engineering · 1 min read
Vector Index Tuning: HNSW Parameters in the Real World
HNSW parameters look intimidating. Here are sensible defaults and the few knobs that actually matter for production RAG.
BABhogar AI TeamProduct & Engineering
HNSW has a dozen parameters; only a few matter. Picking sensible defaults beats over-tuning early and saves your team weeks.
Why it matters
The knobs that actually move the needle: M (graph connectivity), efConstruction (build accuracy), efSearch (query accuracy/latency trade-off).
How Bhogar AI approaches it
Bhogar AI ships HNSW defaults that work for 90% of corpora out of the box; the other 10% are guided through a tuning recipe with measurable trade-offs.
- Sensible HNSW defaults out of the box
- Tuning recipe for advanced users
- Per-KB tuning telemetry
- Recall vs latency dashboards
- Compatible with pgvector and managed vector DBs
What you get
Production-quality vector indexing without weeks of academic-paper reading.