📖 To learn more, take a look at our complete API
📤 Example Request
Generate an api key below👇
Install node-fetch & add the GOOEY_API_KEY to your environment variables.
Never store the api key in your code and don't use direcly in the browser.
$ npm install node-fetch
$ export GOOEY_API_KEY=sk-xxxx
- Use this sample code to call the API.
If you encounter any issues, write to us at [email protected] and make sure to include the full code snippet and the error message.
import fetch from 'node-fetch';
const payload = {
"input_prompt": "{{conversations}}\n\n####\n\nYour job is to analyse the conversations above and summarise the user's responses to questions asked by the assistant.\n\nReturn the summary as a numbered list of 5 representative user responses. Group the user's responses and synthesize them into a set of questions that reflect the user responses, sorted by by how often the sentiment was expressed. Also categorise them based on common themes you observe. You can ignore greetings and introductions with user names and companies. \n\nHere are two examples: \n{\"What's your reaction to everything you've seen so far?\": \"1. Hope: Most people expressed cautious optimism about AI's potential for India\", \n\"Top questions\": \"1. AI and Jobs: What can we do to stem job losses from AI?\" }\n\nReturn your analysis as the following JSON object: { \"What's your reaction to everything you've seen so far?\": \"1. <Theme>: <Response>\\n2. <Theme>: <Response>\\n3. ...\", \"Top questions\": \"1. <Theme>: <Response>\\n2. <Theme>: <Response>\\n3. ...\" }\n\nIf the user responses doesn't fit into any of the categories, return an empty object (`{}`)",
"selected_models": [
"gpt_4_turbo"
]
};
async function gooeyAPI() {
const response = await fetch("https://api.gooey.ai/v2/CompareLLM?example_id=48eqzd62k52t", {
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();
Generate an api key below👇
Install requests & add the GOOEY_API_KEY to your environment variables.
Never store the api key in your code.
$ python3 -m pip install requests
$ export GOOEY_API_KEY=sk-xxxx
- Use this sample code to call the API.
If you encounter any issues, write to us at [email protected] and make sure to include the full code snippet and the error message.
import os
import requests
payload = {
"input_prompt": '{{conversations}}\n\n####\n\nYour job is to analyse the conversations above and summarise the user\'s responses to questions asked by the assistant.\n\nReturn the summary as a numbered list of 5 representative user responses. Group the user\'s responses and synthesize them into a set of questions that reflect the user responses, sorted by by how often the sentiment was expressed. Also categorise them based on common themes you observe. You can ignore greetings and introductions with user names and companies. \n\nHere are two examples: \n{"What\'s your reaction to everything you\'ve seen so far?": "1. Hope: Most people expressed cautious optimism about AI\'s potential for India", \n"Top questions": "1. AI and Jobs: What can we do to stem job losses from AI?" }\n\nReturn your analysis as the following JSON object: { "What\'s your reaction to everything you\'ve seen so far?": "1. <Theme>: <Response>\\n2. <Theme>: <Response>\\n3. ...", "Top questions": "1. <Theme>: <Response>\\n2. <Theme>: <Response>\\n3. ..." }\n\nIf the user responses doesn\'t fit into any of the categories, return an empty object (`{}`)',
"selected_models": ["gpt_4_turbo"],
}
response = requests.post(
"https://api.gooey.ai/v2/CompareLLM?example_id=48eqzd62k52t",
headers={
"Authorization": "bearer " + os.environ["GOOEY_API_KEY"],
},
json=payload,
)
assert response.ok, response.content
result = response.json()
print(response.status_code, result)
Generate an api key below👇
Install curl & add the GOOEY_API_KEY to your environment variables.
Never store the api key in your code.
export GOOEY_API_KEY=sk-xxxx
- Run the following
curl command in your terminal.
If you encounter any issues, write to us at [email protected] and make sure to include the full curl command and the error message.
curl 'https://api.gooey.ai/v2/CompareLLM?example_id=48eqzd62k52t' \
-H "Authorization: bearer $GOOEY_API_KEY" \
-H 'Content-Type: application/json' \
-d '{
"input_prompt": "{{conversations}}\n\n####\n\nYour job is to analyse the conversations above and summarise the user'"'"'s responses to questions asked by the assistant.\n\nReturn the summary as a numbered list of 5 representative user responses. Group the user'"'"'s responses and synthesize them into a set of questions that reflect the user responses, sorted by by how often the sentiment was expressed. Also categorise them based on common themes you observe. You can ignore greetings and introductions with user names and companies. \n\nHere are two examples: \n{\"What'"'"'s your reaction to everything you'"'"'ve seen so far?\": \"1. Hope: Most people expressed cautious optimism about AI'"'"'s potential for India\", \n\"Top questions\": \"1. AI and Jobs: What can we do to stem job losses from AI?\" }\n\nReturn your analysis as the following JSON object: { \"What'"'"'s your reaction to everything you'"'"'ve seen so far?\": \"1. <Theme>: <Response>\\n2. <Theme>: <Response>\\n3. ...\", \"Top questions\": \"1. <Theme>: <Response>\\n2. <Theme>: <Response>\\n3. ...\" }\n\nIf the user responses doesn'"'"'t fit into any of the categories, return an empty object (`{}`)",
"selected_models": [
"gpt_4_turbo"
]
}'
🎁 Example Response
{4 Items"url":
string
"https://gooey.ai/compare-large-language-models/"
"created_at":
string
"2024-05-14T16:27:14.802104+00:00"
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