agentdb-semantic-vector-search

skill_id when-building-semantic-search-use-agentdb-vector-search name agentdb-semantic-vector-search description Build semantic vector search systems with AgentDB for intelligent document retrieval, RAG applications, and knowledge bases using embedding-based similarity matching version 1.0.0 category agentdb subcategory semantic-search trigger_pattern when-building-semantic-search agents ["ml-developer","backend-dev","tester"] complexity intermediate estimated_duration 6-8 hours prerequisites ["AgentDB basics","Embedding models knowledge","REST API development"] outputs ["Semantic search engine","Document retrieval system","RAG-ready infrastructure","Query API endpoints"] validation_criteria ["Search returns relevant results","Retrieval accuracy > 90%","Query latency < 100ms","API functional and documented"] evidence_based_techniques ["Relevance evaluation","Precision/recall metrics","User feedback testing"] metadata {"author":"claude-flow","created":"2025-10-30T00:00:00.000Z","tags":["agentdb","semantic-search","rag","vector-search","embeddings"]} 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 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 : queryEmbedding, topK : 10 , filter : { category : 'tech' } }); Features Semantic Search : Meaning-based retrieval Hybrid Search : Vector + keyword search Filtering : Metadata-based filtering Re-ranking : Improve result relevance RAG Integration : Context for LLMs Success Metrics Retrieval accuracy > 90% Query latency < 100ms Relevant results in top-10: > 95% API uptime > 99.9% Additional Resources Full docs: SKILL.md AgentDB Vector Search: https://agentdb.dev/docs/vector-search