| 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 | [object Object] |
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