🚀 New Features #

• RAG (Retrieval-Augmented Generation) Architecture - Complete modular RAG system with engine-agnostic design
• Multi-Engine RAG Support - Extensible architecture supporting GGUF, SGML, TFLite, and future inference engines
• Document Management System - Add, remove, and manage documents with automatic chunking and embedding generation
• Intelligent Document Chunking - Multiple chunking strategies (sentence, paragraph, fixed-size) with configurable overlap
• Semantic Search - Cosine similarity-based document retrieval with confidence scoring
• Context-Aware Generation - Generate answers using relevant document chunks as context
• Llama RAG Engine - First implementation using llama_cpp_dart for both embeddings and text generation
• Flexible Configuration - Customizable similarity thresholds, chunk sizes, and generation parameters

🔧 RAG System Components #

• RagEngine Interface - Abstract base class for implementing different RAG backends
• DocumentChunk Model - Structured document representation with metadata and relevance scoring
• TextChunker Utility - Advanced text splitting with sentence-aware chunking and overlap management
• SimilarityCalculator - Multiple similarity metrics (cosine, euclidean) for document matching
• RagService Integration - Seamless integration with existing LLM service architecture
• Batch Processing - Efficient batch embedding generation for large document collections

🧠 RAG Features #

• Document Metadata Support - Rich metadata storage and filtering capabilities
• Multi-Document Support - Handle multiple documents with unique identification
• Relevance Scoring - Confidence-based ranking of retrieved document chunks
• Dynamic Context Building - Intelligent context assembly from multiple relevant chunks
• Memory-Efficient Processing - Optimized for mobile devices with limited memory
• Streaming Integration - Compatible with existing streaming text generation

🔧 Technical Improvements #

• Modular Architecture - Clean separation between RAG core, engines, and utilities
• Engine Abstraction - Easy addition of new inference engines (TFLite, ONNX, etc.)
• Error Handling - Comprehensive error management for document processing and retrieval
• Performance Optimization - Efficient embedding caching and similarity computation
• Memory Management - Smart memory usage for large document collections
• Async Operations - Non-blocking document processing and query handling

📚 RAG Configuration Options #

• Chunking Configuration - Customizable chunk size, overlap, and splitting strategies
• Retrieval Parameters - Adjustable similarity thresholds and maximum relevant chunks
• Generation Settings - Temperature, max tokens, and system prompt customization
• Engine-Specific Settings - Tailored configurations for different inference engines

🛠️ Implementation Details #

• LlamaRagEngine - Complete implementation using llama_cpp_dart for embeddings and generation
• Document Processing Pipeline - Automated chunking, embedding, and indexing workflow
• Query Processing - Efficient query embedding and similarity search
• Context Assembly - Smart context building with relevance-based chunk selection
• Response Generation - Context-aware answer generation with source attribution

🔧 Improvements from Previous Version #

• Enhanced Model Browser with tabbed interface (Search, Recommended, Downloaded)
• File Browser Integration - Load models directly from device storage
• Device-Specific Recommendations - Memory-aware model suggestions based on device capabilities
• Advanced Debug Console - Real-time logging with color-coded categories and filtering
• Improved Model Cards - Better file organization, download status, and compatibility indicators
• Native Library Validation - Automatic detection of llama.cpp compatibility and health checks
• Progressive Model Loading - Fallback configurations for memory-constrained devices
• Enhanced Error Handling - Detailed error messages with troubleshooting tips and recovery suggestions

🐛 Bug Fixes #

• Fixed SIGSEGV crashes with problematic quantizations on Android ARM64 devices
• Resolved memory leaks during model loading/unloading cycles
• Fixed download interruption handling and resume functionality
• Corrected model file validation for corrupted GGUF files
• Fixed context size calculation for low-memory devices
• Resolved engine detection issues for certain model formats
• Fixed UI state management during concurrent operations

📚 Documentation Updates #

• RAG Architecture Guide - Comprehensive documentation for implementing RAG systems
• Engine Integration Tutorial - Step-by-step guide for adding new inference engines
• Document Processing Best Practices - Guidelines for optimal chunking and embedding strategies
• Performance Optimization - RAG-specific performance tuning recommendations
• API Reference - Complete API documentation for RAG components and utilities

🎯 Future RAG Roadmap #

• Vector Database Integration - Support for external vector databases (Pinecone, Weaviate)
• Advanced Retrieval Strategies - Hybrid search, re-ranking, and query expansion
• Multi-Modal RAG - Support for images, PDFs, and other document formats
• Distributed RAG - Cloud-based document processing and retrieval
• RAG Analytics - Performance metrics and retrieval quality analysis

🚀 New Features #

• Enhanced Model Browser with tabbed interface (Search, Recommended, Downloaded)
• File Browser Integration - Load models directly from device storage
• Device-Specific Recommendations - Memory-aware model suggestions based on device capabilities
• Advanced Debug Console - Real-time logging with color-coded categories and filtering
• Improved Model Cards - Better file organization, download status, and compatibility indicators
• Native Library Validation - Automatic detection of llama.cpp compatibility and health checks
• Progressive Model Loading - Fallback configurations for memory-constrained devices
• Enhanced Error Handling - Detailed error messages with troubleshooting tips and recovery suggestions

🔧 Improvements #

• Quantization Safety Checks - Automatic detection and warnings for unstable quantizations (Q2_K, IQ1_S, IQ1_M)
• Memory Management - Dynamic context size adjustment based on available device memory
• Download Progress - Real-time progress indicators with speed metrics and ETA
• Model Detection - Improved engine detection for GGUF and TFLite files with better accuracy
• UI/UX Enhancements - Material Design 3 with gradient themes, better accessibility, and responsive design
• Performance Monitoring - Token generation speed tracking, memory usage reporting, and performance analytics
• Search Optimization - Better model discovery with multiple search strategies and relevance ranking

🐛 Bug Fixes #

• Fixed SIGSEGV crashes with problematic quantizations on Android ARM64 devices
• Resolved memory leaks during model loading/unloading cycles
• Fixed download interruption handling and resume functionality
• Corrected model file validation for corrupted GGUF files
• Fixed context size calculation for low-memory devices
• Resolved engine detection issues for certain model formats
• Fixed UI state management during concurrent operations

📚 Documentation #

• Added comprehensive model compatibility guide with device-specific recommendations
• Enhanced troubleshooting documentation for Android devices with common solutions
• Updated API documentation with new configuration options and examples
• Added performance optimization guidelines for mobile deployment

🛠️ Technical Improvements #

• Isolate-based Model Loading - Better crash protection and memory isolation
• Enhanced Logging System - Categorized debug logs with filtering and export capabilities
• Improved Error Recovery - Graceful fallback mechanisms for failed operations
• Better Resource Management - Optimized memory usage and cleanup procedures

🎉 Initial Release #

• Dual Engine Support - Llama (GGUF) and Gemma (TFLite) inference engines
• HuggingFace Integration - Direct model search and download from HuggingFace Hub
• Model Management - Automatic model detection and configuration
• Chat Interface - Real-time streaming text generation with conversation history
• Cross-Platform - Support for Android and iOS devices

🔧 Core Features #

• Model Search - Search and filter models by format, size, and compatibility
• Automatic Downloads - Background model downloading with progress tracking
• Engine Detection - Automatic selection of appropriate inference engine based on model format
• Memory Optimization - Dynamic configuration based on device capabilities
• Error Handling - Comprehensive exception handling and user feedback

📱 Supported Formats #

• GGUF - Quantized models for efficient inference (Q4_K_M, Q4_0, Q5_K_M, Q8_0)
• TFLite - TensorFlow Lite models optimized for mobile devices
• Model Types - Support for Gemma, Llama, Phi, DeepSeek, and custom models

🛠️ Technical Stack #

• Flutter Framework - Cross-platform mobile development
• llama_cpp_dart - Native GGUF model inference with ARM64 optimization
• flutter_gemma - TFLite model inference with GPU acceleration
• Dio HTTP Client - Efficient model downloading and API communication
• Path Provider - Cross-platform file system access and management

🎯 Initial Capabilities #

• Text Generation - Streaming text generation with customizable parameters
• Model Discovery - Browse and search thousands of compatible models
• Local Storage - Efficient model caching and management
• Debug Tools - Basic logging and error reporting
• Configuration Management - Flexible inference configuration options