Generate Vector Embeddings 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:

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.

• Use RetrievalQAChain and set OpenAI's model as LLM and the vector store as the data. Pass the input ...