MongoDB, the New York-based NoSQL database vendor, has announced new generative AI capabilities aimed at simplifying application development for developers. These capabilities include natural language processing (NLP) tools that allow users to interact with data using natural language instead of code, as well as new MongoDB Atlas Vector Search capabilities designed to reduce errors in model outputs. MongoDB unveiled these features at its recent MongoDB.local London event.
Relational databases, which have been around since the 1970s, struggle to discover relationships between data points. With the increasing complexity and volume of data collected by organizations, alternative databases have gained popularity. Graph databases like TigerGraph and Neo4j specialize in uncovering relationships between data points, while document-based databases like MongoDB and Couchbase are designed to handle large sets of distributed data.
MongoDB has focused on generative AI since OpenAI’s launch of ChatGPT in November 2022. In June, the company introduced its initial generative AI capabilities, including a partnership with Google Cloud to integrate Google’s generative AI and large language models with MongoDB Atlas. Now, MongoDB is introducing more specific generative AI tools to allow users to interact with data using natural language.
These new tools include natural language query capabilities in MongoDB Compass, which allow users to generate queries and integrate data assets into applications, and natural language visualization in MongoDB Atlas Charts, which enables developers to create, share, and embed visualizations. Additionally, MongoDB has launched an AI-powered chatbot in MongoDB Documentation, providing users with tutorials, code samples, and reference libraries for building applications with MongoDB.
Stephen Catanzano, an analyst at TechTarget’s Enterprise Strategy Group, notes that other data management and analytics vendors are also developing similar NLP capabilities. However, the full implementation and comparison of these tools will only be known once they are generally available. The objective of integrating generative AI into MongoDB’s products, like other vendors, is to automate and simplify manual and repetitive tasks for data workers.
Apart from the NLP capabilities, MongoDB has also introduced new AI-powered features in MongoDB Relational Migrator to facilitate the migration of data from relational and other database types to MongoDB. These features automatically convert SQL queries to MongoDB Query API syntax. MongoDB Atlas Vector Search, a tool still in preview, has also received improvements to reduce AI hallucinations that can occur in generative AI and large language model outputs.
The most significant addition to MongoDB’s capabilities is vector search. Catanzano emphasizes that most database companies, including MongoDB, are incorporating vector search to support generative AI workloads. Vector search has various use cases and is considered essential for databases that aim to play a role in the generative AI space.
In addition to the new features in Atlas Vector Search, MongoDB has integrated Atlas with data streaming specialist Confluent. This integration allows developers to access streaming data for use in generative AI models. Andrew Davidson, MongoDB’s Senior Vice President of Products, explains that these intelligent developer experience features are designed to accelerate application development within MongoDB’s operational data layer. He also highlights the progress made from theory to practice within just three months since MongoDB’s initial entry into the generative AI field.
Apart from generative AI capabilities, MongoDB has also introduced Atlas for the Edge, a set of capabilities that allow users to deploy MongoDB applications at the edge, where data is created, processed, and stored. This feature not only synchronizes an organization’s edge data with its core data but also enables users to deploy MongoDB in various infrastructures, including on-premises servers and remote locations. Atlas for the Edge also integrates with generative AI and machine learning tools, enabling the development and deployment of generative AI capabilities directly on devices.
In conclusion, MongoDB’s new generative AI capabilities, including NLP tools and Atlas Vector Search enhancements, aim to simplify application development for developers. These features not only reduce the burden on data workers but also improve the accuracy and efficiency of generative AI models. MongoDB’s focus on vector search and the introduction of Atlas for the Edge demonstrate the company’s commitment to staying at the forefront of the generative AI space.
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