Create embeddings

Vector embeddings for RAG, semantic search, and classification. OpenAI-compatible.

POST /embeddings

Create embeddings

cURL

curl --request POST \
  --url https://api.runcrate.ai/v1/embeddings \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "model": "<string>",
  "input": "<string>",
  "encoding_format": "float"
}
'

200

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 123,
      "embedding": [
        123
      ]
    }
  ],
  "model": "<string>",
  "usage": {
    "prompt_tokens": 123,
    "total_tokens": 123
  }
}

Authorizations

Authorization · string · header · required

Use a Runcrate API key with the rc_live_* prefix as the bearer token. Create one at https://www.runcrate.ai/dashboard/api-keys.

Body

application/json

model · string · required

input · string string[] · required

encoding_format · enum<string> · default: float

Available options: float, base64

Response

200

application/json

Embedding vectors.

object · enum<string>

Available options: list

data · object[]

Show child attributes

model · string

usage · object

Show child attributes