import { type ChatCompletionCreateParamsBase } from "https://deno.land/x/openai@v4.19.0/resources/chat/completions.ts";
Properties
A list of messages comprising the conversation so far. Example Python code.
ID of the model to use. See the model endpoint compatibility table for details on which models work with the Chat API.
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.
See more information about frequency and presence penalties.
Deprecated in favor of tool_choice
.
Controls which (if any) function is called by the model. none
means the model
will not call a function and instead generates a message. auto
means the model
can pick between generating a message or calling a function. Specifying a
particular function via {"name": "my_function"}
forces the model to call that
function.
none
is the default when no functions are present. `auto`` is the default if
functions are present.
Deprecated in favor of tools
.
A list of functions the model may generate JSON inputs for.
Modify the likelihood of specified tokens appearing in the completion.
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.
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. Example Python code for counting tokens.
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.
See more information about frequency and presence penalties.
An object specifying the format that the model must output.
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 increased latency and appearance of a "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.
This feature is in Beta. If specified, our system will make a best effort to
sample deterministically, 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 changes
in the backend.
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.
Example Python code.
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.
Controls which (if any) function is called by the model. none
means the model
will not call a function and instead generates a message. auto
means the model
can pick between generating a message or calling a function. Specifying a
particular function via
{"type: "function", "function": {"name": "my_function"}}
forces the model to
call that function.
none
is the default when no functions are present. auto
is the default if
functions are present.
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.
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 unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. Learn more.