Project Overview
A sophisticated data augmentation system that uses Retrieval-Augmented Generation (RAG) to enhance and synthesize review datasets. The system leverages FAISS for efficient vector storage and retrieval, combined with Sentence-BERT for generating high-quality semantic embeddings.
Key Features
- Automated review dataset enhancement
- FAISS-based vector similarity search
- Sentence-BERT semantic embeddings
- RAG pipeline for content generation
- Quality assessment metrics
- Scalable processing pipeline
Technical Challenges
- Maintaining review authenticity in generated content
- Optimizing FAISS indexing for large datasets
- Ensuring semantic coherence in augmented data
- Balancing augmentation quantity with quality