This is Flask application that provides chat functionality with multiple AI models, file processing, text-to-speech capabilities, and web search integration. It features basic memory for follow-up questions, customizable system prompts, and allows you to select from a variety of AI models. Designed for ease of use, it requires minimal setup to get started. Powering a local ChatGPT-like environment. The system is designed to be user-friendly, accurate, and informative, with a focus on providing relevant and contextual responses.
app/
├── __init__.py # Application factory
├── config.py # Configuration settings
├── core/ # Core components
│ ├── clients.py # API clients and integrations
│ ├── models.py # Data models and schemas
│ └── session.py # Session management
├── routes/ # API route handlers
│ ├── chat.py # Chat-related routes
│ ├── main.py # Main application routes
│ └── speech.py # Speech-to-text and text-to-speech routes
├── services/ # Business logic and services
│ ├── file_processor.py # File processing services
│ └── search.py # Search services
├── utils/ # Utility modules
│ └── logging.py # Logging utilities
static/ # Static files (CSS, JS, images)
templates/ # HTML templates
tools/ # External tools and scripts
uploads/ # Directory for file uploads
- Create a Python virtual environment and activate it:
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate- Dependencies
Install all dependencies using pip: cd to jarvis-chat
pip install -r requirements.txt-
Set up environment variables:
a. Copy the example environment file:
cp .env.example .env
b. Edit the
.envfile and fill in your API keys:# Required: AI Model API Keys CEREBRAS_API_KEY=your-cerebras-key GROQ_API_KEY=your-groq-key MISTRAL_API_KEY=your-mistral-key SAMBANOVA_API_KEY=your-sambanova-key GLHF_API_KEY=your-glhf-key GEMINI_API_KEY=your-gemini-key COHERE_API_KEY=your-cohere-key # Optional Configuration FLASK_ENV=development # or "production" PORT=4001
You can obtain API keys from the following providers:
- Cerebras: https://www.cerebras.ai/
- Groq: https://www.groq.com/
- Mistral AI: https://www.mistral.ai/
- SambaNova: https://www.sambanova.ai/
- GLHF: https://glhf.chat/
- Google Gemini: https://ai.google.dev/
- Cohere: https://cohere.com/
Note: The application requires at least one valid API key to function. For optimal performance, it is recommended to provide keys for multiple models.
- Start the application:
python run.py- Access the application at
http://localhost:4001
- Multi-model chat interface
- File upload and processing (PDF, images)
- Text-to-speech and speech-to-text
- Web search integration
- Semantic search in uploaded documents
- Session management
- Streaming responses
- Markdown Rendering: Supports rendering of Markdown syntax for formatted text.
- Code Highlighting: Highlights code snippets with syntax highlighting for better readability.
- Code Copying: Allows users to easily copy generated code with a single click.
- You can also get information from a URL. Jarvis will add the content from the URL as context before providing a response.
- The application uses a factory pattern for initialization
- Configuration is environment-based (development, production, testing)
- Blueprints separate different functional areas
- Services handle business logic
- Core modules manage fundamental functionality
The application includes comprehensive error handling:
- API client initialization errors
- File processing errors
- Network request failures
- Invalid configuration errors
Logging is configured for:
- Chat interactions
- Model usage
- Web searches
- Error tracking
- File upload restrictions
- Session security
- Environment variable validation
- Fork the repository
- Create a feature branch
- Make your changes
- Submit a pull request
This project is licensed under the MIT License.