mtgai

MTGAI is a collection of experiments with Magic: The Gathering data and AI tools.

streamlit-app-2024-10-12-15-10-18.webm

Project Organization

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   ├── raw            <- The original, immutable data dump.
│   └── vectorstore    <- Directory for vector store data.
│
├── docs               <- A default Sphinx project; see sphinx-doc.org for details
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── app            <- Directory for the application code.
│   │
│   ├── data           <- Scripts to download or generate data
│   │   ├── make_raw.py
│   │   ├── make_procesed.py 
│   │   └── make_vectorstore.py
│   │
│   ├── features       <- Scripts to turn raw data into features for modeling
│   │   └── build_features.py
│   │
│   ├── models         <- Scripts to train models and then use trained models to make
│   │   │                 predictions
│   │   ├── predict_model.py
│   │   └── train_model.py
│   │
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
│       └── visualize.py
├── .env            <- Environment configuration file THAT YOU MUST MAKE MAUNALLY
└── tox.ini            <- tox file with settings for running tox; see tox.readthedocs.io

---

Make the .env

You need to start a new file in the root and write the relevant settings for the project. It looks like this:

OPENAI_API_KEY=sk-...
LANGCHAIN_PROJECT="mtgai"
LANGCHAIN_API_KEY='ls__...'
LANGCHAIN_TRACING_V2=True

OPENAI_API_KEY is the key for the OpenAI API. You can get it from the OpenAI website. It's likely that you should use a sk-proj "project" key. If you want to use a different key (such as a key linked to an "organisation" starting with sk-org), this should work, though you may also need to have set the OPENAI_ORG_ID environment variable.

The LANGCHAIN_* variables are for the Langchain Langsmith observability platform, and are optional. You can get them from the Langchain website. The LANGCHAIN_PROJECT is the project name, and the LANGCHAIN_API_KEY is the API key. The LANGCHAIN_TRACING_V2 is a boolean that determines whether to use the new tracing system.

Project based on the cookiecutter data science project template. #cookiecutterdatascience