Chat interface

POST

/v1/chat/completions

Given a hint, the model will return one or more predicted completions and can also return the probability of the surrogate marker for each position.
Complete creation for the provided prompts and parameters

Request

Header Params

Content-Type

string

required

Example:

application/json

string

required

Example:

application/json

Authorization

string

optional

Example:

Bearer {{YOUR_API_KEY}}

Body Params application/json

model

string

required

The ID of the model to use. For more details on which models can be used with the Chat API, refer to the model endpoint compatibility table.

messages

array [object {2}]

required

A list of messages included in the conversation so far. Python code example.

role

string

optional

content

string

optional

temperature

integer

optional

The sampling temperature to use, between 0 and 2. Higher values (e.g., 0.8) make the output more random, while lower values (e.g., 0.2) make it more focused and deterministic. We generally recommend altering this or top_p but not both.

top_p

integer

optional

An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered. We generally recommend altering this or temperature but not both.

integer

optional

Default is 1. The number of chat completion choices to generate for each input message.

stream

boolean

optional

Default is false. If set, partial message increments will be sent like in ChatGPT. Tokens will be sent as server-sent events (SSE) as they become available, and the stream will terminate with a data: [DONE] message. Python code example.

stop

string

optional

Default is null. Up to 4 sequences where the API will stop generating further tokens.

max_tokens

integer

optional

Default is infinity. The maximum number of tokens to generate in the chat completion.

The total length of input tokens and generated tokens is limited by the model's context length. Python code example for token calculation.

presence_penalty

number

optional

A number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the likelihood of the model discussing new topics. See more on frequency and presence penalties.

frequency_penalty

number

optional

Default is 0. A number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text, reducing the likelihood of the model repeating the same line. See more on frequency and presence penalties.

logit_bias

null

optional

Modify the likelihood of specified tokens appearing in the completion.

Accepts a JSON object that maps tokens (specified by their token IDs from the tokenizer) to associated bias values (-100 to 100). Mathematically, the bias is added to the logits generated by the model before sampling. The exact effect varies per model, but values between -1 and 1 should reduce or increase the likelihood of the associated tokens being selected; values like -100 or 100 should disable or exclusively allow the associated tokens.

user

string

optional

A unique identifier representing your end user, which can help OpenAI monitor and detect abuse. Learn more.

response_format

object

optional

An object specifying the format the model must output. Enabling JSON mode with { "type": "json_object" } ensures the model generates valid JSON messages. Important: When using JSON mode, you must also instruct the model to generate JSON through system or user messages. Failure to do so may result in endless blank streams until the token limit is reached, leading to increased latency and the appearance of a 'stuck' request. Also note that if finish_reason="length", the message content may be partially truncated, indicating generation exceeded max_tokens or the conversation exceeded the maximum context length. Show properties.

seen

integer

optional

This feature is experimental. If specified, our system will make a best effort to deterministically sample such that repeated requests with the same seed and parameters should return the same result. Determinism is not guaranteed, and you should refer to the system_fingerprint response parameter to monitor backend changes.

tools

array[string]

required

A list of tools the model can call. Currently, only functions are supported as tools. Use this feature to provide a list of functions the model can generate JSON inputs for.

tool_choice

object

required

Controls which function (if any) the model calls. none means the model will not call a function but will generate a message. auto means the model can choose between generating a message and calling a function. Forcing the model to call a function can be done with {"type": "function", "function": {"name": "my_function"}}. If no functions exist, the default is none. If functions exist, the default is auto. Show possible types.

Example

{
  "model": "gemini-1.5-pro-latest",
  "messages": [
    {
      "role": "user",
      "content": "Who is Messi"
    }
  ]
}

Request samples

Shell

JavaScript

Java

Swift

PHP

Python

HTTP

Objective-C

Ruby

OCaml

Dart

curl --location --request POST '/v1/chat/completions' \
--header 'Accept: application/json' \
--header 'Authorization: Bearer {{YOUR_API_KEY}}' \
--header 'Content-Type: application/json' \
--data-raw '{
  "model": "gemini-1.5-pro-latest",
  "messages": [
    {
      "role": "user",
      "content": "Who is Messi"
    }
  ]
}'

Responses

🟢200OK

application/json

Body

string

required

object

string

required

created

integer

required

choices

array [object {3}]

required

index

integer

optional

message

object

optional

finish_reason

string

optional

usage

object

required

prompt_tokens

integer

required

completion_tokens

integer

required

total_tokens

integer

required

Example

{
    "id": "chatcmpl-123",
    "object": "chat.completion",
    "created": 1677652288,
    "choices": [
        {
            "index": 0,
            "message": {
                "role": "assistant",
                "content": "\n\nHello there, how may I assist you today?"
            },
            "finish_reason": "stop"
        }
    ],
    "usage": {
        "prompt_tokens": 9,
        "completion_tokens": 12,
        "total_tokens": 21
    }
}

Modified at 2025-02-20 06:05:13

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