mobile_rag_engine 0.5.0

A high-performance, on-device RAG (Retrieval-Augmented Generation) engine for Flutter. Run semantic search completely offline on iOS and Android with HNSW vector indexing.

Mobile RAG Engine

Production-ready, fully local RAG (Retrieval-Augmented Generation) engine for Flutter.

Powered by a Rust core, it delivers lightning-fast vector search and embedding generation directly on the device. No servers, no API costs, no latency.

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Why this package?

No Rust Installation Required

You do NOT need to install Rust, Cargo, or Android NDK.

This package includes pre-compiled binaries for iOS, Android, and macOS. Just pub add and run.

Performance

Feature	Pure Dart	Mobile RAG Engine (Rust)
Tokenization	Slow	10x Faster (HuggingFace tokenizers)
Vector Search	O(n)	O(log n) (HNSW Index)
Memory Usage	High	Optimized (Zero-copy FFI)

100% Offline & Private

Data never leaves the user's device. Perfect for privacy-focused apps (journals, secure chats, enterprise tools).

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Features

• Cross-Platform: Works seamlessly on iOS, Android, and macOS
• HNSW Vector Index: Fast approximate nearest neighbor search (proven scale up to 10k+ docs)
• Hybrid Search Ready: Supports semantic search combined with exact matching
• Markdown Structure-Aware Chunking: Preserves headers, code blocks, and tables during chunking with header path inheritance
• PDF/DOCX Text Extraction: Built-in document parsing with smart dehyphenation and page number removal
• Auto-Chunking: Intelligent text splitting strategies included (Unicode-based semantic chunking)
• Model Flexibility: Use standard ONNX models (e.g., bge-m3, all-MiniLM-L6-v2)

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Benchmark Results

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Requirements

Platform	Minimum Version
iOS	13.0+
Android	API 21+ (Android 5.0 Lollipop)
macOS	10.15+ (Catalina)

ONNX Runtime is bundled automatically via the onnxruntime plugin. No additional native setup required.

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Installation

1. Add the dependency

dependencies:
  mobile_rag_engine: ^0.4.3

2. Download Model Files

# Create assets folder
mkdir -p assets && cd assets

# Download BGE-m3 model (INT8 quantized, multilingual)
curl -L -o model.onnx "https://huggingface.co/Teradata/bge-m3/resolve/main/onnx/model_int8.onnx"
curl -L -o tokenizer.json "https://huggingface.co/BAAI/bge-m3/resolve/main/tokenizer.json"

See Model Setup Guide for alternative models and production deployment strategies.

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Quick Start

Initialize the engine and start searching in just a few lines of code:

import 'package:mobile_rag_engine/mobile_rag_engine.dart';

void main() async {
  // 1. Initialize (just 3 lines!)
  await RustLib.init();
  final rag = await RagEngine.initialize(
    config: RagConfig.fromAssets(
      tokenizerAsset: 'assets/tokenizer.json',
      modelAsset: 'assets/model.onnx',
    ),
  );

  // 2. Add Documents (auto-chunked & embedded)
  await rag.addDocument('Flutter is a UI toolkit for building apps.');
  await rag.rebuildIndex();

  // 3. Search with LLM-ready context
  final result = await rag.search('What is Flutter?', tokenBudget: 2000);
  print(result.context.text); // Ready to send to LLM

  // Optional: Format as prompt
  final prompt = rag.formatPrompt('What is Flutter?', result);
}

Advanced Usage: For fine-grained control, you can still use the low-level APIs (initTokenizer, EmbeddingService, SourceRagService) directly. See the API Reference.

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PDF/DOCX Import

Extract text from documents and add to RAG:

import 'dart:io';
import 'package:file_picker/file_picker.dart';
import 'package:mobile_rag_engine/mobile_rag_engine.dart';

Future<void> importDocument(RagEngine rag) async {
  // Pick file
  final result = await FilePicker.platform.pickFiles(
    type: FileType.custom,
    allowedExtensions: ['pdf', 'docx'],
  );
  if (result == null) return;

  // Extract text (handles hyphenation, page numbers automatically)
  final bytes = await File(result.files.single.path!).readAsBytes();
  final text = await extractTextFromDocument(fileBytes: bytes.toList());

  // Add to RAG with auto-chunking
  await rag.addDocument(text, filePath: result.files.single.path);
  await rag.rebuildIndex();
}

Note: file_picker is optional. You can obtain file bytes from any source (network, camera, etc.) and pass to extractTextFromDocument().

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Benchmarks

Rust-powered components (M3 Pro macOS):

Operation	Time	Notes
Tokenization (234 chars)	0.04ms	HuggingFace tokenizers crate
HNSW Search (100 docs)	0.3ms	instant-distance (O(log n))

These are the components where Rust provides 10-100x speedup over pure Dart implementations.

Embedding generation uses ONNX Runtime (platform-dependent, typically 25-100ms per text).

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Architecture

This package bridges the best of two worlds: Flutter for UI and Rust for heavy lifting.

Component	Technology
Embedding	ONNX Runtime with quantized models (INT8)
Storage	SQLite for metadata + memory-mapped vector index
Search	instant-distance (HNSW) for low-latency retrieval
Tokenization	HuggingFace tokenizers crate

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Model Options

Model	Dimensions	Size	Max Tokens	Languages
Teradata/bge-m3 (INT8)	1024	~542 MB	8,194	100+ (multilingual)
all-MiniLM-L6-v2	384	~25 MB	256	English only

Important: The embedding dimension must be consistent across all documents. Switching models requires re-embedding your entire corpus.

Custom Models: Export any Sentence Transformer to ONNX:

pip install optimum[exporters]
optimum-cli export onnx --model sentence-transformers/YOUR_MODEL ./output

See Model Setup Guide for deployment strategies and troubleshooting.

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Documentation

Guide	Description
Quick Start	Get started in 5 minutes
Model Setup	Model selection, download, deployment strategies
FAQ	Frequently asked questions
Troubleshooting	Problem solving guide

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Contributing

Bug reports, feature requests, and PRs are all welcome!

License

This project is licensed under the MIT License.