🤖 WPFLLM

AI Assistant with RAG, Document Analysis & Enterprise Security

A modern WPF desktop application for AI-powered conversations with advanced RAG, document analysis, and enterprise-grade security. Built with .NET 10, featuring offline-first design, local embedding models, and hybrid retrieval.

Features • Quick Start • Architecture • Documentation • Testing • Contributing

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Screenshot: Chat interface with RAG debug panel showing retrieval metrics

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✨ Features

💬 Intelligent Chat

• Real-time streaming responses from LLMs
• Multiple conversation management with history
• Markdown rendering support
• RAG Debug Panel with sources, scores, and latency metrics

📚 Advanced RAG System

• Hybrid Retrieval: Vector search + FTS5 keyword search with RRF fusion
• Import documents (.txt, .md, .json, .csv, .pdf, .docx)
• Configurable TopK, similarity threshold, retrieval mode
• Full pipeline transparency with Flight Recorder

✈️ RAG Debug Panel (Flight Recorder)

The "black box" for your RAG pipeline - understand exactly why the model responded the way it did:

Tab	What it shows
📄 Sources	Selected chunks with Vec/KW/RRF scores and content preview
📊 Candidates	All evaluated chunks (not just selected), with ranking and token counts
⏱️ Timings	Visual breakdown: EmbedQuery, VectorSearch, KeywordSearch, Merge+Rerank

Summary Cards show at a glance:

• Total pipeline time (ms)
• Selected/Total chunks ratio
• Vector matches count
• Keyword matches count

Formula: RRF(k=60) - Reciprocal Rank Fusion combining vector and keyword results

📄 Document Analysis Mode

• Analyze documents and transcripts for insights
• Summary: TL;DR of content
• Detected Intents: Customer/user intentions with confidence scores
• Red Flags: Risk detection with severity levels
• Compliance Checklist: Automatic verification of required items
• Suggested Response: Draft professional replies

🧠 Local Embedding Models

• Download and run embedding models locally (ONNX)
• Support for multilingual E5 models (Small/Base/Large/Instruct)
• No API costs for embeddings
• Privacy-first: your data stays on your machine

🎨 Modern UI/UX

• Clean dark theme with accent colors
• Multi-language support: English, Polski (more coming)
• Status bar with offline/encryption/network indicators
• RAG Debug panel with professional metrics display

🔒 Enterprise Security

• AES-256-GCM encryption for data at rest
• DPAPI key protection tied to Windows user
• Offline mode indicator
• Network calls counter (transparency)
• Zero telemetry, zero data leakage

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🏗 Architecture

Architecture inspired by RAG frameworks like LlamaIndex, implemented natively in .NET for offline mode.

┌─────────────────────────────────────────────────────────────────┐
│                         UI Layer (WPF)                          │
│  ┌─────────┐ ┌─────────┐ ┌──────────┐ ┌─────────┐ ┌──────────┐ │
│  │  Chat   │ │Knowledge│ │ Document │ │Embeddings│ │ Settings │ │
│  │  View   │ │  Base   │ │ Analysis │ │   View   │ │   View   │ │
│  └────┬────┘ └────┬────┘ └────┬─────┘ └────┬─────┘ └────┬─────┘ │
│       │           │           │            │            │       │
│  ┌────┴───────────┴───────────┴────────────┴────────────┴────┐  │
│  │                    ViewModels (MVVM)                       │  │
│  │   IAsyncEnumerable<string> for streaming responses         │  │
│  │   Update UI via Dispatcher (only in UI layer)              │  │
│  └────────────────────────────┬───────────────────────────────┘  │
└───────────────────────────────┼──────────────────────────────────┘
                                │
┌───────────────────────────────┼──────────────────────────────────┐
│                    Application Layer                             │
│  ┌────────────────┐ ┌─────────────────┐ ┌──────────────────────┐ │
│  │ ChatOrchestrator│ │IngestionPipeline│ │  RagQueryEngine     │ │
│  │ (ChatService)   │ │ (Channel<T>)    │ │  (Hybrid Retrieval) │ │
│  └────────────────┘ └─────────────────┘ └──────────────────────┘ │
│  ┌────────────────┐ ┌─────────────────┐ ┌──────────────────────┐ │
│  │DocumentAnalysis│ │ StatusService   │ │ LocalizationService │ │
│  └────────────────┘ └─────────────────┘ └──────────────────────┘ │
└───────────────────────────────┬──────────────────────────────────┘
                                │
┌───────────────────────────────┼──────────────────────────────────┐
│                       Core Interfaces                            │
│  IEmbedder │ IVectorIndex │ IRetriever │ ILLM │ IEncryptor       │
└───────────────────────────────┬──────────────────────────────────┘
                                │
┌───────────────────────────────┼──────────────────────────────────┐
│                    Infrastructure Layer                          │
│  ┌─────────────────┐ ┌─────────────────┐ ┌────────────────────┐  │
│  │ SQLite + FTS5   │ │  ONNX Runtime   │ │  DPAPI + AES-GCM   │  │
│  │ (vector + text) │ │  (CPU/DirectML) │ │  (encryption)      │  │
│  └─────────────────┘ └─────────────────┘ └────────────────────┘  │
└──────────────────────────────────────────────────────────────────┘

Key Architectural Decisions

Decision	Rationale
Hybrid Retrieval (FTS5 + Vectors)	Combines keyword precision with semantic understanding via RRF fusion
Channel<T> Ingestion Queue	Backpressure, cancellation, retry - enterprise-grade document processing
DPAPI + AES-256-GCM	Windows-native key protection, industry-standard encryption
Offline-First Design	Data never leaves device, zero external dependencies for core features
IAsyncEnumerable Streaming	Token-by-token response delivery without blocking UI

Tech Stack

Technology	Purpose
.NET 10.0	Runtime
WPF	UI Framework
CommunityToolkit.Mvvm	MVVM implementation
SQLite + FTS5	Local database with full-text search
ONNX Runtime	Local model inference
AES-GCM + DPAPI	Enterprise encryption

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🚀 Quick Start

Prerequisites

• Windows 10/11 (64-bit)
• .NET 10.0 SDK
• ~2GB disk space for embedding models (optional)

Installation

Option 1: Microsoft Store (Recommended)

Option 2: Build from Source

# Clone the repository
git clone https://github.com/DamianTarnowski/WPFLLM.git
cd WPFLLM

# Build and run
dotnet run

First Run Setup

1. Configure API → Settings → Enter OpenAI/OpenRouter API key
2. Start Chatting → Chat tab → Create conversation → Send message
3. Enable RAG (optional) → Knowledge Base → Add documents → Generate embeddings
4. Go Offline (optional) → Embeddings → Download local model

Build for Production

# Self-contained executable
dotnet publish -c Release -r win-x64 --self-contained -o ./publish

# Framework-dependent (smaller)
dotnet publish -c Release -r win-x64 -o ./publish

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🔧 Configuration

Data Location

%LOCALAPPDATA%\WPFLLM\
├── wpfllm.db           # Encrypted database
└── models/             # Downloaded embedding models
    ├── multilingual-e5-small/
    ├── multilingual-e5-base/
    └── multilingual-e5-large/

Supported API Providers

Provider	Endpoint
OpenAI	https://api.openai.com/v1
Azure OpenAI	https://{resource}.openai.azure.com/
OpenRouter	https://openrouter.ai/api/v1
Ollama	http://localhost:11434/v1
LM Studio	http://localhost:1234/v1

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📊 Embedding Models

Model	Dimensions	Size	Quality	RAM
E5 Small	384	~470MB	★★★☆☆	1-2 GB
E5 Base	768	~1.1GB	★★★★☆	2-3 GB
E5 Large	1024	~2.2GB	★★★★★	4-6 GB

All models support 100+ languages including Polish, English, German, French, and more.

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📖 Documentation

Document	Description
Architecture	System design, MVVM patterns, service layer
Embeddings	Local embedding models, Rust tokenizer, E5 setup
Store Publishing	Microsoft Store submission guide
Privacy Policy	Privacy policy for Store compliance
Contributing	How to contribute, code style, commit conventions
Changelog	Version history and release notes

Key Services

Service	Responsibility
LlmService	OpenAI-compatible API, streaming, embeddings
RagService	Document chunking, hybrid retrieval (FTS5 + vectors)
ChatService	Conversation management, message history
LocalEmbeddingService	ONNX model inference, Rust tokenizer
EncryptionService	AES-256-GCM encryption, DPAPI key protection
IngestionService	Background document processing queue

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🧪 Testing

The project includes 317 tests with comprehensive coverage:

Category	Tests	Description
Unit Tests	184	Services, models, utilities
Integration Tests	120	Database, RAG, chat workflows
Real API Tests	13	Live OpenRouter API validation

Running Tests

# All tests (mocked, no API key needed)
dotnet test

# With verbose output
dotnet test --logger "console;verbosity=normal"

# Only real API tests
dotnet test --filter "TestCategory=RealApi"

Real API Tests

To run tests against the real OpenRouter API:

# Option 1: Environment variable
$env:OPENROUTER_API_KEY = "sk-or-v1-your-key-here"
dotnet test --filter "TestCategory=RealApi"

# Option 2: Create WPFLLM.Tests/.env file
echo "OPENROUTER_API_KEY=sk-or-v1-your-key-here" > WPFLLM.Tests/.env
dotnet test --filter "TestCategory=RealApi"

Note: The .env file is gitignored. Tests are automatically skipped if no API key is found.

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🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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👤 Author

Damian Tarnowski

• GitHub: @DamianTarnowski

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Made with ❤️ and .NET