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

Overview
One of the best tools for improving the quality of responses from large language models (LLMs) is retrieval augmented generation (RAG). RAG is the pattern of retrieving some non-public data and using that data to augment your prompt sent to the LLM. RAG allows the LLM to generate more accurate responses based on the data included in the prompt.
You'll use AlloyDB, Google Cloud's scalable and performant PostgreSQL-compatible database, to store and search by a special kind of vector data called vector embeddings. Vector embeddings can be retrieved using a semantic search, which allows retrieval of the available data that is the best match for a user's natural language query. The retrieved data is then passed to the LLM in the prompt.
You'll also use Vertex AI, Google Cloud's fully-managed, unified AI development platform for building and using generative AI. Your application uses Gemini Pro, a multimodal foundation model that supports adding image, audio, video, and PDF files in text or chat prompts and supports long-context understanding.
What you will learn
In this lab, you'll learn:
How RAG enhances LLM capabilities by retrieving relevant information from a knowledge base.
How AlloyDB can be used to find relevant information using semantic search.
How you can use Vertex AI and Google's foundation models to provide powerful generative AI capabilities to applications.
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