📚 RAG Chat Assistant

A simple yet powerful Retrieval-Augmented Generation (RAG) pipeline built with Streamlit that allows you to upload PDF documents and chat with them using OpenAI's GPT models.

✨ Features

• 📄 Multi-PDF Support: Upload and process multiple PDF documents at once
• 🔍 Smart Chunking: Automatically splits documents into optimal chunks for processing
• 💬 Interactive Chat: Natural conversation interface with your documents
• 📖 Source Citations: See exactly which parts of your documents the AI is referencing
• 🚀 Easy Setup: Simple installation process with helpful setup script
• 💾 Session Memory: Maintains conversation history during your session

🛠️ Quick Start

Prerequisites

• Python 3.8 or higher
• OpenAI API key (Get one here)

Installation

1. Clone the repository

   git clone <your-repo-url>
   cd rag-pipeline

2. Run the setup script

   python setup.py

   This will:

   • Check your Python version
   • Install all dependencies
   • Create a .env file from the template

3. Add your OpenAI API key

   • Open the .env file
   • Replace your-openai-api-key-here with your actual API key

4. Run the application

   streamlit run app.py

📖 How to Use

1. Start the App: Run streamlit run app.py and open the provided URL in your browser

2. Upload Documents:

   • Click on "Browse files" in the sidebar
   • Select one or more PDF files
   • Click "🚀 Process Documents"

3. Start Chatting:

   • Once documents are processed, type your question in the chat input
   • The AI will answer based on the content of your uploaded documents
   • Click "View Sources" to see which parts of the documents were used

4. Clear History: Use the "Clear Chat History" button to start a fresh conversation

🔧 Configuration

You can modify these settings in app.py:

• Model: Change gpt-3.5-turbo to gpt-4 for more advanced responses
• Temperature: Adjust the temperature parameter (0-1) for response creativity
• Chunk Size: Modify chunk_size in the text splitter for different document processing
• Retrieved Documents: Change k value in search_kwargs to retrieve more/fewer sources

📁 Project Structure

rag-pipeline/
├── app.py              # Main Streamlit application
├── setup.py            # Setup helper script
├── requirements.txt    # Python dependencies
├── env_example.txt     # Environment variables template
├── .gitignore         # Git ignore file
└── README.md          # This file

🤔 Troubleshooting

"OpenAI API key not found!"

• Make sure you've created a .env file from env_example.txt
• Ensure your API key is correctly added to the .env file

"No module named 'langchain'"

• Run pip install -r requirements.txt to install all dependencies

"Error processing documents"

• Check that your PDFs are not password-protected
• Ensure you have enough OpenAI API credits
• Try with smaller PDF files first

💡 Tips

• Better Results: Upload documents that are related to each other for more coherent conversations
• Specific Questions: Ask specific questions rather than broad ones
• Multiple Sources: Upload multiple documents to create a comprehensive knowledge base
• Cost Management: Monitor your OpenAI API usage to manage costs

🔒 Security Notes

• Never commit your .env file to version control
• Keep your OpenAI API key secure
• The app processes documents locally before sending to OpenAI

🚀 Future Enhancements

Potential improvements you could make:

• Support for more file formats (TXT, DOCX, etc.)
• Persistent vector storage
• User authentication
• Export chat history
• Fine-tuning options
• Local LLM support

📝 License

This project is open source and available under the MIT License.

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Built with ❤️ using Streamlit, LangChain, and OpenAI