This project is a Generative AI-powered web application that prioritizes the Retrieval-Augmented Generation (RAG) pipeline. It leverages Google's gemini-1.5-flash model to enable question and answer functionality with custom knowledge provided by the user. The application includes customized authentication and a message retrieval option for an enhanced user experience.
- Generative AI Model: Utilizes Google's
gemini-1.5-flashmodel for accurate and context-aware responses. - RAG Pipeline: Combines retrieval and generation capabilities to provide precise answers based on provided documents.
- Custom Knowledge Integration: Users can upload custom documents (PDFs) that the AI will use to generate responses.
- Web Application: A user-friendly web interface built with Django.
- Authentication: Customized user authentication for secure access.
- Message Retrieval: Users can retrieve previous interactions and answers.
- Python 3.8 or later
- Django
- Streamlit
- LangChain
- Pandas
- FAISS
- PyPDF2
- dotenv
- Bootstrap (for front-end styling)
- Sign Up / Log In: Access the application and either sign up for a new account or log in if you already have one.
- Upload Document: Navigate to the upload section and upload your PDF document.
- Ask Questions: Enter your question in the input field and get answers based on the content of the uploaded document.
- Retrieve Messages: View previous interactions and responses.
Contributions are welcome! Please fork the repository and create a pull request with your changes.
This project is licensed under the MIT License.
For any questions or suggestions, feel free to contact the project maintainer at [lokeshsinha746@gmail.com].