chapter eleven
11 Build an AI RAG Agent: Project walkthrough
This chapter covers
- Building a full-stack RAG web application for managing multiple knowledge bases
- Implementing core features including authentication, knowledge and document management, vector search, and conversational AI
- Leveraging tools such as Next.js, Clerk.js, Upstash Redis and Vector, and Langchain.js
- Handling secure user data isolation, document parsing and embedding, state management, and chat interface design
- Addressing resource isolation, state synchronization, document cleaning, and codebase organization
- Improving the app with database architecture optimization, text-to-speech support, and prompt engineering for better AI responses
This project is a more complex full-stack generative AI web application: a Retrieval-Augmented Generation (RAG) app designed to manage multiple knowledge bases. The app uses the Vercel AI SDK and Langchain.js libraries to enable users to upload documents, organize them into knowledge bases, and interact with these knowledge bases via conversational AI. The application also integrates modern authentication, persistence, and vector search technologies to ensure a more robust experience.
We start with the features and how the application behaves, and then go over the technical decisions. We conclude this chapter with some important next steps to your learning path for building robust, full-stack, AI-powered web applications.