Skip to content

Latest commit

 

History

History
105 lines (68 loc) · 3.01 KB

README.md

File metadata and controls

105 lines (68 loc) · 3.01 KB
Raw

Information Retrieval from Multiple PDF 💁💬 with PaLM2

Overview

The Information Retrieval from Multiple PDF app is a Streamlit-based application that allows users to upload and interact with multiple PDF files. Using PaLM2, LangChain, and FAISS, the app enables users to ask questions about the content of the PDFs and receive accurate, contextually relevant responses.

How to Run?

STEPS:

  1. Clone the Repository

    Project repo: https://github.com/Amartya-007/PDF-Reader-App

    git clone https://github.com/Amartya-007/PDF-Reader-App.git
cd PDF-Reader-App

  2. Create a Conda Environment

    After opening the repository, create and activate a Conda environment:

    conda create -n llmapp python=3.8 -y
conda activate llmapp

  3. Install the Requirements

    Install the necessary Python packages:

    pip install -r requirements.txt

  4. Set Up Environment Variables

    Create a .env file in the root directory and add your Google API key. You can generate an API key by following the instructions at Google PaLM API Setup:

    GOOGLE_API_KEY= "xxxxxxxxxxxxxxxxxxxxxxxxxxxxx"

  5. Run the App

    Finally, run the Streamlit application:

    streamlit run app.py

  6. Open the App

    Open your web browser and navigate to:

    http://localhost:8501

Usage

  1. Upload PDF Files

    • Use the sidebar to upload one or more PDF files.
    • Click the "Submit" button to process the files.

  2. Ask Questions

    • Once the PDFs are processed, enter your question in the text input box.
    • The app will provide answers based on the content of the uploaded PDFs.

  3. View Chat History

    • The chat history displays the questions you’ve asked and the app’s responses, simulating a conversation interface.

Project Structure

PDF-Reader-App/
│
├── src/
│   ├── helper.py            # Contains helper functions for text extraction, chunking, vector store, and conversational chain.
│
├── app.py                   # Main Streamlit app file.
├── requirements.txt         # Python dependencies.
├── .env                     # Environment variables.
└── README.md                # Project documentation.

Technologies Used

  • Python: Programming language used for the app.
  • LangChain: For managing conversational AI and text processing.
  • Streamlit: For creating the web application.
  • PaLM2: For generating embeddings and handling language model responses.
  • FAISS: For efficient vector search.

Contributing

Contributions are welcome! If you have suggestions or improvements, please create a pull request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contact

If you have any questions or feedback, feel free to reach out to me at [your-email@example.com].