Metadata filtering lets you scope a vector search to a subset of your index before doing similarity search. Instead of searching all 1 million document chunks, you search only the 50,000 from Q4 2024, or only from the "legal" department, or only documents tagged "product spec."
This dramatically improves precision (less irrelevant noise makes it into top-K) and enables multi-tenant architectures (each user or org only retrieves their own documents). Most vector databases support metadata filtering as a standard feature.
Common metadata fields worth indexing: source (URL or filename), document type, date created/updated, author, department/team, and content tags. Add filtering at design time — retrofitting it into an existing index requires re-ingesting everything.
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