httpie for embeddings. Embed anything from the command line.
Project description
vemb
httpie for embeddings. Embed text, images, audio, video, and PDFs from the command line.
pipx install vemb
export GEMINI_API_KEY=your_key
vemb text "hello world"
Powered by Gemini Embedding 2, the first natively multimodal embedding model. One model, one vector space for everything.
Install
pipx install vemb
# or
pip install vemb
Get a free API key at https://aistudio.google.com/apikey
export GEMINI_API_KEY=your_key
Commands
vemb text "hello world" # embed text
vemb embed photo.jpg # embed any file (auto-detects type)
vemb embed *.jpg --jsonl # batch embed, one JSON per line
vemb image photo.jpg # embed image (PNG, JPEG)
vemb audio clip.mp3 # embed audio (MP3, WAV)
vemb video clip.mp4 # embed video (MP4, MOV)
vemb pdf doc.pdf # embed PDF
vemb similar photo1.jpg photo2.jpg # cosine similarity between two files
vemb search ./photos "sunset at beach" # search a directory
Pipe from stdin:
echo "hello world" | vemb text -
cat document.txt | vemb text -
Output
Default output is JSON:
{
"model": "gemini-embedding-2-preview",
"dimensions": 768,
"values": [0.012, -0.034, ...]
}
Options:
vemb text "hello" --compact # just the vector array
vemb text "hello" --numpy # numpy format
vemb text "hello" --dim 768 # set dimensions (128-3072)
vemb text "hello" --task RETRIEVAL_QUERY # set task type
Batch mode outputs JSONL (one embedding per line):
vemb embed *.jpg --jsonl > embeddings.jsonl
Search
Search indexes a directory and finds files similar to your query:
vemb search ./photos "sunset at beach" --top 5
Embeddings are cached in .vemb/cache.json inside the searched directory. Unchanged files won't be re-embedded on subsequent searches.
Supported formats
| Type | Formats |
|---|---|
| Text | any string, stdin |
| Image | PNG, JPEG |
| Audio | MP3, WAV (up to 80s) |
| Video | MP4, MOV (up to 128s) |
| up to 6 pages |
License
MIT
Project details
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