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vendor/ruvector/npm/packages/graph-node/README.md

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+# @ruvector/graph-node
+
+Native Node.js bindings for RuVector Graph Database with hypergraph support, Cypher queries, and persistence. **10x faster than WASM**.
+
+## Features
+
+- **Native Performance**: Direct NAPI-RS bindings - no WASM overhead
+- **Hypergraph Support**: Multi-node relationships with vector embeddings
+- **Cypher Queries**: Neo4j-compatible query language
+- **Persistence**: ACID-compliant storage with redb backend
+- **Vector Similarity Search**: Fast k-NN search on embeddings
+- **Graph Traversal**: k-hop neighbor discovery
+- **Transactions**: Full ACID support with begin/commit/rollback
+- **Batch Operations**: High-throughput bulk inserts (131K+ ops/sec)
+- **Zero-Copy**: Efficient Float32Array handling
+- **TypeScript**: Full type definitions included
+
+## Installation
+
+```bash
+npm install @ruvector/graph-node
+```
+
+## Quick Start
+
+```javascript
+const { GraphDatabase } = require('@ruvector/graph-node');
+
+// Create an in-memory database
+const db = new GraphDatabase({
+  distanceMetric: 'Cosine',
+  dimensions: 384
+});
+
+// Or create a persistent database
+const persistentDb = new GraphDatabase({
+  distanceMetric: 'Cosine',
+  dimensions: 384,
+  storagePath: './my-graph.db'
+});
+
+// Or open an existing database
+const existingDb = GraphDatabase.open('./my-graph.db');
+
+// Create nodes
+await db.createNode({
+  id: 'alice',
+  embedding: new Float32Array([1.0, 0.0, 0.0, /* ... */]),
+  labels: ['Person', 'Employee'],
+  properties: { name: 'Alice', age: '30' }
+});
+
+// Create edges
+await db.createEdge({
+  from: 'alice',
+  to: 'bob',
+  description: 'KNOWS',
+  embedding: new Float32Array([0.5, 0.5, 0.0, /* ... */]),
+  confidence: 0.95
+});
+
+// Create hyperedges (multi-node relationships)
+await db.createHyperedge({
+  nodes: ['alice', 'bob', 'charlie'],
+  description: 'COLLABORATED_ON_PROJECT',
+  embedding: new Float32Array([0.33, 0.33, 0.33, /* ... */]),
+  confidence: 0.85
+});
+
+// Query with Cypher
+const results = await db.query('MATCH (n:Person) RETURN n');
+
+// Vector similarity search
+const similar = await db.searchHyperedges({
+  embedding: new Float32Array([0.3, 0.3, 0.3, /* ... */]),
+  k: 10
+});
+
+// Get statistics
+const stats = await db.stats();
+console.log(\`Nodes: \${stats.totalNodes}, Edges: \${stats.totalEdges}\`);
+```
+
+## Benchmarks
+
+| Operation | Throughput | Latency |
+|-----------|------------|---------|
+| Node Creation | 9.17K ops/sec | 109ms |
+| Batch Node Creation | 131.10K ops/sec | 7.63ms |
+| Edge Creation | 9.30K ops/sec | 107ms |
+| Vector Search (k=10) | 2.35K ops/sec | 42ms |
+| k-hop Traversal | 10.28K ops/sec | 9.73ms |
+
+## Platform Support
+
+| Platform | Architecture | Status |
+|----------|--------------|--------|
+| Linux | x64 (glibc) | Supported |
+| Linux | arm64 (glibc) | Supported |
+| macOS | x64 | Supported |
+| macOS | arm64 (M1/M2) | Supported |
+| Windows | x64 | Supported |
+
+## License
+
+MIT