📄 Hybrid-RAG: Combining Retrieval and Generation for Intelligent Responses

📌 About the Project

This project implements a hybrid Retrieval-Augmented Generation (RAG) system that uses BERT and GPT-2 to deliver more accurate and contextually relevant responses.

🎯 Key Features

✅ Intelligent Information Retrieval: Uses BERT + FAISS for fast and precise document search.
✅ Optimized Response Generation: Combines retrieved information with the original query and leverages GPT-2 for response generation.
✅ Efficient Vector Processing: Stores and searches documents in a vector index using FAISS.

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🔧 Installation & Requirements

Dependencies

Libraries used in this project:

• torch
• transformers
• faiss-gpu
• numpy
• scipy

Run the Notebook

jupyter notebook hybrid-rag.ipynb

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🔧 How the System Works

1️⃣ Embedding:

• Scientific texts and documents are converted into embeddings.
  2️⃣ Indexing:
• Vectors are stored in a FAISS Index for searchability.
  3️⃣ Retrieval:
• When a query is received, the most relevant documents are searched and retrieved.
  4️⃣ Augmentation:
• The original query + retrieved texts are combined.
  5️⃣ Generation:
• The GPT-2 model uses the new input to generate a precise and relevant response.

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• If you have suggestions for improving the project, please submit a Pull Request.
• To report issues, please open an Issue.

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