In this tutorial, we will implement semantic search on a sample dataset. We will utilize DistilBERT on HuggingFace for vectorization, a lighter and faster version of BERT that maintains similar accuracy. For storing and querying the vectors, Upstash Vector will be used. Here is the outline: 1- Create an index on Upstash Vector and install the required dependencies. 2- Download a sample dataset, which consists of newsgroup documents, available at http://qwone.com/~jason/20Newsgroups/. 3- Vectorize the documents using DistilBERT. 4- Insert the vectors into the database. 5- Conduct a test query. You can find the full tutorial and code in the notebook here.