Gemini Format - Embeddings - AihubMax

curl --request POST \
  --url https://api.aihubmax.com/v1beta/models/{model}:embedContent \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "content": {
    "parts": [
      {
        "text": "What is deep learning?"
      }
    ]
  }
}
'

{
  "embedding": {
    "values": [
      0.0123,
      -0.0456,
      0.0789,
      0.0234,
      -0.0567
    ]
  }
}

{
  "error": {
    "message": "Invalid request parameters",
    "type": "invalid_request_error"
  }
}

{
  "error": {
    "message": "Invalid API Key",
    "type": "authentication_error"
  }
}

{
  "error": {
    "message": "Invalid request parameters",
    "type": "invalid_request_error"
  }
}

{
  "error": {
    "message": "Access denied",
    "type": "permission_error"
  }
}

{
  "error": {
    "message": "Rate limit exceeded",
    "type": "rate_limit_error"
  }
}

{
  "error": {
    "message": "Internal server error",
    "type": "server_error"
  }
}

POST

/

v1beta

/

models

/

{model}

:embedContent

curl --request POST \
  --url https://api.aihubmax.com/v1beta/models/{model}:embedContent \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "content": {
    "parts": [
      {
        "text": "What is deep learning?"
      }
    ]
  }
}
'

{
  "embedding": {
    "values": [
      0.0123,
      -0.0456,
      0.0789,
      0.0234,
      -0.0567
    ]
  }
}

{
  "error": {
    "message": "Invalid request parameters",
    "type": "invalid_request_error"
  }
}

{
  "error": {
    "message": "Invalid API Key",
    "type": "authentication_error"
  }
}

{
  "error": {
    "message": "Invalid request parameters",
    "type": "invalid_request_error"
  }
}

{
  "error": {
    "message": "Access denied",
    "type": "permission_error"
  }
}

{
  "error": {
    "message": "Rate limit exceeded",
    "type": "rate_limit_error"
  }
}

{
  "error": {
    "message": "Internal server error",
    "type": "server_error"
  }
}

Authorizations

Authorization

string

header

required

All endpoints require Bearer Token authentication

Add the following to your request headers:

Authorization: Bearer YOUR_API_KEY

Path Parameters

model

string

required

Embedding model name, e.g. gemini-embedding-2-preview

Example:

"gemini-embedding-2-preview"

Body

application/json

content

object

required

The content to embed

Show child attributes

taskType

enum<string>

Embedding task type, which affects the optimization direction of the embedding vector. Note: gemini-embedding-2-preview does not support this field; that model uses a prompt prefix approach to specify task types (e.g. task: search result | query: {content})

Available options:

RETRIEVAL_QUERY,

RETRIEVAL_DOCUMENT,

SEMANTIC_SIMILARITY,

CLASSIFICATION,

CLUSTERING,

QUESTION_ANSWERING,

FACT_VERIFICATION,

CODE_RETRIEVAL_QUERY

title

string

Document title, only effective when taskType is RETRIEVAL_DOCUMENT

outputDimensionality

integer

Output vector dimensionality, used for dimensionality reduction. Default 3072

Response

Embedding response

embedding

object

Embedding result

Show child attributes