← All models
Intfloat · Embedding model
E5-Base-v2
intfloat/e5-base-v2- Embedding
The e5-base-v2 embedding model encodes English sentences and paragraphs into a 768-dimensional dense vector space, producing efficient and high-quality semantic embeddings optimized for tasks such as semantic search, similarity scoring,...
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/e5-base-v2","input":["brass lever espresso machine","coffee grinder"]}'