Embeddings
Generate vector embeddings for text input. Use for semantic search, clustering, and similarity comparisons. OpenAI-compatible.
Create embeddings
Authorization
api_key AuthorizationBearer <token>
API key authentication using Bearer token format
In: header
Request Body
application/json
dimensions?integer|null
Number of dimensions for output embeddings
Format
int64encoding_format?null|EncodingFormat
input*string||||
Input to embed
input_type?string|null
Hadrian Extension: Input type hint for embedding providers that support it (e.g., Cohere)
model*string
Model to use for embedding
provider?object
Hadrian Extension: Provider routing configuration
user?string|null
User identifier for abuse detection
Response Body
application/json
application/json
curl -X POST "https://loading/api/v1/embeddings" \ -H "Content-Type: application/json" \ -d '{ "dimensions": 1024, "input": [ "First document to embed", "Second document to embed", "Third document to embed" ], "model": "openai/text-embedding-3-large" }'Empty
{
"error": {
"code": "routing_error",
"message": "Model 'invalid-embedding-model' not found"
}
}{
"error": {
"code": "provider_error",
"message": "Upstream provider returned error"
}
}