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如何流式运行可运行对象

Prerequisites

本指南假设您熟悉以下概念：

流式传输对于使基于LLM的应用程序对最终用户感觉响应迅速至关重要。

重要的LangChain原语，如聊天模型、输出解析器、提示、检索器和代理，都实现了LangChain的可运行接口。

此接口提供了两种通用的流内容方法：

  1. 同步 stream 和异步 astream：流式传输的默认实现，从链中流式传输最终输出。
  2. 异步 astream_events 和 异步 astream_log：这些提供了一种从链中流式传输中间步骤和最终输出的方式。

让我们来看看这两种方法，并尝试理解如何使用它们。

info

有关LangChain中流媒体技术的高级概述，请参阅概念指南的这一部分。

使用流​

所有 Runnable 对象都实现了一个名为 stream 的同步方法和一个名为 astream 的异步变体。

这些方法旨在以块的形式流式传输最终输出，一旦每个块可用就立即生成。

只有在程序中的所有步骤都知道如何处理输入流时，才能进行流处理；即一次处理一个输入块，并生成相应的输出块。

这种处理的复杂性可以有所不同，从简单的任务如发出由LLM生成的令牌，到更具挑战性的任务如在完整的JSON完成之前流式传输JSON结果的部分。

开始探索流式处理的最佳地点是LLMs应用程序中最重要的组件——LLMs本身！

LLMs 和聊天模型​

大型语言模型及其聊天变体是基于LLM的应用程序中的主要瓶颈。

大型语言模型可能需要几秒钟来生成对查询的完整响应。这远远慢于约200-300毫秒的阈值，在这个阈值下，应用程序对最终用户来说感觉是响应迅速的。

使应用程序感觉更响应的关键策略是显示中间进度；即，逐个标记地从模型中流式输出。

我们将展示使用聊天模型的流式传输示例。请从以下选项中选择一个：

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_openai import ChatOpenAI

model = ChatOpenAI(model="gpt-4o-mini")

让我们从同步的 stream API 开始：

chunks = []
for chunk in model.stream("what color is the sky?"):
    chunks.append(chunk)
    print(chunk.content, end="|", flush=True)
The| sky| appears| blue| during| the| day|.|

或者，如果您在异步环境中工作，您可以考虑使用异步的 astream API：

chunks = []
async for chunk in model.astream("what color is the sky?"):
    chunks.append(chunk)
    print(chunk.content, end="|", flush=True)
The| sky| appears| blue| during| the| day|.|

让我们检查其中一个块

chunks[0]
AIMessageChunk(content='The', id='run-b36bea64-5511-4d7a-b6a3-a07b3db0c8e7')

我们得到了一些叫做AIMessageChunk的东西。这个块代表了一个AIMessage的一部分。

消息块设计上是可累加的——只需将它们相加即可获得到目前为止的响应状态！

chunks[0] + chunks[1] + chunks[2] + chunks[3] + chunks[4]
AIMessageChunk(content='The sky appears blue during', id='run-b36bea64-5511-4d7a-b6a3-a07b3db0c8e7')

链​

几乎所有LLM应用程序都涉及不仅仅是调用语言模型的步骤。

让我们使用LangChain Expression Language (LCEL)构建一个简单的链，该链结合了提示、模型和解析器，并验证流式处理是否有效。

我们将使用StrOutputParser来解析模型的输出。这是一个简单的解析器，它从AIMessageChunk中提取content字段，从而得到模型返回的token。

tip

LCEL 是一种声明式的方式，通过将不同的 LangChain 原语链接在一起来指定一个“程序”。使用 LCEL 创建的链受益于自动实现的 stream 和 astream，允许流式传输最终输出。事实上，使用 LCEL 创建的链实现了整个标准的 Runnable 接口。

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

prompt = ChatPromptTemplate.from_template("tell me a joke about {topic}")
parser = StrOutputParser()
chain = prompt | model | parser

async for chunk in chain.astream({"topic": "parrot"}):
    print(chunk, end="|", flush=True)
Here|'s| a| joke| about| a| par|rot|:|

A man| goes| to| a| pet| shop| to| buy| a| par|rot|.| The| shop| owner| shows| him| two| stunning| pa|rr|ots| with| beautiful| pl|um|age|.|

"|There|'s| a| talking| par|rot| an|d a| non|-|talking| par|rot|,"| the| owner| says|.| "|The| talking| par|rot| costs| $|100|,| an|d the| non|-|talking| par|rot| is| $|20|."|

The| man| says|,| "|I|'ll| take| the| non|-|talking| par|rot| at| $|20|."|

He| pays| an|d leaves| with| the| par|rot|.| As| he|'s| walking| down| the| street|,| the| par|rot| looks| up| at| him| an|d says|,| "|You| know|,| you| really| are| a| stupi|d man|!"|

The| man| is| stun|ne|d an|d looks| at| the| par|rot| in| dis|bel|ief|.| The| par|rot| continues|,| "|Yes|,| you| got| r|ippe|d off| big| time|!| I| can| talk| just| as| well| as| that| other| par|rot|,| an|d you| only| pai|d $|20| |for| me|!"|

请注意，即使我们在链的末尾使用了parser，我们仍然可以获得流式输出。parser对每个流式块单独操作。许多LCEL原语也支持这种转换风格的直通流式传输，这在构建应用程序时非常方便。

自定义函数可以设计为返回生成器，这些生成器能够对流进行操作。

某些可运行对象，如提示模板和聊天模型，无法处理单个数据块，而是会聚合所有先前的步骤。这样的可运行对象可能会中断流式处理过程。

note

LangChain 表达式语言允许你将链的构建与使用模式（例如，同步/异步、批处理/流处理等）分离开来。如果这与你要构建的内容无关，你也可以依赖标准的命令式编程方法，通过在每个组件上分别调用invoke、batch或stream，将结果分配给变量，然后根据需要在后续步骤中使用它们。

处理输入流​

如果你想在生成时从输出流式传输JSON，该怎么办？

如果你依赖json.loads来解析部分json，解析将会失败，因为部分json不会是有效的json。

你可能会完全不知所措，声称无法流式传输JSON。

好吧，事实证明有一种方法可以做到——解析器需要在输入流上操作，并尝试将部分json“自动完成”为有效状态。

让我们看看这样一个解析器的实际操作，以理解这意味着什么。

from langchain_core.output_parsers import JsonOutputParser

chain = (
    model | JsonOutputParser()
)  # Due to a bug in older versions of Langchain, JsonOutputParser did not stream results from some models
async for text in chain.astream(
    "output a list of the countries france, spain and japan and their populations in JSON format. "
    'Use a dict with an outer key of "countries" which contains a list of countries. '
    "Each country should have the key `name` and `population`"
):
    print(text, flush=True)
API Reference:JsonOutputParser
{}
{'countries': []}
{'countries': [{}]}
{'countries': [{'name': ''}]}
{'countries': [{'name': 'France'}]}
{'countries': [{'name': 'France', 'population': 67}]}
{'countries': [{'name': 'France', 'population': 67413}]}
{'countries': [{'name': 'France', 'population': 67413000}]}
{'countries': [{'name': 'France', 'population': 67413000}, {}]}
{'countries': [{'name': 'France', 'population': 67413000}, {'name': ''}]}
{'countries': [{'name': 'France', 'population': 67413000}, {'name': 'Spain'}]}
{'countries': [{'name': 'France', 'population': 67413000}, {'name': 'Spain', 'population': 47}]}
{'countries': [{'name': 'France', 'population': 67413000}, {'name': 'Spain', 'population': 47351}]}
{'countries': [{'name': 'France', 'population': 67413000}, {'name': 'Spain', 'population': 47351567}]}
{'countries': [{'name': 'France', 'population': 67413000}, {'name': 'Spain', 'population': 47351567}, {}]}
{'countries': [{'name': 'France', 'population': 67413000}, {'name': 'Spain', 'population': 47351567}, {'name': ''}]}
{'countries': [{'name': 'France', 'population': 67413000}, {'name': 'Spain', 'population': 47351567}, {'name': 'Japan'}]}
{'countries': [{'name': 'France', 'population': 67413000}, {'name': 'Spain', 'population': 47351567}, {'name': 'Japan', 'population': 125}]}
{'countries': [{'name': 'France', 'population': 67413000}, {'name': 'Spain', 'population': 47351567}, {'name': 'Japan', 'population': 125584}]}
{'countries': [{'name': 'France', 'population': 67413000}, {'name': 'Spain', 'population': 47351567}, {'name': 'Japan', 'population': 125584000}]}

现在，让我们分解流处理。我们将使用前面的示例，并在最后附加一个提取函数，该函数从最终的JSON中提取国家名称。

warning

链中任何操作最终输入而不是输入流的步骤都可能通过stream或astream破坏流功能。

tip

稍后，我们将讨论astream_events API，它可以从中间步骤流式传输结果。即使链中包含仅对最终输入进行操作的步骤，此API也可以从中间步骤流式传输结果。

from langchain_core.output_parsers import (
    JsonOutputParser,
)


# A function that operates on finalized inputs
# rather than on an input_stream
def _extract_country_names(inputs):
    """A function that does not operates on input streams and breaks streaming."""
    if not isinstance(inputs, dict):
        return ""

    if "countries" not in inputs:
        return ""

    countries = inputs["countries"]

    if not isinstance(countries, list):
        return ""

    country_names = [
        country.get("name") for country in countries if isinstance(country, dict)
    ]
    return country_names


chain = model | JsonOutputParser() | _extract_country_names

async for text in chain.astream(
    "output a list of the countries france, spain and japan and their populations in JSON format. "
    'Use a dict with an outer key of "countries" which contains a list of countries. '
    "Each country should have the key `name` and `population`"
):
    print(text, end="|", flush=True)
API Reference:JsonOutputParser
['France', 'Spain', 'Japan']|

生成器函数​

让我们使用一个可以在输入流上操作的生成器函数来修复流处理。

tip

生成器函数（使用yield的函数）允许编写操作输入流的代码

from langchain_core.output_parsers import JsonOutputParser


async def _extract_country_names_streaming(input_stream):
    """A function that operates on input streams."""
    country_names_so_far = set()

    async for input in input_stream:
        if not isinstance(input, dict):
            continue

        if "countries" not in input:
            continue

        countries = input["countries"]

        if not isinstance(countries, list):
            continue

        for country in countries:
            name = country.get("name")
            if not name:
                continue
            if name not in country_names_so_far:
                yield name
                country_names_so_far.add(name)


chain = model | JsonOutputParser() | _extract_country_names_streaming

async for text in chain.astream(
    "output a list of the countries france, spain and japan and their populations in JSON format. "
    'Use a dict with an outer key of "countries" which contains a list of countries. '
    "Each country should have the key `name` and `population`",
):
    print(text, end="|", flush=True)
API Reference:JsonOutputParser
France|Spain|Japan|
note

因为上面的代码依赖于JSON自动补全，你可能会看到部分国家名称（例如，Sp 和 Spain），这并不是提取结果所期望的！

我们专注于流式处理的概念，而不一定是链的结果。

非流式组件​

一些内置组件如Retrievers不提供任何streaming。如果我们尝试stream它们会发生什么？🤨

from langchain_community.vectorstores import FAISS
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_openai import OpenAIEmbeddings

template = """Answer the question based only on the following context:
{context}

Question: {question}
"""
prompt = ChatPromptTemplate.from_template(template)

vectorstore = FAISS.from_texts(
    ["harrison worked at kensho", "harrison likes spicy food"],
    embedding=OpenAIEmbeddings(),
)
retriever = vectorstore.as_retriever()

chunks = [chunk for chunk in retriever.stream("where did harrison work?")]
chunks
[[Document(page_content='harrison worked at kensho'),
  Document(page_content='harrison likes spicy food')]]

流刚刚从该组件产生了最终结果。

这是可以的 🥹！并非所有组件都必须实现流式传输——在某些情况下，流式传输要么是不必要的，要么是困难的，或者根本没有意义。

tip

使用非流式组件构建的LCEL链，在许多情况下仍然能够进行流式处理，部分输出的流式处理将在链中最后一个非流式步骤之后开始。

retrieval_chain = (
    {
        "context": retriever.with_config(run_name="Docs"),
        "question": RunnablePassthrough(),
    }
    | prompt
    | model
    | StrOutputParser()
)
for chunk in retrieval_chain.stream(
    "Where did harrison work? " "Write 3 made up sentences about this place."
):
    print(chunk, end="|", flush=True)
Base|d on| the| given| context|,| Harrison| worke|d at| K|ens|ho|.|

Here| are| |3| |made| up| sentences| about| this| place|:|

1|.| K|ens|ho| was| a| cutting|-|edge| technology| company| known| for| its| innovative| solutions| in| artificial| intelligence| an|d data| analytics|.|

2|.| The| modern| office| space| at| K|ens|ho| feature|d open| floor| plans|,| collaborative| work|sp|aces|,| an|d a| vib|rant| atmosphere| that| fos|tere|d creativity| an|d team|work|.|

3|.| With| its| prime| location| in| the| heart| of| the| city|,| K|ens|ho| attracte|d top| talent| from| aroun|d the| worl|d,| creating| a| diverse| an|d dynamic| work| environment|.|

现在我们已经了解了stream和astream的工作原理，让我们进入流事件的世界。🏞️

使用流事件​

事件流是一个测试版 API。此API可能会根据反馈进行一些更改。

note

本指南演示了V2 API，并要求langchain-core版本大于等于0.2。对于与旧版本LangChain兼容的V1 API，请参见这里。

import langchain_core

langchain_core.__version__

为了使astream_events API正常工作：

  • 尽可能在代码中使用async（例如，异步工具等）
  • 如果定义自定义函数/可运行对象，请传播回调
  • 每当使用没有LCEL的可运行对象时，确保在LLMs上调用.astream()而不是.ainvoke，以强制LLM流式传输令牌。
  • 如果发现任何功能不如预期，请告诉我们！ :)

事件参考​

以下是一个参考表，显示了各种Runnable对象可能发出的一些事件。

note

当流式处理正确实现时，可运行程序的输入在输入流完全消耗之前将不会知道。这意味着inputs通常只会在end事件中包含，而不是在start事件中。

事件名称块输入输出
on_chat_model_start[模型名称]{"messages": [[SystemMessage, HumanMessage]]}
on_chat_model_stream[model name]AIMessageChunk(content="你好")
on_chat_model_end[model name]{"messages": [[SystemMessage, HumanMessage]]}AIMessageChunk(content="hello world")
on_llm_start[model name]{'input': 'hello'}
on_llm_stream[model name]'你好'
on_llm_end[model name]'你好人类！'
on_chain_startformat_docs
on_chain_streamformat_docs"你好世界！，再见世界！"
on_chain_endformat_docs[Document(...)]"你好世界！，再见世界！"
on_tool_startsome_tool{"x": 1, "y": "2"}
on_tool_endsome_tool{"x": 1, "y": "2"}
on_retriever_start[retriever name]{"query": "hello"}
on_retriever_end[retriever name]{"query": "hello"}[Document(...), ..]
on_prompt_start[template_name]{"question": "你好"}
on_prompt_end[template_name]{"question": "你好"}ChatPromptValue(messages: [SystemMessage, ...])

聊天模型​

让我们首先看一下聊天模型产生的事件。

events = []
async for event in model.astream_events("hello", version="v2"):
    events.append(event)
/home/eugene/src/langchain/libs/core/langchain_core/_api/beta_decorator.py:87: LangChainBetaWarning: This API is in beta and may change in the future.
  warn_beta(
note

嘿，API中那个有趣的version="v2"参数是什么？！😾

这是一个测试版API，我们几乎肯定会对其进行一些更改（事实上，我们已经做了！）

此版本参数将使我们能够最小化对您代码的重大更改。

简而言之，我们现在打扰您，是为了以后不必再打扰您。

v2 仅适用于 langchain-core>=0.2.0。

让我们来看一些开始事件和一些结束事件。

events[:3]
[{'event': 'on_chat_model_start',
  'data': {'input': 'hello'},
  'name': 'ChatAnthropic',
  'tags': [],
  'run_id': 'a81e4c0f-fc36-4d33-93bc-1ac25b9bb2c3',
  'metadata': {}},
 {'event': 'on_chat_model_stream',
  'data': {'chunk': AIMessageChunk(content='Hello', id='run-a81e4c0f-fc36-4d33-93bc-1ac25b9bb2c3')},
  'run_id': 'a81e4c0f-fc36-4d33-93bc-1ac25b9bb2c3',
  'name': 'ChatAnthropic',
  'tags': [],
  'metadata': {}},
 {'event': 'on_chat_model_stream',
  'data': {'chunk': AIMessageChunk(content='!', id='run-a81e4c0f-fc36-4d33-93bc-1ac25b9bb2c3')},
  'run_id': 'a81e4c0f-fc36-4d33-93bc-1ac25b9bb2c3',
  'name': 'ChatAnthropic',
  'tags': [],
  'metadata': {}}]
events[-2:]
[{'event': 'on_chat_model_stream',
  'data': {'chunk': AIMessageChunk(content='?', id='run-a81e4c0f-fc36-4d33-93bc-1ac25b9bb2c3')},
  'run_id': 'a81e4c0f-fc36-4d33-93bc-1ac25b9bb2c3',
  'name': 'ChatAnthropic',
  'tags': [],
  'metadata': {}},
 {'event': 'on_chat_model_end',
  'data': {'output': AIMessageChunk(content='Hello! How can I assist you today?', id='run-a81e4c0f-fc36-4d33-93bc-1ac25b9bb2c3')},
  'run_id': 'a81e4c0f-fc36-4d33-93bc-1ac25b9bb2c3',
  'name': 'ChatAnthropic',
  'tags': [],
  'metadata': {}}]

链​

让我们重新审视解析流式JSON的示例链，以探索流式事件API。

chain = (
    model | JsonOutputParser()
)  # Due to a bug in older versions of Langchain, JsonOutputParser did not stream results from some models

events = [
    event
    async for event in chain.astream_events(
        "output a list of the countries france, spain and japan and their populations in JSON format. "
        'Use a dict with an outer key of "countries" which contains a list of countries. '
        "Each country should have the key `name` and `population`",
        version="v2",
    )
]

如果你检查前几个事件，你会注意到有3个不同的开始事件，而不是2个开始事件。

三个开始事件对应：

  1. 链（模型 + 解析器）
  2. 模型
  3. 解析器
events[:3]
[{'event': 'on_chain_start',
  'data': {'input': 'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of "countries" which contains a list of countries. Each country should have the key `name` and `population`'},
  'name': 'RunnableSequence',
  'tags': [],
  'run_id': '4765006b-16e2-4b1d-a523-edd9fd64cb92',
  'metadata': {}},
 {'event': 'on_chat_model_start',
  'data': {'input': {'messages': [[HumanMessage(content='output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of "countries" which contains a list of countries. Each country should have the key `name` and `population`')]]}},
  'name': 'ChatAnthropic',
  'tags': ['seq:step:1'],
  'run_id': '0320c234-7b52-4a14-ae4e-5f100949e589',
  'metadata': {}},
 {'event': 'on_chat_model_stream',
  'data': {'chunk': AIMessageChunk(content='{', id='run-0320c234-7b52-4a14-ae4e-5f100949e589')},
  'run_id': '0320c234-7b52-4a14-ae4e-5f100949e589',
  'name': 'ChatAnthropic',
  'tags': ['seq:step:1'],
  'metadata': {}}]

你认为如果你查看最后3个事件会看到什么？中间的呢？

让我们使用这个API来输出模型和解析器的流事件。我们忽略开始事件、结束事件和来自链的事件。

num_events = 0

async for event in chain.astream_events(
    "output a list of the countries france, spain and japan and their populations in JSON format. "
    'Use a dict with an outer key of "countries" which contains a list of countries. '
    "Each country should have the key `name` and `population`",
    version="v2",
):
    kind = event["event"]
    if kind == "on_chat_model_stream":
        print(
            f"Chat model chunk: {repr(event['data']['chunk'].content)}",
            flush=True,
        )
    if kind == "on_parser_stream":
        print(f"Parser chunk: {event['data']['chunk']}", flush=True)
    num_events += 1
    if num_events > 30:
        # Truncate the output
        print("...")
        break
Chat model chunk: '{'
Parser chunk: {}
Chat model chunk: '\n  '
Chat model chunk: '"'
Chat model chunk: 'countries'
Chat model chunk: '":'
Chat model chunk: ' ['
Parser chunk: {'countries': []}
Chat model chunk: '\n    '
Chat model chunk: '{'
Parser chunk: {'countries': [{}]}
Chat model chunk: '\n      '
Chat model chunk: '"'
Chat model chunk: 'name'
Chat model chunk: '":'
Chat model chunk: ' "'
Parser chunk: {'countries': [{'name': ''}]}
Chat model chunk: 'France'
Parser chunk: {'countries': [{'name': 'France'}]}
Chat model chunk: '",'
Chat model chunk: '\n      '
Chat model chunk: '"'
Chat model chunk: 'population'
...

因为模型和解析器都支持流式处理，我们可以实时看到来自这两个组件的流式事件！有点酷，不是吗？🦜

过滤事件​

因为这个API会产生很多事件，所以能够对事件进行过滤是非常有用的。

你可以通过组件的name、组件的tags或组件的type进行过滤。

按名称​

chain = model.with_config({"run_name": "model"}) | JsonOutputParser().with_config(
    {"run_name": "my_parser"}
)

max_events = 0
async for event in chain.astream_events(
    "output a list of the countries france, spain and japan and their populations in JSON format. "
    'Use a dict with an outer key of "countries" which contains a list of countries. '
    "Each country should have the key `name` and `population`",
    version="v2",
    include_names=["my_parser"],
):
    print(event)
    max_events += 1
    if max_events > 10:
        # Truncate output
        print("...")
        break
{'event': 'on_parser_start', 'data': {'input': 'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of "countries" which contains a list of countries. Each country should have the key `name` and `population`'}, 'name': 'my_parser', 'tags': ['seq:step:2'], 'run_id': 'e058d750-f2c2-40f6-aa61-10f84cd671a9', 'metadata': {}}
{'event': 'on_parser_stream', 'data': {'chunk': {}}, 'run_id': 'e058d750-f2c2-40f6-aa61-10f84cd671a9', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}}
{'event': 'on_parser_stream', 'data': {'chunk': {'countries': []}}, 'run_id': 'e058d750-f2c2-40f6-aa61-10f84cd671a9', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}}
{'event': 'on_parser_stream', 'data': {'chunk': {'countries': [{}]}}, 'run_id': 'e058d750-f2c2-40f6-aa61-10f84cd671a9', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}}
{'event': 'on_parser_stream', 'data': {'chunk': {'countries': [{'name': ''}]}}, 'run_id': 'e058d750-f2c2-40f6-aa61-10f84cd671a9', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}}
{'event': 'on_parser_stream', 'data': {'chunk': {'countries': [{'name': 'France'}]}}, 'run_id': 'e058d750-f2c2-40f6-aa61-10f84cd671a9', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}}
{'event': 'on_parser_stream', 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67}]}}, 'run_id': 'e058d750-f2c2-40f6-aa61-10f84cd671a9', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}}
{'event': 'on_parser_stream', 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67413}]}}, 'run_id': 'e058d750-f2c2-40f6-aa61-10f84cd671a9', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}}
{'event': 'on_parser_stream', 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67413000}]}}, 'run_id': 'e058d750-f2c2-40f6-aa61-10f84cd671a9', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}}
{'event': 'on_parser_stream', 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67413000}, {}]}}, 'run_id': 'e058d750-f2c2-40f6-aa61-10f84cd671a9', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}}
{'event': 'on_parser_stream', 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67413000}, {'name': ''}]}}, 'run_id': 'e058d750-f2c2-40f6-aa61-10f84cd671a9', 'name': 'my_parser', 'tags': ['seq:step:2'], 'metadata': {}}
...

按类型​

chain = model.with_config({"run_name": "model"}) | JsonOutputParser().with_config(
    {"run_name": "my_parser"}
)

max_events = 0
async for event in chain.astream_events(
    'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of "countries" which contains a list of countries. Each country should have the key `name` and `population`',
    version="v2",
    include_types=["chat_model"],
):
    print(event)
    max_events += 1
    if max_events > 10:
        # Truncate output
        print("...")
        break
{'event': 'on_chat_model_start', 'data': {'input': 'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of "countries" which contains a list of countries. Each country should have the key `name` and `population`'}, 'name': 'model', 'tags': ['seq:step:1'], 'run_id': 'db246792-2a91-4eb3-a14b-29658947065d', 'metadata': {}}
{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='{', id='run-db246792-2a91-4eb3-a14b-29658947065d')}, 'run_id': 'db246792-2a91-4eb3-a14b-29658947065d', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {}}
{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\n  ', id='run-db246792-2a91-4eb3-a14b-29658947065d')}, 'run_id': 'db246792-2a91-4eb3-a14b-29658947065d', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {}}
{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='"', id='run-db246792-2a91-4eb3-a14b-29658947065d')}, 'run_id': 'db246792-2a91-4eb3-a14b-29658947065d', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {}}
{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='countries', id='run-db246792-2a91-4eb3-a14b-29658947065d')}, 'run_id': 'db246792-2a91-4eb3-a14b-29658947065d', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {}}
{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='":', id='run-db246792-2a91-4eb3-a14b-29658947065d')}, 'run_id': 'db246792-2a91-4eb3-a14b-29658947065d', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {}}
{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content=' [', id='run-db246792-2a91-4eb3-a14b-29658947065d')}, 'run_id': 'db246792-2a91-4eb3-a14b-29658947065d', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {}}
{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\n    ', id='run-db246792-2a91-4eb3-a14b-29658947065d')}, 'run_id': 'db246792-2a91-4eb3-a14b-29658947065d', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {}}
{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='{', id='run-db246792-2a91-4eb3-a14b-29658947065d')}, 'run_id': 'db246792-2a91-4eb3-a14b-29658947065d', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {}}
{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\n      ', id='run-db246792-2a91-4eb3-a14b-29658947065d')}, 'run_id': 'db246792-2a91-4eb3-a14b-29658947065d', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {}}
{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='"', id='run-db246792-2a91-4eb3-a14b-29658947065d')}, 'run_id': 'db246792-2a91-4eb3-a14b-29658947065d', 'name': 'model', 'tags': ['seq:step:1'], 'metadata': {}}
...

按标签​

caution

标签由给定可运行组件的子组件继承。

如果您正在使用标签进行过滤，请确保这是您想要的。

chain = (model | JsonOutputParser()).with_config({"tags": ["my_chain"]})

max_events = 0
async for event in chain.astream_events(
    'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of "countries" which contains a list of countries. Each country should have the key `name` and `population`',
    version="v2",
    include_tags=["my_chain"],
):
    print(event)
    max_events += 1
    if max_events > 10:
        # Truncate output
        print("...")
        break
{'event': 'on_chain_start', 'data': {'input': 'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of "countries" which contains a list of countries. Each country should have the key `name` and `population`'}, 'name': 'RunnableSequence', 'tags': ['my_chain'], 'run_id': 'fd68dd64-7a4d-4bdb-a0c2-ee592db0d024', 'metadata': {}}
{'event': 'on_chat_model_start', 'data': {'input': {'messages': [[HumanMessage(content='output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of "countries" which contains a list of countries. Each country should have the key `name` and `population`')]]}}, 'name': 'ChatAnthropic', 'tags': ['seq:step:1', 'my_chain'], 'run_id': 'efd3c8af-4be5-4f6c-9327-e3f9865dd1cd', 'metadata': {}}
{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='{', id='run-efd3c8af-4be5-4f6c-9327-e3f9865dd1cd')}, 'run_id': 'efd3c8af-4be5-4f6c-9327-e3f9865dd1cd', 'name': 'ChatAnthropic', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}}
{'event': 'on_parser_start', 'data': {}, 'name': 'JsonOutputParser', 'tags': ['seq:step:2', 'my_chain'], 'run_id': 'afde30b9-beac-4b36-b4c7-dbbe423ddcdb', 'metadata': {}}
{'event': 'on_parser_stream', 'data': {'chunk': {}}, 'run_id': 'afde30b9-beac-4b36-b4c7-dbbe423ddcdb', 'name': 'JsonOutputParser', 'tags': ['seq:step:2', 'my_chain'], 'metadata': {}}
{'event': 'on_chain_stream', 'data': {'chunk': {}}, 'run_id': 'fd68dd64-7a4d-4bdb-a0c2-ee592db0d024', 'name': 'RunnableSequence', 'tags': ['my_chain'], 'metadata': {}}
{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='\n  ', id='run-efd3c8af-4be5-4f6c-9327-e3f9865dd1cd')}, 'run_id': 'efd3c8af-4be5-4f6c-9327-e3f9865dd1cd', 'name': 'ChatAnthropic', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}}
{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='"', id='run-efd3c8af-4be5-4f6c-9327-e3f9865dd1cd')}, 'run_id': 'efd3c8af-4be5-4f6c-9327-e3f9865dd1cd', 'name': 'ChatAnthropic', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}}
{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='countries', id='run-efd3c8af-4be5-4f6c-9327-e3f9865dd1cd')}, 'run_id': 'efd3c8af-4be5-4f6c-9327-e3f9865dd1cd', 'name': 'ChatAnthropic', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}}
{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content='":', id='run-efd3c8af-4be5-4f6c-9327-e3f9865dd1cd')}, 'run_id': 'efd3c8af-4be5-4f6c-9327-e3f9865dd1cd', 'name': 'ChatAnthropic', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}}
{'event': 'on_chat_model_stream', 'data': {'chunk': AIMessageChunk(content=' [', id='run-efd3c8af-4be5-4f6c-9327-e3f9865dd1cd')}, 'run_id': 'efd3c8af-4be5-4f6c-9327-e3f9865dd1cd', 'name': 'ChatAnthropic', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}}
...

非流式组件​

还记得有些组件因为不操作输入流而无法很好地流式传输吗？

虽然在使用astream时，这些组件可能会中断最终输出的流式传输，但astream_events仍然会从支持流式传输的中间步骤中产生流式事件！

# Function that does not support streaming.
# It operates on the finalizes inputs rather than
# operating on the input stream.
def _extract_country_names(inputs):
    """A function that does not operates on input streams and breaks streaming."""
    if not isinstance(inputs, dict):
        return ""

    if "countries" not in inputs:
        return ""

    countries = inputs["countries"]

    if not isinstance(countries, list):
        return ""

    country_names = [
        country.get("name") for country in countries if isinstance(country, dict)
    ]
    return country_names


chain = (
    model | JsonOutputParser() | _extract_country_names
)  # This parser only works with OpenAI right now

正如预期的那样，astream API 无法正常工作，因为 _extract_country_names 不在流上操作。

async for chunk in chain.astream(
    "output a list of the countries france, spain and japan and their populations in JSON format. "
    'Use a dict with an outer key of "countries" which contains a list of countries. '
    "Each country should have the key `name` and `population`",
):
    print(chunk, flush=True)
['France', 'Spain', 'Japan']

现在，让我们确认一下，使用astream_events我们仍然可以看到模型和解析器的流式输出。

num_events = 0

async for event in chain.astream_events(
    "output a list of the countries france, spain and japan and their populations in JSON format. "
    'Use a dict with an outer key of "countries" which contains a list of countries. '
    "Each country should have the key `name` and `population`",
    version="v2",
):
    kind = event["event"]
    if kind == "on_chat_model_stream":
        print(
            f"Chat model chunk: {repr(event['data']['chunk'].content)}",
            flush=True,
        )
    if kind == "on_parser_stream":
        print(f"Parser chunk: {event['data']['chunk']}", flush=True)
    num_events += 1
    if num_events > 30:
        # Truncate the output
        print("...")
        break
Chat model chunk: '{'
Parser chunk: {}
Chat model chunk: '\n  '
Chat model chunk: '"'
Chat model chunk: 'countries'
Chat model chunk: '":'
Chat model chunk: ' ['
Parser chunk: {'countries': []}
Chat model chunk: '\n    '
Chat model chunk: '{'
Parser chunk: {'countries': [{}]}
Chat model chunk: '\n      '
Chat model chunk: '"'
Chat model chunk: 'name'
Chat model chunk: '":'
Chat model chunk: ' "'
Parser chunk: {'countries': [{'name': ''}]}
Chat model chunk: 'France'
Parser chunk: {'countries': [{'name': 'France'}]}
Chat model chunk: '",'
Chat model chunk: '\n      '
Chat model chunk: '"'
Chat model chunk: 'population'
Chat model chunk: '":'
Chat model chunk: ' '
Chat model chunk: '67'
Parser chunk: {'countries': [{'name': 'France', 'population': 67}]}
...

传播回调​

caution

如果您在工具内部调用可运行对象，您需要将回调传播到可运行对象；否则，不会生成任何流事件。

note

当使用RunnableLambdas或@chain装饰器时，回调会在后台自动传播。

from langchain_core.runnables import RunnableLambda
from langchain_core.tools import tool


def reverse_word(word: str):
    return word[::-1]


reverse_word = RunnableLambda(reverse_word)


@tool
def bad_tool(word: str):
    """Custom tool that doesn't propagate callbacks."""
    return reverse_word.invoke(word)


async for event in bad_tool.astream_events("hello", version="v2"):
    print(event)
API Reference:RunnableLambda | tool
{'event': 'on_tool_start', 'data': {'input': 'hello'}, 'name': 'bad_tool', 'tags': [], 'run_id': 'ea900472-a8f7-425d-b627-facdef936ee8', 'metadata': {}}
{'event': 'on_chain_start', 'data': {'input': 'hello'}, 'name': 'reverse_word', 'tags': [], 'run_id': '77b01284-0515-48f4-8d7c-eb27c1882f86', 'metadata': {}}
{'event': 'on_chain_end', 'data': {'output': 'olleh', 'input': 'hello'}, 'run_id': '77b01284-0515-48f4-8d7c-eb27c1882f86', 'name': 'reverse_word', 'tags': [], 'metadata': {}}
{'event': 'on_tool_end', 'data': {'output': 'olleh'}, 'run_id': 'ea900472-a8f7-425d-b627-facdef936ee8', 'name': 'bad_tool', 'tags': [], 'metadata': {}}

这是一个正确传播回调的重新实现。你会注意到现在我们也能从reverse_word可运行对象获取事件。

@tool
def correct_tool(word: str, callbacks):
    """A tool that correctly propagates callbacks."""
    return reverse_word.invoke(word, {"callbacks": callbacks})


async for event in correct_tool.astream_events("hello", version="v2"):
    print(event)
{'event': 'on_tool_start', 'data': {'input': 'hello'}, 'name': 'correct_tool', 'tags': [], 'run_id': 'd5ea83b9-9278-49cc-9f1d-aa302d671040', 'metadata': {}}
{'event': 'on_chain_start', 'data': {'input': 'hello'}, 'name': 'reverse_word', 'tags': [], 'run_id': '44dafbf4-2f87-412b-ae0e-9f71713810df', 'metadata': {}}
{'event': 'on_chain_end', 'data': {'output': 'olleh', 'input': 'hello'}, 'run_id': '44dafbf4-2f87-412b-ae0e-9f71713810df', 'name': 'reverse_word', 'tags': [], 'metadata': {}}
{'event': 'on_tool_end', 'data': {'output': 'olleh'}, 'run_id': 'd5ea83b9-9278-49cc-9f1d-aa302d671040', 'name': 'correct_tool', 'tags': [], 'metadata': {}}

如果您从Runnable Lambdas或@chains中调用runnables，那么回调将自动代表您传递。

from langchain_core.runnables import RunnableLambda


async def reverse_and_double(word: str):
    return await reverse_word.ainvoke(word) * 2


reverse_and_double = RunnableLambda(reverse_and_double)

await reverse_and_double.ainvoke("1234")

async for event in reverse_and_double.astream_events("1234", version="v2"):
    print(event)
API Reference:RunnableLambda
{'event': 'on_chain_start', 'data': {'input': '1234'}, 'name': 'reverse_and_double', 'tags': [], 'run_id': '03b0e6a1-3e60-42fc-8373-1e7829198d80', 'metadata': {}}
{'event': 'on_chain_start', 'data': {'input': '1234'}, 'name': 'reverse_word', 'tags': [], 'run_id': '5cf26fc8-840b-4642-98ed-623dda28707a', 'metadata': {}}
{'event': 'on_chain_end', 'data': {'output': '4321', 'input': '1234'}, 'run_id': '5cf26fc8-840b-4642-98ed-623dda28707a', 'name': 'reverse_word', 'tags': [], 'metadata': {}}
{'event': 'on_chain_stream', 'data': {'chunk': '43214321'}, 'run_id': '03b0e6a1-3e60-42fc-8373-1e7829198d80', 'name': 'reverse_and_double', 'tags': [], 'metadata': {}}
{'event': 'on_chain_end', 'data': {'output': '43214321'}, 'run_id': '03b0e6a1-3e60-42fc-8373-1e7829198d80', 'name': 'reverse_and_double', 'tags': [], 'metadata': {}}

并且使用@chain装饰器：

from langchain_core.runnables import chain


@chain
async def reverse_and_double(word: str):
    return await reverse_word.ainvoke(word) * 2


await reverse_and_double.ainvoke("1234")

async for event in reverse_and_double.astream_events("1234", version="v2"):
    print(event)
API Reference:chain
{'event': 'on_chain_start', 'data': {'input': '1234'}, 'name': 'reverse_and_double', 'tags': [], 'run_id': '1bfcaedc-f4aa-4d8e-beee-9bba6ef17008', 'metadata': {}}
{'event': 'on_chain_start', 'data': {'input': '1234'}, 'name': 'reverse_word', 'tags': [], 'run_id': '64fc99f0-5d7d-442b-b4f5-4537129f67d1', 'metadata': {}}
{'event': 'on_chain_end', 'data': {'output': '4321', 'input': '1234'}, 'run_id': '64fc99f0-5d7d-442b-b4f5-4537129f67d1', 'name': 'reverse_word', 'tags': [], 'metadata': {}}
{'event': 'on_chain_stream', 'data': {'chunk': '43214321'}, 'run_id': '1bfcaedc-f4aa-4d8e-beee-9bba6ef17008', 'name': 'reverse_and_double', 'tags': [], 'metadata': {}}
{'event': 'on_chain_end', 'data': {'output': '43214321'}, 'run_id': '1bfcaedc-f4aa-4d8e-beee-9bba6ef17008', 'name': 'reverse_and_double', 'tags': [], 'metadata': {}}

下一步​

现在你已经学会了一些使用LangChain流式传输最终输出和内部步骤的方法。

要了解更多信息，请查看本节中的其他操作指南，或Langchain表达式语言的概念指南。

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