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POST
https://llm.ai-nebula.com
/
v1
/
embeddings
Embeddings
curl --request POST \
  --url https://llm.ai-nebula.com/v1/embeddings \
  --header 'Authorization: <authorization>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "model": "<string>",
  "input": {},
  "encoding_format": "<string>",
  "dimensions": 123
}
'
{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [0.0023064255, -0.009327292, 0.015797347, ...]
    }
  ],
  "model": "text-embedding-3-small",
  "usage": {
    "prompt_tokens": 5,
    "total_tokens": 5
  }
}

​
Introduction

Convert text to vector embeddings for semantic search, similarity calculation, and clustering.

​
Authentication

​
Authorization
string
required
Bearer Token, e.g. Bearer sk-xxxxxxxxxx

​
Request Parameters

​
model
string
required
Model name, e.g. text-embedding-3-small, text-embedding-3-large
​
input
string | array
required
Text to embed, string or array of strings
​
encoding_format
string
default:"float"
Return format: float or base64
​
dimensions
integer
Output dimensions (supported by some models)

​
cURL Example

curl https://llm.ai-nebula.com/v1/embeddings \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-XyLy**************************mIqSt" \
  -d '{
    "model": "text-embedding-3-small",
    "input": "Hello, world"
  }'

​
Python Example

from openai import OpenAI

client = OpenAI(
    api_key="sk-XyLy**************************mIqSt",
    base_url="https://llm.ai-nebula.com/v1"
)

response = client.embeddings.create(
    model="text-embedding-3-small",
    input="Hello, world"
)

print(response.data[0].embedding)
print(f"Dimensions: {len(response.data[0].embedding)}")

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [0.0023064255, -0.009327292, 0.015797347, ...]
    }
  ],
  "model": "text-embedding-3-small",
  "usage": {
    "prompt_tokens": 5,
    "total_tokens": 5
  }
}

​
Supported Models

ModelDimensionsDescription
text-embedding-3-small1536Cost-effective for most use cases
text-embedding-3-large3072High precision for demanding applications
text-embedding-ada-0021536Legacy model

​
Notes

  • For batch embedding, pass an array of strings to input
  • Some models support custom dimensions via dimensions parameter
  • Requires openai library: pip install openai