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Generate Vector Embeddings Using LangChain

Generate Vector Embeddings Using LangChain

Learn to generate vector embeddings from the PDF document and storing them using LangChain.

Introduction

Before creating the API endpoint, let's make a script ready that will load a PDF document and the script is able to answer the questions asked. We will be using a node package langchain to implement the question-answer over document. LangChain is an open-source framework designed to simplify the creation of applications powered by LLMs like GPT-3 and others. As discussed, we will use Nvidia's freely available PDF document on transformers as our dataset.

Below is a simple flow to implement the script:

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Steps to implement the script to perform question-answering over a document
Steps to implement the script to perform question-answering over a document

Let's discuss the above steps briefly:

  • Load the PDF document using LangChain's document loader.

  • Split the document using RecursiveCharacterTextSplitter into smaller chunks of text.

  • Generate a vector store to store the documents as vector embeddings so that the text can become searchable. ...