mobile_rag_engine 0.4.0

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.

Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

0.4.0

Changed

• README Cleanup: Removed all emojis and unnecessary sections for cleaner documentation

0.3.9

Fixed

• README Images: Updated image paths to use GitHub raw URLs for pub.dev compatibility

0.3.8

Changed

• ONNX Runtime: Reverted to onnxruntime ^1.4.1 for CocoaPods compatibility (1.23.2 not yet available)
• README: Added benchmark result screenshots (iOS/Android) and architecture diagram
• Platform Support: Removed Linux/Windows from publish (no pre-compiled binaries available)

Removed

• ChunkingTestScreen: Removed unnecessary test screen from example app

Added

• Android Platform: Added Android support to example app

0.3.7

Changed

• ONNX Runtime Upgrade: Migrated from onnxruntime to onnxruntime_v2 (v1.23.2) with optional GPU acceleration support
• README Remake: Completely redesigned README with "No Rust Installation Required" emphasis, accurate benchmarks, and Mermaid architecture diagram
• Benchmark UI Overhaul: Visual separation of Rust-powered (fast) vs ONNX (standard) operations with category headers and icons

Added

• GPU Acceleration Option: EmbeddingService.init() now accepts useGpuAcceleration parameter (CoreML/NNAPI support, disabled by default)
• macOS Support for Example App: Example app now supports macOS platform
• Benchmark Categories: Results now grouped by BenchmarkCategory.rust and BenchmarkCategory.onnx

Fixed

• Pub Point Warning: Removed non-existent assets/ directory reference from pubspec.yaml
• Static Analysis: Fixed all lint issues (unnecessary imports, avoid_print, curly braces)

0.3.5

• Globalization: Removed all Korean text and logic, replaced with English.
• Updated prompt builder and semantic chunker for better international support.
• Updated default language settings to English.

0.3.4

• Fix model download URLs in README (use correct Teradata/bge-m3 and BAAI/bge-m3 sources)
• Add production model deployment strategies guide

0.3.3

• Improve README with Quick Start guide and model download instructions
• Update to pub.dev dependency instead of git

0.3.2

• Update rag_engine_flutter dependency to ^0.3.0 (fixes platform directory issue)

0.3.1 - 2026-01-08

Fixed

• Package structure fix: Update rag_engine_flutter dependency to v0.2.0 which includes rust/ source

0.3.0 - 2026-01-08

Changed

• Package Rename: Rust crate renamed to rag_engine_flutter for pub.dev distribution.
• iOS Podspec Fix: Resolved linker path issues for iOS builds.
• Asset Handling: Force-overwrite asset files to prevent stale cache issues.

Removed

• Deprecated test_app and local-gemma-macos directories.

0.2.0 - 2024-12-08

Added

• LLM-Optimized Chunking: Introduced ChunkingService with Recursive Character Splitting and Overlap support.
• Improved Data Model: Separated storage into Source (original document) and Chunk (searchable parts).
• Context Assembly: Added ContextBuilder to intelligently assemble LLM context within a token budget.
• High-Level API: New SourceRagService for automated chunking, embedding, and indexing pipeline.
• Context Strategies: Support for relevanceFirst, diverseSources, and chronological context assembly strategies.

0.1.0 - 2024-12-08

Added

• Initial release
• On-device semantic search with HNSW vector indexing
• Rust-powered tokenization via HuggingFace tokenizers
• ONNX Runtime integration for embedding generation
• SQLite-based vector storage with content deduplication
• Batch embedding support with progress callback
• Cross-platform support (iOS and Android)

Features

• initDb() - Initialize SQLite database
• addDocument() - Add documents with SHA256 deduplication
• searchSimilar() - HNSW-based semantic search
• rebuildHnswIndex() - Manual index rebuild
• EmbeddingService.embed() - Generate embeddings
• EmbeddingService.embedBatch() - Batch embedding

Performance

• HNSW search: O(log n) complexity
• Tokenization: ~0.8ms for short text
• Embedding: ~4ms for short text, ~36ms for long text
• Search (100 docs): ~1ms