如何從工具串流事件

先決條件

本指南假設您已熟悉以下概念

如果您有工具呼叫聊天模型、檢索器或其他可執行物件，您可能想要存取這些可執行物件的內部事件，或使用其他屬性來配置它們。本指南向您展示如何正確手動傳遞參數，以便您可以使用 astream_events() 方法來執行此操作。

相容性

如果您在 python<=3.10 中執行 async 程式碼，LangChain 無法自動將配置（包括 astream_events() 所需的回呼）傳播到子可執行物件。這是您可能無法看到從自訂可執行物件或工具發出事件的常見原因。

如果您執行的是 python<=3.10，則需要手動將 RunnableConfig 物件傳播到非同步環境中的子可執行物件。如需如何手動傳播配置的範例，請參閱下方 bar RunnableLambda 的實作。

如果您執行的是 python>=3.11，則 RunnableConfig 將自動傳播到非同步環境中的子可執行物件。但是，如果您的程式碼可能在較舊的 Python 版本中執行，手動傳播 RunnableConfig 仍然是一個好主意。

本指南也需要 langchain-core>=0.2.16。

假設您有一個自訂工具，它呼叫一個鏈，該鏈透過提示聊天模型僅返回 10 個單字來縮減其輸入，然後反轉輸出。首先，以樸素的方式定義它

選擇聊天模型

pip install -qU "langchain[openai]"

import getpass
import os

if not os.environ.get("OPENAI_API_KEY"):
  os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter API key for OpenAI: ")

from langchain.chat_models import init_chat_model

model = init_chat_model("gpt-4o-mini", model_provider="openai")

from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.tools import tool


@tool
async def special_summarization_tool(long_text: str) -> str:
    """A tool that summarizes input text using advanced techniques."""
    prompt = ChatPromptTemplate.from_template(
        "You are an expert writer. Summarize the following text in 10 words or less:\n\n{long_text}"
    )

    def reverse(x: str):
        return x[::-1]

    chain = prompt | model | StrOutputParser() | reverse
    summary = await chain.ainvoke({"long_text": long_text})
    return summary

API 參考：StrOutputParser | ChatPromptTemplate | tool

直接調用該工具可以正常運作

LONG_TEXT = """
NARRATOR:
(Black screen with text; The sound of buzzing bees can be heard)
According to all known laws of aviation, there is no way a bee should be able to fly. Its wings are too small to get its fat little body off the ground. The bee, of course, flies anyway because bees don't care what humans think is impossible.
BARRY BENSON:
(Barry is picking out a shirt)
Yellow, black. Yellow, black. Yellow, black. Yellow, black. Ooh, black and yellow! Let's shake it up a little.
JANET BENSON:
Barry! Breakfast is ready!
BARRY:
Coming! Hang on a second.
"""

await special_summarization_tool.ainvoke({"long_text": LONG_TEXT})

'.yad noitaudarg rof tiftuo sesoohc yrraB ;scisyhp seifed eeB'

但是，如果您想存取聊天模型的原始輸出，而不是完整的工具，您可以嘗試使用 astream_events() 方法並尋找 on_chat_model_end 事件。以下是發生的情況

stream = special_summarization_tool.astream_events({"long_text": LONG_TEXT})

async for event in stream:
    if event["event"] == "on_chat_model_end":
        # Never triggers in python<=3.10!
        print(event)

您會注意到（除非您在 python>=3.11 中執行本指南），否則不會從子可執行物件發出任何聊天模型事件！

這是因為上面的範例沒有將工具的配置物件傳遞到內部鏈中。為了修正此問題，請重新定義您的工具以採用類型為 RunnableConfig 的特殊參數（有關更多詳細資訊，請參閱本指南）。您還需要在執行內部鏈時傳遞該參數

from langchain_core.runnables import RunnableConfig


@tool
async def special_summarization_tool_with_config(
    long_text: str, config: RunnableConfig
) -> str:
    """A tool that summarizes input text using advanced techniques."""
    prompt = ChatPromptTemplate.from_template(
        "You are an expert writer. Summarize the following text in 10 words or less:\n\n{long_text}"
    )

    def reverse(x: str):
        return x[::-1]

    chain = prompt | model | StrOutputParser() | reverse
    # Pass the "config" object as an argument to any executed runnables
    summary = await chain.ainvoke({"long_text": long_text}, config=config)
    return summary

API 參考：RunnableConfig

現在，使用您的新工具嘗試與之前相同的 astream_events() 呼叫

stream = special_summarization_tool_with_config.astream_events({"long_text": LONG_TEXT})

async for event in stream:
    if event["event"] == "on_chat_model_end":
        print(event)

{'event': 'on_chat_model_end', 'data': {'output': AIMessage(content='Bee defies physics; Barry chooses outfit for graduation day.', additional_kwargs={}, response_metadata={'stop_reason': 'end_turn', 'stop_sequence': None}, id='run-337ac14e-8da8-4c6d-a69f-1573f93b651e', usage_metadata={'input_tokens': 182, 'output_tokens': 19, 'total_tokens': 201, 'input_token_details': {'cache_creation': 0, 'cache_read': 0}}), 'input': {'messages': [[HumanMessage(content="You are an expert writer. Summarize the following text in 10 words or less:\n\n\nNARRATOR:\n(Black screen with text; The sound of buzzing bees can be heard)\nAccording to all known laws of aviation, there is no way a bee should be able to fly. Its wings are too small to get its fat little body off the ground. The bee, of course, flies anyway because bees don't care what humans think is impossible.\nBARRY BENSON:\n(Barry is picking out a shirt)\nYellow, black. Yellow, black. Yellow, black. Yellow, black. Ooh, black and yellow! Let's shake it up a little.\nJANET BENSON:\nBarry! Breakfast is ready!\nBARRY:\nComing! Hang on a second.\n", additional_kwargs={}, response_metadata={})]]}}, 'run_id': '337ac14e-8da8-4c6d-a69f-1573f93b651e', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['225beaa6-af73-4c91-b2d3-1afbbb88d53e']}

太棒了！這次發出了一個事件。

對於串流，astream_events() 會自動以啟用串流的方式呼叫鏈中的內部可執行物件（如果可能），因此，如果您想要從聊天模型生成的 token 串流，您可以簡單地篩選以尋找 on_chat_model_stream 事件，而無需其他變更

stream = special_summarization_tool_with_config.astream_events({"long_text": LONG_TEXT})

async for event in stream:
    if event["event"] == "on_chat_model_stream":
        print(event)

{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='', additional_kwargs={}, response_metadata={}, id='run-f5e049f7-4e98-4236-87ab-8cd1ce85a2d5', usage_metadata={'input_tokens': 182, 'output_tokens': 2, 'total_tokens': 184, 'input_token_details': {'cache_creation': 0, 'cache_read': 0}})}, 'run_id': 'f5e049f7-4e98-4236-87ab-8cd1ce85a2d5', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['51858043-b301-4b76-8abb-56218e405283']}
{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='Bee', additional_kwargs={}, response_metadata={}, id='run-f5e049f7-4e98-4236-87ab-8cd1ce85a2d5')}, 'run_id': 'f5e049f7-4e98-4236-87ab-8cd1ce85a2d5', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['51858043-b301-4b76-8abb-56218e405283']}
{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content=' defies physics;', additional_kwargs={}, response_metadata={}, id='run-f5e049f7-4e98-4236-87ab-8cd1ce85a2d5')}, 'run_id': 'f5e049f7-4e98-4236-87ab-8cd1ce85a2d5', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['51858043-b301-4b76-8abb-56218e405283']}
{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content=' Barry chooses outfit for', additional_kwargs={}, response_metadata={}, id='run-f5e049f7-4e98-4236-87ab-8cd1ce85a2d5')}, 'run_id': 'f5e049f7-4e98-4236-87ab-8cd1ce85a2d5', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['51858043-b301-4b76-8abb-56218e405283']}
{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content=' graduation day.', additional_kwargs={}, response_metadata={}, id='run-f5e049f7-4e98-4236-87ab-8cd1ce85a2d5')}, 'run_id': 'f5e049f7-4e98-4236-87ab-8cd1ce85a2d5', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['51858043-b301-4b76-8abb-56218e405283']}
{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='', additional_kwargs={}, response_metadata={'stop_reason': 'end_turn', 'stop_sequence': None}, id='run-f5e049f7-4e98-4236-87ab-8cd1ce85a2d5', usage_metadata={'input_tokens': 0, 'output_tokens': 17, 'total_tokens': 17, 'input_token_details': {}})}, 'run_id': 'f5e049f7-4e98-4236-87ab-8cd1ce85a2d5', 'name': 'ChatAnthropic', 'tags': ['seq:step:2'], 'metadata': {'ls_provider': 'anthropic', 'ls_model_name': 'claude-3-5-sonnet-20240620', 'ls_model_type': 'chat', 'ls_temperature': 0.0, 'ls_max_tokens': 1024}, 'parent_ids': ['51858043-b301-4b76-8abb-56218e405283']}

後續步驟

您現在已經了解如何從工具內部串流事件。接下來，查看以下指南，以取得有關使用工具的更多資訊

您還可以查看工具呼叫的一些更具體用途

建立使用工具的鏈和代理
從模型取得結構化輸出

後續步驟​

此頁面是否對您有幫助？

後續步驟