如何创建一个动态（自构建）链

Prerequisites

本指南假设您熟悉以下内容：

有时我们希望在运行时根据链的输入构建链的部分（路由是最常见的例子）。我们可以使用RunnableLambda的一个非常有用的属性来创建动态链，即如果RunnableLambda返回一个Runnable，那么该Runnable本身会被调用。让我们看一个例子。

Select chat model:

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

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

# | echo: false

from langchain_anthropic import ChatAnthropic

llm = ChatAnthropic(model="claude-3-sonnet-20240229")

API Reference:ChatAnthropic

from operator import itemgetter

from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import Runnable, RunnablePassthrough, chain

contextualize_instructions = """Convert the latest user question into a standalone question given the chat history. Don't answer the question, return the question and nothing else (no descriptive text)."""
contextualize_prompt = ChatPromptTemplate.from_messages(
    [
        ("system", contextualize_instructions),
        ("placeholder", "{chat_history}"),
        ("human", "{question}"),
    ]
)
contextualize_question = contextualize_prompt | llm | StrOutputParser()

qa_instructions = (
    """Answer the user question given the following context:\n\n{context}."""
)
qa_prompt = ChatPromptTemplate.from_messages(
    [("system", qa_instructions), ("human", "{question}")]
)


@chain
def contextualize_if_needed(input_: dict) -> Runnable:
    if input_.get("chat_history"):
        # NOTE: This is returning another Runnable, not an actual output.
        return contextualize_question
    else:
        return RunnablePassthrough() | itemgetter("question")


@chain
def fake_retriever(input_: dict) -> str:
    return "egypt's population in 2024 is about 111 million"


full_chain = (
    RunnablePassthrough.assign(question=contextualize_if_needed).assign(
        context=fake_retriever
    )
    | qa_prompt
    | llm
    | StrOutputParser()
)

full_chain.invoke(
    {
        "question": "what about egypt",
        "chat_history": [
            ("human", "what's the population of indonesia"),
            ("ai", "about 276 million"),
        ],
    }
)

API Reference:StrOutputParser | ChatPromptTemplate | Runnable | RunnablePassthrough | chain

"According to the context provided, Egypt's population in 2024 is estimated to be about 111 million."

这里的关键是contextualize_if_needed返回另一个Runnable而不是实际的输出。当整个链执行时，这个返回的Runnable本身会被运行。

查看跟踪记录，我们可以看到，由于我们传入了chat_history，我们执行了contextualize_question链作为完整链的一部分：https://smith.langchain.com/public/9e0ae34c-4082-4f3f-beed-34a2a2f4c991/r

请注意，返回的Runnable的流式处理、批处理等功能都得到了保留

for chunk in contextualize_if_needed.stream(
    {
        "question": "what about egypt",
        "chat_history": [
            ("human", "what's the population of indonesia"),
            ("ai", "about 276 million"),
        ],
    }
):
    print(chunk)

What
 is
 the
 population
 of
 Egypt
?

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