LoSearch 0.1.1

A high-performance Python search library with intelligent relevance scoring, advanced indexing capabilities, and multilingual support for Persian and English.

LoSearch - Advanced Search Engine Library

A high-performance Python search library with intelligent relevance scoring, advanced indexing capabilities, and multilingual support for Persian and English.

Features

• Fast Document Search - Optimized indexing and retrieval
• Smart Query Correction - Automatic typo detection and correction
• Contextual Search - Extract relevant sentences with context
• Multilingual Support - Persian and English language processing
• Performance Optimization - Intelligent caching and speed enhancements
• Accuracy Enhancement - Advanced relevance scoring algorithms
• Text Analysis - Comprehensive text processing utilities
• Flexible API - Simple yet powerful interface

Installation

pip install losearch

Quick Start

import losearch as ls

# Initialize search engine
search = ls.LoSearch(enable_optimization=True)

# Add documents
documents = [
    {"id": "1", "title": "MacBook Pro", "content": "High-performance laptop with M2 chip"},
    {"id": "2", "title": "iPhone 15", "content": "Latest smartphone with advanced camera"}
]

search.add_documents(documents)

# Search with automatic optimization
results = search.search("laptop performance")
for result in results:
    print(f"{result['title']} - Score: {result['score']}")

Core Components

Main Classes

• LoSearch - Primary interface for all search operations
• LoSearchEngine - Advanced search engine with optimization
• TextProcessor - Text normalization and language processing
• ContextExtractor - Intelligent context and sentence extraction
• SpeedOptimizer - Performance optimization and caching
• AccuracyEnhancer - Result quality improvement

Search Methods

Basic Search

results = search.search("machine learning", limit=10)

Smart Search with Auto-Correction

result = search.smart_search("machne lerning")  # Automatically corrects typos

Contextual Search

result = search.contextual_search("AI algorithms", mode='advanced')

Contextual Search with Scoring

result = search.contextual_search_with_scores("deep learning", top_sentences=3)

Advanced Usage

Text Analysis

analysis = search.analyze_text("Machine learning and artificial intelligence")
print(f"Language: {analysis['language']}")
print(f"Terms: {analysis['terms']}")

Query Correction

correction = search.fix_query("machne lerning")
print(f"Corrected: {correction['corrected_query']}")
print(f"Confidence: {correction['confidence']}")

Performance Monitoring

stats = search.get_performance_stats()
print(f"Documents indexed: {stats['documents_indexed']}")
print(f"Cache hit rate: {stats['speed_optimizer']['hit_rate']}")

Multilingual Support

LoSearch provides seamless support for both Persian and English with automatic language detection:

# English search
english_results = search.search("machine learning algorithms")

# Persian search  
persian_results = search.search("یادگیری ماشین")

# Mixed language documents
mixed_docs = [
    {"id": "1", "title": "AI Research", "content": "تحقیقات هوش مصنوعی"},
    {"id": "2", "title": "Python Programming", "content": "برنامه نویسی پایتون"}
]

Architecture

src/losearch/
├── core/                    # Core search functionality
│   ├── engine.py           # Main LoSearchEngine class
│   ├── search_engine.py    # Base search engine
│   ├── speed_optimizer.py  # Caching and optimization
│   └── accuracy_enhancer.py # Result accuracy improvement
├── utils/                   # Utility modules
│   ├── text_processor.py   # Text processing and normalization
│   └── context_extractor.py # Context and sentence extraction
└── __init__.py             # Library interface

Performance Features

Speed Optimization

• Intelligent query caching
• Index optimization
• Memory-efficient processing

Accuracy Enhancement

• Advanced relevance scoring
• Context-aware ranking
• Language-specific processing

Use Cases

• E-commerce product search
• Document management systems
• Knowledge base applications
• Content discovery platforms
• Research and academic tools
• Multilingual applications

Documentation

For comprehensive documentation, examples, and advanced usage:

Complete Tutorial

Performance Tips

1. Enable optimization for better performance:

search = ls.LoSearch(enable_optimization=True)

2. Use batch operations for multiple documents:

search.add_documents(all_documents)  # Preferred over individual adds

3. Use advanced mode for better contextual results:

result = search.contextual_search(query, mode='advanced')

Version 1.0.0

What's New

• Complete rewrite with modular architecture
• Multi-language support (Persian/English)
• Smart query correction and auto-completion
• Contextual search with sentence extraction
• Performance optimization with intelligent caching
• Comprehensive text analysis capabilities
• Enhanced accuracy scoring algorithms

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Support

• Create an issue on GitHub for bug reports
• Check the Complete Tutorial for documentation
• Review examples in the documentation

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LoSearch - Making search intelligent and fast