Creator Support AI - RAG-Powered Customer Support

An AI-powered customer support system that understands your course content and answers student questions automatically using RAG (Retrieval-Augmented Generation).

Live Demo

Link	Description
Live Demo	Try the AI chat
Landing Page	Product homepage
API Health	Backend status

Features

• Content-Aware AI: Indexes course videos, PDFs, and text to answer specific content questions
• Multi-Agent RAG Pipeline: Uses LangGraph to orchestrate retrieval and response generation
• Automatic Transcription: Converts video content to searchable text using OpenAI Whisper
• Embed Widget: Easy-to-integrate chat widget for your course platform
• Creator Dashboard: Review conversations, manage content, and monitor performance
• Credit-Based Billing: Integrated with Lemon Squeezy for payments
• Escalation System: Flags uncertain responses for human review

Tech Stack

Frontend

• Next.js 15 - React framework with App Router
• TypeScript - Type safety
• Tailwind CSS - Styling
• Supabase Auth - Authentication

Backend

• FastAPI - Python web framework
• LangGraph - Multi-agent orchestration
• LangChain - RAG pipeline
• OpenAI - LLM (GPT-4) and embeddings
• Pinecone - Vector database
• Supabase - PostgreSQL database
• Whisper API - Video transcription

Getting Started

Prerequisites

• Node.js 18+ and npm
• Python 3.10+
• OpenAI API key
• Pinecone account
• Supabase project (optional but recommended)
• Lemon Squeezy account (for billing)

Backend Setup

1. Navigate to the backend directory:

   cd backend

2. Create a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Copy the environment template:

   cp .env.example .env

5. Add your API keys to .env:

   OPENAI_API_KEY=your_key_here
   PINECONE_API_KEY=your_key_here
   SUPABASE_URL=your_url_here
   SUPABASE_ANON_KEY=your_key_here
   LEMON_SQUEEZY_API_KEY=your_key_here
   

6. Run the FastAPI server:

   python main.py

   The API will be available at http://localhost:8000

Frontend Setup

1. Navigate to the frontend directory:

   cd frontend

2. Install dependencies:

   npm install

3. Run the development server:

   npm run dev

   The app will be available at http://localhost:3000

Database Setup

If using Supabase:

1. Create a new Supabase project
2. Run the SQL schema from backend/schema.sql in the Supabase SQL editor
3. Enable Row Level Security policies
4. Add your Supabase credentials to .env

Project Structure

strong_mvp/
├── frontend/
│   ├── src/
│   │   ├── app/
│   │   │   ├── page.tsx           # Landing page
│   │   │   ├── demo/              # Chat demo
│   │   │   ├── dashboard/         # Creator dashboard
│   │   │   └── pricing/           # Pricing page
│   │   └── components/
│   │       ├── ChatInterface.tsx  # Chat UI
│   │       ├── EmbedWidget.tsx    # Widget generator
│   │       └── ContentUpload.tsx  # File upload
│   └── package.json
├── backend/
│   ├── app/
│   │   ├── agents/
│   │   │   └── support_agent.py   # LangGraph agent
│   │   ├── services/
│   │   │   ├── vector_store.py    # Pinecone integration
│   │   │   ├── document_processor.py
│   │   │   ├── content_ingestion.py
│   │   │   └── billing.py         # Lemon Squeezy
│   │   └── models/
│   │       └── database.py        # Supabase client
│   ├── main.py                    # FastAPI app
│   ├── requirements.txt
│   └── schema.sql                 # Database schema
└── README.md

Usage

Upload Content

1. Go to the dashboard at /dashboard
2. Navigate to the "Upload Content" section
3. Select content type (PDF, video, or text)
4. Upload your course materials
5. The system will automatically process and index the content

Embed on Your Website

1. Go to the dashboard
2. Click on "Embed Widget"
3. Copy the JavaScript snippet
4. Paste it into your website's HTML

Test the Chat

1. Visit /demo to test the chat interface
2. Ask questions about your uploaded content
3. The AI will respond with citations to specific sources

API Endpoints

POST /upload/content

Upload and process course content

Body:

• file: File upload (PDF, video, or text)
• creator_id: Creator's unique ID
• content_type: Type of content (pdf, video, text)
• title: Optional title

POST /chat

Send a message to the AI assistant

Body:

{
  "message": "What did you cover in Module 3?",
  "creator_id": "creator-id",
  "conversation_id": "optional-conversation-id"
}

Response:

{
  "response": "In Module 3, we covered...",
  "sources": ["Module 3: Advanced Topics (Score: 0.92)"],
  "should_escalate": false,
  "conversation_id": "conv-123"
}

GET /conversations/{creator_id}

Retrieve conversation history

Query Parameters:

• limit: Number of conversations (default: 50)

Deployment

Current Production Setup

Component	Service	URL
Frontend	Vercel	https://never-afk-ai-lngm.vercel.app
Backend	Railway	https://neverafkai-production.up.railway.app
Database	Supabase	PostgreSQL (managed)
Vector Store	Pinecone	Cloud-hosted

Backend (FastAPI) - Railway

1. Create Railway project
2. Set root directory to /backend
3. Add environment variables:
   • OPENAI_API_KEY
   • PINECONE_API_KEY
   • SUPABASE_URL
   • SUPABASE_ANON_KEY
4. Deploy via GitHub integration

Frontend (Next.js) - Vercel

1. Create Vercel project
2. Set root directory to frontend
3. Add environment variable:
   • NEXT_PUBLIC_API_URL = Railway backend URL
4. Deploy via GitHub integration

Alternative Deployment Options

Backend:

• Render
• AWS Lambda (with Mangum)
• Google Cloud Run

Frontend:

• Netlify
• AWS Amplify

Pricing Strategy

• Free: 100 responses/month
• Starter ($29/mo): 1,000 responses/month
• Pro ($49/mo): Unlimited responses

Roadmap

• Multi-language support
• Voice responses using ElevenLabs
• Video avatar responses using HeyGen
• Advanced analytics dashboard
• A/B testing for responses
• Integration with Zapier
• Mobile app

Contributing

This is a personal MVP project. Contributions and feedback are welcome!

License

MIT License

Support

For issues and questions, please open an issue on GitHub.