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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
  }
}

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Overview

The Embeddings API generates vector representations of text that capture semantic meaning. These vectors can be used for semantic search, clustering, classification, anomaly detection, and retrieval-augmented generation (RAG). CometAPI supports embedding models from multiple providers. Pass one or more text strings and receive back numerical vectors that you can store in a vector database or use directly for similarity calculations.

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Available Models

ModelDimensionsMax TokensBest For
text-embedding-3-large3,072 (adjustable)8,191Highest quality embeddings
text-embedding-3-small1,536 (adjustable)8,191Cost-effective, fast
text-embedding-ada-0021,536 (fixed)8,191Legacy compatibility
See the model list for all available embedding models and pricing.

​
Important Notes

Reducing Dimensions — The text-embedding-3-* models support the dimensions parameter, allowing you to shorten the embedding vector without significant loss of accuracy. This can reduce storage costs by up to 75% while retaining most of the semantic information.
Batch Input — You can embed multiple texts in a single request by passing an array of strings to the input parameter. This is significantly more efficient than making individual requests for each text.

Authorizations

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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"

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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).

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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
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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"

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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"

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usage
object

Token usage statistics for this request.