Agentdb Hybrid Search

Hybrid Search

Combine sparse (BM25, exact terms) and dense (vector, semantic) search via Reciprocal Rank Fusion. Catches both "exact-string" queries and "intent" queries in one call.

When to use

• Queries with code symbols (getUserById, JWT_SECRET)
• Queries with proper nouns (file names, package names, project IDs)
• Mixed-intent queries — "the auth module's token refresh logic"
• When you've seen pure vector search miss obvious string matches

API

agentdb_hybrid_search(
  query:    <string>
  k:        5
  weights?: { bm25: 0.5, vector: 0.5 }   // default; bandit can pick
  filters?: { ... }                        // metadata filters
)

How RRF works

Each result gets a rank in BM25 list (r_bm25) and a rank in vector list (r_vec). Final score:

score(i) = w_bm25 / (60 + r_bm25(i)) + w_vec / (60 + r_vec(i))

The constant 60 (k in the RRF paper) prevents top-1 hits from dominating; the additive structure rewards being well-ranked in both lists.

When NOT to use hybrid

• Pure-natural-language queries with no identifiers → vector alone is fine and faster
• Pure-keyword queries (regex, exact match) → use BM25 directly
• Cross-language queries (query in English, docs in Japanese) → BM25 won't help; stick with vector

Don't

• Don't tune weights manually per query. Let the bandit (weights: 'auto') learn the right balance.
• Don't run hybrid on small corpora (< 1000 docs). BM25's IDF signal is noisy at that scale; vector alone is usually better.