Summary

In this chapter, you’ve explored an integration pattern that combines RAG and generative AI models to build a chatbot capable of answering questions based on a document corpus. You’ve learned that RAG leverages the strengths of retrieval systems and generative models, allowing the system to retrieve relevant context from existing knowledge sources and generate contextual responses, preventing hallucinations and ensuring accuracy.

We proposed an architecture that utilized a serverless, event-driven approach built on Google Cloud. It consists of an ingestion layer for accepting user queries, a document corpus management layer for storing embeddings, an AI processing layer integrating with Google Gemini on Vertex AI, and monitoring and logging components. The entry point handles various input modalities like text, audio, and images, pre-processing them as needed.

You’ve learned that the core of the RAG pipeline involves generating embeddings from...