Vectorizing Images with LLMs [Full Stack Deep Dive]
This video walks through building a complete end-to-end image vectorization and similarity search system using modern AI tools, including multimodal LLMs and GPU-powered infrastructure. You'll learn how to generate image embeddings with an OpenAI model, store vectors efficiently with ChromaDB, and integrate LLM microservices for semantic search, all while exploring the architecture and full demo of the solution.
Description
In this video, we walk through how to build a complete end-to-end image vectorization and similarity search system using modern AI tools, multimodal LLMs, and GPU-powered infrastructure.
🔍 What You’ll Learn
• How anything can be represented as a vector — including images
• How to generate image embeddings using an OpenAI multimodal model
• How to store vectors efficiently using ChromaDB, a high-performance vector database
• How LLM microservices (running on vLLM) power the semantic search logic
•A deep dive into the architecture, design decisions, and multi-service setup
• A full demo of the final solution live in the browser
You’ll see exactly how these pieces fit together to deliver an end-to-end multimodal AI experience.
💻 Source Code:
Source code from the video is available on GitHub for review, reuse, and extension:
https://github.com/robkerr/robkerrai-demo-code/tree/main/dgx-spark-image-vectorization
0:00 Introduction
033 Demo
1:38 Architecture
3:40 Model Card
4:00 Kaggle Dataset
5:01 What are Vectors?
6:02 Vector DB Loading
7:15 Embedding Model Code
10:00 Run the Loading Script
11:08 Re-test the web app
11:42 Summary