98 lines
3.3 KiB
JavaScript
98 lines
3.3 KiB
JavaScript
import defineAction from '../../../../helpers/define-action.js';
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const castFloatOrUndefined = (value) => {
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return value === '' ? undefined : parseFloat(value);
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};
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export default defineAction({
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name: 'Send prompt',
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key: 'sendPrompt',
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description: 'Creates a completion for the provided prompt and parameters.',
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arguments: [
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{
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label: 'Prompt',
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key: 'prompt',
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type: 'string',
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required: true,
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variables: true,
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description: 'The text to analyze.',
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},
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{
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label: 'Temperature',
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key: 'temperature',
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type: 'string',
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required: false,
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variables: true,
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description:
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'What sampling temperature to use, between 0 and 2. Higher values means the model will take more risks. Try 0.9 for more creative applications, and 0 (argmax sampling) for ones with a well-defined answer. We generally recommend altering this or Top P but not both.',
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},
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{
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label: 'Maximum tokens',
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key: 'maxTokens',
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type: 'string',
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required: false,
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variables: true,
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description:
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'The maximum number of tokens to generate in the completion.',
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},
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{
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label: 'Stop Sequence',
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key: 'stopSequence',
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type: 'string',
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required: false,
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variables: true,
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description:
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'Single stop sequence where the API will stop generating further tokens. The returned text will not contain the stop sequence.',
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},
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{
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label: 'Top P',
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key: 'topP',
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type: 'string',
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required: false,
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variables: true,
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description:
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'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.',
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},
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{
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label: 'Frequency Penalty',
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key: 'frequencyPenalty',
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type: 'string',
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required: false,
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variables: true,
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description: `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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},
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{
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label: 'Presence Penalty',
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key: 'presencePenalty',
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type: 'string',
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required: false,
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variables: true,
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description: `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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},
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],
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async run($) {
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const payload = {
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model: $.step.parameters.model,
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prompt: $.step.parameters.prompt,
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temperature: castFloatOrUndefined($.step.parameters.temperature),
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max_tokens: castFloatOrUndefined($.step.parameters.maxTokens),
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stop: $.step.parameters.stopSequence || null,
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top_p: castFloatOrUndefined($.step.parameters.topP),
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frequency_penalty: castFloatOrUndefined(
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$.step.parameters.frequencyPenalty
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),
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presence_penalty: castFloatOrUndefined($.step.parameters.presencePenalty),
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};
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const { data } = await $.http.post(
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`/deployments/${$.auth.data.deploymentId}/completions`,
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payload
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);
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$.setActionItem({
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raw: data,
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});
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},
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});
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