Calls GPT4 and returns the result as a string.


Uses the openai API key:


const aigur = createClient({
apiKeys: {
openai: process.env.OPENAI_API_KEY



import { gpt4Prediction } from '@aigur/client';
flow.node(gpt4Prediction, () => ({
messages: [
role: 'user',
content: 'Hello, how are you?'
})) // --> {text: 'I'm doing well, thank you. How about you?'}


Property Type Required Description Default Value
messages Array<{role: 'user' | 'system' | 'assistant', content: string}> Yes The prompt to pass GPT3.5-Turbo
model enum('gpt-4', 'gpt-4-32k') No ID of the model to use gpt-4
max_tokens number No The maximum number of tokens to generate in the completion. The token count of your prompt plus max_tokens cannot exceed the model's context length. Most models have a context length of 2048 tokens (except for the newest models, which support 4096). 200
temperature number No 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. 0.7
top_p number No 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. It is generally recommend altering this or temperature but not both. 1
frequency_penalty number No 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. 0
presence_penalty number No 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. 0


Property Type
text string