Metadata Filtering

Filtering

Part of: Vector Databases & Production Search

A user on your German support site asks a question and your retriever confidently returns the perfect answer, in English, from a document marked "internal only," dated three years before the feature existed. The vectors were similar. The result was useless. Metadata filtering is how you keep similarity search inside the boundaries reality demands. What it is Metadata is the structured data you attach to each vector: language, source, author, date, access level, document type. Metadata filtering restricts a similarity search to only the vectors whose metadata matches a condition, so "closest in meaning" is computed only over the allowed subset . The query becomes two-part: a vector ("what is this about?") plus a filter ("which records are even eligible?"). How it works You store metadata next to each embedding, then pass a filter at query time: There are two ways a vector DB can apply this, and the difference matters: - Pre-filtering narrows the candidate set first , then runs nearest-neighbor search only over the survivors. Accurate, but can be slow if the filter is complex. - Post-filtering runs nearest-neighbor search first, then throws away results that fail the filter. Fast, bu

Challenge: Metadata-Filtered Retrieval