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pydo.embeddings.create() | DigitalOcean Documentation

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pydo.embeddings.create()

Generated on 3 Aug 2026 from pydo version v0.40.0

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Usage 

client.embeddings.create(
    model="qwen3-embedding-0.6b",
    input=["hello world", "goodbye world"],
    encoding_format="float",
    user="user-1234",
)
Returns JSONRaises HttpResponseError

Serverless inference methods can authenticate with a model access key instead of a DigitalOcean API token:

client = Client(token=os.environ.get("MODEL_ACCESS_KEY"))

Description

Create vector embeddings for one or more text inputs. OpenAI-compatible request and response. Unknown fields in the request body are rejected. There is no streaming response for this endpoint.

Parameters

model string required

Example: qwen3-embedding-0.6b

Model id to use for embeddings. Must match a model your account can access.

input object required

Example: hello world

A single string or 12048 strings; each string produces one row in data, in order.

user string optional

Example: user-1234

Optional end-user identifier to help with abuse monitoring.

encoding_format string optional

How embedding values are returned in each data[].embedding field.

One of: float, base64

Default: float

Request Sample

Show Request Sample
import os
from pydo import Client

client = Client(token=os.environ.get("DIGITALOCEAN_TOKEN"))

resp = client.embeddings.create(
    model="qwen3-embedding-0.6b",
    input=["hello world", "goodbye world"],
    encoding_format="float",
    user="user-1234",
)

for item in resp.data:
    print(item.index, item.embedding[:8])

Response Example

Show Response Example
{
  "object": "list",
  "model": "qwen3-embedding-0.6b",
  "data": [
    {
      "index": 0,
      "object": "embedding",
      "embedding": [
        0.0123,
        -0.0456,
        0.0001
      ]
    }
  ],
  "usage": {
    "prompt_tokens": 6,
    "total_tokens": 6
  }
}

More Information

See /v1/embeddings in the API reference for additional detail on responses, headers, parameters, and more.

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