Google Cloud offers many different options to build Retrieval Augmented Generation (RAG) powered applications. This includes discrete components that comprise RAG solutions (embeddings, vector search, LLMs) but also includes options that combine multiple steps or even the entire RAG application in a single service. The best option for you will depend on factors such as your use case, engineering expertise, existing tech stack and future needs.
Let's start with a set of use cases and design a solution architecture using the most appropriate options. After that let's go through a detailed breakdown of the full list of services with the pros, cons and recommendations for when to use each.
