MAVANN: Metadata-Aware Vector Approximate Nearest Neighbor
Nithin Mani
Research
·8 min read

1. Introduction
2. Comparison with Traditional Filtering Approaches
2.1 Pre-Filtering
2.2 Post-Filtering
2.3 MAVANN's Novel Approach
3. Vector Structure in MAVANN
4. Phased Distance Metric Computation
4.1 Step 1: Metadata Segment Evaluation
4.2 Step 2: Full Vector Similarity Computation
5. Index Structure
5.1 Base Vector Representation
5.2 Metadata Segment Encoding
Dimension Allocation
5.3 Metadata Schema
Updating Metadata Schema
5.4 Hierarchical Metadata Routing Structure
Pseudo-metadata Nodes
Pseudo-root Node
5.5 Vector Extension and Indexing Process
Phase 1: Pseudo-metadata Structure Creation
Phase 2: Metadata Node Insertion
6. Query Processing
6.1 Pure Similarity Search
6.2 Metadata Filtering Queries
6.3 Query Vector Encoding Details
Equality Filter Encoding
Inequality Filter Encoding
7. Performance Considerations
8. Trade-offs and Considerations
9. Conclusion
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