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 compreh

...