OpenAI · Schema

OpenAI Embedding Response

A response object returned by the OpenAI Embeddings API. Contains a list of embedding vectors representing the input text as floating-point numbers, along with the model used and token usage statistics.

AIArtificial IntelligenceLarge Language ModelsT1

Properties

Name Type Description
object string The object type, which is always list.
data array The list of embedding objects, one for each input.
model string The name of the model used to generate the embeddings (e.g., text-embedding-3-small, text-embedding-3-large, text-embedding-ada-002).
usage object
View JSON Schema on GitHub

JSON Schema

openai-embedding-schema.json Raw ↑
{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "$id": "https://platform.openai.com/schemas/openai/embedding.json",
  "title": "OpenAI Embedding Response",
  "description": "A response object returned by the OpenAI Embeddings API. Contains a list of embedding vectors representing the input text as floating-point numbers, along with the model used and token usage statistics.",
  "type": "object",
  "required": ["object", "data", "model", "usage"],
  "properties": {
    "object": {
      "type": "string",
      "const": "list",
      "description": "The object type, which is always list."
    },
    "data": {
      "type": "array",
      "description": "The list of embedding objects, one for each input.",
      "items": {
        "$ref": "#/$defs/Embedding"
      }
    },
    "model": {
      "type": "string",
      "description": "The name of the model used to generate the embeddings (e.g., text-embedding-3-small, text-embedding-3-large, text-embedding-ada-002)."
    },
    "usage": {
      "$ref": "#/$defs/Usage"
    }
  },
  "$defs": {
    "Embedding": {
      "type": "object",
      "description": "Represents an embedding vector returned by the Embeddings API. Each embedding corresponds to one input in the request.",
      "required": ["object", "embedding", "index"],
      "properties": {
        "object": {
          "type": "string",
          "const": "embedding",
          "description": "The object type, which is always embedding."
        },
        "embedding": {
          "oneOf": [
            {
              "type": "array",
              "description": "The embedding vector, which is a list of floats. The length of vector depends on the model (1536 for text-embedding-ada-002, up to 3072 for text-embedding-3-large) and the dimensions parameter if specified.",
              "items": {
                "type": "number",
                "format": "float"
              }
            },
            {
              "type": "string",
              "description": "The embedding vector as a base64-encoded string, returned when encoding_format is base64."
            }
          ],
          "description": "The embedding vector representing the input text. The format depends on the encoding_format request parameter."
        },
        "index": {
          "type": "integer",
          "minimum": 0,
          "description": "The index of the embedding in the list of embeddings, corresponding to the position of the input in the request."
        }
      }
    },
    "Usage": {
      "type": "object",
      "description": "Token usage statistics for the embedding request.",
      "required": ["prompt_tokens", "total_tokens"],
      "properties": {
        "prompt_tokens": {
          "type": "integer",
          "minimum": 0,
          "description": "The number of tokens in the input text."
        },
        "total_tokens": {
          "type": "integer",
          "minimum": 0,
          "description": "The total number of tokens used in the request. For embeddings, this is the same as prompt_tokens."
        }
      }
    }
  }
}