When working on applications that require vector, semantic, or similarity search, it’s often useful to have a quick and easy way to create vector embeddings of data and save them in a vector database for further querying. 

This blog will walk you through a simple web application that allows you to quickly generate vector embeddings for various document types and store them directly in Azure Cosmos DB. Once stored, this data can be leveraged by other applications for tasks like vector search, part of a Retrieval-Augmented Generation (RAG) workflow, and more.