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agentdb-semantic-vector-search

// Build semantic vector search systems with AgentDB for intelligent document retrieval, RAG applications, and knowledge bases using embedding-based similarity matching

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stars:194
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updated:March 4, 2026
SKILL.mdreadonly
SKILL.md Frontmatter
skill_idwhen-building-semantic-search-use-agentdb-vector-search
nameagentdb-semantic-vector-search
descriptionBuild semantic vector search systems with AgentDB for intelligent document retrieval, RAG applications, and knowledge bases using embedding-based similarity matching
version1.0.0
categoryagentdb
subcategorysemantic-search
trigger_patternwhen-building-semantic-search
agentsml-developer,backend-dev,tester
complexityintermediate
estimated_duration6-8 hours
prerequisitesAgentDB basics,Embedding models knowledge,REST API development
outputsSemantic search engine,Document retrieval system,RAG-ready infrastructure,Query API endpoints
validation_criteriaSearch returns relevant results,Retrieval accuracy > 90%,Query latency < 100ms,API functional and documented
evidence_based_techniquesRelevance evaluation,Precision/recall metrics,User feedback testing
metadata[object Object]

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