Retrieval-Augmented Generation (RAG) with LangChain : URL and PDF

With Retrieval-Augmented Generation (RAG), the LangChain framework provides chat interaction with RAG by extracting information from URL or PDF sources using OpenAI embedding and Gemini LLM (Large Language Model).

How to Use

To run the project on your local machine, follow these steps:

1. Clone the project repository:

   git clone https://github.com/serkanyasr/RAG-with-LangChain-URL-PDF.git

2. Navigate to the project directory:

   cd RAG-with-LangChain-URL-PDF

3. Install the required dependencies:

   pip install -r requirements.txt

4. Run the application:

   streamlit run app.py

RAG-with-LangChain-URL-PDF.mp4

Configuration

• Update API keys and environment in app.py.

Contributing

If you'd like to contribute to this project, please follow these guidelines:

1. Fork the repository.
2. Create a new branch for your feature: git checkout -b feature-name.
3. Make your changes and commit them: git commit -m 'Add some feature'.
4. Push to the branch: git push origin feature-name.
5. Submit a pull request.

License

This project is licensed under the MIT License.