RAG Chatbot Application

Description

This Streamlit application creates a pedagogical chatbot augmented by document retrieval (RAG - Retrieval Augmented Generation). The chatbot uses texts extracted from PDF documents and images uploaded by the user to respond to context-based questions. It is a valuable tool for education and quick information retrieval from various documents.

Features

• Upload and process multiple PDF files and images.
• Extract text from PDFs and images.
• Generate contextual responses based on the extracted content.

Installation

To install and run this application, follow these steps:

1. Prerequisites:

   • Python 3.10 or newer.
   • Tesseract-OCR for text extraction from images.

2. Installing Tesseract-OCR: On Ubuntu:

   sudo apt update
   sudo apt install tesseract-ocr

   On Windows, download and install Tesseract from this link, and make sure to add the path to the Tesseract executable to your PATH environment variable.

3. Clone the repository or download the files:

   git clone [URL_OF_REPO]
   cd rag3

4. Install Python dependencies:

   pip install -r requirements.txt

5. Configure environment variables: Copy the .env.sample file to .env and adjust the necessary values:

   cp .env.sample .env

6. Launch the application:

   streamlit run app.py

Usage

• Launch the application and navigate through the Streamlit interface.
• Use the file uploader to upload PDF documents and images.
• Pose your questions to the chatbot via the user interface to receive answers based on the content of the uploaded documents.

Development

• app.py: The main application file, modify it to adjust the user interface or high-level logic.
• models.py: Contains logic for file processing and response generation; modify this file to adjust processing or interaction algorithms.

Support

For assistance or to report issues, open an issue in the application's GitHub repository or contact the developer by email.