🎤 New Features - Speech Recognition (ASR) #

• Complete ASR Integration - Full TensorFlow Lite ASR engine with Whisper model support
• Real-time Speech Recognition - Live streaming transcription with continuous audio processing
• Multi-format Audio Support - WAV, PCM16, PCM32, Float32 audio format compatibility
• Advanced Audio Preprocessing - Whisper-compatible mel-spectrogram generation with FFT processing
• Voice Activity Detection (VAD) - Smart silence detection and automatic recording termination
• Chunked Audio Processing - Efficient handling of long audio files with overlap management
• ASR Service Layer - High-level service wrapper for easy speech recognition integration
• Microphone Integration - Native microphone access with permission handling

🔊 ASR Technical Features #

• Enhanced Audio Pipeline - Optimized mel-filter bank computation with isolate processing
• Adaptive Processing - Dynamic audio length optimization based on content characteristics
• Silence Detection - Advanced RMS-based silence detection with configurable thresholds
• Audio Resampling - High-quality interpolation-based audio resampling for format compatibility
• Model Type Detection - Automatic ASR model type detection (Whisper, Wav2Vec2, DeepSpeech)
• Streaming Optimization - Low-latency streaming with configurable chunk sizes and overlap
• Memory Efficient - Optimized for mobile devices with minimal memory footprint
• Error Recovery - Robust error handling with graceful fallbacks for audio processing

🎯 ASR Configuration Options #

• ASRConfig Class - Comprehensive configuration for sample rates, bit rates, and streaming parameters
• Quality Presets - Pre-built configurations for high-quality, low-latency, mobile, and streaming scenarios
• VAD Settings - Configurable voice activity detection with silence timeouts and thresholds
• Performance Tuning - Adjustable confidence thresholds and token limits for optimal performance
• Streaming Parameters - Customizable streaming intervals and chunk processing settings

🔧 ASR Service Methods #

• File Transcription - transcribeFile() for processing audio files with enhanced chunking
• Byte Transcription - transcribeBytes() for direct audio data processing
• Live Recording - startRecording() / stopRecording() for real-time audio capture
• Streaming Recognition - startStreamingTranscription() for continuous live transcription
• VAD Recording - recordWithVAD() for intelligent voice-activated recording
• Timed Recording - recordAndTranscribe() for fixed-duration audio capture

🎤 ASR Audio Processing #

• WAV Header Parsing - Complete WAV file format validation and metadata extraction
• Multi-channel Support - Automatic stereo-to-mono conversion for ASR compatibility
• Audio Validation - Comprehensive audio file validation with detailed error reporting
• Format Detection - Automatic audio format detection and appropriate decoder selection
• Quality Metrics - Audio quality assessment and processing recommendations

⚠️ Current Limitations #

• ASR Status: ASR functionality is implemented but currently experiencing stability issues and may not work reliably in all scenarios
• Multimodal Status: Multimodal image+text generation is implemented but not thoroughly tested - use with caution
• Platform Compatibility: ASR features may have varying performance across different Android devices
• Model Compatibility: Some ASR models may require specific preprocessing that hasn't been fully optimized

🔧 Improvements from Previous Version #

• Enhanced Multimodal Support - Improved Gemma multimodal integration with proper image handling
• Better Engine Detection - More accurate ASR model type detection and configuration
• Optimized Memory Usage - Reduced memory footprint for ASR processing on mobile devices
• Improved Error Messages - More descriptive error messages for ASR-related issues
• Debug Enhancements - Extended debug console with ASR-specific logging and metrics

🐛 Bug Fixes #

• Fixed audio preprocessing pipeline for Whisper model compatibility
• Resolved memory leaks in continuous ASR processing
• Corrected mel-spectrogram generation for various audio durations
• Fixed streaming audio buffer management and overlap handling
• Resolved TensorFlow Lite model loading issues for ASR engines
• Fixed microphone permission handling across different Android versions

📚 Documentation Updates #

• ASR Integration Guide - Complete guide for implementing speech recognition features
• Audio Processing Tutorial - Technical documentation for audio preprocessing and format handling
• Streaming Best Practices - Guidelines for optimal real-time speech recognition performance
• Troubleshooting Guide - Common ASR issues and their solutions
• Model Compatibility Matrix - Supported ASR models and their specific requirements

🔮 Upcoming in Next Release #

• ASR Stability Improvements - Focus on resolving current ASR reliability issues
• Multimodal Testing - Comprehensive testing and validation of image+text generation
• Additional ASR Models - Support for more ASR model architectures and formats
• Performance Optimizations - Further memory and speed optimizations for mobile deployment

🚀 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