model2vec 1.0.0

A high-performance Dart wrapper for model2vec-rs using Rust FFI. Generate fast, local, and static text embeddings with minimal memory footprint using Native Assets.

example/main.dart

import 'dart:io';

import 'package:model2vec/model2vec.dart';

/// Example demonstrating production-ready usage of the Model2Vec package.
void main() {
  try {
    // 1. Initialize the API via shared instance
    final m2v = Model2Vec.instance;
    stdout.writeln('✅ Model2Vec library loaded successfully.');

    // 3. Explore Recommended Models
    final models = m2v.getRecommendedModels();
    stdout.writeln('\n📦 Available models:');
    for (final m in models) {
      final name = m['name']! as String;

      stdout.writeln('  - ${name.padRight(25)} ID: ${m['id']}');
    }

    // 4. Initialize a Model
    const modelId = 'minishlab/potion-base-2M';
    stdout.writeln('\n🚀 Initializing $modelId...');
    final sw = Stopwatch()..start();
    m2v.initEmbedder(modelId);
    stdout
      ..writeln('✨ Initialized in ${sw.elapsedMilliseconds}ms')
      // 5. Inspect Model Metadata
      ..writeln('\n📊 Model Metadata:')
      ..writeln('  - Dimension:           ${m2v.embeddingDimension}')
      ..writeln('  - Vocabulary Size:     ${m2v.vocabularySize}')
      ..writeln('  - Is Normalized:       ${m2v.isNormalized}')
      ..writeln('  - Median Token Length: ${m2v.medianTokenLength}');

    // 6. Tokenization Demo
    const text = 'Model2Vec is incredibly fast!';
    final tokens = m2v.tokenize(text);
    stdout
      ..writeln('\n🔍 Tokenization: "$text"')
      ..writeln('  - Tokens: $tokens')
      // 7. Single Embedding
      ..writeln('\n🧠 Generating single embedding...');
    final embedding = m2v.generateEmbedding(text);
    stdout
      ..writeln('  - Vector (first 3): ${embedding.take(3).toList()}')
      ..writeln('  - Total Length:    ${embedding.length}');

    // 8. Batch Embedding (Production Optimization)
    final texts = [
      'The first sentence.',
      'A second, slightly longer sentence for the batch.',
      'Third one.',
    ];
    stdout.writeln(
      '\n⚡ Generating batch embeddings for ${texts.length} sentences...',
    );
    final batchStartTime = DateTime.now();
    final batch = m2v.generateBatchEmbeddings(texts);
    final batchDuration = DateTime.now().difference(batchStartTime);

    stdout.writeln('  - Processed in ${batchDuration.inMicroseconds}μs');
    for (var i = 0; i < batch.length; i++) {
      stdout.writeln('  - Result $i length: ${batch[i].length}');
    }

    stdout.writeln('\n🎉 All operations completed successfully.');
  } on Model2VecException catch (e) {
    stdout.writeln('\n❌ Model2Vec Error: ${e.message}');
    if (e.code != null) {
      stdout.writeln('   Error Code: ${e.code}');
    }
  } on Object catch (e) {
    stdout.writeln('\n💥 Unexpected Error: $e');
  }
}