Chat with PDF Documents
A powerful RAG (Retrieval-Augmented Generation) chat application built with Streamlit, LlamaIndex, and Nebius AI's Qwen3 model. This application allows users to upload PDF documents and interact with them through an AI-powered chat interface.
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
Prerequisites
- Python 3.10
- Nebius AI API Keys
Installation
1. Clone the repository
git clone https://github.com/nebius/token-factory-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
Running
- Run the Streamlit application:
If using uv
uv run streamlit run main.py
If using pip
streamlit run main.py
-
Open your web browser and navigate to the provided local URL (typically http://localhost:8501)
-
Upload a PDF document using the sidebar
-
Start chatting with your document!
Features in Detail
Document Processing
- Supports PDF file uploads
- Real-time document preview in the sidebar
- Automatic document indexing using LlamaIndex
Chat Interface
- Clean and intuitive chat UI
- Support for multiple message types
- Clear chat history functionality
- Expandable AI reasoning display
Model Options
- Primary: Qwen3-235B-A22B
- Alternative: DeepSeek-V3
- Embedding Model: BAAI/bge-en-icl
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
Contributing
Feel free to submit issues and enhancement requests!
Credits
This example is adopted with thanks from here