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How to Convert Text Into Dense Vector Representations

Last verified 22 Jun 2026

Inference provides a single control plane for managing inference workflows. It includes a Model Catalog where you can view available foundation models, including both DigitalOcean-hosted and third-party commercial models, compare model capabilities and pricing, use routing to match inference requests to the best-fit model, and run inference using serverless or dedicated deployments.

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Use the Embeddings API to convert text into dense vector representations for use in semantic search, retrieval-augmented generation (RAG), clustering, classification, and similarity matching. Embedding models include Qwen3 Embedding 0.6B, BGE-M3, and E5-Large. They offer a range of options across multilingual support, token window sizes, and dimensionality to match your use case.

For embedding requests, send a POST request to the https://inference.do-ai.run base URL using your existing Model Access Key. Embeddings are returned synchronously as float arrays that you can store in a vector database or use directly in your application pipeline.

The following cURL and Python PyDo examples generates a vector embedding using the Qwen3 Embedding 0.6B model:

Create a model access key and save it for use with the API.

Python

Using PyDo, the official DigitalOcean API client for Python:

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])

JavaScript

Using dots, the official DigitalOcean API client for JavaScript:

import { InferenceClient } from "@digitalocean/dots";

const client = new InferenceClient({
    apiKey: process.env.DIGITALOCEAN_TOKEN,
});

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

for (const item of resp.data) {
    console.log(item.index, item.embedding.slice(0, 8));
}

cURL

Send a POST request to https://inference.do-ai.run/v1/embeddings.

Using cURL:

curl -X POST \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $DIGITALOCEAN_TOKEN" \
  -d '{"model":"qwen3-embedding-0.6b","input":["hello world","goodbye world"],"encoding_format":"float","user":"user-1234"}' \
  "https://inference.do-ai.run/v1/embeddings"

How to Convert Text Into Dense Vector Representations

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