Spring AI 面试平台与 RAG 知识库

Spring AI 面试平台与 RAG 知识库 is an open-source AI-powered interview assistance and knowledge base question-answering platform designed for Java backend developers. 0, the project addresses the need for more advanced and practical AI application experience in technical resumes and interview preparation. It integrates resume analysis, AI-driven mock interviews, retrieval-augmented generation (RAG) knowledge base Q&A, voice interview capabilities, asynchronous task processing, and distributed rate limiting into a unified system.

Key features include intelligent resume analysis, where users can upload their resume for multidimensional scoring and improvement suggestions generated by AI. The mock interview module creates personalized interview questions based on resume content, supporting real-time Q&A and answer evaluation. The RAG knowledge base Q&A feature allows users to upload technical documents to build a private knowledge base, enabling vector-based retrieval-augmented intelligent question answering. The platform also incorporates asynchronous processing of intensive AI tasks such as resume analysis and vectorization using Redis Stream, and supports distributed rate limiting for robust backend operations.

The system architecture follows a frontend-backend separation model. The backend exposes unified REST APIs and manages core services such as resume handling, interview session management, knowledge base operations, and RAG chat. Data storage is handled via PostgreSQL with pgvector for both structured data and vector similarity search, Redis for session caching and message queues, and S3-compatible object storage for file management. AI integration is achieved through Spring AI and DashScope, providing a unified interface for large language model operations, including text generation and vectorization. The frontend is developed with React, TypeScript, and a modern UI stack, delivering an interactive user experience.

Spring AI 面试平台与 RAG 知识库 is completely free and open source, with no paid or Pro versions. The project is positioned as a practical, high-value learning and portfolio resource for backend developers seeking to demonstrate skills in AI application development, RAG, and agent engineering. Comprehensive tutorials and resources are available for in-depth learning, though full access to accompanying instructional content may require membership in the JavaGuide community.