Multimodal (Decoder) RAG

Todos

• System (chat/ rag) prompt
• Autoregressive Transfomer response

Installation

1. Clone the repsitory:

git clone git@github.com:CKeibel/FHSWF-deep-learning.git

2. Checkout directory:

cd FHSWF-deep-learning

Installing with pip

1. Create a virtual environment:

python -m venv .venv

2. Activate the newly create virtual env named "venv":

source .venv/bin/activate

Now (.venv) should be displayed in front of your command prompt.

3. Install project dependencies with pip:

python -m pip install -e .

Poetry

Install poetry

pip install poetry

Install project dependencies with poetry:

poetry install

Usage

Stat the application via python main.py.

When starting up, two urls will be available to access the interface. Use the local url when you are working on your local machine. If the app runs on a remote cluster (e.g. the fh-swf cluster) use the public url.

IMPORTANT: When you start the app on the fh-swf cluster make sure that your “current working directory” is set correctly in vscode. This is absolutely necessary to read the models.yml when starting the app.

# launch.json
{
    "configurations": [
        {
            "name": "App",
            "type": "python",
            "request": "launch",
            ...
            "cwd": "/home/<USER>/FHSWF-deep-learning/", # set <USER>
            "program": "main.py",
            "console": "integratedTerminal",
            "justMyCode": true,
            ...
        }
    ]
}

Dev

pre-commit

Install pre-commit

pip install pre-commit

Install pre-commit hooks

pre-commit install
-------------------
>> pre-commit installed at .git/hooks/pre_commit