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Math Tutor Agent: Development Summary

📋 Chain of thought | Fun facts:

  • Leaked API key doesn't work anymore, generate yours on developer.wolframalpha.com

  • I created an Agent with 2 tools: tool 1 - checks if a query is about math, and the 2nd is just a whole logic of math tutoring.

  • Streamlit frontend, just open an .ipynb file and use it straight in browser. LINK to the video example!

  • I use Gemma 2 9B instruct because it can generate Ukrainian quite well.

  • The ChromaDB dataset doesn't even exist, but if you asked the agent the same question - it'll create a brand new database and give you back a previously saved answer. (for generation of more robust answers we can use a bigger model, but even the current one is great at math questions (though it can't solve image input))

    image

  • My goal was to use the small 3B Llama 3.1 for agent logic and use Gemma 2 9B for explanation (now it's Gemma 2 9B instruct for both)

  • For equations, I use the Wolfram Simple API.

  • I was planning to add a button to tasks generation on the sidebar (but didn't make it yet)

  • I was planning to generate ZNO questions for ChromaDB + Image library (but didn't make it). The problem is with understanding images by LLMs and image generation for the answer.

  • Validation of dataset isn't working without a dataset, because it's not a RAG but an Agent without a default dataset.

  • I've planned to save questions, answers with explanation[s] and with pictures, and I made it.

  • I use Ollama instead of vLLM because I'm working on a laptop with 4GB GPU AND I want to use as models as small as possible to understand how to use them properly.

📋 Overview

The project represents an intelligent system for solving mathematical problems using a combination of modern technologies and approaches.

✅ Implemented Components

1. Core Infrastructure

  • Wolfram Alpha integration for precise calculations
  • Result caching system using ChromaDB
  • Bilingual support (Ukrainian/English)
  • Web interface based on Streamlit

2. Problem Processing

  • Automatic mathematical problem type detection
  • Step-by-step explanation generation
  • Result visualization through graphs and diagrams
  • Solution storage and reuse

3. System Reliability

  • User input validation
  • Error handling at all levels
  • Fallback mechanism for local calculations
  • API key and connection verification

🔄 Technology Stack

  • Core Components:

    • Wolfram Alpha API
    • ChromaDB
    • Streamlit
    • Ollama
    • Google Translator

  • Supporting Tools:

    • PIL for image processing
    • JSON for data structuring
    • Requests for API calls

📈 Potential Improvements

1. Functionality Extension

  • Implementation of task generator
  • Addition of evaluation system
  • Development of multi-agent architecture

2. Quality Improvements

  • Knowledge base expansion
  • Enhanced explanations
  • Translation optimization

3. Technical Enhancements

  • Caching optimization
  • Error handling improvements
  • Expanded visualization format support

📊 Conclusions

The project demonstrates good basic functionality for mathematical problem solving but requires further development in task generation and quality assessment. The core system components work reliably and efficiently, providing a solid foundation for future development.

Key Achievements:

  1. Created a working prototype with basic functionality
  2. Implemented a reliable mathematical problem processing system
  3. Provided bilingual support and user-friendly interface

Development Directions:

  1. Implementation of task generation
  2. Development of evaluation system
  3. Realization of multi-agent architecture

💡 Technical Implementation Details

RAG System

  • Uses ChromaDB for efficient storage and retrieval

  • Implements similarity search for cached solutions

    image2

  • Maintains metadata for better search results

Translation Pipeline

  • Bidirectional translation between Ukrainian and English
  • Handles mathematical terminology accurately
  • Preserves formatting during translation

Error Handling

  • Multiple fallback mechanisms
  • Graceful degradation when services are unavailable
  • Comprehensive error messages in Ukrainian

Visualization

  • Dynamic image generation through Wolfram Alpha
  • Local image storage and management
  • Responsive display in web interface

🔍 Code Quality

Strengths

  • Well-structured modular design
  • Comprehensive error handling
  • Clear separation of concerns
  • Type hints and documentation

Areas for Improvement

  • Need for more unit tests
  • Better configuration management
  • Code optimization opportunities
  • Enhanced logging system

📚 Documentation Status

Complete

  • Basic setup instructions
  • API documentation
  • Configuration guide