The MongoDB team has made a number of announcements regarding new features and products at the recent New York staging of its MongoDB.Local conference series.  

MongoDB is a NoSQL document database that stores its documents in a JSON-like format with schema. MongoDB Atlas is the fully-managed cloud database from the MongoDB team. 
MongoDB Community Edition is the free, source-available version of MongoDB while MongoDB Enterprise Server is the commercial version of MongoDB’s core database.

The team announced that users of the MongoDB Community Edition and Enterprise Server now finally get the Search and Vector Search features that were added to Mongo's Atlas cloud platform several years ago. The additions are only in preview, but will avoid the need for customers to rely on a third-party service for vector databases. 

Both editions now have two complementary search capabilities - full-text and vector search. Vector search is accessed via the MongoDB Query API, and can be used by developers to build applications that use semantic search and generative AI. It can provide results when users are vague about what they're looking for.

The developers say there are no functional limitations on the core search aggregation stages in this public preview. This means that $search, $searchMeta, and $vectorSearch are all supported with functional parity to what is available in Atlas, excluding features in a preview state. 

The second announcement of note at the conference was the launch of the MongoDB Application Modernization Platform, or AMP. This is designed to make it easier to take existing legacy systems and convert them into services. The announcement said that MongoDB AMP integrates agentic AI workflows into the company's modernization methodology. 

"It is a repeatable, end-to-end platform that combines AI-powered tooling, proven techniques, and specialized talent to reinvent critical business systems while minimizing cost and risk."

The reference to 'specialized talent' is key, here. Shilpa Kolhar of MongoDB said that the process begins with AI agents handling tasks such as functional tests to show exactly what the system really does, and creating missing documentation. Then engineers from MongoDB take over to deal with the much smaller elements that the AI can’t handle on its own.

The company is also extending its Queryable Encryption feature with support for prefix, suffix, and substring queries. Developed by the MongoDB Cryptography Research Group, Queryable Encryption can be used to encrypt sensitive application data, store it in encrypted form in the MongoDB database, and perform expressive queries directly on that encrypted data.

The new feature means it can be used to perform flexible text searches on encrypted data, such as matching partial names, keywords, or identifiers. Until now this required decryption of the underlying data. 

More Information

MongoDB Website

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