Vector Databases and Hybrid Search for AI Apps

Vector Search Is Only One Retrieval Strategy

Vector databases help retrieve semantically similar content, but production systems often need hybrid search that combines embeddings, keyword matching, metadata filters, reranking, and freshness rules.

Engineering Considerations

• Use metadata filters for tenant, role, document type, date, language, and access level.
• Combine keyword search with semantic retrieval for exact terms, IDs, product names, and code symbols.
• Use reranking when initial recall is high but precision is weak.
• Track index freshness and document deletion behavior.
• Evaluate retrieval with realistic user questions, not just embedding similarity scores.

Permissions Are Part of Retrieval

A retrieval system is also an access-control system. The model should never receive documents the user or workflow is not allowed to access.