Qubrid AI Embeddings API

openapi: 3.2.0
info:
  title: Qubrid AI Inference Embeddings API
  description: The Qubrid AI Inference API provides a single, OpenAI-compatible endpoint for orchestrating 40+ open-source models running on NVIDIA GPU infrastructure.
  version: 1.0.0
  contact:
    name: Qubrid AI Support
    url: https://www.qubrid.com/contact
  termsOfService: https://www.qubrid.com/terms-of-service
servers:
- url: https://platform.qubrid.com/v1
  description: Qubrid AI Production Server
security:
- bearerAuth: []
tags:
- name: Embeddings
  description: Generate vector embeddings from text input using embedding models hosted on the Qubrid AI platform, suitable for semantic search, clustering, and retrieval-augmented generation workflows.
paths:
  /embeddings:
    post:
      operationId: createEmbedding
      summary: Create embeddings
      description: Generates vector embeddings for the provided input text using a specified embedding model on the Qubrid AI platform. Embeddings can be used for semantic search, clustering, recommendations, and retrieval-augmented generation workflows.
      tags:
      - Embeddings
      requestBody:
        required: true
        content:
          application/json:
            schema:
              $ref: '#/components/schemas/EmbeddingRequest'
      responses:
        '200':
          description: Successfully generated embeddings for the input text.
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/EmbeddingResponse'
        '400':
          description: The request was malformed or contained invalid parameters.
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/ErrorResponse'
        '401':
          description: Authentication failed due to a missing or invalid bearer token.
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/ErrorResponse'
        '404':
          description: The specified embedding model was not found.
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/ErrorResponse'
components:
  schemas:
    EmbeddingObject:
      type: object
      properties:
        object:
          type: string
          enum:
          - embedding
          description: The object type, always embedding.
        embedding:
          type: array
          items:
            type: number
          description: The embedding vector, which is a list of floating point numbers.
        index:
          type: integer
          description: The index of the embedding in the list of embeddings.
    EmbeddingResponse:
      type: object
      properties:
        object:
          type: string
          enum:
          - list
          description: The object type, always list.
        data:
          type: array
          description: A list of embedding objects.
          items:
            $ref: '#/components/schemas/EmbeddingObject'
        model:
          type: string
          description: The model used to generate the embeddings.
        usage:
          type: object
          properties:
            prompt_tokens:
              type: integer
              description: The number of tokens in the input.
            total_tokens:
              type: integer
              description: The total number of tokens processed.
    ErrorResponse:
      type: object
      properties:
        error:
          type: object
          properties:
            message:
              type: string
              description: A human-readable error message describing what went wrong.
            type:
              type: string
              description: The type of error that occurred.
            code:
              type: string
              description: A machine-readable error code.
    EmbeddingRequest:
      type: object
      required:
      - model
      - input
      properties:
        model:
          type: string
          description: The identifier of the embedding model to use for generating vector representations of the input text.
        input:
          oneOf:
          - type: string
          - type: array
            items:
              type: string
          description: The input text to embed. Can be a single string or an array of strings for batch embedding.
        encoding_format:
          type: string
          enum:
          - float
          - base64
          description: The format to return the embeddings in. Defaults to float.
          default: float
  securitySchemes:
    bearerAuth:
      type: http
      scheme: bearer
      bearerFormat: QUBRID_API_KEY
      description: Qubrid AI API key passed as a bearer token in the Authorization header. Obtain your API key from the Qubrid AI platform dashboard at https://platform.qubrid.com.
externalDocs:
  description: Qubrid AI Documentation
  url: https://docs.platform.qubrid.com