openapi: 3.1.0
info:
  title: OpenAI Embeddings API
  description: >-
    Get a vector representation of a given input that can be easily consumed by
    machine learning models and algorithms. OpenAI's text embeddings measure the
    relatedness of text strings. Embeddings are commonly used for search,
    clustering, recommendations, anomaly detection, diversity measurement, and
    classification. An embedding is a vector (list of floating point numbers)
    where the distance between two vectors measures their relatedness.
  version: 2.0.0
  termsOfService: https://openai.com/policies/terms-of-use
  contact:
    name: OpenAI Support
    url: https://help.openai.com/
    email: support@openai.com
  license:
    name: MIT
    url: https://github.com/openai/openai-openapi/blob/master/LICENSE
servers:
- url: https://api.openai.com/v1
  description: OpenAI API production server
security:
- BearerAuth: []
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: Openai Create Embeddings
      description: >-
        Creates an embedding vector representing the input text. The input can
        be a single string or an array of strings. Each input must not exceed
        the max input tokens for the model (8191 tokens for text-embedding-3-small
        and text-embedding-3-large, 8191 tokens for text-embedding-ada-002).
      requestBody:
        required: true
        content:
          application/json:
            schema:
              $ref: '#/components/schemas/CreateEmbeddingRequest'
            examples:
              CreateembeddingRequestExample:
                summary: Default createEmbedding request
                x-microcks-default: true
                value:
                  model: example_value
                  input: example_value
                  encoding_format: float
                  dimensions: 10
                  user: example_value
      responses:
        '200':
          description: Successful response with embedding vectors.
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/CreateEmbeddingResponse'
              examples:
                Createembedding200Example:
                  summary: Default createEmbedding 200 response
                  x-microcks-default: true
                  value:
                    object: list
                    data:
                    - object: embedding
                      embedding: example_value
                      index: 10
                    model: example_value
                    usage:
                      prompt_tokens: 10
                      total_tokens: 10
        '400':
          description: Bad request - invalid parameters or input too long.
        '401':
          description: Unauthorized - invalid or missing API key.
        '429':
          description: Rate limit exceeded.
        '500':
          description: Internal server error.
      x-microcks-operation:
        delay: 0
        dispatcher: FALLBACK
components:
  securitySchemes:
    BearerAuth:
      type: http
      scheme: bearer
      bearerFormat: API Key
      description: >-
        OpenAI API key. Obtain from https://platform.openai.com/api-keys.
        Pass as Authorization: Bearer YOUR_API_KEY.
  schemas:
    CreateEmbeddingRequest:
      type: object
      required:
      - model
      - input
      properties:
        model:
          type: string
          description: >-
            ID of the model to use. You can use the List Models API to see
            all available models, or see the Model overview for descriptions.
          examples:
          - text-embedding-3-small
          - text-embedding-3-large
          - text-embedding-ada-002
        input:
          oneOf:
          - type: string
            description: The string to embed.
          - type: array
            description: The array of strings to embed.
            items:
              type: string
            minItems: 1
            maxItems: 2048
          - type: array
            description: >-
              The array of integers (token IDs) to embed. Each array must
              have 8191 or fewer elements.
            items:
              type: integer
            minItems: 1
          - type: array
            description: >-
              The array of arrays containing integers (token IDs) to embed.
            items:
              type: array
              items:
                type: integer
              minItems: 1
            minItems: 1
          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.
          example: example_value
        encoding_format:
          type: string
          enum:
          - float
          - base64
          default: float
          description: >-
            The format to return the embeddings in. Can be either float or
            base64. Defaults to float.
          example: float
        dimensions:
          type: integer
          minimum: 1
          description: >-
            The number of dimensions the resulting output embeddings should
            have. Only supported in text-embedding-3 and later models.
          example: 10
        user:
          type: string
          description: >-
            A unique identifier representing your end-user, which can help
            OpenAI to monitor and detect abuse.
          example: example_value
    CreateEmbeddingResponse:
      type: object
      required:
      - object
      - data
      - model
      - usage
      properties:
        object:
          type: string
          enum:
          - list
          description: The object type, always list.
          example: list
        data:
          type: array
          description: The list of embedding objects.
          items:
            $ref: '#/components/schemas/Embedding'
          example: []
        model:
          type: string
          description: The name of the model used to generate the embedding.
          example: example_value
        usage:
          $ref: '#/components/schemas/EmbeddingUsage'
    Embedding:
      type: object
      required:
      - object
      - embedding
      - index
      properties:
        object:
          type: string
          enum:
          - embedding
          description: The object type, always embedding.
          example: embedding
        embedding:
          oneOf:
          - type: array
            description: >-
              The embedding vector as an array of floats. The length of the
              vector depends on the model and dimensions parameter.
            items:
              type: number
              format: float
          - type: string
            description: >-
              The embedding vector as a base64-encoded string when
              encoding_format is base64.
          example: example_value
        index:
          type: integer
          description: >-
            The index of the embedding in the list of embeddings, corresponding
            to the position of the input.
          example: 10
    EmbeddingUsage:
      type: object
      required:
      - prompt_tokens
      - total_tokens
      properties:
        prompt_tokens:
          type: integer
          description: The number of tokens in the input.
          example: 10
        total_tokens:
          type: integer
          description: The total number of tokens used.
          example: 10