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ChatPDF

This project is a Document Retrieval application that utilizes Retrieval-Augmented Generation (RAG) techniques to enable users to interact with uploaded PDF documents. By leveraging a Large Language Model (LLM), users can ask questions about the content of the documents and receive accurate answers based on the information retrieved.

Features

  • PDF Upload: Users can upload PDF files for processing.
  • AI Interaction: Ask questions about the content of the uploaded PDFs.
  • Machine Learning Integration: Utilizes advanced machine learning models for document processing and question answering.

Technologies Used

  • Backend: FastAPI
  • Frontend: Streamlit
  • Machine Learning: Langchain, Hugging Face Transformers
  • Vector Store: FAISS for efficient similarity search

Installation

  1. Clone the repository:

    git clone https://github.com/yourusername/chatpdf.git
cd chatpdf

  2. Create a virtual environment and activate it:

    python -m venv .venv
source .venv/bin/activate  # On Windows use .venv\Scripts\activate

  3. Install the required packages:

    pip install -r requirements.txt

Usage

  1. Start the FastAPI server:

    uvicorn app.main:app --reload

  2. Open the Streamlit app in another terminal:

    streamlit run app/streamlit_app.py

  3. Navigate to http://localhost:8501 in your web browser to access the application.

API Endpoints

  • GET /: Returns a welcome message.

  • POST /upload_pdf/: Uploads a PDF file for processing.

    • Request: Multipart form data with the PDF file.
    • Response: Success message upon successful upload and processing.

  • POST /ask/: Asks a question about the uploaded PDF.

    • Request: JSON body with the question.
    • Response: The answer to the question based on the PDF content.

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Testing

  1. To run the tests, use:

    streamlit run app/streamlit_app.py