| name | agentdb-hybrid-search |
| description | Hybrid search — BM25 keyword + dense vector fused with Reciprocal Rank Fusion. Use when queries have specific identifiers, code symbols, or proper nouns that pure semantic search might miss. |
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.
