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1493bab017
Implements the three missing delete primitives on GraphDatabase.prototype,
unblocking the ruflo bridge from relying solely on the SQL fallback path.
**API additions:**
deleteNode(id, {cascade?}) → {deletedNode, deletedEdges}
deleteEdge(id) → {deleted}
deleteHyperedge(id) → {deleted}
cascade=true on deleteNode removes all incident hyperedges atomically
(no racy enumerate-then-delete required by callers).
**Rust changes:**
- ruvector-core/hypergraph: HypergraphIndex::remove_entity(cascade)
+ remove_hyperedge() with full bipartite-index + temporal-index cleanup
- ruvector-graph/graph: GraphDB::delete_hyperedge() + delete_hyperedges_by_node()
symmetric to create_hyperedge, propagates to GraphStorage when enabled
- ruvector-graph-node/lib: three new #[napi] async NAPI methods, each
propagating through HypergraphIndex → GraphDB → GraphStorage in order
- ruvector-graph-node/types: JsDeleteNodeOptions, JsDeleteNodeResult,
JsDeleteResult return types
**Versions:** workspace 2.2.1 → 2.2.2; @ruvector/graph-node 2.0.3 → 2.0.4
(platform optionalDependencies aligned to 2.0.4)
Co-Authored-By: claude-flow <ruv@ruv.net>
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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
npm install @ruvector/graph-node
Quick Start
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