engineering · 1 min read
Hybrid Search: Combining Dense Vectors with BM25
Pure vector search misses exact matches. Pure keyword search misses paraphrases. Hybrid search wins both. Here is how Bhogar AI implements it at scale.
BABhogar AI TeamProduct & Engineering
Vector search alone misses exact identifiers - order numbers, SKU codes, function names. Keyword search alone misses paraphrases. Hybrid search, properly fused, beats either approach on virtually every benchmark.
Why it matters
Most teams ship vector-only RAG and learn the hard way that customers ask questions like "what about order 4429?" - a query no embedding will retrieve correctly. Hybrid is the answer.
How Bhogar AI approaches it
Bhogar AI runs dense and sparse retrievers in parallel and fuses results with reciprocal rank fusion. Hybrid is the default for new knowledge bases; pure vector or pure keyword are available as overrides for narrow use cases.
- Dense + BM25 retrieval in a single API call
- Reciprocal rank fusion as the default reranker
- Per-KB override to dense-only or sparse-only
- Filtered search by metadata, tag and tenant
- Latency budget gates that fall back gracefully under load
What you get
On internal evals across customer support and technical docs, hybrid search lifts top-3 recall 20-30 points over vector-only.