python/resources/files/methods/retrieve/index.md 2026-07-10 23:02 UTC to 2026-07-12 06:58 UTC

Retrieve file

files.retrieve(strfile_id) -> FileObject

get /files/{file_id}

Returns information about a specific file.

Parameters

• file_id: str

Returns

• class FileObject: …

  The File object represents a document that has been uploaded to OpenAI.

  • id: str

    The file identifier, which can be referenced in the API endpoints.

  • bytes: int

    The size of the file, in bytes.

  • created_at: int

    The Unix timestamp (in seconds) for when the file was created.

  • filename: str

    The name of the file.

  • object: Literal["file"]

    The object type, which is always file.

    • "file"

  • purpose: Literal["assistants", "assistants_output", "batch", 5 more]

    The intended purpose of the file. Supported values are assistants, assistants_output, batch, batch_output, fine-tune, fine-tune-results, vision, and user_data.

    • "assistants"

    • "assistants_output"

    • "batch"

    • "batch_output"

    • "fine-tune"

    • "fine-tune-results"

    • "vision"

    • "user_data"

  • status: Literal["uploaded", "processed", "error"]

    Deprecated. The current status of the file, which can be either uploaded, processed, or error.

    • "uploaded"

    • "processed"

    • "error"

  • expires_at: Optional[int]

    The Unix timestamp (in seconds) for when the file will expire.

  • status_details: Optional[str]

    Deprecated. For details on why a fine-tuning training file failed validation, see the error field on fine_tuning.job.

Example

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ.get("OPENAI_API_KEY"),  # This is the default and can be omitted
)
file_object = client.files.retrieve(
    "file_id",
)
print(file_object.id)

Response

{
  "id": "id",
  "bytes": 0,
  "created_at": 0,
  "filename": "filename",
  "object": "file",
  "purpose": "assistants",
  "status": "uploaded",
  "expires_at": 0,
  "status_details": "status_details"
}

Example

from openai import OpenAI
client = OpenAI()

client.files.retrieve("file-abc123")

Response

{
  "id": "file-abc123",
  "object": "file",
  "bytes": 120000,
  "created_at": 1677610602,
  "expires_at": 1677614202,
  "filename": "mydata.jsonl",
  "purpose": "fine-tune",
}