Get embeddings for multiple data sources

Hi,

Following my first short post about RAGs, I would like to provide a brief overview about embeddings, which are used to find similiar objects in a vector database. To better understand how various transformer models handle different input data types, I created this notebook. I explore therefor, text, image, audio and video data.

I’ve chosen to skip the more traditional text embeddings (TF-IDF, Word2Vec or GloVe), because there are already very good tutorials available. Additionally, I plan to discuss the training of embedding models in a separate blog post. For this post, I use mostly pretrained classification models, where I use the last layer before the prediction head as embedding.

This is a brief overview of the embedding component of a RAG system. Just have a look.

Thank you for your attention.