blitztext 0.1.0

A library for fast keywords extraction and replacement in strings.

BlitzText

BlitzText is a high-performance library for efficient keyword extraction and replacement in strings. It is based on the FlashText and Aho-Corasick algorithm. There are both Rust and Python implementations. Main difference form Aho-Corasick is that BlitzText only matches the longest pattern in a greedy manner.

Table of Contents

• Installation
• Usage
  • Rust Usage
  • Python Usage
• Features
  • 1. Parallel Processing
  • 2. Fuzzy Matching
  • 3. Case Sensitivity
  • 4. Overlapping Matches
  • 5. Custom Non-Word Boundaries
• Performance
• Contributing
• License

Installation

Rust

Add this to your Cargo.toml:

[dependencies]
blitztext = "0.1.0"

or

cargo add blitztext

Python

Install the library using pip:

pip install blitztext

Usage

Rust Usage

use blitztext::KeywordProcessor;

fn main() {
    let mut processor = KeywordProcessor::new();
    processor.add_keyword("rust", Some("Rust Lang"));
    processor.add_keyword("programming", Some("Coding"));

    let text = "I love rust programming";
    let matches = processor.extract_keywords(text, None);
    
    for m in matches {
        println!("Found '{}' at [{}, {}]", m.keyword, m.start, m.end);
    }

    let replaced = processor.replace_keywords(text, None);
    println!("Replaced text: {}", replaced);
    // Output: "I love Rust Lang Coding"
}

Python Usage

from blitztext import KeywordProcessor

processor = KeywordProcessor()
processor.add_keyword("rust", "Rust Lang")
processor.add_keyword("programming", "Coding")

text = "I love rust programming"
matches = processor.extract_keywords(text)

for m in matches:
    print(f"Found '{m.keyword}' at [{m.start}, {m.end}]")

replaced = processor.replace_keywords(text)
// Output: "I love Rust Lang Coding"

print(f"Replaced text: {replaced}")

Features

1. Parallel Processing

For processing multiple texts in parallel:

// Rust
let texts = vec!["Text 1", "Text 2", "Text 3"];
let results = processor.parallel_extract_keywords_from_texts(&texts, None);

# Python
texts = ["Text 1", "Text 2", "Text 3"]
results = processor.parallel_extract_keywords_from_texts(texts)

2. Fuzzy Matching

Both Rust and Python implementations support fuzzy matching:

// Rust
let matches = processor.extract_keywords(text, Some(0.8));

# Python
matches = processor.extract_keywords(text, threshold=0.8)

3. Case Sensitivity

You can enable case-sensitive matching:

// Rust
let mut processor = KeywordProcessor::with_options(true, false);
processor.add_keyword("Rust", Some("Rust Lang"));
let matches = processor.extract_keywords("I love Rust and rust", None);
// Only "Rust" will be matched, not "rust"

# Python
processor = KeywordProcessor(case_sensitive=True)
processor.add_keyword("Rust", "Rust Lang")
matches = processor.extract_keywords("I love Rust and rust")
# Only "Rust" will be matched, not "rust"

4. Overlapping Matches

Enable overlapping matches:

// Rust
let mut processor = KeywordProcessor::with_options(false, true);
processor.add_keyword("word", None);
processor.add_keyword("sword", None);
let matches = processor.extract_keywords("I have a sword", None);
// "word" will be matched

# Python
processor = KeywordProcessor(allow_overlaps=True)
processor.add_keyword("word")
matches = processor.extract_keywords("I have a sword")
# "word" will be matched

5. Custom Non-Word Boundaries

This library uses the concept of non-word boundaries to determine where words begin and end. By default, alphanumeric characters and underscores are considered part of a word. You can customize this behavior to fit your specific needs.

Understanding Non-Word Boundaries

• Characters defined as non-word boundaries are considered part of a word.
• Characters not defined as non-word boundaries are treated as word separators.

Example

// Rust
let mut processor = KeywordProcessor::new();

processor.add_keyword("rust", None);
processor.add_keyword("programming", Some("coding"));

let text = "I-love-rust-programming-and-1coding2";

// Default behavior: '-' is a word separator
let matches = processor.extract_keywords(text, None);
assert_eq!(matches.len(), 2);
// Matches: "rust" and "coding"

// Add '-' as a non-word boundary
processor.add_non_word_boundary('-');

// Now '-' is considered part of words
let matches = processor.extract_keywords(text, None);
assert_eq!(matches.len(), 0);
// No matches, because "rust" and "programming" are now part of larger "words"

# Python
processor = KeywordProcessor()

processor.add_keyword("rust")
processor.add_keyword("programming", "coding")

text = "I-love-rust-programming-and-1coding2"

# Default behavior: '-' is a word separator
matches = processor.extract_keywords(text)
assert len(matches) == 2
# Matches: "rust" and "coding"

# Add '-' as a non-word boundary
processor.add_non_word_boundary('-')

# Now '-' is considered part of words
matches = processor.extract_keywords(text)
assert len(matches) == 0
# No matches, because "rust" and "programming" are now part of larger "words"

Setting a whole new set of non-word boundaries

// Rust
processor.set_non_word_boundaries(&['-', '_', '@']);

# Python
processor.set_non_word_boundaries(['-', '_', '@'])

Performance

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BlitzText is designed for high performance, making it suitable for processing large volumes of text. Benchmark details here.

Mult-threaded performance:

Contributing

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

Issues

If you encounter any problems, please file an issue along with a detailed description.

License

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This project is licensed under the MIT License.