Project overview

This is a Question-Answering program based on TinyLlama/TinyLlama-1.1B-Chat-v1.0 llm from HuggingFace. The project also involves RAG system based on 1300+ Towards DataScience Medium Articles Dataset data set from kaggle. Additionally I added rerank method, which works as follows:

1. Get k(default 10) chunks from database
2. With the help of Cohere API, rerank this articles and select top_n(default 1) chunks relatively to the previously selcted chunks. And use only them for context of LLM query

This method suppose to help to get more meaningful and precise information from the LLM.

Retrieve Augmented Generation in my program works as follows:

Set up and configuration

You can download the repository via the command

git clone https://github.com/DzmitryPihulski/LLM_question_and_answer_system_with_RAG.git

Install all the libraries by running, I used python version 3.11.4

pip install -r requirements.txt

You will also need a .env file in the folder with the project. In the .env file you need to place your Cohere API Key. You can create yours for free on theirs website. In the file paste your key:

COHERE_API_KEY = "YOUR_API_KEY_FROM_THE_WEBSITE"

And after that run the main.py It will generate a response for the test question. If you want to change the question, you must change it in the main.py, "test_query" variable.