mediapipe_retrieval 0.1.0

Google's MediaPipe retrieval tasks for Flutter: Universal Embedder, one embedding space for text, images and audio, and Semantic Retriever, an on-device vector index over them.

mediapipe_retrieval

Google's MediaPipe retrieval tasks for Dart and Flutter. Universal Embedder turns text, images and audio into vectors in one embedding space; Semantic Retriever indexes documents, images, audio and mixed records on the device and finds the ones nearest a query. Every call runs Google's official MediaPipe pipeline.

Platforms

Task	Class	Android	iOS	macOS arm64	Linux x64	Windows x64	Web
Universal Embedder	UniversalEmbedder	✓	✓	✓	✓	✓	✓
Semantic Retriever	SemanticRetriever	✓	✓	✓	✓	✓	✓

The runtimes are Google's MediaPipe 1.2.0: its per-family retrieval C library, which this package's build hook downloads and checks against its SHA-256 on Android 9+, iOS 15+, macOS 14+, Linux and Windows, on the CPU; and @mediapipe/tasks-retrieval 1.2.0-rc.20261018 in browsers, on the GPU delegate. Google's browser Universal Embedder creates a WebGPU device for itself and has no CPU path (UP-052), so the capability queries offer browsers only the GPU, and only on a hardware WebGPU adapter; there both tasks answer as the native library does, except in WebKit, the engine of Safari and of every browser on iOS, where Google's runtime returns zeros for every input with both models, so the queries offer neither task there (UP-054). Two more differences the browser runtime imposes: images and audio go in as bytes and samples there, never as file paths, and the index stays in memory, so databasePath is for the native platforms. In browsers the package fetches the model itself, since Google's task reads a modelAssetPath as a file (UP-051).

Quick start

Add mediapipe_retrieval: ^0.1.0 to your app's dependencies, then:

import 'package:mediapipe_retrieval/mediapipe_retrieval.dart';

Future<void> search() async {
  final embedder = await UniversalEmbedder.create(
    UniversalEmbedderOptions(model: RetrievalModels.embeddingGemma2TextVision),
  );
  final retriever = await SemanticRetriever.create(
    SemanticRetrieverOptions(embedder: embedder),
  );
  try {
    await retriever.insertDocument(
      'dog',
      'A dog chases a ball across the park on a sunny afternoon.',
      metadata: {'topic': 'animals'},
    );
    await retriever.insertDocument(
      'recipe',
      'Whisk the eggs with sugar, then fold in the flour to make the batter.',
      metadata: {'topic': 'cooking'},
    );
    final result = await retriever.retrieve('A puppy playing fetch.', limit: 1);
    print('${result.records.first.id}: ${result.records.first.score}');

    final a = await embedder.embedText('A dog chases a ball.');
    final b = await embedder.embedText('A puppy plays fetch.');
    print(
      UniversalEmbedder.cosineSimilarity(
        a.embeddings.first,
        b.embeddings.first,
      ),
    );
  } finally {
    await retriever.dispose();
    await embedder.dispose();
  }
}

retrieve returns at most limit records scoring at least minSimilarity (Google's default 0.5), best first, each with its id, text, score and metadata; metadataFilter keeps only records carrying every given pair. retrieveContent takes a multimodal query of TextPart, ImagePart and AudioPart, and insertImage, insertAudio and insertContent index the other modalities. On Android, iOS and the desktops an image or audio record's text is the path it was inserted with, or its id when it came from bytes or samples alone. Audio needs the full model, and Google's audio embeddings vary slightly from call to call (UP-058). A retriever runs on its embedder's worker: dispose it before the embedder.

Models

RetrievalModels pins Google's EmbeddingGemma 2 models with a vision encoder, from Google's LiteRT community on Hugging Face (Apache 2.0): the text and vision model (embeddingGemma2TextVision, 388 MB) and the full model with audio (embeddingGemma2, 485 MB). Both embed into 768 dimensions. Google's engine refuses the text-only EmbeddingGemma 2 for this task, so it is not listed. An app bundles a model by naming it under hooks.user_defines.mediapipe_retrieval.models in its pubspec and running dart run mediapipe_core:bundle_models, or passes modelPath or modelBytes of its own.

Capabilities

queryUniversalEmbedderCapabilities() and querySemanticRetrieverCapabilities() report, without loading a model, the delegates this platform runs the tasks on: the CPU on Android, iOS, macOS, Linux and Windows, and in browsers the GPU, once the plugin has registered and on a hardware WebGPU adapter. The other delegate is refused by create with a RuntimeUnavailableException: the native library's GPU path is not validated, and Google's browser runtime has no CPU path.

Testing

dart test runs the unit tests and, with Google's model downloaded by dart tool/download_models.dart, the native suite, which compares every embedding and retrieval with the answers Google's own Python API gave, as tool/generate_reference.py records them: text and images from the official mediapipe 1.1.0 wheel (test/fixtures/embedding_gemma_2_text_vision_reference.json), and audio, whose embeddings Google changed in 1.2.0, from its mediapipe-nightly 1.2.0rc20261018 wheel, the pinned build (test/fixtures/embedding_gemma_2_audio_reference.json).