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 |
JSON Schema
{
"$schema": "https://json-schema.org/draft/2020-12/schema",
"$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."
}
}
}
}
}