What is Metadata Filtering?
Metadata filtering is a technique that narrows search results based on document attributes or properties before or during similarity search, enabling more precise retrieval by combining semantic similarity with structured constraints. Filters are applied to metadata fields like document type, creation date, author, category, source, or custom tags, restricting retrieval to only those documents that match both the semantic query and the metadata criteria.
The filtering can occur at different stages in the retrieval pipeline. Pre-filtering applies constraints before similarity search, reducing the search space to only matching documents. Post-filtering performs similarity search across all documents then removes results that don't match metadata criteria. Hybrid approaches use database indexes to efficiently combine semantic and metadata constraints in a single query operation, offering the best performance for large-scale systems.
Metadata filtering is essential for many practical applications. Enterprise search might filter by department or document type, customer support systems might filter by product or date range, and personalized systems might filter by user preferences or permissions. Most modern vector databases support metadata filtering as a first-class feature, allowing developers to express complex filter conditions alongside vector similarity queries. Effective metadata extraction during document ingestion and thoughtful metadata schema design significantly impact filtering capabilities.