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-3.5 Turbo 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

📚 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-3.5 Turbo 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

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README.md Unescape Escape

🧠 Math Tutor Agent – Agentic RAG with Feedback Loop