Beyond Keywords: Optimizing Vector Search with Filters and Caching (Part 2)
Welcome back! If you landed here without reading Part 1: Beyond Keywords: Implementing Semantic Search in Java With Spring Data, I recommend …
Mongo
The Cost of Not Knowing MongoDB - Part 2
This is the second part of the series “The Cost of Not Knowing MongoDB,” where we go through many ways we can model our MongoDB schemas for …
Beyond Keywords: Implementing Semantic Search in Java With Spring Data (Part 1)
Have you ever tried to search for something such as a product, a song, or a movie but couldn’t quite remember its exact name? Maybe you …
The Cost of Not Knowing MongoDB (Part 1)
The primary focus of this series is to show how much performance you can gain, and as a consequence, the cost you can save when using …
Keywords Meet Vectors: Hybrid Search on MongoDB
Hybrid search in MongoDB brings together two complementary search techniques: Full text search (BM25 via Atlas Search)—optimized for exact …
Working with Geo Location Data in MongoDB
MongoDB makes it really easy to work with location data (sometimes called Geo Data) by simplifying how to store this type of data and …
Agents Meet Databases: The Future of Agentic Architectures
With 2025 hailed as "the year of agents" by NVIDIA CEO Jensen Huang and OpenAI CPO Kevin Weil, AI agents are increasingly of interest to …
Building an AI Semantic Movie Recommender with Vector Search
Last time, we created a vector search index in a local MongoDB Atlas cluster. Now, let’s put it to work with a real case: building an …
Introduction to Data-Driven Testing with Java and MongoDB
Data-driven testing (DDT) is important because it enables you to validate business behavior across various scenarios without having to …
Power your AI application with Vector Search
MongoDB Vector Search enables semantic queries. For example, finding support tickets with similar meanings even if they use different words. …