From agentdb-search
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.
How this skill is triggered — by the user, by Claude, or both
Slash command
/agentdb-search:agentdb-hybrid-searchThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
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.
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.
getUserById, JWT_SECRET)agentdb_hybrid_search(
query: <string>
k: 5
weights?: { bm25: 0.5, vector: 0.5 } // default; bandit can pick
filters?: { ... } // metadata filters
)
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.
weights manually per query. Let the bandit (weights: 'auto') learn the right balance.npx claudepluginhub ruvnet/agentdb --plugin agentdb-searchFuses vector and keyword search results using RRF, linear combination, and reranking for better recall in RAG systems and search engines.
Combines vector similarity and keyword search for improved retrieval in RAG systems and search engines.
Searches MemPalace before answering questions about past work, people, projects, or prior decisions. Returns verbatim stored content instead of guessing from model memory.