RAG: Chat with any PDFs 📚

A Streamlit-based application that leverages Retrieval-Augmented Generation (RAG) to allow users to interact with PDF documents. Users can upload PDFs, ask questions about their contents, and receive AI-generated responses based on the uploaded documents.

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Features

• PDF Upload: Upload and view PDFs directly in the app sidebar.
• Text Cleaning: Ensures text integrity by normalizing Unicode and removing invalid characters.
• Text Splitting: Splits large PDF content into manageable chunks for efficient processing.
• Vector Database: Creates and stores embeddings for document chunks using Google Generative AI Embeddings.
• Question Answering: Uses Groq’s llama3-70b-8192 model to answer user queries based on the uploaded PDFs.

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How It Works

1. PDF Upload:

   • Users upload PDFs, which are rendered in the sidebar for easy viewing.
   • The text content of the PDF is extracted and cleaned.

2. Text Processing:

   • The extracted text is split into chunks using the RecursiveCharacterTextSplitter.

3. Embedding and Storage:

   • Each text chunk is embedded using the GoogleGenerativeAIEmbeddings model.
   • The embeddings are stored in a FAISS vector database.

4. Question Answering:

   • Users submit queries through a chat interface.
   • Relevant chunks are retrieved using similarity search.
   • Answers are generated using Groq’s llama3-70b-8192 model.

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Installation

1. Clone the Repository:

   git clone https://github.com/Osama-Abo-Bakr/RAG-Chat-with-any-PDFs.git
   cd RAG-Chat-with-any-PDFs

2. Install Dependencies:

   • Create a virtual environment:

     python -m venv venv
     source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

   • Install required packages:

     pip install -r requirements.txt

3. Set Up Environment Variables:

   • Create a .env file in the project directory and add your credentials:

     GOOGLE_API_KEY=your_google_api_key
     GROQ_API_KEY=your_groq_api_key
     

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Usage

1. Run the Application:

   streamlit run main.py

2. Upload and Interact:

   • Use the sidebar to upload your PDF.
   • Enter your questions in the chat input.
   • View the AI-generated answers in real time.

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Dependencies

• Streamlit: For building the interactive web application.
• LangChain: For document loaders, text splitting, and chains.
• FAISS: For efficient similarity search.
• Google Generative AI: For generating embeddings.
• Groq: For large language model-based question answering.

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Future Improvements

• Add support for multi-file uploads.
• Enhance UI/UX for better user experience.
• Integrate additional document formats (e.g., Word, TXT).
• Allow saving and exporting of chat interactions.

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Contributing

Contributions are welcome! Feel free to fork the repository and submit a pull request.

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License

This project is licensed under the MIT License. See the LICENSE file for details.

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Author

Osama Abo Bakr
GitHub: Osama-Abo-Bakr

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Acknowledgments

• Special thanks to the developers of Streamlit, LangChain, FAISS, Google Generative AI, and Groq for their amazing tools!