如何链式调用可运行对象

前提条件

本指南假定您熟悉以下概念

关于 LangChain 表达式语言的一个要点是，任何两个可运行对象都可以“链式”连接成序列。前一个可运行对象的 .invoke() 调用的输出将作为下一个可运行对象的输入传递。这可以使用管道运算符 (|)，或更显式的 .pipe() 方法来完成，它们的作用相同。

生成的 RunnableSequence 本身也是一个可运行对象，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式连接。以这种方式链式连接可运行对象的优点是高效的流式处理（序列将在输出可用时立即流式传输输出），以及使用 LangSmith 等工具进行调试和追踪。

管道运算符：`|`

为了展示其工作原理，让我们看一个示例。我们将逐步介绍 LangChain 中的一个常见模式：使用提示词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。

选择聊天模型

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

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

chain = prompt | model | StrOutputParser()

API 参考：StrOutputParser | ChatPromptTemplate

提示词和模型都是可运行对象，并且提示词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式连接在一起。然后，我们可以像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

我们甚至可以将此链与更多可运行对象组合以创建另一个链。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，假设我们想将笑话生成链与另一个评估生成的笑话是否有趣的链组合起来。

我们需要小心如何格式化下一个链的输入。在下面的示例中，链中的字典会自动解析并转换为 RunnableParallel，它并行运行其所有值并返回一个包含结果的字典。

这恰好是下一个提示词模板期望的相同格式。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 参考：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.'

函数也将被强制转换为可运行对象，因此你也可以向链中添加自定义逻辑。下面的链产生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!"

但是，请记住，像这样使用函数可能会干扰流式处理等操作。有关更多信息，请参阅此部分。

`.pipe()` 方法

我们也可以使用 .pipe() 方法组成相同的序列。这是它的样子

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 参考：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."

或缩写形式

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

流式处理：查看流式处理指南以了解链的流式处理行为

管道运算符：|​

强制转换​

.pipe() 方法​

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管道运算符：`|`

强制转换

`.pipe()` 方法

相关内容