High-Performance Vector Databases
Retrieval-Augmented Generation (RAG) relies on converting documents into high-dimensional vector embeddings. Querying these databases at scale requires advanced indexing techniques to avoid slow linear searches.
Vector Quantization Strategies
• Product Quantization (PQ): Compress vectors by dividing them into sub-vectors and quantizing each sub-vector independently, saving up to 90% memory.
• Hierarchical Navigable Small World (HNSW): Build a multi-layered graph index to search nearest-neighbor vectors with logarithmic complexity.
• Cosine vs. L2 Distance: Choose the right distance metric matching your LLM embedder model's output configuration.
RAG Inference Optimizations
1. Semantic Chunking: Split source documents by semantic shifts rather than fixed character lengths to preserve sentence context.
2. Hybrid Search: Combine keyword search (BM25) with vector search to capture both exact token matches and conceptual meaning.
3. Reranking: Run retrieved candidates through a cross-encoder model to sort by relevance before passing context to the LLM.