This project was developed for the 2024 UPDate Hackathon powered by NuCC and AbuseTotal, during the 2023/2024 academic year, at the Faculty of Sciences of the University of Porto, by the students André Sousa, António Cardoso, Bárbara Santos e Paulo Silva and you may find its original repository on Github.
To run this build, you must have a Twitch API Key and an OpenAI Key. Additionally, at the end of this file, you may find the Python and library versions used.
We were tasked with creating an AI service/agent that leverages LLMs to process, refine and/or enrich this data to produce intelligence on a daily basis. For this task, we decided to create a platform that fetches Twitch data about old and recent games and produces a possible future scoring and review for recently released games based on old similar ones.
To do this, we start by using a Twitch API Key in order to have access to the data we will be using through queries. This extracted data was then engineered into a dataset and fed onto an XGBoost machine learning model in order to learn how to rate a new game.
On the other hand, some other part of our data goes to an embedder, in order help the LLM to produce its answer. We utilize the embedder to encode the context of some lexicalgraphic data, so that we know which pre-stored context might be useful to synthesize the LLM's answer.
Finally, with all of this information being within our knowledge, any new game that enters the platform before the next report will be refined in order for the machine learning model to output a possible rating for the game and the LLM to consequently give an example review that gives some insight into the positives and negatives of said game.
main.pyholds the main code to access our solution. Simply runpython main.pyto get started.model.ipynbholds the parameter tuning for the machine learning model used, along with a quick PCA analysis.llm.pyholds functions to access the llm and the embeddings used for generation.xgb.pyholds a function to generate a xgb model with the correct PCA function (stored inpca.pkl) and generate the model used in the main file.
| Module | Version |
|---|---|
| Python | 3.10.13 |
| xgboost | 2.0.3 |
| matplotlib | 3.8.3 |
| scikit-learn | 1.4.1.post1 |
| Jinja2 | 3.1.2 |
| numpy | 1.25.1 |
| openai | 1.13.3 |
| pandas | 2.0.3 |
| scipy | 1.12.0 |