Optimizing Vector Search Performance with Quantization Techniques for Large Scale Production RAG Systems
Table of Contents Introduction Background: Vector Search & Retrieval‑Augmented Generation (RAG) Challenges of Large‑Scale Production Deployments Fundamentals of Quantization 4.1 Scalar vs. Vector Quantization 4.2 Product Quantization (PQ) and Variants Quantization Techniques for Vector Search 5.1 Uniform (Scalar) Quantization 5.2 Product Quantization (PQ) 5.3 Optimized Product Quantization (OPQ) 5.4 Additive Quantization (AQ) 5.5 Binary & Hamming‑Based Quantization Integrating Quantization into RAG Pipelines 6.1 Index Construction 6.2 Query Processing Performance Metrics and Trade‑offs Practical Implementation Walk‑throughs 8.1 FAISS Example: Training & Using PQ 8.2 ScaNN Example: End‑to‑End Pipeline Hyper‑parameter Tuning Strategies Real‑World Case Studies Best Practices & Common Pitfalls 12Future Directions Conclusion Resources Introduction Retrieval‑Augmented Generation (RAG) has become the de‑facto paradigm for building LLM‑powered applications that need up‑to‑date, factual knowledge. At the heart of any RAG system lies a vector search engine that can quickly locate the most relevant passages, documents, or multimodal embeddings from a corpus that can easily stretch into billions of items. ...