RAG-TA: RAG-based Intelligent Teaching Assistant System

The RAG Intelligent Teaching Assistant System is an intelligent teaching assistance platform based on Multimodal Retrieval-Augmented Generation (Multimodal RAG), specifically designed for educational scenarios. The system integrates the following core functions:

🤖 Intelligent Question Answering System

• Multimodal Understanding: Supports complex question answering with text and images, capable of understanding charts, formulas, and image content in course materials.
• Contextual Retrieval: Precise semantic retrieval based on the ChromaDB vector database.
• Hybrid Retrieval: Combines dense vector retrieval and sparse retrieval (BM25) to improve retrieval accuracy.
• Source Tracing: Automatically annotates the source file and page number of the answer, ensuring information traceability.

📚 Knowledge Base Management

• Multi-format Support: Supports various formats including PDF, PPTX, DOCX, TXT, MD, and images.
• Intelligent Indexing: Automatically extracts text and image content to build a multimodal vector index.
• Incremental Updates: Intelligently detects file changes and updates only the changed parts, improving efficiency.
• Folder Management: Supports hierarchical directory structure for easy organization of course materials.

💬 Conversation Management

• History: Complete conversation history saving and management.
• Multiple Answer Options: Supports multiple answer versions for the same question, allowing users to switch between them.
• Folder Classification: Supports conversation folder management for easy course classification.
• Thinking Mode: Visualizes the AI reasoning process to help understand the answer logic.

🖼️ Multimodal Interaction

• Image Understanding: Automatically extracts and describes image content in PDFs/PPTXs.
• Real-time Upload: Supports users uploading images and documents for instant question answering.
• Visual Question Answering: Provides comprehensive answers combining image content and text knowledge.