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Building with Langchain: Leveraging Open source AI models

·3 mins
Table of Contents

Introduction #

LangChain is a powerful tool for building conversational AI systems, and while OpenAI’s GPT-3 is widely used, there are excellent open-source alternatives available. These alternatives can provide more control, flexibility, and cost-effectiveness. In this post, we’ll explore some of the best open-source large language models (LLMs) and demonstrate how to integrate one with LangChain using Jupyter Notebooks.

GPT-4All #

GPT-4All is designed to run on local machines and is optimized for various tasks. It’s a versatile option compatible with LangChain.

Hugging Face Transformers #

The Hugging Face Transformers library offers numerous pre-trained models, such as BERT and GPT-2. These models can be fine-tuned for specific applications and are compatible with LangChain.

GPT-J #

Developed by EleutherAI, GPT-J is an open-source alternative to GPT-3. It’s suitable for a wide range of NLP tasks and integrates well with LangChain.

BLOOM #

BLOOM is an open-access multilingual language model supporting multiple languages, making it ideal for diverse NLP applications.

LLaMA (Large Language Model Meta AI) #

Created by Meta (formerly Facebook), LLaMA is another robust open-source language model that pairs well with LangChain.

Integrating GPT-J with LangChain #

Let’s walk through how to integrate GPT-J, an open-source LLM, with LangChain using a Jupyter Notebook.

Step 1: Install Necessary Libraries #

First, ensure you have the required libraries installed. You can do this using pip:

pip install langchain transformers

Step 2: Load the Model and Tokenizer #

Next, you’ll load the GPT-J model and tokenizer from Hugging Face. Then, you’ll initialize a LangChain conversation with the loaded model.

# Import necessary libraries
from transformers import AutoModelForCausalLM, AutoTokenizer
from langchain import Conversation

# Load GPT-J model and tokenizer from Hugging Face
model_name = "EleutherAI/gpt-j-6B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Initialize LangChain Conversation
conversation = Conversation(model=model, tokenizer=tokenizer)

# Add a message and generate a response
conversation.add_message("Hello, how can I assist you today?")
response = conversation.generate_reply()
print(response)

Explanation #

  • AutoModelForCausalLM and AutoTokenizer: These classes from the Hugging Face Transformers library are used to load the GPT-J model and tokenizer.
  • Conversation: This is a class from LangChain that facilitates managing a conversation with the loaded model.
  • add_message: This method adds a message to the conversation.
  • generate_reply: This method generates a response based on the conversation history.

Conclusion #

By leveraging open-source LLMs like GPT-J with LangChain, you can build powerful and cost-effective conversational AI systems. This approach gives you greater control over your models and can be a great alternative to proprietary solutions like OpenAI’s GPT-3.

Whether you’re looking to save costs, require more customization, or prefer open-source tools, integrating models like GPT-J with LangChain is a viable and effective solution.

Feel free to experiment with other models from Hugging Face or explore additional features of LangChain to enhance your AI applications further.

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