openapi: 3.1.0
info:
  title: Mistral AI Mistral Embeddings API
  description: >-
    Generate vector embeddings for text using Mistral's embedding models.
    Useful for retrieval-augmented generation, clustering, classification,
    and semantic search use cases.
  version: '1.0'
  contact:
    name: Mistral AI Support
    url: https://docs.mistral.ai/
    email: support@mistral.ai
  termsOfService: https://mistral.ai/terms/
externalDocs:
  description: Mistral Embeddings API Documentation
  url: https://docs.mistral.ai/api/#embeddings
servers:
  - url: https://api.mistral.ai/v1
    description: Mistral AI Production
tags:
  - name: Embeddings
    description: Text embedding operations
security:
  - bearerAuth: []
paths:
  /embeddings:
    post:
      operationId: createEmbedding
      summary: Mistral AI Create embeddings
      description: >-
        Generate vector embeddings for the given input text. Supports single
        or batch text inputs and returns high-dimensional float vectors.
      tags:
        - Embeddings
      requestBody:
        required: true
        content:
          application/json:
            schema:
              $ref: '#/components/schemas/EmbeddingRequest'
      responses:
        '200':
          description: Embedding response
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/EmbeddingResponse'
        '400':
          description: Bad request
        '401':
          description: Unauthorized - invalid or missing API key
        '429':
          description: Rate limit exceeded
components:
  securitySchemes:
    bearerAuth:
      type: http
      scheme: bearer
      description: Mistral AI API key passed as a Bearer token
  schemas:
    EmbeddingRequest:
      type: object
      required:
        - model
        - input
      properties:
        model:
          type: string
          description: ID of the embedding model to use
          examples:
            - mistral-embed
        input:
          oneOf:
            - type: string
            - type: array
              items:
                type: string
          description: Text to embed, as a string or array of strings
        encoding_format:
          type: string
          enum:
            - float
          default: float
          description: The format of the output embeddings
    EmbeddingResponse:
      type: object
      properties:
        id:
          type: string
          description: Unique identifier for the embedding request
        object:
          type: string
          enum:
            - list
        data:
          type: array
          items:
            $ref: '#/components/schemas/Embedding'
        model:
          type: string
          description: The model used to generate the embeddings
        usage:
          $ref: '#/components/schemas/Usage'
    Embedding:
      type: object
      properties:
        object:
          type: string
          enum:
            - embedding
        embedding:
          type: array
          items:
            type: number
          description: The embedding vector
        index:
          type: integer
          description: Index of the embedding in the input list
    Usage:
      type: object
      properties:
        prompt_tokens:
          type: integer
          description: Number of tokens in the input
        total_tokens:
          type: integer
          description: Total tokens used