At MongoDB.local in London, the non-relational database giant has introduced a set of generative AI features across various tools to streamline and enhance application development and modernisation. 

The MongoDB Relational Migrator now includes AI-powered capabilities that significantly improve the migration process from legacy database technologies to MongoDB Atlas. This tool automates the conversion of SQL queries and stored procedures in legacy applications to development-ready MongoDB Query API syntax, allowing organisations to accelerate their migration efforts without requiring extensive knowledge of MongoDB Query Syntax API.

In MongoDB Compass, the data interaction tool, developers can now leverage natural language to swiftly generate executable MongoDB Query API syntax. By entering commands such as ‘Filter pizza orders by size, group the remaining documents by pizza name, and calculate the total quantity,’ developers receive suggested code to execute the necessary aggregation pipeline stages. This natural language capability enables developers to focus more on shipping data-driven applications, reducing the manual effort required for complex queries and aggregations.

Data visualisation tool MongoDB Atlas Charts has integrated AI-powered capabilities to facilitate the creation of visualisations using natural language commands. Developers can input queries like ‘Show me a comparison of annual revenue by country and product,’ and MongoDB Atlas Charts will swiftly generate the requested visualization. The familiar drag-and-drop interface then allows for further refinement and customization, enabling developers to efficiently create, share, and embed visualizations.

Additionally, MongoDB Documentation now features an AI-powered chatbot that provides quick and intuitive answers to developers’ questions. Developers can ask about MongoDB’s products and services, troubleshoot issues during software development, and receive step-by-step instructions, example code, and links to references. The chatbot, an open-source project utilising MongoDB Atlas Vector Search, facilitates information retrieval with context, allowing developers to build and deploy their own chatbots for various use cases. This integration of generative AI features across MongoDB tools aims to reduce the time and effort spent on undifferentiated tasks, allowing developers to focus on innovation and creating exceptional end-user experiences.

Two weeks ago, the NY-based company introduced features in MongoDB Atlas Vector Search that benefit generative AI application development. These features enhance information LLMs by expanding query capabilities and facilitating a dedicated data aggregation stage, reducing inaccuracies. The platform also accelerates data indexing for generative AI applications by simplifying the indexing process for operational data, metadata, and vector data, thereby speeding up the development of AI-powered applications. 

Read more: Is MongoDB Vector Search the Panacea for all LLM Problems?