hjLabs AI Playground
Starting the on-device runtime…
Turn sentences into vectors with all-MiniLM-L6-v2 (384 dimensions, 23 MB), bge-small-en-v1.5 or mxbai-embed-large — the same encoders people run server-side for semantic search and RAG, executed here on your GPU. You get the raw vector, cosine similarity between any two inputs, and a copyable JSON payload. For prototyping a retrieval pipeline this removes both the API bill and the round trip: a MiniLM embedding takes a few milliseconds once the model is warm.
text embeddings onlinesentence embeddings browserall-minilm-l6-v2 demofree embedding api alternativebge embeddingscosine similarity toolrag embeddings localvector embeddings generator
Yes, within quantization error. The ONNX export is the same network; at fp32 the outputs match the PyTorch reference to several decimal places, and q8 shifts values slightly without meaningfully changing similarity ranking.
You can embed as many strings as you like — there is no quota — but the browser tab is doing the work, so batch through a few thousand chunks rather than a million. Keep the tab focused; browsers throttle background tabs.