When NOT to Use This Skill

Local-only operations with no vector search needs
Simple key-value storage without semantic similarity
Real-time streaming data without persistence requirements
Operations that do not require embedding-based retrieval

Success Criteria

Vector search query latency: <10ms for 99th percentile
Embedding generation: <100ms per document
Index build time: <1s per 1000 vectors
Recall@10: >0.95 for similar documents
Database connection success rate: >99.9%
Memory footprint: <2GB for 1M vectors with quantization

Edge Cases & Error Handling

Rate Limits: AgentDB local instances have no rate limits; cloud deployments may vary
Connection Failures: Implement retry logic with exponential backoff (max 3 retries)
Index Corruption: Maintain backup indices; rebuild from source if corrupted
Memory Overflow: Use quantization (4-bit, 8-bit) to reduce memory by 4-32x
Stale Embeddings: Implement TTL-based refresh for dynamic content
Dimension Mismatch: Validate embedding dimensions (384 for sentence-transformers) before insertion

Guardrails & Safety

NEVER expose database connection strings in logs or error messages
ALWAYS validate vector dimensions before insertion
ALWAYS sanitize metadata to prevent injection attacks
NEVER store PII in vector metadata without encryption
ALWAYS implement access control for multi-tenant deployments
ALWAYS validate search results before returning to users

Evidence-Based Validation

Verify database health: Check connection status and index integrity
Validate search quality: Measure recall/precision on test queries
Monitor performance: Track query latency, throughput, and memory usage
Test failure recovery: Simulate connection drops and index corruption
Benchmark improvements: Compare against baseline metrics (e.g., 150x speedup claim)

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.db

# Custom 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

# Shows:
# - Total patterns/vectors
# - Database size
# - Average confidence
# - Domains distribution
# - Index status

Performance Characteristics

Vector Search: <100µs (HNSW indexing)
Pattern Retrieval: <1ms (with cache)
Batch Insert: 2ms for 100 vectors
Memory Efficiency: 4-32x reduction with quantization
Scalability: Handles 1M+ vectors efficiently
Latency: Sub-millisecond for most operations

Learn More

GitHub: https://github.com/ruvnet/agentic-flow/tree/main/packages/agentdb
Documentation: node_modules/agentic-flow/docs/AGENTDB_INTEGRATION.md
MCP Integration: npx agentdb@latest mcp for Claude Code
Website: https://agentdb.ruv.io
CLI Help: npx agentdb@latest --help
Command Help: npx agentdb@latest help <command>

agentdb-vector-search