Graph Foundry bridges the gap between empirical success and theoretical rigor in approximate nearest neighbor search. We establish formal accuracy guarantees for existing graph-based algorithms while enhancing their integration with relational infrastructure.
Explore Our ResearchWe enhance existing graph-based ANN algorithms (focusing on HNSW) with measurable accuracy guarantees, robust relational integration, and joint optimization with full-text indexing beyond simple reranking approaches.
Formal guarantees for graph-based ANN algorithms
Graph-based approximate nearest neighbor algorithms achieve state-of-the-art empirical performance but lack formal accuracy guarantees, limiting their adoption in critical applications.
Popular graph-based ANN algorithms like HNSW demonstrate remarkable empirical performance but operate without formal accuracy bounds. This creates uncertainty in deployment and limits applications in domains requiring reliability guarantees.
Example: Product database with vector embeddings and relational attributes
| Product ID | Product Name | Price | Image Embedding |
|---|---|---|---|
| 101 | Wireless Headphones | $79.99 | [0.24, -0.12, 0.87, ...] |
| 102 | Smart Watch | $149.99 | [-0.45, 0.33, 0.21, ...] |
| 103 | Bluetooth Speaker | $59.99 | [0.67, 0.05, -0.32, ...] |
| 104 | Gaming Mouse | $89.99 | [-0.12, 0.78, 0.09, ...] |
Challenge: Efficiently executing hybrid queries combining vector similarity with relational filters while maintaining accuracy guarantees.
Graph Foundry develops theoretically grounded enhancements to existing graph-based ANN algorithms with practical integration frameworks.
We establish formal accuracy bounds for existing graph-based ANN algorithms, starting with HNSW. Our approach maintains empirical performance while adding measurable guarantee certificates for query results.
Key Insight: By analyzing graph connectivity properties and search path distributions, we derive probabilistic bounds on recall and error rates.
We design optimization frameworks that embed graph-based ANN algorithms directly within relational query processors, enabling efficient hybrid queries over vector and attribute data.
Innovation: Our approach moves beyond post-processing reranking to integrated query planning with cost-based optimization for joint vector/relational operations.
We develop novel data structures and algorithms for unified graph and full-text indexing, enabling optimized multi-modal retrieval beyond simple reranking approaches.
Breakthrough: Our fused index architecture enables simultaneous traversal of semantic and lexical similarity spaces with formal convergence guarantees.
Current approaches to multi-modal retrieval use separate indices with reranking. We develop fused indexing structures that enable simultaneous traversal.
Inverted indexes with BM25 scoring, term positions, and linguistic analysis
Hierarchical Navigable Small World graphs with vector embeddings
Unified traversal with joint optimization, avoiding suboptimal reranking
Graph Foundry is an academic research initiative. We welcome collaborations with researchers and industry partners.
For research collaborations, access to datasets, or implementation details:
Preprints and conference papers
Reference implementations
Benchmarks and evaluation tools