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Chat
Completions

Get chat completion

chat.completions.retrieve(strcompletion_id) -> ChatCompletion
GET/chat/completions/{completion_id}

Get a stored chat completion. Only Chat Completions that have been created with the store parameter set to true will be returned.

ParametersExpand Collapse
completion_id: str
ReturnsExpand Collapse
class ChatCompletion: …

Represents a chat completion response returned by model, based on the provided input.

id: str

A unique identifier for the chat completion.

choices: List[Choice]

A list of chat completion choices. Can be more than one if n is greater than 1.

finish_reason: Literal["stop", "length", "tool_calls", 2 more]

The reason the model stopped generating tokens. This will be stop if the model hit a natural stop point or a provided stop sequence, length if the maximum number of tokens specified in the request was reached, content_filter if content was omitted due to a flag from our content filters, tool_calls if the model called a tool, or function_call (deprecated) if the model called a function.

Accepts one of the following:
"stop"
"length"
"tool_calls"
"content_filter"
"function_call"
index: int

The index of the choice in the list of choices.

logprobs: Optional[ChoiceLogprobs]

Log probability information for the choice.

content: Optional[List[ChatCompletionTokenLogprob]]

A list of message content tokens with log probability information.

token: str

The token.

bytes: Optional[List[int]]

A list of integers representing the UTF-8 bytes representation of the token. Useful in instances where characters are represented by multiple tokens and their byte representations must be combined to generate the correct text representation. Can be null if there is no bytes representation for the token.

logprob: float

The log probability of this token, if it is within the top 20 most likely tokens. Otherwise, the value -9999.0 is used to signify that the token is very unlikely.

top_logprobs: List[TopLogprob]

List of the most likely tokens and their log probability, at this token position. In rare cases, there may be fewer than the number of requested top_logprobs returned.

token: str

The token.

bytes: Optional[List[int]]

A list of integers representing the UTF-8 bytes representation of the token. Useful in instances where characters are represented by multiple tokens and their byte representations must be combined to generate the correct text representation. Can be null if there is no bytes representation for the token.

logprob: float

The log probability of this token, if it is within the top 20 most likely tokens. Otherwise, the value -9999.0 is used to signify that the token is very unlikely.

refusal: Optional[List[ChatCompletionTokenLogprob]]

A list of message refusal tokens with log probability information.

token: str

The token.

bytes: Optional[List[int]]

A list of integers representing the UTF-8 bytes representation of the token. Useful in instances where characters are represented by multiple tokens and their byte representations must be combined to generate the correct text representation. Can be null if there is no bytes representation for the token.

logprob: float

The log probability of this token, if it is within the top 20 most likely tokens. Otherwise, the value -9999.0 is used to signify that the token is very unlikely.

top_logprobs: List[TopLogprob]

List of the most likely tokens and their log probability, at this token position. In rare cases, there may be fewer than the number of requested top_logprobs returned.

token: str

The token.

bytes: Optional[List[int]]

A list of integers representing the UTF-8 bytes representation of the token. Useful in instances where characters are represented by multiple tokens and their byte representations must be combined to generate the correct text representation. Can be null if there is no bytes representation for the token.

logprob: float

The log probability of this token, if it is within the top 20 most likely tokens. Otherwise, the value -9999.0 is used to signify that the token is very unlikely.

message: ChatCompletionMessage

A chat completion message generated by the model.

content: Optional[str]

The contents of the message.

refusal: Optional[str]

The refusal message generated by the model.

role: Literal["assistant"]

The role of the author of this message.

annotations: Optional[List[Annotation]]

Annotations for the message, when applicable, as when using the web search tool.

type: Literal["url_citation"]

The type of the URL citation. Always url_citation.

url_citation: AnnotationURLCitation

A URL citation when using web search.

end_index: int

The index of the last character of the URL citation in the message.

start_index: int

The index of the first character of the URL citation in the message.

title: str

The title of the web resource.

url: str

The URL of the web resource.

audio: Optional[ChatCompletionAudio]

If the audio output modality is requested, this object contains data about the audio response from the model. Learn more.

id: str

Unique identifier for this audio response.

data: str

Base64 encoded audio bytes generated by the model, in the format specified in the request.

expires_at: int

The Unix timestamp (in seconds) for when this audio response will no longer be accessible on the server for use in multi-turn conversations.

transcript: str

Transcript of the audio generated by the model.

Deprecatedfunction_call: Optional[FunctionCall]

Deprecated and replaced by tool_calls. The name and arguments of a function that should be called, as generated by the model.

arguments: str

The arguments to call the function with, as generated by the model in JSON format. Note that the model does not always generate valid JSON, and may hallucinate parameters not defined by your function schema. Validate the arguments in your code before calling your function.

name: str

The name of the function to call.

tool_calls: Optional[List[ChatCompletionMessageToolCallUnion]]

The tool calls generated by the model, such as function calls.

Accepts one of the following:
class ChatCompletionMessageFunctionToolCall: …

A call to a function tool created by the model.

id: str

The ID of the tool call.

function: Function

The function that the model called.

arguments: str

The arguments to call the function with, as generated by the model in JSON format. Note that the model does not always generate valid JSON, and may hallucinate parameters not defined by your function schema. Validate the arguments in your code before calling your function.

name: str

The name of the function to call.

type: Literal["function"]

The type of the tool. Currently, only function is supported.

class ChatCompletionMessageCustomToolCall: …

A call to a custom tool created by the model.

id: str

The ID of the tool call.

custom: Custom

The custom tool that the model called.

input: str

The input for the custom tool call generated by the model.

name: str

The name of the custom tool to call.

type: Literal["custom"]

The type of the tool. Always custom.

created: int

The Unix timestamp (in seconds) of when the chat completion was created.

model: str

The model used for the chat completion.

object: Literal["chat.completion"]

The object type, which is always chat.completion.

service_tier: Optional[Literal["auto", "default", "flex", 2 more]]

Specifies the processing type used for serving the request.

  • If set to 'auto', then the request will be processed with the service tier configured in the Project settings. Unless otherwise configured, the Project will use 'default'.
  • If set to 'default', then the request will be processed with the standard pricing and performance for the selected model.
  • If set to 'flex' or 'priority', then the request will be processed with the corresponding service tier.
  • When not set, the default behavior is 'auto'.

When the service_tier parameter is set, the response body will include the service_tier value based on the processing mode actually used to serve the request. This response value may be different from the value set in the parameter.

Accepts one of the following:
"auto"
"default"
"flex"
"scale"
"priority"
Deprecatedsystem_fingerprint: Optional[str]

This fingerprint represents the backend configuration that the model runs with.

Can be used in conjunction with the seed request parameter to understand when backend changes have been made that might impact determinism.

usage: Optional[CompletionUsage]

Usage statistics for the completion request.

completion_tokens: int

Number of tokens in the generated completion.

prompt_tokens: int

Number of tokens in the prompt.

total_tokens: int

Total number of tokens used in the request (prompt + completion).

completion_tokens_details: Optional[CompletionTokensDetails]

Breakdown of tokens used in a completion.

accepted_prediction_tokens: Optional[int]

When using Predicted Outputs, the number of tokens in the prediction that appeared in the completion.

audio_tokens: Optional[int]

Audio input tokens generated by the model.

reasoning_tokens: Optional[int]

Tokens generated by the model for reasoning.

rejected_prediction_tokens: Optional[int]

When using Predicted Outputs, the number of tokens in the prediction that did not appear in the completion. However, like reasoning tokens, these tokens are still counted in the total completion tokens for purposes of billing, output, and context window limits.

prompt_tokens_details: Optional[PromptTokensDetails]

Breakdown of tokens used in the prompt.

audio_tokens: Optional[int]

Audio input tokens present in the prompt.

cached_tokens: Optional[int]

Cached tokens present in the prompt.

Get chat completion

from openai import OpenAI
client = OpenAI()

completions = client.chat.completions.list()
first_id = completions[0].id
first_completion = client.chat.completions.retrieve(completion_id=first_id)
print(first_completion)

{
  "object": "chat.completion",
  "id": "chatcmpl-abc123",
  "model": "gpt-4o-2024-08-06",
  "created": 1738960610,
  "request_id": "req_ded8ab984ec4bf840f37566c1011c417",
  "tool_choice": null,
  "usage": {
    "total_tokens": 31,
    "completion_tokens": 18,
    "prompt_tokens": 13
  },
  "seed": 4944116822809979520,
  "top_p": 1.0,
  "temperature": 1.0,
  "presence_penalty": 0.0,
  "frequency_penalty": 0.0,
  "system_fingerprint": "fp_50cad350e4",
  "input_user": null,
  "service_tier": "default",
  "tools": null,
  "metadata": {},
  "choices": [
    {
      "index": 0,
      "message": {
        "content": "Mind of circuits hum,  \nLearning patterns in silence—  \nFuture's quiet spark.",
        "role": "assistant",
        "tool_calls": null,
        "function_call": null
      },
      "finish_reason": "stop",
      "logprobs": null
    }
  ],
  "response_format": null
}
Returns Examples
{
  "object": "chat.completion",
  "id": "chatcmpl-abc123",
  "model": "gpt-4o-2024-08-06",
  "created": 1738960610,
  "request_id": "req_ded8ab984ec4bf840f37566c1011c417",
  "tool_choice": null,
  "usage": {
    "total_tokens": 31,
    "completion_tokens": 18,
    "prompt_tokens": 13
  },
  "seed": 4944116822809979520,
  "top_p": 1.0,
  "temperature": 1.0,
  "presence_penalty": 0.0,
  "frequency_penalty": 0.0,
  "system_fingerprint": "fp_50cad350e4",
  "input_user": null,
  "service_tier": "default",
  "tools": null,
  "metadata": {},
  "choices": [
    {
      "index": 0,
      "message": {
        "content": "Mind of circuits hum,  \nLearning patterns in silence—  \nFuture's quiet spark.",
        "role": "assistant",
        "tool_calls": null,
        "function_call": null
      },
      "finish_reason": "stop",
      "logprobs": null
    }
  ],
  "response_format": null
}