GPTs API (Special Model)
POSThttps://api.cometapi.com/v1/completionsYou can easily chat with any gpts in ChatGPT GPTs store through GPTs api. This is CometAPI's special provided model.
By requesting a specific GPTs, you need find its gpts_id first. By clicking on the GPTs you wanted, you will get a url like https://chatgpt.com/g/g-kZ0eYXlJe-scholar-gpt. The gpts_id for this Scholar GPTs is g-kZ0eYXlJe, make sure to remove the "-scholar-gpt" at the end.
Then, you can request the model gpt-4-gizmo-g-kZ0eYXlJe, you will get 1:1 experience using the Scholar GPT in ChatGPT website.
Request
Your API-KEY or Token
ID of the model to use. See the model list for details on which models work with the Chat API.
A list of messages comprising the conversation so far.
Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim.
Accepts a JSON object that maps tokens (specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect will vary per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token.
Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities of each output token returned in the content of message.
An integer between 0 and 20 specifying the number of most likely tokens to return at each token position, each with an associated log probability. logprobs must be set to true if this parameter is used.
The maximum number of tokens that can be generated in the chat completion. The total length of input tokens and generated tokens is limited by the model's context length. Example Python code for counting tokens.
How many chat completion choices to generate for each input message. Note that you will be charged based on the number of generated tokens across all of the choices. Keep n as 1 to minimize costs.
Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics.
An object specifying the format that the model must output. Compatible with GPT-4 Turbo and all GPT-3.5 Turbo models newer than gpt-3.5-turbo-1106. Setting to { "type": "json_object" } enables JSON mode, which guarantees the message the model generates is valid JSON. Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly "stuck" request. Also note that the message content may be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length.
Up to 4 sequences where the API will stop generating further tokens.
If set, partial message deltas will be sent, like in ChatGPT. Tokens will be sent as data-only server-sent events as they become available, with the stream terminated by a data: [DONE] message.
What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. We generally recommend altering this or top_p but not both.
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.
A list of tools the model may call. Currently, only functions are supported as a tool. Use this to provide a list of functions the model may generate JSON inputs for. A max of 128 functions are supported.
Controls which (if any) tool is called by the model. none means the model will not call any tool and instead generates a message. auto means the model can pick between generating a message or calling one or more tools. required means the model must call one or more tools. Specifying a particular tool via {"type": "function", "function": {"name": "my_function"}} forces the model to call that tool. none is the default when no tools are present. auto is the default if tools are present.
Optional Defaults to true Whether to enable parallel function calling during tool use.
{
"model": "gpt-4-gizmo-g-kZ0eYXlJe",
"messages": [
{
"role": "user",
"content": "Who are you?"
}
]
}Request samples
[api.label.responses]
{
"id": "chatcmpl-123",
"object": "chat.completion",
"created": 1677652288,
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "I am Scholar GPT, a specialized research assistant designed to help with academic and technical queries. My capabilities include data analysis, visualization, web research for academic papers, processing data, and more. How can I assist you with your research today?"
},
"finish_reason": "stop"
}
],
"usage": {
"prompt_tokens": 9,
"completion_tokens": 12,
"total_tokens": 21
}
}