Vector search finds “what it’s like.” Full-text search finds “what it says.” Relational filters handle “who” and “where.” With seekdb, you can combine all three in a single query and run embedding and reranking inside the database. The fusion logic (e.g., RRF) and the AI Functions API are open on GitHub ( https://github.com/oceanbase/seekdb ) — you can review, modify, and send PRs. This post walks through how it works and how to use it in RAG and knowledge-base setups, and how you can contribute. 1. Hybrid Search: Why One SQL Beats Multi-Stage Retrieval The usual approach: hit a vector store, hit a full-text store, and then normalize, fuse scores (e.g., RRF), and rerank in the application layer. The catch: extra network hops, custom fusion logic, and filter conditions that can drift between systems (e.g.,“only this user’s data” has to be expressed in both stores). seekdb’s hybrid search is different: one table has both a vector index and a full-text index.…