Metadata Filtering

Metadata Filtering is a technique used in vector databases and search systems to narrow search results by applying constraints based on structured metadata attributes associated with documents or data points. This process combines semantic similarity search with traditional filtering criteria such as date ranges, document types, author information, categories, tags, or custom attributes. Metadata filtering enables precise control over search scope, allowing users to retrieve semantically similar content within specific contexts or domains. The filtering can be applied pre-search to limit the search space or post-search to refine results. Common implementation approaches include WHERE clauses in vector queries, filtered approximate nearest neighbor (ANN) searches, and hybrid filtering strategies that balance search performance with precision. Metadata filtering is essential for enterprise applications requiring compliance, access control, temporal restrictions, or domain-specific constraints while maintaining the benefits of semantic similarity search in retrieval-augmented generation and recommendation systems.