---
title: "Creates an embedding vector representing the input text."
url: "https://kongair.terwilligar.com/apis/openai-api-2-3-0/versions/ea92d048-c746-4ead-a6a8-da28d93137d1/operations/createEmbedding"
---

> Full API specification: https://kongair.terwilligar.com/apis/openai-api-2-3-0/versions/ea92d048-c746-4ead-a6a8-da28d93137d1.md

# Creates an embedding vector representing the input text.

`POST` `/embeddings`

Operation ID: `createEmbedding`

## Request body (required)

Content types: `application/json`

## Responses

- `200` - OK

## OpenAPI definition

```yaml
openapi: 3.0.0
info:
  title: OpenAI API
  version: 2.3.0
servers:
  - url: https://api.openai.com/v1
paths:
  /embeddings:
    post:
      operationId: createEmbedding
      tags:
        - Embeddings
      summary: Creates an embedding vector representing the input text.
      requestBody:
        required: true
        content:
          application/json:
            schema:
              $ref: "#/components/schemas/CreateEmbeddingRequest"
      responses:
        "200":
          description: OK
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/CreateEmbeddingResponse"
      x-oaiMeta:
        name: Create embeddings
        group: embeddings
        returns: A list of [embedding](/docs/api-reference/embeddings/object) objects.
        examples:
          request:
            curl: |
              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"
                }'
            python: |
              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"
              )
            node.js: |-
              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();
          response: |
            {
              "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
              }
            }
security:
  - ApiKeyAuth: []
components:
  schemas:
    CreateEmbeddingRequest:
      type: object
      additionalProperties: false
      properties:
        input:
          description: |
            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 `text-embedding-ada-002`), cannot be an empty string, and any array must be 2048 dimensions or less. [Example Python code](https://cookbook.openai.com/examples/how_to_count_tokens_with_tiktoken) for counting tokens. Some models may also impose a limit on total number of tokens summed across inputs.
          example: The quick brown fox jumped over the lazy dog
          oneOf:
            - type: string
              title: string
              description: The string that will be turned into an embedding.
              default: ""
              example: This is a test.
            - type: array
              title: array
              description: The array of strings that will be turned into an embedding.
              minItems: 1
              maxItems: 2048
              items:
                type: string
                default: ""
                example: "['This is a test.']"
            - type: array
              title: array
              description: The array of integers that will be turned into an embedding.
              minItems: 1
              maxItems: 2048
              items:
                type: integer
              example: "[1212, 318, 257, 1332, 13]"
            - type: array
              title: array
              description: The array of arrays containing integers that will be turned into an
                embedding.
              minItems: 1
              maxItems: 2048
              items:
                type: array
                minItems: 1
                items:
                  type: integer
              example: "[[1212, 318, 257, 1332, 13]]"
          x-oaiExpandable: true
        model:
          description: >
            ID of the model to use. You can use the [List
            models](/docs/api-reference/models/list) API to see all of your
            available models, or see our [Model overview](/docs/models) for
            descriptions of them.
          example: text-embedding-3-small
          anyOf:
            - type: string
            - type: string
              enum:
                - text-embedding-ada-002
                - text-embedding-3-small
                - text-embedding-3-large
          x-oaiTypeLabel: string
        encoding_format:
          description: The format to return the embeddings in. Can be either `float` or
            [`base64`](https://pypi.org/project/pybase64/).
          example: float
          default: float
          type: string
          enum:
            - float
            - base64
        dimensions:
          description: >
            The number of dimensions the resulting output embeddings should
            have. Only supported in `text-embedding-3` and later models.
          type: integer
          minimum: 1
        user:
          type: string
          example: user-1234
          description: >
            A unique identifier representing your end-user, which can help
            OpenAI to monitor and detect abuse. [Learn
            more](/docs/guides/safety-best-practices#end-user-ids).
      required:
        - model
        - input
    CreateEmbeddingResponse:
      type: object
      properties:
        data:
          type: array
          description: The list of embeddings generated by the model.
          items:
            $ref: "#/components/schemas/Embedding"
        model:
          type: string
          description: The name of the model used to generate the embedding.
        object:
          type: string
          description: The object type, which is always "list".
          enum:
            - list
          x-stainless-const: true
        usage:
          type: object
          description: The usage information for the request.
          properties:
            prompt_tokens:
              type: integer
              description: The number of tokens used by the prompt.
            total_tokens:
              type: integer
              description: The total number of tokens used by the request.
          required:
            - prompt_tokens
            - total_tokens
      required:
        - object
        - model
        - data
        - usage
    Embedding:
      type: object
      description: |
        Represents an embedding vector returned by embedding endpoint.
      properties:
        index:
          type: integer
          description: The index of the embedding in the list of embeddings.
        embedding:
          type: array
          description: >
            The embedding vector, which is a list of floats. The length of
            vector depends on the model as listed in the [embedding
            guide](/docs/guides/embeddings).
          items:
            type: number
        object:
          type: string
          description: The object type, which is always "embedding".
          enum:
            - embedding
          x-stainless-const: true
      required:
        - index
        - object
        - embedding
      x-oaiMeta:
        name: The embedding object
        example: |
          {
            "object": "embedding",
            "embedding": [
              0.0023064255,
              -0.009327292,
              .... (1536 floats total for ada-002)
              -0.0028842222,
            ],
            "index": 0
          }
  securitySchemes:
    ApiKeyAuth:
      type: http
      scheme: bearer
```