Create Embeddings

💡 Troubleshooting

Encountering errors during API calls? See the API Error Codes Reference for a complete list of error types and troubleshooting solutions.

POST https://zenmux.ai/api/v1/embeddings

The Create Embeddings endpoint is compatible with OpenAI's Create Embeddings API, used to convert input text into vector representations (embeddings).

Request headers

Authorization string

Bearer Token authentication

Content-Type string

Request content type, defaults to application/json

Request

input string or array

The input text to generate embeddings for. Can be a string or an array of strings. To embed multiple inputs in a single request, pass an array of strings.

Supported input types:

• string — A text string to be embedded
• array of strings — An array of text strings to be embedded
• array of integers — An array of token integers to be embedded
• array of integer arrays — A 2D array of token integers to be embedded

Input Limitations

• A single input cannot exceed the model's maximum input token limit (e.g., 8192 tokens for the text-embedding-3 series)
• Input cannot be an empty string
• Array length cannot exceed 2048
• Total token count across all inputs in a single request cannot exceed 300,000

model string

The model ID to use, in the format <provider>/<model-name>.

Currently supported embedding model:

• openai/text-embedding-3-small — Efficient embedding model with 1536 output dimensions

encoding_format string

The encoding format for the returned embeddings.

• float — Returns as an array of floating-point numbers (default)
• base64 — Returns as a Base64-encoded string

dimensions integer

The number of dimensions for the output embeddings. Only supported by text-embedding-3 and newer models.

By specifying fewer dimensions, you can shorten the embedding vector without significantly losing semantic expressiveness, thereby reducing storage and retrieval costs.

user string

A unique identifier representing the end user, which can help monitor and detect abuse.

Response

Returns a JSON object containing a list of embedding vectors.

object string

The object type, always list.

data array

An array of embedding objects, each corresponding to the embedding result of an input text.

---

data object

object string

The object type, always embedding.

embedding array of float

The embedding vector, consisting of a list of floating-point numbers. The vector length depends on the model used and the dimensions parameter.

index integer

The index position of this embedding in the input list.

---

model string

The name of the model used to generate the embeddings.

usage object

Token usage information for this request.

• prompt_tokens integer — Number of tokens consumed by the input
• total_tokens integer — Total number of tokens consumed

TypeScript

import OpenAI from "openai";

const openai = new OpenAI({
  baseURL: 'https://zenmux.ai/api/v1', 
  apiKey: '<ZENMUX_API_KEY>', 
});

async function main() {
  const embedding = await openai.embeddings.create({
    model: "openai/text-embedding-3-small",
    input: "ZenMux is an LLM API aggregation service.",
    encoding_format: "float",
  });

  console.log(embedding.data[0].embedding);
}

main();

Python

from openai import OpenAI

client = OpenAI(
    base_url="https://zenmux.ai/api/v1",  
    api_key="<your_ZENMUX_API_KEY>",  
)

embedding = client.embeddings.create(
    model="openai/text-embedding-3-small",
    input="ZenMux is an LLM API aggregation service.",
    encoding_format="float",
)

print(embedding.data[0].embedding)

cURL

curl https://zenmux.ai/api/v1/embeddings \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $ZENMUX_API_KEY" \
  -d '{
    "model": "openai/text-embedding-3-small",
    "input": "ZenMux is an LLM API aggregation service.",
    "encoding_format": "float"
  }'

json

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "embedding": [
        0.0023064255,
        -0.009327292,
        -0.0028842222,
        0.018182484,
        -0.012395813,
        0.0073498537,
        -0.020456877,
        0.0118837105,
        0.015375832,
        -0.0036487724
      ],
      "index": 0
    }
  ],
  "model": "openai/text-embedding-3-small",
  "usage": {
    "prompt_tokens": 9,
    "total_tokens": 9
  }
}