Labeling conversation with GPT

library
// Sets labels that GPT can apply to conversations after an analysis.
// These labels help in collecting and analyzing statistics from various conversations.
// You are encouraged to add any additional labels that you may find relevant.
// The name of a label will be an output after the conversation ends. The description helps GPT to accurately assign a label to the conversation.
context
{
    // control of the labeling
    isLabellingEnabled: boolean = true;
    
    input openAiApiKey: string? = null;
    input label_descriptions:
    {
        name: string; description: string;
    }
    [] = [
    {
        name: "CallBack", description: "when the conversation ends with assistant saying they will call the user back later."
    }
    ,
    {
        name: "Escalation", description: "when assistant escalated or transferred the call to a human agent."
    }
    ,
    {
        name: "DontCall", description: "when human ask not call any more."
    }
    ,
    {
        name: "NotInterested", description: "when user is not interested in the converstation."
    }
    ];
    output labels: string[] = [];
}

block makeLabels(openAiApiKey: string?, label_descriptions:
{
    name: string; description: string;
}
[]): string[]
{
    context
    {
        analyzePrompt: string = `Instructions: Analyze the following conversation between user and assistant and label it with the following possible labels:
        {{labels}}
        None: use this label in case of there are no appropriate labels.
        
        ---conversation log start---
        {{transcription}}
        ---conversation log end---
        
        Reply with one and more label using ',' as a separator.
        `;
    }
    
    start node root
    {
        do
        {
            var labels: string = "";
            var result: string[] = [];
            for (var entry in $label_descriptions)
            {
                set labels += "\n" + entry.name + ": " + entry.description;
            }
            
            var responses = #getTranscription(options:
            {
                channelIds: []
            }
            );

            var hasResponses = false;
            
            for (var response in responses)
            {
                if (response.source != "ai")
                {
                    set hasResponses = true;
                }
            }
            // checks if there was any response from the human
            if (!hasResponses)
            {
                result.push("None");
                return result;
            }
            
            var transcription = #getFormattedTranscription(options:
            {
                channelIds: []
            }
            );
            var res = #askGPT($analyzePrompt, gptOptions:
            {
                model: "openai/gpt-4o-mini",
                openai_apikey: $openAiApiKey,
                function_call: "none",
                history_length: 0
            }
            );
            for (var label in res.responseText.split(","))
            {
                result.push(label.trim());
            }
            
            return result;
        }
    }
}

when exiting make_labels do
{
    if(!$isLabellingEnabled) 
    {
        return;
    }

    var newLabels = blockcall makeLabels($openAiApiKey, $label_descriptions);
    for (var x in newLabels)
    {
        $labels.push(x);
    }

    return;
}