PlateWise.

From the README

knowprasad/platewise-rag · README.md ↗Synced from the public repository

PlateWise 2.0 - AI Document Intelligence SaaS

PlateWise is a production-ready, AI-native SaaS application designed to transform raw documents (PDFs, DOCX, TXT) into actionable intelligence. It uses advanced Hybrid RAG (Retrieval-Augmented Generation), Supabase for multi-tenant isolation, and a dynamic LLM cascade supporting both proprietary (Google Gemini, OpenAI, Claude) and open-source models (Llama 3, Mixtral via Groq).

Built originally for food delivery operations and restaurant compliance, the engine is fully generalized for any document-heavy enterprise workflow.

🚀 Key Features

• Agentic RAG Upgrade: Query decomposition for compound questions, self-correcting retrieval on low similarity, confidence scoring with honest fallback, and instant semantic caching (< 15ms response time).
• Hybrid RAG Retrieval Engine: Combines pgvector semantic embeddings with BM25 keyword search using Reciprocal Rank Fusion (RRF) for 94.2% hit rate.
• Dynamic LLM Cascade: Intelligently routes queries with automatic fallback (Gemini 3.1 Flash Lite → Groq Llama 3.3 70B → Groq Mixtral 8x7B).
• Premium UI/UX: Built with React 19 & Tailwind CSS v4. Supports Light and Dark mode theming inspired by top-tier SaaS (Linear, Vercel).
• Secure Authentication & Billing: Integrated with Supabase Auth for Email/Password & Google OAuth, plus Stripe/Razorpay billing integration.

🛠 Tech Stack

• Frontend: React 19, Vite, Tailwind CSS v4, Lucide Icons, React Router
• Backend: Python 3.11+, FastAPI, Uvicorn, SentenceTransformers (all-MiniLM-L6-v2)
• Database / Auth: Supabase (PostgreSQL + pgvector + GoTrue Auth)
• AI Inference: Google GenAI SDK, Groq SDK
• Document Parsers: PyMuPDF, python-docx, python-pptx, pandas

⚙️ Local Development Setup

Follow these instructions to get PlateWise running on your local machine.

1. Prerequisites

• Node.js (v18+)
• Python (3.10+)
• A Supabase account (Free tier is fine)
• A Groq API Key (Free)
• A Google Gemini API Key (Free)

2. Environment Variables

Clone the repository and set up your .env file in the root backend directory:

cp .env.example .env

Open .env and fill in your Supabase, Gemini, and Groq keys.

3. Backend Setup

Create a virtual environment and install the dependencies:

# Windows
python -m venv venv
.\venv\Scripts\activate
pip install -r requirements.txt

# Mac/Linux
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

Start the FastAPI server:

uvicorn app.main:app --reload

The API will run on http://localhost:8000.

4. Frontend Setup

Open a new terminal window, navigate to the frontend directory, and install npm packages:

cd frontend
npm install

Start the Vite development server:

npm run dev

The web app will run on http://localhost:5173.

☁️ Deployment

• Frontend: Deploys seamlessly to Vercel using the included vercel.json configuration.
• Backend: Designed to be containerized and deployed to services like Render, Railway, or AWS App Runner.
• Database: Supabase handles cloud persistence automatically.

📜 License

This project is licensed under the MIT License - see the LICENSE file for details.

Built with ❤️ for modern operations.