openapi: 3.1.0 info: title: Mistral AI Embeddings API description: >- The Mistral AI Embeddings API allows developers to compute document and text embeddings using Mistral's embedding models. These embeddings can be used for semantic search, clustering, classification, and retrieval augmented generation workflows. The API accepts text inputs and returns high-dimensional vector representations suitable for a variety of natural language processing tasks. version: '1.0.0' contact: name: Mistral AI Support url: https://docs.mistral.ai termsOfService: https://mistral.ai/terms externalDocs: description: Mistral AI Embeddings Documentation url: https://docs.mistral.ai/capabilities/embeddings servers: - url: https://api.mistral.ai/v1 description: Mistral AI Production Server tags: - name: Embeddings description: >- Endpoints for generating vector embeddings from text inputs using Mistral embedding models. security: - bearerAuth: [] paths: /embeddings: post: operationId: createEmbedding summary: Create embeddings description: >- Creates an embedding vector representing the input text. The input can be a single string or an array of strings for batch processing. Returns high-dimensional vector representations suitable for semantic search, clustering, and classification tasks. tags: - Embeddings requestBody: required: true content: application/json: schema: $ref: '#/components/schemas/EmbeddingRequest' responses: '200': description: Successful embedding response content: application/json: schema: $ref: '#/components/schemas/EmbeddingResponse' '400': description: Bad request due to invalid parameters content: application/json: schema: $ref: '#/components/schemas/Error' '401': description: Unauthorized due to missing or invalid API key content: application/json: schema: $ref: '#/components/schemas/Error' '429': description: Rate limit exceeded content: application/json: schema: $ref: '#/components/schemas/Error' components: securitySchemes: bearerAuth: type: http scheme: bearer bearerFormat: API Key schemas: EmbeddingRequest: type: object required: - model - input properties: model: type: string description: >- ID of the model to use for generating embeddings. example: mistral-embed input: oneOf: - type: string - type: array items: type: string description: >- Input text to embed. Can be a single string or an array of strings for batch processing. encoding_format: type: string description: >- The format to return the embeddings in. enum: - float default: float EmbeddingResponse: type: object properties: id: type: string description: >- A unique identifier for the embedding request. object: type: string description: >- The object type, always list. enum: - 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: $ref: '#/components/schemas/Usage' EmbeddingObject: type: object properties: object: type: string description: >- The object type, always embedding. enum: - embedding embedding: type: array description: >- The embedding vector as an array of floats. items: type: number format: float index: type: integer description: >- The index of the embedding in the list of embeddings. Usage: type: object properties: prompt_tokens: type: integer description: >- Number of tokens in the input. total_tokens: type: integer description: >- Total number of tokens used in the request. Error: type: object properties: message: type: string description: >- A human-readable error message. type: type: string description: >- The type of error. code: type: integer description: >- The HTTP status code.