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Build RAG systems, semantic search engines, and knowledge bases.\n\n## SOP Framework: 5-Phase Semantic Search\n\n### Phase 1: Setup Vector Database (1-2 hours)\n- Initialize AgentDB\n- Configure embedding model\n- Setup database schema\n\n### Phase 2: Embed Documents (1-2 hours)\n- Process document corpus\n- Generate embeddings\n- Store vectors with metadata\n\n### Phase 3: Build Search Index (1-2 hours)\n- Create HNSW index\n- Optimize search parameters\n- Test retrieval accuracy\n\n### Phase 4: Implement Query Interface (1-2 hours)\n- Create REST API endpoints\n- Add filtering and ranking\n- Implement hybrid search\n\n### Phase 5: Refine and Optimize (1-2 hours)\n- Improve relevance\n- Add re-ranking\n- Performance tuning\n\n## Quick Start\n\n```typescript\nimport { AgentDB, EmbeddingModel } from 'agentdb-vector-search';\n\n\u002F\u002F Initialize\nconst db = new AgentDB({ name: 'semantic-search', dimensions: 1536 });\nconst embedder = new EmbeddingModel('openai\u002Fada-002');\n\n\u002F\u002F Embed documents\nfor (const doc of documents) {\n  const embedding = await embedder.embed(doc.text);\n  await db.insert({\n    id: doc.id,\n    vector: embedding,\n    metadata: { title: doc.title, content: doc.text }\n  });\n}\n\n\u002F\u002F Search\nconst query = 'machine learning tutorials';\nconst queryEmbedding = await embedder.embed(query);\nconst results = await db.search({\n  vector: queryEmbedding,\n  topK: 10,\n  filter: { category: 'tech' }\n});\n```\n\n## Features\n\n- **Semantic Search**: Meaning-based retrieval\n- **Hybrid Search**: Vector + keyword search\n- **Filtering**: Metadata-based filtering\n- **Re-ranking**: Improve result relevance\n- **RAG Integration**: Context for LLMs\n\n## Success Metrics\n\n- Retrieval accuracy > 90%\n- Query latency \u003C 100ms\n- Relevant results in top-10: > 95%\n- API uptime > 99.9%\n\n## Additional Resources\n\n- Full docs: SKILL.md\n- AgentDB Vector Search: https:\u002F\u002Fagentdb.dev\u002Fdocs\u002Fvector-search\n","# AgentDB Semantic Vector Search\n\n## Overview\n\nImplement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Build RAG systems, semantic search engines, and knowledge bases.\n\n## SOP Framework: 5-Phase Semantic Search\n\n### Phase 1: Setup Vector Database (1-2 hours)\n- Initialize AgentDB\n- Configure embedding model\n- Setup database schema\n\n### Phase 2: Embed Documents (1-2 hours)\n- Process document corpus\n- Generate embeddings\n- Store vectors with metadata\n\n### Phase 3: Build Search Index (1-2 hours)\n- Create HNSW index\n- Optimize search parameters\n- Test retrieval accuracy\n\n### Phase 4: Implement Query Interface (1-2 hours)\n- Create REST API endpoints\n- Add filtering and ranking\n- Implement hybrid search\n\n### Phase 5: Refine and Optimize (1-2 hours)\n- Improve relevance\n- Add re-ranking\n- Performance tuning\n\n## Quick Start\n\n```typescript\nimport { AgentDB, EmbeddingModel } from 'agentdb-vector-search';\n\n\u002F\u002F Initialize\nconst db = new AgentDB({ name: 'semantic-search', dimensions: 1536 });\nconst embedder = new EmbeddingModel('openai\u002Fada-002');\n\n\u002F\u002F Embed documents\nfor (const doc of documents) {\n  const embedding = await embedder.embed(doc.text);\n  await db.insert({\n    id: doc.id,\n    vector: embedding,\n    metadata: { title: doc.title, content: doc.text }\n  });\n}\n\n\u002F\u002F Search\nconst query = 'machine learning tutorials';\nconst queryEmbedding = await embedder.embed(query);\nconst results = await db.search({\n  vector: queryEmbedding,\n  topK: 10,\n  filter: { category: 'tech' }\n});\n```\n\n## Features\n\n- **Semantic Search**: Meaning-based retrieval\n- **Hybrid Search**: Vector + keyword search\n- **Filtering**: Metadata-based filtering\n- **Re-ranking**: Improve result relevance\n- **RAG Integration**: Context for LLMs\n\n## Success Metrics\n\n- Retrieval accuracy > 90%\n- Query latency \u003C 100ms\n- Relevant results in top-10: > 95%\n- API uptime > 99.9%\n\n## Additional Resources\n\n- Full docs: SKILL.md\n- AgentDB Vector Search: 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