example/example.md

Mobile RAG Engine - Example #

A complete on-device RAG (Retrieval-Augmented Generation) implementation.

Quick Start #

import 'package:flutter/services.dart';
import 'package:path_provider/path_provider.dart';
import 'package:mobile_rag_engine/mobile_rag_engine.dart';

void main() async {
  WidgetsFlutterBinding.ensureInitialized();
  
  // 1. Initialize Rust library
  await RustLib.init();
  
  // 2. Load tokenizer
  final dir = await getApplicationDocumentsDirectory();
  await initTokenizer(tokenizerPath: '${dir.path}/tokenizer.json');
  
  // 3. Load ONNX embedding model
  final modelBytes = await rootBundle.load('assets/model.onnx');
  await EmbeddingService.init(modelBytes.buffer.asUint8List());
  
  // 4. Initialize RAG service
  final ragService = SourceRagService(dbPath: '${dir.path}/rag.db');
  await ragService.init();
  
  runApp(MyApp(ragService: ragService));
}

Adding Documents #

// Add a document with automatic chunking and embedding
final result = await ragService.addSourceWithChunking(
  'Flutter is Google\'s UI toolkit for building beautiful apps...',
  onProgress: (done, total) => print('Embedding: $done/$total'),
);

print('Created ${result.chunkCount} chunks');

// Rebuild HNSW index after adding documents
await ragService.rebuildIndex();

Semantic Search #

// Search for relevant chunks
final searchResult = await ragService.search(
  'How to build mobile apps?',
  topK: 5,
  tokenBudget: 2000,
);

// Get assembled context for LLM
print('Found ${searchResult.chunks.length} chunks');
print('Context tokens: ${searchResult.context.estimatedTokens}');

// Format prompt for LLM
final prompt = ragService.formatPrompt(
  'How to build mobile apps?',
  searchResult,
);

Batch Embedding #

// Embed multiple texts efficiently
final embeddings = await EmbeddingService.embedBatch(
  ['Text 1', 'Text 2', 'Text 3'],
  onProgress: (done, total) => print('Progress: $done/$total'),
);

Performance #

See the full example app in the GitHub repository.