Build Multimodal RAG AI Application with Voyage AI & KDB.AI | Image + Text

Learn how to build a powerful Multimodal RAG AI App that retrieves and analyzes text + images using Voyage AI’s multimodal embedding model and KDB.AI vector database. In this tutorial, you'll embed text and images into a shared vector space and use LLM to generate AI responses based on user queries. Details: In this project, a Multimodal RAG AI Application has been built with Python (pandas, PIL, Streamlit etc.), Voyage AI and KDB.AI with free API Key. In Multimodal RAG (Retrieval Augmented Generation), it stores and retrieves both text and image data using a vector database. Then we use LLM to generate a response based on the retrieved data (image + text) and a user’s query. Our goal is to embed images and texts into a unified vector space by using a multimodal embedding model to enable simultaneous vector searches across both media types. It is done by embedding our data i.e converting data into numerical vector representations and storing them in the KDB.AI vector database. A multimodal LLM can then be augmented with retrieved embeddings to generate a response based on the user query. The multimodal embedding model used in this project is called “voyage-multimodal-3” which was developed by Voyage AI. voyage-multimodal-3 can embed data modalities including text and images. First, images and their corresponding text data are prepared and kept in 2 different folders. Orders, names of images and text data should match. In the App, user can write a query in the Query text box and then they just need to click on the "Multimodal Search" button. It will first create embeddings of our multimodal data (image + text) and store them into vector database. Then it will also convert the user’s query to a query vector and search in the vector database based on the user’s query and find the best matched image and also its corresponding text and finally give the response.
GitHub Link: https://github.com/dharsandip/multimo... LinkedIn:   / sandip-dhar-40145546  

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