feat: Convert all app files to JS

packages/backend/src/apps/azure-openai/actions/send-prompt/index.js

@@ -0,0 +1,97 @@
+import defineAction from '../../../../helpers/define-action.js';
+
+const castFloatOrUndefined = (value) => {
+  return value === '' ? undefined : parseFloat(value);
+};
+
+export default defineAction({
+  name: 'Send prompt',
+  key: 'sendPrompt',
+  description: 'Creates a completion for the provided prompt and parameters.',
+  arguments: [
+    {
+      label: 'Prompt',
+      key: 'prompt',
+      type: 'string',
+      required: true,
+      variables: true,
+      description: 'The text to analyze.',
+    },
+    {
+      label: 'Temperature',
+      key: 'temperature',
+      type: 'string',
+      required: false,
+      variables: true,
+      description:
+        '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.',
+    },
+    {
+      label: 'Maximum tokens',
+      key: 'maxTokens',
+      type: 'string',
+      required: false,
+      variables: true,
+      description:
+        'The maximum number of tokens to generate in the completion.',
+    },
+    {
+      label: 'Stop Sequence',
+      key: 'stopSequence',
+      type: 'string',
+      required: false,
+      variables: true,
+      description:
+        'Single stop sequence where the API will stop generating further tokens. The returned text will not contain the stop sequence.',
+    },
+    {
+      label: 'Top P',
+      key: 'topP',
+      type: 'string',
+      required: false,
+      variables: true,
+      description:
+        '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.',
+    },
+    {
+      label: 'Frequency Penalty',
+      key: 'frequencyPenalty',
+      type: 'string',
+      required: false,
+      variables: true,
+      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.`,
+    },
+    {
+      label: 'Presence Penalty',
+      key: 'presencePenalty',
+      type: 'string',
+      required: false,
+      variables: true,
+      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.`,
+    },
+  ],
+
+  async run($) {
+    const payload = {
+      model: $.step.parameters.model,
+      prompt: $.step.parameters.prompt,
+      temperature: castFloatOrUndefined($.step.parameters.temperature),
+      max_tokens: castFloatOrUndefined($.step.parameters.maxTokens),
+      stop: $.step.parameters.stopSequence || null,
+      top_p: castFloatOrUndefined($.step.parameters.topP),
+      frequency_penalty: castFloatOrUndefined(
+        $.step.parameters.frequencyPenalty
+      ),
+      presence_penalty: castFloatOrUndefined($.step.parameters.presencePenalty),
+    };
+
+    const { data } = await $.http.post(
+      `/deployments/${$.auth.data.deploymentId}/completions`,
+      payload
+    );
+
+    $.setActionItem({
+      raw: data,
+    });
+  },
+});