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: 2025-10-30 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