As generative AI applications mature, retrieval-augmented generation (RAG) has become popular for improving large language model-based apps. We expect teams to move beyond basic RAG to autonomous agents and generative loops. We'll set up a Weaviate vector database on Google Kubernetes Engine (GKE) and Gemini to showcase generative feedback loops.

After this session, a Google Cloud GKE user should be able to: - Deploy Weaviate open source on GKE - Set up a pipeline to ingest data from the Cloud Storage bucket - Query, RAG, and enhance the responses

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Learn the strategies and tools for a successful migration from Oracle databases to CloudSQL/AlloyDB. This session covers everything from schema conversion to data replication, offering insights into leveraging Google Cloud technology for a smooth transition to open source database engines. Join us for this Mini Talk at 'Meet the Experts, hosted by Google Cloud Consulting' at Expo. Seating is limited and on a first-come, first served basis; standing areas are available.

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At Google I/O last year, we announced several new AI Firebase Extensions using the PaLM API. This year, we’ve added support for Google's latest Gemini models. Easily add a chatbot, text summarizer, content generator, vector database pipeline, and more to your app without learning new APIs. In this session, get an end-to-end view of how you can use Firebase and Gemini to create an enterprise-ready customer support app. Build many apps with the powerful combo of Gemini's multimodal features and Firebase's convenient suite of developer tools.

Click the blue “Learn more” button above to tap into special offers designed to help you implement what you are learning at Google Cloud Next 25.