Cortex AI

Cortex AI is an advanced AI-powered search and chat platform that integrates multiple language models and search providers for enhanced information retrieval and interaction.

Features

• Multiple Search Providers: SearxNG, Tavily, Serper, Bing
• Multiple LLM Support: Supports OpenAI, Groq, Azure, Ollama and custom models
• Docker Integration: Fully dockerized setup for easy deployment
• Modern UI: Sleek interface built with Next.js and Tailwind CSS
• Local Modes: Support for running with local models

Project Structure

├── src/
│   ├── backend/             # Python FastAPI backend
│   │   ├── alembic/         # Database migrations
│   │   ├── db/              # Database models and utilities
│   │   ├── llm/             # LLM integrations
│   │   ├── search/          # Search provider integrations
│   │   └── main.py          # Main FastAPI application
│   │
│   └── frontend/            # Next.js frontend
│       ├── app/             # Next.js app router
│       ├── components/      # UI components
│       ├── hooks/           # React hooks
│       ├── lib/             # Utility functions
│       └── styles/          # CSS styles
│
├── docker-compose.dev.yaml  # Docker Compose configuration
├── standalone.Dockerfile    # All-in-one Docker setup
├── .env-example             # Environment variables template
└── start.sh                 # Startup script

Installation and Setup

Prerequisites

• Docker and Docker Compose
• Node.js (for local development)
• Python 3.11+ (for local development)

Running with Docker

1. Clone the repository:

   git clone https://github.com/i-anubhav-anand/Cortex_AI.git
   cd Cortex_AI

2. Copy the environment template:

   cp .env-example .env

3. Update the .env file with your API keys and preferences

4. Run using Docker Compose:

   ./start.sh

   Or manually:

   docker-compose -f docker-compose.dev.yaml up --build

5. Access the application:

   • Frontend: http://localhost:3000
   • Backend API: http://localhost:8000
   • SearxNG: http://localhost:8080

Running Locally (Development)

Backend

1. Navigate to the backend directory:

   cd src/backend

2. Create a virtual environment and install dependencies:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   pip install poetry
   poetry install

3. Start the backend server:

   uvicorn main:app --reload --host 0.0.0.0 --port 8000

Frontend

1. Navigate to the frontend directory:

   cd src/frontend

2. Install dependencies:

   pnpm install

3. Start the development server:

   pnpm dev

Contributing

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

License

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