d803bfe2b1
git-subtree-dir: vendor/ruvector git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900
107 lines
2.8 KiB
Markdown
107 lines
2.8 KiB
Markdown
# @ruvector/graph-node
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Native Node.js bindings for RuVector Graph Database with hypergraph support, Cypher queries, and persistence. **10x faster than WASM**.
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## Features
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- **Native Performance**: Direct NAPI-RS bindings - no WASM overhead
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- **Hypergraph Support**: Multi-node relationships with vector embeddings
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- **Cypher Queries**: Neo4j-compatible query language
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- **Persistence**: ACID-compliant storage with redb backend
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- **Vector Similarity Search**: Fast k-NN search on embeddings
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- **Graph Traversal**: k-hop neighbor discovery
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- **Transactions**: Full ACID support with begin/commit/rollback
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- **Batch Operations**: High-throughput bulk inserts (131K+ ops/sec)
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- **Zero-Copy**: Efficient Float32Array handling
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- **TypeScript**: Full type definitions included
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## Installation
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```bash
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npm install @ruvector/graph-node
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```
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## Quick Start
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```javascript
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const { GraphDatabase } = require('@ruvector/graph-node');
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// Create an in-memory database
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const db = new GraphDatabase({
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distanceMetric: 'Cosine',
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dimensions: 384
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});
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// Or create a persistent database
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const persistentDb = new GraphDatabase({
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distanceMetric: 'Cosine',
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dimensions: 384,
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storagePath: './my-graph.db'
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});
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// Or open an existing database
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const existingDb = GraphDatabase.open('./my-graph.db');
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// Create nodes
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await db.createNode({
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id: 'alice',
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embedding: new Float32Array([1.0, 0.0, 0.0, /* ... */]),
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labels: ['Person', 'Employee'],
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properties: { name: 'Alice', age: '30' }
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});
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// Create edges
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await db.createEdge({
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from: 'alice',
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to: 'bob',
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description: 'KNOWS',
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embedding: new Float32Array([0.5, 0.5, 0.0, /* ... */]),
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confidence: 0.95
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});
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// Create hyperedges (multi-node relationships)
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await db.createHyperedge({
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nodes: ['alice', 'bob', 'charlie'],
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description: 'COLLABORATED_ON_PROJECT',
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embedding: new Float32Array([0.33, 0.33, 0.33, /* ... */]),
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confidence: 0.85
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});
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// Query with Cypher
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const results = await db.query('MATCH (n:Person) RETURN n');
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// Vector similarity search
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const similar = await db.searchHyperedges({
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embedding: new Float32Array([0.3, 0.3, 0.3, /* ... */]),
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k: 10
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});
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// Get statistics
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const stats = await db.stats();
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console.log(\`Nodes: \${stats.totalNodes}, Edges: \${stats.totalEdges}\`);
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```
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## Benchmarks
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| Operation | Throughput | Latency |
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|-----------|------------|---------|
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| Node Creation | 9.17K ops/sec | 109ms |
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| Batch Node Creation | 131.10K ops/sec | 7.63ms |
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| Edge Creation | 9.30K ops/sec | 107ms |
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| Vector Search (k=10) | 2.35K ops/sec | 42ms |
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| k-hop Traversal | 10.28K ops/sec | 9.73ms |
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## Platform Support
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| Platform | Architecture | Status |
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|----------|--------------|--------|
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| Linux | x64 (glibc) | Supported |
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| Linux | arm64 (glibc) | Supported |
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| macOS | x64 | Supported |
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| macOS | arm64 (M1/M2) | Supported |
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| Windows | x64 | Supported |
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## License
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MIT
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