This project aims to analyze NFL (National Football League) data from the year 2008 to the present using Python and Llama-2. The project involves processing, analyzing, and visualizing various aspects of NFL data to derive insights and patterns for fantasy football.
- SQL Database: SQL Lite database ready for use of NFL data.
- Data Analysis: Performing exploratory data analysis (EDA) to understand trends, patterns, and relationships within the dataset.
- Chroma DB: Creation of Chroma DB and embeddings for use in RAG.
- Visualization: Creating informative visualizations such as plots, charts, and graphs to illustrate findings in a intuitive front end.
- RAG and SQL agent duo: Utilize a comboination of RAG and SQL agents in Langchain to balance precision of data (SQL) and semantic understanding of unstructured data (RAG) in responses.
- LLM Interface in Streamlit: Spin up streamlit QA chat bot to discuss roster formation.
- API Integration from Major Fantasy Providers: ESPN and Sleeper Roster ingestion in front end interface.
- Python 3.x
- Pandas
- NumPy
- Langchain
- Chromadb
- Ollama
-
Clone the repository:
git clone https://github.com/chasenuzum/nfl_llm.git -
Install the required Python packages:
pip install -r requirements.txt
- Spin up Ollama and Llama2 https://ollama.com/download
- Run the python file:
python run.py - Wait until download, embeddings, finish
- Open local port listed in terminal
Contributions are welcome! If you'd like to contribute to this project, please follow these steps:
- Fork the repository.
- Create a new branch (
git checkout -b feature/new-feature). - Make your changes.
- Commit your changes (
git commit -am 'Add new feature'). - Push to the branch (
git push origin feature/new-feature). - Create a new Pull Request.
This project is licensed under the MIT License - see the LICENSE file for details.
- The nfl-data-py team for providing the dataset.
- Contributors to libraries used in this project