Vektör Veritabanları

It can support semantic search, recommendations, and retrieval for AI applications. It does not independently establish the truth, permission, or usefulness of a retrieved record.

Key takeaways

• Version embeddings with their source and model.
• Evaluate approximation against task relevance.
• Treat deletion and permissions as first-class requirements.

An embedding model maps an item such as a passage or image into a vector. The database indexes those vectors so a query representation can retrieve nearby items. Keep the original content, source identifier, and relevant metadata available with the vector. The embedding model and similarity measure must be compatible with the application. Changing the model can change the meaning or dimensions of stored representations. A migration may require recomputing embeddings and rebuilding an index rather than mixing old and new vectors. Exact nearest-neighbor search compares candidates directly, while approximate methods trade some retrieval fidelity for speed or memory efficiency. Evaluate the tradeoff using representative queries and the actual collection size. Database latency alone does not measure the relevance of the returned evidence. Plan updates, deletion, and authorization as part of the design. A removed document should not remain discoverable through an old embedding. Metadata filters need to enforce the requesting user’s permitted scope. Test concurrent updates and fallback behavior when the index is unavailable.

1. Imagine an index built with model A and a new query service using model B.
2. Even if both produce the same number of dimensions, their coordinate meanings need not match. Mixing them can make similarity results meaningless.
3. Build a separately versioned index using model B, evaluate it, and switch queries only after validating the complete migration.

The constructed scenario shows why embedding versioning is part of data integrity.