Embeddings and Rerank

final vectors = await RunAnywhere.embeddings.embed(['hello', 'world']);

for (final embedding in vectors) {
  print('${embedding.index}: ${embedding.vector.length} dimensions');
}

Future<List<Embedding>> embed(
  List<String> texts, {
  String? model,
  EmbedOptions? options,
});

final vectors = await RunAnywhere.embeddings.embed(
  texts,
  options: EmbedOptions(
    normalize: EmbeddingsNormalizeMode.EMBEDDINGS_NORMALIZE_MODE_L2,
    pooling: EmbeddingsPoolingStrategy.EMBEDDINGS_POOLING_STRATEGY_MEAN,
  ),
);

double cosine(Float32List a, Float32List b) {
  var sum = 0.0;
  for (var i = 0; i < a.length; i++) {
    sum += a[i] * b[i];
  }
  return sum;
}

final vectors = await RunAnywhere.embeddings.embed(['a cat', 'a kitten']);
print(cosine(vectors[0].vector, vectors[1].vector));

final ranked = await RunAnywhere.rerank.rerank(
  'how do cats sleep',
  documents,
  topN: 3,
);

for (final item in ranked) {
  print('${documents[item.index]}  ${item.relevanceScore}');
}

Future<List<RankedResult>> rerank(
  String query,
  List<String> documents, {
  int? topN,
});

final matches = await session.search(question, topK: 20);
final ranked = await RunAnywhere.rerank.rerank(
  question,
  matches.map((m) => m.text).toList(),
  topN: 5,
);

final best = [for (final item in ranked) matches[item.index]];

import 'package:runanywhere_onnx/runanywhere_onnx.dart';

await Onnx.register();