This is a minimal implementation of the RAG model for question answering.

This is an educational project where all of the codes where explained (step by step) via a set of Arabic youtube videos. Please check the list:

• Python 3.10

sudo apt update
sudo apt install libpq-dev gcc python3-dev

1. Download and install MiniConda from here
2. Create a new environment using the following command:

$ conda create -n mini-rag python=3.10

3. Activate the environment:

$ conda activate mini-rag

export PS1="\[\033[01;32m\]\u@\h:\w\n\[\033[00m\]\$ "

• Check the notebook + Video

$ pip install -r requirements.txt

$ cp .env.example .env

$ alembic upgrade head

Set your environment variables in the .env file. Like OPENAI_API_KEY value.

$ cd docker
$ cp .env.example .env

• update .env with your credentials

$ cd docker
$ sudo docker compose up -d

• FastAPI: http://localhost:8000
• Flower Dashboard: http://localhost:5555 (admin/password from env)
• Grafana: http://localhost:3000
• Prometheus: http://localhost:9090

$ uvicorn main:app --reload --host 0.0.0.0 --port 5000

For development, you can run Celery services manually instead of using Docker:

To Run the Celery worker, you need to run the following command in a separate terminal:

$ python -m celery -A celery_app worker --queues=default,file_processing,data_indexing --loglevel=info

To run the Beat scheduler, you can run the following command in a separate terminal:

$ python -m celery -A celery_app beat --loglevel=info

To Run Flower Dashboard, you can run the following command in a separate terminal:

$ python -m celery -A celery_app flower --conf=flowerconfig.py

open your browser and go to http://localhost:5555 to see the dashboard.

Download the POSTMAN collection from /assets/mini-rag-app.postman_collection.json