Are you tired of manually searching through lengthy documents for the information you need? In this video, we’ll show you how to build a powerful QA system using vector search, Langchain, and OPENAI that can help you quickly and accurately find answers to your questions.
First, we’ll explain the concept of vector search and how it can be used to represent text data as vectors, making it easier to search and analyze. We’ll then introduce Langchain, a natural language processing tool that can help identify relevant sections of a document based on the question being asked.
Next, we’ll dive into how to leverage OPENAI’s state-of-the-art language model to generate more accurate answers to questions. We’ll walk you through the steps of building a QA system that uses all three of these tools in combination to create a robust and efficient system for single document search.
Throughout the video, we’ll provide examples of how the system works in practice, and how it can be customized to fit your specific needs. By the end of the video, you’ll have a solid understanding of how to build a powerful QA system using vector search, Langchain, and OPENAI, and how to use it to find the answers you need quickly and accurately.
So, if you’re looking to improve your document search capabilities, this video is a must-watch!
The code is given at this github repo:
https://github.com/shahzaibhamid/Langchain_single_document_QA
