| # kagglex-final-project | |
| A prototype written in Python to illustrate/demonstrate querying the Learning Path Index Dataset (see [Kaggle Dataset](https://www.kaggle.com/datasets/neomatrix369/learning-path-index-dataset) and [GitHub repo](https://github.com/neomatrix369/learning-path-index)), with the help of the OpenAI GPT technology (InstructHPT model and embeddings model), [Langchain](https://python.langchain.com/) and using [Facebook's FAISS library](https://faiss.ai/). | |
|  | |
| The end-to-end process can be learnt by going through the code base as well as by observing the console logs when using both the Streamlit and the CLI versions. | |
| ## Pre-requisites | |
| - Python 3.8.x or above | |
| - OpenAI API Key (see [How to get an OpenAI API Key](https://www.howtogeek.com/885918/how-to-get-an-openai-api-key/) -- note it's may not be FREE anymore) | |
| - Install dependencies from `requirements.txt` | |
| - Basic Command-line experience | |
| - Basic git and GitHub experience | |
| ## Install and run | |
| Copy the `.env_template` to `.env` in the current folder and then add your OpenAI API Key to `.env`. | |
| **Please don't modify the `.env_template` file.** | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| ### Interactive session via CLI app | |
| ```bash | |
| python main.py | |
| ``` | |
|  | |
| ### Interactive session via Streamlit app | |
| ```bash | |
| streamlit run main.py | |
| ``` | |
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