https://github.com/pythontogo/rag_chatbot
A Streamlit-based chatbot that uses RAG (Retrieval-Augmented Generation) to answer questions about uploaded PDF documents.
https://github.com/pythontogo/rag_chatbot
openai rag-chatbot streamlit
Last synced: about 2 months ago
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A Streamlit-based chatbot that uses RAG (Retrieval-Augmented Generation) to answer questions about uploaded PDF documents.
- Host: GitHub
- URL: https://github.com/pythontogo/rag_chatbot
- Owner: PythonToGo
- Created: 2025-06-21T23:02:41.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-06-21T23:21:50.000Z (about 1 year ago)
- Last Synced: 2025-06-22T00:18:49.367Z (about 1 year ago)
- Topics: openai, rag-chatbot, streamlit
- Language: Python
- Homepage:
- Size: 16.6 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# PDF RAG Chatbot
## just for fun; not yet deployed;
A Streamlit-based chatbot that uses RAG (Retrieval-Augmented Generation) to answer questions about uploaded PDF documents.
## Features
- **PDF Upload & Processing**: Upload PDF files and automatically process them for RAG
- **Vector Storage**: Uses FAISS for efficient document retrieval
- **RAG Integration**: Leverages OpenAI's GPT models for intelligent question answering
- **PDF Visualization**: View PDF pages as images alongside responses
- **Context Display**: See the source documents used to generate answers
## Project Structure
```
chatbot/
├── src/
│ ├── config/
│ │ ├── __init__.py
│ │ └── settings.py # Configuration and constants
│ ├── core/
│ │ ├── __init__.py
│ │ ├── document_processor.py # PDF processing and vector storage
│ │ └── rag_chain.py # RAG chain implementation
│ ├── utils/
│ │ ├── __init__.py
│ │ └── pdf_converter.py # PDF to image conversion
│ ├── ui/
│ │ ├── __init__.py
│ │ └── streamlit_app.py # Streamlit UI implementation
│ └── __init__.py
├── data/
│ ├── temp_pdfs/ # Temporary PDF storage
│ ├── vector_store/ # FAISS vector database
│ └── pdf_images/ # Converted PDF images
├── tests/ # Test files
├── docs/ # Documentation
├── main.py # Application entry point
├── requirements.txt # Python dependencies
└── README.md # This file
```
## Installation
1. Clone the repository:
```bash
git clone https://github.com/PythonToGo/rag_chatbot.git
cd chatbot
```
2. Create a virtual environment:
```bash
python -m venv venv
source venv/bin/activate
```
3. Install dependencies:
```bash
pip install -r requirements.txt
```
4. Set up environment variables:
Create a `.env` file in the root directory with your OpenAI API key:
```
OPENAI_API_KEY=your_openai_api_key_here
```
## Usage
1. Run the application:
```bash
streamlit run main.py
```
2. Open your browser and navigate to the provided URL (usually `http://localhost:8501`)
3. Upload a PDF file using the file uploader
4. Ask questions about the uploaded PDF in the text input field
5. View the generated responses and related document context
## Configuration
You can modify the application settings in `src/config/settings.py`:
- **Model Settings**: Change the embedding and chat models
- **Document Processing**: Adjust chunk size and overlap
- **Retrieval Settings**: Modify the number of retrieved documents
- **Image Conversion**: Change DPI settings for PDF to image conversion
## Dependencies
- **Streamlit**: Web application framework
- **LangChain**: RAG framework and document processing
- **OpenAI**: Language models and embeddings
- **FAISS**: Vector similarity search
- **PyMuPDF**: PDF processing and image conversion
## Development
### Running Tests
```bash
# Add test files to the tests/ directory
python -m pytest tests/
```
### Code Structure
The application follows a modular architecture:
- **DocumentProcessor**: Handles PDF loading, chunking, and vector storage
- **RAGChain**: Manages the RAG pipeline and question processing
- **PDFConverter**: Converts PDF pages to images for display
- **StreamlitApp**: Main UI application with clean separation of concerns
### Adding New Features
1. Create new modules in the appropriate directory (`core/`, `utils/`, `ui/`)
2. Update configuration in `src/config/settings.py` if needed
3. Add tests in the `tests/` directory
4. Update this README with new features
## License
MIT License,
Copyright PythonToGo 2025.