Creating a RAG Application for Text Prompts

We’ll learn to implement RAG with LangChain using Google Gemini models and a knowledge base.

What is text retrieval with RAG?

Text retrieval simply means retrieving relevant text from a large dataset. Text retrieval with RAG is an approach where the retrieval and generation models are combined to generate the most relevant and accurate responses to text queries. This way, we combine the information retrieval with the power generation models to generate contextually rich responses.

Let’s discuss a scenario for text retrieval with RAG.

Customer service assistant

Suppose you work in a shoe retail company that sells different brands of shoes. All the brand names and details of the shoes are available on the store’s website, but the user has to find them by manually navigating through the entire website. That’s why the company receives thousands of user requests about shoe styles, details, prices, availability, and more. It is difficult to handle all these queries manually because it is time-consuming to scroll through the entire list of shoe data. Your goal is to automate this task.

To achieve accurate results using your own company data, you decide to implement an RAG system using Gemini. RAG requires a knowledge base to provide relevant and accurate information. The data on the company website can’t be used directly as a knowledge base for the RAG process because there will be scattered data, navigation issues, and dynamic content on the website. We need to structure data to use for the retrieval process. To do this, we have scraped the website data and stored it. All the details of the shoes are in a PDF file, including the shoe name, style, release date, style code, original retail price, store location, and description, along with the image of the shoe.

Note: You can access and download the knowledge base, ShoesStore.pdf.

Here are a few snapshots of the PDF file:

RAG will utilize this PDF that contains all the relevant details of the shoes available in the store, and Gemini will help generate responses to user queries. The LangChain toolkit will also help us structure the process by providing the components for indexing, retrieval, etc. ...