I kept writing the same boilerplate to call embedding APIs from scripts. Wanted something like httpie but for embeddings.
vemb wraps Gemini Embedding 2, which is the first model that natively embeds text, images, audio, video, and PDFs into the same vector space. Free API key from Google AI Studio.
One command: `vemb text "query"` gives you a vector. `vemb search ./docs "find similar"` searches a directory with caching. `vemb similar a.jpg b.jpg` gives you cosine similarity.
The multimodal part is the differentiator: you can search a folder of images using a text query, or compare a PDF to an audio file. Same embedding space, no transcription step.
Would love feedback on what output formats or integrations would be most useful.
vemb wraps Gemini Embedding 2, which is the first model that natively embeds text, images, audio, video, and PDFs into the same vector space. Free API key from Google AI Studio.
One command: `vemb text "query"` gives you a vector. `vemb search ./docs "find similar"` searches a directory with caching. `vemb similar a.jpg b.jpg` gives you cosine similarity.
The multimodal part is the differentiator: you can search a folder of images using a text query, or compare a PDF to an audio file. Same embedding space, no transcription step.
Would love feedback on what output formats or integrations would be most useful.