Implements a complete Retrieval-Augmented Generation (RAG) chat interface with vector search and document citations.
AI RAG Chat transforms static documents into interactive knowledge bases by integrating a robust RAG pipeline into Next.js applications. It leverages vector search to retrieve relevant document chunks, injects them into the AI's context, and provides a streaming chat UI complete with clickable source citations and session persistence. This skill is ideal for developers building internal knowledge tools, customer support bots, or any application requiring precise, document-grounded AI responses.
Key Features
01Customizable UI components including citation badges and prompt inputs
02Vector-based document retrieval for context-aware responses
033 GitHub stars
04Streaming chat interface with real-time AI feedback
05Automated source citations with document titles and page numbers
06Conversation persistence using database-backed chat sessions
Use Cases
01Developing a customer support bot that answers queries using an existing knowledge base
02Building a 'Chat with your PDF' feature for document-heavy applications
03Creating an internal technical documentation assistant for engineering teams
