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如何链式运行

Prerequisites

本指南假设您熟悉以下概念：

关于LangChain表达式语言的一点是，任何两个可运行对象都可以“链接”在一起形成序列。前一个可运行对象的.invoke()调用的输出将作为输入传递给下一个可运行对象。这可以使用管道操作符（|）或更明确的.pipe()方法来完成，它们的功能相同。

生成的 RunnableSequence 本身是一个可运行的对象，这意味着它可以像任何其他可运行对象一样被调用、流式传输或进一步链接。以这种方式链接可运行对象的优势包括高效的流式传输（序列将在输出可用时立即流式传输），以及使用 LangSmith 等工具进行调试和跟踪。

管道操作符: |​

为了展示这是如何工作的，让我们通过一个例子来说明。我们将介绍LangChain中的一个常见模式：使用提示模板将输入格式化为聊天模型，最后使用输出解析器将聊天消息输出转换为字符串。

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

prompt = ChatPromptTemplate.from_template("tell me a joke about {topic}")

chain = prompt | model | StrOutputParser()

提示和模型都是可运行的，提示调用的输出类型与聊天模型的输入类型相同，因此我们可以将它们链接在一起。然后我们可以像调用任何其他可运行对象一样调用生成的序列：

chain.invoke({"topic": "bears"})
"Here's a bear joke for you:\n\nWhy did the bear dissolve in water?\nBecause it was a polar bear!"

强制转换​

我们甚至可以把这个链与更多的可运行对象结合起来，创建另一个链。这可能涉及使用其他类型的可运行对象进行一些输入/输出格式化，具体取决于链组件所需的输入和输出。

例如，假设我们想要将生成笑话的链与另一个评估生成的笑话是否有趣的链组合在一起。

我们需要注意如何格式化输入到下一个链中。在下面的示例中，链中的字典会自动解析并转换为RunnableParallel，它会并行运行所有值并返回一个包含结果的字典。

这恰好是下一个提示模板期望的格式。以下是它的实际应用：

from langchain_core.output_parsers import StrOutputParser

analysis_prompt = ChatPromptTemplate.from_template("is this a funny joke? {joke}")

composed_chain = {"joke": chain} | analysis_prompt | model | StrOutputParser()

composed_chain.invoke({"topic": "bears"})
API Reference:StrOutputParser
'Haha, that\'s a clever play on words! Using "polar" to imply the bear dissolved or became polar/polarized when put in water. Not the most hilarious joke ever, but it has a cute, groan-worthy pun that makes it mildly amusing. I appreciate a good pun or wordplay joke.'

函数也会被强制转换为可运行的，因此你也可以向你的链中添加自定义逻辑。下面的链产生了与之前相同的逻辑流程：

composed_chain_with_lambda = (
    chain
    | (lambda input: {"joke": input})
    | analysis_prompt
    | model
    | StrOutputParser()
)

composed_chain_with_lambda.invoke({"topic": "beets"})
"Haha, that's a cute and punny joke! I like how it plays on the idea of beets blushing or turning red like someone blushing. Food puns can be quite amusing. While not a total knee-slapper, it's a light-hearted, groan-worthy dad joke that would make me chuckle and shake my head. Simple vegetable humor!"

然而，请记住，使用这样的函数可能会干扰像流处理这样的操作。有关更多信息，请参阅本节。

The .pipe() 方法​

我们也可以使用.pipe()方法来组合相同的序列。以下是它的样子：

from langchain_core.runnables import RunnableParallel

composed_chain_with_pipe = (
    RunnableParallel({"joke": chain})
    .pipe(analysis_prompt)
    .pipe(model)
    .pipe(StrOutputParser())
)

composed_chain_with_pipe.invoke({"topic": "battlestar galactica"})
API Reference:RunnableParallel
"I cannot reproduce any copyrighted material verbatim, but I can try to analyze the humor in the joke you provided without quoting it directly.\n\nThe joke plays on the idea that the Cylon raiders, who are the antagonists in the Battlestar Galactica universe, failed to locate the human survivors after attacking their home planets (the Twelve Colonies) due to using an outdated and poorly performing operating system (Windows Vista) for their targeting systems.\n\nThe humor stems from the juxtaposition of a futuristic science fiction setting with a relatable real-world frustration – the use of buggy, slow, or unreliable software or technology. It pokes fun at the perceived inadequacies of Windows Vista, which was widely criticized for its performance issues and other problems when it was released.\n\nBy attributing the Cylons' failure to locate the humans to their use of Vista, the joke creates an amusing and unexpected connection between a fictional advanced race of robots and a familiar technological annoyance experienced by many people in the real world.\n\nOverall, the joke relies on incongruity and relatability to generate humor, but without reproducing any copyrighted material directly."

或者简写为：

composed_chain_with_pipe = RunnableParallel({"joke": chain}).pipe(
    analysis_prompt, model, StrOutputParser()
)
  • Streaming: 查看流式指南以了解链的流式行为

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