flutter_gemma_embeddings 1.0.1

On-device text embeddings (Gecko / EmbeddingGemma .tflite via LiteRT + dart:ffi) for flutter_gemma. Opt-in EmbeddingBackendProvider for on-device RAG.

example/README.md

flutter_gemma_embeddings example

flutter_gemma_embeddings is an opt-in text-embedding backend for flutter_gemma. It runs Gecko / EmbeddingGemma .tflite models via the LiteRT C API (dart:ffi on the 5 native platforms, LiteRT.js on web). Register the backend once at startup, then embed text and feed the vectors into any RAG vector store.

import 'package:flutter/widgets.dart';
import 'package:flutter_gemma/flutter_gemma.dart';
import 'package:flutter_gemma_embeddings/flutter_gemma_embeddings.dart';

Future<void> main() async {
  WidgetsFlutterBinding.ensureInitialized();

  // Opt into the LiteRT embedding backend.
  await FlutterGemma.initialize(
    embeddingBackends: [LiteRtEmbeddingBackend()],
  );

  // Install an embedding model (downloads + sets it active).
  await FlutterGemma.installEmbedder(
    modelType: EmbeddingModelType.embeddingGemma,
  ).fromNetwork(
    'https://example.com/embeddinggemma.tflite',
    tokenizer: 'https://example.com/sentencepiece.model',
  ).install();

  // Create the embedding model and embed text.
  final embedder = await FlutterGemma.getActiveEmbeddingModel();
  final vector = await embedder.generateEmbedding('Gemma runs on-device.');
  print('embedding dim: ${vector.length}');

  await embedder.close();
}

Pair this with a RAG vector store (flutter_gemma_rag_qdrant on native, flutter_gemma_rag_sqlite for web) to build on-device retrieval. A full runnable app lives in the flutter_gemma example.