OpenAI Embeddings

Text embeddings API converts text into high-dimensional vector representations for semantic search, text classification, and clustering.

Endpoint

Method	Endpoint
POST	https://neolink.com/api/v1/embeddings

Authentication

Method	Description
Bearer Token	Include Authorization: Bearer <API_key> in the request header for account verification.

Request Headers

Parameter	Type	Required	Description
Content-Type	string	Yes	Media type of the request body, set to application/json
Authorization	string	Yes	Bearer API_key for account verification

Request Body

Parameter	Type	Required	Default	Description
model	string	Yes	-	Model ID to use
input	string or array	Yes	-	Input text to convert to vectors. Can be a single string or an array of strings (batch processing)
encoding_format	string	No	float	Encoding format for returned vectors. Options: float or base64
dimensions	int	No	-	Number of dimensions for returned vectors. Only supported by some models
user	string	No	-	Unique identifier for the end-user, used for monitoring and abuse detection

Request Example

cURL

curl --location --request POST 'https://neolink.com/api/v1/embeddings' \
     --header 'Content-Type: application/json' \
     --header 'Authorization: Bearer <API Key>' \
     --data-raw '{
         "model": "<Model ID>",
         "input": "What is artificial intelligence"
     }'

Python

import requests

url = "https://neolink.com/api/v1/embeddings"

headers = {
    "Authorization": "Bearer <API Key>"
}

files = {
    "model": (None, "<Model ID>"),
    "input": (None, "What is artificial intelligence"),
}

response = requests.post(url, headers=headers, files=files)

print(response.status_code)
print(response.text)

Supported Models:

• text-embedding-3-large
• text-embedding-3-small
• text-embedding-ada-002
• text-embedding-v4