Vector search best practices

This section covers the best practices for improving the accuracy of your vector search through intelligent data modeling, deployment model options, and considerations for prototype and production use cases. By following the guidance in this section, you will be more likely to improve the quality of your vector search results and operate your search system in a scalable, production-ready manner.

Data modeling

In the context of MongoDB, data modeling refers to the process of designing the structure of the data stored in the database. Unlike traditional relational databases, MongoDB is a NoSQL database that uses a flexible, schema-less model, allowing for more dynamic and hierarchical data storage. The big idea about data modeling for vector search centers around the notion that embedding models are not infinitely capable, and users can take control of the relevance search problems in embedding models by using vectors along with the other data they...