What is Faceted Search

How It Works

Faceted search augments search results with aggregated counts of attribute values, enabling users to progressively narrow results by selecting filters. When a user selects a facet value (e.g., 'modality: video'), the system applies that filter and recalculates counts for remaining facets. This iterative refinement helps users navigate large multimodal collections without knowing exact search terms.

Technical Details

Facets are computed by aggregating indexed fields across the result set. Elasticsearch and Solr provide built-in facet aggregation. For vector databases, faceted search combines vector similarity with payload filtering. Common facet types include terms (categorical values), ranges (numeric intervals), and hierarchical (nested categories). Efficient facet computation requires proper field indexing and may use approximate counts for large datasets.

Best Practices

• Show facet counts to help users understand the distribution of results before filtering
• Order facets by relevance or frequency, not alphabetically, for better discoverability
• Support multi-select within a facet and combine selections with OR logic
• Use hierarchical facets for taxonomy-style attributes (category > subcategory)

Common Pitfalls

• Displaying too many facets, overwhelming users with choices
• Not updating facet counts when filters are applied, showing stale or misleading counts
• Using facets on high-cardinality fields (unique IDs), producing unusable filter lists
• Ignoring the performance impact of computing facets across large result sets

Advanced Tips

• Build modality-specific facets (resolution for images, duration for video, language for audio) in multimodal search
• Combine faceted filtering with vector similarity for filtered semantic search
• Use dynamic facets that change based on query context and current result set
• Implement facet suggestions using AI to recommend the most useful filters for each query