— name: "AgentDB Vector Search" description: "Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases." — # AgentDB Vector Search ## What This Skill Does Implements vector-based semantic search using AgentDB's high-performance vector database with **150x-12,500x faster** operations than traditional solutions. Features HNSW indexing, quantization, and sub-millisecond search (<100µs). ## Prerequisites – Node.js 18+ – AgentDB v1.0.7+ (via agentic-flow or standalone) – OpenAI API key (for embeddings) or custom embedding model ## Quick Start with CLI ### Initialize Vector Database “`bash # Initialize with default dimensions (1536 for OpenAI ada-002) npx agentdb@latest init ./vectors.db # Custom dimensions for different embedding models npx agentdb@latest init ./vectors.db –dimension 768 # sentence-transformers npx agentdb@latest init ./vectors.db –dimension 384 # all-MiniLM-L6-v2 # Use preset configurations npx agentdb@latest init ./vectors.db –preset small # <10K vectors npx agentdb@latest init ./vectors.db –preset medium # 10K-100K vectors npx agentdb@latest init ./vectors.db –preset large # >100K vectors # In-memory database for testing npx agentdb@latest init ./vectors.db –in-memory “` ### Query Vector Database “`bash # Basic similarity search npx agentdb@latest query ./vectors.db "[0.1,0.2,0.3,…]" # Top-k results npx agentdb@latest query ./vectors.db "[0.1,0.2,0.3]" -k 10 # With similarity threshold (cosine similarity) npx agentdb@latest query ./vectors.db "0.1 0.2 0.3" -t 0.75 -m cosine # Different distance metrics npx agentdb@latest query ./vectors.db "[…]" -m euclidean # L2 distance npx agentdb@latest query ./vectors.db "[…]" -m dot # Dot product # JSON output for automation npx agentdb@latest query ./vectors.db "[…]" -f json -k 5 # Verbose output with distances npx agentdb@latest query ./vectors.db "[…]" -v “` ### Import/Export Vectors “`bash # Export vectors to JSON npx agentdb@latest export ./vectors.db ./backup.json # Import vectors from JSON npx agentdb@latest import ./backup.json # Get database statistics npx agentdb@latest stats ./vectors.db “` ## Quick Start with API “`typescript import { createAgentDBAdapter, computeEmbedding } from 'agentic-flow/reasoningbank'; // Initialize with vector search optimizations const adapter = await createAgentDBAdapter({ dbPath: '.agentdb/vectors.db', enableLearning: false, // Vector search only enableReasoning: true, // Enable semantic matching quantizationType: 'binary', // 32x memory reduction cacheSize: 1000, // Fast retrieval }); // Store document with embedding const text = "The quantum computer achieved 100 qubits"; const embedding = await computeEmbedding(text); await adapter.insertPattern({ id: '', type: 'document', domain: 'technology', pattern_data: JSON.stringify({ embedding, text, metadata: { category: "quantum", date: "2025-01-15" } }), confidence: 1.0, usage_count: 0, success_count: 0, created_at: Date.now(), last_used: Date.now(), }); // Semantic search with MMR (Maximal Marginal Relevance) const queryEmbedding = await computeEmbedding("quantum computing advances"); const results = await adapter.retrieveWithReasoning(queryEmbedding, { domain: 'technology', k: 10, useMMR: true, // Diverse results synthesizeContext: true, // Rich context }); “` ## Core Features ### 1. Vector Storage “`typescript // Store with automatic embedding await db.storeWithEmbedding({ content: "Your document text", metadata: { source: "docs", page: 42 } }); “` ### 2. Similarity Search “`typescript // Find similar documents const similar = await db.findSimilar("quantum computing", { limit: 5, minScore: 0.75 }); “` ### 3. Hybrid Search (Vector + Metadata) “`typescript // Combine vector similarity with metadata filtering const results = await db.hybridSearch({ query: "machine learning models", filters: { category: "research", date: { $gte: "2024-01-01" } }, limit: 20 }); “` ## Advanced Usage ### RAG (Retrieval Augmented Generation) “`typescript // Build RAG pipeline async function ragQuery(question: string) { // 1. Get relevant context const context = await db.searchSimilar( await embed(question), { limit: 5, threshold: 0.7 } ); // 2. Generate answer with context const prompt = `Context: ${context.map(c => c.text).join('\n')} Question: ${question}`; return await llm.generate(prompt); } “` ### Batch Operations “`typescript // Efficient batch storage await db.batchStore(documents.map(doc => ({ text: doc.content, embedding: doc.vector, metadata: doc.meta }))); “` ## MCP Server Integration “`bash # Start AgentDB MCP server for Claude Code npx agentdb@latest mcp # Add to Claude Code (one-time setup) claude mcp add agentdb npx agentdb@latest mcp # Now use MCP tools in Claude Code: # – agentdb_query: Semantic vector search # – agentdb_store: Store documents with embeddings # – agentdb_stats: Database statistics “` ## Performance Benchmarks “`bash # Run comprehensive benchmarks npx agentdb@latest benchmark # Results: # ✅ Pattern Search: 150x faster (100µs vs 15ms) # ✅ Batch Insert: 500x faster (2ms vs 1s for 100 vectors) # ✅ Large-scale Query: 12,500x faster (8ms vs 100s at 1M vectors) # ✅ Memory Efficiency: 4-32x reduction with quantization “` ## Quantization Options AgentDB provides multiple quantization strategies for memory efficiency: ### Binary Quantization (32x reduction) “`typescript const adapter = await createAgentDBAdapter({ quantizationType: 'binary', // 768-dim → 96 bytes }); “` ### Scalar Quantization (4x reduction) “`typescript const adapter = await createAgentDBAdapter({ quantizationType: 'scalar', // 768-dim → 768 bytes }); “` ### Product Quantization (8-16x reduction) “`typescript const adapter = await createAgentDBAdapter({ quantizationType: 'product', // 768-dim → 48-96 bytes }); “` ## Distance Metrics “`bash # Cosine similarity (default, best for most use cases) npx agentdb@latest query ./db.sqlite "[…]" -m cosine # Euclidean distance (L2 norm) npx agentdb@latest query ./db.sqlite "[…]" -m euclidean # Dot product (for normalized vectors) npx agentdb@latest query ./db.sqlite "[…]" -m dot “` ## Advanced Features ### HNSW Indexing – **O(log n) search complexity** – **Sub-millisecond retrieval** (<100µs) – **Automatic index building** ### Caching – **1000 pattern in-memory cache** – **<1ms pattern retrieval** – **Automatic cache invalidation** ### MMR (Maximal Marginal Relevance) – **Diverse result sets** – **Avoid redundancy** – **Balance relevance and diversity** ## Performance Tips 1. **Enable HNSW indexing**: Automatic with AgentDB, 10-100x faster 2. **Use quantization**: Binary (32x), Scalar (4x), Product (8-16x) memory reduction 3. **Batch operations**: 500x faster for bulk inserts 4. **Match dimensions**: 1536 (OpenAI), 768 (sentence-transformers), 384 (MiniLM) 5. **Similarity threshold**: Start at 0.7 for quality, adjust based on use case 6. **Enable caching**: 1000 pattern cache for frequent queries ## Troubleshooting ### Issue: Slow search performance “`bash # Check if HNSW indexing is enabled (automatic) npx agentdb@latest stats ./vectors.db # Expected: <100µs search time “` ### Issue: High memory usage “`bash # Enable binary quantization (32x reduction) # Use in adapter: quantizationType: 'binary' “` ### Issue: Poor relevance “`bash # Adjust similarity threshold npx agentdb@latest query ./db.sqlite "[…]" -t 0.8 # Higher threshold # Or use MMR for diverse results # Use in adapter: useMMR: true “` ### Issue: Wrong dimensions “`bash # Check embedding model dimensions: # – OpenAI ada-002: 1536 # – sentence-transformers: 768 # – all-MiniLM-L6-v2: 384 npx agentdb@latest init ./db.sqlite –dimension 768 “` ## Database Statistics “`bash # Get comprehensive stats npx agentdb@latest stats ./vectors.db # Shows: # – Total patterns/vectors # – Database size # – Average confidence # – Domains distribution # – Index status “` ## Performance Characteristics – **Vector Search**: <100µs (HNSW indexing) – **Pattern Retrieval**: <1ms (with cache) – **Batch Insert**: 2ms for 100 vectors – **Memory Efficiency**: 4-32x reduction with quantization – **Scalability**: Handles 1M+ vectors efficiently – **Latency**: Sub-millisecond for most operations ## Learn More – GitHub: https://github.com/ruvnet/agentic-flow/tree/main/packages/agentdb – Documentation: node_modules/agentic-flow/docs/AGENTDB_INTEGRATION.md – MCP Integration: `npx agentdb@latest mcp` for Claude Code – Website: https://agentdb.ruv.io – CLI Help: `npx agentdb@latest –help` – Command Help: `npx agentdb@latest help <command>`