🚀 FuzzyBolt - A Fast & Efficient Fuzzy Search Library for Dart #

FuzzyBolt is a high-performance fuzzy search library for Dart, designed for fast and accurate matching of search queries within large datasets. It leverages Jaro-Winkler similarity and Damerau-Levenshtein distance to provide ranked results with typo tolerance.

✨ Features #

• 🔍 Fast & Optimized - Uses Jaro-Winkler and Damerau-Levenshtein for accurate matching.
• ⚡ Isolate Support - Automatically switches to isolates for large datasets.
• 🔢 Configurable Thresholds - Adjust strict and typo-matching sensitivity.
• 📝 Easy-to-Use API - Simple function calls with named parameters.
• 🧪 Well-Tested - Comes with robust test cases.

📦 Installation #

Add fuzzy_bolt to your project's pubspec.yaml:

dependencies:
  fuzzy_bolt: ^1.0.0

Then, run:

dart pub get

🚀 Usage Example #

import 'package:fuzzy_bolt/fuzzy_bolt.dart';

void main() async {
  final fuzzyBolt = FuzzyBolt();

  List<String> dataset = [
    "apple",
    "banana",
    "grapefruit",
    "strawberry",
    "raspberry",
    "blackberry",
    "blueberry",
    "pineapple",
  ];

  final results = await fuzzyBolt.search(
    dataset: dataset,
    query: "black rasp",
    strictThreshold: 0.85,
    typoThreshold: 0.7,
  );

  print("Top Matches:");
  for (var result in results) {
    print("${result['value']} (Score: ${result['rank']})");
  }
}

🛠 Output Example #

Top Matches:
blackberry (Score: 0.89)
raspberry (Score: 0.85)
blueberry (Score: 0.72)

⚡ API Reference #

FuzzyBolt.search({dataset, query, strictThreshold, typoThreshold}) → Future<List<Map<String, dynamic>>>

Parameters: #

🔹 dataset (List

📚 Example Use Cases #

✅ Search & Auto-Suggestions - Improve search bars with intelligent suggestions.
✅ Spell Checking - Detect and correct minor spelling errors.
✅ Command Line Interfaces - Enhance fuzzy matching in CLI applications.
✅ Data Deduplication - Identify similar records in datasets.

🔬 Running Tests #

To ensure FuzzyBolt works correctly, run:

dart test

📜 License #

This package is licensed under the MIT License. See the LICENSE file for details.

❤️ Contributing #

Contributions are welcome! Feel free to open issues, submit PRs, or discuss improvements.

💬 Have questions? Reach out on GitHub Issues!
🌟 Like this package? Give it a star on GitHub! ⭐

🔹 Made with ❤️ by Vishwa Karthik. #