Agentdb semantic vector search

Skill aiskillstore/marketplace/skills/dnyoussef/agentdb-semantic-vector-search

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npx -y skills add aiskillstore/marketplace --skill agentdb-semantic-vector-search

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

SKILL.md

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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