Multimodal SearchTwo ways to ask. One search space.
Find an image with a sentence or another image, using a shared embedding space.
- 01Words or image
- 02CLIP embedding
- 03Vector search
- 04Image results
Two ways to ask. One search space.
This application connects a text-or-image query to image retrieval with CLIP embeddings, ChromaDB, a FastAPI backend, and a React interface.
The problem to work through
Keyword search depends on labels. An image collection becomes more flexible to explore when a query can describe its meaning or simply be another image.
Shared representation
I used CLIP to represent text and images in a shared embedding space.
An end-to-end retrieval path
ChromaDB handles vector retrieval and FastAPI exposes the search flow.
A usable model
The React interface connects the query to returned results so the model can be explored as an application.
Text + image retrieval
An application workflow. No retrieval-accuracy or production-scale benchmark is claimed.
What I take from it
The model becomes useful through the whole query-to-result experience, including the interface that lets someone understand what it found.