🪐 QuantaOrion

QuantaOrion-Agent is a next-generation, multimodal AI platform engineered to transcend the boundaries of traditional conversational agents. Harnessing the power of Retrieval-Augmented Generation (RAG), advanced web search, and dynamic document analysis, QuantaOrion-Agent orchestrates a seamless fusion of real-time web intelligence, contextual document retrieval, and deep conversational memory.

At its core, QuantaOrion-Agent leverages a sophisticated pipeline that unifies web-scale search APIs, state-of-the-art embedding models, and large language models (LLMs) to deliver contextually aware, evidence-backed responses. Whether synthesizing insights from the latest web content, extracting knowledge from user-uploaded documents, or maintaining coherent multi-turn dialogues, QuantaOrion-Agent adapts fluidly to the user’s intent—empowering research, decision-making, and discovery.

QuantaOrion-Agent is not just an assistant—it is a stellar nexus of knowledge, designed to illuminate, synthesize, and guide users through the ever-expanding universe of information.

🐛 Report Bug | 💡 Request Feature

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✨ Key Features

📄 Document Intelligence

• Multi-format Support: Upload documents (PDF, DOCX, TXT, MD)
• Instant Summarization: Receive AI-generated summaries upon upload
• Contextual Q&A: Ask questions based on uploaded content
• Smart Extraction: Advanced text processing and chunking

🔍 Hybrid Document + Web Search

When you ask a question after uploading a document:

1. 🎯 Document Context: Locates relevant snippets in your uploaded documents
2. 🌐 Real-time Web Search: Performs live web search for current information
3. 🧠 Intelligent Synthesis: Returns a comprehensive 3-part response:
   • 🔗 Relevant web links with summaries
   • 📄 Matching document excerpts
   • 💡 AI-generated answer combining both sources

🌐 Website Summarization

• URL Analysis: Paste any URL for instant content summarization
• Structured Output: Clear, organized summaries with key points
• Content Extraction: Smart parsing of web content

💬 Smart Chat (Advanced Mode)

• Enhanced Capabilities: Outperforms standard chatbots in real-world scenarios
• Real-time Information: Access to current events, people, and concepts
• Context Awareness: Maintains conversation flow and memory
• Multi-source Responses: Combines web search with AI reasoning

🧠 Persistent Memory System

• Conversation Storage: All chats saved in Supabase database
• Thread Management: Create and continue multiple conversation threads
• Document Persistence: Uploaded documents remain accessible across sessions
• Search History: Track and revisit previous queries

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🛠️ Tech Stack

Layer	Technology	Purpose
Frontend	Next.js 14 (React)	Modern, responsive UI with server-side rendering
Backend	Flask (Python)	High-performance async API with CORS support
Database	Supabase (PostgreSQL)	Real-time database with built-in auth
AI Engine	OpenAI GPT-4 / Google Gemini	Advanced language model integration
Web Search	Serper API / NewsAPI	Real-time web search capabilities
Vector Store	FAISS / Sentence Transformers	Efficient document embedding storage
Deployment	Vercel + Cloud Platforms	Scalable, global deployment

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🚀 Quick Start

Prerequisites

• Python 3.8+
• Node.js 18+
• npm or yarn
• Git

1️⃣ Clone the Repository

git clone https://github.com/Likhith623/advanced-rag-agent.git
cd advanced-rag-agent

2️⃣ Backend Setup (Flask)

# Create virtual environment
python -m venv venv

# Activate virtual environment
# On macOS/Linux:
source venv/bin/activate
# On Windows:
venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Create environment file
cp .env.example .env
# Edit .env with your API keys (see Environment Variables section)

# Start the backend server
python main.py

3️⃣ Frontend Setup (Next.js)

# Navigate to frontend directory (new terminal)
cd front_end

# Install dependencies
npm install
# or
yarn install

# Create environment file
cp .env.local.example .env.local
# Edit .env.local with your configuration

# Start the development server
npm run dev
# or
yarn dev

4️⃣ Access the Application

• Frontend: http://localhost:3000
• Backend API: http://localhost:8080
• Health Check: http://localhost:8080/health

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🔐 Environment Variables

Backend .env

# Database
SUPABASE_URL=your_supabase_project_url
SUPABASE_KEY=your_supabase_anon_key

# AI APIs
OPENAI_API_KEY=your_openai_api_key
GEMINI_API_KEY=your_google_gemini_key

# Web Search
SERPER_API_KEY=your_serper_api_key
NEWS_API_KEY=your_newsapi_key

# Application
DEBUG=true
LOG_LEVEL=info
PORT=8080

Frontend .env.local

# API Configuration
NEXT_PUBLIC_API_URL=http://localhost:8080
NEXT_PUBLIC_SUPABASE_URL=your_supabase_project_url
NEXT_PUBLIC_SUPABASE_ANON_KEY=your_supabase_anon_key

# Application
NEXT_PUBLIC_APP_NAME=Advanced RAG Agent
NEXT_PUBLIC_APP_VERSION=1.0.0

🔑 Getting API Keys

Click to expand API key setup instructions

Supabase

1. Go to supabase.com
2. Create a new project
3. Copy URL and anon key from Settings > API

OpenAI

1. Visit platform.openai.com
2. Create account and add billing
3. Generate API key in API Keys section

Google Gemini

1. Go to ai.google.dev
2. Get API key for Gemini Pro

Serper (Web Search)

1. Visit serper.dev
2. Sign up for free tier
3. Copy API key from dashboard

NewsAPI

1. Go to newsapi.org
2. Register for free account
3. Get your API key

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📂 Project Structure

advanced-rag-agent/
├── 📁 front_end/              # Next.js frontend
│   ├── 📁 app/                # App Router (Next.js 13+)
│   │   ├── 📄 layout.js       # Root layout
│   │   ├── 📄 page.js         # Main chat interface
│   │   └── 📄 globals.css     # Global styles
│   ├── 📁 components/         # React components
│   ├── 📁 lib/                # Utility libraries
│   ├── 📄 package.json        # Node dependencies
│   ├── 📄 next.config.mjs     # Next.js configuration
│   └── 📄 .env.local.example  # Environment template
├── 📄 main.py                 # Flask backend entry point
├── 📄 requirements.txt        # Python dependencies
├── 📁 uploads/                # File upload directory
├── 📄 comprehensive_news_knowledge.txt # Knowledge base
├── 📄 .env.example            # Environment template
├── 📄 .gitignore             # Git ignore rules
├── 📄 README.md              # This file
└── 📄 LICENSE                # MIT License

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🎯 Usage Examples

Document Upload and Query

# 1. Upload a research paper
POST /upload
- file: research_paper.pdf
- conversation_id: conv_123

# 2. Ask questions about the document
POST /api/news
{
  "query": "What are the main findings in this research?",
  "user_email": "user@example.com",
  "conversation_id": "conv_123"
}

# Response includes:
# - Document context from uploaded PDF
# - Related web search results
# - AI-synthesized answer

Web-Only Search

POST /api/news
{
  "query": "latest developments in quantum computing",
  "user_email": "user@example.com"
}

URL Summarization

POST /api/news
{
  "query": "https://example.com/article",
  "user_email": "user@example.com"
}

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🚀 Deployment

Frontend (Vercel)

# Install Vercel CLI
npm install -g vercel

# Deploy frontend
cd front_end
vercel --prod

# Set environment variables in Vercel dashboard

Backend (Railway/Render/Heroku)

# For Railway
railway login
railway init
railway up

# For Render
# Connect your GitHub repo and deploy

# For Google Cloud Run
gcloud run deploy advanced-rag-agent \
  --source . \
  --platform managed \
  --region us-central1 \
  --allow-unauthenticated

Docker Deployment

# Dockerfile for backend
FROM python:3.9-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
EXPOSE 8080
CMD ["python", "main.py"]

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🧪 Testing

Backend Health Check

curl http://localhost:8080/health

Frontend Development

cd front_end
npm run test        # Run unit tests
npm run build       # Production build
npm run lint        # Code linting

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Development Workflow

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

Code Standards

• Python: Follow PEP 8, use Black formatter
• JavaScript: Follow ESLint rules, use Prettier
• Commits: Use conventional commit messages

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🐛 Troubleshooting

Common Issues and Solutions

Backend won't start

# Check Python version
python --version  # Should be 3.8+

# Reinstall dependencies
pip install -r requirements.txt --force-reinstall

# Check environment variables
cat .env

Frontend build errors

# Clear cache and reinstall
rm -rf node_modules package-lock.json
npm install

# Check Node version
node --version  # Should be 18+

Database connection issues

• Verify Supabase URL and key
• Check network connectivity
• Ensure database is not paused

File upload issues

• Check file size limits
• Verify supported file formats
• Ensure uploads/ directory exists

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👨‍💻 Author

Likhith Vasireddy

• GitHub: @Likhith623

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