| # Starting Point for the Final Project of the "From Beginner to Advanced LLM Developer" course | |
| ## Overview | |
| This repository contains the code of the final "Part 4; Building Your Own advanced LLM + RAG Project to receive certification" lesson of the "From Beginner to Advanced LLM Developer" course. | |
| Congrats, you are at the last step of the course! In this final project you'll have the possibility to practice with all the techniques that you learned and earn your certification. | |
| If you want, you can use this repository as starting point for your final project. The code here is the same as in the "Building and Deploying a Gradio UI on Hugging Face Spaces" lesson, so you should be already familiar with it. If you want to use it for your project, fork this repository here on GitHub. By doing so, you'll create a copy of this repository in your GitHub account that you can modify as you want. | |
| ## Setup | |
| 1. Create a `.env` file and add there your OpenAI API key. Its content should be something like: | |
| ```bash | |
| OPENAI_API_KEY="sk-..." | |
| ``` | |
| 2. Create a local virtual environment, for example using the `venv` module. Then, activate it. | |
| ```bash | |
| python -m venv venv | |
| source venv/bin/activate | |
| ``` | |
| 3. Install the dependencies. | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| 4. Launch the Gradio app. | |
| ```bash | |
| python app.py | |
| ``` |