kottoization / RAG-LLM

RAG-LLM enables interactive question answering leveraging RAG architecture and Large Language Models (LLMs) applied to custom dataset regarding Medium articles.

RAG LLM Project

Description

The RAG LLM Project is an artificial intelligence system that utilizes RAG (Retrieval-Augmented Generation) and LLM (Large Language Model) to provide answers to questions regarding data from Medium articles. This system enables quick search and content generation based on the given queries.

Requirements

To run the project, you need to have Python and pip installed. The project also relies on the requirements.txt file, which contains all the necessary dependencies.

Installation

1. Clone the repository to your local machine.
2. Create a virtual Python environment:

   python -m venv venv
   

3. Activate the virtual environment:
   • Windows:

   venv\Scripts\activate
   

   • macOS/Linux:

   source venv/bin/activate
   

4. Install the dependencies using the requirements.txt file:

   pip install -r requirements.txt
   

Configuration

The project uses the .env file for environment variable configuration. Before running the project, create the .env file and add OpenAI API key.

Example .env file:

OPENAI_API_KEY=YOUR_OPENAI_API_KEY_HERE

Usage

After successfully installing and configuring the project, you can run the main.py file. The project will be ready to use, and the user can input questions to which the system will respond.

Example of running the project:

python main.py

After running the main.py code, a new csv file with embedded values will be created. If this file already exists the user will be asked if he/she want's to create a new file or to open the existing one.