engine-embeddings

Create engine embeddings

Create embedding vectors for input text using specified engine

Request

POST /engines/{model}/embeddings

Path Parameters

Parameter	Type	Required	Description
model	string	Yes	Engine model name

Request Body

Field	Type	Required	Description
input	string	Yes	Input text to embed, encoded as a string or array of tokens.
encoding_format	string (float, base64)	No	The format to return the embeddings in. Can be either “float” or “base64”.
dimensions	integer	No	The number of dimensions the resulting output embeddings should have.
user	string	No	A unique identifier representing your end-user.

Request Examples

Create embedding using engine

{
  "input": "The quick brown fox jumps over the lazy dog"
}

Batch create embeddings

{
  "input": [
    "First text",
    "Second text"
  ],
  "encoding_format": "float"
}

Specify dimensions and encoding format

{
  "input": [
    "Customer feedback: great service!"
  ],
  "encoding_format": "base64",
  "dimensions": 512
}

Successful response

Response Schema

Field	Type	Required	Description
object	string	Yes	The object type, which is always “list”.
data	Array<EmbeddingObject>	Yes	The list of embeddings generated by the model.
model	string	Yes	The name of the model used to generate the embedding.
usage	object	Yes	The usage information for the request.

Code Examples

JavaScript (Fetch)

const response = await fetch('https://api.r9s.ai/v1/engines/{model}/embeddings', {
  method: 'POST',
  headers: {
    'Authorization': 'Bearer YOUR_API_KEY',
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
  "input": "The quick brown fox jumps over the lazy dog"
})
});

const data = await response.json();
console.log(data);

Python (requests)

import requests

url = "https://api.r9s.ai/v1/engines/{model}/embeddings"
headers = {
    "Authorization": "Bearer YOUR_API_KEY",
    "Content-Type": "application/json"
}

response = requests.post(url, json={
  "input": "The quick brown fox jumps over the lazy dog"
}, headers=headers)
data = response.json()
print(data)

cURL

curl -X POST "https://api.r9s.ai/v1/engines/{model}/embeddings" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"input":"The quick brown fox jumps over the lazy dog"}'

Schema Reference

EngineEmbeddingRequest

Field	Type	Required	Description
input	string	Yes	Input text to embed, encoded as a string or array of tokens.
encoding_format	string (float, base64)	No	The format to return the embeddings in. Can be either “float” or “base64”.
dimensions	integer	No	The number of dimensions the resulting output embeddings should have.
user	string	No	A unique identifier representing your end-user.

EmbeddingResponse

Field	Type	Required	Description
object	string	Yes	The object type, which is always “list”.
data	Array<EmbeddingObject>	Yes	The list of embeddings generated by the model.
model	string	Yes	The name of the model used to generate the embedding.
usage	object	Yes	The usage information for the request.

EmbeddingObject

Field	Type	Required	Description
object	string	Yes	The object type, which is always “embedding”.
embedding	Array	Yes	The embedding vector, which is a list of floats. The length of vector depends on the model.
index	integer	Yes	The index of the embedding in the list of embeddings.

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• API Overview - Learn about authentication and basic information
• models - View models related APIs
• chat - View chat related APIs
• responses - View responses related APIs
• messages - View messages related APIs
• completions - View completions related APIs
• edits - View edits related APIs
• images - View images related APIs
• embeddings - View embeddings related APIs
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