Alright, here's a professional-grade folder structure + code scaffolding plan for the AI Knowledge Engine, designed to keep it simple, clean, and deployable within 10 hours.

📁 Project Folder Structure

knowledge_engine/
├── backend/
│   ├── manage.py
│   ├── requirements.txt
│   ├── backend/              # Django project folder
│   │   ├── __init__.py
│   │   ├── settings.py
│   │   ├── urls.py
│   │   ├── wsgi.py
│   ├── api/                  # Django app
│   │   ├── __init__.py
│   │   ├── views.py
│   │   ├── urls.py
│   │   ├── embeddings.py     # Embedding + retrieval logic
│   │   ├── llm_response.py   # Call HuggingFace / LLM for final answer
│   │   ├── sample_data.txt   # Preloaded knowledge base (sample text)
├── frontend/
│   ├── index.html
│   ├── assets/
│   │   ├── styles.css
│   │   ├── script.js
├── README.md

🛠 Code Scaffolding Plan

1. Backend (Django + DRF)

• backend/settings.py

  • Setup basic Django config + CORS allow all origins

  • Add rest_framework, corsheaders, api to INSTALLED_APPS

• backend/urls.py

  • Route all API endpoints (e.g., /api/ask)

• api/urls.py

  • Define path('ask/', AskView.as_view())

• api/views.py

  • Class AskView(APIView):

    • POST method: receives question

    • Calls retrieval function

    • Calls LLM response generation

    • Returns final answer as JSON

• api/embeddings.py

  • Load sample_data.txt

  • Embed text chunks

  • Implement basic semantic search (cosine similarity or FAISS)

• api/llm_response.py

  • Use HuggingFace pipeline (or API) to generate answer from matched context.

• Djangodjangorestframeworktransformerssentence-transformerstorchfaiss-cpudjango-cors-headers

2. Frontend (HTML + Bootstrap + Vanilla JS)

• index.html

  • Simple Bootstrap layout

    • Header

    • Input field for question

    • "Ask" button

    • Answer card

• assets/styles.css

  • Custom minor styling (loading spinner, answer card formatting)

• assets/script.js

  • Fetch question via POST /api/ask

  • Show loading spinner

  • Render the answer dynamically on the page

3. Deployment Notes

• Add gunicorn to requirements.txt for production

• web: gunicorn backend.wsgi

• Set environment variables like DJANGO_ALLOWED_HOSTS=*

• Frontend can be pushed as a static site if needed, or served via Django staticfiles.

⚡ Fast Milestones Timeline

Time SlotDeliverable1st HourDjango + DRF skeleton setup2nd-3rd HourBuild embedding + retrieval modules4th HourSetup API + Test with Postman5th HourFrontend HTML + Bootstrap UI6th HourJS fetch integration7th-8th HourFinal polish: loaders, error handling9th HourDeployment setup10th HourLive Demo + GitHub push

📦 Output at the End

✅ Working full-stack AI Knowledge Engine✅ Hosted live version (Render or HuggingFace Spaces)✅ GitHub repo with clean README

Would you also like me to give you a starter views.py and script.js template?(Literally plug-and-play in class.) 🚀Ready if you want it!