Elevating Large Language Models to New Levels of Relevance and Accuracy

Implement Retrieval-Augmented Generation(RAG) with LangChain to Enhance Large Language Models(LLMs) to New Levels of Relevance and Factual Correctness

Discover RAG fundamentals with clarity and simplicity, free from technical intricacies here.

This blog post will walk you through how to implement Retrieval-Augmented Generation (RAG) using LangChain and Python.

RAG is a powerful technique that combines the strengths of large language models (LLMs) with external data sources to generate more comprehensive, context-aware, and accurate responses.

In this post, we will implement RAG to answer questions about a PDF file. We will start by reading and processing the PDF’s contents, chunking the large PDF content, and storing the embeddings in a vector database like FAISS or Pinecone. Finally, we will pose a question related to the PDF to the OpenAI LLM and receive a response that utilizes RAG to provide relevant and factual information.

Image by the author(inspired by Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks)

Preparation of External Sources of Information and Storing Indexes in Vector Database

Loading the PDF

Here I have used a research paper on Contextual Confidence and Generative AI published on Nov 2, 2023

PyPDFLoader class loads and processes the PDF files

load() method of the PyPDFLoader instance extracts the text content from the loaded PDF file, and the extracted text is stored in the pages variable

from langchain.document_loaders import PyPDFLoader

#Loads the specified PDF file and extracts the text content into pages variable for further processing
loader=PyPDFLoader('Contextual Confidence and Generative AI.pdf')
pages = loader.load()

Read the PDF contents

Once the PDF is loaded and extracted, iterate through the pages extracted from the PDF file and concatenate the text content into a single string. Replace all tab characters (\t) with spaces to ensure consistent whitespace formatting.