# 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

# 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

# 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

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
});

// Store with automatic embedding
await db.storeWithEmbedding({
  content: "Your document text",
  metadata: { source: "docs", page: 42 }
});

// Find similar documents
const similar = await db.findSimilar("quantum computing", {
  limit: 5,
  minScore: 0.75
});

// 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
});

// 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);
}

// Efficient batch storage
await db.batchStore(documents.map(doc => ({
  text: doc.content,
  embedding: doc.vector,
  metadata: doc.meta
})));

# 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

# 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

const adapter = await createAgentDBAdapter({
  quantizationType: 'binary',  // 768-dim → 96 bytes
});

const adapter = await createAgentDBAdapter({
  quantizationType: 'scalar',  // 768-dim → 768 bytes
});

const adapter = await createAgentDBAdapter({
  quantizationType: 'product',  // 768-dim → 48-96 bytes
});

# 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

# Check if HNSW indexing is enabled (automatic)
npx agentdb@latest stats .$vectors.db

# Expected: <100µs search time

# Enable binary quantization (32x reduction)
# Use in adapter: quantizationType: 'binary'

# 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

# Check embedding model dimensions:
# - OpenAI ada-002: 1536
# - sentence-transformers: 768
# - all-MiniLM-L6-v2: 384

npx agentdb@latest init .$db.sqlite --dimension 768

# Get comprehensive stats
npx agentdb@latest stats .$vectors.db

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