An AI-powered learning assistant that assesses your knowledge and teaches your chosen topics, powered by GaiaNet's Node.
This advanced AI learning assistant, powered by GaiaNet's Node offers a highly personalized and interactive educational experience. The application is built using Python and Streamlit, with the core AI functionality provided by a LLaMA 3 8B model accessed through GaiaNet's API. Here's a comprehensive breakdown of its structure and functionality:
1. Initialization and Setup:
- The app uses Streamlit for the user interface and session state management.
- It initializes the ChatOpenAI model with GaiaNet's base URL and a specific model ("Llama-3-8B-Instruct").
- A detailed system prompt is defined to guide the AI's behavior during the assessment phase.
2. Main Application Flow:
- The main() function sets up the Streamlit interface and calls the chat() function.
- The chat() function manages the entire conversation flow and state transitions.
3. Topic Selection:
- The app starts by prompting the user to choose a learning topic.
- The extract_topic() function uses the AI to distill the core subject from the user's input.
4. Knowledge Assessment:
- The app enters an assessment stage, asking up to three questions.
- generate_assessment_question() creates tailored questions based on the topic and previous responses.
- generate_assessment_feedback() provides specific feedback on each user answer.
- The assessment adapts dynamically, adjusting question difficulty based on user responses.
5. Comprehensive Evaluation:
- After the assessment, generate_comprehensive_assessment() creates two key outputs:
a) User feedback: A summary of the user's knowledge and readiness.
b) Teaching guidance: A plan for addressing knowledge gaps and teaching approach.
6. Personalized Teaching:
- The app transitions to the teaching stage, managed by generate_teaching_response().
- This function uses the comprehensive assessment to tailor the teaching approach.
- Each response includes concept explanations, examples, and a follow-up question.
7. Continuous Interaction:
- The app maintains an ongoing dialogue, storing the conversation history in Streamlit's session state.
- It uses this history to inform each new response, ensuring continuity and relevance.
8. Error Handling and Robustness:
- Try-except blocks are implemented throughout to gracefully handle potential API errors.
- Fallback responses are provided to ensure the conversation can continue even if an error occurs.
9. Helper Functions:
- format_message_history(): Formats the conversation for AI input.
- remove_ending_questions(): Ensures AI responses don't end with questions when not appropriate.
10. Streamlit Integration:
- The app leverages Streamlit's chat interface for a user-friendly experience.
- It uses session state to manage conversation stages and persist data between reruns.
This application demonstrates advanced use of AI in education, combining large language model capabilities with careful prompt engineering and state management to create a responsive, adaptive learning environment. It showcases the potential of GaiaNet's Node in powering sophisticated AI applications.
I built a basic Streamlit interface connected to GaiaNet's Node for simple topic conversations. To structure the experience, I added stages: topic selection, knowledge assessment, and personalized teaching. I refined the extract_topic() function for accurate interest identification and developed dynamic question generation that adapts to responses, providing tailored feedback. The teaching module was enhanced to match explanations with the user's knowledge level, while robust error handling and continuous UI improvements ensured a smooth, adaptive learning experience.