Vector Search: A Practical Guide for Developers

This post provides a practical guide to production vector search, detailing index trade-offs (FLAT vs. HNSW) and their impact on recall, speed, and memory. It explains distance metrics (cosine, Euclidean, dot product) and their relevance to embedding models. The post outlines common production patterns like RAG, hybrid search, semantic caching, recommendations, and agent memory, highlighting how vector search fits into these architectures. It also addresses key failure modes such as memory constraints with large datasets (leading to quantization) and recall degradation due to metadata filtering, along with the challenge of embedding drift when models are updated.