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Intfloat · Embedding model
Multilingual-E5-Large
intfloat/multilingual-e5-large- Embedding
The multilingual-e5-large embedding model encodes sentences, paragraphs, and documents across over 90 languages into a 1024-dimensional dense vector space, delivering robust semantic embeddings optimized for multilingual retrieval, cross-language similarity, and...
Specifications
- Input
- text
- Output
- vector per input (float array; base64 on request)
- Batch
- up to 512 inputs per call
- Billing
- per input token
Use it
POST /api/v1/embeddings · MCP embed · full reference
curl https://omnirail.org/api/v1/embeddings \
-H "Authorization: Bearer $OMNIRAIL_KEY" -H "Content-Type: application/json" \
-d '{"model":"intfloat/multilingual-e5-large","input":["brass lever espresso machine","coffee grinder"]}'