cohere · Schema

EmbedRequest

Properties

Name	Type	Description
model	string	The name of the embedding model to use.
texts	array	An array of strings for the model to embed. Maximum number of texts per call depends on the model.
images	array	An array of image data for the model to embed. Used with models that support image embedding.
input_type	string	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, classific
embedding_types	array	Specifies the types of embeddings to return. Can include one or more of float, int8, uint8, binary, and base64.
truncate	string	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.

View JSON Schema on GitHub

JSON Schema

{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "$id": "#/components/schemas/EmbedRequest",
  "title": "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."
    }
  }
}