Case studies and real-world applications

Vector search is a powerful tool that enables you to build sophisticated systems for finding information based on its meaning, rather than just its exact words. By understanding the context and relationships between data points, vector search helps you retrieve highly relevant results. So far, you have learned about the different concepts involved with vector search and some of the different offerings that exist in the market, but how do businesses integrate vector search into their applications?

In this section, you will explore three popular methods for leveraging vector search: semantic search, RAG, and robotic process automation (RPA). You will look at existing case studies of MongoDB Atlas Vector Search that fit into each of these buckets, and how these applications deliver value to the end user through more accurate search that wasn’t previously possible. Each of the following case studies was originally published as a part of...