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Features

  • PDF Document Upload and Preview
  • Interactive Chat Interface
  • Powered by Qwen3-235B-A22B Model
  • Advanced RAG Implementation using LlamaIndex
  • High-quality Embeddings with BAAI/bge-en-icl
  • Real-time Document Processing
  • Transparent AI Reasoning Display

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Prerequisites

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Setup

  1. Clone the repository: 
    git   clone    https://github.com/nebius/ai-studio-cookbook/
cd rag/chat-with-pdf
  2. Install the required packages: If using uv : 
    uv sync
    If using pip: 
    pip install -r requirements.txt
  3. Set up your environment variables: Create a .env file in the project root and add your Nebius API key: 
    NEBIUS_API_KEY=your_api_key_here

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Running

  1. Run the Streamlit application: If using uv : 
    uv run streamlit  run main.py
    If using pip: 
    streamlit run main.py
  2. Open your web browser and navigate to the provided local URL (typically http://localhost:8501)
  3. Upload a PDF document using the sidebar
  4. Start chatting with your document!

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Features in Detail

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Document Processing

  • Supports PDF file uploads
  • Real-time document preview in the sidebar
  • Automatic document indexing using LlamaIndex

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Chat Interface

  • Clean and intuitive chat UI
  • Support for multiple message types
  • Clear chat history functionality
  • Expandable AI reasoning display

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Model Options

  • Primary: Qwen3-235B-A22B
  • Alternative: DeepSeek-V3
  • Embedding Model: BAAI/bge-en-icl

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Architecture

The application uses a combination of:
  • Streamlit for the web interface
  • LlamaIndex for document processing and RAG implementation
  • Nebius AI’s models for embeddings and generation
  • PyPDF2 for PDF handling

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Contributing

Feel free to submit issues and enhancement requests!

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Credits

This example is adopted with thanks from here