Chat with PDF Assistant with Langchain and Streamlit

Welcome to the ChatGPT-Powered PDF Assistant project repository by the DorkyDuos team.

This comprehensive project embarks on a project-based journey where we leverage Langchain and Streamlit to develop an interactive ChatGPT for your PDF documents. With the power of an LLM (Large Language Model) such as OpenAI's ChatGPT, we will create an application that enables you to ask questions about PDFs and receive accurate answers.

Team Members

• Madhav N (Team Leader)
  Branch: B.Tech Information Technology
• Mathangi N
  Branch: B.Tech Computer Science Engineering (AI & DS)

Table of Contents

1. Introduction
2. Abstract
3. Features
4. Tech Stack Used
5. Solution
6. Dataset
7. Working
8. Model Architecture
9. Pipeline
10. Primary Goals & Statistics
11. Business Model
12. Proof of Concept
13. Contact

Introduction

In this project, we'll guide you through building a fully functional Streamlit application. Train GPT on PDF documents and fine-tune it to your specific use case. Experience the seamless user interface as you upload PDFs, ask questions, and receive prompt answers from the LLM.

Abstract

Learn how to harness the power of Langchain, an open-source Python (and JavaScript) framework, to create intelligent applications. Discover Langchain's capabilities in training GPT models on your data and generating personalized LLMs. Explore text embeddings and their integration with Langchain using OpenAI's API.

Features

• Interactive PDF Assistant: Ask questions about your PDF documents and receive accurate answers.
• Leveraging Langchain: Utilize Langchain's capabilities in training GPT models and generating personalized LLMs.
• Streamlit Application: A fully functional Streamlit application for seamless user experience.
• Text Embeddings: Integration with Langchain using OpenAI's API for text embeddings.
• Task Automation: Automate tasks and improve efficiency using Langchain with Streamlit.

Tech Stack Used

• Langchain: For training GPT models and generating personalized LLMs.
• Streamlit: For building the interactive user interface.
• OpenAI's API: For text embeddings and GPT capabilities.
• Python: The primary programming language used for development.

Solution

Objective of the System

• Real-time PDF Interaction: Instantly answers questions about PDF content, enhancing document analysis.
• Customizable Responses: Fine-tune responses based on specific use cases and data.
• Advanced Technologies: Utilizes machine learning, natural language processing, and deep learning to analyze PDF documents.

Dataset

To ensure accuracy and effectiveness, we use a collection of PDF documents with varied content. This dataset enables the model to learn and respond accurately to different types of questions.

Working

1. Upload PDFs: Users upload their PDF documents.
2. Question Input: Users input their questions about the document.
3. LLM Processing: The trained GPT model processes the input and generates answers based on the PDF content.
4. Real-time Response: Users receive accurate and prompt answers.

Model Architecture

1. Input PDF Data
2. Data Preprocessing: Text extraction and embedding generation.
3. Training and Testing: The model is trained and tested on the processed data.
4. Inference: The system makes real-time inferences based on the analysis.
5. Output: Generates accurate answers to user queries.

Pipeline

1. Data Acquisition
2. Data Labeling
3. Data Preprocessing
4. Feature Extraction
5. Model Selection
6. Model Training
7. Model Evaluation
8. Real-Time Inference
9. Answer Generation
10. User-Interface Integration
11. Deployment
12. Monitoring and Maintenance

Primary Goals & Statistics

• Enhanced Document Analysis
• Real-time Interaction
• Feedback Based Improvement
• High Accuracy
• Seamless User Experience
• Adaptive Learning
• Instant Answers
• Comprehensive PDF Understanding

Business Model

• Subscription Model: Offer a subscription-based service to individuals and organizations.
• API Access: Provide an API and charge a fee based on usage.
• Data Insights Subscription: Offer insights and analytics based on collected data.
• Freemium Model: Offer a basic version for free with premium upgrades.
• White Label Solution: Provide a customizable solution for organizations.

Proof of Concept

Our system's proof of concept includes real-time interaction with PDF documents demonstrating the accurate and prompt generation of answers.

Contact

• Madhav N: madhavnarayanan2004@gmail.com
• Mathangi N: mathanginarayanan2004@gmail.com