Can I Use Anthropic’s Claude 2 in LangChain?

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πŸ’‘ Langchain is an open-source toolkit for building language model applications. It’s designed to make it easier to integrate language models into your AI apps, giving you a set of tools and APIs for this purpose. Langchain is not a language model itself but a framework for using language models in development projects.

Anthropic’s Claude 2 is a powerful and popular large language model (LLM).

Can You Integrate Anthropic’s LLMs, such as Claude 2, into LangChain?

Yes, you can use Anthropic’s Claude 2 in conjunction with Langchain.

  • Langchain is a toolkit for building end-to-end language model applications, including those that involve Language Learning Models (LLMs) like Claude 2.
  • Claude 2 is a chat-based model trained on conversational data, and its integration with Langchain is well-supported.

Using Anthropic’s Claude v2 model with Langchain involves importing the ChatAnthropic module from Langchain and then using it to work with Claude 2. This process slightly differs from using Claude with the client SDKs provided by Anthropic.

Here’s an example of using Anthropic’s Claude 2 model in Python LangChain (source):

from langchain.chat_models import ChatAnthropic
model = ChatAnthropic()

ChatAnthropic is a subclass of ChatModel in LangChain, optimized for ChatPromptTemplate.

To use ChatModels, design your prompts using ChatPromptTemplate:

from langchain.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful chatbot"),
    ("human", "Tell me a joke about {topic}"),
])

Combine this prompt with the model in a chain:

chain = prompt | model
chain.invoke({"topic": "bears"})

To understand the prompt formatting, use this code:

prompt_value = prompt.format_prompt(topic="bears")
model.convert_prompt(prompt_value)

This results in:

'\n\nYou are a helpful chatbot\n\nHuman: Tell me a joke about bears\n\nAssistant:'

LangChain doesn’t add prefix/suffix to SystemMessages.

Anthropic requires all prompts to end with assistant messages, so if the last message isn’t from the assistant, Assistant: is automatically added.

Using a standard PromptTemplate, the process is slightly different:

from langchain.prompts import PromptTemplate

prompt = PromptTemplate.from_template("Tell me a joke about {topic}")
prompt_value = prompt.format_prompt(topic="bears")
model.convert_prompt(prompt_value)

This yields:

'\n\nHuman: Tell me a joke about bears\n\nAssistant:'

Here, the string is first converted to a human message, then an empty assistant message is appended, specific to Anthropic.


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