Merge commit 'd803bfe2b1fe7f5e219e50ac20d6801a0a58ac75' as 'vendor/ruvector'

vendor/ruvector/examples/nodejs/semantic_search.js

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+/**
+ * Semantic Search Example (Node.js)
+ *
+ * Demonstrates building a semantic search system with Ruvector
+ */
+
+const { VectorDB } = require('ruvector');
+
+// Mock embedding function (in production, use a real embedding model)
+function mockEmbedding(text, dims = 384) {
+    // Simple hash-based mock embedding
+    let hash = 0;
+    for (let i = 0; i < text.length; i++) {
+        hash = ((hash << 5) - hash) + text.charCodeAt(i);
+        hash = hash & hash;
+    }
+
+    const embedding = new Float32Array(dims);
+    for (let i = 0; i < dims; i++) {
+        embedding[i] = Math.sin((hash + i) * 0.01);
+    }
+    return embedding;
+}
+
+async function main() {
+    console.log('🔍 Semantic Search Example\n');
+
+    // 1. Setup database
+    console.log('1. Setting up search index...');
+    const db = new VectorDB({
+        dimensions: 384,
+        storagePath: './semantic_search.db',
+        distanceMetric: 'cosine',
+        hnsw: {
+            m: 32,
+            efConstruction: 200,
+            efSearch: 100
+        }
+    });
+    console.log('   ✓ Database created\n');
+
+    // 2. Index documents
+    console.log('2. Indexing documents...');
+    const documents = [
+        {
+            id: 'doc_001',
+            text: 'The quick brown fox jumps over the lazy dog',
+            category: 'animals'
+        },
+        {
+            id: 'doc_002',
+            text: 'Machine learning is a subset of artificial intelligence',
+            category: 'technology'
+        },
+        {
+            id: 'doc_003',
+            text: 'Python is a popular programming language for data science',
+            category: 'technology'
+        },
+        {
+            id: 'doc_004',
+            text: 'The cat sat on the mat while birds sang outside',
+            category: 'animals'
+        },
+        {
+            id: 'doc_005',
+            text: 'Neural networks are inspired by biological neurons',
+            category: 'technology'
+        },
+        {
+            id: 'doc_006',
+            text: 'Dogs are loyal companions and great pets',
+            category: 'animals'
+        },
+        {
+            id: 'doc_007',
+            text: 'Deep learning requires large amounts of training data',
+            category: 'technology'
+        },
+        {
+            id: 'doc_008',
+            text: 'Birds migrate south during winter months',
+            category: 'animals'
+        }
+    ];
+
+    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 ${documents.length} documents\n`);
+
+    // 3. Perform semantic searches
+    const queries = [
+        'artificial intelligence and neural networks',
+        'pets and domestic animals',
+        'programming and software development'
+    ];
+
+    for (const query of queries) {
+        console.log(`Query: "${query}"`);
+        console.log('─'.repeat(60));
+
+        const queryEmbedding = mockEmbedding(query);
+        const results = await db.search({
+            vector: queryEmbedding,
+            k: 3,
+            includeMetadata: true
+        });
+
+        results.forEach((result, i) => {
+            console.log(`${i + 1}. ${result.metadata.text}`);
+            console.log(`   Category: ${result.metadata.category}, Similarity: ${(1 - result.distance).toFixed(4)}`);
+        });
+        console.log();
+    }
+
+    // 4. Filtered semantic search
+    console.log('Filtered search (category: technology)');
+    console.log('─'.repeat(60));
+
+    const techQuery = mockEmbedding('computers and algorithms');
+    const filteredResults = await db.search({
+        vector: techQuery,
+        k: 3,
+        filter: { category: 'technology' },
+        includeMetadata: true
+    });
+
+    filteredResults.forEach((result, i) => {
+        console.log(`${i + 1}. ${result.metadata.text}`);
+        console.log(`   Similarity: ${(1 - result.distance).toFixed(4)}`);
+    });
+    console.log();
+
+    console.log('✅ Semantic search example completed!');
+    console.log('\n💡 In production:');
+    console.log('   • Use a real embedding model (OpenAI, Sentence Transformers, etc.)');
+    console.log('   • Add more documents to your knowledge base');
+    console.log('   • Implement filters for category, date, author, etc.');
+    console.log('   • Add hybrid search (vector + keyword) for better results');
+}
+
+main().catch(console.error);