advanced-search-library/examples/advanced-usage.js

Intelligent search library with typo correction, autocomplete, and flexible data structure support

/**
 * Advanced Search Library - Advanced Usage Example
 * 
 * @author Rıdvan Sevindik <sevindikbusiness@gmail.com>
 * @github https://github.com/Ridvan0
 * @linkedin https://www.linkedin.com/in/ridvansevindik/
 */

const AdvancedSearch = require('../src/AdvancedSearch');

console.log('🔍 Advanced Search Library - Advanced Usage\n');

// Create search with custom options
const search = new AdvancedSearch({
    maxResults: 20,
    minQueryLength: 2,
    typoThreshold: 3,
    historyLimit: 100
});

console.log('✅ Search engine created with custom settings');

// Advanced data with custom fields
const advancedData = [
    {
        id: 1,
        productName: "Premium Coffee Machine",
        description: "Automatic espresso machine with built-in grinder",
        specifications: "15 bar pressure, stainless steel, programmable",
        manufacturer: "Delonghi",
        category: "Appliances",
        price: 1200,
        rating: 4.8,
        keywords: ["coffee", "espresso", "automatic", "grinder"],
        features: ["programmable", "stainless steel", "built-in grinder"]
    },
    {
        id: 2,
        title: "Professional Drum Kit",
        content: "Complete 7-piece drum set for professional musicians",
        details: "Includes cymbals, hardware, and throne",
        brand: "Pearl",
        type: "Musical Instruments",
        cost: 3500,
        score: 4.9,
        searchTerms: ["drum", "kit", "professional", "percussion"],
        attributes: ["complete", "professional", "7-piece"]
    },
    {
        id: 3,
        itemName: "Gaming Laptop",
        summary: "High-performance gaming laptop with RTX graphics",
        specs: "Intel i7, 16GB RAM, RTX 4060, 1TB SSD",
        company: "ASUS",
        segment: "Computers",
        value: 2800,
        rating: 4.7,
        tags: ["gaming", "laptop", "high-performance", "RTX"],
        properties: ["Intel i7", "RTX 4060", "16GB RAM"]
    }
];

// Add data with auto field detection
search.addData(advancedData);

console.log('✅ Advanced data added with auto field detection');
console.log(`   Detected fields: ${search.searchableFields.map(f => f.field).join(', ')}`);

// Performance test with large dataset
console.log('\n📊 Performance Test:');
const largeDataset = [];
for (let i = 0; i < 5000; i++) {
    largeDataset.push({
        id: i + 100,
        name: `Product ${i}`,
        description: `Description for product ${i} with various keywords`,
        category: `Category ${i % 20}`,
        price: Math.floor(Math.random() * 1000) + 10,
        rating: Math.round((Math.random() * 5) * 10) / 10,
        tags: [`tag${i % 10}`, `keyword${i % 15}`, `feature${i % 8}`]
    });
}

search.addData(largeDataset);

const startTime = performance.now();
const perfResults = search.search('product');
const endTime = performance.now();

console.log(`   Search time: ${(endTime - startTime).toFixed(2)}ms`);
console.log(`   Results found: ${perfResults.length}`);
console.log(`   Total items: ${search.getStats().totalItems}`);

// Advanced filtering combinations
console.log('\n🔧 Advanced Filtering:');

// Combined filters
const complexFilter = {
    priceRange: [100, 2000],
    category: 'Appliances',
    minRating: 4.5,
    sortBy: 'rating_desc'
};

const filteredResults = search.search('coffee', complexFilter);
console.log(`   Complex filter search found ${filteredResults.length} results:`);
filteredResults.forEach(item => {
    const name = item.productName || item.name || item.title;
    const category = item.category || item.type || item.segment;
    const price = item.price || item.cost || item.value;
    const rating = item.rating || item.score;
    console.log(`   - ${name} (${category}) - $${price} - ⭐${rating}`);
});

// Custom field priority search
console.log('\n🎯 Custom Field Priority:');
const customSearch = new AdvancedSearch({
    customFields: [
        { field: 'productName', priority: 20, exact: 40 },
        { field: 'title', priority: 20, exact: 40 },
        { field: 'name', priority: 15, exact: 30 },
        { field: 'description', priority: 8, exact: 15 },
        { field: 'keywords', priority: 12, exact: 20 },
        { field: 'tags', priority: 10, exact: 18 }
    ]
});

customSearch.addData(advancedData);

const priorityResults = customSearch.search('coffee');
console.log(`   Custom priority search found ${priorityResults.length} results:`);
priorityResults.forEach(item => {
    const name = item.productName || item.name || item.title;
    console.log(`   - ${name} (Score: ${item.relevanceScore})`);
});

// Multi-word and phrase search
console.log('\n📝 Multi-word Search:');
const multiWordQueries = [
    'gaming laptop',
    'coffee machine',
    'professional drum',
    'high performance'
];

multiWordQueries.forEach(query => {
    const results = search.search(query);
    console.log(`   "${query}": ${results.length} results`);
    if (results.length > 0) {
        const topResult = results[0];
        const name = topResult.productName || topResult.name || topResult.title || topResult.itemName;
        console.log(`     Top: ${name} (Score: ${topResult.relevanceScore})`);
    }
});

// Search history analysis
console.log('\n📈 Search History Analysis:');
const history = search.getSearchHistory();
const queryFrequency = {};

history.forEach(entry => {
    queryFrequency[entry.query] = (queryFrequency[entry.query] || 0) + 1;
});

const popularQueries = Object.entries(queryFrequency)
    .sort(([,a], [,b]) => b - a)
    .slice(0, 5);

console.log(`   Most popular queries:`);
popularQueries.forEach(([query, count]) => {
    console.log(`     "${query}": ${count} times`);
});

// Autocomplete with context
console.log('\n💡 Contextual Autocomplete:');
const autocompleteTests = ['cof', 'gam', 'pro', 'lap'];

autocompleteTests.forEach(partial => {
    const suggestions = search.autocomplete(partial);
    console.log(`   "${partial}": [${suggestions.join(', ')}]`);
});

// Final statistics
console.log('\n📊 Final Statistics:');
const finalStats = search.getStats();
console.log(`   Total items indexed: ${finalStats.totalItems}`);
console.log(`   Total searches performed: ${finalStats.totalSearches}`);
console.log(`   Unique search queries: ${finalStats.uniqueSearches}`);
console.log(`   Average results per search: ${finalStats.averageResultsPerSearch.toFixed(2)}`);

console.log('\n✅ Advanced usage example completed!');
console.log('   The library has successfully processed large datasets and complex queries.');