🧭 Compass for Canvas

Fall 2024 AI Hackathon Project by Team Knead Uhjahb

Compass is an AI-driven web application that helps students quickly find specific information from their Canvas courses, addressing the problem of knowledge bottlenecks. By integrating a full Retrieval-Augmented Generation (RAG) system, Compass offers accurate, context-aware responses, simplifying the process of studying and reviewing course materials.

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🚀 Project Overview

Problem Space

Students often face the challenge of locating specific course information amidst extensive lecture notes and textbooks. This can result in time wasted and hindered academic progress.

Solution Space

Compass addresses these issues by providing:

• Reduced AI Hallucinations: Accurate answers sourced directly from course materials.
• Fast and Relevant Responses: Saves time by pinpointing information students need most.
• User-Friendly Interface: Seamless and easy to navigate.
• Canvas Integration: Direct connection to Canvas courses for optimized user experience.

💡 How It Works

Compass leverages the following technologies to create an efficient and accurate information retrieval system:

• Retrieval-Augmented Generation (RAG): Combining generative AI with retrieval capabilities to ensure relevant, specific responses.
• Key Components:
  • Llama-Index: Supports structured data retrieval.
  • Groq: Enhances processing efficiency.
  • Canvas API: Provides direct integration with Canvas courses.
  • React & FastAPI: A modern, robust tech stack to power the front and back end.
  • Python: Core language for system integration and processing.

✅ Validation

Our model has undergone extensive testing to ensure that it:

• Only answers relevant questions.
• Utilizes information directly from course materials to avoid AI hallucinations.
• Delivers concise, accurate responses quickly, helping students study effectively.

📈 Impact & Benefits

With Compass, students can:

• Find course information faster than traditional study methods.
• Engage in interactive learning experiences through back-and-forth queries.
• Save valuable study time with auto-generated summaries.

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Preview

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🛠️ How to Run

The frontend and backend are located in separate folders.

Frontend

From the frontend folder, run:

npm install
npm run start

Backend

From the backend folder, follow these steps:

1. Preferably, create a virtual Python environment:

   python -m venv venv

2. Activate the environment:

   • Windows:

     .\venv\Scripts\activate

   • Linux/Mac:

     source venv/bin/activate

3. Install required packages:

   pip install python-dotenv canvasapi llama-index fastapi openai uvicorn

4. Rename .env.example to .env and add your API keys:

   CANVAS_API=
   GROQ_API_KEY=
   

5. Run the backend:

   python index.py

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👥 Meet the Team

• Gabriel Malek - Programmer
• William Lorence - Software Engineer
• Kevin Esquivel - Developer
• Rahul Gupta - Coder

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Join us on our journey to redefine academic study tools and make learning more accessible!

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