Description
Finding the right image in a large collection can be a challenge, especially when relying on traditional keyword-based search. This presentation is a HOWTO instruction on building a system that combines AI model with pgvector to enable natural language image retrieval and similarity search. By embedding images into a high-dimensional space using CLIP model and storing these embeddings in a pgvector PostgreSQL database, the system allows users to search for images using simple text descriptions—no need for exact keywords or metadata. The result is an intuitive, AI-powered search experience that returns visually relevant results based on semantic meaning. We’ll dive into how the system works, discuss real-world use cases, and explore performance considerations for scaling. To make it even more interesting, the photos that I'll be using are from my personal collection.
