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PDF-ingestion-QnA-App

Overview

The PDF-ingestion-QnA-App based on RAG(Retrieval-augmented generation) is a powerful application that allows users to upload PDF documents and ask questions based on the content of those documents. Leveraging advanced language models and retrieval-augmented generation techniques, this application provides concise and accurate answers from the uploaded PDFs.

Screenshot

Features

  • Upload and process PDF documents.
  • Ask questions about the content of the uploaded PDFs.
  • Efficient text splitting and embedding using state-of-the-art models.
  • In-memory vector storage for fast retrieval of document chunks.
  • User-friendly interface built with Streamlit.

Technologies Used

  • Python: The programming language used for the implementation.
  • LangChain: A framework for developing applications with language models.
  • Streamlit: A library for building interactive web applications.
  • Hugging Face: For pre-trained models and embeddings.
  • Groq: For efficient model execution.

Installation

To get started with the PDF Q&A System, follow these steps:

  1. Clone the repository: 
    git clone https://github.com/vishnun0027/PDF-ingestion-QnA-App.git
PDF-ingestion-QnA-App

2.Install the required packages:

   pip install -r requirements.txt
  1. Set up environment variables: Create a .env file in the root directory of the project and add your API keys:
  GROQ_API_KEY=your_groq_api_key
  HF_API_KEY=your_hugging_face_api_key

Usage

  1. Run the Streamlit application:

  2. run app

   streamlit run app.py

  1. Upload a PDF: Use the sidebar to upload your PDF document.

  2. Ask Questions: After processing, enter your questions in the chat input field to receive answers based on the content of the PDF.