RAG Database Design: SQL, Full-Text Search, Vector Search, and Context Retrieval

Short summary

Production RAG systems need more than just a vector database—they require relational stores for source-of-truth data, full-text search for lexical retrieval, caches for session state, and vector indexes for semantic retrieval. The article maps each retrieval job to the appropriate database model and argues teams should identify access rules, filters, and evaluation plans before choosing a vector index. A Kyoto travel assistant example illustrates how different storage engines serve different pipeline stages.

• •RAG pipelines need multiple database types: relational for source-of-truth, full-text for lexical retrieval, vector for semantic retrieval
• •Teams should design retrieval jobs, access rules, and evaluation plans before selecting a vector index
• •A Kyoto travel assistant example demonstrates how different databases serve different pipeline stages

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