When should metadata filters be applied in vector retrieval?

Uses pre-filtering for authorization and hard constraints while recognizing engine-specific recall and performance behavior.

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TL;DR

I apply mandatory authorization and domain constraints before an item can become retrieval evidence. A metadata filter might restrict results to the caller's tenant, approved documents, the requested product, and currently effective versions. Post-filtering a small top-k list can lose the best permitted matches and must never be the only security control.

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Hard constraints, pre-filter versus post-filter behavior, and secure retrieval.

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Overview
A similarity score cannot replace hard constraints such as tenant, status, locale, or effective date.
I apply mandatory authorization and domain constraints before an item can become retrieval evidence. A metadata filter might restrict results to the caller's tenant, approved documents, the requested product, and currently effective versions. Post-filtering a small top-k list can lose the best permitted matches and must never be the only security control.
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Stronger answer
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Filter execution differs by search engine: some perform exact pre-filtering, while others use iterative or approximate strategies that affect recall and latency. I test selective filters because a very narrow candidate set can behave differently from the unfiltered corpus. If hybrid retrieval has multiple paths, every path receives equivalent mandatory constraints.

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Details

Applying tenant filtering only after retrieving top results.
Letting the model construct raw authorization filters.
Assuming filter behavior is identical across vector engines.

When should metadata filters be applied in vector retrieval?

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