Question and Answer Generation App using LLM: Elevate Your Exam Prep with Generative AI 📚🤖

In this informative video, the host guides viewers through the process of building a Question and Answer (Q&A) Generation App using Large Language Models (LLMs). The app is designed to be beneficial for teachers, professors, students, and anyone preparing for exams. It allows users to upload documents, such as PDFs, and generate Q&A pairs based on the content. The video demonstrates how to use the app with a Sustainable Development Goals (SDG) document provided by the United Nations as an example. The host also explains how the generated Q&A pairs can be used to create datasets for fine-tuning LLMs. The app is developed using FastAPI for the backend and Bootstrap 5.3 for the frontend, with the option to extend the functionality for handling more documents. The process involves using the OpenAI API with the GPT-3.5 Turbo model for accurate responses. The final step is to download the generated Q&A pairs in CSV or Excel format, which can be further utilized for various purposes like academic studies or training modules.

Takeaways

• 📚 The video introduces a project to develop an application that generates question-answer pairs from documents, which can be useful for educators, students, and professionals preparing for exams.
• 🔍 The application uses a large language model (LLM) to generate questions and answers from uploaded documents, such as PDFs, leveraging the power of the OpenAI API.
• 🚀 The process involves uploading a document, which is then processed to generate a list of questions and corresponding answers, aiding in exam preparation or content summarization.
• 📈 The application can be extended for creating datasets for fine-tuning large language models, which is beneficial for those looking to customize AI behaviors for specific tasks.
• 💻 The backend is built using FastAPI, a modern, fast web framework for building APIs, while the frontend utilizes Bootstrap for a responsive design.
• 📈 The script mentions the use of the LangChain framework, which provides a set of tools for building generative AI applications.
• 📝 The generated questions and answers can be exported in CSV or Excel format, allowing users to download and use the data for various purposes.
• 📉 A limit is set on the number of pages processed from the uploaded document to manage the load on the LLM and to control the cost of API calls.
• 🔗 The video provides a GitHub link where viewers can access the code for the application, allowing them to modify, extend, or use it for their projects.
• 🌐 The application is designed to be web-based, providing a user interface for uploading documents and downloading the generated question-answer pairs.
• 🔧 The video offers insights into the technical details of building the application, including code snippets and explanations of the tools and frameworks used.

Q & A

What is the main purpose of the project discussed in the video?

The main purpose of the project is to develop an application that generates question and answer pairs from a provided document, which can be particularly helpful for educators, students, and professionals preparing for exams.

Which framework is used for developing the web application?

The web application is developed using the Bootstrap framework, specifically Bootstrap 5.3.

What type of document can be uploaded to the application?

The application supports the upload of PDF documents, which can be from various sources like study materials, reference guides, or documents related to sustainable development goals as mentioned in the video.

How does the application handle the process of generating questions and answers?

The application uses the OpenAI API and the GPD 3.5 Turbo model to generate questions and answers. It processes the uploaded document, creates a question list, generates respective answers, and then merges them together.

What is the significance of the 'Sustainable Development Goals (SDG)' PDF document mentioned in the video?

The 'Sustainable Development Goals (SDG)' PDF document is an example used in the video to demonstrate how the application can generate questions and answers. It is a document from the United Nations outlining 17 goals for sustainable development.

How can the generated question and answer pairs be utilized?

The generated Q&A pairs can be used in various ways, such as study materials for students, preparation for exams, creating a curriculum, or even for fine-tuning a large language model by creating a dataset for training.

What is the file format for the generated question and answer pairs?

The generated question and answer pairs are provided in a CSV (Comma Separated Values) file format, which can be easily opened and processed in spreadsheet applications.

How does the application ensure the quality of the generated questions?

The application uses a prompt template to guide the language model in generating questions that are relevant and insightful. It also filters the questions to ensure they end with a question mark, indicating a proper question format.

What is the role of the 'fast API' in the development of the application?

Fast API is used as the backend framework to create an API-based application. It handles the routing, server tasks, and responds to the requests from the frontend.

How can the generated dataset be used for fine-tuning a language model?

The generated dataset of question and answer pairs can be used to fine-tune a large language model like LLMa 2 by providing it with specific, curated information that the model can learn from, thus improving its performance on similar tasks.

What are the limitations or considerations when uploading a document for question generation?

The application sets a limit on the number of pages considered from the uploaded document to ensure efficient processing. This is because passing the entire document to a large language model can be computationally expensive and time-consuming.

Outlines

00:00

📚 Introduction to the AI Project for Generating QA Pairs

The video begins with an introduction to an AI project aimed at generating question and answer pairs from documents. The host explains that the application will be useful for educators, professors, students, and anyone preparing for exams. It will allow users to upload documents, such as PDFs, and generate a set of questions and answers that can be exported to Excel or CSV formats. The project also touches on how the generated QA pairs can be used to create datasets for fine-tuning large language models.

05:01

🔍 Demonstrating the Question Answer Generator Application

The host demonstrates the application's functionality, showing how to upload a PDF document and generate questions and answers. The application uses the OpenAI GPD 3.5 Turbo model to process the document and create the QA pairs. The host also discusses the application's potential use cases, such as creating study materials, summarizing answers for study notes, and preparing datasets for language model fine-tuning.

10:02

💻 Backend Code Explanation and Setup

The host starts writing the backend code for the application using Fast API, an asynchronous Python web framework. The code includes importing necessary modules and setting up the application structure. The host also outlines the folder structure for static files, templates, and the main app setup. Additionally, the host discusses the use of the LangChain framework for handling generative AI applications.

15:03

📝 Frontend Interaction and File Upload Limitations

The host discusses the frontend aspect of the application, where users can upload a PDF file and interact with the application to generate QA pairs. It is mentioned that there is a limit to the number of pages processed from the uploaded PDF to manage the load on the large language model and the OpenAI API. The host also provides a brief explanation of the application's interface and its components.

20:03

🔗 Utilizing LangChain and Fast API for QA Generation

The host continues with the backend code, integrating LangChain for question and answer generation. The process involves defining a file processing function, utilizing LangChain's text splitting and document handling capabilities, and setting up an LLM pipeline with the necessary prompt templates for question generation. The host also explains the use of the Fast API for handling HTTP requests and responses.

25:05

🚀 Finalizing the Application and Running the Server

The host finalizes the application by writing the API endpoints for uploading and analyzing documents. The Fast API is used to create asynchronous endpoints that handle file uploads and trigger the QA pair generation process. The host then demonstrates how to run the application using Uvicorn, a lightweight ASGI server, and shows the application in action on a local server.

30:08

🌟 Conclusion and Future Content Teasers

The host concludes the video by summarizing the functionality of the completed application and its potential applications. They also provide information on where to find the GitHub repository containing the application's code. The host teases upcoming content, including multilingual chatbots and audio-related projects, and encourages viewers to subscribe, like, and share the video.

Keywords

LLM (Large Language Model)

A Large Language Model (LLM) is an advanced artificial intelligence system designed to process and understand large volumes of human language data. In the context of this video, the LLM is utilized to generate question and answer pairs from a given document. The LLM used here is OpenAI's GPT 3.5 Turbo, which is instrumental in creating educational content, study materials, and aiding in exam preparation.

QA (Question and Answer) Generation

QA Generation refers to the process of automatically creating questions and their corresponding answers from a text document. In the video, this process is central to the application being developed. It is particularly useful for educators, students, and professionals looking to create study guides or prepare for exams.

API (Application Programming Interface)

An API is a set of rules and protocols that allows software applications to communicate and interact with each other. In the script, the OpenAI API is used to leverage the capabilities of the LLM for generating questions and answers. The FastAPI framework is also mentioned, which is used for creating the backend of the web application.

CSV (Comma Separated Values)

CSV is a file format used to store tabular data, where each row represents a record, and each field within the row is separated by a comma. The video script discusses generating a CSV file that contains the automatically generated questions and answers, which can be used for further analysis or as a dataset for training other AI models.

PDF (Portable Document Format)

PDF is a widely accepted file format for documents that maintains its formatting across different platforms. In the video, users can upload a PDF file, which will then be processed by the application to generate a set of questions and answers, as demonstrated by uploading an SDG (Sustainable Development Goals) document.

Bootstrap

Bootstrap is a popular front-end web development framework that helps create responsive and mobile-first websites. In the context of the video, it is used to design the user interface of the question and answer generation app, providing a clean and user-friendly design.

Sustainable Development Goals (SDG)

SDG refers to the 17 global goals set by the United Nations for sustainable development. The video uses an SDG PDF document to showcase how the application can generate questions and answers related to the content of the document, which is beneficial for educational purposes or sustainability-focused organizations.

Fine-tuning

In machine learning, fine-tuning involves training a model on a specific task after it has been pre-trained on a more general task. The video mentions the possibility of using the generated QA pairs to fine-tune a large language model like LLaMa 2, which can help improve the model's performance on specific tasks or datasets.

FastAPI

FastAPI is a modern, fast web framework for building APIs with Python. It is used in the video to create the backend of the question and answer generation application, handling HTTP requests and responses, and providing endpoints for uploading documents and generating QA pairs.

Embeddings

In the context of NLP and AI, embeddings are a representation of words or phrases in a multi-dimensional space where the distance between points corresponds to their semantic similarity. The video discusses using embeddings to create a vector representation of the questions, which is then used for retrieval in the QA generation process.

Vector Store

A vector store is a database designed to store and retrieve embedding vectors efficiently. In the video, a vector store is used in conjunction with the embeddings generated from the questions to retrieve relevant answers from the uploaded document.

Highlights

A project is introduced to generate question and answer pairs from documents using a large language model (LLM).

The application is aimed at assisting teachers, professors, students, and anyone preparing for exams by generating study materials.

Users can upload documents, and the system generates a QA pair in an Excel or CSV format.

The generated QA pairs can be used to fine-tune a large language model like LLMa 2.

The application uses a Bootstrap template for its user interface.

The project utilizes the OpenAI API for generating questions and answers from uploaded PDF files.

A limit is set on the number of pages processed from the uploaded document to manage the load on the LLM.

The system uses the LangChain framework for building generative AI applications.

The QA generation process involves using chains like the RetrievalQA chain and the Summarize chain.

The application allows users to download the generated QA pairs in CSV format.

The generated data can be used in academia, for creating curricula, or for workplace training modules.

The application is built using FastAPI for the backend and can be extended for more complex operations.

The source code and templates for the application will be available on GitHub for users to modify and extend.

The project includes a PDF reader utility to process the content of uploaded documents.

The application uses a token limit to control the number of questions generated and manage costs.

The system provides a simple and clean UI for users to interact with and generate their study materials.

The video includes a live demonstration of the application generating questions and answers from a provided SDG PDF document.

The application is designed to be helpful for creating question databases for educational purposes or for training modules in a professional setting.