545563e776
- Complete NestJS TypeScript implementation with WebSocket support - Direct messaging (DM) and group chat functionality - End-to-end encryption with AES encryption and key pairs - Media file support (images, videos, audio, documents) up to 100MB - Push notifications with Firebase Cloud Messaging integration - Mention alerts and real-time typing indicators - User authentication with JWT and Passport - SQLite database with TypeORM entities and relationships - Comprehensive API documentation with Swagger/OpenAPI - File upload handling with secure access control - Online/offline status tracking and presence management - Message editing, deletion, and reply functionality - Notification management with automatic cleanup - Health check endpoint for monitoring - CORS configuration for cross-origin requests - Environment-based configuration management - Structured for Flutter SDK integration Features implemented: ✅ Real-time messaging with Socket.IO ✅ User registration and authentication ✅ Direct messages and group chats ✅ Media file uploads and management ✅ End-to-end encryption ✅ Push notifications ✅ Mention alerts ✅ Typing indicators ✅ Message read receipts ✅ Online status tracking ✅ File access control ✅ Comprehensive API documentation Ready for Flutter SDK development and production deployment.
fast-levenshtein - Levenshtein algorithm in Javascript
An efficient Javascript implementation of the Levenshtein algorithm with locale-specific collator support.
Features
- Works in node.js and in the browser.
- Better performance than other implementations by not needing to store the whole matrix (more info).
- Locale-sensitive string comparisions if needed.
- Comprehensive test suite and performance benchmark.
- Small: <1 KB minified and gzipped
Installation
node.js
Install using npm:
$ npm install fast-levenshtein
Browser
Using bower:
$ bower install fast-levenshtein
If you are not using any module loader system then the API will then be accessible via the window.Levenshtein object.
Examples
Default usage
var levenshtein = require('fast-levenshtein');
var distance = levenshtein.get('back', 'book'); // 2
var distance = levenshtein.get('我愛你', '我叫你'); // 1
Locale-sensitive string comparisons
It supports using Intl.Collator for locale-sensitive string comparisons:
var levenshtein = require('fast-levenshtein');
levenshtein.get('mikailovitch', 'Mikhaïlovitch', { useCollator: true});
// 1
Building and Testing
To build the code and run the tests:
$ npm install -g grunt-cli
$ npm install
$ npm run build
Performance
Thanks to Titus Wormer for encouraging me to do this.
Benchmarked against other node.js levenshtein distance modules (on Macbook Air 2012, Core i7, 8GB RAM):
Running suite Implementation comparison [benchmark/speed.js]...
>> levenshtein-edit-distance x 234 ops/sec ±3.02% (73 runs sampled)
>> levenshtein-component x 422 ops/sec ±4.38% (83 runs sampled)
>> levenshtein-deltas x 283 ops/sec ±3.83% (78 runs sampled)
>> natural x 255 ops/sec ±0.76% (88 runs sampled)
>> levenshtein x 180 ops/sec ±3.55% (86 runs sampled)
>> fast-levenshtein x 1,792 ops/sec ±2.72% (95 runs sampled)
Benchmark done.
Fastest test is fast-levenshtein at 4.2x faster than levenshtein-component
You can run this benchmark yourself by doing:
$ npm install
$ npm run build
$ npm run benchmark
Contributing
If you wish to submit a pull request please update and/or create new tests for any changes you make and ensure the grunt build passes.
See CONTRIBUTING.md for details.
License
MIT - see LICENSE.md