Building a Production-Grade RAG Document Q&A System: From Architecture Design to Full Implementation

This article introduces an open-source production-grade RAG document Q&A system using a complete tech stack including Next.js frontend, Express backend, Redis message queue, Qdrant vector database, and Groq LLM, supporting PDF document processing. The project is open-sourced by developer DharitriM and provides a full-stack architecture implementation, which is of high reference value for developers who want to understand or build RAG systems.

Retrieval-Augmented Generation (RAG) is one of the mainstream architectures for large language model applications. By combining external knowledge bases with generation models, it solves the problems of knowledge timeliness and hallucinations, helping enterprises convert private documents into intelligent Q&A capabilities at low cost. This project, open-sourced by DharitriM, provides a fully functional RAG document Q&A system where users can upload PDF documents, and the system automatically processes, indexes, and supports intelligent Q&A. It uses a modern full-stack architecture and has high reference value.

Frontend Layer

• Next.js 15: React server component framework with excellent performance and development experience
• React19: Supports concurrency features and improved rendering mechanisms
• Tailwind CSS: Utility-first CSS framework
• shadcn/ui + Lucide React: Component library and icon system
• Clerk: Identity authentication solution

Backend Layer

• Node.js + Express.js: Lightweight and efficient API server
• Multer: Handles file uploads
• BullMQ + Valkey/Redis: Asynchronous task queue
• LangChain: LLM application orchestration framework
• Qdrant: High-performance vector database
• Hugging Face Models: Text embedding models
• Groq LLaMA3.1: Large language model inference service

The system's document Q&A process is divided into four stages:

1. Document Upload: Users upload PDFs, Clerk authenticates, Multer processes and saves
2. Task Queueing: The backend delivers processing tasks to the Valkey queue, BullMQ manages status and order
3. Document Processing and Vectorization: Background workers perform PDF parsing → text chunking → embedding generation → vector storage in Qdrant
4. Intelligent Q&A: User asks a question → question vectorization to retrieve relevant text from Qdrant → context construction → Groq LLaMA3.1 generates answer → stream returns

Asynchronous Task Queue Design

Document processing is a compute-intensive operation. Using BullMQ queues brings:

• User experience: Immediate return after upload without waiting
• System stability: Avoids request blocking
• Scalability: Horizontal scaling by adding worker instances

Vector Database Selection

Qdrant advantages: High performance, ease of use, native support for filter queries and hybrid search, smooth integration with RESTful API and Node.js

Multi-Model Collaboration

Hugging Face handles embedding (lightweight and fast), Groq handles generation (high-performance cloud), balancing cost and effectiveness

Local development environment configuration:

1. Infrastructure: Docker Compose starts Valkey and Qdrant with one click
2. Backend services: Express API server + Worker processes
3. Frontend application: Next.js development server

Need to configure API keys: Hugging Face (embedding model), Groq (LLM inference), Clerk (user authentication)

This project is an extensible RAG system template. Developers can extend it by:

• Integrating more document formats (Word, Markdown, web pages, etc.)
• Implementing multi-tenant isolation
• Adding conversation history to support multi-turn Q&A
• Integrating re-ranking models to improve retrieval accuracy
• Connecting to enterprise knowledge bases to build internal Q&A assistants

For teams that want to deeply understand RAG architecture or quickly build document Q&A applications, it provides a clear implementation reference and reusable code foundation