mini-rag

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

Requirements

• Python 3.10

Install Dependencies

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

Install Python using MiniConda

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

(Optional) Setup you command line interface for better readability

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

(Optional) Run Ollama Local LLM Server using Colab + Ngrok

• Check the notebook + Video

Installation

Install the required packages

$ pip install -r requirements.txt

Setup the environment variables

$ cp .env.example .env

Run Alembic Migration

$ alembic upgrade head

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

Run Docker Compose Services

$ cd docker
$ cp .env.example .env

• update '.env' with your credentials

$ cd docker
$ sudo docker compose up -d

Run the FastAPI Server

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

POSTMAN Collection

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