This guide shows how to turn raw documents into semantic embeddings using a pre-trained sentence-transformers model, reduce their dimensionality with UMAP, and then apply HDBSCAN to uncover topic clusters without labels. It walks through data loading, embedding generation, dimensionality reduction, clustering, and sampling of cluster contents in Python.