如何创建动态（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构建链的部分，这取决于链的输入（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，则该Runnable会被调用。让我们看一个例子。

pip install -qU langchain-openai

import getpass
import os

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

from langchain_openai import ChatOpenAI

llm = 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

llm = 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

llm = 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

llm = 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

llm = 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

llm = 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

llm = 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

llm = 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

llm = 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

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

<!--IMPORTS:[{"imported": "ChatAnthropic", "source": "langchain_anthropic", "docs": "https://python.langchain.com/api_reference/anthropic/chat_models/langchain_anthropic.chat_models.ChatAnthropic.html", "title": "How to create a dynamic (self-constructing) chain"}]-->
# | echo: false

from langchain_anthropic import ChatAnthropic

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

<!--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 create a dynamic (self-constructing) chain"}, {"imported": "ChatPromptTemplate", "source": "langchain_core.prompts", "docs": "https://python.langchain.com/api_reference/core/prompts/langchain_core.prompts.chat.ChatPromptTemplate.html", "title": "How to create a dynamic (self-constructing) chain"}, {"imported": "Runnable", "source": "langchain_core.runnables", "docs": "https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html", "title": "How to create a dynamic (self-constructing) chain"}, {"imported": "RunnablePassthrough", "source": "langchain_core.runnables", "docs": "https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.passthrough.RunnablePassthrough.html", "title": "How to create a dynamic (self-constructing) chain"}, {"imported": "chain", "source": "langchain_core.runnables", "docs": "https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.chain.html", "title": "How to create a dynamic (self-constructing) chain"}]-->
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"),
        ],
    }
)

"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 返回另一个运行接口，而不是实际的输出。这个返回的运行接口在完整链被执行时会被运行。

从追踪中我们可以看到，由于我们传入了聊天历史，我们在完整链中执行了 contextualize_question 链： https://smith.langchain.com/public/9e0ae34c-4082-4f3f-beed-34a2a2f4c991/r

请注意，返回的运行接口的流式处理、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
?

如何创建动态（自构建）链

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