Semantic Image Search CLI tool.
CLI tool for semantic image search, locally without using third party APIs.
Powered by node-mlx, a machine
learning framework for Node.js.
https://github.com/user-attachments/assets/66e6e437-c27b-48cf-80cc-a5a0c8c0bdfb
GPU support:
CPU support:
(No support for Windows yet, but I might try to make MLX work on it in future)
For platforms without GPU support, the index command will be much slower, and
will take many hours indexing tens of thousands of images. The index is only
built for new and modified files, so once your have done the initial building,
updating index for new images will be much easier.
Install:
npm install -g @frost-beta/sisi
CLI:
━━━ Semantic Image Search CLI - 0.0.1-dev ━━━━━━━━━━━━━━━━
$ sisi <command>
━━━ General commands ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
sisi index <target>
Build or update index for images under target directory.
sisi list-index
List the directories in the index.
sisi remove-index <target>
Remove index for all items under target directory.
sisi search [--in #0] [--max #0] [--print] <query>
Search the query string from indexed images.
Build index for ~/Pictures/:
sisi index ~/Pictures/
Search pictures from all indexed images:
sisi search 'cat jumping'
Search from the ~/Pictures/ directory:
sisi search cat --in ~/Pictures/
Search images with image:
sisi search https://images.pexels.com/photos/45201/kitty-cat-kitten-pet-45201.jpeg
It works with local files too:
sisi search file:///Users/Your/Pictures/cat.jpg
The index is built by computing the embeddings of images using the CLIP
model, and then stored in a binary JSON file.
Searching the images is computing cosine similarities between the query string
and the indexed embeddings. There is no database involved here, everytime you do
a search the computation is done for all the embeddings stored, which is very
fast even when you have tens of thousands of pictures.
The JavaScript implementation of the CLIP model is in a separate module:
frost-beta/clip.
MIT