feat: CRM Clinicas SaaS - MVP completo

- Auth: Login/Register con creacion de clinica - Dashboard: KPIs reales, graficas recharts - Pacientes: CRUD completo con busqueda - Agenda: FullCalendar, drag-and-drop, vista recepcion - Expediente: Notas SOAP, signos vitales, CIE-10 - Facturacion: Facturas con IVA, campos CFDI SAT - Inventario: Productos, stock, movimientos, alertas - Configuracion: Clinica, equipo, catalogo servicios - Supabase self-hosted: 18 tablas con RLS multi-tenant - Docker + Nginx para produccion Co-Authored-By: claude-flow <ruv@ruv.net>
2026-03-03 07:04:14 +00:00
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+---
+name: "AgentDB Vector Search"
+description: "Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases."
+---
+
+# 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
+
+```bash
+# 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
+
+```bash
+# 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
+
+```bash
+# 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
+
+```typescript
+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
+```typescript
+// Store with automatic embedding
+await db.storeWithEmbedding({
+  content: "Your document text",
+  metadata: { source: "docs", page: 42 }
+});
+```
+
+### 2. Similarity Search
+```typescript
+// Find similar documents
+const similar = await db.findSimilar("quantum computing", {
+  limit: 5,
+  minScore: 0.75
+});
+```
+
+### 3. Hybrid Search (Vector + Metadata)
+```typescript
+// 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)
+```typescript
+// 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
+```typescript
+// Efficient batch storage
+await db.batchStore(documents.map(doc => ({
+  text: doc.content,
+  embedding: doc.vector,
+  metadata: doc.meta
+})));
+```
+
+## MCP Server Integration
+
+```bash
+# 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
+
+```bash
+# 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)
+```typescript
+const adapter = await createAgentDBAdapter({
+  quantizationType: 'binary',  // 768-dim → 96 bytes
+});
+```
+
+### Scalar Quantization (4x reduction)
+```typescript
+const adapter = await createAgentDBAdapter({
+  quantizationType: 'scalar',  // 768-dim → 768 bytes
+});
+```
+
+### Product Quantization (8-16x reduction)
+```typescript
+const adapter = await createAgentDBAdapter({
+  quantizationType: 'product',  // 768-dim → 48-96 bytes
+});
+```
+
+## Distance Metrics
+
+```bash
+# 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
+
+1. **Enable HNSW indexing**: Automatic with AgentDB, 10-100x faster
+2. **Use quantization**: Binary (32x), Scalar (4x), Product (8-16x) memory reduction
+3. **Batch operations**: 500x faster for bulk inserts
+4. **Match dimensions**: 1536 (OpenAI), 768 (sentence-transformers), 384 (MiniLM)
+5. **Similarity threshold**: Start at 0.7 for quality, adjust based on use case
+6. **Enable caching**: 1000 pattern cache for frequent queries
+
+## Troubleshooting
+
+### Issue: Slow search performance
+```bash
+# Check if HNSW indexing is enabled (automatic)
+npx agentdb@latest stats ./vectors.db
+
+# Expected: <100µs search time
+```
+
+### Issue: High memory usage
+```bash
+# Enable binary quantization (32x reduction)
+# Use in adapter: quantizationType: 'binary'
+```
+
+### Issue: Poor relevance
+```bash
+# 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
+```bash
+# 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
+
+```bash
+# 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>`