orl embeddings create
Turn text into vectors, part of the orl CLI for OpenRelay GPU VMs, pods, batches and billing. Usage, every flag and an example.
orl embeddings create --input <input> --model <model>Turns text into vectors for search, retrieval, clustering and deduplication in the OpenAI embeddings shape, served by openrelay/bge-m3 today. openrelay/bge-m3 is multilingual and returns 1024-dimension dense vectors; check a model's supported_apis on GET /v1/models for embeddings.
Requires a funded balance. No output rate: billed only on usage.prompt_tokens. stream is dropped. Limits: 8192 tokens per input item, 1024 items per request, 10 MiB request body. Also runs through the Batch API at endpoint /v1/embeddings, at the batch rate.
Flags
| Flag | Type | Required | |
|---|---|---|---|
--input | json | yes | The text to embed. An array embeds each item independently and returns one vector per item, in order. |
--model | string | yes | An OpenRelay public model id, e.g. openrelay/bge-m3. |
--max-cost | float64 | no | refuse (exit 8) when the worst-case cost exceeds this many dollars (default: ORL_MAX_COST_USD) |
--body | string | no | a JSON object of request body fields, merged under any of this command's own flags that are also set, e.g. {"key":"value"} |
--body-file | string | no | read the --body JSON object from a file (- for stdin) |
This wraps the createEmbedding API operation.
orl doctor
Check the install, credentials, both API hosts, the key's access and budget, and the balance, part of the orl CLI for OpenRelay GPU VMs, pods, batches and…
orl estimate
Bound what a batch file or a token count will cost, before anything runs, part of the orl CLI for OpenRelay GPU VMs, pods, batches and billing.