Jewbot: A Retrieval Augmented Generation (RAG) Approach to Jewish Learning 📚

Jewbot is not merely a project; it's a groundbreaking platform that has captivated over 2,000 users with its rich database on Jewish knowledge. Seamlessly integrating technology with tradition, Jewbot offers users an unprecedented journey through Jewish history, traditions, and culture.

🌐 Project Overview

Jewbot represents the forefront of digital Jewish learning. Developed with precision, it serves as a bridge connecting users to the vast expanse of Jewish wisdom. It stands out as a beacon of innovation, making Jewish knowledge accessible to a global audience.

Try jewbot: Jewbot

Visit our landing page: Jewbot Landing Page

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🎯 My Mission

Developer of Jewbot, my mission was clear: to harness the potential of AI in service of Jewish education. My role extended beyond coding; it was about envisioning a future where technology and tradition coalesce. Through Jewbot, I aimed to make Jewish learning accessible, engaging, and interactive for everyone, everywhere.

🛠️ Technologies Used

• LLM and Embedding models : OpenAi
• Database Management: Supabase
• Web Development: Next js

🏗️ Project Architecture

Jewbot's architecture is a prime example of Retrieval-Augmented Generation (RAG), a cutting-edge AI methodology. Here’s a breakdown of the architecture and its significance in the project:

1. Chat History and New Question: The system starts by taking into account the user's chat history and their new question. This context is crucial for generating relevant responses.

2. Standalone Question Generation: The new question, combined with the chat history, is processed by a large language model (GPT-4o) to generate a standalone question.

3. Document Embedding: Relevant documents are identified and converted into embeddings using OpenAI's embedding model (text-embedding-3-large). These embeddings are stored in a VectorStore for efficient retrieval.

4. Document Retrieval: When a new question is posed, the system checks for relevant documents in the VectorStore. The documents are split into chunks and converted to text before creating embeddings.

5. Generating the Answer: Finally, the large language model uses the standalone question and the relevant documents to generate a comprehensive response, ensuring the information provided is accurate and contextually appropriate.

📈 Skills Acquired

• AI Integration: Leveraging ChatGPT and OpenAI's models to create intelligent, context-aware conversations.
• RAG Methodology: Implementing Retrieval-Augmented Generation to enhance the chatbot's response accuracy and relevance.
• Document Embedding and Retrieval: Applying advanced techniques to manage and retrieve large sets of documents, ensuring the bot's responses are well-informed and precise.
• Database Management: Utilizing Supabase for efficient data storage and retrieval.
• Web Development: Building a robust web platform using Next.js, ensuring a seamless user experience.

🎉 Conclusion

Jewbot continues to evolve, promising even more innovative features and a deeper exploration into the wealth of Jewish knowledge. As my first Retrieval-Augmented Generation (RAG) project, I thoroughly enjoyed working on Jewbot. It was an excellent experience that significantly enhanced my technical skills and my passion for integrating technology with education.