Free SKILL.md scraped from GitHub. Clone the repo or copy the file directly into your Claude Code skills directory.
npx versuz@latest install aiskillstore-marketplace-skills-dnyoussef-agentdb-semantic-vector-searchgit clone https://github.com/aiskillstore/marketplace.gitcp marketplace/SKILL.MD ~/.claude/skills/aiskillstore-marketplace-skills-dnyoussef-agentdb-semantic-vector-search/SKILL.md---
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: 2025-10-30
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
```typescript
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