agentdb-semantic-vector-searchlisted

# AgentDB Semantic Vector Search ## Overview Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Build RAG systems, semantic search engines, and knowledge bases. ## SOP Framework: 5-Phase Semantic Search ### Phase 1: Setup Vector Database (1-2 hours) - Initialize AgentDB - Configure embedding model - Setup database schema ### Phase 2: Embed Documents (1-2 hours) - Process document corpus - Generate embeddings - Store vectors with metadata ### Phase 3: Build Search Index (1-2 hours) - Create HNSW index - Optimize search parameters - Test retrieval accuracy ### Phase 4: Implement Query Interface (1-2 hours) - Create REST API endpoints - Add filtering and ranking - Implement hybrid search ### Phase 5: Refine and Optimize (1-2 hours) - Improve relevance - Add re-ranking - Performance tuning ## Quick Start ```typescript import { AgentDB, EmbeddingModel } from 'agentdb-vector-search'; // Initialize const db = new AgentDB({ name: 'semantic-search', dimensions: 1536 }); const embedder = new EmbeddingModel('openai/ada-002'); // Embed documents for (const doc of documents) { const embedding = await embedder.embed(doc.text); await db.insert({ id: doc.id, vector: embedding, metadata: { title: doc.title, content: doc.text } }); } // Search const query = 'machine learning tutorials'; const queryEmbedding = await embedder.embed(query); const results = await db.search({ vector: que