Creating a RAG Application for Image Prompts

In this lesson, we’ll learn how to implement RAG for image prompts with LangChain using Google Gemini models and a knowledge base.

What is image retrieval with RAG?

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

We’ll implement image retrieval for our previously discussed scenario of “customer service assistant.” Now, if a customer sends an image as a query, the retriever will extract the relevant information from the PDF file containing the data of various shoe brands. It will generate precise answers to customer queries with the help of LangChain and Gemini, ensuring that customers get answers promptly.

Before going into implementation details, let’s get deeper into the RAG process for this scenario:

Process workflow

The text retrieval task will involve the following six main steps:

Input query

The customer will pass an image input query.

Indexing

The knowledge base, which is the PDF, will be processed to extract textual and image details of all types from all the pages of the PDF. Now, we need to process both text and images of the knowledge base. The extracted text of each page will be divided into smaller chunks. We’ll use Google’s MobileNetV3-Small model for image embedding. The images will be converted to MediaPipe images and individually passed to an embedding model to generate each image’s embeddings.

Once we have the text chunks and embedding of images, we’ll save them in a vector ...