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POST
/
v1
/
embeddings
from openai import OpenAI

client = OpenAI(
    base_url="https://api.cometapi.com/v1",
    api_key="<COMETAPI_KEY>",
)

response = client.embeddings.create(
    model="text-embedding-3-small",
    input="The food was delicious and the waiter was friendly.",
)

print(response.data[0].embedding[:5])  # First 5 dimensions
print(f"Dimensions: {len(response.data[0].embedding)}")
{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [
        -0.0021,
        -0.0491,
        0.0209,
        0.0314,
        -0.0453
      ]
    }
  ],
  "model": "text-embedding-3-small",
  "usage": {
    "prompt_tokens": 2,
    "total_tokens": 2
  }
}

Documentation Index

Fetch the complete documentation index at: https://apidoc.cometapi.com/llms.txt

Use this file to discover all available pages before exploring further.

CometAPI supports embedding models from multiple providers through a single endpoint. Pass one or more text strings and receive numerical vectors for semantic search, clustering, classification, or retrieval-augmented generation (RAG). See the model list for available embedding models and pricing.
The text-embedding-3-* models support the dimensions parameter, which shortens the embedding vector without significant accuracy loss. This can reduce storage costs while retaining most semantic information.
To embed multiple texts in a single request, pass an array of strings to the input parameter. Batch input is significantly more efficient than making individual requests.

Authorizations

​
Authorization
string
header
required

Bearer token authentication. Use your CometAPI key.

Body

application/json
​
model
string
required

The embedding model to use. See the Models page for current embedding model IDs.

Example:

"text-embedding-3-small"

​
input
required

The text to embed. Can be a single string, an array of strings, or an array of token arrays. Each input must not exceed the model's maximum token limit (8,191 tokens for text-embedding-3-* models).

​
encoding_format
enum<string>
default:float

The format of the returned embedding vectors. float returns an array of floating-point numbers. base64 returns a base64-encoded string representation, which can reduce response size for large batches.

Available options:
float,
base64
​
dimensions
integer

The number of dimensions for the output embedding vector. Only supported by text-embedding-3-* models. Reducing dimensions can lower storage costs while maintaining most of the embedding's utility.

Required range: x >= 1
​
user
string

A unique identifier for your end-user, which can help monitor and detect abuse.

Response

200 - application/json

A list of embedding vectors for the input text(s).

​
object
enum<string>

The object type, always list.

Available options:
list
Example:

"list"

​
data
object[]

An array of embedding objects, one per input text. When multiple inputs are provided, results are returned in the same order as the input.

​
model
string

The model used to generate the embeddings.

Example:

"text-embedding-3-small"

​
usage
object

Token usage statistics for this request.