This application allows users to upload PDF files, enter a search query, and receive relevant text snippets from the PDF as search results.
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
- Docker
- Python 3.8 or higher (if running outside Docker)
A step-by-step series of examples that tell you how to get a development environment running.
- Clone the repository to your local machine.
- Navigate to the cloned directory.
docker build -t pdf-search-app .
docker run -p 8501:8501 pdf-search-app
git clone https://github.com/abdullah-alnahas/pdf-ai/
cd pdf-ai
pip install poetry==1.7.1
poetry install
poetry run streamlit run app.py
- Navigate to
http://localhost:8501
in your web browser. - Upload a PDF file using the file uploader.
- Enter your search query in the text box.
- Click on the "Search" button to perform the search.
- View the search results displayed on the page.
- Streamlit - The web framework used
- Qdrant - Vector search engine for embedding storage and search
- Docker - Containerization
- Abdullah Al Nahas (BAKIRCI)
This project is licensed under the MIT License - see the LICENSE.md file for details