🚀 Vector Search Pro - Next-Gen Content Intelligence

The most powerful, lightweight, and intelligent vector search package for modern applications

✨ What's New in v2.0.1

• 🧠 Content Intelligence Engine: Analyze content tone, style, and generate DNA fingerprints
• 🎯 Tone Detection: Automatically detect professional, casual, technical, formal, and conversational tones
• 🎨 Style Analysis: Analyze writing style, readability, and complexity
• 🧬 Content DNA: Generate unique content fingerprints and relationship maps
• 🔗 Content Fusion: Combine multiple sources into coherent summaries with conflict detection
• ⚡ Adaptive Optimization: Self-optimizing chunk sizes and search algorithms
• 📊 Performance Analytics: Real-time performance tracking and optimization recommendations

🚀 Quick Start

npm install vector-chunk

import { VectorSearch } from 'vector-chunk';

// Initialize with all intelligent features
const searchEngine = new VectorSearch();

// Basic search (your original function)
const results = await searchEngine.searchContent(
  "Your document content here...", 
  "search term"
);

// Content analysis
const analysis = await searchEngine.analyzeContent("Your content here");

// Multi-source fusion
const fusion = await searchEngine.fuseContent([
  "Source 1 content...",
  "Source 2 content...",
  "Source 3 content..."
]);

🎯 How to Use All Functions

1. Content Analysis & Tone Detection

const analysis = await searchEngine.analyzeContent(content);

// What you get:
// - Tone: professional/casual/technical/formal/conversational with confidence
// - Style: sentence length, vocabulary complexity, readability score
// - DNA: semantic signature, complexity, coherence
// - Summary: auto-generated content summary
// - Keywords: extracted important terms
// - Quality score: overall content quality assessment
// - Insights: actionable recommendations

Use Cases: Content marketing, document quality assessment, writing style analysis, tone consistency checking

2. Content Fusion & Multi-source Summarization

const fusion = await searchEngine.fuseContent([source1, source2, source3]);

// What you get:
// - Coherent summary combining all sources
// - Conflict detection between sources
// - Information gaps identification
// - Source relationship mapping
// - Coherence scoring

Use Cases: Research paper synthesis, multi-document summarization, content aggregation, fact-checking

3. Adaptive Performance Optimization

// Record performance metrics
searchEngine.recordPerformanceMetrics({
  searchTime: 45,
  chunkSize: 512,
  memoryUsage: 2.5,
  accuracy: 0.85
});

// Get optimization recommendations
const recommendations = searchEngine.getOptimizationRecommendations();

// Get performance analytics
const analytics = searchEngine.getPerformanceAnalytics();

Use Cases: Production system optimization, performance monitoring, automatic tuning, scalability improvement

4. Advanced Search with Intelligence

// Search with content understanding
const results = await searchEngine.searchContent(content, query);

// Get fusion insights
const insights = searchEngine.getFusionInsights(fusion);

// Update configurations dynamically
searchEngine.updateOptimizationConfig({ learningRate: 0.15 });

Use Cases: Intelligent document search, content recommendation, similarity matching, knowledge discovery

🔧 Configuration Options

const searchEngine = new VectorSearch(
  // Search configuration
  {
    similarityMetric: 'cosine',
    maxResults: 10,
    threshold: 0.0
  },
  // Optimization configuration
  {
    enableAutoOptimization: true,
    learningRate: 0.1,
    performanceThreshold: 0.8
  },
  // Adaptive configuration
  {
    enableLearning: true,
    optimizationStrategy: 'balanced'
  }
);

📊 Performance Features

• Zero Dependencies: Pure JavaScript/TypeScript implementation
• Self-Optimizing: Automatically tunes parameters based on usage
• Real-time Analytics: Continuous performance monitoring
• Adaptive Learning: Improves over time with usage patterns
• Memory Efficient: Optimized for large document collections

🌟 Unique Capabilities

Content Intelligence

• Tone Detection: Understand content mood and style
• Style Matching: Find content with similar writing characteristics
• DNA Fingerprinting: Generate unique content signatures
• Quality Assessment: Score content readability and complexity

Smart Processing

• Conflict Detection: Identify contradictions between sources
• Gap Analysis: Find missing information across documents
• Relationship Mapping: Discover connections between content pieces
• Coherence Scoring: Measure how well content flows together

Adaptive Optimization

• Self-Tuning: Automatically optimize chunk sizes and search parameters
• Performance Learning: Learn from usage patterns to improve efficiency
• Predictive Optimization: Anticipate and prevent performance issues
• Dynamic Configuration: Update settings without restarting

🎯 Perfect For

• Content Management Systems: Intelligent document organization and search
• E-commerce Platforms: Smart product search and recommendation engines
• Knowledge Bases: Instant answers from large document collections
• Research Tools: Academic paper analysis and discovery
• Legal Systems: Contract and policy search with conflict detection
• Marketing Platforms: Content tone analysis and style optimization
• Educational Platforms: Content quality assessment and improvement
• Enterprise Search: Intelligent document discovery and relationship mapping

🚀 Getting Started

Installation

npm install vector-chunk

Basic Usage

import { VectorSearch } from 'vector-chunk';

const searchEngine = new VectorSearch();

// Your original search function
const results = await searchEngine.searchContent(
  "Your document content...", 
  "search term"
);

Advanced Usage

// Content analysis
const analysis = await searchEngine.analyzeContent(content);
console.log(`Tone: ${analysis.tone.dominantTone}`);
console.log(`Quality: ${(analysis.qualityScore * 100).toFixed(1)}%`);

// Multi-source fusion
const fusion = await searchEngine.fuseContent(sources);
console.log(`Summary: ${fusion.summary}`);
console.log(`Conflicts: ${fusion.conflicts.length}`);

// Performance optimization
searchEngine.recordPerformanceMetrics(metrics);
const recommendations = searchEngine.getOptimizationRecommendations();

🔧 Configuration Options

Search Configuration

• similarityMetric: Similarity algorithm (cosine)
• maxResults: Maximum results to return
• threshold: Minimum similarity threshold

Optimization Configuration

• enableAutoOptimization: Enable automatic optimization
• learningRate: How fast to adapt (0.1 = 10% per iteration)
• performanceThreshold: Target performance level
• optimizationInterval: How often to optimize

Adaptive Configuration

• enableLearning: Enable learning from usage patterns
• performanceTracking: Track performance metrics
• autoTuning: Automatically tune parameters
• optimizationStrategy: aggressive/balanced/conservative

📈 Performance Tips

1. Start with defaults: The package is pre-optimized for most use cases
2. Monitor performance: Use built-in analytics to track improvements
3. Let it learn: Performance improves automatically over time
4. Batch operations: Process multiple documents together for better efficiency
5. Use insights: Follow recommendations from the optimization engine

🤝 Contributing

We welcome contributions! Please see our contributing guidelines for details.

📄 License

MIT License - see LICENSE file for details.

🙏 Acknowledgements

• Built with pure JavaScript/TypeScript
• No external dependencies or AI services
• All algorithms are free and license-secure
• Designed for enterprise-scale applications

💬 Support

• Documentation: Comprehensive examples and API reference
• Issues: Report bugs and request features on GitHub
• Community: Join discussions and share use cases

Vector Search Pro - Where content meets intelligence, powered by zero dependencies and unlimited possibilities! 🚀✨