PippaQ — Premium Q&A & FAQ Platform

A high-performance, semantic query-resolution and FAQ generation platform with a QP (Quality Point) reputation economy, role-based access control, and a production-ready RAG (Retrieval-Augmented Generation) duplicate detection engine.

Built with React, Express, MongoDB, Qdrant Cloud, and local Sentence Transformers.

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🏗️ Architecture

D:\faq\
├── client/          React + Vite frontend (Tailwind CSS + Premium Typography)
├── server/          Express.js backend (MongoDB/Mongoose + Qdrant Integration)
├── rag-engine/      Semantic Decision Engine (Sentence Transformer + Qdrant ANN)
├── 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, JWT secret, and QDRANT_URL

3. Start MongoDB & Qdrant

Ensure your local or cloud MongoDB and Qdrant instances are accessible.

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 Duplicate Detection Engine

Questions are evaluated semantically against the FAQ and RTQ collections stored in Qdrant:

User Question 
      │
      ▼
Generate Embedding ──► Local Sentence Transformer (all-MiniLM-L6-v2, 384-dim)
      │
      ▼
Qdrant HNSW ANN Search ──► Compare with FAQ/RTQ Collections
      │
      ▼
Apply Decision Tree Rules:
┌───────────────────────────────┬───────────────────────────────┐
│ F1: FAQ similarity > 80%      │ REJECT, -5 QP, upvote FAQ     │
├───────────────────────────────┼───────────────────────────────┤
│ F2: FAQ similarity 50–80%     │                               │
│  ├── R1: RTQ > 60%            │ REJECT, -5 QP, upvote FAQ     │
│  ├── R2: RTQ 20–60%           │ REJECT (no penalty)           │
│  └── R3: RTQ ≤ 20%            │ ACCEPT → Route to RTQ         │
├───────────────────────────────┼───────────────────────────────┤
│ F3: FAQ similarity ≤ 50%      │                               │
│  ├── R1: RTQ > 60%            │ REJECT (no penalty), upvote RTQ│
│  └── R2/R3: RTQ ≤ 60%         │ ACCEPT → Route to RTQ         │
└───────────────────────────────┴───────────────────────────────┘

Performance Optimization: Includes an LRU Embedding Cache (500 entries) and model warmup on server startup to ensure lightning-fast semantic queries under 10ms.

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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 + Premium Typography (Playfair Display & Outfit)
• Backend: Express.js, Mongoose, JWT (bcryptjs)
• Vector Store: Qdrant Cloud (HNSW indexes, cosine distance, metadata payload filters)
• Embeddings: Native Node.js @xenova/transformers (local WebAssembly ONNX execution of all-MiniLM-L6-v2)
• Dev Tools: concurrently to run client + server together