Key Takeaway
By the end of this blueprint you will have a production vector search system using pgvector with HNSW indexing, tuned for sub-50ms queries at millions of vectors, with pre-filtered metadata search, a result caching layer, and a re-ranking stage that maximizes precision for your RAG or semantic search application.
Prerequisites
- PostgreSQL 16+ with pgvector 0.7+ extension
- Understanding of embedding models and vector similarity concepts
- Python 3.11+ for the query and ingestion code
- Redis for query result caching
- Familiarity with PostgreSQL EXPLAIN ANALYZE for query tuning
Index Algorithm Selection
The choice of index algorithm determines the tradeoff between query speed, recall accuracy, memory usage, and build time. HNSW (Hierarchical Navigable Small World) is the best general-purpose choice: it provides excellent recall with logarithmic query time and supports incremental inserts without full index rebuilds. IVF (Inverted File Index) uses less memory but requires periodic rebuilds and has lower recall at the same latency. For very large corpora that do not fit in RAM, DiskANN style indices enable disk-resident search with acceptable latency.
| Algorithm | Query Speed | Recall@10 | Memory | Incremental Insert | Best For |
|---|---|---|---|---|---|
| HNSW | 1-10ms | 95-99% | High (2-4x vectors) | Yes | General purpose, <50M vectors |
| IVF-Flat | 5-20ms | 90-95% | Low (1x vectors) | Requires rebuild | Cost-sensitive, batch updates |
| IVF-PQ | 2-10ms | 85-92% | Very low (0.1x) | Requires rebuild | Billions of vectors, memory-constrained |
| DiskANN | 10-50ms | 95-98% | Minimal RAM | Limited | Disk-resident, very large corpora |
pgvector HNSW Tuning
pgvector's HNSW index has two critical parameters: m (the number of connections per node, controlling graph density) and ef_construction (the beam width during index building, controlling build-time recall). Higher values produce better recall but consume more memory and build time. At query time, ef_search controls the beam width and directly trades latency for recall. The defaults are conservative — tuning these parameters for your specific dataset and latency budget can improve recall by 5-10 percentage points.
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