class OpenAI::CreateChatCompletion

Overview

POST https://api.openai.com/v1/chat/completions

Included Modules

Extended Modules

Defined in:

open_ai/models/chat_completion.cr

Constructors

Instance Method Summary

Constructor Detail

def self.new(model : String, messages : Array(OpenAI::Message)) #

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def self.new(pull : JSON::PullParser) #

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Instance Method Detail

def frequency_penalty : Float64 #

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.


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def frequency_penalty=(frequency_penalty : Float64) #

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.


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def logit_bias : Hash(String, Float64) | Nil #

Modify the likelihood of specified tokens appearing in the completion. You can use this tokenizer tool (which works for both GPT-2 and GPT-3) to convert text to token IDs


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def logit_bias=(logit_bias : Hash(String, Float64) | Nil) #

Modify the likelihood of specified tokens appearing in the completion. You can use this tokenizer tool (which works for both GPT-2 and GPT-3) to convert text to token IDs


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def messages : Array(Message) #

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def messages=(messages : Array(Message)) #

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def model : String #

the model id


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def model=(model : String) #

the model id


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def num_completions : Int32 #

How many completions to generate for each prompt.


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def num_completions=(num_completions : Int32) #

How many completions to generate for each prompt.


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def presence_penalty : Float64 #

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.


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def presence_penalty=(presence_penalty : Float64) #

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.


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def stop : String | Array(String) | Nil #

Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence.


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def stop=(stop : String | Array(String) | Nil) #

Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence.


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def stream : Bool #

Whether to stream back partial progress.


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def stream=(stream : Bool) #

Whether to stream back partial progress.


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def temperature : Float64 #

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.


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def temperature=(temperature : Float64) #

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.


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def top_p : Float64 #

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. Alter this or temperature but not both.


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def top_p=(top_p : Float64) #

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. Alter this or temperature but not both.


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def user : String | Nil #

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


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def user=(user : String | Nil) #

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


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