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
    identifier: MIT
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: Create embeddings
      description: 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'
        '429':
          $ref: '#/components/responses/InferenceRateLimited'
        '503':
          $ref: '#/components/responses/InferenceServiceUnavailable'
      x-oaiMeta:
        group: embeddings
        examples:
          request:
            curl: "curl https://api.openai.com/v1/embeddings \\\n  -H \"Authorization: Bearer $OPENAI_API_KEY\" \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\n    \"input\": \"The food was delicious and the waiter...\",\n    \"model\": \"text-embedding-ada-002\",\n    \"encoding_format\": \"float\"\n  }'\n"
            python: "from openai import OpenAI\nclient = OpenAI()\n\nclient.embeddings.create(\n  model=\"text-embedding-ada-002\",\n  input=\"The food was delicious and the waiter...\",\n  encoding_format=\"float\"\n)\n"
            javascript: "import OpenAI from \"openai\";\n\nconst openai = new OpenAI();\n\nasync function main() {\n  const embedding = await openai.embeddings.create({\n    model: \"text-embedding-ada-002\",\n    input: \"The quick brown fox jumped over the lazy dog\",\n    encoding_format: \"float\",\n  });\n\n  console.log(embedding);\n}\n\nmain();\n"
            csharp: "using System;\n\nusing OpenAI.Embeddings;\n\nEmbeddingClient client = new(\n    model: \"text-embedding-3-small\",\n    apiKey: Environment.GetEnvironmentVariable(\"OPENAI_API_KEY\")\n);\n\nOpenAIEmbedding embedding = client.GenerateEmbedding(input: \"The quick brown fox jumped over the lazy dog\");\nReadOnlyMemory<float> vector = embedding.ToFloats();\n\nfor (int i = 0; i < vector.Length; i++)\n{\n    Console.WriteLine($\"  [{i,4}] = {vector.Span[i]}\");\n}\n"
          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:
    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](https://developers.openai.com/api/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"
    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](https://developers.openai.com/api/reference/resources/models/methods/list) API to see all of your available models, or see our [Model overview](https://developers.openai.com/api/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](https://developers.openai.com/api/docs/guides/safety-best-practices#implement-safety-identifiers).

            '
      required:
      - model
      - input
    _MisalignmentErrorType:
      anyOf:
      - type: string
      - type: string
        enum:
        - potentially_unintended_data_transfer
        - potentially_unintended_data_access
        - potentially_unintended_destructive_activity
        - other
    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
    _MisalignmentSteer:
      properties:
        message:
          type: string
          description: The public continuation instruction.
      type: object
      required:
      - message
    Error:
      type: object
      properties:
        code:
          anyOf:
          - type: string
          - type: 'null'
        message:
          type: string
        param:
          anyOf:
          - type: string
          - type: 'null'
        type:
          type: string
        misalignment:
          $ref: '#/components/schemas/MisalignmentErrorDetailsResource'
      required:
      - type
      - message
      - param
      - code
    ErrorResponse:
      type: object
      properties:
        error:
          $ref: '#/components/schemas/Error'
      required:
      - error
    MisalignmentErrorDetailsResource:
      properties:
        error_type:
          $ref: '#/components/schemas/_MisalignmentErrorType'
          description: An optional classification; clients must accept additional values.
        detailed_explanation:
          type: string
          description: The public explanation for this block.
        steer:
          $ref: '#/components/schemas/_MisalignmentSteer'
          description: An optional public continuation instruction.
      type: object
      required: []
  responses:
    InferenceRateLimited:
      description: The request was rejected because a rate limit was exceeded. A slow_down error means traffic increased too quickly; reduce your request rate, then increase it gradually.
      headers:
        Retry-After:
          description: The minimum number of seconds to wait before retrying. This header is returned when the server has computed a retry delay and may be omitted.
          schema:
            type: integer
            minimum: 1
      content:
        application/json:
          schema:
            $ref: '#/components/schemas/ErrorResponse'
          examples:
            slowDown:
              summary: Traffic increased too quickly
              value:
                error:
                  message: Your request rate increased too quickly. Please reduce the request rate and gradually increase it again.
                  type: rate_limit_error
                  param: null
                  code: slow_down
    InferenceServiceUnavailable:
      description: The service is temporarily unavailable. A server_is_overloaded error means the requested model is temporarily overloaded; retry after a brief delay.
      headers:
        Retry-After:
          description: The minimum number of seconds to wait before retrying. This header is returned when the server has computed a retry delay and may be omitted.
          schema:
            type: integer
            minimum: 1
      content:
        application/json:
          schema:
            $ref: '#/components/schemas/ErrorResponse'
          examples:
            serverOverloaded:
              summary: The requested model is temporarily overloaded
              value:
                error:
                  message: The model is temporarily overloaded. Please retry your request after a brief delay.
                  type: service_unavailable_error
                  param: null
                  code: server_is_overloaded
  securitySchemes:
    ApiKeyAuth:
      type: http
      scheme: bearer
    AdminApiKeyAuth:
      type: http
      scheme: bearer
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  - id: containers
    title: Containers
  - id: live
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  - id: realtime
    title: Realtime
  - id: chat
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  - id: assistants
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      '
    navigationGroup: endpoints
    sections:
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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](https://developers.openai.com/api/docs/guides/webhooks).

      '
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  - id: images-streaming
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      [Learn more about image streaming](https://developers.openai.com/api/docs/guides/image-generation).

      '
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    - type: object
      key: RealtimeClientEventInputAudioBufferCommit
      path: <auto>
    - type: object
      key: RealtimeClientEventInputAudioBufferClear
      path: <auto>
    - type: object
      key: RealtimeClientEventConversationItemCreate
      path: <auto>
    - type: object
      key: RealtimeClientEventConversationItemRetrieve
      path: <auto>
    - type: object
      key: RealtimeClientEventConversationItemTruncate
      path: <auto>
    - type: object
      key: RealtimeClientEventConversationItemDelete
      path: <auto>
    - type: object
      key: RealtimeClientEventResponseCreate
      path: <auto>
    - type: object
      key: RealtimeClientEventResponseCancel
      path: <auto>
    - type: object
      key: RealtimeClientEventOutputAudioBufferClear
      path: <auto>
  - id: realtime-server-events
    title: Server events
    description: 'These are events emitted from the OpenAI Realtime WebSocket server to the client.

      '
    navigationGroup: realtime
    sections:
    - type: object
      key: RealtimeServerEventError
      path: <auto>
    - type: object
      key: RealtimeServerEventSessionCreated
      path: <auto>
    - type: object
      key: RealtimeServerEventSessionUpdated
      path: <auto>
    - type: object
      key: RealtimeServerEventConversationItemAdded
      path: <auto>
    - type: object
      key: RealtimeServerEventConversationItemDone
      path: <auto>
    - type: object
      key: RealtimeServerEventConversationItemRetrieved
      path: <auto>
    - type: object
      key: RealtimeServerEventConversationItemInputAudioTranscriptionCompleted
      path: <auto>
    - type: object
      key: RealtimeServerEventConversationItemInputAudioTranscriptionDelta
      path: <auto>
    - type: object
      key: RealtimeServerEventConversationItemInputAudioTranscriptionSegment
      path: <auto>
    - type: object
      key: RealtimeServerEventConversationItemInputAudioTranscriptionFailed
      path: <auto>
    - type: object
      key: RealtimeServerEventConversationItemTruncated
      path: <auto>
    - type: object
      key: RealtimeServerEventConversationItemDeleted
      path: <auto>
    - type: object
      key: RealtimeServerEventInputAudioBufferCommitted
      path: <auto>
    - type: object
      key: RealtimeServerEventInputAudioBufferDtmfEventReceived
      path: <auto>
    - type: object
      key: RealtimeServerEventInputAudioBufferCleared
      path: <auto>
    - type: object
      key: RealtimeServerEventInputAudioBufferSpeechStarted
      path: <auto>
    - type: object
      key: RealtimeServerEventInputAudioBufferSpeechStopped
      path: <auto>
    - type: object
      key: RealtimeServerEventInputAudioBufferTimeoutTriggered
      path: <auto>
    - type: object
      key: RealtimeServerEventOutputAudioBufferStarted
      path: <auto>
    - type: object
      key: RealtimeServerEventOutputAudioBufferStopped
      path: <auto>
    - type: object
      key: RealtimeServerEventOutputAudioBufferCleared
      path: <auto>
    - type: object
      key: RealtimeServerEventResponseCreated
      path: <auto>
    - type: object
      key: RealtimeServerEventResponseDone
      path: <auto>
    - type: object
      key: RealtimeServerEventResponseOutputItemAdded
      path: <auto>
    - type: object
      key: RealtimeServerEventResponseOutputItemDone
      path: <auto>
    - type: object
      key: RealtimeServerEventResponseContentPartAdded
      path: <auto>
    - type: object
      key: RealtimeServerEventResponseContentPartDone
      path: <auto>
    - type: object
      key: RealtimeServerEventResponseTextDelta
      path: <auto>
    - type: object
      key: RealtimeServerEventResponseTextDone
      path: <auto>
    - type: object
      key: RealtimeServerEventResponseAudioTranscriptDelta
      path: <auto>
    - type: object
      key: RealtimeServerEventResponseAudioTranscriptDone
      path: <auto>
    - type: object
      key: RealtimeServerEventResponseAudioDelta
      path: <auto>
    - type: object
      key: RealtimeServerEventResponseAudioDone
      path: <auto>
    - type: object
      key: RealtimeServerEventResponseFunctionCallArgumentsDelta
      path: <auto>
    - type: object
      key: RealtimeServerEventResponseFunctionCallArgumentsDone
      path: <auto>
    - type: object
      key: RealtimeServerEventResponseMCPCallArgumentsDelta
      path: <auto>
    - type: object
      key: RealtimeServerEventResponseMCPCallArgumentsDone
      path: <auto>
    - type: object
      key: RealtimeServerEventResponseMCPCallInProgress
      path: <auto>
    - type: object
      key: RealtimeServerEventResponseMCPCallCompleted
      path: <auto>
    - type: object
      key: RealtimeServerEventResponseMCPCallFailed
      path: <auto>
    - type: object
      key: RealtimeServerEventMCPListToolsInProgress
      path: <auto>
    - type: object
      key: RealtimeServerEventMCPListToolsCompleted
      path: <auto>
    - type: object
      key: RealtimeServerEventMCPListToolsFailed
      path: <auto>
    - type: object
      key: RealtimeServerEventRateLimitsUpdated
      path: <auto>


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# Full source: https://raw.githubusercontent.com/api-evangelist/openai/refs/heads/main/openapi/openai-embeddings-api-openapi.yml