In the lifecycle of a Retrieval-Augmented Generation (RAG) application, there is a specific breaking point. It usually happens when your vector dataset migrates from a clean 50k-row proof-of-concept to a messy, real-world dataset of 1 to 10 million rows. Suddenly, sub-millisecond queries spike to 300ms+, or worse, your LLM starts hallucinating because the vector database failed to retrieve the most semantically relevant chunks. The culprit is almost always a misunderstanding of the Hierarchical Navigable Small Worlds (HNSW) index parameters in pgvector . Developers often apply the default index settings, unaware that HNSW is an approximate algorithm where latency and recall are diametrically opposed trade-offs. The Root Cause: How HNSW Breaks Down Unlike IVFFlat (Inverted File Flat), which partitions space into clusters, HNSW builds a multi-layered graph. Think of it as a skip-list for vectors. Upper Layers: sparse graphs with long "highways" that allow the algorithm ...
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