commit

README.md

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----
-title: Enterprise Rag Assistant
-emoji: 🚀
-colorFrom: red
-colorTo: red
-sdk: docker
-app_port: 8501
-tags:
-- streamlit
-pinned: false
-short_description: 'Enterprise AI Assistant with RAG - Complete Implementation '
-license: mit
----
+# 📄 Enterprise Rag Assistant with IBM Granite
+
+A powerful Retrieval-Augmented Generation (RAG) application that allows you to upload PDF documents and ask intelligent questions about their content using IBM's Granite AI model.
+
+## 🌟 Features
+
+- **PDF Text Extraction**: Extract text from PDF documents with detailed progress tracking
+- **Intelligent Chunking**: Split documents into manageable chunks with overlap for better context preservation
+- **Semantic Search**: Find relevant content using advanced sentence embeddings
+- **AI-Powered Q&A**: Generate accurate answers using IBM Granite language model
+- **Interactive UI**: User-friendly Streamlit interface with real-time status updates
+- **GPU/CPU Support**: Automatically detects and utilizes available hardware
+- **Memory Optimization**: Efficient processing for large documents
+
+## 🚀 Quick Start
+
+### Prerequisites
+
+- Python 3.8 or higher
+- pip package manager
+- At least 4GB RAM (8GB+ recommended)
+- Optional: CUDA-compatible GPU for faster processing
+
+### Installation
+
+1. **Clone the repository:**
+```bash
+git clone https://huggingface.co/spaces/SimranShaikh/enterprise-rag-assistant.git
+cd pdf-rag-granite
+```
+
+2. **Create a virtual environment:**
+```bash
+python -m venv venv
+source venv/bin/activate  # On Windows: venv\Scripts\activate
+```
+
+3. **Install dependencies:**
+```bash
+pip install -r requirements.txt
+```
+
+4. **Run the application:**
+```bash
+streamlit run app.py
+```
+
+5. **Open your browser** and navigate to `http://localhost:8501`
+
+## 📦 Dependencies
+
+Create a `requirements.txt` file with the following content:
+
+```txt
+streamlit>=1.28.0
+PyPDF2>=3.0.1
+sentence-transformers>=2.2.2
+transformers>=4.30.0
+torch>=2.0.0
+numpy>=1.24.0
+scikit-learn>=1.3.0
+```
+
+## 🔧 Usage
+
+### Step 1: Load Models
+1. Click the **"🤖 Load Models"** button
+2. Wait for the models to download and load (this may take a few minutes on first run)
+3. Models are cached locally for faster subsequent loads
+
+### Step 2: Upload PDF
+1. Click **"Browse files"** and select your PDF document
+2. Supported formats: PDF files only
+3. Maximum recommended size: 50MB
+
+### Step 3: Process PDF
+1. Click **"📖 Process PDF"** after uploading
+2. The system will:
+   - Extract text from all pages
+   - Split text into overlapping chunks
+   - Generate embeddings for semantic search
+   - Display processing progress
+
+### Step 4: Ask Questions
+1. Type your question in the text input field
+2. Click **"🔍 Get Answer"**
+3. View the AI-generated answer and source references
+
+### Example Questions
+- "What is the main topic of this document?"
+- "Summarize the key findings"
+- "What are the recommendations mentioned?"
+- "Who are the main authors or contributors?"
+- "What methodology was used?"
+
+## 🏗️ Architecture
+
+```
+┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
+│   PDF Upload    │───▶│  Text Extraction │───▶│   Text Chunking │
+└─────────────────┘    └──────────────────┘    └─────────────────┘
+                                                         │
+┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
+│   User Query    │───▶│ Semantic Search  │◀───│   Embeddings    │
+└─────────────────┘    └──────────────────┘    └─────────────────┘
+         │                       │
+         │              ┌─────────────────┐
+         └─────────────▶│  Answer Gen.    │
+                        │ (IBM Granite)   │
+                        └─────────────────┘
+```
+
+## 🔧 Configuration
+
+### Model Configuration
+
+You can modify the models used in the `SimplePDFRAG` class:
+
+```python
+# Embedding model options
+embedding_model = SentenceTransformer('all-MiniLM-L6-v2')  # Default
+# embedding_model = SentenceTransformer('all-mpnet-base-v2')  # Better quality
+
+# Language model options
+model_name = "ibm-granite/granite-3-2b-instruct"  # Default
+# model_name = "ibm-granite/granite-3-8b-instruct"  # Better performance
+# model_name = "google/flan-t5-base"  # Alternative
+```
+
+### Chunking Parameters
+
+Adjust text chunking settings:
+
+```python
+def chunk_text(self, text, chunk_size=400, overlap=50):
+    # chunk_size: Number of words per chunk
+    # overlap: Number of overlapping words between chunks
+```
+
+### Search Parameters
+
+Modify search behavior:
+
+```python
+def search_documents(self, query, top_k=3):
+    # top_k: Number of relevant chunks to retrieve
+    # min_threshold: Minimum similarity score (0.1 default)
+```
+
+## 📊 Performance Tips
+
+### For Better Performance:
+- Use a GPU-enabled environment
+- Increase chunk overlap for better context
+- Use larger language models (8B+ parameters)
+- Process smaller PDF files (< 20MB)
+
+### Memory Management:
+- The app automatically manages GPU memory
+- Use the "Reset Everything" button to clear memory
+- Process one PDF at a time for optimal performance
+
+## 🐛 Troubleshooting
+
+### Common Issues:
 
-# Welcome to Streamlit!
+**1. Models not loading:**
+```
+Error: Model loading failed
+```
+- **Solution**: Check internet connection and try again
+- **Alternative**: Use smaller models or CPU-only mode
 
-Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
+**2. PDF text extraction fails:**
+```
+Error: No text could be extracted
+```
+- **Solution**: Ensure PDF contains selectable text (not just images)
+- **Alternative**: Use OCR preprocessing tools
+
+**3. Out of memory errors:**
+```
+Error: CUDA out of memory
+```
+- **Solution**: Reduce batch size or use CPU mode
+- **Alternative**: Process smaller documents
+
+**4. Slow processing:**
+- **Solution**: Enable GPU acceleration
+- **Alternative**: Use smaller embedding models
+
+### Debug Mode
+
+Enable debug logging by setting:
+```python
+logging.basicConfig(level=logging.DEBUG)
+```
+
+## 🚀 Deployment
+
+### Local Development
+```bash
+streamlit run app.py
+```
+
+### Docker Deployment
+```dockerfile
+FROM python:3.9-slim
+
+WORKDIR /app
+COPY requirements.txt .
+RUN pip install -r requirements.txt
+
+COPY . .
+EXPOSE 8501
+
+CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+```
+
+### Cloud Deployment
+
+**Streamlit Cloud:**
+1. Push code to GitHub
+2. Connect repository to Streamlit Cloud
+3. Deploy with one click
+
+**Heroku:**
+```bash
+git init
+heroku create your-app-name
+git add .
+git commit -m "Initial commit"
+git push heroku main
+```
+
+## 📈 Advanced Features
+
+### Custom Models
+
+Add support for custom models:
+
+```python
+def load_custom_model(self, model_path):
+    """Load a custom trained model"""
+    self.granite_model = AutoModelForCausalLM.from_pretrained(model_path)
+    self.tokenizer = AutoTokenizer.from_pretrained(model_path)
+```
+
+### Batch Processing
+
+Process multiple PDFs:
+
+```python
+def process_multiple_pdfs(self, pdf_files):
+    """Process multiple PDFs simultaneously"""
+    all_documents = []
+    all_embeddings = []
+    
+    for pdf_file in pdf_files:
+        # Process each PDF
+        documents, embeddings = self.process_single_pdf(pdf_file)
+        all_documents.extend(documents)
+        all_embeddings.extend(embeddings)
+    
+    return all_documents, all_embeddings
+```
+
+### Export Results
+
+Save Q&A sessions:
+
+```python
+def export_qa_session(self, qa_pairs, filename):
+    """Export Q&A session to file"""
+    import json
+    with open(filename, 'w') as f:
+        json.dump(qa_pairs, f, indent=2)
+```
+
+## 🤝 Contributing
+
+We welcome contributions! Please follow these steps:
+
+1. **Fork the repository**
+2. **Create a feature branch:**
+   ```bash
+   git checkout -b feature/amazing-feature
+   ```
+3. **Make your changes and commit:**
+   ```bash
+   git commit -m "Add amazing feature"
+   ```
+4. **Push to your branch:**
+   ```bash
+   git push origin feature/amazing-feature
+   ```
+5. **Create a Pull Request**
+
+### Development Guidelines
+
+- Follow PEP 8 style guidelines
+- Add docstrings to all functions
+- Include unit tests for new features
+- Update documentation as needed
+
+## 📝 License
+
+This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
+
+```
+MIT License
+
+Copyright (c) 2024 Your Name
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+```
+
+## 🙏 Acknowledgments
+
+- **IBM** for the Granite language models
+- **Hugging Face** for the transformers library
+- **Sentence Transformers** for embedding models
+- **Streamlit** for the web framework
+- **PyPDF2** for PDF processing
+
+
+---
 
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+**⭐ If you find this project helpful, please consider giving it a star on GitHub!**