similator

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Similator: Revolutionary Python-Rust Hybrid for Lightning-Fast Text Validation

Similator is a cutting-edge Python library that leverages the power of Rust to offer unparalleled speed and efficiency in text validation and comparison. By combining the ease of Python with the performance of Rust, Similator achieves up to 10x faster processing compared to traditional implementations. This fusion represents a significant leap forward in text processing technology.

🚀 Features

• Blazing-Fast Performance: Experience up to 10x speed improvement with our Rust-powered engine, drastically reducing the time required for complex text operations.
• Hybrid Python-Rust Implementation: Seamlessly integrates Python and Rust to deliver optimal performance without compromising on ease of use.
• Customizable Similarity Search: Adjust similarity thresholds to find the most relevant matches quickly and accurately.
• Automatic Caching: Enhance performance with optional caching for repetitive searches, making your workflow more efficient.
• Advanced Scoring Mechanism: Benefit from an improved scoring system that ensures meaningful similarity results based on your criteria.
• Flexible Case Sensitivity: Choose between case-sensitive and case-insensitive comparisons to suit your specific needs.

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📦 Installation

Install Similator quickly and easily using pip:

pip install similator

🌟 Quickstart Guide

Here's a quick example to get you up and running with Similator:

1. Import and Initialize

from similator import TextSimilator, ValidData

# Example data
valid_strings = ["Hello", "World", "Text", "Example", "Python"]

# Initialize ValidData
valid_data_instance = ValidData(valid_strings, encoding='utf-8')

# Initialize TextSimilator with ValidData
text_similator = TextSimilator(valid_data_instance, encoding='utf-8', case_sensitive=False)

2. Perform a Search

Search for a string within the valid data with a similarity threshold:

search_value = "hello"
results = text_similator.search(search_value, threshold=0.85)
print(results)
# Output: [('hello', 2.0)]

3. Compare Two Strings

Directly compare two strings to obtain a similarity score:

value1 = "hello"
value2 = "hell"
similarity_score = text_similator.compare(value1, value2)
print(similarity_score)
# Output: 1.94

Advanced Usage

Enabling Caching for Repeated Searches

If your application involves repeated searches with similar queries, you can enable caching to improve performance:

# Enable caching with a maximum size of 50 cached results
text_similator_with_cache = TextSimilator(valid_data_instance, auto_cached=True, max_cache_size=50)

# Perform a search and it will be cached
results_cached = text_similator_with_cache.search("python", threshold=0.9)

Exporting and Loading Cached Data

You can export the cache to a file and reload it later for persistent storage:

# Export the current cache to a JSON file
text_similator_with_cache.memory.export_memory("cache.json")

# Load the cache from a JSON file
text_similator_with_cache.memory.load_memory("cache.json")

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💬 Contact

If you have any questions, suggestions, or just want to say hello, feel free to contact me:

• Email: [e-mail]
• GitHub: DSAV-code
• Twitter/X: @dsav_v2

🛠️ Contributing

Contributions are welcome! If you have any ideas, suggestions, or issues, feel free to open an issue or submit a pull request.

📝 License

This project is licensed under the GNU General Public License v3.0 - see the LICENSE file for details.

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