QnA Chatbot Using PDF

Overview

This project is focused on developing a Question and Answer (QnA) Chatbot that utilizes PDF documents to answer queries. It provides an interactive interface for users to ask questions and receive relevant answers extracted from PDF content.

Features

• Chatbot GUI: An interactive Graphical User Interface.
• PDF Question Answering: Process and answer questions based on PDF content.
• Model Training Notebooks: Jupyter notebooks for training sentence transformer models.
• Trained Models: Pre-trained models using sentence transformers.

Contents

• CHATBOT.pptx: PowerPoint presentation detailing the chatbot project.
• Chatbot_GUI: Files for the Chatbot's graphical user interface.
• Model_trained: Trained weights of the transformer models.
• pytorch_sbert_training.ipynb: Jupyter notebook for training a model using PyTorch and SBERT.
• requirements.txt: Lists necessary Python packages.
• sbert_test_c: Additional resources or tests related to SBERT.
• sentence_transformer_training.ipynb: Notebook for training sentence transformer models.
• testing.ipynb: Notebook for testing and evaluating the models.

Installation

1. Clone this repository.
2. Install required packages using pip install -r requirements.txt.

Usage

Chatbot GUI:

• Navigate to the Chatbot_GUI directory.
• Start the application following the provided instructions.
• The Chatbot GUI is ready for use upon launch.

Sentence Model and GPT API Key:

• Pre-trained sentence model hosted on Hugging Face is integrated within the Chatbot via LangChain.
• Responses are formulated after comparing embeddings with the database content.
• GPT API is used with a secret key (free trial version). Update the key in utils.py at the start of the Chatbot_GUI folder.

Working with PDFs:

• Chatbot allows PDF uploads.
• Ask questions based on the PDF's content.
• Chatbot processes queries and returns answers extracted from the uploaded PDF.

Model Training and Weights

Model Weights

• Model_trained Folder: Contains weights for a transformer model, trained for understanding user queries.
• sbert_train Folder: Stores weights for another model, possibly fine-tuned for processing responses from PDFs.

Training Notebooks

• pytorch_sbert_training.ipynb: Details the training process of the model in Model_trained, using PyTorch and SBERT.
• sentence_transformer_training.ipynb: Corresponds to the model in sbert_train, outlining the training approach and parameters used.

Integrating Models with Chatbot

• Models are crucial for the chatbot's functionality, with one handling user queries and the other processing PDF content to generate responses.

Using Only Chatbot Feature

Chatbot GUI:

• To use the Chatbot feature without model training, navigate to the Chatbot_GUI directory.
• Start the application following the provided instructions.
• The Chatbot GUI is ready for immediate use upon launch.

Sentence Model and GPT API Key:

• Pre-trained sentence model integrated within the Chatbot via LangChain.
• Responses are formulated after comparing embeddings with the database content, then sent through the GPT API using a secret key.
• Note: The provided GPT API key is a free trial version and may need updating. Locate the key at the start of utils.py in the Chatbot_GUI folder to replace it with a new trial or paid version.

Contributions

Contributions to this project are welcome. Please follow the standard fork-and-pull request workflow.

License

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