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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：

如果您有调用聊天模型、检索器或其他可运行对象的工具，则可能需要从这些可运行对象访问内部事件或使用其他属性配置它们。本指南介绍如何正确手动传递参数，以便您可以使用astream_events()方法。

兼容性

LangChain 无法自动传播配置，包括astream_events()，如果正在运行asynccode inpython<=3.10.这是您可能无法看到从自定义可运行对象或工具发出的事件的常见原因。

如果您运行的是 python<=3.10，则需要手动传播RunnableConfigobject 添加到异步环境中的子 runnable 中。有关如何手动传播配置的示例，请参阅barRunnableLambda 的 Lambda。

如果您运行的是 python>=3.11，则RunnableConfig将自动传播到异步环境中的子可运行对象。但是，将RunnableConfigmanually （如果您的代码可能在较旧的 Python 版本中运行）。

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，该链通过提示聊天模型仅返回 10 个单词，然后反转输出来压缩其输入。首先，以一种天真的方式定义它：

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 参考：StrOutputParser | ChatPromptTemplate 模板 | 工具

直接调用该工具就可以正常工作了：

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，如果您想访问聊天模型的原始输出而不是完整工具，则可以尝试使用astream_events()方法并查找on_chat_model_end事件。以下是发生的情况：

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)

您会注意到（除非您正在python>=3.11），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 config 对象传递到内部链中。要解决此问题，请重新定义您的工具以采用类型为RunnableConfig（有关更多详细信息，请参阅本指南）。在执行该参数时，您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

现在尝试同样的作astream_events()像以前一样使用新工具调用：

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']}

棒！这次发出了一个事件。

对于流式处理，astream_events()如果可能，在启用流式处理的情况下自动调用链中的内部 Runnables，因此，如果您想在从聊天模型生成令牌流时，只需过滤以查找on_chat_model_stream没有其他更改的事件：

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']}

后续步骤

您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有关使用工具的更多信息：

您还可以查看工具调用的一些更具体的用途：