openapi: 3.2.0 info: title: Cohere Chat Embed API description: The Cohere Chat API enables developers to integrate large language model text generation capabilities into their applications through a conversational interface. It supports multi-turn conversations, tool use with JSON schema definitions, retrieval-augmented generation, and streaming responses. The API is available via the v2 endpoint and works with Cohere's Command family of models. version: '2.0' contact: name: Cohere Support url: https://support.cohere.com termsOfService: https://cohere.com/terms-of-use servers: - url: https://api.cohere.com description: Cohere Production Server security: - bearerAuth: [] tags: - name: Embed description: Endpoints for generating vector embeddings from text and image inputs using Cohere embedding models. paths: /v2/embed: post: operationId: embed summary: Generate embeddings description: Returns vector embeddings for the provided text or image inputs. The embeddings capture semantic information about the content and can be used for semantic search, clustering, classification, and other tasks requiring similarity comparisons. Requires specifying an input_type for embedding models v3 and higher. tags: - Embed requestBody: required: true content: application/json: schema: $ref: '#/components/schemas/EmbedRequest' responses: '200': description: Successful embedding response content: application/json: schema: $ref: '#/components/schemas/EmbedResponse' '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: schemas: EmbedResponse: type: object properties: id: type: string description: Unique identifier for the embedding request. embeddings: type: object description: An object containing the generated embeddings organized by embedding type. Each type maps to an array of embedding vectors corresponding to the input texts. properties: float: type: array description: Float embeddings for each input. items: type: array items: type: number int8: type: array description: Signed int8 embeddings for each input. items: type: array items: type: integer uint8: type: array description: Unsigned int8 embeddings for each input. items: type: array items: type: integer binary: type: array description: Signed binary embeddings for each input. items: type: array items: type: integer base64: type: array description: Base64-encoded embeddings for each input. items: type: string texts: type: array description: The text entries for which embeddings were returned. items: type: string meta: type: object description: Metadata about the API request. properties: api_version: type: object properties: version: type: string description: The API version used for the request. Error: type: object properties: message: type: string description: A human-readable error message describing what went wrong. EmbedRequest: type: object required: - model - input_type properties: model: type: string description: The name of the embedding model to use. example: embed-english-v3.0 texts: type: array description: An array of strings for the model to embed. Maximum number of texts per call depends on the model. items: type: string images: type: array description: An array of image data for the model to embed. Used with models that support image embedding. items: type: string input_type: type: string enum: - search_document - search_query - classification - clustering - image description: Specifies the type of input passed to the model. Required for embedding models v3 and higher. Use search_document for embeddings stored in a vector database, search_query for search queries, classification for text classifiers, clustering for clustering tasks, and image for image inputs. embedding_types: type: array description: Specifies the types of embeddings to return. Can include one or more of float, int8, uint8, binary, and base64. items: type: string enum: - float - int8 - uint8 - binary - base64 truncate: type: string enum: - NONE - START - END description: Specifies how the API handles inputs longer than the maximum token length. START discards the beginning, END discards the end. NONE returns an error if the input is too long. securitySchemes: bearerAuth: type: http scheme: bearer description: Bearer authentication using a Cohere API key. Pass the API key in the Authorization header as Bearer . externalDocs: description: Cohere Chat API Documentation url: https://docs.cohere.com/reference/chat