StartupHub

A comprehensive AI-powered platform combining intelligent funding advisory for Indian startups with advanced document ingestion and RAG (Retrieval-Augmented Generation) capabilities.

🌟 Features

Startup Funding Intelligence

AI Funding Co-Founder: Personalized funding advice using Google Gemini
Founder Profile Management: Save and manage startup context (stage, sector, location, funding goals)
Funding Readiness Score: Get a 0-100 score based on your startup's profile
Actionable Checklists: Receive 5-step recommendations tailored to your funding journey
Multi-language Support: Get advice in preferred Indian languages
India-focused: Tailored specifically for the Indian startup ecosystem

Document Intelligence System

PDF Ingestion Pipeline: Process and clean PDF documents
Vector Database: ChromaDB-powered semantic search
RAG Engine: Advanced question-answering with context retrieval
Semantic Search: Find relevant information across documents
OCR Support: Extract text from image-based PDFs

Modern Web Application

React Frontend: Built with Vite and TypeScript
Responsive UI: Shadcn/ui components with Radix UI primitives
Real-time Updates: React Query for efficient data fetching
Smooth Animations: Framer Motion for engaging user experience
Multi-page Navigation: Dashboard, Onboarding, and more

🛠️ Tech Stack

Frontend

Framework: React 18 with TypeScript
Build Tool: Vite
Styling: Tailwind CSS
UI Components: Shadcn/ui + Radix UI
State Management: React Context API
Data Fetching: TanStack React Query
Animations: Framer Motion
Routing: Wouter

Backend

Main Server: Express.js with TypeScript
RAG Backend: FastAPI (Python)
Database: PostgreSQL with Drizzle ORM
Vector Database: ChromaDB
Session Management: connect-pg-simple

AI/ML

Primary LLM: Google Gemini 1.5 Pro
Alternative LLM: GROQ API
Embeddings: ChromaDB embeddings
OCR: Integrated OCR for document processing

Deployment

Platform: Vercel-ready
Alternative: Render (for Python backend)

📁 Project Structure

ai-verse/
├── client/                    # React frontend
│   ├── src/
│   │   ├── components/       # UI components
│   │   ├── pages/           # Application pages
│   │   ├── context/         # React Context providers
│   │   ├── hooks/           # Custom React hooks
│   │   └── lib/             # Utilities and API clients
│   └── public/              # Static assets
│
├── server/                    # Express.js backend
│   ├── index.ts             # Server entry point
│   ├── routes.ts            # API routes
│   └── storage.ts           # Database logic
│
├── startup-rag/               # AI Funding Advisory System
│   └── backend/
│       ├── app/
│       │   ├── main.py      # FastAPI application
│       │   ├── rag.py       # RAG implementation
│       │   ├── gemini_client.py  # Gemini integration
│       │   └── prompts.py   # LLM prompts
│       └── requirements.txt
│
├── Data Ingestion/            # Document Processing Pipeline
│   ├── app.py               # Main control center
│   ├── ingestion/           # PDF processing modules
│   ├── vector_store/        # Vector DB management
│   ├── rag/                 # RAG engine
│   └── data/
│       ├── raw/             # Original PDFs
│       ├── processed/       # Cleaned text
│       ├── chunks/          # Text chunks
│       └── vector_db/       # ChromaDB storage
│
└── shared/                    # Shared TypeScript schemas

⚡ Quick Commands

Run Backend (Python 3.10)

cd startup-rag/backend
py -3.10 -m uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload

Run Frontend

cd ai-verse
npm run dev:client

Access:

Backend: http://localhost:8000
Frontend: http://localhost:5001
Health Check: http://localhost:8000/health

🚀 Quick Start

Prerequisites

Node.js: v18 or higher
Python: 3.10 or higher (3.10 recommended for RAG compatibility)
PostgreSQL: For main database (optional for MVP)
API Keys:
- Google Gemini API key (required)
- GROQ API key (optional - for Data Ingestion)

1. Clone the Repository

git clone <repository-url>
cd ai-verse

2. Frontend & Main Backend Setup

# Install dependencies
npm install

# Configure environment variables
# Create .env file with:
# DATABASE_URL=postgresql://user:password@localhost:5432/aiverse
# SESSION_SECRET=your-session-secret

# Push database schema
npm run db:push

# Start development server
npm run dev

The application will be available at http://localhost:5000

3. Startup RAG Backend Setup

cd startup-rag/backend

# Install Python dependencies (use Python 3.10)
py -3.10 -m pip install -r requirements.txt

# Install RAG dependencies
py -3.10 -m pip install sentence-transformers==2.2.2 "numpy<2.0" chromadb langdetect python-multipart

# Configure environment
# Create .env file with:
# GEMINI_API_KEY=your_gemini_api_key_here

# Start the FastAPI server (Python 3.10)
py -3.10 -m uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

Get Gemini API Key:

Visit Google AI Studio
Create a new API key
Add to startup-rag/backend/.env file
See GEMINI_API_KEY_SETUP.md for detailed instructions

4. Data Ingestion System Setup

cd "Data Ingestion"

# Install Python dependencies
pip install -r requirements.txt

# Configure GROQ API Key (PowerShell)
$env:GROQ_API_KEY="your_groq_api_key_here"

# Run the ingestion pipeline
python app.py

📖 Usage

Startup Funding Advisory

Create Founder Profile:
```
POST http://localhost:8000/founder/profile
```
Provide startup stage, sector, location, and funding goals
Get Funding Advice:
```
POST http://localhost:8000/funding/advice
```
Ask questions about funding strategy and receive personalized recommendations

Document Intelligence

Ingest PDFs: Place PDF files in Data Ingestion/data/raw/
Process Documents: Run the pipeline to extract and clean text
Build Vector DB: Create embeddings for semantic search
Query System: Ask questions and get context-aware answers

🔧 Development

Available Scripts

# Frontend development
npm run dev:client          # Start Vite dev server

# Full stack development
npm run dev                 # Start Express server with hot reload

# Production build
npm run build              # Build for production
npm start                  # Start production server

# Database
npm run db:push            # Push schema changes to database

# Type checking
npm run check              # Run TypeScript compiler checks

Testing

Backend API Tests

cd startup-rag/backend

# Quick test
python test_api_quick.py

# Full test suite
python test_api.py

# Gemini integration test
python test_gemini_integration.py

🌐 API Endpoints

Startup RAG Backend (Port 8000)

Save Founder Profile

POST /founder/profile

Get Funding Advice

POST /funding/advice

Main Application (Port 5000)

See server/routes.ts for complete API documentation.

🔑 Environment Variables

Main Application (.env)

DATABASE_URL=postgresql://user:password@localhost:5432/aiverse
SESSION_SECRET=your-session-secret-here
NODE_ENV=development

Startup RAG Backend (.env)

GEMINI_API_KEY=your_gemini_api_key_here

Data Ingestion System

GROQ_API_KEY=your_groq_api_key_here

📦 Deployment

✅ Deployment Status: PRODUCTION READY

See FINAL_DEPLOYMENT_READINESS.md for complete deployment verification.

Vercel (Frontend)

# Deploy to Vercel
cd client
vercel deploy --prod

Environment Variables (Vercel):

VITE_API_URL=https://your-backend.onrender.com

Render (Python Backend)

Push to GitHub: https://github.com/akhil151/ai-verse
Connect repository to Render
Use render.yaml configuration

Add environment variables in Render dashboard:

GEMINI_API_KEY=your_gemini_api_key
ALLOWED_ORIGINS=https://your-frontend.vercel.app

Render will auto-deploy on push

Important: Backend requires Python 3.10+ runtime in Render settings.

🤝 Contributing

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request

📄 License

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

🙏 Acknowledgments

Google Gemini: AI-powered funding advice
ChromaDB: Vector database for semantic search
Shadcn/ui: Beautiful UI components
FastAPI: High-performance Python backend
Vercel: Deployment platform

📞 Support

For questions and support:

Open an issue in the repository
Check existing documentation in /startup-rag/backend/README.md
Review setup guides in /Data Ingestion/SETUP_API_KEY.md

Name		Name	Last commit message	Last commit date
Data Ingestion		Data Ingestion
attached_assets/generated_images		attached_assets/generated_images
client		client
script		script
server		server
shared		shared
startup-rag/backend		startup-rag/backend
.env.example		.env.example
.gitignore		.gitignore
README.md		README.md
components.json		components.json
drizzle.config.ts		drizzle.config.ts
package-lock.json		package-lock.json
package.json		package.json
postcss.config.js		postcss.config.js
tsconfig.json		tsconfig.json
vercel.json		vercel.json
vite.config.ts		vite.config.ts

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