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claude-codeagentdb

Marketplace 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

install
source · Clone the upstream repo
git clone https://github.com/aiskillstore/marketplace
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/aiskillstore/marketplace "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/dnyoussef/agentdb-semantic-vector-search" ~/.claude/skills/aiskillstore-marketplace-agentdb-semantic-vector-search && rm -rf "$T"
manifest: skills/dnyoussef/agentdb-semantic-vector-search/SKILL.md
source content

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