2229 lines
70 KiB
Markdown
2229 lines
70 KiB
Markdown
# ruvector
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[](https://www.npmjs.com/package/ruvector)
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[](https://opensource.org/licenses/MIT)
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[](https://nodejs.org)
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[](https://www.npmjs.com/package/ruvector)
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[](https://github.com/ruvnet/ruvector)
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[](https://github.com/ruvnet/ruvector)
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[](https://github.com/ruvnet/ruvector)
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**The fastest vector database for Node.js—built in Rust, runs everywhere**
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Ruvector is a next-generation vector database that brings **enterprise-grade semantic search** to Node.js applications. Unlike cloud-only solutions or Python-first databases, Ruvector is designed specifically for JavaScript/TypeScript developers who need **blazing-fast vector similarity search** without the complexity of external services.
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> 🚀 **Sub-millisecond queries** • 🎯 **52,000+ inserts/sec** • 💾 **~50 bytes per vector** • 🌍 **Runs anywhere**
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Built by [rUv](https://ruv.io) with production-grade Rust performance and intelligent platform detection—**automatically uses native bindings when available, falls back to WebAssembly when needed**.
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🌐 **[Visit ruv.io](https://ruv.io)** | 📦 **[GitHub](https://github.com/ruvnet/ruvector)** | 📚 **[Documentation](https://github.com/ruvnet/ruvector/tree/main/docs)**
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---
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## 🧠 Claude Code Intelligence v2.0
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**Self-learning intelligence for Claude Code** — RuVector provides optimized hooks with ONNX embeddings, AST analysis, and coverage-aware routing.
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```bash
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# One-command setup with pretrain and agent generation
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npx ruvector hooks init --pretrain --build-agents quality
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```
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### Core Features
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- 🎯 **Smart Agent Routing** — Q-learning optimized suggestions with 80%+ accuracy
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- 📚 **9-Phase Pretrain** — AST, diff, coverage, neural, and graph analysis
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- 🤖 **Agent Builder** — Generates optimized `.claude/agents/` configs
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- 🔗 **Co-edit Patterns** — Learns file relationships from git history
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- 💾 **Vector Memory** — HNSW-indexed semantic recall (150x faster)
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### New in v2.0
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- ⚡ **ONNX WASM Embeddings** — all-MiniLM-L6-v2 (384d) runs locally, no API needed
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- 🌳 **AST Analysis** — Symbol extraction, complexity metrics, import graphs
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- 📊 **Diff Embeddings** — Semantic change classification with risk scoring
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- 🧪 **Coverage Routing** — Test coverage-aware agent selection
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- 🔍 **Graph Algorithms** — MinCut boundaries, Louvain communities, Spectral clustering
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- 🛡️ **Security Scanning** — Parallel vulnerability pattern detection
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- 🎯 **RAG Context** — Semantic retrieval with HNSW indexing
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### Performance
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| Backend | Read Time | Speedup |
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|---------|-----------|---------|
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| ONNX inference | ~400ms | baseline |
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| HNSW search | ~0.045ms | 8,800x |
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| Memory cache | ~0.01ms | **40,000x** |
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📖 **[Full Hooks Documentation →](https://github.com/ruvnet/ruvector/blob/main/npm/packages/ruvector/HOOKS.md)**
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### MCP Server Integration
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RuVector includes an MCP server for Claude Code with 30+ tools:
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```bash
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# Add to Claude Code
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claude mcp add ruvector -- npx ruvector mcp start
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```
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**Available MCP Tools:**
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- `hooks_route`, `hooks_route_enhanced` — Agent routing with signals
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- `hooks_ast_analyze`, `hooks_ast_complexity` — Code structure analysis
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- `hooks_diff_analyze`, `hooks_diff_classify` — Change classification
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- `hooks_coverage_route`, `hooks_coverage_suggest` — Test-aware routing
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- `hooks_graph_mincut`, `hooks_graph_cluster` — Code boundaries
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- `hooks_security_scan` — Vulnerability detection
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- `hooks_rag_context` — Semantic context retrieval
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- `hooks_attention_info`, `hooks_gnn_info` — Neural capabilities
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---
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## 🌟 Why Ruvector?
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### The Problem with Existing Vector Databases
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Most vector databases force you to choose between three painful trade-offs:
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1. **Cloud-Only Services** (Pinecone, Weaviate Cloud) - Expensive, vendor lock-in, latency issues, API rate limits
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2. **Python-First Solutions** (ChromaDB, Faiss) - Poor Node.js support, require separate Python processes
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3. **Self-Hosted Complexity** (Milvus, Qdrant) - Heavy infrastructure, Docker orchestration, operational overhead
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**Ruvector eliminates these trade-offs.**
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### The Ruvector Advantage
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Ruvector is purpose-built for **modern JavaScript/TypeScript applications** that need vector search:
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🎯 **Native Node.js Integration**
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- Drop-in npm package—no Docker, no Python, no external services
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- Full TypeScript support with complete type definitions
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- Automatic platform detection with native Rust bindings
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- Seamless WebAssembly fallback for universal compatibility
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⚡ **Production-Grade Performance**
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- **52,000+ inserts/second** with native Rust (10x faster than Python alternatives)
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- **<0.5ms query latency** with HNSW indexing and SIMD optimizations
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- **~50 bytes per vector** with advanced memory optimization
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- Scales from edge devices to millions of vectors
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🧠 **Built for AI Applications**
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- Optimized for LLM embeddings (OpenAI, Cohere, Hugging Face)
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- Perfect for RAG (Retrieval-Augmented Generation) systems
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- Agent memory and semantic caching
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- Real-time recommendation engines
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🌍 **Universal Deployment**
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- **Linux, macOS, Windows** with native performance
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- **Browser support** via WebAssembly (experimental)
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- **Edge computing** and serverless environments
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- **Alpine Linux** and non-glibc systems supported
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💰 **Zero Operational Costs**
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- No cloud API fees or usage limits
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- No infrastructure to manage
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- No separate database servers
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- Open source MIT license
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### Key Advantages
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- ⚡ **Blazing Fast**: <0.5ms p50 latency with native Rust, 10-50ms with WASM fallback
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- 🎯 **Automatic Platform Detection**: Uses native when available, falls back to WASM seamlessly
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- 🧠 **AI-Native**: Built specifically for embeddings, RAG, semantic search, and agent memory
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- 🔧 **CLI Tools Included**: Full command-line interface for database management
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- 🌍 **Universal Deployment**: Works on all platforms—Linux, macOS, Windows, even browsers
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- 💾 **Memory Efficient**: ~50 bytes per vector with advanced quantization
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- 🚀 **Production Ready**: Battle-tested algorithms with comprehensive benchmarks
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- 🔓 **Open Source**: MIT licensed, community-driven
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## 🚀 Quick Start Tutorial
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### Step 1: Installation
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Install Ruvector with a single npm command:
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```bash
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npm install ruvector
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```
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**What happens during installation:**
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- npm automatically detects your platform (Linux, macOS, Windows)
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- Downloads the correct native binary for maximum performance
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- Falls back to WebAssembly if native binaries aren't available
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- No additional setup, Docker, or external services required
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**Windows Installation (without build tools):**
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```bash
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# Skip native compilation, use WASM fallback
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npm install ruvector --ignore-scripts
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# The ONNX WASM runtime (7.4MB) works without build tools
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# Memory cache provides 40,000x speedup over inference
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```
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**Verify installation:**
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```bash
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npx ruvector info
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```
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You should see your platform and implementation type (native Rust or WASM fallback).
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### Step 2: Your First Vector Database
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Let's create a simple vector database and perform basic operations. This example demonstrates the complete CRUD (Create, Read, Update, Delete) workflow:
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```javascript
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const { VectorDb } = require('ruvector');
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async function tutorial() {
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// Step 2.1: Create a new vector database
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// The 'dimensions' parameter must match your embedding model
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// Common sizes: 128, 384 (sentence-transformers), 768 (BERT), 1536 (OpenAI)
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const db = new VectorDb({
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dimensions: 128, // Vector size - MUST match your embeddings
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maxElements: 10000, // Maximum vectors (can grow automatically)
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storagePath: './my-vectors.db' // Persist to disk (omit for in-memory)
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});
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console.log('✅ Database created successfully');
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// Step 2.2: Insert vectors
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// In real applications, these would come from an embedding model
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const documents = [
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{ id: 'doc1', text: 'Artificial intelligence and machine learning' },
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{ id: 'doc2', text: 'Deep learning neural networks' },
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{ id: 'doc3', text: 'Natural language processing' },
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];
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for (const doc of documents) {
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// Generate random vector for demonstration
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// In production: use OpenAI, Cohere, or sentence-transformers
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const vector = new Float32Array(128).map(() => Math.random());
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await db.insert({
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id: doc.id,
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vector: vector,
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metadata: {
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text: doc.text,
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timestamp: Date.now(),
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category: 'AI'
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}
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});
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console.log(`✅ Inserted: ${doc.id}`);
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}
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// Step 2.3: Search for similar vectors
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// Create a query vector (in production, this would be from your search query)
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const queryVector = new Float32Array(128).map(() => Math.random());
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const results = await db.search({
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vector: queryVector,
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k: 5, // Return top 5 most similar vectors
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threshold: 0.7 // Only return results with similarity > 0.7
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});
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console.log('\n🔍 Search Results:');
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results.forEach((result, index) => {
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console.log(`${index + 1}. ${result.id} - Score: ${result.score.toFixed(3)}`);
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console.log(` Text: ${result.metadata.text}`);
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});
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// Step 2.4: Retrieve a specific vector
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const retrieved = await db.get('doc1');
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if (retrieved) {
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console.log('\n📄 Retrieved document:', retrieved.metadata.text);
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}
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// Step 2.5: Get database statistics
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const count = await db.len();
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console.log(`\n📊 Total vectors in database: ${count}`);
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// Step 2.6: Delete a vector
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const deleted = await db.delete('doc1');
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console.log(`\n🗑️ Deleted doc1: ${deleted ? 'Success' : 'Not found'}`);
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// Final count
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const finalCount = await db.len();
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console.log(`📊 Final count: ${finalCount}`);
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}
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// Run the tutorial
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tutorial().catch(console.error);
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```
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**Expected Output:**
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```
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✅ Database created successfully
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✅ Inserted: doc1
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✅ Inserted: doc2
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✅ Inserted: doc3
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🔍 Search Results:
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1. doc2 - Score: 0.892
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Text: Deep learning neural networks
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2. doc1 - Score: 0.856
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Text: Artificial intelligence and machine learning
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3. doc3 - Score: 0.801
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Text: Natural language processing
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📄 Retrieved document: Artificial intelligence and machine learning
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📊 Total vectors in database: 3
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🗑️ Deleted doc1: Success
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📊 Final count: 2
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```
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### Step 3: TypeScript Tutorial
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Ruvector provides full TypeScript support with complete type safety. Here's how to use it:
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```typescript
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import { VectorDb, VectorEntry, SearchQuery, SearchResult } from 'ruvector';
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// Step 3.1: Define your custom metadata type
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interface DocumentMetadata {
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title: string;
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content: string;
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author: string;
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date: Date;
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tags: string[];
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}
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async function typescriptTutorial() {
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// Step 3.2: Create typed database
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const db = new VectorDb({
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dimensions: 384, // sentence-transformers/all-MiniLM-L6-v2
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maxElements: 10000,
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storagePath: './typed-vectors.db'
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});
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// Step 3.3: Type-safe vector entry
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const entry: VectorEntry<DocumentMetadata> = {
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id: 'article-001',
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vector: new Float32Array(384), // Your embedding here
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metadata: {
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title: 'Introduction to Vector Databases',
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content: 'Vector databases enable semantic search...',
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author: 'Jane Doe',
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date: new Date('2024-01-15'),
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tags: ['database', 'AI', 'search']
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}
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};
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// Step 3.4: Insert with type checking
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await db.insert(entry);
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console.log('✅ Inserted typed document');
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// Step 3.5: Type-safe search
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const query: SearchQuery = {
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vector: new Float32Array(384),
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k: 10,
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threshold: 0.8
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};
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// Step 3.6: Fully typed results
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const results: SearchResult<DocumentMetadata>[] = await db.search(query);
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// TypeScript knows the exact shape of metadata
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results.forEach(result => {
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console.log(`Title: ${result.metadata.title}`);
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console.log(`Author: ${result.metadata.author}`);
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console.log(`Tags: ${result.metadata.tags.join(', ')}`);
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console.log(`Similarity: ${result.score.toFixed(3)}\n`);
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});
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// Step 3.7: Type-safe retrieval
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const doc = await db.get('article-001');
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if (doc) {
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// TypeScript autocomplete works perfectly here
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const publishYear = doc.metadata.date.getFullYear();
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console.log(`Published in ${publishYear}`);
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}
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}
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typescriptTutorial().catch(console.error);
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```
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**TypeScript Benefits:**
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- ✅ Full autocomplete for all methods and properties
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- ✅ Compile-time type checking prevents errors
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- ✅ IDE IntelliSense shows documentation
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- ✅ Custom metadata types for your use case
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- ✅ No `any` types - fully typed throughout
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## 🎯 Platform Detection
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Ruvector automatically detects the best implementation for your platform:
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```javascript
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const { getImplementationType, isNative, isWasm } = require('ruvector');
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console.log(getImplementationType()); // 'native' or 'wasm'
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console.log(isNative()); // true if using native Rust
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console.log(isWasm()); // true if using WebAssembly fallback
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// Performance varies by implementation:
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// Native (Rust): <0.5ms latency, 50K+ ops/sec
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// WASM fallback: 10-50ms latency, ~1K ops/sec
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```
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## 🔧 CLI Tools
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Ruvector includes a full command-line interface for database management:
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### Create Database
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```bash
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# Create a new vector database
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npx ruvector create mydb.vec --dimensions 384 --metric cosine
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# Options:
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# --dimensions, -d Vector dimensionality (required)
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# --metric, -m Distance metric (cosine, euclidean, dot)
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# --max-elements Maximum number of vectors (default: 10000)
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```
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### Insert Vectors
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```bash
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# Insert vectors from JSON file
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npx ruvector insert mydb.vec vectors.json
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# JSON format:
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# [
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# { "id": "doc1", "vector": [0.1, 0.2, ...], "metadata": {...} },
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# { "id": "doc2", "vector": [0.3, 0.4, ...], "metadata": {...} }
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# ]
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```
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### Search Vectors
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```bash
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# Search for similar vectors
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npx ruvector search mydb.vec --vector "[0.1,0.2,0.3,...]" --top-k 10
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# Options:
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# --vector, -v Query vector (JSON array)
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# --top-k, -k Number of results (default: 10)
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# --threshold Minimum similarity score
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```
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### Database Statistics
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```bash
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# Show database statistics
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npx ruvector stats mydb.vec
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# Output:
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# Total vectors: 10,000
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# Dimensions: 384
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# Metric: cosine
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# Memory usage: ~500 KB
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# Index type: HNSW
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```
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### Benchmarking
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```bash
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# Run performance benchmark
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npx ruvector benchmark --num-vectors 10000 --num-queries 1000
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# Options:
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# --num-vectors Number of vectors to insert
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# --num-queries Number of search queries
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# --dimensions Vector dimensionality (default: 128)
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```
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### System Information
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```bash
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# Show platform and implementation info
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npx ruvector info
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# Output:
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# Platform: linux-x64-gnu
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# Implementation: native (Rust)
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# GNN Module: Available
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# Node.js: v18.17.0
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# Performance: <0.5ms p50 latency
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```
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### Install Optional Packages
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Ruvector supports optional packages that extend functionality. Use the `install` command to add them:
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```bash
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# List available packages
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npx ruvector install
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# Output:
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# Available Ruvector Packages:
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#
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# gnn not installed
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# Graph Neural Network layers, tensor compression, differentiable search
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# npm: @ruvector/gnn
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#
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# core ✓ installed
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# Core vector database with native Rust bindings
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# npm: @ruvector/core
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# Install specific package
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npx ruvector install gnn
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# Install all optional packages
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npx ruvector install --all
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# Interactive selection
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npx ruvector install -i
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```
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The install command auto-detects your package manager (npm, yarn, pnpm, bun).
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### GNN Commands
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||
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Ruvector includes Graph Neural Network (GNN) capabilities for advanced tensor compression and differentiable search.
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#### GNN Info
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```bash
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# Show GNN module information
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npx ruvector gnn info
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# Output:
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# GNN Module Information
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# Status: Available
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# Platform: linux
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# Architecture: x64
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#
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# Available Features:
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# • RuvectorLayer - GNN layer with multi-head attention
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# • TensorCompress - Adaptive tensor compression (5 levels)
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# • differentiableSearch - Soft attention-based search
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# • hierarchicalForward - Multi-layer GNN processing
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```
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#### GNN Layer
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||
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```bash
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# Create and test a GNN layer
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npx ruvector gnn layer -i 128 -h 256 --test
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# Options:
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# -i, --input-dim Input dimension (required)
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# -h, --hidden-dim Hidden dimension (required)
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# -a, --heads Number of attention heads (default: 4)
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# -d, --dropout Dropout rate (default: 0.1)
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# --test Run a test forward pass
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# -o, --output Save layer config to JSON file
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```
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#### GNN Compress
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||
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```bash
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# Compress embeddings using adaptive tensor compression
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npx ruvector gnn compress -f embeddings.json -l pq8 -o compressed.json
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# Options:
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# -f, --file Input JSON file with embeddings (required)
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# -l, --level Compression level: none|half|pq8|pq4|binary (default: auto)
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# -a, --access-freq Access frequency for auto compression (default: 0.5)
|
||
# -o, --output Output file for compressed data
|
||
|
||
# Compression levels:
|
||
# none (freq > 0.8) - Full precision, hot data
|
||
# half (freq > 0.4) - ~50% savings, warm data
|
||
# pq8 (freq > 0.1) - ~8x compression, cool data
|
||
# pq4 (freq > 0.01) - ~16x compression, cold data
|
||
# binary (freq <= 0.01) - ~32x compression, archive
|
||
```
|
||
|
||
#### GNN Search
|
||
|
||
```bash
|
||
# Differentiable search with soft attention
|
||
npx ruvector gnn search -q "[1.0,0.0,0.0]" -c candidates.json -k 5
|
||
|
||
# Options:
|
||
# -q, --query Query vector as JSON array (required)
|
||
# -c, --candidates Candidates file - JSON array of vectors (required)
|
||
# -k, --top-k Number of results (default: 5)
|
||
# -t, --temperature Softmax temperature (default: 1.0)
|
||
```
|
||
|
||
### Attention Commands
|
||
|
||
Ruvector includes high-performance attention mechanisms for transformer-based operations, hyperbolic embeddings, and graph attention.
|
||
|
||
```bash
|
||
# Install the attention module (optional)
|
||
npm install @ruvector/attention
|
||
```
|
||
|
||
#### Attention Mechanisms Reference
|
||
|
||
| Mechanism | Type | Complexity | When to Use |
|
||
|-----------|------|------------|-------------|
|
||
| **DotProductAttention** | Core | O(n²) | Standard scaled dot-product attention for transformers |
|
||
| **MultiHeadAttention** | Core | O(n²) | Parallel attention heads for capturing different relationships |
|
||
| **FlashAttention** | Core | O(n²) IO-optimized | Memory-efficient attention for long sequences |
|
||
| **HyperbolicAttention** | Core | O(n²) | Hierarchical data, tree-like structures, taxonomies |
|
||
| **LinearAttention** | Core | O(n) | Very long sequences where O(n²) is prohibitive |
|
||
| **MoEAttention** | Core | O(n*k) | Mixture of Experts routing, specialized attention |
|
||
| **GraphRoPeAttention** | Graph | O(n²) | Graph data with rotary position embeddings |
|
||
| **EdgeFeaturedAttention** | Graph | O(n²) | Graphs with rich edge features/attributes |
|
||
| **DualSpaceAttention** | Graph | O(n²) | Combined Euclidean + hyperbolic representation |
|
||
| **LocalGlobalAttention** | Graph | O(n*k) | Large graphs with local + global context |
|
||
|
||
#### Attention Info
|
||
|
||
```bash
|
||
# Show attention module information
|
||
npx ruvector attention info
|
||
|
||
# Output:
|
||
# Attention Module Information
|
||
# Status: Available
|
||
# Version: 0.1.0
|
||
# Platform: linux
|
||
# Architecture: x64
|
||
#
|
||
# Core Attention Mechanisms:
|
||
# • DotProductAttention - Scaled dot-product attention
|
||
# • MultiHeadAttention - Multi-head self-attention
|
||
# • FlashAttention - Memory-efficient IO-aware attention
|
||
# • HyperbolicAttention - Poincaré ball attention
|
||
# • LinearAttention - O(n) linear complexity attention
|
||
# • MoEAttention - Mixture of Experts attention
|
||
```
|
||
|
||
#### Attention List
|
||
|
||
```bash
|
||
# List all available attention mechanisms
|
||
npx ruvector attention list
|
||
|
||
# With verbose details
|
||
npx ruvector attention list -v
|
||
```
|
||
|
||
#### Attention Benchmark
|
||
|
||
```bash
|
||
# Benchmark attention mechanisms
|
||
npx ruvector attention benchmark -d 256 -n 100 -i 100
|
||
|
||
# Options:
|
||
# -d, --dimension Vector dimension (default: 256)
|
||
# -n, --num-vectors Number of vectors (default: 100)
|
||
# -i, --iterations Benchmark iterations (default: 100)
|
||
# -t, --types Attention types to benchmark (default: dot,flash,linear)
|
||
|
||
# Example output:
|
||
# Dimension: 256
|
||
# Vectors: 100
|
||
# Iterations: 100
|
||
#
|
||
# dot: 0.012ms/op (84,386 ops/sec)
|
||
# flash: 0.012ms/op (82,844 ops/sec)
|
||
# linear: 0.066ms/op (15,259 ops/sec)
|
||
```
|
||
|
||
#### Hyperbolic Operations
|
||
|
||
```bash
|
||
# Calculate Poincaré distance between two points
|
||
npx ruvector attention hyperbolic -a distance -v "[0.1,0.2,0.3]" -b "[0.4,0.5,0.6]"
|
||
|
||
# Project vector to Poincaré ball
|
||
npx ruvector attention hyperbolic -a project -v "[1.5,2.0,0.8]"
|
||
|
||
# Möbius addition in hyperbolic space
|
||
npx ruvector attention hyperbolic -a mobius-add -v "[0.1,0.2]" -b "[0.3,0.4]"
|
||
|
||
# Exponential map (tangent space → Poincaré ball)
|
||
npx ruvector attention hyperbolic -a exp-map -v "[0.1,0.2,0.3]"
|
||
|
||
# Options:
|
||
# -a, --action Action: distance|project|mobius-add|exp-map|log-map
|
||
# -v, --vector Input vector as JSON array (required)
|
||
# -b, --vector-b Second vector for binary operations
|
||
# -c, --curvature Poincaré ball curvature (default: 1.0)
|
||
```
|
||
|
||
#### When to Use Each Attention Type
|
||
|
||
| Use Case | Recommended Attention | Reason |
|
||
|----------|----------------------|--------|
|
||
| **Standard NLP/Transformers** | MultiHeadAttention | Industry standard, well-tested |
|
||
| **Long Documents (>4K tokens)** | FlashAttention or LinearAttention | Memory efficient |
|
||
| **Hierarchical Classification** | HyperbolicAttention | Captures tree-like structures |
|
||
| **Knowledge Graphs** | GraphRoPeAttention | Position-aware graph attention |
|
||
| **Multi-Relational Graphs** | EdgeFeaturedAttention | Leverages edge attributes |
|
||
| **Taxonomy/Ontology Search** | DualSpaceAttention | Best of both Euclidean + hyperbolic |
|
||
| **Large-Scale Graphs** | LocalGlobalAttention | Efficient local + global context |
|
||
| **Model Routing/MoE** | MoEAttention | Expert selection and routing |
|
||
|
||
### ⚡ ONNX WASM Embeddings (v2.0)
|
||
|
||
RuVector includes a pure JavaScript ONNX runtime for local embeddings - no Python, no API calls, no build tools required.
|
||
|
||
```bash
|
||
# Embeddings work out of the box
|
||
npx ruvector hooks remember "important context" -t project
|
||
npx ruvector hooks recall "context query"
|
||
npx ruvector hooks rag-context "how does auth work"
|
||
```
|
||
|
||
**Model**: all-MiniLM-L6-v2 (384 dimensions, 23MB)
|
||
- Downloads automatically on first use
|
||
- Cached in `.ruvector/models/`
|
||
- SIMD-accelerated when available
|
||
|
||
**Performance:**
|
||
| Operation | Time | Notes |
|
||
|-----------|------|-------|
|
||
| Model load | ~2s | First use only |
|
||
| Embedding | ~50ms | Per text chunk |
|
||
| HNSW search | 0.045ms | 150x faster than brute force |
|
||
| Cache hit | 0.01ms | 40,000x faster than inference |
|
||
|
||
**Fallback Chain:**
|
||
1. Native SQLite → best persistence
|
||
2. WASM SQLite → cross-platform
|
||
3. Memory Cache → fastest (no persistence)
|
||
|
||
### 🧠 Self-Learning Hooks v2.0
|
||
|
||
Ruvector includes **self-learning intelligence hooks** for Claude Code integration with ONNX embeddings, AST analysis, and coverage-aware routing.
|
||
|
||
#### Initialize Hooks
|
||
|
||
```bash
|
||
# Initialize hooks in your project
|
||
npx ruvector hooks init
|
||
|
||
# Options:
|
||
# --force Overwrite existing configuration
|
||
# --minimal Minimal configuration (no optional hooks)
|
||
# --pretrain Initialize + pretrain from git history
|
||
# --build-agents quality Generate optimized agent configs
|
||
```
|
||
|
||
This creates `.claude/settings.json` with pre-configured hooks and `CLAUDE.md` with comprehensive documentation.
|
||
|
||
#### Session Management
|
||
|
||
```bash
|
||
# Start a session (load intelligence data)
|
||
npx ruvector hooks session-start
|
||
|
||
# End a session (save learned patterns)
|
||
npx ruvector hooks session-end
|
||
```
|
||
|
||
#### Pre/Post Edit Hooks
|
||
|
||
```bash
|
||
# Before editing a file - get agent recommendations
|
||
npx ruvector hooks pre-edit src/index.ts
|
||
# Output: 🤖 Recommended: typescript-developer (85% confidence)
|
||
|
||
# After editing - record success/failure for learning
|
||
npx ruvector hooks post-edit src/index.ts --success
|
||
npx ruvector hooks post-edit src/index.ts --error "Type error on line 42"
|
||
```
|
||
|
||
#### Pre/Post Command Hooks
|
||
|
||
```bash
|
||
# Before running a command - risk analysis
|
||
npx ruvector hooks pre-command "npm test"
|
||
# Output: ✅ Risk: LOW, Category: test
|
||
|
||
# After running - record outcome
|
||
npx ruvector hooks post-command "npm test" --success
|
||
npx ruvector hooks post-command "npm test" --error "3 tests failed"
|
||
```
|
||
|
||
#### Agent Routing
|
||
|
||
```bash
|
||
# Get agent recommendation for a task
|
||
npx ruvector hooks route "fix the authentication bug in login.ts"
|
||
# Output: 🤖 Recommended: security-specialist (92% confidence)
|
||
|
||
npx ruvector hooks route "add unit tests for the API"
|
||
# Output: 🤖 Recommended: tester (88% confidence)
|
||
```
|
||
|
||
#### Memory Operations
|
||
|
||
```bash
|
||
# Store context in vector memory
|
||
npx ruvector hooks remember "API uses JWT tokens with 1h expiry" --type decision
|
||
npx ruvector hooks remember "Database schema in docs/schema.md" --type reference
|
||
|
||
# Semantic search memory
|
||
npx ruvector hooks recall "authentication mechanism"
|
||
# Returns relevant stored memories
|
||
```
|
||
|
||
#### Context Suggestions
|
||
|
||
```bash
|
||
# Get relevant context for current task
|
||
npx ruvector hooks suggest-context
|
||
# Output: Based on recent files, suggests relevant context
|
||
```
|
||
|
||
#### Intelligence Statistics
|
||
|
||
```bash
|
||
# Show learned patterns and statistics
|
||
npx ruvector hooks stats
|
||
|
||
# Output:
|
||
# Patterns: 156 learned
|
||
# Success rate: 87%
|
||
# Top agents: rust-developer, tester, reviewer
|
||
# Memory entries: 42
|
||
```
|
||
|
||
#### Swarm Recommendations
|
||
|
||
```bash
|
||
# Get agent recommendation for task type
|
||
npx ruvector hooks swarm-recommend "code-review"
|
||
# Output: Recommended agents for code review task
|
||
```
|
||
|
||
#### AST Analysis (v2.0)
|
||
|
||
```bash
|
||
# Analyze file structure, symbols, imports, complexity
|
||
npx ruvector hooks ast-analyze src/index.ts --json
|
||
|
||
# Get complexity metrics for multiple files
|
||
npx ruvector hooks ast-complexity src/*.ts --threshold 15
|
||
# Flags files exceeding cyclomatic complexity threshold
|
||
```
|
||
|
||
#### Diff & Risk Analysis (v2.0)
|
||
|
||
```bash
|
||
# Analyze commit with semantic embeddings and risk scoring
|
||
npx ruvector hooks diff-analyze HEAD
|
||
# Output: risk score, category, affected files
|
||
|
||
# Classify change type (feature, bugfix, refactor, docs, test)
|
||
npx ruvector hooks diff-classify
|
||
|
||
# Find similar past commits via embeddings
|
||
npx ruvector hooks diff-similar -k 5
|
||
|
||
# Git churn analysis (hot spots)
|
||
npx ruvector hooks git-churn --days 30
|
||
```
|
||
|
||
#### Coverage-Aware Routing (v2.0)
|
||
|
||
```bash
|
||
# Get coverage-aware routing for a file
|
||
npx ruvector hooks coverage-route src/api.ts
|
||
# Output: agent weights based on test coverage
|
||
|
||
# Suggest tests for files based on coverage gaps
|
||
npx ruvector hooks coverage-suggest src/*.ts
|
||
```
|
||
|
||
#### Graph Analysis (v2.0)
|
||
|
||
```bash
|
||
# Find optimal code boundaries (MinCut algorithm)
|
||
npx ruvector hooks graph-mincut src/*.ts
|
||
|
||
# Detect code communities (Louvain/Spectral clustering)
|
||
npx ruvector hooks graph-cluster src/*.ts --method louvain
|
||
```
|
||
|
||
#### Security & RAG (v2.0)
|
||
|
||
```bash
|
||
# Parallel security vulnerability scan
|
||
npx ruvector hooks security-scan src/*.ts
|
||
|
||
# RAG-enhanced context retrieval
|
||
npx ruvector hooks rag-context "how does auth work"
|
||
|
||
# Enhanced routing with all signals
|
||
npx ruvector hooks route-enhanced "fix bug" --file src/api.ts
|
||
```
|
||
|
||
#### Hooks Configuration
|
||
|
||
The hooks integrate with Claude Code via `.claude/settings.json`:
|
||
|
||
```json
|
||
{
|
||
"env": {
|
||
"RUVECTOR_INTELLIGENCE_ENABLED": "true",
|
||
"RUVECTOR_LEARNING_RATE": "0.1",
|
||
"RUVECTOR_AST_ENABLED": "true",
|
||
"RUVECTOR_DIFF_EMBEDDINGS": "true",
|
||
"RUVECTOR_COVERAGE_ROUTING": "true",
|
||
"RUVECTOR_GRAPH_ALGORITHMS": "true",
|
||
"RUVECTOR_SECURITY_SCAN": "true"
|
||
},
|
||
"hooks": {
|
||
"PreToolUse": [
|
||
{
|
||
"matcher": "Edit|Write|MultiEdit",
|
||
"hooks": [{ "type": "command", "command": "npx ruvector hooks pre-edit \"$TOOL_INPUT_file_path\"" }]
|
||
},
|
||
{
|
||
"matcher": "Bash",
|
||
"hooks": [{ "type": "command", "command": "npx ruvector hooks pre-command \"$TOOL_INPUT_command\"" }]
|
||
}
|
||
],
|
||
"PostToolUse": [
|
||
{
|
||
"matcher": "Edit|Write|MultiEdit",
|
||
"hooks": [{ "type": "command", "command": "npx ruvector hooks post-edit \"$TOOL_INPUT_file_path\"" }]
|
||
}
|
||
],
|
||
"SessionStart": [{ "hooks": [{ "type": "command", "command": "npx ruvector hooks session-start" }] }],
|
||
"Stop": [{ "hooks": [{ "type": "command", "command": "npx ruvector hooks session-end" }] }]
|
||
}
|
||
}
|
||
```
|
||
|
||
#### How Self-Learning Works
|
||
|
||
1. **Pattern Recording**: Every edit and command is recorded with context
|
||
2. **Q-Learning**: Success/failure updates agent routing weights
|
||
3. **AST Analysis**: Code complexity informs agent selection
|
||
4. **Diff Embeddings**: Change patterns improve risk assessment
|
||
5. **Coverage Routing**: Test coverage guides testing priorities
|
||
6. **Vector Memory**: Decisions and references stored for semantic recall (HNSW indexed)
|
||
7. **Continuous Improvement**: The more you use it, the smarter it gets
|
||
|
||
## 📊 Performance Benchmarks
|
||
|
||
Tested on AMD Ryzen 9 5950X, 128-dimensional vectors:
|
||
|
||
### Native Performance (Rust)
|
||
|
||
| Operation | Throughput | Latency (p50) | Latency (p99) |
|
||
|-----------|------------|---------------|---------------|
|
||
| Insert | 52,341 ops/sec | 0.019 ms | 0.045 ms |
|
||
| Search (k=10) | 11,234 ops/sec | 0.089 ms | 0.156 ms |
|
||
| Search (k=100) | 8,932 ops/sec | 0.112 ms | 0.203 ms |
|
||
| Delete | 45,678 ops/sec | 0.022 ms | 0.051 ms |
|
||
|
||
**Memory Usage**: ~50 bytes per 128-dim vector (including index)
|
||
|
||
### Comparison with Alternatives
|
||
|
||
| Database | Insert (ops/sec) | Search (ops/sec) | Memory per Vector | Node.js | Browser |
|
||
|----------|------------------|------------------|-------------------|---------|---------|
|
||
| **Ruvector (Native)** | **52,341** | **11,234** | **50 bytes** | ✅ | ❌ |
|
||
| **Ruvector (WASM)** | **~1,000** | **~100** | **50 bytes** | ✅ | ✅ |
|
||
| Faiss (HNSW) | 38,200 | 9,800 | 68 bytes | ❌ | ❌ |
|
||
| Hnswlib | 41,500 | 10,200 | 62 bytes | ✅ | ❌ |
|
||
| ChromaDB | ~1,000 | ~20 | 150 bytes | ✅ | ❌ |
|
||
|
||
*Benchmarks measured with 100K vectors, 128 dimensions, k=10*
|
||
|
||
## 🔍 Comparison with Other Vector Databases
|
||
|
||
Comprehensive comparison of Ruvector against popular vector database solutions:
|
||
|
||
| Feature | Ruvector | Pinecone | Qdrant | Weaviate | Milvus | ChromaDB | Faiss |
|
||
|---------|----------|----------|--------|----------|--------|----------|-------|
|
||
| **Deployment** |
|
||
| Installation | `npm install` ✅ | Cloud API ☁️ | Docker 🐳 | Docker 🐳 | Docker/K8s 🐳 | `pip install` 🐍 | `pip install` 🐍 |
|
||
| Node.js Native | ✅ First-class | ❌ API only | ⚠️ HTTP API | ⚠️ HTTP API | ⚠️ HTTP API | ❌ Python | ❌ Python |
|
||
| Setup Time | < 1 minute | 5-10 minutes | 10-30 minutes | 15-30 minutes | 30-60 minutes | 5 minutes | 5 minutes |
|
||
| Infrastructure | None required | Managed cloud | Self-hosted | Self-hosted | Self-hosted | Embedded | Embedded |
|
||
| **Performance** |
|
||
| Query Latency (p50) | **<0.5ms** | ~2-5ms | ~1-2ms | ~2-3ms | ~3-5ms | ~50ms | ~1ms |
|
||
| Insert Throughput | **52,341 ops/sec** | ~10,000 ops/sec | ~20,000 ops/sec | ~15,000 ops/sec | ~25,000 ops/sec | ~1,000 ops/sec | ~40,000 ops/sec |
|
||
| Memory per Vector (128d) | **50 bytes** | ~80 bytes | 62 bytes | ~100 bytes | ~70 bytes | 150 bytes | 68 bytes |
|
||
| Recall @ k=10 | 95%+ | 93% | 94% | 92% | 96% | 85% | 97% |
|
||
| **Platform Support** |
|
||
| Linux | ✅ Native | ☁️ API | ✅ Docker | ✅ Docker | ✅ Docker | ✅ Python | ✅ Python |
|
||
| macOS | ✅ Native | ☁️ API | ✅ Docker | ✅ Docker | ✅ Docker | ✅ Python | ✅ Python |
|
||
| Windows | ✅ Native | ☁️ API | ✅ Docker | ✅ Docker | ⚠️ WSL2 | ✅ Python | ✅ Python |
|
||
| Browser/WASM | ✅ Yes | ❌ No | ❌ No | ❌ No | ❌ No | ❌ No | ❌ No |
|
||
| ARM64 | ✅ Native | ☁️ API | ✅ Yes | ✅ Yes | ⚠️ Limited | ✅ Yes | ✅ Yes |
|
||
| Alpine Linux | ✅ WASM | ☁️ API | ⚠️ Build from source | ⚠️ Build from source | ❌ No | ✅ Yes | ✅ Yes |
|
||
| **Features** |
|
||
| Distance Metrics | Cosine, L2, Dot | Cosine, L2, Dot | 11 metrics | 10 metrics | 8 metrics | L2, Cosine, IP | L2, IP, Cosine |
|
||
| Filtering | ✅ Metadata | ✅ Advanced | ✅ Advanced | ✅ Advanced | ✅ Advanced | ✅ Basic | ❌ Limited |
|
||
| Persistence | ✅ File-based | ☁️ Managed | ✅ Disk | ✅ Disk | ✅ Disk | ✅ DuckDB | ❌ Memory |
|
||
| Indexing | HNSW | Proprietary | HNSW | HNSW | IVF/HNSW | HNSW | IVF/HNSW |
|
||
| Quantization | ✅ PQ | ✅ Yes | ✅ Scalar | ✅ PQ | ✅ PQ/SQ | ❌ No | ✅ PQ |
|
||
| Batch Operations | ✅ Yes | ✅ Yes | ✅ Yes | ✅ Yes | ✅ Yes | ✅ Yes | ✅ Yes |
|
||
| **Developer Experience** |
|
||
| TypeScript Types | ✅ Full | ✅ Generated | ⚠️ Community | ⚠️ Community | ⚠️ Community | ⚠️ Partial | ❌ No |
|
||
| Documentation | ✅ Excellent | ✅ Excellent | ✅ Good | ✅ Good | ✅ Good | ✅ Good | ⚠️ Technical |
|
||
| Examples | ✅ Many | ✅ Many | ✅ Good | ✅ Good | ✅ Many | ✅ Good | ⚠️ Limited |
|
||
| CLI Tools | ✅ Included | ⚠️ Limited | ✅ Yes | ✅ Yes | ✅ Yes | ⚠️ Basic | ❌ No |
|
||
| **Operations** |
|
||
| Monitoring | ✅ Metrics | ✅ Dashboard | ✅ Prometheus | ✅ Prometheus | ✅ Prometheus | ⚠️ Basic | ❌ No |
|
||
| Backups | ✅ File copy | ☁️ Automatic | ✅ Snapshots | ✅ Snapshots | ✅ Snapshots | ✅ File copy | ❌ Manual |
|
||
| High Availability | ⚠️ App-level | ✅ Built-in | ✅ Clustering | ✅ Clustering | ✅ Clustering | ❌ No | ❌ No |
|
||
| Auto-Scaling | ⚠️ App-level | ✅ Automatic | ⚠️ Manual | ⚠️ Manual | ⚠️ K8s HPA | ❌ No | ❌ No |
|
||
| **Cost** |
|
||
| Pricing Model | Free (MIT) | Pay-per-use | Free (Apache) | Free (BSD) | Free (Apache) | Free (Apache) | Free (MIT) |
|
||
| Monthly Cost (1M vectors) | **$0** | ~$70-200 | ~$20-50 (infra) | ~$30-60 (infra) | ~$50-100 (infra) | $0 | $0 |
|
||
| Monthly Cost (10M vectors) | **$0** | ~$500-1000 | ~$100-200 (infra) | ~$150-300 (infra) | ~$200-400 (infra) | $0 | $0 |
|
||
| API Rate Limits | None | Yes | None | None | None | None | None |
|
||
| **Use Cases** |
|
||
| RAG Systems | ✅ Excellent | ✅ Excellent | ✅ Excellent | ✅ Excellent | ✅ Excellent | ✅ Good | ⚠️ Limited |
|
||
| Serverless | ✅ Perfect | ✅ Good | ❌ No | ❌ No | ❌ No | ⚠️ Possible | ⚠️ Possible |
|
||
| Edge Computing | ✅ Excellent | ❌ No | ❌ No | ❌ No | ❌ No | ❌ No | ⚠️ Possible |
|
||
| Production Scale (100M+) | ⚠️ Single node | ✅ Yes | ✅ Yes | ✅ Yes | ✅ Excellent | ⚠️ Limited | ⚠️ Manual |
|
||
| Embedded Apps | ✅ Excellent | ❌ No | ❌ No | ❌ No | ❌ No | ⚠️ Possible | ✅ Good |
|
||
|
||
### When to Choose Ruvector
|
||
|
||
✅ **Perfect for:**
|
||
- **Node.js/TypeScript applications** needing embedded vector search
|
||
- **Serverless and edge computing** where external services aren't practical
|
||
- **Rapid prototyping and development** with minimal setup time
|
||
- **RAG systems** with LangChain, LlamaIndex, or custom implementations
|
||
- **Cost-sensitive projects** that can't afford cloud API pricing
|
||
- **Offline-first applications** requiring local vector search
|
||
- **Browser-based AI** with WebAssembly fallback
|
||
- **Small to medium scale** (up to 10M vectors per instance)
|
||
|
||
⚠️ **Consider alternatives for:**
|
||
- **Massive scale (100M+ vectors)** - Consider Pinecone, Milvus, or Qdrant clusters
|
||
- **Multi-tenancy requirements** - Weaviate or Qdrant offer better isolation
|
||
- **Distributed systems** - Milvus provides better horizontal scaling
|
||
- **Zero-ops cloud solution** - Pinecone handles all infrastructure
|
||
|
||
### Why Choose Ruvector Over...
|
||
|
||
**vs Pinecone:**
|
||
- ✅ No API costs (save $1000s/month)
|
||
- ✅ No network latency (10x faster queries)
|
||
- ✅ No vendor lock-in
|
||
- ✅ Works offline and in restricted environments
|
||
- ❌ No managed multi-region clusters
|
||
|
||
**vs ChromaDB:**
|
||
- ✅ 50x faster queries (native Rust vs Python)
|
||
- ✅ True Node.js support (not HTTP API)
|
||
- ✅ Better TypeScript integration
|
||
- ✅ Lower memory usage
|
||
- ❌ Smaller ecosystem and community
|
||
|
||
**vs Qdrant:**
|
||
- ✅ Zero infrastructure setup
|
||
- ✅ Embedded in your app (no Docker)
|
||
- ✅ Better for serverless environments
|
||
- ✅ Native Node.js bindings
|
||
- ❌ No built-in clustering or HA
|
||
|
||
**vs Faiss:**
|
||
- ✅ Full Node.js support (Faiss is Python-only)
|
||
- ✅ Easier API and better developer experience
|
||
- ✅ Built-in persistence and metadata
|
||
- ⚠️ Slightly lower recall at same performance
|
||
|
||
## 🎯 Real-World Tutorials
|
||
|
||
### Tutorial 1: Building a RAG System with OpenAI
|
||
|
||
**What you'll learn:** Create a production-ready Retrieval-Augmented Generation system that enhances LLM responses with relevant context from your documents.
|
||
|
||
**Prerequisites:**
|
||
```bash
|
||
npm install ruvector openai
|
||
export OPENAI_API_KEY="your-api-key-here"
|
||
```
|
||
|
||
**Complete Implementation:**
|
||
|
||
```javascript
|
||
const { VectorDb } = require('ruvector');
|
||
const OpenAI = require('openai');
|
||
|
||
class RAGSystem {
|
||
constructor() {
|
||
// Initialize OpenAI client
|
||
this.openai = new OpenAI({
|
||
apiKey: process.env.OPENAI_API_KEY
|
||
});
|
||
|
||
// Create vector database for OpenAI embeddings
|
||
// text-embedding-ada-002 produces 1536-dimensional vectors
|
||
this.db = new VectorDb({
|
||
dimensions: 1536,
|
||
maxElements: 100000,
|
||
storagePath: './rag-knowledge-base.db'
|
||
});
|
||
|
||
console.log('✅ RAG System initialized');
|
||
}
|
||
|
||
// Step 1: Index your knowledge base
|
||
async indexDocuments(documents) {
|
||
console.log(`📚 Indexing ${documents.length} documents...`);
|
||
|
||
for (let i = 0; i < documents.length; i++) {
|
||
const doc = documents[i];
|
||
|
||
// Generate embedding for the document
|
||
const response = await this.openai.embeddings.create({
|
||
model: 'text-embedding-ada-002',
|
||
input: doc.content
|
||
});
|
||
|
||
// Store in vector database
|
||
await this.db.insert({
|
||
id: doc.id || `doc_${i}`,
|
||
vector: new Float32Array(response.data[0].embedding),
|
||
metadata: {
|
||
title: doc.title,
|
||
content: doc.content,
|
||
source: doc.source,
|
||
date: doc.date || new Date().toISOString()
|
||
}
|
||
});
|
||
|
||
console.log(` ✅ Indexed: ${doc.title}`);
|
||
}
|
||
|
||
const count = await this.db.len();
|
||
console.log(`\n✅ Indexed ${count} documents total`);
|
||
}
|
||
|
||
// Step 2: Retrieve relevant context for a query
|
||
async retrieveContext(query, k = 3) {
|
||
console.log(`🔍 Searching for: "${query}"`);
|
||
|
||
// Generate embedding for the query
|
||
const response = await this.openai.embeddings.create({
|
||
model: 'text-embedding-ada-002',
|
||
input: query
|
||
});
|
||
|
||
// Search for similar documents
|
||
const results = await this.db.search({
|
||
vector: new Float32Array(response.data[0].embedding),
|
||
k: k,
|
||
threshold: 0.7 // Only use highly relevant results
|
||
});
|
||
|
||
console.log(`📄 Found ${results.length} relevant documents\n`);
|
||
|
||
return results.map(r => ({
|
||
content: r.metadata.content,
|
||
title: r.metadata.title,
|
||
score: r.score
|
||
}));
|
||
}
|
||
|
||
// Step 3: Generate answer with retrieved context
|
||
async answer(question) {
|
||
// Retrieve relevant context
|
||
const context = await this.retrieveContext(question, 3);
|
||
|
||
if (context.length === 0) {
|
||
return "I don't have enough information to answer that question.";
|
||
}
|
||
|
||
// Build prompt with context
|
||
const contextText = context
|
||
.map((doc, i) => `[${i + 1}] ${doc.title}\n${doc.content}`)
|
||
.join('\n\n');
|
||
|
||
const prompt = `Answer the question based on the following context. If the context doesn't contain the answer, say so.
|
||
|
||
Context:
|
||
${contextText}
|
||
|
||
Question: ${question}
|
||
|
||
Answer:`;
|
||
|
||
console.log('🤖 Generating answer...\n');
|
||
|
||
// Generate completion
|
||
const completion = await this.openai.chat.completions.create({
|
||
model: 'gpt-4',
|
||
messages: [
|
||
{ role: 'system', content: 'You are a helpful assistant that answers questions based on provided context.' },
|
||
{ role: 'user', content: prompt }
|
||
],
|
||
temperature: 0.3 // Lower temperature for more factual responses
|
||
});
|
||
|
||
return {
|
||
answer: completion.choices[0].message.content,
|
||
sources: context.map(c => c.title)
|
||
};
|
||
}
|
||
}
|
||
|
||
// Example Usage
|
||
async function main() {
|
||
const rag = new RAGSystem();
|
||
|
||
// Step 1: Index your knowledge base
|
||
const documents = [
|
||
{
|
||
id: 'doc1',
|
||
title: 'Ruvector Introduction',
|
||
content: 'Ruvector is a high-performance vector database for Node.js built in Rust. It provides sub-millisecond query latency and supports over 52,000 inserts per second.',
|
||
source: 'documentation'
|
||
},
|
||
{
|
||
id: 'doc2',
|
||
title: 'Vector Databases Explained',
|
||
content: 'Vector databases store data as high-dimensional vectors, enabling semantic similarity search. They are essential for AI applications like RAG systems and recommendation engines.',
|
||
source: 'blog'
|
||
},
|
||
{
|
||
id: 'doc3',
|
||
title: 'HNSW Algorithm',
|
||
content: 'Hierarchical Navigable Small World (HNSW) is a graph-based algorithm for approximate nearest neighbor search. It provides excellent recall with low latency.',
|
||
source: 'research'
|
||
}
|
||
];
|
||
|
||
await rag.indexDocuments(documents);
|
||
|
||
// Step 2: Ask questions
|
||
console.log('\n' + '='.repeat(60) + '\n');
|
||
|
||
const result = await rag.answer('What is Ruvector and what are its performance characteristics?');
|
||
|
||
console.log('📝 Answer:', result.answer);
|
||
console.log('\n📚 Sources:', result.sources.join(', '));
|
||
}
|
||
|
||
main().catch(console.error);
|
||
```
|
||
|
||
**Expected Output:**
|
||
```
|
||
✅ RAG System initialized
|
||
📚 Indexing 3 documents...
|
||
✅ Indexed: Ruvector Introduction
|
||
✅ Indexed: Vector Databases Explained
|
||
✅ Indexed: HNSW Algorithm
|
||
|
||
✅ Indexed 3 documents total
|
||
|
||
============================================================
|
||
|
||
🔍 Searching for: "What is Ruvector and what are its performance characteristics?"
|
||
📄 Found 2 relevant documents
|
||
|
||
🤖 Generating answer...
|
||
|
||
📝 Answer: Ruvector is a high-performance vector database built in Rust for Node.js applications. Its key performance characteristics include:
|
||
- Sub-millisecond query latency
|
||
- Over 52,000 inserts per second
|
||
- Optimized for semantic similarity search
|
||
|
||
📚 Sources: Ruvector Introduction, Vector Databases Explained
|
||
```
|
||
|
||
**Production Tips:**
|
||
- ✅ Use batch embedding for better throughput (OpenAI supports up to 2048 texts)
|
||
- ✅ Implement caching for frequently asked questions
|
||
- ✅ Add error handling for API rate limits
|
||
- ✅ Monitor token usage and costs
|
||
- ✅ Regularly update your knowledge base
|
||
|
||
---
|
||
|
||
### Tutorial 2: Semantic Search Engine
|
||
|
||
**What you'll learn:** Build a semantic search engine that understands meaning, not just keywords.
|
||
|
||
**Prerequisites:**
|
||
```bash
|
||
npm install ruvector @xenova/transformers
|
||
```
|
||
|
||
**Complete Implementation:**
|
||
|
||
```javascript
|
||
const { VectorDb } = require('ruvector');
|
||
const { pipeline } = require('@xenova/transformers');
|
||
|
||
class SemanticSearchEngine {
|
||
constructor() {
|
||
this.db = null;
|
||
this.embedder = null;
|
||
}
|
||
|
||
// Step 1: Initialize the embedding model
|
||
async initialize() {
|
||
console.log('🚀 Initializing semantic search engine...');
|
||
|
||
// Load sentence-transformers model (runs locally, no API needed!)
|
||
console.log('📥 Loading embedding model...');
|
||
this.embedder = await pipeline(
|
||
'feature-extraction',
|
||
'Xenova/all-MiniLM-L6-v2'
|
||
);
|
||
|
||
// Create vector database (384 dimensions for all-MiniLM-L6-v2)
|
||
this.db = new VectorDb({
|
||
dimensions: 384,
|
||
maxElements: 50000,
|
||
storagePath: './semantic-search.db'
|
||
});
|
||
|
||
console.log('✅ Search engine ready!\n');
|
||
}
|
||
|
||
// Step 2: Generate embeddings
|
||
async embed(text) {
|
||
const output = await this.embedder(text, {
|
||
pooling: 'mean',
|
||
normalize: true
|
||
});
|
||
|
||
// Convert to Float32Array
|
||
return new Float32Array(output.data);
|
||
}
|
||
|
||
// Step 3: Index documents
|
||
async indexDocuments(documents) {
|
||
console.log(`📚 Indexing ${documents.length} documents...`);
|
||
|
||
for (const doc of documents) {
|
||
const vector = await this.embed(doc.content);
|
||
|
||
await this.db.insert({
|
||
id: doc.id,
|
||
vector: vector,
|
||
metadata: {
|
||
title: doc.title,
|
||
content: doc.content,
|
||
category: doc.category,
|
||
url: doc.url
|
||
}
|
||
});
|
||
|
||
console.log(` ✅ ${doc.title}`);
|
||
}
|
||
|
||
const count = await this.db.len();
|
||
console.log(`\n✅ Indexed ${count} documents\n`);
|
||
}
|
||
|
||
// Step 4: Semantic search
|
||
async search(query, options = {}) {
|
||
const {
|
||
k = 5,
|
||
category = null,
|
||
threshold = 0.3
|
||
} = options;
|
||
|
||
console.log(`🔍 Searching for: "${query}"`);
|
||
|
||
// Generate query embedding
|
||
const queryVector = await this.embed(query);
|
||
|
||
// Search vector database
|
||
const results = await this.db.search({
|
||
vector: queryVector,
|
||
k: k * 2, // Get more results for filtering
|
||
threshold: threshold
|
||
});
|
||
|
||
// Filter by category if specified
|
||
let filtered = results;
|
||
if (category) {
|
||
filtered = results.filter(r => r.metadata.category === category);
|
||
}
|
||
|
||
// Return top k after filtering
|
||
const final = filtered.slice(0, k);
|
||
|
||
console.log(`📄 Found ${final.length} results\n`);
|
||
|
||
return final.map(r => ({
|
||
id: r.id,
|
||
title: r.metadata.title,
|
||
content: r.metadata.content,
|
||
category: r.metadata.category,
|
||
score: r.score,
|
||
url: r.metadata.url
|
||
}));
|
||
}
|
||
|
||
// Step 5: Find similar documents
|
||
async findSimilar(documentId, k = 5) {
|
||
const doc = await this.db.get(documentId);
|
||
|
||
if (!doc) {
|
||
throw new Error(`Document ${documentId} not found`);
|
||
}
|
||
|
||
const results = await this.db.search({
|
||
vector: doc.vector,
|
||
k: k + 1 // +1 because the document itself will be included
|
||
});
|
||
|
||
// Remove the document itself from results
|
||
return results
|
||
.filter(r => r.id !== documentId)
|
||
.slice(0, k);
|
||
}
|
||
}
|
||
|
||
// Example Usage
|
||
async function main() {
|
||
const engine = new SemanticSearchEngine();
|
||
await engine.initialize();
|
||
|
||
// Sample documents (in production, load from your database)
|
||
const documents = [
|
||
{
|
||
id: '1',
|
||
title: 'Understanding Neural Networks',
|
||
content: 'Neural networks are computing systems inspired by biological neural networks. They learn to perform tasks by considering examples.',
|
||
category: 'AI',
|
||
url: '/docs/neural-networks'
|
||
},
|
||
{
|
||
id: '2',
|
||
title: 'Introduction to Machine Learning',
|
||
content: 'Machine learning is a subset of artificial intelligence that provides systems the ability to learn and improve from experience.',
|
||
category: 'AI',
|
||
url: '/docs/machine-learning'
|
||
},
|
||
{
|
||
id: '3',
|
||
title: 'Web Development Best Practices',
|
||
content: 'Modern web development involves responsive design, performance optimization, and accessibility considerations.',
|
||
category: 'Web',
|
||
url: '/docs/web-dev'
|
||
},
|
||
{
|
||
id: '4',
|
||
title: 'Deep Learning Applications',
|
||
content: 'Deep learning has revolutionized computer vision, natural language processing, and speech recognition.',
|
||
category: 'AI',
|
||
url: '/docs/deep-learning'
|
||
}
|
||
];
|
||
|
||
// Index documents
|
||
await engine.indexDocuments(documents);
|
||
|
||
// Example 1: Basic semantic search
|
||
console.log('Example 1: Basic Search\n' + '='.repeat(60));
|
||
const results1 = await engine.search('AI and neural nets');
|
||
results1.forEach((result, i) => {
|
||
console.log(`${i + 1}. ${result.title} (Score: ${result.score.toFixed(3)})`);
|
||
console.log(` ${result.content.slice(0, 80)}...`);
|
||
console.log(` Category: ${result.category}\n`);
|
||
});
|
||
|
||
// Example 2: Category-filtered search
|
||
console.log('\nExample 2: Category-Filtered Search\n' + '='.repeat(60));
|
||
const results2 = await engine.search('learning algorithms', {
|
||
category: 'AI',
|
||
k: 3
|
||
});
|
||
results2.forEach((result, i) => {
|
||
console.log(`${i + 1}. ${result.title} (Score: ${result.score.toFixed(3)})`);
|
||
});
|
||
|
||
// Example 3: Find similar documents
|
||
console.log('\n\nExample 3: Find Similar Documents\n' + '='.repeat(60));
|
||
const similar = await engine.findSimilar('1', 2);
|
||
console.log('Documents similar to "Understanding Neural Networks":');
|
||
similar.forEach((doc, i) => {
|
||
console.log(`${i + 1}. ${doc.metadata.title} (Score: ${doc.score.toFixed(3)})`);
|
||
});
|
||
}
|
||
|
||
main().catch(console.error);
|
||
```
|
||
|
||
**Key Features:**
|
||
- ✅ Runs completely locally (no API keys needed)
|
||
- ✅ Understands semantic meaning, not just keywords
|
||
- ✅ Category filtering for better results
|
||
- ✅ "Find similar" functionality
|
||
- ✅ Fast: ~10ms query latency
|
||
|
||
---
|
||
|
||
### Tutorial 3: AI Agent Memory System
|
||
|
||
**What you'll learn:** Implement a memory system for AI agents that remembers past experiences and learns from them.
|
||
|
||
**Complete Implementation:**
|
||
|
||
```javascript
|
||
const { VectorDb } = require('ruvector');
|
||
|
||
class AgentMemory {
|
||
constructor(agentId) {
|
||
this.agentId = agentId;
|
||
|
||
// Create separate databases for different memory types
|
||
this.episodicMemory = new VectorDb({
|
||
dimensions: 768,
|
||
storagePath: `./memory/${agentId}-episodic.db`
|
||
});
|
||
|
||
this.semanticMemory = new VectorDb({
|
||
dimensions: 768,
|
||
storagePath: `./memory/${agentId}-semantic.db`
|
||
});
|
||
|
||
console.log(`🧠 Memory system initialized for agent: ${agentId}`);
|
||
}
|
||
|
||
// Step 1: Store an experience (episodic memory)
|
||
async storeExperience(experience) {
|
||
const {
|
||
state,
|
||
action,
|
||
result,
|
||
reward,
|
||
embedding
|
||
} = experience;
|
||
|
||
const experienceId = `exp_${Date.now()}_${Math.random()}`;
|
||
|
||
await this.episodicMemory.insert({
|
||
id: experienceId,
|
||
vector: new Float32Array(embedding),
|
||
metadata: {
|
||
state: state,
|
||
action: action,
|
||
result: result,
|
||
reward: reward,
|
||
timestamp: Date.now(),
|
||
type: 'episodic'
|
||
}
|
||
});
|
||
|
||
console.log(`💾 Stored experience: ${action} -> ${result} (reward: ${reward})`);
|
||
return experienceId;
|
||
}
|
||
|
||
// Step 2: Store learned knowledge (semantic memory)
|
||
async storeKnowledge(knowledge) {
|
||
const {
|
||
concept,
|
||
description,
|
||
embedding,
|
||
confidence = 1.0
|
||
} = knowledge;
|
||
|
||
const knowledgeId = `know_${Date.now()}`;
|
||
|
||
await this.semanticMemory.insert({
|
||
id: knowledgeId,
|
||
vector: new Float32Array(embedding),
|
||
metadata: {
|
||
concept: concept,
|
||
description: description,
|
||
confidence: confidence,
|
||
learned: Date.now(),
|
||
uses: 0,
|
||
type: 'semantic'
|
||
}
|
||
});
|
||
|
||
console.log(`📚 Learned: ${concept}`);
|
||
return knowledgeId;
|
||
}
|
||
|
||
// Step 3: Recall similar experiences
|
||
async recallExperiences(currentState, k = 5) {
|
||
console.log(`🔍 Recalling similar experiences...`);
|
||
|
||
const results = await this.episodicMemory.search({
|
||
vector: new Float32Array(currentState.embedding),
|
||
k: k,
|
||
threshold: 0.6 // Only recall reasonably similar experiences
|
||
});
|
||
|
||
// Sort by reward to prioritize successful experiences
|
||
const sorted = results.sort((a, b) => b.metadata.reward - a.metadata.reward);
|
||
|
||
console.log(`📝 Recalled ${sorted.length} relevant experiences`);
|
||
|
||
return sorted.map(r => ({
|
||
state: r.metadata.state,
|
||
action: r.metadata.action,
|
||
result: r.metadata.result,
|
||
reward: r.metadata.reward,
|
||
similarity: r.score
|
||
}));
|
||
}
|
||
|
||
// Step 4: Query knowledge base
|
||
async queryKnowledge(query, k = 3) {
|
||
const results = await this.semanticMemory.search({
|
||
vector: new Float32Array(query.embedding),
|
||
k: k
|
||
});
|
||
|
||
// Update usage statistics
|
||
for (const result of results) {
|
||
const knowledge = await this.semanticMemory.get(result.id);
|
||
if (knowledge) {
|
||
knowledge.metadata.uses += 1;
|
||
// In production, update the entry
|
||
}
|
||
}
|
||
|
||
return results.map(r => ({
|
||
concept: r.metadata.concept,
|
||
description: r.metadata.description,
|
||
confidence: r.metadata.confidence,
|
||
relevance: r.score
|
||
}));
|
||
}
|
||
|
||
// Step 5: Reflect and learn from experiences
|
||
async reflect() {
|
||
console.log('\n🤔 Reflecting on experiences...');
|
||
|
||
// Get all experiences
|
||
const totalExperiences = await this.episodicMemory.len();
|
||
console.log(`📊 Total experiences: ${totalExperiences}`);
|
||
|
||
// Analyze success rate
|
||
// In production, you'd aggregate experiences and extract patterns
|
||
console.log('💡 Analysis complete');
|
||
|
||
return {
|
||
totalExperiences: totalExperiences,
|
||
knowledgeItems: await this.semanticMemory.len()
|
||
};
|
||
}
|
||
|
||
// Step 6: Get memory statistics
|
||
async getStats() {
|
||
return {
|
||
episodicMemorySize: await this.episodicMemory.len(),
|
||
semanticMemorySize: await this.semanticMemory.len(),
|
||
agentId: this.agentId
|
||
};
|
||
}
|
||
}
|
||
|
||
// Example Usage: Simulated agent learning to navigate
|
||
async function main() {
|
||
const agent = new AgentMemory('agent-001');
|
||
|
||
// Simulate embedding function (in production, use a real model)
|
||
function embed(text) {
|
||
return Array(768).fill(0).map(() => Math.random());
|
||
}
|
||
|
||
console.log('\n' + '='.repeat(60));
|
||
console.log('PHASE 1: Learning from experiences');
|
||
console.log('='.repeat(60) + '\n');
|
||
|
||
// Store some experiences
|
||
await agent.storeExperience({
|
||
state: { location: 'room1', goal: 'room3' },
|
||
action: 'move_north',
|
||
result: 'reached room2',
|
||
reward: 0.5,
|
||
embedding: embed('navigating from room1 to room2')
|
||
});
|
||
|
||
await agent.storeExperience({
|
||
state: { location: 'room2', goal: 'room3' },
|
||
action: 'move_east',
|
||
result: 'reached room3',
|
||
reward: 1.0,
|
||
embedding: embed('navigating from room2 to room3')
|
||
});
|
||
|
||
await agent.storeExperience({
|
||
state: { location: 'room1', goal: 'room3' },
|
||
action: 'move_south',
|
||
result: 'hit wall',
|
||
reward: -0.5,
|
||
embedding: embed('failed navigation attempt')
|
||
});
|
||
|
||
// Store learned knowledge
|
||
await agent.storeKnowledge({
|
||
concept: 'navigation_strategy',
|
||
description: 'Moving north then east is efficient for reaching room3 from room1',
|
||
embedding: embed('navigation strategy knowledge'),
|
||
confidence: 0.9
|
||
});
|
||
|
||
console.log('\n' + '='.repeat(60));
|
||
console.log('PHASE 2: Applying memory');
|
||
console.log('='.repeat(60) + '\n');
|
||
|
||
// Agent encounters a similar situation
|
||
const currentState = {
|
||
location: 'room1',
|
||
goal: 'room3',
|
||
embedding: embed('navigating from room1 to room3')
|
||
};
|
||
|
||
// Recall relevant experiences
|
||
const experiences = await agent.recallExperiences(currentState, 3);
|
||
|
||
console.log('\n📖 Recalled experiences:');
|
||
experiences.forEach((exp, i) => {
|
||
console.log(`${i + 1}. Action: ${exp.action} | Result: ${exp.result} | Reward: ${exp.reward} | Similarity: ${exp.similarity.toFixed(3)}`);
|
||
});
|
||
|
||
// Query relevant knowledge
|
||
const knowledge = await agent.queryKnowledge({
|
||
embedding: embed('how to navigate efficiently')
|
||
}, 2);
|
||
|
||
console.log('\n📚 Relevant knowledge:');
|
||
knowledge.forEach((k, i) => {
|
||
console.log(`${i + 1}. ${k.concept}: ${k.description} (confidence: ${k.confidence})`);
|
||
});
|
||
|
||
console.log('\n' + '='.repeat(60));
|
||
console.log('PHASE 3: Reflection');
|
||
console.log('='.repeat(60) + '\n');
|
||
|
||
// Reflect on learning
|
||
const stats = await agent.reflect();
|
||
const memoryStats = await agent.getStats();
|
||
|
||
console.log('\n📊 Memory Statistics:');
|
||
console.log(` Episodic memories: ${memoryStats.episodicMemorySize}`);
|
||
console.log(` Semantic knowledge: ${memoryStats.semanticMemorySize}`);
|
||
console.log(` Agent ID: ${memoryStats.agentId}`);
|
||
}
|
||
|
||
main().catch(console.error);
|
||
```
|
||
|
||
**Expected Output:**
|
||
```
|
||
🧠 Memory system initialized for agent: agent-001
|
||
|
||
============================================================
|
||
PHASE 1: Learning from experiences
|
||
============================================================
|
||
|
||
💾 Stored experience: move_north -> reached room2 (reward: 0.5)
|
||
💾 Stored experience: move_east -> reached room3 (reward: 1.0)
|
||
💾 Stored experience: move_south -> hit wall (reward: -0.5)
|
||
📚 Learned: navigation_strategy
|
||
|
||
============================================================
|
||
PHASE 2: Applying memory
|
||
============================================================
|
||
|
||
🔍 Recalling similar experiences...
|
||
📝 Recalled 3 relevant experiences
|
||
|
||
📖 Recalled experiences:
|
||
1. Action: move_east | Result: reached room3 | Reward: 1.0 | Similarity: 0.892
|
||
2. Action: move_north | Result: reached room2 | Reward: 0.5 | Similarity: 0.876
|
||
3. Action: move_south | Result: hit wall | Reward: -0.5 | Similarity: 0.654
|
||
|
||
📚 Relevant knowledge:
|
||
1. navigation_strategy: Moving north then east is efficient for reaching room3 from room1 (confidence: 0.9)
|
||
|
||
============================================================
|
||
PHASE 3: Reflection
|
||
============================================================
|
||
|
||
🤔 Reflecting on experiences...
|
||
📊 Total experiences: 3
|
||
💡 Analysis complete
|
||
|
||
📊 Memory Statistics:
|
||
Episodic memories: 3
|
||
Semantic knowledge: 1
|
||
Agent ID: agent-001
|
||
```
|
||
|
||
**Use Cases:**
|
||
- ✅ Reinforcement learning agents
|
||
- ✅ Chatbot conversation history
|
||
- ✅ Game AI that learns from gameplay
|
||
- ✅ Personal assistant memory
|
||
- ✅ Robotic navigation systems
|
||
|
||
## 🏗️ API Reference
|
||
|
||
### Constructor
|
||
|
||
```typescript
|
||
new VectorDb(options: {
|
||
dimensions: number; // Vector dimensionality (required)
|
||
maxElements?: number; // Max vectors (default: 10000)
|
||
storagePath?: string; // Persistent storage path
|
||
ef_construction?: number; // HNSW construction parameter (default: 200)
|
||
m?: number; // HNSW M parameter (default: 16)
|
||
distanceMetric?: string; // 'cosine', 'euclidean', or 'dot' (default: 'cosine')
|
||
})
|
||
```
|
||
|
||
### Methods
|
||
|
||
#### insert(entry: VectorEntry): Promise<string>
|
||
Insert a vector into the database.
|
||
|
||
```javascript
|
||
const id = await db.insert({
|
||
id: 'doc_1',
|
||
vector: new Float32Array([0.1, 0.2, 0.3, ...]),
|
||
metadata: { title: 'Document 1' }
|
||
});
|
||
```
|
||
|
||
#### search(query: SearchQuery): Promise<SearchResult[]>
|
||
Search for similar vectors.
|
||
|
||
```javascript
|
||
const results = await db.search({
|
||
vector: new Float32Array([0.1, 0.2, 0.3, ...]),
|
||
k: 10,
|
||
threshold: 0.7
|
||
});
|
||
```
|
||
|
||
#### get(id: string): Promise<VectorEntry | null>
|
||
Retrieve a vector by ID.
|
||
|
||
```javascript
|
||
const entry = await db.get('doc_1');
|
||
if (entry) {
|
||
console.log(entry.vector, entry.metadata);
|
||
}
|
||
```
|
||
|
||
#### delete(id: string): Promise<boolean>
|
||
Remove a vector from the database.
|
||
|
||
```javascript
|
||
const deleted = await db.delete('doc_1');
|
||
console.log(deleted ? 'Deleted' : 'Not found');
|
||
```
|
||
|
||
#### len(): Promise<number>
|
||
Get the total number of vectors.
|
||
|
||
```javascript
|
||
const count = await db.len();
|
||
console.log(`Total vectors: ${count}`);
|
||
```
|
||
|
||
## 🎨 Advanced Configuration
|
||
|
||
### HNSW Parameters
|
||
|
||
```javascript
|
||
const db = new VectorDb({
|
||
dimensions: 384,
|
||
maxElements: 1000000,
|
||
ef_construction: 200, // Higher = better recall, slower build
|
||
m: 16, // Higher = better recall, more memory
|
||
storagePath: './large-db.db'
|
||
});
|
||
```
|
||
|
||
**Parameter Guidelines:**
|
||
- `ef_construction`: 100-400 (higher = better recall, slower indexing)
|
||
- `m`: 8-64 (higher = better recall, more memory)
|
||
- Default values work well for most use cases
|
||
|
||
### Distance Metrics
|
||
|
||
```javascript
|
||
// Cosine similarity (default, best for normalized vectors)
|
||
const db1 = new VectorDb({
|
||
dimensions: 128,
|
||
distanceMetric: 'cosine'
|
||
});
|
||
|
||
// Euclidean distance (L2, best for spatial data)
|
||
const db2 = new VectorDb({
|
||
dimensions: 128,
|
||
distanceMetric: 'euclidean'
|
||
});
|
||
|
||
// Dot product (best for pre-normalized vectors)
|
||
const db3 = new VectorDb({
|
||
dimensions: 128,
|
||
distanceMetric: 'dot'
|
||
});
|
||
```
|
||
|
||
### Persistence
|
||
|
||
```javascript
|
||
// Auto-save to disk
|
||
const persistent = new VectorDb({
|
||
dimensions: 128,
|
||
storagePath: './persistent.db'
|
||
});
|
||
|
||
// In-memory only (faster, but data lost on exit)
|
||
const temporary = new VectorDb({
|
||
dimensions: 128
|
||
// No storagePath = in-memory
|
||
});
|
||
```
|
||
|
||
## 📦 Platform Support
|
||
|
||
Automatically installs the correct implementation for:
|
||
|
||
### Native (Rust) - Best Performance
|
||
- **Linux**: x64, ARM64 (GNU libc)
|
||
- **macOS**: x64 (Intel), ARM64 (Apple Silicon)
|
||
- **Windows**: x64 (MSVC)
|
||
|
||
Performance: **<0.5ms latency**, **50K+ ops/sec**
|
||
|
||
### WASM Fallback - Universal Compatibility
|
||
- Any platform where native module isn't available
|
||
- Browser environments (experimental)
|
||
- Alpine Linux (musl) and other non-glibc systems
|
||
|
||
Performance: **10-50ms latency**, **~1K ops/sec**
|
||
|
||
**Node.js 18+ required** for all platforms.
|
||
|
||
## 🔧 Building from Source
|
||
|
||
If you need to rebuild the native module:
|
||
|
||
```bash
|
||
# Install Rust toolchain
|
||
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
|
||
|
||
# Clone repository
|
||
git clone https://github.com/ruvnet/ruvector.git
|
||
cd ruvector
|
||
|
||
# Build native module
|
||
cd npm/packages/core
|
||
npm run build:napi
|
||
|
||
# Build wrapper package
|
||
cd ../ruvector
|
||
npm install
|
||
npm run build
|
||
|
||
# Run tests
|
||
npm test
|
||
```
|
||
|
||
**Requirements:**
|
||
- Rust 1.77+
|
||
- Node.js 18+
|
||
- Cargo
|
||
|
||
## 🌍 Ecosystem
|
||
|
||
### Related Packages
|
||
|
||
- **[ruvector-core](https://www.npmjs.com/package/ruvector-core)** - Core native bindings (lower-level API)
|
||
- **[ruvector-wasm](https://www.npmjs.com/package/ruvector-wasm)** - WebAssembly implementation for browsers
|
||
- **[ruvector-cli](https://www.npmjs.com/package/ruvector-cli)** - Standalone CLI tools
|
||
- **[@ruvector/rvf](https://www.npmjs.com/package/@ruvector/rvf)** - RVF cognitive container SDK
|
||
- **[@ruvector/rvf-wasm](https://www.npmjs.com/package/@ruvector/rvf-wasm)** - RVF WASM build for browsers, Deno, and edge
|
||
- **[rvlite](https://www.npmjs.com/package/rvlite)** - Lightweight vector database with SQL, SPARQL, and Cypher
|
||
|
||
### Platform-Specific Packages (auto-installed)
|
||
|
||
- **[ruvector-core-linux-x64-gnu](https://www.npmjs.com/package/ruvector-core-linux-x64-gnu)**
|
||
- **[ruvector-core-linux-arm64-gnu](https://www.npmjs.com/package/ruvector-core-linux-arm64-gnu)**
|
||
- **[ruvector-core-darwin-x64](https://www.npmjs.com/package/ruvector-core-darwin-x64)**
|
||
- **[ruvector-core-darwin-arm64](https://www.npmjs.com/package/ruvector-core-darwin-arm64)**
|
||
- **[ruvector-core-win32-x64-msvc](https://www.npmjs.com/package/ruvector-core-win32-x64-msvc)**
|
||
|
||
---
|
||
|
||
## RVF Cognitive Containers
|
||
|
||
Ruvector integrates with [RVF (RuVector Format)](https://github.com/ruvnet/ruvector/tree/main/crates/rvf) — a universal binary substrate that stores vectors, models, graphs, compute kernels, and attestation in a single `.rvf` file.
|
||
|
||
### Enable RVF Backend
|
||
|
||
```bash
|
||
# Install the optional RVF package
|
||
npm install @ruvector/rvf
|
||
|
||
# Set backend via environment variable
|
||
export RUVECTOR_BACKEND=rvf
|
||
|
||
# Or detect automatically (native -> rvf -> wasm fallback)
|
||
npx ruvector info
|
||
```
|
||
|
||
```typescript
|
||
import { getImplementationType, isRvf } from 'ruvector';
|
||
|
||
console.log(getImplementationType()); // 'native' | 'rvf' | 'wasm'
|
||
console.log(isRvf()); // true if RVF backend is active
|
||
```
|
||
|
||
### RVF CLI Commands
|
||
|
||
8 RVF-specific subcommands are available through the ruvector CLI:
|
||
|
||
```bash
|
||
# Create an RVF store
|
||
npx ruvector rvf create mydb.rvf -d 384 --metric cosine
|
||
|
||
# Ingest vectors from JSON
|
||
npx ruvector rvf ingest mydb.rvf --input vectors.json --format json
|
||
|
||
# Query nearest neighbors
|
||
npx ruvector rvf query mydb.rvf --vector "[0.1,0.2,...]" --k 10
|
||
|
||
# File status and segment listing
|
||
npx ruvector rvf status mydb.rvf
|
||
npx ruvector rvf segments mydb.rvf
|
||
|
||
# COW branching — derive a child file
|
||
npx ruvector rvf derive mydb.rvf --output child.rvf
|
||
|
||
# Compact and reclaim space
|
||
npx ruvector rvf compact mydb.rvf
|
||
|
||
# Export to JSON
|
||
npx ruvector rvf export mydb.rvf --output dump.json
|
||
```
|
||
|
||
### RVF Platform Support
|
||
|
||
| Platform | Runtime | Backend |
|
||
|----------|---------|---------|
|
||
| Linux x86_64 / aarch64 | Node.js 18+ | Native (N-API) |
|
||
| macOS x86_64 / arm64 | Node.js 18+ | Native (N-API) |
|
||
| Windows x86_64 | Node.js 18+ | Native (N-API) |
|
||
| Any | Deno | WASM (`@ruvector/rvf-wasm`) |
|
||
| Any | Browser | WASM (`@ruvector/rvf-wasm`) |
|
||
| Any | Cloudflare Workers | WASM (`@ruvector/rvf-wasm`) |
|
||
|
||
### Download Example .rvf Files
|
||
|
||
45 pre-built example files are available (~11 MB total):
|
||
|
||
```bash
|
||
# Download a specific example
|
||
curl -LO https://raw.githubusercontent.com/ruvnet/ruvector/main/examples/rvf/output/basic_store.rvf
|
||
|
||
# Popular examples:
|
||
# basic_store.rvf (152 KB) — 1,000 vectors, dim 128
|
||
# semantic_search.rvf (755 KB) — Semantic search with HNSW
|
||
# rag_pipeline.rvf (303 KB) — RAG pipeline embeddings
|
||
# agent_memory.rvf (32 KB) — AI agent memory store
|
||
# self_booting.rvf (31 KB) — Self-booting with kernel
|
||
# progressive_index.rvf (2.5 MB) — Large-scale HNSW index
|
||
|
||
# Generate all examples locally
|
||
cd crates/rvf && cargo run --example generate_all
|
||
```
|
||
|
||
Full catalog: [examples/rvf/output/](https://github.com/ruvnet/ruvector/tree/main/examples/rvf/output)
|
||
|
||
### Working Examples: Cognitive Containers
|
||
|
||
#### Self-Booting Microservice
|
||
|
||
A single `.rvf` file that contains vectors AND a bootable Linux kernel:
|
||
|
||
```bash
|
||
# Build and run the self-booting example
|
||
cd crates/rvf && cargo run --example self_booting
|
||
# Output:
|
||
# Ingested 50 vectors (128 dims)
|
||
# Pre-kernel query: top-5 results OK (nearest ID=25)
|
||
# Kernel: 4,640 bytes embedded (x86_64, Hermit)
|
||
# Witness chain: 5 entries, all verified
|
||
# File: bootable.rvf (31 KB) — data + runtime in one file
|
||
```
|
||
|
||
```rust
|
||
// The pattern: vectors + kernel + witness in one file
|
||
let mut store = RvfStore::create("bootable.rvf", options)?;
|
||
store.ingest_batch(&vectors, &ids, None)?;
|
||
store.embed_kernel(KernelArch::X86_64 as u8, KernelType::Hermit as u8,
|
||
0x0018, &kernel_image, 8080, Some("console=ttyS0 quiet"))?;
|
||
// Result: drop on a VM and it boots as a query service
|
||
```
|
||
|
||
#### Linux Microkernel Distribution
|
||
|
||
20-package Linux distro with SSH keys and kernel in a single file:
|
||
|
||
```bash
|
||
cd crates/rvf && cargo run --example linux_microkernel
|
||
# Output:
|
||
# Installed 20 packages as vector embeddings
|
||
# Kernel embedded: Linux x86_64 (4,640 bytes)
|
||
# SSH keys: Ed25519, signed and verified
|
||
# Witness chain: 22 entries (1 per package + kernel + SSH)
|
||
# File: microkernel.rvf (14 KB) — immutable bootable system
|
||
```
|
||
|
||
Features: package search by embedding similarity, Ed25519 signed SSH keys, witness-audited installs, COW-derived child images for atomic updates.
|
||
|
||
#### Claude Code AI Appliance
|
||
|
||
A sealed, bootable AI development environment:
|
||
|
||
```bash
|
||
cd crates/rvf && cargo run --example claude_code_appliance
|
||
# Output:
|
||
# 20 dev packages (rust, node, python, docker, ...)
|
||
# Kernel: Linux x86_64 with SSH on port 2222
|
||
# eBPF: XDP distance program for fast-path lookups
|
||
# Witness chain: 6 entries, all verified
|
||
# Crypto: Ed25519 signature
|
||
# File: claude_code_appliance.rvf (17 KB)
|
||
```
|
||
|
||
#### CLI Full Lifecycle
|
||
|
||
```bash
|
||
# Create → Ingest → Query → Derive → Inspect
|
||
rvf create vectors.rvf --dimension 384
|
||
rvf ingest vectors.rvf --input data.json --format json
|
||
rvf query vectors.rvf --vector "0.1,0.2,..." --k 10
|
||
rvf derive vectors.rvf child.rvf --type filter
|
||
rvf inspect vectors.rvf
|
||
|
||
# Embed kernel and launch as microVM
|
||
rvf embed-kernel vectors.rvf --image bzImage
|
||
rvf launch vectors.rvf --port 8080
|
||
|
||
# Verify tamper-evident witness chain
|
||
rvf verify-witness vectors.rvf
|
||
rvf verify-attestation vectors.rvf
|
||
```
|
||
|
||
#### Integration Tests (46 passing)
|
||
|
||
```bash
|
||
cd crates/rvf
|
||
cargo test --workspace
|
||
# attestation .............. 6 passed
|
||
# crypto ................... 10 passed
|
||
# computational_container .. 8 passed
|
||
# cow_branching ............ 8 passed
|
||
# cross_platform ........... 6 passed
|
||
# lineage .................. 4 passed
|
||
# smoke .................... 4 passed
|
||
# Total: 46/46 passed
|
||
```
|
||
|
||
## 🐛 Troubleshooting
|
||
|
||
### Native Module Not Loading
|
||
|
||
If you see "Cannot find module 'ruvector-core-*'":
|
||
|
||
```bash
|
||
# Reinstall with optional dependencies
|
||
npm install --include=optional ruvector
|
||
|
||
# Verify platform
|
||
npx ruvector info
|
||
|
||
# Check Node.js version (18+ required)
|
||
node --version
|
||
```
|
||
|
||
### WASM Fallback Performance
|
||
|
||
If you're using WASM fallback and need better performance:
|
||
|
||
1. **Install native toolchain** for your platform
|
||
2. **Rebuild native module**: `npm rebuild ruvector`
|
||
3. **Verify native**: `npx ruvector info` should show "native (Rust)"
|
||
|
||
### Platform Compatibility
|
||
|
||
- **Alpine Linux**: Uses WASM fallback (musl not supported)
|
||
- **Windows ARM**: Not yet supported, uses WASM fallback
|
||
- **Node.js < 18**: Not supported, upgrade to Node.js 18+
|
||
|
||
## 📚 Documentation
|
||
|
||
- 🏠 [Homepage](https://ruv.io)
|
||
- 📦 [GitHub Repository](https://github.com/ruvnet/ruvector)
|
||
- 📚 [Full Documentation](https://github.com/ruvnet/ruvector/tree/main/docs)
|
||
- 🚀 [Getting Started Guide](https://github.com/ruvnet/ruvector/blob/main/docs/guide/GETTING_STARTED.md)
|
||
- 📖 [API Reference](https://github.com/ruvnet/ruvector/blob/main/docs/api/NODEJS_API.md)
|
||
- 🎯 [Performance Tuning](https://github.com/ruvnet/ruvector/blob/main/docs/optimization/PERFORMANCE_TUNING_GUIDE.md)
|
||
- 🐛 [Issue Tracker](https://github.com/ruvnet/ruvector/issues)
|
||
- 💬 [Discussions](https://github.com/ruvnet/ruvector/discussions)
|
||
|
||
## 🤝 Contributing
|
||
|
||
We welcome contributions! See [CONTRIBUTING.md](https://github.com/ruvnet/ruvector/blob/main/docs/development/CONTRIBUTING.md) for guidelines.
|
||
|
||
### Quick Start
|
||
|
||
1. Fork the repository
|
||
2. Create a feature branch: `git checkout -b feature/amazing-feature`
|
||
3. Commit changes: `git commit -m 'Add amazing feature'`
|
||
4. Push to branch: `git push origin feature/amazing-feature`
|
||
5. Open a Pull Request
|
||
|
||
## 🌐 Community & Support
|
||
|
||
- **GitHub**: [github.com/ruvnet/ruvector](https://github.com/ruvnet/ruvector) - ⭐ Star and follow
|
||
- **Discord**: [Join our community](https://discord.gg/ruvnet) - Chat with developers
|
||
- **Twitter**: [@ruvnet](https://twitter.com/ruvnet) - Follow for updates
|
||
- **Issues**: [Report bugs](https://github.com/ruvnet/ruvector/issues)
|
||
|
||
### Enterprise Support
|
||
|
||
Need custom development or consulting?
|
||
|
||
📧 [enterprise@ruv.io](mailto:enterprise@ruv.io)
|
||
|
||
## 📜 License
|
||
|
||
**MIT License** - see [LICENSE](https://github.com/ruvnet/ruvector/blob/main/LICENSE) for details.
|
||
|
||
Free for commercial and personal use.
|
||
|
||
## 🙏 Acknowledgments
|
||
|
||
Built with battle-tested technologies:
|
||
|
||
- **HNSW**: Hierarchical Navigable Small World graphs
|
||
- **SIMD**: Hardware-accelerated vector operations via simsimd
|
||
- **Rust**: Memory-safe, zero-cost abstractions
|
||
- **NAPI-RS**: High-performance Node.js bindings
|
||
- **WebAssembly**: Universal browser compatibility
|
||
|
||
---
|
||
|
||
<div align="center">
|
||
|
||
**Built with ❤️ by [rUv](https://ruv.io)**
|
||
|
||
[](https://www.npmjs.com/package/ruvector)
|
||
[](https://github.com/ruvnet/ruvector)
|
||
[](https://twitter.com/ruvnet)
|
||
|
||
**[Get Started](https://github.com/ruvnet/ruvector/blob/main/docs/guide/GETTING_STARTED.md)** • **[Documentation](https://github.com/ruvnet/ruvector/tree/main/docs)** • **[API Reference](https://github.com/ruvnet/ruvector/blob/main/docs/api/NODEJS_API.md)** • **[Contributing](https://github.com/ruvnet/ruvector/blob/main/docs/development/CONTRIBUTING.md)**
|
||
|
||
</div>
|