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java/resources/embeddings/methods/create/index.md 2026-06-10 15:48 UTC to 2026-06-12 00:01 UTC

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Create embeddings

CreateEmbeddingResponse embeddings().create(EmbeddingCreateParamsparams, RequestOptionsrequestOptions = RequestOptions.none())

post /embeddings

Creates an embedding vector representing the input text.

Parameters

  • EmbeddingCreateParams params

    • Input input

      Input text to embed, encoded as a string or array of tokens. To embed multiple inputs in a single request, pass an array of strings or array of token arrays. The input must not exceed the max input tokens for the model (8192 tokens for all embedding models), cannot be an empty string, and any array must be 2048 dimensions or less. Example Python code for counting tokens. In addition to the per-input token limit, all embedding models enforce a maximum of 300,000 tokens summed across all inputs in a single request.

      • String

      • List<String>

      • List<long>

      • List<List<long>>

    • EmbeddingModel model

      ID of the model to use. You can use the List models API to see all of your available models, or see our Model overview for descriptions of them.

    • Optional<Long> dimensions

      The number of dimensions the resulting output embeddings should have. Only supported in text-embedding-3 and later models.

    • Optional<EncodingFormat> encodingFormat

      The format to return the embeddings in. Can be either float or base64.

      • FLOAT("float")

      • BASE64("base64")

    • Optional<String> user

      A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. Learn more.

Returns

  • class CreateEmbeddingResponse:

    • List<Embedding> data

      The list of embeddings generated by the model.

      • List<double> embedding

        The embedding vector, which is a list of floats. The length of vector depends on the model as listed in the embedding guide.

      • long index

        The index of the embedding in the list of embeddings.

      • JsonValue; object_ "embedding"constant

        The object type, which is always "embedding".

        • EMBEDDING("embedding")

    • String model

      The name of the model used to generate the embedding.

    • JsonValue; object_ "list"constant

      The object type, which is always "list".

      • LIST("list")

    • Usage usage

      The usage information for the request.

      • long promptTokens

        The number of tokens used by the prompt.

      • long totalTokens

        The total number of tokens used by the request.

Example

package com.openai.example;

import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.embeddings.CreateEmbeddingResponse;
import com.openai.models.embeddings.EmbeddingCreateParams;
import com.openai.models.embeddings.EmbeddingModel;

public final class Main {
    private Main() {}

    public static void main(String[] args) {
        OpenAIClient client = OpenAIOkHttpClient.fromEnv();

        EmbeddingCreateParams params = EmbeddingCreateParams.builder()
            .input("The quick brown fox jumped over the lazy dog")
            .model(EmbeddingModel.TEXT_EMBEDDING_3_SMALL)
            .build();
        CreateEmbeddingResponse createEmbeddingResponse = client.embeddings().create(params);
    }
}

Response

{
  "data": [
    {
      "embedding": [
        0
      ],
      "index": 0,
      "object": "embedding"
    }
  ],
  "model": "model",
  "object": "list",
  "usage": {
    "prompt_tokens": 0,
    "total_tokens": 0
  }
}