如何在子链之间路由
先决条件
本指南假设您熟悉以下概念：
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。通过允许您定义状态并将与这些状态相关的信息用作模型调用的上下文，路由可以帮助为与模型的交互提供结构和一致性。
有两种执行路由的方法：
- 有条件地从
RunnableLambda返回运行器（推荐） - 使用
RunnableBranch（旧版）
我们将使用一个两步序列来说明这两种方法，第一步将输入问题分类为关于 LangChain、Anthropic 或 Other，然后路由到相应的提示链。
示例设置
首先，让我们创建一个链来识别传入的问题是关于 LangChain、Anthropic 还是 Other：
from langchain_anthropic 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(model_name="claude-3-haiku-20240307")
| StrOutputParser()
)
chain.invoke({"question": "how do I call Anthropic?"})
'Anthropic'
现在，我们来创建三个子链：
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(model_name="claude-3-haiku-20240307")
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(model_name="claude-3-haiku-20240307")
general_chain = PromptTemplate.from_template(
"""Respond to the following question:
Question: {question}
Answer:"""
) | ChatAnthropic(model_name="claude-3-haiku-20240307")
使用自定义函数（推荐）
你也可以使用自定义函数来路由不同的输出。示例如下：
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
)
API Reference:RunnableLambda
full_chain.invoke({"question": "how do I use Anthropic?"})
AIMessage(content="As Dario Amodei told me, to use Anthropic, you can start by exploring the company's website and learning about their mission, values, and the different services and products they offer. Anthropic is focused on developing safe and ethical AI systems, so they have a strong emphasis on transparency and responsible AI development. \n\nDepending on your specific needs, you can look into Anthropic's AI research and development services, which cover areas like natural language processing, computer vision, and reinforcement learning. They also offer consulting and advisory services to help organizations navigate the challenges and opportunities of AI integration.\n\nAdditionally, Anthropic has released some open-source AI models and tools that you can explore and experiment with. These can be a great way to get hands-on experience with Anthropic's approach to AI development.\n\nOverall, Anthropic aims to be a reliable and trustworthy partner in the AI space, so I'd encourage you to reach out to them directly to discuss how they can best support your specific requirements.", response_metadata={'id': 'msg_01CtLFgFSwvTaJomrihE87Ra', 'content': [ContentBlock(text="As Dario Amodei told me, to use Anthropic, you can start by exploring the company's website and learning about their mission, values, and the different services and products they offer. Anthropic is focused on developing safe and ethical AI systems, so they have a strong emphasis on transparency and responsible AI development. \n\nDepending on your specific needs, you can look into Anthropic's AI research and development services, which cover areas like natural language processing, computer vision, and reinforcement learning. They also offer consulting and advisory services to help organizations navigate the challenges and opportunities of AI integration.\n\nAdditionally, Anthropic has released some open-source AI models and tools that you can explore and experiment with. These can be a great way to get hands-on experience with Anthropic's approach to AI development.\n\nOverall, Anthropic aims to be a reliable and trustworthy partner in the AI space, so I'd encourage you to reach out to them directly to discuss how they can best support your specific requirements.", type='text')], 'model': 'claude-3-haiku-20240307', 'role': 'assistant', 'stop_reason': 'end_turn', 'stop_sequence': None, 'type': 'message', 'usage': Usage(input_tokens=53, output_tokens=219)})
full_chain.invoke({"question": "how do I use LangChain?"})
AIMessage(content="As Harrison Chase told me, using LangChain involves a few key steps:\n\n1. **Set up your environment**: Install the necessary Python packages, including the LangChain library itself, as well as any other dependencies your application might require, such as language models or other integrations.\n\n2. **Understand the core concepts**: LangChain revolves around a few core concepts, like Agents, Chains, and Tools. Familiarize yourself with these concepts and how they work together to build powerful language-based applications.\n\n3. **Identify your use case**: Determine what kind of task or application you want to build using LangChain, such as a chatbot, a question-answering system, or a document summarization tool.\n\n4. **Choose the appropriate components**: Based on your use case, select the right LangChain components, such as agents, chains, and tools, to build your application.\n\n5. **Integrate with language models**: LangChain is designed to work seamlessly with various language models, such as OpenAI's GPT-3 or Anthropic's models. Connect your chosen language model to your LangChain application.\n\n6. **Implement your application logic**: Use LangChain's building blocks to implement the specific functionality of your application, such as prompting the language model, processing the response, and integrating with other services or data sources.\n\n7. **Test and iterate**: Thoroughly test your application, gather feedback, and iterate on your design and implementation to improve its performance and user experience.\n\nAs Harrison Chase emphasized, LangChain provides a flexible and powerful framework for building language-based applications, making it easier to leverage the capabilities of modern language models. By following these steps, you can get started with LangChain and create innovative solutions tailored to your specific needs.", response_metadata={'id': 'msg_01H3UXAAHG4TwxJLpxwuuVU7', 'content': [ContentBlock(text="As Harrison Chase told me, using LangChain involves a few key steps:\n\n1. **Set up your environment**: Install the necessary Python packages, including the LangChain library itself, as well as any other dependencies your application might require, such as language models or other integrations.\n\n2. **Understand the core concepts**: LangChain revolves around a few core concepts, like Agents, Chains, and Tools. Familiarize yourself with these concepts and how they work together to build powerful language-based applications.\n\n3. **Identify your use case**: Determine what kind of task or application you want to build using LangChain, such as a chatbot, a question-answering system, or a document summarization tool.\n\n4. **Choose the appropriate components**: Based on your use case, select the right LangChain components, such as agents, chains, and tools, to build your application.\n\n5. **Integrate with language models**: LangChain is designed to work seamlessly with various language models, such as OpenAI's GPT-3 or Anthropic's models. Connect your chosen language model to your LangChain application.\n\n6. **Implement your application logic**: Use LangChain's building blocks to implement the specific functionality of your application, such as prompting the language model, processing the response, and integrating with other services or data sources.\n\n7. **Test and iterate**: Thoroughly test your application, gather feedback, and iterate on your design and implementation to improve its performance and user experience.\n\nAs Harrison Chase emphasized, LangChain provides a flexible and powerful framework for building language-based applications, making it easier to leverage the capabilities of modern language models. By following these steps, you can get started with LangChain and create innovative solutions tailored to your specific needs.", type='text')], 'model': 'claude-3-haiku-20240307', 'role': 'assistant', 'stop_reason': 'end_turn', 'stop_sequence': None, 'type': 'message', 'usage': Usage(input_tokens=50, output_tokens=400)})
full_chain.invoke({"question": "whats 2 + 2"})
AIMessage(content='4', response_metadata={'id': 'msg_01UAKP81jTZu9fyiyFYhsbHc', 'content': [ContentBlock(text='4', type='text')], 'model': 'claude-3-haiku-20240307', 'role': 'assistant', 'stop_reason': 'end_turn', 'stop_sequence': None, 'type': 'message', 'usage': Usage(input_tokens=28, output_tokens=5)})
使用 RunnableBranch
RunnableBranch 是 一种特殊的 runnable，它允许您定义一组条件和 runnable，并根据输入来执行它们。它不提供任何您无法在前述的自定义函数中实现的功能，因此我们建议您改用自定义函数。
RunnableBranch 使用 (条件, runnable) 对的列表和默认 runnable 来初始化。它通过将调用时传入的输入传递给每个条件来选择要执行的分支。它选择第一个评估为 True 的条件，并使用该输入运行与该条件相对应的 runnable。
如果所有提供的条件均不匹配，它将运行默认的 runnable。
以下是它在实际应用中的示例：
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?"})
API Reference:RunnableBranch
AIMessage(content="As Dario Amodei told me, to use Anthropic, you should first familiarize yourself with our mission and principles. Anthropic is committed to developing safe and beneficial artificial intelligence that can help solve important problems facing humanity. \n\nTo get started, I recommend exploring the resources on our website, which cover our research, products, and approach to AI development. You can also reach out to our team to learn more about how Anthropic's technology and services can support your specific needs.\n\nThe key is to engage with us in a way that aligns with our values of transparency, ethical AI, and a commitment to the wellbeing of humanity. We're here to collaborate and help you harness the power of advanced AI responsibly.", response_metadata={'id': 'msg_0187BVnpniPDJnVvwf3M1LdY', 'content': [ContentBlock(text="As Dario Amodei told me, to use Anthropic, you should first familiarize yourself with our mission and principles. Anthropic is committed to developing safe and beneficial artificial intelligence that can help solve important problems facing humanity. \n\nTo get started, I recommend exploring the resources on our website, which cover our research, products, and approach to AI development. You can also reach out to our team to learn more about how Anthropic's technology and services can support your specific needs.\n\nThe key is to engage with us in a way that aligns with our values of transparency, ethical AI, and a commitment to the wellbeing of humanity. We're here to collaborate and help you harness the power of advanced AI responsibly.", type='text')], 'model': 'claude-3-haiku-20240307', 'role': 'assistant', 'stop_reason': 'end_turn', 'stop_sequence': None, 'type': 'message', 'usage': Usage(input_tokens=53, output_tokens=160)})
full_chain.invoke({"question": "how do I use LangChain?"})
AIMessage(content="As Harrison Chase told me, using LangChain involves several key steps. First, you'll need to install the LangChain library and import the necessary modules. Then, you'll want to define your language model, any data sources you plan to use, and the specific tasks you want to accomplish, such as question answering, text generation, or agent-based reasoning. \n\nLangChain provides a flexible framework for building applications that leverage large language models. It includes abstractions for things like retrievers, prompts, and chains, which allow you to compose different components together to create powerful workflows. \n\nThe documentation on the LangChain website is excellent and covers many common use cases in detail. I'd recommend starting there to get a solid understanding of the core concepts and how to apply them to your specific needs. And of course, feel free to reach out if you have any other questions - I'm always happy to share more insights from my conversations with Harrison.", response_metadata={'id': 'msg_01T1naS99wGPkEAP4LME8iAv', 'content': [ContentBlock(text="As Harrison Chase told me, using LangChain involves several key steps. First, you'll need to install the LangChain library and import the necessary modules. Then, you'll want to define your language model, any data sources you plan to use, and the specific tasks you want to accomplish, such as question answering, text generation, or agent-based reasoning. \n\nLangChain provides a flexible framework for building applications that leverage large language models. It includes abstractions for things like retrievers, prompts, and chains, which allow you to compose different components together to create powerful workflows. \n\nThe documentation on the LangChain website is excellent and covers many common use cases in detail. I'd recommend starting there to get a solid understanding of the core concepts and how to apply them to your specific needs. And of course, feel free to reach out if you have any other questions - I'm always happy to share more insights from my conversations with Harrison.", type='text')], 'model': 'claude-3-haiku-20240307', 'role': 'assistant', 'stop_reason': 'end_turn', 'stop_sequence': None, 'type': 'message', 'usage': Usage(input_tokens=50, output_tokens=205)})
full_chain.invoke({"question": "whats 2 + 2"})
AIMessage(content='4', response_metadata={'id': 'msg_01T6T3TS6hRCtU8JayN93QEi', 'content': [ContentBlock(text='4', type='text')], 'model': 'claude-3-haiku-20240307', 'role': 'assistant', 'stop_reason': 'end_turn', 'stop_sequence': None, 'type': 'message', 'usage': Usage(input_tokens=28, output_tokens=5)})
按语义相似性路由
一个特别有用的技术是使用 embeddings 将查询路由到最相关的 prompt。下面是一个例子。
from langchain_community.utils.math import cosine_similarity
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import PromptTemplate
from langchain_core.runnables import RunnableLambda, RunnablePassthrough
from langchain_openai import OpenAIEmbeddings
physics_template = """You are a very smart physics professor. \
You are great at answering questions about physics in a concise and easy to understand manner. \
When you don't know the answer to a question you admit that you don't know.
Here is a question:
{query}"""
math_template = """You are a very good mathematician. You are great at answering math questions. \
You are so good because you are able to break down hard problems into their component parts, \
answer the component parts, and then put them together to answer the broader question.
Here is a question:
{query}"""
embeddings = OpenAIEmbeddings()
prompt_templates = [physics_template, math_template]
prompt_embeddings = embeddings.embed_documents(prompt_templates)
def prompt_router(input):
query_embedding = embeddings.embed_query(input["query"])
similarity = cosine_similarity([query_embedding], prompt_embeddings)[0]
most_similar = prompt_templates[similarity.argmax()]
print("Using MATH" if most_similar == math_template else "Using PHYSICS")
return PromptTemplate.from_template(most_similar)
chain = (
{"query": RunnablePassthrough()}
| RunnableLambda(prompt_router)
| ChatAnthropic(model="claude-3-haiku-20240307")
| StrOutputParser()
)
API Reference:cosine_similarity | StrOutputParser | PromptTemplate | RunnableLambda | RunnablePassthrough | OpenAIEmbeddings
print(chain.invoke("What's a black hole"))
Using PHYSICS
As a physics professor, I would be happy to provide a concise and easy-to-understand explanation of what a black hole is.
A black hole is an incredibly dense region of space-time where the gravitational pull is so strong that nothing, not even light, can escape from it. This means that if you were to get too close to a black hole, you would be pulled in and crushed by the intense gravitational forces.
The formation of a black hole occurs when a massive star, much larger than our Sun, reaches the end of its life and collapses in on itself. This collapse causes the matter to become extremely dense, and the gravitational force becomes so strong that it creates a point of no return, known as the event horizon.
Beyond the event horizon, the laws of physics as we know them break down, and the intense gravitational forces create a singularity, which is a point of infinite density and curvature in space-time.
Black holes are fascinating and mysterious objects, and there is still much to be learned about their properties and behavior. If I were unsure about any specific details or aspects of black holes, I would readily admit that I do not have a complete understanding and would encourage further research and investigation.
print(chain.invoke("What's a path integral"))
Using MATH
A path integral is a powerful mathematical concept in physics, particularly in the field of quantum mechanics. It was developed by the renowned physicist Richard Feynman as an alternative formulation of quantum mechanics.
In a path integral, instead of considering a single, definite path that a particle might take from one point to another, as in classical mechanics, the particle is considered to take all possible paths simultaneously. Each path is assigned a complex-valued weight, and the total probability amplitude for the particle to go from one point to another is calculated by summing (integrating) over all possible paths.
The key ideas behind the path integral formulation are:
1. Superposition principle: In quantum mechanics, particles can exist in a superposition of multiple states or paths simultaneously.
2. Probability amplitude: The probability amplitude for a particle to go from one point to another is calculated by summing the complex-valued weights of all possible paths.
3. Weighting of paths: Each path is assigned a weight based on the action (the time integral of the Lagrangian) along that path. Paths with lower action have a greater weight.
4. Feynman's approach: Feynman developed the path integral formulation as an alternative to the traditional wave function approach in quantum mechanics, providing a more intuitive and conceptual understanding of quantum phenomena.
The path integral approach is particularly useful in quantum field theory, where it provides a powerful framework for calculating transition probabilities and understanding the behavior of quantum systems. It has also found applications in various areas of physics, such as condensed matter, statistical mechanics, and even in finance (the path integral approach to option pricing).
The mathematical construction of the path integral involves the use of advanced concepts from functional analysis and measure theory, making it a powerful and sophisticated tool in the physicist's arsenal.
后续步骤
现在您已经学会了如何为已组成的 LCEL 链添加路由。
接下来，请查看本节中关于 runnables 的其他操作指南。