Overview

A flexible, production-ready implementation of Retrieval-Augmented Generation (RAG) in PyTorch. This framework combines dense vector retrieval with large language models to provide accurate, context-grounded answers to user queries.

Key Components

• Dense Retrieval: FAISS-based vector store for efficient similarity search
• LLM Integration: Support for multiple LLM backends
• Context Augmentation: Automatically retrieves and incorporates relevant context
• Customizable Pipeline: Modular design for easy extension

Capabilities

• Index custom document collections
• Query with semantic understanding
• Generate answers grounded in retrieved documents
• Evaluate retrieval quality

Why I Built It

Most RAG tutorials stop at the demo stage — a working pipeline but nothing you’d trust in production. I built this to understand the full stack from retrieval quality to inference latency, with enough structure to extend it to real use cases rather than starting from scratch each time.

Technologies

PyTorch · FAISS · Transformers · Hugging Face Models