HNSW at Scale: Why Your RAG System Gets Worse as the Vector Database Grows

Approximate vector search, a core component of retrieval-augmented generation (RAG) systems, experiences a silent decline in recall accuracy as the size of the vector database increases, leading to degraded retrieval quality. This phenomenon occurs because the probabilistic nature of approximate algorithms introduces errors that accumulate with scale, necessitating new strategies such as adaptive indexing or hybrid search methods to maintain high recall rates in large-scale vector databases.