API: Default Copilot Analysis Script

$ npm install node-fetch
$ export GOOEY_API_KEY=sk-xxxx

import fetch from 'node-fetch';

const payload = {
  "input_prompt": "{# \nThis workflow will run after your copilot responds to any incoming message. Hence, you can use it to categorize the conversation, track whether the bot answered the user's question or infer key data from the user's conversation about their gender, location or any factor you find important. \n\nOnce all the conversation data is categorized, you can easily then visualize it on the Analytics dashboard. For more: https://docs.gooey.ai/guides/copilot/conversation-analysis \n  \nThe following variables are included with incoming run of this workflow and can be useful to enrich your analysis:\n- Last message: {{ user_msg }}  {{ assistant_msg }} or in local language {{ user_msg_local }} {{ assistant_msg_local }}\n- Current user's conversation messages: {{ messages }}\n- Messages from all conversations from all users: {{ conversations }} \n- Any other variable from the bot's run, e.g. {{ platform }} {{ user_wa_phone_number}} etc.\n- Any API request/response parameter, e.g. {{ input_prompt }} {{ input_audio }} {{ output_text }}  etc. from https://api.gooey.ai/docs#operation/video-bots\n#}\n\nConversation: \"\"\"\n{{ messages }}\n\"\"\"\n\nLast Messages: \"\"\"\nUser: {{ user_msg }}\nAssistant: {{ assistant_msg }}\n\"\"\"\n\n###\n\nGiven the Conversation above, your job is to analyze the user's messages.\n\nReturn your analysis as the following json object: {\n    \"Category\": \"<category>\",  // category of the user's question\n    {# Add suggested categories or topics here to constrain the category set - e.g. if you were building an farming agent, you might specify the potential categories as:\n  (Corn, Carrots, Livestock) \n#}\n    \"Answered\": true/false  // whether the bot was able to answer the user's last question\n    {# add any other fields you'd like to see in your analyis below e.g. Location, Gender etc. #}\n}",
  "selected_models": [
    "gpt_4_1"
  ]
};

async function gooeyAPI() {
  const response = await fetch("https://test.api.gooey.ai/v2/CompareLLM?example_id=8qqg3xb84ddc", {
    method: "POST",
    headers: {
      "Authorization": "bearer " + process.env["GOOEY_API_KEY"],
      "Content-Type": "application/json",
    },
    body: JSON.stringify(payload),
  });

  if (!response.ok) {
    throw new Error(response.status);
  }

  const result = await response.json();
  console.log(response.status, result);
}

gooeyAPI();

$ python3 -m pip install requests
$ export GOOEY_API_KEY=sk-xxxx

import os
import requests

payload = {
    "input_prompt": '{# \nThis workflow will run after your copilot responds to any incoming message. Hence, you can use it to categorize the conversation, track whether the bot answered the user\'s question or infer key data from the user\'s conversation about their gender, location or any factor you find important. \n\nOnce all the conversation data is categorized, you can easily then visualize it on the Analytics dashboard. For more: https://docs.gooey.ai/guides/copilot/conversation-analysis \n  \nThe following variables are included with incoming run of this workflow and can be useful to enrich your analysis:\n- Last message: {{ user_msg }}  {{ assistant_msg }} or in local language {{ user_msg_local }} {{ assistant_msg_local }}\n- Current user\'s conversation messages: {{ messages }}\n- Messages from all conversations from all users: {{ conversations }} \n- Any other variable from the bot\'s run, e.g. {{ platform }} {{ user_wa_phone_number}} etc.\n- Any API request/response parameter, e.g. {{ input_prompt }} {{ input_audio }} {{ output_text }}  etc. from https://api.gooey.ai/docs#operation/video-bots\n#}\n\nConversation: """\n{{ messages }}\n"""\n\nLast Messages: """\nUser: {{ user_msg }}\nAssistant: {{ assistant_msg }}\n"""\n\n###\n\nGiven the Conversation above, your job is to analyze the user\'s messages.\n\nReturn your analysis as the following json object: {\n    "Category": "<category>",  // category of the user\'s question\n    {# Add suggested categories or topics here to constrain the category set - e.g. if you were building an farming agent, you might specify the potential categories as:\n  (Corn, Carrots, Livestock) \n#}\n    "Answered": true/false  // whether the bot was able to answer the user\'s last question\n    {# add any other fields you\'d like to see in your analyis below e.g. Location, Gender etc. #}\n}',
    "selected_models": ["gpt_4_1"],
}

response = requests.post(
    "https://test.api.gooey.ai/v2/CompareLLM?example_id=8qqg3xb84ddc",
    headers={
        "Authorization": "bearer " + os.environ["GOOEY_API_KEY"],
    },
    json=payload,
)
assert response.ok, response.content

result = response.json()
print(response.status_code, result)

export GOOEY_API_KEY=sk-xxxx

curl 'https://test.api.gooey.ai/v2/CompareLLM?example_id=8qqg3xb84ddc' \
  -H "Authorization: bearer $GOOEY_API_KEY" \
  -H 'Content-Type: application/json' \
  -d '{
  "input_prompt": "{# \nThis workflow will run after your copilot responds to any incoming message. Hence, you can use it to categorize the conversation, track whether the bot answered the user'"'"'s question or infer key data from the user'"'"'s conversation about their gender, location or any factor you find important. \n\nOnce all the conversation data is categorized, you can easily then visualize it on the Analytics dashboard. For more: https://docs.gooey.ai/guides/copilot/conversation-analysis \n  \nThe following variables are included with incoming run of this workflow and can be useful to enrich your analysis:\n- Last message: {{ user_msg }}  {{ assistant_msg }} or in local language {{ user_msg_local }} {{ assistant_msg_local }}\n- Current user'"'"'s conversation messages: {{ messages }}\n- Messages from all conversations from all users: {{ conversations }} \n- Any other variable from the bot'"'"'s run, e.g. {{ platform }} {{ user_wa_phone_number}} etc.\n- Any API request/response parameter, e.g. {{ input_prompt }} {{ input_audio }} {{ output_text }}  etc. from https://api.gooey.ai/docs#operation/video-bots\n#}\n\nConversation: \"\"\"\n{{ messages }}\n\"\"\"\n\nLast Messages: \"\"\"\nUser: {{ user_msg }}\nAssistant: {{ assistant_msg }}\n\"\"\"\n\n###\n\nGiven the Conversation above, your job is to analyze the user'"'"'s messages.\n\nReturn your analysis as the following json object: {\n    \"Category\": \"<category>\",  // category of the user'"'"'s question\n    {# Add suggested categories or topics here to constrain the category set - e.g. if you were building an farming agent, you might specify the potential categories as:\n  (Corn, Carrots, Livestock) \n#}\n    \"Answered\": true/false  // whether the bot was able to answer the user'"'"'s last question\n    {# add any other fields you'"'"'d like to see in your analyis below e.g. Location, Gender etc. #}\n}",
  "selected_models": [
    "gpt_4_1"
  ]
}'