Embeddings

Create an embedding

POST /openai/v1/embeddings

Creates an embedding from the supplied request.

要求本文

The request body.

Content-Type: application/json

input string | string[] | OpenAI.integer[] | OpenAI.integer[][] 必要

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.

下列其中一項：

string

string[]

OpenAI.integer[]

OpenAI.integer[][]

model string | enum 必要

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.

以下任何一項：

string

enum

text-embedding-ada-002text-embedding-3-smalltext-embedding-3-large

encoding_format enum

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

floatbase64

dimensions integer

user string

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

回覆

The request has succeeded.

主體

Content-Type: application/json

data OpenAI.Embedding[] 必要

The list of embeddings generated by the model.

model string 必要

The name of the model used to generate the embedding.

object enum 必要

The object type, which is always "list".

list

usage OpenAI.CreateEmbeddingResponseUsage 必要

prompt_tokens integer 必要

total_tokens integer 必要

Request

curl https://api.openai.com/v1/embeddings \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "input": "The food was delicious and the waiter...",
    "model": "text-embedding-ada-002",
    "encoding_format": "float"
  }'

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ.get("OPENAI_API_KEY"),  # This is the default and can be omitted
)
create_embedding_response = client.embeddings.create(
    input="The quick brown fox jumped over the lazy dog",
    model="text-embedding-3-small",
)
print(create_embedding_response.data)

import OpenAI from "openai";

const openai = new OpenAI();

async function main() {
  const embedding = await openai.embeddings.create({
    model: "text-embedding-ada-002",
    input: "The quick brown fox jumped over the lazy dog",
    encoding_format: "float",
  });

  console.log(embedding);
}

main();

using System;

using OpenAI.Embeddings;

EmbeddingClient client = new(
    model: "text-embedding-3-small",
    apiKey: Environment.GetEnvironmentVariable("OPENAI_API_KEY")
);

OpenAIEmbedding embedding = client.GenerateEmbedding(input: "The quick brown fox jumped over the lazy dog");
ReadOnlyMemory<float> vector = embedding.ToFloats();

for (int i = 0; i < vector.Length; i++)
{
    Console.WriteLine($"  [{i,4}] = {vector.Span[i]}");
}

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: process.env['OPENAI_API_KEY'], // This is the default and can be omitted
});

const createEmbeddingResponse = await client.embeddings.create({
  input: 'The quick brown fox jumped over the lazy dog',
  model: 'text-embedding-3-small',
});

console.log(createEmbeddingResponse.data);

package main

import (
    "context"
    "fmt"

    "github.com/openai/openai-go"
    "github.com/openai/openai-go/option"
)

func main() {
    client := openai.NewClient(
        option.WithAPIKey("My API Key"),
    )
    createEmbeddingResponse, err := client.Embeddings.New(context.TODO(), openai.EmbeddingNewParams{
        Input: openai.EmbeddingNewParamsInputUnion{
            OfString: openai.String("The quick brown fox jumped over the lazy dog"),
        },
        Model: openai.EmbeddingModelTextEmbedding3Small,
    })
    if err != nil {
        panic(err.Error())
    }
    fmt.Printf("%+v
", createEmbeddingResponse.Data)
}

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);
    }
}

require "openai"

openai = OpenAI::Client.new(api_key: "My API Key")

create_embedding_response = openai.embeddings.create(
  input: "The quick brown fox jumped over the lazy dog",
  model: :"text-embedding-3-small"
)

puts(create_embedding_response)

回應

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "embedding": [
        0.0023064255,
        -0.009327292,
        .... (1536 floats total for ada-002)
        -0.0028842222,
      ],
      "index": 0
    }
  ],
  "model": "text-embedding-ada-002",
  "usage": {
    "prompt_tokens": 8,
    "total_tokens": 8
  }
}