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Agent skill
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 this agent skill to your Project
npx add-skill https://github.com/aiskillstore/marketplace/tree/main/skills/dnyoussef/agentdb-semantic-vector-search
Metadata
Additional technical details for this skill
- tags
-
agentdb semantic-search rag vector-search embeddings
- author
- claude-flow
- created
- 1761782400
SKILL.md
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
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