OpenAI Embeddings API

openapi: 3.2.0
info:
  title: OpenAI Embeddings API
  description: The OpenAI REST API. Please see https://platform.openai.com/docs/api-reference for more details.
  version: 2.3.0
  termsOfService: https://openai.com/policies/terms-of-use
  contact:
    name: OpenAI Support
    url: https://help.openai.com/
  license:
    name: MIT
    url: https://github.com/openai/openai-openapi/blob/master/LICENSE
servers:
- url: https://api.openai.com/v1
security:
- ApiKeyAuth: []
tags:
- name: Embeddings
  description: Get a vector representation of a given input that can be easily consumed by machine learning models and algorithms.
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
        examples:
          request:
            curl: "curl https://api.openai.com/v1/embeddings \\\n  -H \"Authorization: Bearer $OPENAI_API_KEY\" \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\n    \"input\": \"The food was delicious and the waiter...\",\n    \"model\": \"text-embedding-ada-002\",\n    \"encoding_format\": \"float\"\n  }'\n"
            python: "import os\nfrom openai import OpenAI\n\nclient = OpenAI(\n    api_key=os.environ.get(\"OPENAI_API_KEY\"),  # This is the default and can be omitted\n)\ncreate_embedding_response = client.embeddings.create(\n    input=\"The quick brown fox jumped over the lazy dog\",\n    model=\"text-embedding-3-small\",\n)\nprint(create_embedding_response.data)"
            javascript: "import OpenAI from \"openai\";\n\nconst openai = new OpenAI();\n\nasync function main() {\n  const embedding = await openai.embeddings.create({\n    model: \"text-embedding-ada-002\",\n    input: \"The quick brown fox jumped over the lazy dog\",\n    encoding_format: \"float\",\n  });\n\n  console.log(embedding);\n}\n\nmain();\n"
            csharp: "using System;\n\nusing OpenAI.Embeddings;\n\nEmbeddingClient client = new(\n    model: \"text-embedding-3-small\",\n    apiKey: Environment.GetEnvironmentVariable(\"OPENAI_API_KEY\")\n);\n\nOpenAIEmbedding embedding = client.GenerateEmbedding(input: \"The quick brown fox jumped over the lazy dog\");\nReadOnlyMemory<float> vector = embedding.ToFloats();\n\nfor (int i = 0; i < vector.Length; i++)\n{\n    Console.WriteLine($\"  [{i,4}] = {vector.Span[i]}\");\n}\n"
            node.js: "import OpenAI from 'openai';\n\nconst client = new OpenAI({\n  apiKey: process.env['OPENAI_API_KEY'], // This is the default and can be omitted\n});\n\nconst createEmbeddingResponse = await client.embeddings.create({\n  input: 'The quick brown fox jumped over the lazy dog',\n  model: 'text-embedding-3-small',\n});\n\nconsole.log(createEmbeddingResponse.data);"
            go: "package main\n\nimport (\n\t\"context\"\n\t\"fmt\"\n\n\t\"github.com/openai/openai-go\"\n\t\"github.com/openai/openai-go/option\"\n)\n\nfunc main() {\n\tclient := openai.NewClient(\n\t\toption.WithAPIKey(\"My API Key\"),\n\t)\n\tcreateEmbeddingResponse, err := client.Embeddings.New(context.TODO(), openai.EmbeddingNewParams{\n\t\tInput: openai.EmbeddingNewParamsInputUnion{\n\t\t\tOfString: openai.String(\"The quick brown fox jumped over the lazy dog\"),\n\t\t},\n\t\tModel: openai.EmbeddingModelTextEmbedding3Small,\n\t})\n\tif err != nil {\n\t\tpanic(err.Error())\n\t}\n\tfmt.Printf(\"%+v\\n\", createEmbeddingResponse.Data)\n}\n"
            java: "package com.openai.example;\n\nimport com.openai.client.OpenAIClient;\nimport com.openai.client.okhttp.OpenAIOkHttpClient;\nimport com.openai.models.embeddings.CreateEmbeddingResponse;\nimport com.openai.models.embeddings.EmbeddingCreateParams;\nimport com.openai.models.embeddings.EmbeddingModel;\n\npublic final class Main {\n    private Main() {}\n\n    public static void main(String[] args) {\n        OpenAIClient client = OpenAIOkHttpClient.fromEnv();\n\n        EmbeddingCreateParams params = EmbeddingCreateParams.builder()\n            .input(\"The quick brown fox jumped over the lazy dog\")\n            .model(EmbeddingModel.TEXT_EMBEDDING_3_SMALL)\n            .build();\n        CreateEmbeddingResponse createEmbeddingResponse = client.embeddings().create(params);\n    }\n}"
            ruby: "require \"openai\"\n\nopenai = OpenAI::Client.new(api_key: \"My API Key\")\n\ncreate_embedding_response = openai.embeddings.create(\n  input: \"The quick brown fox jumped over the lazy dog\",\n  model: :\"text-embedding-3-small\"\n)\n\nputs(create_embedding_response)"
          response: "{\n  \"object\": \"list\",\n  \"data\": [\n    {\n      \"object\": \"embedding\",\n      \"embedding\": [\n        0.0023064255,\n        -0.009327292,\n        .... (1536 floats total for ada-002)\n        -0.0028842222,\n      ],\n      \"index\": 0\n    }\n  ],\n  \"model\": \"text-embedding-ada-002\",\n  \"usage\": {\n    \"prompt_tokens\": 8,\n    \"total_tokens\": 8\n  }\n}\n"
components:
  schemas:
    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
    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 all embedding models), 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. 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.

            '
          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]]'
        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
    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
            format: float
        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: "{\n  \"object\": \"embedding\",\n  \"embedding\": [\n    0.0023064255,\n    -0.009327292,\n    .... (1536 floats total for ada-002)\n    -0.0028842222,\n  ],\n  \"index\": 0\n}\n"
  securitySchemes:
    ApiKeyAuth:
      type: http
      scheme: bearer
    AdminApiKeyAuth:
      type: http
      scheme: bearer
x-oaiMeta:
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  - id: endpoints
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  - id: vector_stores
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  - id: chatkit
    title: ChatKit
    beta: true
  - id: containers
    title: Containers
  - id: realtime
    title: Realtime
  - id: chat
    title: Chat Completions
  - id: assistants
    title: Assistants
    deprecated: true
  - id: administration
    title: Administration
  - id: legacy
    title: Legacy
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    title: Streaming events
    description: 'When you [create a Response](/docs/api-reference/responses/create) with

      `stream` set to `true`, the server will emit server-sent events to the

      client as the Response is generated. This section contains the events that

      are emitted by the server.


      [Learn more about streaming responses](/docs/guides/streaming-responses?api-mode=responses).

      '
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  - id: webhook-events
    title: Webhook Events
    description: 'Webhooks are HTTP requests sent by OpenAI to a URL you specify when certain

      events happen during the course of API usage.


      [Learn more about webhooks](/docs/guides/webhooks).

      '
    navigationGroup: webhooks
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      [Learn more about image streaming](/docs/guides/image-generation).

      '
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      '
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  - id: chat-streaming
    title: Streaming
    description: 'Stream Chat Completions in real time. Receive chunks of completions

      returned from the model using server-sent events.

      [Learn more](/docs/guides/streaming-responses?api-mode=chat).

      '
    navigationGroup: chat
    sections:
    - type: object
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      path: streaming
  - id: assistants-streaming
    title: Streaming
    beta: true
    description: 'Stream the result of executing a Run or resuming a Run after submitting tool outputs.

      You can stream events from the [Create Thread and Run](/docs/api-reference/runs/createThreadAndRun),

      [Create Run](/docs/api-reference/runs/createRun), and [Submit Tool Outputs](/docs/api-reference/runs/submitToolOutputs)

      endpoints by passing `"stream": true`. The response will be a [Server-Sent events](https://html.spec.whatwg.org/multipage/server-sent-events.html#server-sent-events) stream.

      Our Node and Python SDKs provide helpful utilities to make streaming easy. Reference the

      [Assistants API quickstart](/docs/assistants/overview) to learn more.

      '
    navigationGroup: assistants
    sections:
    - type: object
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      path: events
  - id: realtime-beta-client-events
    title: Realtime Beta client events
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      '
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      path: <auto>
    - type: object
      key: RealtimeBetaClientEventInputAudioBufferClear
      path: <auto>
    - type: object
      key: RealtimeBetaClientEventConversationItemCreate
      path: <auto>
    - type: object
      key: RealtimeBetaClientEventConversationItemRetrieve
      path: <auto>
    - type: object
      key: RealtimeBetaClientEventConversationItemTruncate
      path: <auto>
    - type: object
      key: RealtimeBetaClientEventConversationItemDelete
      path: <auto>
    - type: object
      key: RealtimeBetaClientEventResponseCreate
      path: <auto>
    - type: object
      key: RealtimeBetaClientEventResponseCancel
      path: <auto>
    - type: object
      key: RealtimeBetaClientEventTranscriptionSessionUpdate
      path: <auto>
    - type: object
      key: RealtimeBetaClientEventOutputAudioBufferClear
      path: <auto>
  - id: realtime-beta-server-events
    title: Realtime Beta server events
    description: 'These are events emitted from the OpenAI Realtime WebSocket server to the client.

      '
    navigationGroup: legacy
    sections:
    - type: object
      key: RealtimeBetaServerEventError
      path: <auto>
    - type: object
      key: RealtimeBetaServerEventSessionCreated
      path: <auto>
    - type: object
      key: RealtimeBetaServerEventSessionUpdated
      path: <auto>
    - type: object
      key: RealtimeBetaServerEventTranscriptionSessionCreated
      path: <auto>
    - type: object
      key: RealtimeBetaServerEventTranscriptionSessionUpdated
      path: <auto>
    - type: object
      key: RealtimeBetaServerEventConversationItemCreated
      path: <auto>
    - type: object
      key: RealtimeBetaServerEventConversationItemRetrieved
      path: <auto>
    - type: object
      key: RealtimeBetaServerEventConversationItemInputAudioTranscriptionCompleted
      path: <auto>
    - type: object
      key: RealtimeBetaServerEventConversationItemInputAudioTranscriptionDelta
      path: <auto>
    - type: object
      key: RealtimeBetaServerEventConversationItemInputAudioTranscriptionSegment
      path: <auto>
    - type: object
      key: RealtimeBetaServerEventConversationItemInputAudioTranscriptionFailed
      path: <auto>
    - type: object
      key: RealtimeBetaServerEventConversationItemTruncated
      path: <auto>
    - type: object
      key: RealtimeBetaServerEventConversationItemDeleted
      path: <auto>
    - type: object
      key: RealtimeBetaServerEventInputAudioBufferCommitted
      path: <auto>
    - type: object
      key: RealtimeBetaServerEventInputAudioBufferCleared
      path: <auto>
    - type: object
      key: RealtimeBetaServerEventInputAudioBufferSpeechStarted
      path: <auto>
    - type: object
      key: RealtimeBetaServerEventInputAudioBufferSpeechStopped
      path: <auto>
    - type: object
      key: RealtimeServerEventInputAudioBufferTimeoutTriggered
      path: <auto>
    - type: object
      key: RealtimeBetaServerEventResponseCreated
      path: <auto>
    - type: object
      key: RealtimeBetaServerEventResponseDone
      path: <auto>
    - type: object
      key: RealtimeBetaServerEventResponseOutputItemAdded
      path: <auto>
    - type: object
      key: RealtimeBetaServerEventResponseOutputItemDone
      path: <auto>
    - type: object
      key: RealtimeBetaServerEventResponseContentPartAdded
      path: <auto>
    - type: object
      key: RealtimeBetaServerEventResponseContentPartDone
      path: <auto>
    - type: object
      key: RealtimeBetaServerEventResponseTextDelta
      path: <auto>
    - type: object
      key: RealtimeBetaServerEventResponseTextDone
      path: <auto>
    - type: object
      key: RealtimeBetaServerEventResponseAudioTranscriptDelta
      path: <auto>
    - type: object
      key: RealtimeBetaServerEventResponseAudioTranscriptDone
      path: <auto>
    - type: object
      key: RealtimeBetaServerEventResponseAudioDelta
      path: <auto>
    - type: object
      key: RealtimeBetaServerEventResponseAudioDone
      path: <auto>
    - type: object
      key: RealtimeBetaServerEventResponseFunctionCallArgumentsDelta
      path: <auto>
    - type: object
      key: RealtimeBetaServerEventResponseFunctionCallArgumentsDone
      path: <auto>
    - type: object
      key: RealtimeBetaServerEventResponseMCPCallArgumentsDelta
      path: <auto>
    - type: object
      key: RealtimeBetaServerEventResponseMCPCallArgumentsDone
      path: <auto>
    - type: object
      key: RealtimeBetaServerEventResponseMCPCallInProgress
      path: <auto>
    - type: object
      key: RealtimeBetaServerEventResponseMCPCallCompleted
      path: <auto>
    - type: object
      key: RealtimeBetaServerEventResponseMCPCallFailed
      path: <auto>
    - type: object
      key: RealtimeBetaServerEventMCPListToolsInProgress
      path: <auto>
    - type: object
      key: RealtimeBetaServerEventMCPListToolsCompleted
      path: <auto>
    - type: object
      key: RealtimeBetaServerEventMCPListToolsFailed
      path: <auto>
    - type: object
      key: RealtimeBetaServerEventRateLimitsUpdated
      path: <auto>