Project Goal: Build a Knowledge Base Project
Description: We build a powerful and scalable mathematical knowledge base. This knowledge base will be part of our educational products, aiming to provide a personalized learning experience and support our AI-driven tutoring system.
1. Knowledge Base Development:
o Build a comprehensive mathematical knowledge base that can read file contents in various formats, such as TXT, DOCX, PDF, etc.
o Use the Pandas library to read CSV file contents and the Pydub library to process audio files.
o Perform data cleaning and preprocessing, including noise removal, format standardization, data segmentation, etc.
2. Artificial Intelligence Integration:
o Implement AI models to enhance knowledge base capabilities, such as automatic problem solving, personalized content recommendation, intelligent tutoring, etc.
o Leverage Hugging Face's pre-trained models (such as BERT, MiniLM) for natural language processing.
o Explore the use of GPT-4 or similar models to generate explanations, answer complex questions, and interact with students.
3. Vector search and similarity matching:
o Implement FAISS (Facebook AI Similarity Search) technology to achieve efficient similarity search and mathematical concept clustering.
o Develop tools to convert local mathematical data into vector format for efficient retrieval and matching.
o MathJax: Parse mathematical formulas into a processable format.
o Symbolic parsing: Use tools such as SymPy to parse mathematical expressions.
o LaTeX to Vector: Convert LaTeX format formulas into vector representations.
o Ensure that the knowledge base supports dense vector search and provides contextually accurate answers.
4. Data processing:
o Design a system to securely handle local private data and ensure compliance with all relevant data protection regulations.
o Use
5. Interactive Q&A system:
o Create an interactive Q&A interface that combines the knowledge base and search results to provide natural language answers.
6. Dynamic data visualization:
o Provide data visualization tools to help users intuitively understand search results and data patterns and improve user experience.
7. Error handling
o Add error handling mechanisms during data import and processing.
o Add error handling mechanisms during data output and processing.
8. Adaptive User Interface:
o Integrate machine learning algorithms so that the knowledge base can continuously optimize and learn based on user interaction and feedback.
Required Expertise:
• Artificial Intelligence and Machine Learning: Expertise in building and deploying AI models, especially in an educational setting.
• Natural Language Processing (NLP): Deep understanding of NLP techniques, especially using pre-trained models such as BERT and GPT-4.
• Embedding and Vectorization: Experience in embedding and vectorization.
• Vector Databases: Familiarity with vector databases such as Pinecone, Weaviate, or FAISS.
• Similarity Search: Proficiency in FAISS for similarity search and clustering.
• Programming Skills: Strong Python programming skills and proficiency in using related libraries (such as Hugging Face Transformers, PyTorch, TensorFlow).
• Data Processing: Ability to process various data types (text, audio, video).
• API Integration: Experience in integrating various APIs to enhance system functionality.
Deliverables?