Local RAG query tool for PDFs

This is a simple Retrieval Augmented Generation (RAG) tool built in Python which allows us to read information from a PDF document and then generate a response based on the information in the document.

We use Ollama to run the tool Locally and we use llama3 currently as the model for the RAG tool.

Setup

After cloning the repository, you can install the required packages using the requirements.txt file. To install the packages, you can run the following command:

pip install -r requirements.txt

After installing the required packages, you also need to place the PDF documents you want to get information about in the data folder.

You also need to download Ollama if you haven't already. After downloading Ollama, make sure you download the 'llama3' model in the terminal by running the following command:

ollama pull llama3

This will download the llama3 model which we will use for the RAG tool. Learn more about Ollama implementation in my Guide to Ollama. You can also download other models if you want but then you will have to change the MODEL_NAME in the main.py file.

Usage

To use the tool, you can run the following command:

python main.py

This will start the script and you can then input a question and it will generate a response based on the information in the PDF document.