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.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
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.まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
日本語の概要は準備中です。原文の説明を表示しています。
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
日本語の概要は準備中です。原文の説明を表示しています。
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
日本語の概要は準備中です。原文の説明を表示しています。
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
日本語の概要は準備中です。原文の説明を表示しています。
Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.
日本語の概要は準備中です。原文の説明を表示しています。
Walk the causal graph in AgentDB to explain why two memories are connected, or trace a root cause. Use when the user asks "why did X happen", "what led to Y", or after an incident.
日本語の概要は準備中です。原文の説明を表示しています。