Squashed 'vendor/ruvector/' content from commit b64c2172

git-subtree-dir: vendor/ruvector
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crates/ruvector-node/examples/semantic-search.mjs

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+#!/usr/bin/env node
+
+/**
+ * Semantic search example with text embeddings
+ *
+ * Note: This example assumes you have a way to generate embeddings.
+ * In practice, you would use an embedding model like sentence-transformers
+ * or OpenAI's API to generate actual embeddings.
+ */
+
+import { VectorDB } from '../index.js';
+
+// Mock embedding function (in practice, use a real embedding model)
+function mockEmbedding(text, dim = 384) {
+  // Simple deterministic "embedding" based on text
+  const hash = text.split('').reduce((acc, char) => {
+    return ((acc << 5) - acc) + char.charCodeAt(0);
+  }, 0);
+
+  const vector = new Float32Array(dim);
+  for (let i = 0; i < dim; i++) {
+    vector[i] = Math.sin(hash * (i + 1) * 0.1);
+  }
+
+  // Normalize
+  const norm = Math.sqrt(vector.reduce((sum, val) => sum + val * val, 0));
+  for (let i = 0; i < dim; i++) {
+    vector[i] /= norm;
+  }
+
+  return vector;
+}
+
+async function main() {
+  console.log('🚀 Ruvector Semantic Search Example\n');
+
+  // Sample documents
+  const documents = [
+    { id: 'doc1', text: 'The cat sat on the mat', category: 'animals' },
+    { id: 'doc2', text: 'The dog played in the park', category: 'animals' },
+    { id: 'doc3', text: 'Python is a programming language', category: 'tech' },
+    { id: 'doc4', text: 'JavaScript is used for web development', category: 'tech' },
+    { id: 'doc5', text: 'Machine learning models learn from data', category: 'tech' },
+    { id: 'doc6', text: 'The bird flew over the tree', category: 'animals' },
+    { id: 'doc7', text: 'Rust is a systems programming language', category: 'tech' },
+    { id: 'doc8', text: 'The fish swam in the ocean', category: 'animals' },
+    { id: 'doc9', text: 'Neural networks are inspired by the brain', category: 'tech' },
+    { id: 'doc10', text: 'The horse galloped across the field', category: 'animals' },
+  ];
+
+  // Create database
+  const db = new VectorDB({
+    dimensions: 384,
+    distanceMetric: 'Cosine',
+    storagePath: './semantic-search.db',
+  });
+
+  console.log('✅ Created vector database');
+
+  // Index documents
+  console.log('\n📝 Indexing documents...');
+
+  const entries = documents.map((doc) => ({
+    id: doc.id,
+    vector: mockEmbedding(doc.text),
+    metadata: {
+      text: doc.text,
+      category: doc.category,
+    },
+  }));
+
+  await db.insertBatch(entries);
+  console.log(`  Indexed ${documents.length} documents`);
+
+  // Search queries
+  const queries = [
+    'animals in nature',
+    'programming languages',
+    'artificial intelligence',
+    'pets and animals',
+  ];
+
+  console.log('\n🔍 Running semantic searches...\n');
+
+  for (const query of queries) {
+    console.log(`Query: "${query}"`);
+
+    const results = await db.search({
+      vector: mockEmbedding(query),
+      k: 3,
+    });
+
+    console.log('  Top results:');
+    results.forEach((result, i) => {
+      console.log(`    ${i + 1}. [${result.metadata?.category}] ${result.metadata?.text}`);
+      console.log(`       Score: ${result.score.toFixed(4)}`);
+    });
+    console.log();
+  }
+
+  // Category-filtered search
+  console.log('🎯 Filtered search (tech category only)...\n');
+
+  const techQuery = 'coding and software';
+  console.log(`Query: "${techQuery}"`);
+
+  const techResults = await db.search({
+    vector: mockEmbedding(techQuery),
+    k: 3,
+    filter: { category: 'tech' },
+  });
+
+  console.log('  Top results:');
+  techResults.forEach((result, i) => {
+    console.log(`    ${i + 1}. [${result.metadata?.category}] ${result.metadata?.text}`);
+    console.log(`       Score: ${result.score.toFixed(4)}`);
+  });
+
+  // Update a document
+  console.log('\n📝 Updating a document...');
+
+  await db.delete('doc3');
+  await db.insert({
+    id: 'doc3',
+    vector: mockEmbedding('Python is great for machine learning and AI'),
+    metadata: {
+      text: 'Python is great for machine learning and AI',
+      category: 'tech',
+    },
+  });
+
+  console.log('  Updated doc3');
+
+  // Search again to see the change
+  const updatedResults = await db.search({
+    vector: mockEmbedding('artificial intelligence'),
+    k: 3,
+  });
+
+  console.log('\n  Results after update:');
+  updatedResults.forEach((result, i) => {
+    console.log(`    ${i + 1}. [${result.metadata?.category}] ${result.metadata?.text}`);
+    console.log(`       Score: ${result.score.toFixed(4)}`);
+  });
+
+  console.log('\n✨ Semantic search example complete!');
+  console.log('\n💡 Tip: In production, use real embeddings from models like:');
+  console.log('   - sentence-transformers (e.g., all-MiniLM-L6-v2)');
+  console.log('   - OpenAI embeddings (text-embedding-ada-002)');
+  console.log('   - Cohere embeddings');
+}
+
+main().catch((err) => {
+  console.error('Error:', err);
+  process.exit(1);
+});