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.,
    : embedding,
    : { : doc., : doc. }
  });
}


 query = ;
 queryEmbedding =  embedder.(query);
 results =  db.({
  : queryEmbedding,
  : ,
  : { :  }
});