例：

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 Reference:ChatAnthropic | BaseCallbackHandler | BaseMessage | LLMResult | 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')

如果已经存在与模块关联的回调函数，这些回调函数将在运行时与任何传入的回调函数一起运行。

下一步

你现在已经学会了如何在运行时传递回调函数。

接下来，查看本节中的其他操作指南，例如如何将回调传递给模块构造函数。

下一步​

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