agentdb-vector-searchlisted

# 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 ./vect