🧠 Math Tutor Agent – Agentic RAG with Feedback Loop

This project implements an Agentic-RAG architecture to simulate a math professor that solves JEE-level math questions with step-by-step explanations. The system smartly routes queries between a vector database and web search, applies input/output guardrails, and incorporates human feedback for continuous learning.

📌 Features

• ✅ Input Guardrails (DSPy): Accepts only academic math questions.
• 📚 Knowledge Base Search: Uses Qdrant Vector DB with OpenAI Embeddings to match known questions.
• 🌐 Web Fallback: Integrates Tavily API when no good match is found.
• ✍️ GPT-4.1 Explanations: Generates step-by-step math solutions.
• 🛡️ Output Guardrails: Filters for correctness and safety.
• 👍 Human-in-the-Loop Feedback: Users rate answers (Yes/No), logged for future learning.
• 📊 Benchmarking: Evaluated on JEEBench dataset with adjustable question limits.
• 💻 Streamlit UI: Interactive dashboard with multiple tabs.

🚀 Architecture Flow

<img width="465" alt="Screenshot 2025-05-04 at 3 45 58 PM" src="https://github.com/user-attachments/assets/c0a9e612-2ef0-413c-b779-c99fe9f48619" />

📚 Knowledge Base

• Dataset: JEEBench (HuggingFace)
• Vector DB: Qdrant (with OpenAI Embeddings)
• Storage: Built with llama-index to persist embeddings and perform top-1 similarity search

🌐 Web Search

• Uses Tavily API for fallback search when the KB doesn't contain a good match
• Fetched content is piped into GPT-4o for clean explanation

🔐 Guardrails

• Input Guardrail (DSPy): Accepts only math-related academic questions
• Output Guardrail (DSPy): Blocks hallucinated or off-topic content

👨‍🏫 Human-in-the-Loop Feedback

• Streamlit UI allows students to give 👍 / 👎 after seeing the answer
• Feedback is logged to a local JSON file for future improvement

📊 Benchmarking

• Evaluated on 50 random JEEBench Math Questions
• Current Accuracy: 66%
• Benchmark results saved to: benchmark/results.csv

🚀 Demo

To run the app with Streamlit:

streamlit run app/streamlit.py