如何流式运行可执行项

Prerequisites

本指南假设您熟悉以下概念：

流式处理对于基于大型语言模型的应用程序在用户端的响应性至关重要。

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

该接口提供了两种流式内容的通用方法：

同步 stream 和异步 astream：一种默认实现的流式处理，从链中流式传输最终输出。
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法。

让我们看看这两种方法，并尝试理解如何使用它们。

info

有关LangChain中流式处理技术的更高层次概述，请参见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，尽快生成每个块。

只有当程序中的所有步骤都知道如何处理输入流时，流式处理才是可能的；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能有所不同，从简单的任务（如发出LLM生成的标记）到更具挑战性的任务（如在整个JSON完成之前流式传输JSON结果的部分）。

探索流式处理的最佳起点是LLM应用中最重要的组件——LLM本身！

LLM和聊天模型

大型语言模型及其聊天变体是基于LLM的应用中的主要瓶颈。

大型语言模型生成完整响应可能需要几秒钟。这远远慢于应用程序对最终用户感觉响应的~200-300毫秒阈值。

使应用程序感觉更具响应性的关键策略是显示中间进度；即，从模型逐个令牌流式输出。

我们将展示使用聊天模型的流式示例。请从以下选项中选择一个：

pip install -qU langchain-openai

import getpass
import os

os.environ["OPENAI_API_KEY"] = getpass.getpass()

from langchain_openai import ChatOpenAI

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

pip install -qU langchain-anthropic

import getpass
import os

os.environ["ANTHROPIC_API_KEY"] = getpass.getpass()

from langchain_anthropic import ChatAnthropic

model = ChatAnthropic(model="claude-3-5-sonnet-20240620")

pip install -qU langchain-openai

import getpass
import os

os.environ["AZURE_OPENAI_API_KEY"] = getpass.getpass()

from langchain_openai import AzureChatOpenAI

model = AzureChatOpenAI(
    azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
    azure_deployment=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],
    openai_api_version=os.environ["AZURE_OPENAI_API_VERSION"],
)

pip install -qU langchain-google-vertexai

import getpass
import os

os.environ["GOOGLE_API_KEY"] = getpass.getpass()

from langchain_google_vertexai import ChatVertexAI

model = ChatVertexAI(model="gemini-1.5-flash")

pip install -qU langchain-cohere

import getpass
import os

os.environ["COHERE_API_KEY"] = getpass.getpass()

from langchain_cohere import ChatCohere

model = ChatCohere(model="command-r-plus")

pip install -qU langchain-nvidia-ai-endpoints

import getpass
import os

os.environ["NVIDIA_API_KEY"] = getpass.getpass()

from langchain import ChatNVIDIA

model = ChatNVIDIA(model="meta/llama3-70b-instruct")

pip install -qU langchain-fireworks

import getpass
import os

os.environ["FIREWORKS_API_KEY"] = getpass.getpass()

from langchain_fireworks import ChatFireworks

model = ChatFireworks(model="accounts/fireworks/models/llama-v3p1-70b-instruct")

pip install -qU langchain-groq

import getpass
import os

os.environ["GROQ_API_KEY"] = getpass.getpass()

from langchain_groq import ChatGroq

model = ChatGroq(model="llama3-8b-8192")

pip install -qU langchain-mistralai

import getpass
import os

os.environ["MISTRAL_API_KEY"] = getpass.getpass()

from langchain_mistralai import ChatMistralAI

model = ChatMistralAI(model="mistral-large-latest")

pip install -qU langchain-openai

import getpass
import os

os.environ["TOGETHER_API_KEY"] = getpass.getpass()

from langchain_openai import ChatOpenAI

model = ChatOpenAI(
    base_url="https://api.together.xyz/v1",
    api_key=os.environ["TOGETHER_API_KEY"],
    model="mistralai/Mixtral-8x7B-Instruct-v0.1",
)

让我们从同步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|.|

另外，如果您在异步环境中工作，您可以考虑使用异步 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|.|

让我们检查一下其中的一个块

chunks[0]

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

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

消息块的设计是可叠加的——可以简单地将它们相加，以获取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

几乎所有的 LLM 应用都涉及比仅仅调用语言模型更多的步骤。

让我们使用 LangChain 表达式 (LCEL) 构建一个简单的链，结合一个提示、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，从 AIMessageChunk 中提取 content 字段，给我们模型返回的 token。

tip

LCEL 是一种 声明性 的方式，通过将不同的 LangChain 原语链在一起指定一个“程序”。使用 LCEL 创建的链受益于 stream 和 astream 的自动实现，允许最终输出的流式处理。实际上，使用 LCEL 创建的链实现了整个标准的运行接口。

<!--IMPORTS:[{"imported": "StrOutputParser", "source": "langchain_core.output_parsers", "docs": "https://python.langchain.com/api_reference/core/output_parsers/langchain_core.output_parsers.string.StrOutputParser.html", "title": "How to stream runnables"}, {"imported": "ChatPromptTemplate", "source": "langchain_core.prompts", "docs": "https://python.langchain.com/api_reference/core/prompts/langchain_core.prompts.chat.ChatPromptTemplate.html", "title": "How to stream runnables"}]-->
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，即使我们在上面的链的末尾使用了 parser，我们仍然得到了流式输出。parser 针对每个流式块单独操作。许多 LCEL 原语也支持这种变换风格的直通流式处理，这在构建应用时非常方便。

自定义函数可以设计为返回生成器，能够在流上操作。

某些可运行对象，如提示词模板和聊天模型，无法处理单个块，而是聚合所有先前的步骤。这些可运行对象可能会中断流式处理过程。

note

LangChain表达式语言允许您将链的构建与其使用模式（例如，同步/异步、批处理/流式等）分开。如果这与您正在构建的内容无关，您也可以依赖标准的命令式编程方法，通过在每个组件上单独调用invoke、batch或stream，将结果分配给变量，然后根据需要在下游使用它们。

处理输入流

如果您想在生成时流式传输JSON，该怎么办？

如果您依赖json.loads来解析部分JSON，解析将失败，因为部分JSON将不是有效的JSON。

您可能会完全不知道该怎么办，并声称无法流式传输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“自动完成”部分JSON，使其成为有效状态。

让我们看看这样的解析器是如何工作的，以理解这意味着什么。

<!--IMPORTS:[{"imported": "JsonOutputParser", "source": "langchain_core.output_parsers", "docs": "https://python.langchain.com/api_reference/core/output_parsers/langchain_core.output_parsers.json.JsonOutputParser.html", "title": "How to stream runnables"}]-->
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)

{}
{'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，让我们中断流式处理。我们将使用之前的示例，并在最后添加一个提取函数，从最终的 JSON 中提取国家名称。

warning

链中的任何步骤如果操作的是最终输入而不是输入流，都可能通过 stream 或 astream 中断流式功能。

tip

稍后，我们将讨论 astream_events API，它从中间步骤流式传输结果。即使链中包含仅操作最终输入的步骤，该 API 也会从中间步骤流式传输结果。

<!--IMPORTS:[{"imported": "JsonOutputParser", "source": "langchain_core.output_parsers", "docs": "https://python.langchain.com/api_reference/core/output_parsers/langchain_core.output_parsers.json.JsonOutputParser.html", "title": "How to stream runnables"}]-->
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)

['France', 'Spain', 'Japan']|

生成器函数

让我们使用一个可以操作输入流的生成器函数来修复流式处理。

tip

生成器函数（使用 yield 的函数）允许编写操作输入流的代码。

<!--IMPORTS:[{"imported": "JsonOutputParser", "source": "langchain_core.output_parsers", "docs": "https://python.langchain.com/api_reference/core/output_parsers/langchain_core.output_parsers.json.JsonOutputParser.html", "title": "How to stream runnables"}]-->
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)

France|Spain|Japan|

note

由于上面的代码依赖于 JSON 自动补全，您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希望的！

我们关注的是流式处理的概念，而不一定是链的结果。

非流式组件

一些内置组件，如检索器，并不提供任何 streaming。如果我们尝试对它们进行 stream 会发生什么？🤨

<!--IMPORTS:[{"imported": "FAISS", "source": "langchain_community.vectorstores", "docs": "https://python.langchain.com/api_reference/community/vectorstores/langchain_community.vectorstores.faiss.FAISS.html", "title": "How to stream runnables"}, {"imported": "StrOutputParser", "source": "langchain_core.output_parsers", "docs": "https://python.langchain.com/api_reference/core/output_parsers/langchain_core.output_parsers.string.StrOutputParser.html", "title": "How to stream runnables"}, {"imported": "ChatPromptTemplate", "source": "langchain_core.prompts", "docs": "https://python.langchain.com/api_reference/core/prompts/langchain_core.prompts.chat.ChatPromptTemplate.html", "title": "How to stream runnables"}, {"imported": "RunnablePassthrough", "source": "langchain_core.runnables", "docs": "https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.passthrough.RunnablePassthrough.html", "title": "How to stream runnables"}, {"imported": "OpenAIEmbeddings", "source": "langchain_openai", "docs": "https://python.langchain.com/api_reference/openai/embeddings/langchain_openai.embeddings.base.OpenAIEmbeddings.html", "title": "How to stream runnables"}]-->
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')]]

流刚刚从该组件中产生了最终结果。

这很好 🥹！并不是所有组件都必须实现流式处理——在某些情况下，流式处理要么不必要，要么困难，或者根本没有意义。

tip

使用非流式组件构建的LCEL链，在很多情况下仍然能够进行流式处理，流式部分输出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是如何工作的，让我们进入流式事件的世界。🏞️

使用流事件

事件流式处理是一个测试版API。该API可能会根据反馈有所变化。

note

本指南演示了V2 API，并要求langchain-core >= 0.2。有关与旧版本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/可运行程序，请传播回调
在不使用 LCEL 的情况下使用可运行对象时，请确保在大型语言模型（LLMs）上调用 .astream() 而不是 .ainvoke，以强制 LLM 流式传输令牌。
如果有任何问题，请告诉我们！:)

事件参考

下面是一个参考表，显示了各种可运行对象可能发出的某些事件。

note

当流式处理正确实现时，输入到可运行对象的内容在输入流完全消耗之前是未知的。这意味着 inputs 通常仅在 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	[模型名称]	AIMessageChunk(content="hello")
on_chat_model_end	[模型名称]		{"messages": [[SystemMessage, HumanMessage]]}	AIMessageChunk(content="hello world")
on_llm_start	[模型名称]		{'输入': '你好'}
on_llm_stream	[模型名称]	'你好'
on_llm_end	[模型名称]		'你好，人类！'
on_chain_start	格式化文档
on_chain_stream	格式化文档	"你好，世界！再见，世界！"
on_chain_end	格式化文档		[文档(...)]	"你好，世界！再见，世界！"
on_tool_start	一些工具		{"x": 1, "y": "2"}
on_tool_end	一些工具			{"x": 1, "y": "2"}
on_retriever_start	[检索器名称]		{"查询": "你好"}
on_retriever_end	[检索器名称]		{"查询": "你好"}	[文档(...), ..]
on_prompt_start	[模板名称]		{"question": "hello"}
on_prompt_end	[模板名称]		{"question": "hello"}	ChatPromptValue(messages: [SystemMessage, ...])

聊天模型

让我们先看看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（实际上，我们已经做过了！）

这个版本参数将允许我们将对您代码的破坏性更改降到最低。

简而言之，我们现在让您烦恼，以便以后不再让您烦恼。

v2 仅适用于 langchain-core>=0.2.0。

让我们看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

让我们重新审视一下解析流式 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",
    )
]

如果你查看前几个事件，你会注意到有 3 个不同的开始事件，而不是 2 个开始事件。

这三个开始事件对应于：

链（模型 + 解析器）
模型
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': {}}]

如果你查看最后 3 个事件，你认为会看到什么？中间的呢？

让我们使用这个 API 从模型和解析器中输出流事件。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，我们实时看到来自两个组件的流式事件！这不是很酷吗？🦜

过滤事件

由于此API产生了许多事件，因此能够对事件进行过滤是很有用的。

您可以通过组件的 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。

如果您使用标签进行过滤，请确保这正是您想要的。

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

还记得有些组件由于不在 输入流 上操作而无法很好地流式处理吗？

虽然这些组件在使用 astream 时可能会中断最终输出的流式处理，但 astream_events 仍会从支持流式处理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 工作不正常，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，让我们确认使用 astream_events 时，我们仍然能看到来自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

如果您在工具中使用可运行的调用，您需要将回调传播到可运行的对象；否则，将不会生成流事件。

note

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

<!--IMPORTS:[{"imported": "RunnableLambda", "source": "langchain_core.runnables", "docs": "https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.RunnableLambda.html", "title": "How to stream runnables"}, {"imported": "tool", "source": "langchain_core.tools", "docs": "https://python.langchain.com/api_reference/core/tools/langchain_core.tools.convert.tool.html", "title": "How to stream runnables"}]-->
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)

{'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。您会注意到现在我们也从 reverse_word 可运行对象中获得事件。

@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 中调用可运行对象，则回调将自动代表您传递。

<!--IMPORTS:[{"imported": "RunnableLambda", "source": "langchain_core.runnables", "docs": "https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.RunnableLambda.html", "title": "How to stream runnables"}]-->
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)

{'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：

<!--IMPORTS:[{"imported": "chain", "source": "langchain_core.runnables", "docs": "https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.chain.html", "title": "How to stream runnables"}]-->
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)

{'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

现在您已经学习了一些使用LangChain流式处理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使用指南，或查看LangChain表达式的概念指南。

如何流式运行可执行项

使用流

LLM和聊天模型

链

处理输入流

生成器函数

非流式组件

使用流事件

事件参考

聊天模型

链

过滤事件

按名称

按类型

按标签

非流式组件

传播回调

下一步

Was this page helpful?

You can also leave detailed feedback on GitHub.

使用流​

LLM和聊天模型​

链​

处理输入流​

生成器函数​

非流式组件​

使用流事件​

事件参考​

聊天模型​

链​

过滤事件​

按名称​

按类型​

按标签​

非流式组件​

传播回调​

下一步​

Was this page helpful?

You can also leave detailed feedback on GitHub.

使用流

LLM和聊天模型

链

处理输入流

生成器函数

非流式组件

使用流事件

事件参考

聊天模型

链

过滤事件

按名称

按类型

按标签

非流式组件

传播回调

下一步