ruvector/crates/rvlite/README.md

RvLite - Standalone Vector Database

Status: Proof of Concept (v0.1.0)

RvLite is a lightweight, standalone vector database that runs entirely in WebAssembly. It provides SQL, SPARQL, and Cypher query interfaces, along with graph neural networks and self-learning capabilities.

🎯 Vision

A complete vector database that runs anywhere JavaScript runs:

• ✅ Browsers (Chrome, Firefox, Safari, Edge)
• ✅ Node.js
• ✅ Deno
• ✅ Bun
• ✅ Cloudflare Workers
• ✅ Vercel Edge Functions

🏗️ Architecture

RvLite is a thin orchestration layer over battle-tested WASM crates:

┌─────────────────────────────────────────┐
│  RvLite (Orchestration)                 │
│  ├─ SQL executor                        │
│  ├─ SPARQL executor                     │
│  ├─ Storage adapter                     │
│  └─ Unified WASM API                    │
└──────────────┬──────────────────────────┘
               │ depends on (100% reuse)
               ▼
┌──────────────────────────────────────────┐
│  Existing WASM Crates                    │
├──────────────────────────────────────────┤
│  • ruvector-core (vectors, SIMD)         │
│  • ruvector-wasm (storage, indexing)     │
│  • ruvector-graph-wasm (Cypher)          │
│  • ruvector-gnn-wasm (GNN layers)        │
│  • sona (ReasoningBank learning)         │
│  • micro-hnsw-wasm (ultra-fast HNSW)     │
└──────────────────────────────────────────┘

🚀 Quick Start (Future)

import { RvLite } from '@rvlite/wasm';

// Create database
const db = await RvLite.create();

// SQL with vector search
await db.sql(`
  CREATE TABLE docs (
    id SERIAL PRIMARY KEY,
    content TEXT,
    embedding VECTOR(384)
  )
`);

await db.sql(`
  SELECT id, content, embedding <=> $1 AS distance
  FROM docs
  ORDER BY distance
  LIMIT 10
`, [queryVector]);

// Cypher graph queries
await db.cypher(`
  CREATE (a:Person {name: 'Alice'})-[:KNOWS]->(b:Person {name: 'Bob'})
`);

// SPARQL RDF queries
await db.sparql(`
  SELECT ?name WHERE {
    ?person foaf:name ?name .
  }
`);

// GNN embeddings
const embeddings = await db.gnn.computeEmbeddings('social_network', [
  db.gnn.createLayer('gcn', { inputDim: 128, outputDim: 64 })
]);

// Self-learning with ReasoningBank
await db.learning.recordTrajectory({ state: [0.1], action: 2, reward: 1.0 });
await db.learning.train({ algorithm: 'q-learning', iterations: 1000 });

📦 Current Status (v0.1.0 - POC)

This is a proof of concept to validate:

• ✅ Basic WASM compilation with ruvector-core
• ✅ WASM bindings setup (wasm-bindgen)
• ⏳ Integration with other WASM crates (pending)
• ⏳ Bundle size measurement (pending)
• ⏳ Performance benchmarks (pending)

🛠️ Development

Build

# Install wasm-pack
curl https://rustwasm.github.io/wasm-pack/installer/init.sh -sSf | sh

# Build for web
cd crates/rvlite
wasm-pack build --target web --release

# Build for Node.js
wasm-pack build --target nodejs --release

Test

# Run Rust unit tests
cargo test

# Run WASM tests (requires Chrome/Firefox)
wasm-pack test --headless --chrome
wasm-pack test --headless --firefox

Size Analysis

# Build optimized
wasm-pack build --release

# Check size
ls -lh pkg/*.wasm
du -sh pkg/

📖 Documentation

See /crates/rvlite/docs/ for comprehensive documentation:

• 00_EXISTING_WASM_ANALYSIS.md - Analysis of existing WASM infrastructure
• 01_SPECIFICATION.md - Complete requirements specification
• 02_API_SPECIFICATION.md - TypeScript API design
• 03_IMPLEMENTATION_ROADMAP.md - Original 5-week timeline
• 04_REVISED_ARCHITECTURE_MAX_REUSE.md - Optimized 2-3 week plan
• 05_ARCHITECTURE_REVIEW_AND_VALIDATION.md - Architecture validation
• SPARC_OVERVIEW.md - SPARC methodology overview

🎯 Roadmap

Phase 1: Proof of Concept (Current)

• Create rvlite crate structure
• Set up WASM bindings
• Basic compilation test
• Measure bundle size
• Integration with ruvector-wasm
• Integration with ruvector-graph-wasm

Phase 2: Core Integration (Week 1)

• Storage adapter implementation
• SPARQL extraction from ruvector-postgres
• SQL parser integration (sqlparser-rs)
• Basic query routing

Phase 3: Full Features (Week 2)

• GNN layer integration
• ReasoningBank integration
• Hyperbolic embeddings
• Comprehensive testing

Phase 4: Production Release (Week 3)

• Documentation
• Examples (browser, Node.js, Deno)
• Performance benchmarks
• NPM package publication

📊 Size Budget

Target: < 3MB gzipped

Expected breakdown:

• ruvector-core: ~500KB
• SQL parser: ~200KB
• SPARQL executor: ~300KB
• Cypher (ruvector-graph-wasm): ~600KB
• GNN layers: ~300KB
• ReasoningBank (sona): ~300KB
• Orchestration: ~100KB

Total estimated: ~2.3MB gzipped ✅

🤝 Contributing

This project reuses existing battle-tested WASM crates. Contributions should focus on:

1. Integration and orchestration
2. SQL/SPARQL/Cypher query routing
3. Storage adapter implementation
4. Testing and benchmarks
5. Documentation and examples

📄 License

MIT OR Apache-2.0

🙏 Acknowledgments

RvLite is built on the shoulders of:

• ruvector-core - Vector operations and SIMD
• ruvector-wasm - WASM vector database
• ruvector-graph - Cypher and graph database
• ruvector-gnn - Graph neural networks
• sona - Self-learning and ReasoningBank
• micro-hnsw-wasm - Ultra-lightweight HNSW

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Status: Proof of Concept - Architecture Validated ✅ Next Step: Build and measure bundle size