A distributed vector database that learns. Store embeddings, query with Cypher, scale horizontally with Raft consensus, and let the index improve itself through Graph Neural Networks.

npx ruvector

All-in-One Package: The core ruvector package includes everything — vector search, graph queries, GNN layers, distributed clustering, AI routing, and WASM support. No additional packages needed.

Traditional vector databases just store and search. When you ask "find similar items," they return results but never get smarter. They don't scale horizontally. They can't route AI requests intelligently.

RuVector is different:

1. Store vectors like any vector DB (embeddings from OpenAI, Cohere, etc.)
2. Query with Cypher like Neo4j (MATCH (a)-[:SIMILAR]->(b) RETURN b)
3. The index learns — GNN layers make search results improve over time
4. Scale horizontally — Raft consensus, multi-master replication, auto-sharding
5. Route AI requests — Semantic routing and FastGRNN neural inference for LLM optimization
6. Compress automatically — 2-32x memory reduction with adaptive tiered compression
7. Run anywhere — Node.js, browser (WASM), HTTP server, or native Rust

Think of it as: Pinecone + Neo4j + PyTorch + etcd in one Rust package.

# Install
npm install ruvector

# Or try instantly
npx ruvector

cargo add ruvector-raft ruvector-cluster ruvector-replication

Real benchmark results on standard hardware:

Production-validated metrics at hyperscale:

*With PQ8 compression. Benchmarks on Apple M2 / Intel i7.

Traditional vector search:

Query → HNSW Index → Top K Results

RuVector with GNN:

Query → HNSW Index → GNN Layer → Enhanced Results
                ↑                      │
                └──── learns from ─────┘

The GNN layer:

1. Takes your query and its nearest neighbors
2. Applies multi-head attention to weigh which neighbors matter
3. Updates representations based on graph structure
4. Returns better-ranked results

Over time, frequently-accessed paths get reinforced, making common queries faster and more accurate.

The architecture adapts to your data. Hot paths get full precision and maximum compute. Cold paths compress automatically and throttle resources. Recent data stays crystal clear; historical data optimizes itself in the background.

Think of it like your computer's memory hierarchy—frequently accessed data lives in fast cache, while older files move to slower, denser storage. RuVector does this automatically for your vectors:

No configuration needed. RuVector tracks access patterns and automatically promotes/demotes vectors between tiers. Your hot data stays fast; your cold data shrinks.

RAG (Retrieval-Augmented Generation)

const context = ruvector.search(questionEmbedding, 5);
const prompt = `Context: ${context.join('\n')}\n\nQuestion: ${question}`;

Recommendation Systems

MATCH (user:User)-[:VIEWED]->(item:Product)
MATCH (item)-[:SIMILAR_TO]->(rec:Product)
RETURN rec ORDER BY rec.score DESC LIMIT 10

Knowledge Graphs

MATCH (concept:Concept)-[:RELATES_TO*1..3]->(related)
RETURN related

All crates are published to crates.io under the ruvector-* namespace.

Crate	Description	crates.io
ruvector-core	Vector database engine with HNSW indexing
ruvector-collections	Collection and namespace management
ruvector-filter	Vector filtering and metadata queries
ruvector-metrics	Performance metrics and monitoring
ruvector-snapshot	Snapshot and persistence management

Crate	Description	crates.io
ruvector-graph	Hypergraph database with Neo4j-style Cypher
ruvector-graph-node	Node.js bindings for graph operations
ruvector-graph-wasm	WASM bindings for browser graph queries
ruvector-gnn	Graph Neural Network layers and training
ruvector-gnn-node	Node.js bindings for GNN inference
ruvector-gnn-wasm	WASM bindings for browser GNN

Crate	Description	crates.io
ruvector-cluster	Cluster management and coordination
ruvector-raft	Raft consensus implementation
ruvector-replication	Data replication and synchronization

Crate	Description	crates.io
ruvector-tiny-dancer-core	FastGRNN neural inference for AI routing
ruvector-tiny-dancer-node	Node.js bindings for AI routing
ruvector-tiny-dancer-wasm	WASM bindings for browser AI routing

Crate	Description	crates.io
ruvector-router-core	Core semantic routing engine
ruvector-router-cli	CLI for router testing and benchmarking
ruvector-router-ffi	FFI bindings for other languages
ruvector-router-wasm	WASM bindings for browser routing

Crate	Description	crates.io
ruvector-node	Main Node.js bindings (napi-rs)
ruvector-wasm	Main WASM bindings for browsers
ruvector-cli	Command-line interface
ruvector-server	HTTP/gRPC server

Package	Description	npm
ruvector	All-in-one CLI & package (vectors, graphs, GNN)
@ruvector/core	Core vector database with native Rust bindings
@ruvector/gnn	Graph Neural Network layers & tensor compression
@ruvector/graph-node	Hypergraph database with Cypher queries
@ruvector/tiny-dancer	FastGRNN neural inference for AI agent routing
@ruvector/router	Semantic router with HNSW vector search
@ruvector/agentic-synth	Synthetic data generator for AI/ML

Platform-specific native bindings (auto-detected):

• @ruvector/node-linux-x64-gnu, @ruvector/node-linux-arm64-gnu, @ruvector/node-darwin-x64, @ruvector/node-darwin-arm64, @ruvector/node-win32-x64-msvc
• @ruvector/gnn-linux-x64-gnu, @ruvector/gnn-linux-arm64-gnu, @ruvector/gnn-darwin-x64, @ruvector/gnn-darwin-arm64, @ruvector/gnn-win32-x64-msvc
• @ruvector/tiny-dancer-linux-x64-gnu, @ruvector/tiny-dancer-linux-arm64-gnu, @ruvector/tiny-dancer-darwin-x64, @ruvector/tiny-dancer-darwin-arm64, @ruvector/tiny-dancer-win32-x64-msvc
• @ruvector/router-linux-x64-gnu, @ruvector/router-linux-arm64-gnu, @ruvector/router-darwin-x64, @ruvector/router-darwin-arm64, @ruvector/router-win32-x64-msvc

See GitHub Issue #20 for multi-platform npm package roadmap.

# Install all-in-one package
npm install ruvector

# Or install individual packages
npm install @ruvector/core @ruvector/gnn @ruvector/graph-node

# List all available packages
npx ruvector install

const ruvector = require('ruvector');

// Vector search
const db = new ruvector.VectorDB(128);
db.insert('doc1', embedding1);
const results = db.search(queryEmbedding, 10);

// Graph queries (Cypher)
db.execute("CREATE (a:Person {name: 'Alice'})-[:KNOWS]->(b:Person {name: 'Bob'})");
db.execute("MATCH (p:Person)-[:KNOWS]->(friend) RETURN friend.name");

// GNN-enhanced search
const layer = new ruvector.GNNLayer(128, 256, 4);
const enhanced = layer.forward(query, neighbors, weights);

// Compression (2-32x memory savings)
const compressed = ruvector.compress(embedding, 0.3);

// Tiny Dancer: AI agent routing
const router = new ruvector.Router();
const decision = router.route(candidates, { optimize: 'cost' });

cargo add ruvector-graph ruvector-gnn

use ruvector_graph::{GraphDB, NodeBuilder};
use ruvector_gnn::{RuvectorLayer, differentiable_search};

let db = GraphDB::new();

let doc = NodeBuilder::new("doc1")
    .label("Document")
    .property("embedding", vec![0.1, 0.2, 0.3])
    .build();
db.create_node(doc)?;

// GNN layer
let layer = RuvectorLayer::new(128, 256, 4, 0.1);
let enhanced = layer.forward(&query, &neighbors, &weights);

use ruvector_raft::{RaftNode, RaftNodeConfig};
use ruvector_cluster::{ClusterManager, ConsistentHashRing};
use ruvector_replication::{SyncManager, SyncMode};

// Configure a 5-node Raft cluster
let config = RaftNodeConfig {
    node_id: "node-1".into(),
    cluster_members: vec!["node-1", "node-2", "node-3", "node-4", "node-5"]
        .into_iter().map(Into::into).collect(),
    election_timeout_min: 150,  // ms
    election_timeout_max: 300,  // ms
    heartbeat_interval: 50,     // ms
};
let raft = RaftNode::new(config);

// Auto-sharding with consistent hashing (150 virtual nodes per real node)
let ring = ConsistentHashRing::new(64, 3); // 64 shards, replication factor 3
let shard = ring.get_shard("my-vector-key");

// Multi-master replication with conflict resolution
let sync = SyncManager::new(SyncMode::SemiSync { min_replicas: 2 });

crates/
├── ruvector-core/           # Vector DB engine (HNSW, storage)
├── ruvector-graph/          # Graph DB + Cypher parser + Hyperedges
├── ruvector-gnn/            # GNN layers, compression, training
├── ruvector-tiny-dancer-core/  # AI agent routing (FastGRNN)
├── ruvector-*-wasm/         # WebAssembly bindings
└── ruvector-*-node/         # Node.js bindings (napi-rs)

We welcome contributions! See CONTRIBUTING.md.

# Run tests
cargo test --workspace

# Run benchmarks
cargo bench --workspace

# Build WASM
cargo build -p ruvector-gnn-wasm --target wasm32-unknown-unknown

MIT License — free for commercial and personal use.