SKILL.md
AgentDB Vector Search
What This Skill Does
Implements vector-based semantic search using AgentDB's high-performance vector database with 150x-12,500x faster operations than traditional solutions. Features HNSW indexing, quantization, and sub-millisecond search (<100µs).
Prerequisites
- Node.js 18+
- AgentDB v1.0.7+ (via agentic-flow or standalone)
- OpenAI API key (for embeddings) or custom embedding model
Quick Start with CLI
Initialize Vector Database
# Initialize with default dimensions (1536 for OpenAI ada-002)
npx agentdb@latest init .$vectors.dbCustom dimensions for different embedding models
npx agentdb@latest init .$vectors.db --dimension 768 # sentence-transformers
npx agentdb@latest init .$vectors.db --dimension 384 # all-MiniLM-L6-v2
Use preset configurations
npx agentdb@latest init .$vectors.db --preset small # <10K vectors
npx agentdb@latest init .$vectors.db --preset medium # 10K-100K vectors
npx agentdb@latest init .$vectors.db --preset large # >100K vectors
In-memory database for testing
npx agentdb@latest init .$vectors.db --in-memory
### Query Vector Database
Basic similarity search
npx agentdb@latest query .$vectors.db "[0.1,0.2,0.3,...]"
Top-k results
npx agentdb@latest query .$vectors.db "[0.1,0.2,0.3]" -k 10
With similarity threshold (cosine similarity)
npx agentdb@latest query .$vectors.db "0.1 0.2 0.3" -t 0.75 -m cosine
Different distance metrics
npx agentdb@latest query .$vectors.db "[...]" -m euclidean # L2 distance
npx agentdb@latest query .$vectors.db "[...]" -m dot # Dot product
JSON output for automation
npx agentdb@latest query .$vectors.db "[...]" -f json -k 5
Verbose output with distances
npx agentdb@latest query .$vectors.db "[...]" -v
### Import/Export Vectors
Export vectors to JSON
npx agentdb@latest export .$vectors.db .$backup.json
Import vectors from JSON
npx agentdb@latest import .$backup.json
Get database statistics
npx agentdb@latest stats .$vectors.db
## Quick Start with API
import { createAgentDBAdapter, computeEmbedding } from 'agentic-flow$reasoningbank';
// Initialize with vector search optimizations
const adapter = await createAgentDBAdapter({
dbPath: '.agentdb$vectors.db',
enableLearning: false, // Vector search only
enableReasoning: true, // Enable semantic matching
quantizationType: 'binary', // 32x memory reduction
cacheSize: 1000, // Fast retrieval
});
// Store document with embedding
const text = "The quantum computer achieved 100 qubits";
const embedding = await computeEmbedding(text);
await adapter.insertPattern({
id: '',
type: 'document',
domain: 'technology',
pattern_data: JSON.stringify({
embedding,
text,
metadata: { category: "quantum", date: "2025-01-15" }
}),
confidence: 1.0,
usage_count: 0,
success_count: 0,
created_at: Date.now(),
last_used: Date.now(),
});
// Semantic search with MMR (Maximal Marginal Relevance)
const queryEmbedding = await computeEmbedding("quantum computing advances");
const results = await adapter.retrieveWithReasoning(queryEmbedding, {
domain: 'technology',
k: 10,
useMMR: true, // Diverse results
synthesizeContext: true, // Rich context
});
## Core Features
### 1. Vector Storage
// Store with automatic embedding
await db.storeWithEmbedding({
content: "Your document text",
metadata: { source: "docs", page: 42 }
});
### 2. Similarity Search
// Find similar documents
const similar = await db.findSimilar("quantum computing", {
limit: 5,
minScore: 0.75
});
### 3. Hybrid Search (Vector + Metadata)
// Combine vector similarity with metadata filtering
const results = await db.hybridSearch({
query: "machine learning models",
filters: {
category: "research",
date: { $gte: "2024-01-01" }
},
limit: 20
});
## Advanced Usage
### RAG (Retrieval Augmented Generation)
// Build RAG pipeline
async function ragQuery(question: string) {
// 1. Get relevant context
const context = await db.searchSimilar(
await embed(question),
{ limit: 5, threshold: 0.7 }
);
// 2. Generate answer with context
const prompt = Context: ${context.map(c => c.text).join('\n')}
Question: ${question};
return await llm.generate(prompt);
}
### Batch Operations
// Efficient batch storage
await db.batchStore(documents.map(doc => ({
text: doc.content,
embedding: doc.vector,
metadata: doc.meta
})));
## MCP Server Integration
Start AgentDB MCP server for Claude Code
npx agentdb@latest mcp
Add to Claude Code (one-time setup)
claude mcp add agentdb npx agentdb@latest mcp
Now use MCP tools in Claude Code:
- agentdb_query: Semantic vector search
- agentdb_store: Store documents with embeddings
- agentdb_stats: Database statistics
## Performance Benchmarks
Run comprehensive benchmarks
npx agentdb@latest benchmark
Results:
✅ Pattern Search: 150x faster (100µs vs 15ms)
✅ Batch Insert: 500x faster (2ms vs 1s for 100 vectors)
✅ Large-scale Query: 12,500x faster (8ms vs 100s at 1M vectors)
✅ Memory Efficiency: 4-32x reduction with quantization
## Quantization Options
AgentDB provides multiple quantization strategies for memory efficiency:
### Binary Quantization (32x reduction)
const adapter = await createAgentDBAdapter({
quantizationType: 'binary', // 768-dim → 96 bytes
});
### Scalar Quantization (4x reduction)
const adapter = await createAgentDBAdapter({
quantizationType: 'scalar', // 768-dim → 768 bytes
});
### Product Quantization (8-16x reduction)
const adapter = await createAgentDBAdapter({
quantizationType: 'product', // 768-dim → 48-96 bytes
});
## Distance Metrics
Cosine similarity (default, best for most use cases)
npx agentdb@latest query .$db.sqlite "[...]" -m cosine
Euclidean distance (L2 norm)
npx agentdb@latest query .$db.sqlite "[...]" -m euclidean
Dot product (for normalized vectors)
npx agentdb@latest query .$db.sqlite "[...]" -m dot
## Advanced Features
### HNSW Indexing
- **O(log n) search complexity**
- **Sub-millisecond retrieval** (<100µs)
- **Automatic index building**
### Caching
- **1000 pattern in-memory cache**
- **<1ms pattern retrieval**
- **Automatic cache invalidation**
### MMR (Maximal Marginal Relevance)
- **Diverse result sets**
- **Avoid redundancy**
- **Balance relevance and diversity**
## Performance Tips
- **Enable HNSW indexing**: Automatic with AgentDB, 10-100x faster
- **Use quantization**: Binary (32x), Scalar (4x), Product (8-16x) memory reduction
- **Batch operations**: 500x faster for bulk inserts
- **Match dimensions**: 1536 (OpenAI), 768 (sentence-transformers), 384 (MiniLM)
- **Similarity threshold**: Start at 0.7 for quality, adjust based on use case
- **Enable caching**: 1000 pattern cache for frequent queries
## Troubleshooting
### Issue: Slow search performance
Check if HNSW indexing is enabled (automatic)
npx agentdb@latest stats .$vectors.db
Expected: <100µs search time
### Issue: High memory usage
Enable binary quantization (32x reduction)
Use in adapter: quantizationType: 'binary'
### Issue: Poor relevance
Adjust similarity threshold
npx agentdb@latest query .$db.sqlite "[...]" -t 0.8 # Higher threshold
Or use MMR for diverse results
Use in adapter: useMMR: true
### Issue: Wrong dimensions
Check embedding model dimensions:
- OpenAI ada-002: 1536
- sentence-transformers: 768
- all-MiniLM-L6-v2: 384
npx agentdb@latest init .$db.sqlite --dimension 768
## Database Statistics
Get comprehensive stats
npx agentdb@latest stats .$vectors.db