跳到主要内容

根据输入动态路由逻辑

本笔记本介绍了如何使用 LangChain 表达式语言进行路由。

路由允许您创建非确定性链，其中上一步的输出定义下一步。路由有助于提供与法学硕士交互的结构和一致性。

执行路由的方法有两种：

  1. 有条件地从 RunnableLambda 返回可运行对象（推荐）
  2. 使用 RunnableBranch。

我们将使用两步序列来说明这两种方法，其中第一步将输入问题分类为有关 LangChain、Anthropic 或其他，然后路由到相应的提示链。

设置示例​

首先，让我们创建一个链，将传入的问题识别为有关 LangChain、Anthropic 或其他的问题：

from langchain_community.chat_models import ChatAnthropic
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import PromptTemplate

chain = (
    PromptTemplate.from_template(
        """Given the user question below, classify it as either being about `LangChain`, `Anthropic`, or `Other`.

Do not respond with more than one word.

<question>
{question}
</question>

Classification:"""
    )
    | ChatAnthropic()
    | StrOutputParser()
)

chain.invoke({"question": "how do I call Anthropic?"})
' Anthropic'

现在，我们创建三个子链：

langchain_chain = (
    PromptTemplate.from_template(
        """You are an expert in langchain. \
Always answer questions starting with "As Harrison Chase told me". \
Respond to the following question:

Question: {question}
Answer:"""
    )
    | ChatAnthropic()
)
anthropic_chain = (
    PromptTemplate.from_template(
        """You are an expert in anthropic. \
Always answer questions starting with "As Dario Amodei told me". \
Respond to the following question:

Question: {question}
Answer:"""
    )
    | ChatAnthropic()
)
general_chain = (
    PromptTemplate.from_template(
        """Respond to the following question:

Question: {question}
Answer:"""
    )
    | ChatAnthropic()
)

使用自定义函数（推荐）​

您还可以使用自定义函数在不同输出之间进行路由。这是一个例子：

def route(info):
    if "anthropic" in info["topic"].lower():
        return anthropic_chain
    elif "langchain" in info["topic"].lower():
        return langchain_chain
    else:
        return general_chain

from langchain_core.runnables import RunnableLambda

full_chain = {"topic": chain, "question": lambda x: x["question"]} | RunnableLambda(
    route
)

full_chain.invoke({"question": "how do I use Anthropic?"})

AIMessage(content=' As Dario Amodei told me, to use Anthropic IPC you first need to import it:\n\n```python\nfrom anthroipc import ic\n```\n\nThen you can create a client and connect to the server:\n\n```python \nclient = ic.connect()\n```\n\nAfter that, you can call methods on the client and get responses:\n\n```python\nresponse = client.ask("What is the meaning of life?")\nprint(response)\n```\n\nYou can also register callbacks to handle events: \n\n```python\ndef on_poke(event):\n  print("Got poked!")\n\nclient.on(\'poke\', on_poke)\n```\n\nAnd that\'s the basics of using the Anthropic IPC client library for Python! Let me know if you have any other questions!', additional_kwargs={}, example=False)


full_chain.invoke({"question": "how do I use LangChain?"})

AIMessage(content=' As Harrison Chase told me, to use LangChain you first need to sign up for an API key at platform.langchain.com. Once you have your API key, you can install the Python library and write a simple Python script to call the LangChain API. Here is some sample code to get started:\n\n```python\nimport langchain\n\napi_key = "YOUR_API_KEY"\n\nlangchain.set_key(api_key)\n\nresponse = langchain.ask("What is the capital of France?")\n\nprint(response.response)\n```\n\nThis will send the question "What is the capital of France?" to the LangChain API and print the response. You can customize the request by providing parameters like max_tokens, temperature, etc. The LangChain Python library documentation has more details on the available options. The key things are getting an API key and calling langchain.ask() with your question text. Let me know if you have any other questions!', additional_kwargs={}, example=False)


full_chain.invoke({"question": "whats 2 + 2"})

AIMessage(content=' 4', additional_kwargs={}, example=False)

使用 RunnableBranch​

RunnableBranch 是一种特殊类型的可运行对象，它允许您定义一组条件和可运行对象以根据输入执行。它不提供上述自定义函数无法实现的任何功能，因此我们建议改用自定义函数。

RunnableBranch 使用（条件、可运行）对列表和默认可运行来初始化。它通过传递调用它的输入的每个条件来选择哪个分支。它选择第一个条件来评估为 True，并使用输入运行与该条件相对应的可运行程序。

如果没有提供的条件匹配，它将运行默认的可运行程序。

下面是实际效果的示例：

from langchain_core.runnables import RunnableBranch

branch = RunnableBranch(
    (lambda x: "anthropic" in x["topic"].lower(), anthropic_chain),
    (lambda x: "langchain" in x["topic"].lower(), langchain_chain),
    general_chain,
)
full_chain = {"topic": chain, "question": lambda x: x["question"]} | branch
full_chain.invoke({"question": "how do I use Anthropic?"})
AIMessage(content=" As Dario Amodei told me, here are some ways to use Anthropic:\n\n- Sign up for an account on Anthropic's website to access tools like Claude, Constitutional AI, and Writer. \n\n- Use Claude for tasks like email generation, customer service chat, and QA. Claude can understand natural language prompts and provide helpful responses.\n\n- Use Constitutional AI if you need an AI assistant that is harmless, honest, and helpful. It is designed to be safe and aligned with human values.\n\n- Use Writer to generate natural language content for things like marketing copy, stories, reports, and more. Give it a topic and prompt and it will create high-quality written content.\n\n- Check out Anthropic's documentation and blog for tips, tutorials, examples, and announcements about new capabilities as they continue to develop their AI technology.\n\n- Follow Anthropic on social media or subscribe to their newsletter to stay up to date on new features and releases.\n\n- For most people, the easiest way to leverage Anthropic's technology is through their website - just create an account to get started!", additional_kwargs={}, example=False)


full_chain.invoke({"question": "how do I use LangChain?"})

AIMessage(content=' As Harrison Chase told me, here is how you use LangChain:\n\nLangChain is an AI assistant that can have conversations, answer questions, and generate text. To use LangChain, you simply type or speak your input and LangChain will respond. \n\nYou can ask LangChain questions, have discussions, get summaries or explanations about topics, and request it to generate text on a subject. Some examples of interactions:\n\n- Ask general knowledge questions and LangChain will try to answer factually. For example "What is the capital of France?"\n\n- Have conversations on topics by taking turns speaking. You can prompt the start of a conversation by saying something like "Let\'s discuss machine learning"\n\n- Ask for summaries or high-level explanations on subjects. For example "Can you summarize the main themes in Shakespeare\'s Hamlet?" \n\n- Give creative writing prompts or requests to have LangChain generate text in different styles. For example "Write a short children\'s story about a mouse" or "Generate a poem in the style of Robert Frost about nature"\n\n- Correct LangChain if it makes an inaccurate statement and provide the right information. This helps train it.\n\nThe key is interacting naturally and giving it clear prompts and requests', additional_kwargs={}, example=False)


full_chain.invoke({"question": "whats 2 + 2"})

AIMessage(content=' 2 + 2 = 4', additional_kwargs={}, example=False)