🤖 Rusty Bot

A modern AI chatbot built entirely in Rust with RAG capabilities

Features • Installation • Usage • Development

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🎯 About

Rusty Bot is an open-source AI chatbot application that demonstrates the power of Rust in building modern web applications. This project serves as both a functional chatbot and an educational resource, showcasing how to integrate large language models with semantic search capabilities using a full-stack Rust approach.

Academic Context: This project is part of the Final Degree Project by Alejandro López Martínez, a student in the 2nd year of Multiplatform Application Development in Almería, Spain.

✨ Features

🧠 AI-Powered Conversations

• Integration with language models via Kalosm
• Real-time streaming responses for better user experience
• Conversation history management with reset functionality

🔍 Semantic Search & RAG

• Document embedding using BERT models
• Context-aware responses through Retrieval-Augmented Generation (RAG)
• Vector database powered by SurrealDB for semantic search

🎨 Modern Web Interface

• Responsive design built with Dioxus framework
• Real-time streaming text display
• Markdown support with syntax highlighting
• Dark theme optimized for extended use

🛠 Full-Stack Rust Architecture

• Use of Dioxus as full-stack framework.
• Type-safe communication between client and server
• Zero JavaScript dependencies (except for styling with TailwindCSS)

Core Components

• Frontend: Dioxus-based reactive UI compiled to WebAssembly
• Backend: Axum server with Dioxus server functions
• LLM Engine: Kalosm integration with Qwen 2.5 7B model
• Vector Database: SurrealDB with embedding-based semantic search
• Context System: Markdown-based document ingestion with RAG

🚀 Installation

Prerequisites

Ensure you have the following installed on your system:

1. Rust Toolchain

# Install Rust using rustup
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

# Follow the on-screen instructions and restart your terminal
# Verify installation
rustc --version
cargo --version

2. Dioxus CLI

# Install Dioxus CLI for development
cargo install dioxus-cli

# Verify installation
dx --version

3. System Requirements

• RAM: At least 8GB (16GB recommended for LLM operations)
• Storage: ~10GB free space for model downloads
• OS: Linux, macOS, or Windows with WSL2

🔧 Setup

1. Clone the Repository

   git clone https://github.com/your-username/rusty_bot.git
   cd rusty_bot

2. Create Context Directory (if it doesn't exist)

   mkdir -p context

3. Add Your Documents (Optional)

   Place your Markdown files in the context/ directory. These will be used for RAG-based responses.

🎮 Usage

dx serve --platform web --release

💬 Using the Chatbot

1. Initialize: The application will automatically download and initialize the language model on first run (this may take several minutes)

2. Chat: Type your message in the text area and press Enter or click Send

3. Context Toggle: Enable the "Context" toggle to use RAG-based responses with your documents

4. Reset: Click the reset button (↻) in the top-left to start a new conversation

🛠 Development

Project Structure

rusty_bot/
├── src/
│   ├── components/          # UI components
│   │   ├── conversation.rs  # Main chat interface
│   │   └── message.rs       # Individual message rendering
│   ├── model/               # Data models
│   │   ├── chat.rs          # Chat message structures
│   │   └── document.rs      # Document result structures
│   ├── server/              # Server-side modules
│   │   ├── llm.rs           # Language model integration
│   │   ├── embedding.rs     # Text embedding functionality
│   │   └── database_impl.rs # Database operations
│   ├── server_functions/    # Dioxus server functions
│   └── main.rs              # Application entry point
├── context/                 # Knowledge base documents
├── assets/                  # Static assets
└── Cargo.toml              # Project dependencies

Adding New Features

1. Server Functions: Add new server functions in src/server_functions/
2. UI Components: Create new components in src/components/
3. Models: Define data structures in src/model/
4. Context: Add knowledge base documents to context/

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

📄 License

This project is licensed under the GNU General Public License v3.0 - see the LICENSE file for details.

🙏 Acknowledgments

• Kalosm - For providing excellent Rust LLM integration
• Dioxus - For the amazing full-stack Rust framework
• SurrealDB - For the powerful multi-model database
• The Rust community for creating such amazing tools and libraries

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Made with ❤️ and 🦀 by Alejandro López Martínez (develatter)