如何从工具流式传输事件

先决条件

本指南假定您熟悉以下概念

如果你有调用聊天模型、检索器或其他可运行对象的工具，你可能希望访问这些可运行对象的内部事件，或为其配置额外的属性。本指南将向你展示如何正确手动传递参数，以便你可以使用 astream_events() 方法来完成此操作。

兼容性

如果你在 python<=3.10 中运行 async 代码，LangChain 无法自动将配置（包括 astream_events() 所需的回调）传播到子可运行对象。这是你可能无法看到自定义可运行对象或工具发出事件的常见原因。

如果你正在运行 python<=3.10，你需要在异步环境中手动将 RunnableConfig 对象传播到子可运行对象。有关如何手动传播配置的示例，请参阅下面 bar RunnableLambda 的实现。

如果你正在运行 python>=3.11，RunnableConfig 将在异步环境中自动传播到子可运行对象。但是，如果你的代码可能在较旧的 Python 版本中运行，手动传播 RunnableConfig 仍然是一个好主意。

本指南还需要 langchain-core>=0.2.16。

假设你有一个自定义工具，它调用一个链，通过提示聊天模型返回仅 10 个词来压缩其输入，然后反转输出。首先，以一种简单的方式定义它

选择聊天模型

pip install -qU "langchain[google-genai]"

import getpass
import os

if not os.environ.get("GOOGLE_API_KEY"):
  os.environ["GOOGLE_API_KEY"] = getpass.getpass("Enter API key for Google Gemini: ")

from langchain.chat_models import init_chat_model

model = init_chat_model("gemini-2.0-flash", model_provider="google_genai")

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 参考：StrOutputParser | ChatPromptTemplate | tool

直接调用工具效果很好

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，如果你想访问聊天模型的原始输出而不是整个工具的输出，你可以尝试使用astream_events() 方法并查找 on_chat_model_end 事件。结果如下

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)

你会注意到（除非你正在 python>=3.11 中运行本指南）子运行中没有发出聊天模型事件！

这是因为上面的示例没有将工具的配置对象传递到内部链中。为了解决这个问题，重新定义你的工具，使其接受一个类型为 RunnableConfig 的特殊参数（有关更多详细信息，请参阅本指南）。在执行时，你还需要将该参数传递到内部链中。

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 参考：RunnableConfig

现在尝试使用你的新工具调用与之前相同的 astream_events()

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！这次有一个事件发出。

对于流式传输，astream_events() 如果可能，会自动调用链中启用了流式传输的内部可运行对象，因此，如果你想要从聊天模型生成令牌流，你可以简单地过滤查找 on_chat_model_stream 事件，无需进行其他更改

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']}

下一步

你现在已经了解了如何从工具中流式传输事件。接下来，查看以下指南以了解更多关于使用工具的信息

你还可以查看工具调用的其他一些特定用法

构建使用工具的链和代理
从模型获取结构化输出

下一步​

下一步