!pip install -r requirements.txt 

• this t5 model for question answering (grazie gli 🇮🇹)
• cosine similarity model
• crossencoder

I'm kinda new to the frontend stuff so I made heavy use of this playlist on youtube for Flask + Slack API integration. Also for the chromadb part refer to their docs.

That being said, the pipline itself fully doesn't require Slack integration, I just figured it would be nice to provide an interface to everything. So if you want to get a feel for the model/workflow check out the t5skeleton.ipynb file which is essentially the rough draft of this project entirely offline.

If you want to regenerate any of the data included in the clean folder you'll need a functioning YouTube API key and to dig into the youtube-api.ipynb notebook. Same goes for the embeddings but this time you'll need to use the t5skeleton.ipynb. Otherwise I've included the cleaned subtitle .vtt's as well as the embeddings I generated for the demo here in the repo already.

Hopefully didn't leave any of my API keys around, that would be bad.