The partnership between MongoDB Atlas and LangChain, a developer tool for building AI applications, has been announced. This partnership enables developers to build AI-powered chatbots using RAG (Retrieval-Augmented Generation) pattern with ease, thanks to the creation of LangChain Templates. These templates provide a reference architecture that can be easily deployed as a REST API using LangServe. Additionally, MongoDB Atlas and LangChain are working together to release new features for Atlas Vector Search, including a dedicated vector search aggregation stage `$vectorSearch`. This partnership aims to help developers build AI-powered applications more efficiently and effectively, while also providing them with the tools they need to modernize their edge-to-cloud strategy. The emergence of Big Data and AI/ML is pushing enterprises to adopt more sophisticated systems that enable data-driven organizations. Modern IoT solutions can capture and visualize edge data in real-time, resulting in rich insights into operations. By bringing computing to the edge, businesses can deploy a wide variety of applications that help them take action on critical data right then and there, delivering significant efficiency into operations. The partnership between MongoDB Atlas and WeKan aims to help enterprises modernize their edge-to-cloud stack with solutions that are easily implemented and adaptable to their growing data needs. By leveraging Realm, MongoDB Atlas provides an infrastructure needed for IoT solutions across industries. The solution is cloud-agnostic and compatible with services from any of the cloud providers (AWS, GCP, AZURE). It offers performant behavior for heavy client-side insert-only workloads of structured & unstructured data, streams it to Atlas with automatic clean-up, and provides out-of-the-box synchronization allowing seamless and secure transport of data from the device to the cloud using Atlas Device Sync. Finally, MongoDB Atlas Search plays a crucial role in maximizing the value of Operational Data Stores (ODS). It enables users to explore, analyze, and gain valuable insights from their data by incorporating search capabilities into an ODS. This allows organizations to streamline data retrieval, accelerate development cycles, and enable users to interact with data in a more intuitive and efficient manner.