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Major additions: - ruvector-gnn: Complete GNN implementation with RuvectorLayer, multi-head attention, GRU cell - Tensor compression: 5-tier adaptive compression (f32→f16→PQ8→PQ4→Binary, 2-32x) - Differentiable search: Soft attention k-NN with gradient flow - Training: InfoNCE contrastive loss, SGD optimizer - Query API: RuvectorQuery, QueryResult, SubGraph types - MmapManager: Memory-mapped embeddings with gradient accumulation - Tensor operations: Full tensor math library Bindings: - ruvector-gnn-wasm: Full WASM bindings for browser - ruvector-gnn-node: napi-rs bindings for Node.js Fixes: - WASM compatibility for ruvector-graph (conditional compilation) - Feature flags for storage/hnsw modules Updated README with GNN architecture overview and tutorials |
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