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Your Private, Local-First AI Desktop Assistant.

Baiss_demo.mp4

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💡 Why Baiss?

In an era where data privacy is paramount, Baiss brings the power of Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) directly to your desktop—running entirely locally.

No cloud subscriptions, no data leaks, just pure AI productivity. Whether you're a developer needing a coding assistant or a researcher organizing documents, Baiss provides a unified, cross-platform interface to interact with your data and models.

⚡ Key Features

• 🔒 Privacy First: Runs local LLMs (via llama.cpp) and vector search on your machine. Your data never leaves your device.
• 🧠 Advanced RAG: Built-in Retrieval-Augmented Generation using DuckDB for high-performance vector storage and FlashRank for re-ranking.
• 🎨 Cross-Platform UI: A beautiful, responsive interface built with Avalonia UI, running natively on macOS, Windows, and Linux.
• 🔌 Extensible Architecture: Designed with Clean Architecture principles, making it easy for developers to add new AI providers, tools, or plugins.
• 🐍 Python Power: Leverages a robust Python backend (FastAPI) for heavy AI lifting, seamlessly integrated with the .NET frontend.

🛠️ The Tech Stack

Frontend & Core:

• C# / .NET 8: The backbone of the application.
• Avalonia UI: For a pixel-perfect cross-platform user experience.
• Clean Architecture: Separation of concerns (Domain, Application, Infrastructure, UI).

AI Backend:

• Python & FastAPI: Handles AI logic and API endpoints.
• DuckDB: Embedded SQL OLAP database for efficient vector search.
• Llama.cpp: For running quantized LLMs locally with hardware acceleration.
• HuggingFace & Transformers: For embeddings and model management.

🚀 Getting Started

Prerequisites

Ensure you have the following installed:

• .NET 8 SDK: Download here
• Python 3.10+: Download here

Installation

1. Clone the repository:

   git clone https://github.com/Tbeninnovation/Baiss.git
   cd Baiss

2. Set up the Python Environment: Navigate to the core directory and install dependencies.

   cd core/baiss
   pip install -r requirements.txt

Usage

To run the application locally:

# Navigate to the UI project
cd Baiss.UI

# Run the application
dotnet run

Note: On the first run, Baiss may need to download default models or configure the local database. Please check the console output for status updates.

📂 Project Structure

Here's a quick look at the codebase organization:

Baiss/
├── Baiss.UI/              # Avalonia UI Frontend & Entry Point
├── Baiss.Application/     # Business Logic, Interfaces, Use Cases
├── Baiss.Domain/          # Core Entities & Value Objects
├── Baiss.Infrastructure/  # Services, DB Access, External APIs
└── core/
    └── baiss/             # Python Backend (FastAPI, Agents, RAG)
        ├── requirements.txt
        └── shared/python/baiss_agents/

🤝 Contributing

We love contributions! Whether it's fixing a bug, improving the UI, or adding support for a new AI model, your help is welcome.

1. clone the repo and create a branch from dev
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 to dev
6. We will test in dev and merge to main when ready.

Contributors

A huge thank you to the brilliant minds building Baiss:

• @taharbmn - Thought this was a 2-week project. That was 6 months ago.
• @Abdelmathin - The voice of reason we muted on Meetings.
• @L0Abdellah - The only one who knows why the search results actually work.
• @AYoubZarda - Burned a laptop developing this (RIP)
• @DraGSsine - Laptop Survived, My Code Didn’t

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Baiss — Empowering your desktop with local AI.