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디렉토리 플랫폼 템플릿

Open-source multi-model database with native vector embedding support alongside graph, document, and more.

Embedded vector + graph + columnar database with HNSW indexing.

Unified graph transformer with proof-gated mutation substrate for verified graph-vector operations, featuring 8 modules and 186 tests.

Node.js NAPI-RS bindings for ruvector-graph-transformer with 22+ methods and 20 tests.

WASM bindings for browser-side graph transformers with proof verification.

Library providing sublinear-time solvers for large-scale math problems like PageRank, graph Laplacians, and AI attention using 8 algorithms including Neumann Series, Conjugate Gradient, Forward/Backward Push, and more. Optimized for scale with SIMD SpMV, fused kernels, and arena allocators; supports WASM and NAPI bindings.

Incremental graph sparsifier that compresses large graphs into a 'shadow graph' preserving key properties like connectivity, cuts, and flow. Uses random walks for importance scoring, spectral sampling, union-find backbone, and periodic auditing to maintain accuracy without full rebuilds.

Microsoft's approach to RAG that uses knowledge graphs to enhance retrieval. GraphRAG builds structured representations of documents enabling better context understanding and multi-hop reasoning for complex queries.

Graph-based algorithm for approximate nearest neighbor search that maintains multi-layer graph structures for efficient vector similarity search with logarithmic complexity, widely used in modern vector databases.