This is a guide for folks who are looking for a way to quickly and easily try out the Vector Search feature in Azure Cosmos DB for NoSQL. This app uses a simple dataset of movies to find similar movies based on a given criteria. It’s implemented in four languages — Python, TypeScript, .NET and Java. There are instructions that walk you through the process of setting things up, loading data, and then executing similarity search queries.
A vector database is designed to store and manage vector embeddings, which are mathematical representations of data in a high-dimensional space. In this space, each dimension corresponds to a feature of the data, and tens of thousands of dimensions might be used to represent data. A vector’s position in this space represents its characteristics. Words, phrases, or entire documents, and images, audio, and other types of data can all be vectorized. These vector embeddings are used in similarity search, multi-modal search, recommendations engines, large language models (LLMs), etc.