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README.md

@@ -1,264 +1,10 @@
 ---
-title: Agentic RAG System
-emoji: 🤖
-license: mit
+title: OrgAI
+emoji: 🏃
+colorFrom: yellow
+colorTo: green
+sdk: docker
+pinned: false
 ---
 
-# 🤖 Agentic RAG System
-
-A production-ready **Retrieval-Augmented Generation (RAG)** system with multi-domain support, advanced AI features, and intelligent web search integration. Built with FastAPI, React, and Google Gemini API.
-
-## ✨ Features
-
-### 🎯 Multi-Domain Support
-- **Medical & Healthcare**: Medical documents, research papers, clinical guidelines
-- **Legal & Compliance**: Legal documents, contracts, regulations, case law
-- **Financial & Analytics**: Financial reports, analysis, market research
-- **Technical Documentation**: Technical docs, APIs, code, architecture
-- **Academic Research**: Research papers, academic publications, studies
-
-### 🚀 Advanced AI Capabilities
-- **Query Improvement**: Automatic query enhancement with abbreviation expansion
-- **Dual-LLM Verification**: Two-stage answer verification using Gemini Pro
-- **Web Search Integration**: Augment answers with real-time web search via Tavily
-- **Conversation Memory**: Context-aware responses with conversation history
-- **Multimodal Processing**: Support for images, tables, and equations (MinerU parser)
-- **Smart Reranking**: Gemini-powered relevance reranking for better results
-- **Streaming Responses**: Real-time token streaming for responsive UX
-
-### 🔧 Technical Features
-- **Gemini API Integration**: Free-tier Gemini Flash & Pro models
-- **Async Processing**: Background document processing with status tracking
-- **RESTful API**: Clean, well-documented FastAPI endpoints
-- **Modern React Frontend**: Beautiful, responsive UI with Tailwind CSS
-- **Docker Support**: One-command deployment with docker-compose
-- **Performance Optimized**: Query caching, fast mode (2-3x speedup), batch processing
-
-## 🚀 Quick Start (Docker)
-
-### Prerequisites
-- Docker and Docker Compose
-- Google Gemini API Key ([Get one free](https://makersuite.google.com/app/apikey))
-- (Optional) Tavily API Key for web search ([Get one free](https://tavily.com))
-
-### 1. Clone the Repository
-```bash
-git clone <your-repo-url>
-cd Agentic_RAG
-```
-
-### 2. Set Up Environment Variables
-Create a `.env` file in the project root:
-```bash
-GEMINI_API_KEY=your_gemini_api_key_here
-GEMINI_TEXT_MODEL=models/gemini-flash-latest
-GEMINI_VERIFIER_MODEL=models/gemini-pro-latest
-GEMINI_VISION_MODEL=models/gemini-flash-latest
-GEMINI_EMBEDDING_MODEL=models/text-embedding-004
-TAVILY_API_KEY=your_tavily_api_key_here  # Optional, for web search
-```
-
-### 3. Start the Application
-```bash
-docker-compose up -d
-```
-
-### 4. Access the Application
-- **Frontend**: http://localhost:3000
-- **Backend API**: http://localhost:8000
-- **API Docs**: http://localhost:8000/docs
-
-## 📖 Usage
-
-### Upload Documents
-1. Navigate to the frontend at http://localhost:3000
-2. Select a domain (Medical, Legal, Financial, Technical, or Academic)
-3. Upload PDF, DOCX, TXT, or other supported documents
-4. Wait for processing to complete (tracked with real-time status)
-
-### Query Documents
-1. Enter your question in the query interface
-2. Select query mode:
-   - **Mix**: Balanced combination of local and global search (recommended)
-   - **Local**: Focused chunk-based search
-   - **Global**: Knowledge graph entity search
-   - **Hybrid**: Advanced combination
-   - **Web**: RAG + real-time web search
-3. Toggle advanced features:
-   - **Query Improvement**: Enhance your query automatically
-   - **Verification**: Dual-LLM quality check
-   - **Web Search**: Augment with real-time web results
-   - **Fast Mode**: 2-3x faster queries (slightly lower quality)
-4. Get streaming responses with sources and confidence scores
-
-### API Usage
-```python
-import requests
-
-# Upload document
-files = {"file": open("document.pdf", "rb")}
-data = {"domain": "medical"}
-response = requests.post("http://localhost:8000/upload", files=files, data=data)
-print(response.json())
-
-# Query documents
-query_data = {
-    "query": "What are the treatment options for hypertension?",
-    "domain": "medical",
-    "mode": "mix",
-    "enable_web_search": False,
-    "fast_mode": False,
-    "return_metadata": True
-}
-response = requests.post("http://localhost:8000/query", json=query_data)
-print(response.json())
-```
-
-## 🏗️ Architecture
-
-```
-Agentic_RAG/
-├── backend/                 # FastAPI backend
-│   ├── main.py             # Main API server
-│   ├── reranker.py         # Gemini-powered reranking
-│   ├── web_search.py       # Tavily web search integration
-│   ├── url_fetcher.py      # URL content fetching
-│   ├── requirements.txt    # Python dependencies
-│   └── Dockerfile          # Backend container
-├── frontend/               # React frontend
-│   ├── src/               # React components
-│   ├── public/            # Static assets
-│   ├── package.json       # Node dependencies
-│   ├── Dockerfile         # Frontend container
-│   └── nginx.conf         # Nginx configuration
-├── storage/               # RAG storage (created at runtime)
-│   ├── medical/          # Medical domain storage
-│   ├── legal/            # Legal domain storage
-│   └── ...               # Other domains
-├── uploads/              # Uploaded documents
-├── docker-compose.yml    # Docker orchestration
-├── Dockerfile            # Hugging Face Space Dockerfile
-└── README.md            # This file
-```
-
-## 🔑 Environment Variables
-
-| Variable | Description | Required | Default |
-|----------|-------------|----------|---------|
-| `GEMINI_API_KEY` | Google Gemini API key | Yes | - |
-| `GEMINI_TEXT_MODEL` | Text generation model | No | `models/gemini-flash-latest` |
-| `GEMINI_VERIFIER_MODEL` | Verification model | No | `models/gemini-pro-latest` |
-| `GEMINI_VISION_MODEL` | Vision processing model | No | `models/gemini-flash-latest` |
-| `GEMINI_EMBEDDING_MODEL` | Embedding model | No | `models/text-embedding-004` |
-| `TAVILY_API_KEY` | Tavily web search API key | No | - |
-
-## 📊 API Endpoints
-
-### Health Check
-```bash
-GET /health
-```
-
-### List Domains
-```bash
-GET /domains
-```
-
-### Upload Document
-```bash
-POST /upload
-Content-Type: multipart/form-data
-
-file: <document file>
-domain: medical
-```
-
-### Query Documents (Streaming)
-```bash
-POST /query/stream
-Content-Type: application/json
-
-{
-  "query": "What are the treatment options?",
-  "domain": "medical",
-  "mode": "mix",
-  "enable_web_search": false,
-  "fast_mode": false
-}
-```
-
-### Query Documents (Standard)
-```bash
-POST /query
-Content-Type: application/json
-
-{
-  "query": "What are the treatment options?",
-  "domain": "medical",
-  "mode": "mix"
-}
-```
-
-### Check Processing Status
-```bash
-GET /status/{processing_id}
-```
-
-### List Documents
-```bash
-GET /documents?domain=medical
-```
-
-### Delete Document
-```bash
-DELETE /documents/{doc_id}
-```
-
-## 🎯 Performance
-
-- **Fast Mode**: 2-3x faster queries with optimized parameters
-- **Query Caching**: 5-minute TTL cache for repeated queries
-- **Batch Processing**: Parallel document processing (up to 10 documents)
-- **Streaming**: Real-time token streaming for responsive UX
-- **Reranking**: Gemini-powered relevance scoring
-
-## 🛠️ Development
-
-### Backend Development
-```bash
-cd backend
-pip install -r requirements.txt
-python main.py
-```
-
-### Frontend Development
-```bash
-cd frontend
-npm install
-npm start
-```
-
-## 🤝 Contributing
-
-Contributions are welcome! Please feel free to submit a Pull Request.
-
-## 📝 License
-
-This project is licensed under the MIT License - see the LICENSE file for details.
-
-## 🙏 Acknowledgments
-
-- [LightRAG](https://github.com/HKUDS/LightRAG) - RAG framework
-- [Google Gemini](https://ai.google.dev/) - LLM and embeddings
-- [Tavily](https://tavily.com/) - Web search API
-- [MinerU](https://github.com/opendatalab/MinerU) - Document parsing
-- [FastAPI](https://fastapi.tiangolo.com/) - Backend framework
-- [React](https://react.dev/) - Frontend framework
-
-## 📧 Support
-
-For issues and questions, please open an issue on GitHub.
-
----
-
-Built with ❤️ using FastAPI, React, and Google Gemini
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference