Microsoft Azure Embeddings API

openapi: 3.2.0
info:
  title: Microsoft Azure Azure OpenAI Service Embeddings API
  description: Azure OpenAI Service provides REST API access to OpenAI's language models including GPT-4, GPT-4 Turbo, GPT-3.5-Turbo, and the Embeddings model series. These models can be adapted to tasks including content generation, summarization, semantic search, and natural language to code translation. Users access the service through REST APIs, Python SDK, or the web-based interface in the Azure AI Foundry portal.
  version: '2024-06-01'
  contact:
    name: Microsoft Azure Support
    url: https://azure.microsoft.com/en-us/support/
    email: azuresupport@microsoft.com
  license:
    name: Microsoft API License
    url: https://learn.microsoft.com/en-us/legal/
  x-logo:
    url: https://azure.microsoft.com/svghandler/azure-logo.png
servers:
- url: https://{endpoint}/openai
  description: Azure OpenAI Service endpoint
  variables:
    endpoint:
      default: your-resource-name.openai.azure.com
      description: 'The Azure OpenAI resource endpoint. Format: https://{resource-name}.openai.azure.com'
security:
- apiKey: []
- oauth2:
  - https://cognitiveservices.azure.com/.default
tags:
- name: Embeddings
  description: Operations for generating text embeddings
paths:
  /deployments/{deployment-id}/embeddings:
    post:
      operationId: Embeddings_Create
      summary: Microsoft Azure Create Embeddings
      description: Get a vector representation of a given input that can be easily consumed by machine learning models and algorithms.
      tags:
      - Embeddings
      parameters:
      - $ref: '#/components/parameters/DeploymentIdParameter'
      - $ref: '#/components/parameters/ApiVersionParameter'
      requestBody:
        required: true
        content:
          application/json:
            schema:
              $ref: '#/components/schemas/CreateEmbeddingRequest'
            examples:
              EmbeddingsCreateRequestExample:
                summary: Default Embeddings_Create request
                x-microcks-default: true
                value:
                  input: example_value
                  user: example_value
                  input_type: example_value
                  encoding_format: float
                  dimensions: 10
      responses:
        '200':
          description: Successfully created embeddings.
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/CreateEmbeddingResponse'
              examples:
                EmbeddingsCreate200Example:
                  summary: Default Embeddings_Create 200 response
                  x-microcks-default: true
                  value:
                    object: list
                    data:
                    - index: 10
                      object: embedding
                      embedding:
                      - {}
                    model: example_value
                    usage:
                      prompt_tokens: 10
                      completion_tokens: 10
                      total_tokens: 10
        default:
          description: An error occurred.
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/ErrorResponse'
              examples:
                EmbeddingsCreatedefaultExample:
                  summary: Default Embeddings_Create default response
                  x-microcks-default: true
                  value:
                    error:
                      code: example_value
                      message: example_value
                      param: example_value
                      type: example_value
                      inner_error:
                        code: example_value
                        content_filter_result: example_value
      x-microcks-operation:
        delay: 0
        dispatcher: FALLBACK
components:
  schemas:
    CreateEmbeddingRequest:
      type: object
      description: Request body for creating embeddings.
      properties:
        input:
          description: Input text to embed, encoded as a string or array of strings.
          oneOf:
          - type: string
          - type: array
            items:
              type: string
            minItems: 1
          example: example_value
        user:
          type: string
          description: A unique identifier representing your end-user.
          example: example_value
        input_type:
          type: string
          description: The input type for the embedding request.
          example: example_value
        encoding_format:
          type: string
          enum:
          - float
          - base64
          description: The format to return the embeddings in.
          example: float
        dimensions:
          type: integer
          minimum: 1
          description: The number of dimensions the resulting output embeddings should have.
          example: 10
      required:
      - input
    Usage:
      type: object
      description: Usage statistics for the API call.
      properties:
        prompt_tokens:
          type: integer
          description: Number of tokens in the prompt.
          example: 10
        completion_tokens:
          type: integer
          description: Number of tokens in the generated completion.
          example: 10
        total_tokens:
          type: integer
          description: Total number of tokens used in the request.
          example: 10
      required:
      - prompt_tokens
      - total_tokens
    CreateEmbeddingResponse:
      type: object
      description: Response from the embeddings API.
      properties:
        object:
          type: string
          enum:
          - list
          description: The object type.
          example: list
        data:
          type: array
          description: The list of embeddings generated.
          items:
            type: object
            properties:
              index:
                type: integer
                description: The index of the embedding.
              object:
                type: string
                enum:
                - embedding
                description: The object type.
              embedding:
                type: array
                items:
                  type: number
                description: The embedding vector.
            required:
            - index
            - object
            - embedding
          example: []
        model:
          type: string
          description: The model used to generate the embeddings.
          example: example_value
        usage:
          $ref: '#/components/schemas/Usage'
      required:
      - object
      - data
      - model
      - usage
    ErrorResponse:
      type: object
      description: Error response.
      properties:
        error:
          type: object
          description: The error object.
          properties:
            code:
              type: string
              description: The error code.
            message:
              type: string
              description: The error message.
            param:
              type: string
              description: The parameter that caused the error.
            type:
              type: string
              description: The error type.
            inner_error:
              type: object
              description: Inner error details.
              properties:
                code:
                  type: string
                  description: The inner error code.
                content_filter_result:
                  type: object
                  description: Content filter results, if applicable.
          required:
          - code
          - message
          example: example_value
  parameters:
    DeploymentIdParameter:
      name: deployment-id
      in: path
      required: true
      description: The deployment ID of the model to use for this request.
      schema:
        type: string
    ApiVersionParameter:
      name: api-version
      in: query
      required: true
      description: The API version to use for this operation.
      schema:
        type: string
        default: '2024-06-01'
  securitySchemes:
    apiKey:
      type: apiKey
      name: api-key
      in: header
      description: Azure OpenAI API key authentication.
    oauth2:
      type: oauth2
      flows:
        implicit:
          authorizationUrl: https://login.microsoftonline.com/common/oauth2/v2.0/authorize
          scopes:
            https://cognitiveservices.azure.com/.default: Access Azure OpenAI