Build an LLM and RAG-based Chat Application with AlloyDB and Vertex AI

In this session, we will explore how to overcome the knowledge limitations of language models using Recall Augmented Generation (RAG). We will analyze a robust architecture on Google Cloud that uses AlloyDB for the storage and semantic search of vector embeddings, allowing the model to access private information in real time.

We will see how the MCP Toolbox acts as secure middleware to manage database operations and how Gemini Pro in Vertex AI synthesizes this information to deliver accurate, multimodal, and context-rich responses.