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RAG (Retrieval Augmented Generation) based solution to chat with documents using OpenAI models.

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Application Documentation

Installation

  1. Clone the repository
git clone https://github.com/Scoutflo/ai-debugger.git
cd ai-debugger
  1. Create and activate virtual environment
python -m venv venv
source venv/bin/activate
  1. Install requirements
pip install -r requirements.txt

Building with Docker

docker build -t my-python-app .

Running the Application

docker run -p 8000:8000 my-python-app

Environment Variables

The application can be configured using environment variables when running the Docker container. Example:

docker run -e VAR_NAME=value -p 8000:8000 my-python-app

Dependencies

All required packages are listed in requirements.txt and automatically installed during the Docker build process.

Project Structure Overview

.
├── Dockerfile
├── README.md
├── requirements.txt
├── app.py          # Main application entrypoint
└── (other project files)

The Dockerfile copies the entire directory structure into the container's /app working directory.

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RAG (Retrieval Augmented Generation) based solution to chat with documents using OpenAI models.

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  • Python 86.7%
  • Dockerfile 13.3%