---
skill_id: when-building-semantic-search-use-agentdb-vector-search
name: agentdb-semantic-vector-search-d46b72dea269
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


---
Source attribution: DNYoussef
Original project: https://github.com/DNYoussef/ai-chrome-extension/blob/a6c374009d5e0bb8650549060dfad84bfbe75293/.claude/skills/agentdb/when-building-semantic-search-use-agentdb-vector-search/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2025 ReadFlow Contributors

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: DNYoussef
Original project: https://github.com/DNYoussef/ai-chrome-extension/blob/a6c374009d5e0bb8650549060dfad84bfbe75293/.claude/skills/agentdb/when-building-semantic-search-use-agentdb-vector-search/SKILL.md
Attribution basis: Source repository record

Fields of work: Data & Analytics, Development & Engineering, Product Management
Roles: Backend Developer, Data Scientist, ML / AI Engineer, Product Manager, QA / Test Engineer
Specialties: API, Backend
Tools: Agentdb
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: bf0a32cc43bdb02761e2082eb4152606930f26adcbe57722eea68a3afa1ac4b1