📚 RAG Document Analyzer

A powerful Retrieval-Augmented Generation (RAG) application that transforms your documents into intelligent, interactive conversations. Built with Streamlit, Google Gemini AI, and FAISS vector database for accurate document analysis and question-answering.

🌟 Features

• Multi-Format Support: Process PDF, DOCX, and TXT files
• Intelligent Q&A: Ask questions and get contextual answers from your documents
• RAG Architecture: Combines document retrieval with AI generation for accurate responses
• Beautiful UI: Modern, responsive interface with intuitive design
• Real-time Processing: Stream responses for better user experience
• Ethical Guidelines: Built-in safeguards for responsible AI usage

🔧 Technology Stack

• Frontend: Streamlit
• AI Model: Google Gemini 1.5 Flash
• Embeddings: Google Generative AI Embeddings
• Vector Database: FAISS (Facebook AI Similarity Search)
• Document Processing: PyPDF2, python-docx
• Text Processing: LangChain

🚀 Quick Start

Prerequisites

• Python 3.8 or higher
• Google API Key for Gemini AI

Installation

1. Clone the repository

   git clone https://github.com/alaaashraf24/rag-document-analyzer.git
   cd rag-document-analyzer

2. Install dependencies

   pip install -r requirements.txt

3. Set up your Google API Key

   Option 1: Streamlit Secrets (Recommended for deployment) Create a .streamlit/secrets.toml file:

   GOOGLE_API_KEY = "your_google_api_key_here"

   Option 2: Environment Variable Create a .env file:

   GOOGLE_API_KEY=your_google_api_key_here

4. Run the application

   streamlit run app.py

Getting Your Google API Key

1. Go to Google AI Studio
2. Sign in with your Google account
3. Click "Create API Key"
4. Copy the generated key and use it in your configuration

📖 How to Use

1. Upload Documents: Use the sidebar to upload PDF, DOCX, or TXT files
2. Process Documents: Click "🔄 Process Documents" to create embeddings
3. Ask Questions: Type your questions in the chat interface
4. Get Answers: Receive contextual answers based on your documents

Example Usage

Upload: research_paper.pdf, meeting_notes.docx
Question: "What are the main findings in the research paper?"
Answer: [AI provides summary based on document content]

🎯 Use Cases

• 📚 Research & Literature Review: Analyze academic papers and research documents
• 📝 Document Summarization: Extract key insights from lengthy documents
• 🎓 Educational Support: Understand complex materials and textbooks
• 📁 Content Organization: Query and organize large document collections
• 🔍 Information Extraction: Find specific information across multiple documents

⚡ Key Features Explained

RAG Architecture

The application uses Retrieval-Augmented Generation to:

1. Chunk Documents: Break documents into manageable pieces
2. Create Embeddings: Generate vector representations of text chunks
3. Store in FAISS: Use efficient similarity search
4. Retrieve Context: Find relevant chunks for user queries
5. Generate Answers: Use Google Gemini with retrieved context

Ethical AI Usage

• Built-in guidelines for responsible usage
• Designed to enhance learning, not replace it
• Encourages proper citation and academic integrity
• Prevents misuse for academic dishonesty

🛠️ Configuration

Supported File Types

• PDF: .pdf files
• Word Documents: .docx files
• Text Files: .txt files

Customizable Parameters

• Chunk Size: 1000 characters (configurable in code)
• Chunk Overlap: 200 characters
• Similarity Search: Top 5 relevant chunks (k=5)
• Model: Google Gemini 1.5 Flash

📁 Project Structure

rag-document-analyzer/
│
├── app.py                 # Main Streamlit application
├── requirements.txt       # Python dependencies
├── README.md             # Project documentation
├── .streamlit/
│   └── secrets.toml      # Streamlit secrets (create this)
└── .env                  # Environment variables (optional)

🔒 Security & Privacy

• API Keys: Store securely using Streamlit secrets or environment variables
• Document Privacy: Documents are processed locally and not stored permanently
• No Data Retention: Chat history is session-based only
• Secure Processing: Uses Google's secure AI APIs

🚀 Deployment

Deploy on Streamlit Cloud

1. Push your code to GitHub
2. Connect your GitHub repo to Streamlit Cloud
3. Add your GOOGLE_API_KEY in the Streamlit Cloud secrets
4. Deploy with one click!

Local Development

# Install in development mode
pip install -e .

# Run with debug mode
streamlit run app.py --logger.level=debug

📊 System Requirements

• Memory: Minimum 4GB RAM (8GB recommended for large documents)
• Storage: 1GB free space for dependencies
• Internet: Required for Google AI API calls
• Browser: Modern web browser (Chrome, Firefox, Safari, Edge)

Built with ❤️ using Streamlit and Google Gemini AI

Transform your documents into intelligent conversations today!