# When Building Semantic Search Use Agentdb Vector Search > When Building Semantic Search Use Agentdb Vector Search - Skill: `dnyoussef/when-building-semantic-search-use-agentdb-vector-search` (Agent Skill, multi-file: 5 files) - Install (CLI): `npx skillmds@latest add dnyoussef/when-building-semantic-search-use-agentdb-vector-search` - Raw SKILL.md: https://api.skillmd.com/api/skills/dnyoussef/when-building-semantic-search-use-agentdb-vector-search/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: DNYoussef (https://skillmd.com/u/dnyoussef) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/dnyoussef/when-building-semantic-search-use-agentdb-vector-search --- /*============================================================================*/ /* AGENTDB SEMANTIC VECTOR SEARCH SKILL :: VERILINGUA x VERIX EDITION */ /*============================================================================*/ --- name: AgentDB Semantic Vector Search version: 1.0.0 description: | [assert|neutral] AgentDB Semantic Vector Search skill for agentdb workflows [ground:given] [conf:0.95] [state:confirmed] category: agentdb tags: - general author: system cognitive_frame: primary: evidential goal_analysis: first_order: "Execute AgentDB Semantic Vector Search workflow" second_order: "Ensure quality and consistency" third_order: "Enable systematic agentdb processes" --- /*----------------------------------------------------------------------------*/ /* S0 META-IDENTITY */ /*----------------------------------------------------------------------------*/ [define|neutral] SKILL := { name: "AgentDB Semantic Vector Search", category: "agentdb", version: "1.0.0", layer: L1 } [ground:given] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S1 COGNITIVE FRAME */ /*----------------------------------------------------------------------------*/ [define|neutral] COGNITIVE_FRAME := { frame: "Evidential", source: "Turkish", force: "How do you know?" } [ground:cognitive-science] [conf:0.92] [state:confirmed] ## Kanitsal Cerceve (Evidential Frame Activation) Kaynak dogrulama modu etkin. /*----------------------------------------------------------------------------*/ /* S2 TRIGGER CONDITIONS */ /*----------------------------------------------------------------------------*/ [define|neutral] TRIGGER_POSITIVE := { keywords: ["AgentDB Semantic Vector Search", "agentdb", "workflow"], context: "user needs AgentDB Semantic Vector Search capability" } [ground:given] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S3 CORE CONTENT */ /*----------------------------------------------------------------------------*/ # AgentDB Semantic Vector Search ## Kanitsal Cerceve (Evidential Frame Activation) Kaynak dogrulama modu etkin. ## 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: 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 - [assert|neutral] Retrieval accuracy > 90% [ground:acceptance-criteria] [conf:0.90] [state:provisional] - [assert|neutral] Query latency < 100ms [ground:acceptance-criteria] [conf:0.90] [state:provisional] - [assert|neutral] Relevant results in top-10: > 95% [ground:acceptance-criteria] [conf:0.90] [state:provisional] - [assert|neutral] API uptime > 99.9% [ground:acceptance-criteria] [conf:0.90] [state:provisional] ## MCP Requirements This skill operates using AgentDB's npm package and API only. No additional MCP servers required. All AgentDB operations are performed through: - npm CLI: `npx agentdb@latest` - TypeScript/JavaScript API: `import { AgentDB } from 'agentdb-vector-search'` ## Additional Resources - Full docs: SKILL.md - AgentDB Vector Search: https://agentdb.dev/docs/vector-search --- ## Core Principles AgentDB Semantic Vector Search operates on 3 fundamental principles for building intelligent document retrieval systems: ### Principle 1: Meaning Over Keywords Semantic search retrieves documents based on meaning similarity rather than exact keyword matching, enabling context-aware retrieval. In practice: - Generate embeddings for documents using models like OpenAI ada-002 (1536 dimensions) to capture semantic meaning - Store vectors with rich metadata (title, content, category, tags) to enable hybrid search combining semantic and keyword filters - Search using query embeddings to find semantically similar documents even when exact keywords differ - Implement distance metrics (cosine similarity, euclidean) to rank results by semantic relevance rather than keyword frequency ### Principle 2: Performance Through Indexing Build HNSW indexes for 150x faster vector search compared to exhaustive search, essential for production-scale retrieval. In practice: - Create HNSW (Hierarchical Navigable Small World) indexes after document ingestion to enable fast approximate nearest neighbor search - Optimize search parameters (ef_construction, M) based on accuracy vs speed tradeoffs for your use case - Test retrieval accuracy with evaluation datasets to en /*----------------------------------------------------------------------------*/ /* S4 SUCCESS CRITERIA */ /*----------------------------------------------------------------------------*/ [define|neutral] SUCCESS_CRITERIA := { primary: "Skill execution completes successfully", quality: "Output meets quality thresholds", verification: "Results validated against requirements" } [ground:given] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S5 MCP INTEGRATION */ /*----------------------------------------------------------------------------*/ [define|neutral] MCP_INTEGRATION := { memory_mcp: "Store execution results and patterns", tools: ["mcp__memory-mcp__memory_store", "mcp__memory-mcp__vector_search"] } [ground:witnessed:mcp-config] [conf:0.95] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S6 MEMORY NAMESPACE */ /*----------------------------------------------------------------------------*/ [define|neutral] MEMORY_NAMESPACE := { pattern: "skills/agentdb/AgentDB Semantic Vector Search/{project}/{timestamp}", store: ["executions", "decisions", "patterns"], retrieve: ["similar_tasks", "proven_patterns"] } [ground:system-policy] [conf:1.0] [state:confirmed] [define|neutral] MEMORY_TAGGING := { WHO: "AgentDB Semantic Vector Search-{session_id}", WHEN: "ISO8601_timestamp", PROJECT: "{project_name}", WHY: "skill-execution" } [ground:system-policy] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S7 SKILL COMPLETION VERIFICATION */ /*----------------------------------------------------------------------------*/ [direct|emphatic] COMPLETION_CHECKLIST := { agent_spawning: "Spawn agents via Task()", registry_validation: "Use registry agents only", todowrite_called: "Track progress with TodoWrite", work_delegation: "Delegate to specialized agents" } [ground:system-policy] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S8 ABSOLUTE RULES */ /*----------------------------------------------------------------------------*/ [direct|emphatic] RULE_NO_UNICODE := forall(output): NOT(unicode_outside_ascii) [ground:windows-compatibility] [conf:1.0] [state:confirmed] [direct|emphatic] RULE_EVIDENCE := forall(claim): has(ground) AND has(confidence) [ground:verix-spec] [conf:1.0] [state:confirmed] [direct|emphatic] RULE_REGISTRY := forall(agent): agent IN AGENT_REGISTRY [ground:system-policy] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* PROMISE */ /*----------------------------------------------------------------------------*/ [commit|confident] AGENTDB SEMANTIC VECTOR SEARCH_VERILINGUA_VERIX_COMPLIANT [ground:self-validation] [conf:0.99] [state:confirmed]