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POST
/
v2
/
router
/
embeddings
Create embeddings
curl --request POST \
  --url https://api.orq.ai/v2/router/embeddings \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "input": "<string>",
  "model": "<string>",
  "encoding_format": "float",
  "dimensions": 123,
  "user": "<string>",
  "name": "<string>",
  "fallbacks": [
    {
      "model": "openai/text-embedding-3-small"
    }
  ],
  "retry": {
    "count": 3,
    "on_codes": [
      429,
      500,
      502,
      503,
      504
    ]
  },
  "cache": {
    "type": "exact_match",
    "ttl": 3600
  },
  "load_balancer": {
    "type": "weight_based",
    "models": [
      {
        "model": "openai/gpt-4o",
        "weight": 0.7
      }
    ]
  },
  "timeout": {
    "call_timeout": 30000
  },
  "orq": {
    "name": "<string>",
    "fallbacks": [
      {
        "model": "openai/gpt-4o-mini"
      }
    ],
    "cache": {
      "type": "exact_match",
      "ttl": 3600
    },
    "retry": {
      "count": 3,
      "on_codes": [
        429,
        500,
        502,
        503,
        504
      ]
    },
    "identity": {
      "id": "contact_01ARZ3NDEKTSV4RRFFQ69G5FAV",
      "display_name": "Jane Doe",
      "email": "jane.doe@example.com",
      "metadata": [
        {
          "department": "Engineering",
          "role": "Senior Developer"
        }
      ],
      "logo_url": "https://example.com/avatars/jane-doe.jpg",
      "tags": [
        "hr",
        "engineering"
      ]
    },
    "contact": {
      "id": "contact_01ARZ3NDEKTSV4RRFFQ69G5FAV",
      "display_name": "Jane Doe",
      "email": "jane.doe@example.com",
      "metadata": [
        {
          "department": "Engineering",
          "role": "Senior Developer"
        }
      ],
      "logo_url": "https://example.com/avatars/jane-doe.jpg",
      "tags": [
        "hr",
        "engineering"
      ]
    },
    "load_balancer": {
      "type": "weight_based",
      "models": [
        {
          "model": "openai/gpt-4o",
          "weight": 0.7
        },
        {
          "model": "anthropic/claude-3-5-sonnet",
          "weight": 0.3
        }
      ]
    },
    "timeout": {
      "call_timeout": 30000
    }
  }
}
'
{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "embedding": [
        123
      ],
      "index": 123
    }
  ],
  "model": "<string>",
  "usage": {
    "prompt_tokens": 123,
    "total_tokens": 123
  }
}

Authorizations

​
Authorization
string
header
required

Bearer authentication header of the form Bearer <token>, where <token> is your auth token.

Body

application/json

input

​
input
required

Input text to embed, encoded as a string or array of tokens.

Minimum string length: 1
​
model
string
required

ID of the model to use

​
encoding_format
enum<string>
default:float

Type of the document element

Available options:
base64,
float
​
dimensions
number

The number of dimensions the resulting output embeddings should have.

​
user
string

A unique identifier representing your end-user

​
name
string

The name to display on the trace. If not specified, the default system name will be used.

​
fallbacks
object[]

Array of fallback models to use if primary model fails

​
retry
object

Retry configuration for the request

​
cache
object

Cache configuration for the request.

​
load_balancer
object

Load balancer configuration for the request.

​
timeout
object

Timeout configuration to apply to the request. If the request exceeds the timeout, it will be retried or fallback to the next model if configured.

​
orq
object

Response

200 - application/json

Returns the embedding vector.

​
object
enum<string>
required
Available options:
list
​
data
object[]
required
​
model
string
required

ID of the model to used.

​
usage
object
required

The usage information for the request.