Summary
This video introduces a project focused on generating question-answer pairs from documents like study materials and reference guides, targeting teachers, professors, students, and exam preparation. The demonstration showcases the process of uploading a PDF document to create question-answer pairs using open AI API and summarization techniques. The speaker discusses code development using FastAPI, integrating functions for processing files, creating question-answer pairs, and running a language model pipeline, and highlights the utilization of tools like Vector Store and fast Dot for embeddings. The video also covers the use of Chroma DB and GPT 3.5 Turbo for answer generation and presents the implementation of Quizlet, filtering questions, and establishing the answer generation chain. Additionally, the speaker demonstrates the process of downloading the generated question-answer pairs in a CSV format for further use.
Chapters
Introduction to Project
Application Development
Uploading Documents
Generating QA Pair
Downloading Output
Building the Application
Setting up Vector Store for Embeddings
Passing Embeddings and Answer Generation
Creating Quizlet and Filtered Questions List
Writing CSV File and Building API Code
Executing the Application and Generating Question-Answer Pairs
Introduction to Project
Introducing the project focused on generating question-answer pairs from documents like study materials, reference guides, etc. Targeted towards teachers, professors, students, and anyone preparing for exams.
Application Development
Developing an application to generate question-answer pairs from uploaded documents. Exploring the potential use cases like fine-tuning language models and creating datasets.
Uploading Documents
Demonstrating the process of uploading a PDF document (e.g., the Sustainable Development Goals document by the United Nations) to generate question-answer pairs.
Generating QA Pair
Illustrating the process of generating question-answer pairs from the uploaded document using open AI API and summarization techniques.
Downloading Output
Downloading the generated question-answer pairs in CSV format for further use, such as in academia, study materials, or for fine-tuning language models.
Building the Application
Walking through the code development process using FastAPI, defining functions for processing files, creating question-answer pairs, and running a language model pipeline.
Setting up Vector Store for Embeddings
The speaker installs Vector Store and discusses using fast Dot from underscore documents for embeddings. Additionally, the speaker mentions potential issues with using Chroma and suggests exploring other options like Millwork for better performance.
Passing Embeddings and Answer Generation
The speaker demonstrates passing embeddings for fast AI, discusses using Chroma DB, and introduces Excuse me document Quest answer for answer generation. The speaker also adjusts the temperature setting for the model and mentions using GPT 3.5 Turbo for answer generation.
Creating Quizlet and Filtered Questions List
The speaker creates a Quizlet, splits questions, and filters questions based on specific criteria. They discuss implementing validation checks for question endings and proceed to establish the answer Generation chain using retrieval QA Dot from underscore chain type.
Writing CSV File and Building API Code
The speaker demonstrates writing data to a CSV file and explains the code for saving outputs. They proceed to write API code for loading index.html file, uploading files, and analyzing data with functions like get CSV and defining API endpoints.
Executing the Application and Generating Question-Answer Pairs
The speaker runs the application, discusses the process of generating question-answer pairs, and showcases the functionality of uploading PDF files to extract information. Finally, the speaker demonstrates downloading a CSV file containing questions and answers.
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