Embeddings

Get a vector representation of a given input that can be easily consumed by machine learning models and algorithms. Related guide: Embeddings

Create embeddings

post https://api.openai.com/v1/embeddings

Creates an embedding vector representing the input text.

Request body

input

string or array

Required

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.

model

string

Required

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.

dimensions

integer

Optional

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

encoding_format

string

Optional

Defaults to float

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

user

string

Optional

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

Returns

A list of embedding objects.

Example request

curl

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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"
  }'

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from openai import OpenAI
client = OpenAI()

client.embeddings.create(
  model="text-embedding-ada-002",
  input="The food was delicious and the waiter...",
  encoding_format="float"
)

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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();

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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]}");
}

Response

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{
  "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
  }
}

The embedding object

Represents an embedding vector returned by embedding endpoint.

embedding

array

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

index

integer

The index of the embedding in the list of embeddings.

object

string

The object type, which is always "embedding".

OBJECT The embedding object

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{
  "object": "embedding",
  "embedding": [
    0.0023064255,
    -0.009327292,
    .... (1536 floats total for ada-002)
    -0.0028842222,
  ],
  "index": 0
}