In this session, we'll discuss how data is stored, retrieved, augmented and isolated for users, and how index types, quantization, multi-tenancy, sharding, and replication affect their behaviour and performance. We will also discuss vector databases' integration with AI models that can generate vectors, or use retrieved data to produce augmented, or transformed outputs. When you emerge from this deep dive, you will have seen the inner workings of a vector database, and the key aspects that make them different to your grandma's database.

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

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.