Intelligent Exam Paper Generation System Based on LangGraph and Qwen3: A Multi-Agent Collaborative RAG Practice

This project is an intelligent exam paper generation system integrating LangGraph agent orchestration, Qwen3 large language model, BGE-M3 embedding, and Qdrant vector search, aiming to solve the time-consuming and labor-intensive problem of exam paper generation in the education field. It demonstrates a typical practice of modern RAG architecture in the vertical education domain, adopting a multi-agent collaborative workflow, deployed via a microservice architecture, and equipped with a Next.js front-end analysis dashboard. Its application scenarios cover educational institutions, online education platforms, and the publishing industry. Future expansion directions include multi-modality and adaptive learning.

In the field of educational technology, exam paper generation is a time-consuming task requiring professional knowledge. Teachers need to consider multiple dimensions such as syllabi, difficulty levels, and question types, and the process often takes hours or even days. With the development of large language models and RAG technology, automated exam paper generation has become possible. This project integrates the LangGraph agent orchestration framework, Qwen3 large model, BGE-M3 embedding model, and Qdrant vector database, representing a typical practice model of RAG applications in vertical domains.

Core Technology Stack

• LangGraph: A member of the LangChain ecosystem, used to build complex agent workflows supporting loops, conditional branches, and state management. In this system, it orchestrates multi-agent collaborative processes such as content retrieval, difficulty assessment, and question type design.
• Qwen3: The latest version of Alibaba's Tongyi Qianwen, supporting multi-language and long contexts. It undertakes core generation tasks like question generation, option design, answer analysis, and difficulty control, with excellent performance in Chinese contexts.
• BGE-M3: A multi-language embedding model developed by the Beijing Academy of Artificial Intelligence (BAAI), supporting over 100 languages, used for semantic retrieval, duplicate detection, and topic clustering.
• Qdrant: An open-source vector database supporting ANN search, hybrid search, and metadata filtering. It stores vectors of historical questions and teaching resources, serving as the core of the RAG retrieval layer.

System Architecture Adopts a containerized microservice architecture, including an API gateway layer, agent orchestration service, embedding service, generation service, vector storage service, and data persistence layer. The front-end is built with Next.js, providing functions like exam paper generation interface, real-time preview, history management, and data analysis.

Retrieval Phase: Parse user request → BGE-M3 encodes query into vector → Qdrant similarity search → Metadata filtering → Assemble context. Generation Phase: Structured prompt engineering → Call Qwen3 to generate questions → Post-processing to extract fields → Quality check → Format output. Feedback Loop: Supports cyclic workflows, such as automatic re-generation if quality is not up to standard, adjusting retrieval strategies based on user feedback, and dynamically balancing the number of questions for each knowledge point.

Educational Institutions: Rapid exam paper generation, question bank expansion, personalized practice; Online Education Platforms: Intelligent practice, exam simulation, knowledge point diagnosis; Publishing Industry: Supplementary teaching material generation, question duplicate removal.

Question Quality Control: Challenge: Generated questions may have errors or ambiguities. Solutions: Multi-agent verification, expert feedback loop, quality scoring mechanism; Difficulty Consistency: Challenge: Ensuring difficulty meets requirements. Solutions: Establish difficulty grading standards, train difficulty prediction models, difficulty filtering in the retrieval phase; Copyright and Originality: Challenge: Avoiding duplication of existing content. Solutions: RAG used only for reference, similarity detection, originality scoring.

Deployment & Operation: Adopts Docker containerized deployment, with independent images for each service, facilitating consistent environments, rapid scaling, and fault isolation. Resource optimization includes model quantization, caching strategies, and asynchronous processing. Future Development Directions: Multi-modality support (images/formulas), adaptive learning (personalized practice), collaborative editing (multi-person joint exam paper generation).

This project demonstrates the in-depth application of AI technology in the education field, building a fully functional RAG system through multi-technology collaboration. For developers, it serves as a technical integration example, RAG best practice, and microservice template. For educators, it foreshadows the prospect of AI-assisted teaching, which can free them from repetitive work and allow them to focus on creative teaching. As large models and RAG technology mature, they will drive education toward efficiency and personalization.