Programming Tutorials

  In the early stages of building a Retrieval-Augmented Generation (RAG) pipeline, vector search feels like magic. You insert a few thousand PDF chunks, run a cosine similarity search, and get relevant context in milliseconds. Then, you scale. Your dataset grows from 10,000 vectors to 10 million. Suddenly, that snappy 50ms query latency spikes to 2 seconds. Your LLM is left waiting for context, user retention drops, and your database CPU usage sits at 100%. This is the "Vector Latency Cliff." It occurs when your dataset exceeds the capability of exact nearest neighbor search, forcing the database to perform full table scans. This article details how to transition from brute-force search to optimized Approximate Nearest Neighbor (ANN) search using PostgreSQL and  pgvector . We will focus on tuning the Hierarchical Navigable Small World (HNSW) algorithm, the current industry standard for high-performance RAG. The Root Cause: Why Exact Search Fails at Scale To understand the fix...