Project Title: Developing an Extensible Mathematical Knowledge Base
Objective: We are building a robust and scalable knowledge base of mathematics as part of our educational offerings. The system is designed to provide a personalized learning experience and enhance our AI-powered tutoring capabilities.
Project scope:
1. Development of knowledge base:
- l Content integration: Build a comprehensive mathematical knowledge base with the ability to read and work with a variety of file formats (such as TXT, DOCX, PDF, CSV, and audio files).
- l Data preprocessing: Implement data preprocessing techniques such as noise reduction, format standardization, data segmentation, etc.
- l Technology Stack: Please outline the libraries and tools you intend to use for these tasks.
2. Artificial intelligence integration:
- l AI model implementation: Integrate AI models to enhance the functionality of the knowledge base, including automatic problem solving, personalized content recommendation, and intelligent tutoring.
Use natural language processing: Use pre-trained models (such as the model in Hug Face) to complete natural language processing tasks.
- l Advanced AI Models: Explore the use of GPT-4 or similar advanced models to generate explanations, answer complex questions, and interact with students.
- l Technology stack: Please specify the AI technology and model you plan to use.
3. Vector search and similarity matching:
- l Similarity search: Implementing efficient similarity search mechanisms for mathematical concepts and clustering.
- l Vector Conversion: Develop tools that convert native mathematical data (formulas and contexts) to vector format for efficient retrieval and matching.
- l Mathematical Formula Parsing: Parses mathematical formulas into formats that can be converted to vector representations.
- l Technology Stack: Please specify the technology you will use for these tasks.
4. Data processing:
- l Safe data handling: Design a system to handle local private data safely and ensure compliance with relevant data protection regulations.
- l Compliance with data protection: Use technology to ensure data privacy and security.
5. Interactive question answering system:
- l Q&A interface: Create an interactive Q&A interface that combines knowledge base insights with search results, provides answers in natural language, and supports multiple answer forms such as text, formulas, and charts.
6. Dynamic data visualization:
- l Visualization tools: Provide dynamic data visualization tools to help users intuitively understand search results and data patterns, and improve user experience.
7. Error handling:
- l Robust error management: Implement error handling mechanisms in data import, processing, and output phases.
- l Provides user-friendly error prompts.
8. Adaptive user interface:
o UI optimization: Integrates machine learning algorithms to enable the knowledge base to continuously learn from user interactions and feedback, optimizing the user interface over time.
- l Technology Stack: Please outline the technology you plan to use for your adaptive UI implementation.
Required majors:
• Artificial Intelligence and Machine Learning: Expertise in building and deploying artificial intelligence models, especially in educational environments.
• Natural Language Processing (NLP): Gain an in-depth understanding of NLP technologies, especially pre-trained models like GPT-4.
• Embedding and vectorization: Experience with embedding and vectorization techniques.
• Vector database: Proficient in using vector database and similarity search, and understand FAISS and other tools.
• Programming skills: Proficient Python programming skills and familiarity with related libraries (e.g., hugs Face Transformers, PyTorch).
• Data processing: Ability to manage various data types (text, audio, video).
• API integration: Experience in integrating various APIs to enhance system functionality.
Deliverables: If you can suggest any improvements or alternative solutions to provide a more efficient system, please let us know.