AI / LlamaIndex Interview Questions
Why should you use metadata filtering in retrieval?
Metadata filtering lets you narrow a similarity search to only the Nodes whose metadata matches specific conditions, such as a date range, a source document, or a tenant ID, before or alongside the embedding comparison.
This matters because vector similarity alone doesn't understand hard constraints. A query about "the 2025 contract" might semantically match text from 2023 just as strongly if the wording is similar, so without filtering, the wrong document could be retrieved. Attaching MetadataFilters with an exact-match condition on a year field enforces the constraint directly.
It's also the standard way to implement multi-tenant access control in a shared index: tagging every Node with a tenant_id at ingestion and filtering on it at query time keeps one customer's retrieval from ever surfacing another's data.
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