Vector Filter

A vector filter is a structured predicate combined with a vector similarity query that restricts results to vectors matching specific metadata conditions — for example, retrieving the most similar documents among only those tagged as published, in English, and authored after a specific date. Vector filters can be applied as pre-filters (narrow the candidate set before similarity search) or post-filters (run similarity search then filter), with major trade-offs in performance and recall. Pre-filters are accurate but can dramatically slow searches when the filter is highly selective, while post-filters are fast but can return fewer than the requested top-k results when the filter eliminates most candidates. Modern vector databases like Qdrant, Weaviate, and Milvus implement sophisticated filter optimization that picks the right strategy automatically based on the filter's selectivity. AI governance use cases for vector filters include enforcing per-user access control, language-specific retrieval, and content classification boundaries. Filtered vector search is a hard requirement for any production RAG system handling permissions, sensitivity labels, or jurisdiction-specific compliance rules.

Filtered vector search in Centralpoint: Centralpoint enforces per-user, per-audience, and per-tenant vector filters across whatever vector backend you operate, ensuring chatbots only retrieve content the user is authorized to see. Tokens are metered centrally, prompts stay local, and filter-aware chatbots embed across portals with one line of JavaScript and complete audit trails.


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