Set up a full-stack RAG application page with split-pane PDF chat and citation navigation.
npx skills add https://github.com/mattwoodco/skills --skill ai-rag-app
Please help me install this Agent Skill. Skill: ai-rag-app Source: https://github.com/mattwoodco/skills/tree/main/skills/ai-rag-app Command: npx skills add https://github.com/mattwoodco/skills --skill ai-rag-app
This Skill provides a complete, ready-to-integrate page for building Retrieval Augmented Generation (RAG) applications, enabling users to chat with their documents and get precise, source-cited answers.
Set up the AI RAG application page by navigating to the /rag route in your application.
High-intent search queries and answers about installing and using this skill.
To build a RAG application with Next.js, use a Skill that sets up a split-pane page for PDF upload, document management, vector search, and AI chat. It provides a ready-to-integrate interface at the /rag route, requiring App Router and authentication.
PDF citation navigation works by linking AI chat answers to the exact source text in the document viewer. When you ask questions, the RAG interface provides clickable citations that highlight the corresponding text within the uploaded PDF.
Yes, authentication is required to set up the document ingestion and vector search interface. The full-stack RAG application page integrates these functions and mandates user authentication to manage documents and access the chat features.
Yes, you can use TypeScript to add source-cited document Q&A to your existing app. The Skill provides a complete page that integrates document ingestion, embedding, and AI chat, allowing users to interact with uploaded knowledge bases.
The best way to implement a split-pane UI for document upload and viewing is using a full-stack RAG application page. It combines PDF upload, document list management, chat interface, and a PDF viewer in a cohesive layout for precise source-cited answers.
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