Microsoft Reactor

From OpenAI's ChatGPT to the NBA to Mercedes-Benz, Azure Cosmos DB is enabling intelligent apps that change the way we live and work. Join us for an engaging session as we delve into the versatile vector database capabilities of Azure Cosmos DB for MongoDB vCore. Discover the seamless integration of your operational and transactional data with native vector indexing and search functionalities, specifically tailored for AI applications. Learn how to swiftly build RAG solutions by seamlessly connecting with Azure AI Studio to create chatbots using your own data. Explore the power of Azure Cosmos DB's Copilot in simplifying complex query writing, enhancing accuracy, and improving performance. Additionally, we'll showcase our seamless integrations with Semantic Kernel, Langchain, and LlamaIndex. Gain valuable insights into KPMG's success story, where Azure Cosmos DB played a pivotal role in the creation of a generative AI assistant for business operations.

Learning Objectives
• Gain a comprehensive understanding of vector database capabilities for Azure Cosmos DB for MongoDB vCore.
• Discover how to seamlessly integrate operational and transactional data with vector data within a single database to enhance your AI applications.
• Discover how to build RAG solutions, including chatbots, using Azure AI Studio and your own data.
• Explore the power of Azure Cosmos DB's Copilot in simplifying complex query writing, enhancing data accuracy, and improving performance.
• Gain insights from KPMG's success story of using Cosmos DB for MongoDB vCore for a Generative AI agent in business operations.

This session is ideal for the startup community who are:
• Interested in leveraging cutting-edge database technologies for intelligent applications and AI-driven solutions.
• Seeking to enhance data integration, query efficiency, and overall performance using Azure Cosmos DB.
• Inspired by real-world success stories like KPMG's generative AI assistant.
• Looking to seamlessly integrate AI Studio, Semantic Kernel, Langchain, and LlamaIndex for enhanced functionality and efficiency in their applications.