HatiData provides a long-term memory system for AI agents that combines vector similarity search with structured SQL metadata. Agents can store, search, and retrieve memories across sessions, enabling persistent context that survives restarts and can be shared between agents.
Build a retrieval-augmented generation system where AI agents query structured data via SQL and semantic memory through a single HatiData connection. This playbook demonstrates how to combine SQL queries with vector-based memory search to give agents persistent, context-aware reasoning.
JOIN_VECTOR, semantic_match(), and semantic_rank() reference