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Q&A Platform

A semantic query-resolution and FAQ generation platform with a QP (Quality Point) reputation economy and role-based access control. Built with React, Express, MongoDB, and a custom RAG engine.

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🏗️ Architecture

D:\faq\
├── client/          React + Vite frontend (Tailwind CSS)
├── server/          Express.js backend (MongoDB/Mongoose)
├── rag-engine/      Custom TF-IDF RAG pipeline (no external AI API)
├── shared/          Shared constants (QP rules, RAG thresholds, roles)
└── docs/            Project documentation

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🚀 Quick Start

1. Install dependencies

npm run install:all

2. Configure environment

cp server/.env.example server/.env
# Edit server/.env — set your MongoDB URI and JWT secret

3. Start MongoDB

Make sure MongoDB is running locally or update MONGO_URI in server/.env.

4. Run the app

npm run dev

• Client: http://localhost:3000
• Server: http://localhost:5000

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👥 Roles & Access

Role	Capabilities
Student	Ask RTQ questions, answer (once per question), upvote, track own questions
Moderator	+ Approve answers, accept/reject questions, mark trendy
Senior	+ Create FAQs manually, convert RTQ→FAQ, delete any content, remove RTQs
Admin	+ Manage email whitelist, assign/revoke roles

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💰 QP (Quality Point) Economy

Earning QP

Action	QP
Answer a question	+2
Answer approved by Moderator/Senior	+5
Answer selected for FAQ	+10 bonus
Question accepted (valid RTQ entry)	+5
Question promoted to FAQ	+20
Senior creates new FAQ manually	+15

Penalties

Action	QP
Duplicate FAQ match (F1)	-5
F2+R1 match	-5
Answer removed by Senior	-3
Question removed	-5

Thresholds

• QP < 50 → Cannot raise questions
• QP ≥ 500 → Auto-request Moderator promotion

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🧠 RAG Decision Tree

Questions are evaluated against the FAQ and RTQ vector stores:

User Question → TF-IDF Embed (384-dim) → Compare with FAQ/RTQ vectors

F1: FAQ similarity > 80%   → REJECT + -5 QP (duplicate)
F2: 50–80% FAQ similarity
  + R1: RTQ > 60%          → REJECT + -5 QP (similar RTQ exists)
  + R2: 20–60% RTQ         → REJECT (no penalty)
  + R3: RTQ ≤ 20%          → ACCEPT → Add to RTQ
F3: FAQ ≤ 50% similarity
  + R1: RTQ > 60%          → REJECT (no penalty)
  + R2/R3: RTQ ≤ 60%       → ACCEPT → Add to RTQ

Uses TF-IDF character n-grams (no external AI API required). Swap for OpenAI/sentence-transformers in production.

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📂 Key Routes

Route	Description
/api/auth/signup	Register + email OTP
/api/auth/login	JWT login
/api/faq	FAQ CRUD + upvote
/api/rtq	RTQ list + submit question
/api/rtq/:id/answer	Add answer to RTQ
/api/rag/evaluate-question	Run RAG decision tree
/api/users	User management
/api/qp/my-score	Current QP balance
/api/admin/assign-role	Admin: change user role

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🛠️ Tech Stack

• Frontend: React 18, Vite, React Router v6, Axios, Tailwind CSS
• Backend: Express.js, Mongoose, JWT (bcryptjs for passwords)
• RAG Engine: Custom TF-IDF n-gram embedder (384-dim), cosine similarity, in-memory vector DB
• Database: MongoDB (vectors stored in documents — swap for Qdrant/Pinecone in production)
• Dev: concurrently to run client + server together

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📝 Notes

• OTP is console-logged in development. Wire to an email provider (SendGrid, Resend) for production.
• Vector DB is in-memory (vectors stored in MongoDB documents). For production, swap to Qdrant or Pinecone.
• RESTRICTED users (flagged by seniors) are blocked from asking questions and answering.

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Maintained with git. Run git log --oneline for full history.

FAQ

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