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Abhishek Biswal
Abhishek Biswal

Posted on • Updated on

Classification of Phrases/Quotes using Chat GPT API and Firebase Cloud Functions

Generating topics for a given quote can be challenging, but with advancements in AI and natural language processing, now it's possible to generate topics with high accuracy. In this article, we'll go through how to use the Chat GPT API to generate topics for a given quote and host the method in the Firebase cloud function HTTP method.

What is Chat-GPT-API?
Chat-GPT-API is an API that provides access to OpenAI's GPT (Generative Pre-trained Transformer) language model. GPT is a machine learning model that is trained to generate human-like text based on a given prompt. The Chat-GPT-API allows developers to integrate the GPT model into their applications and generate text based on user input.

What are Firebase Cloud Functions?
Firebase Cloud Functions is a serverless compute service that allows developers to run code in response to events and automatically scale based on traffic. Firebase Cloud Functions supports Node.js, Python, Java, Go, and .NET. In this article, we will use Firebase Cloud Functions with Node.js to host our code.

Now that we have an understanding of the technologies we will be using, let's take a look at the process.

The first step in creating a tag/category is to create a list of topics from which to choose. In our example, we defined a list of default topics as an array of objects with an id and name field. Below is an example:

const defaultTags = [
    { id: "1", name: "a" },
    { id: "2", name: "b" },
    { id: "3", name: "c" },
    { id: "4", name: "d" },
    { id: "5", name: "x" },
    { id: "6", name: "z" }
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Next, we need to set up the configuration for the OpenAI API using our API key.

const configuration = new Configuration({
    apiKey: process.env.OPENAI_API_KEY,
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The function that creates the topics can now be defined. We will host this function in Firebase cloud function HTTP way so that we may access it via an API endpoint.

async function generateTopics(req, res){
    var data = req.body
    var tagNames = =>;

    #initialize openaiapi
    const openai = new OpenAIApi(configuration);
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Next create a prompt:
var userPrompt =
"Assign multiple topics as an array from the topic list given below to the following quote:\nQuote - " +
data.content +
"\n\ntopics = [" +
tagNames.join(", ") +
"]\n\n Output should be in the format `topics = ['']`";

then create a openAIApi response using the prompt:

const response = await openai.createChatCompletion({
        model: "gpt-3.5-turbo",
        messages: [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role":"user", "content": userPrompt}],
        temperature: 0.8,
        max_tokens: 150,
        top_p: 1,
        frequency_penalty: 0,
        presence_penalty: 0.6
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Here we are using gpt-3.5-turbo model to get the response.

Then we have to parse the response to get the message. The structure of the response is like the below:

  "id": "chatcmpl-123",
  "object": "chat.completion",
  "created": 1677652288,
  "choices": [{
    "index": 0,
    "message": {
      "role": "user",
      "content": "topics = ['a', 'b', 'c']",
    "finish_reason": "stop"
  "usage": {
    "prompt_tokens": 9,
    "completion_tokens": 12,
    "total_tokens": 21
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We have to get the message which is required further to get the topics.
const message =[0]
const json = message.message
const content = JSON.stringify(json.content)

Then we can use regular expression to parse the topics from the result:

const topicsArr = str.match(/\[([^[\]]*)\]/)[1].match(/'[^']*'/g).map(topic => topic.slice(1, -1));
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After this we can use map and filter to assign respective ids from the default topic list

const selectedTagObjects = updatedTags.filter(tag => generatedTopics.includes(
        .map(({ id, name }) => ({ id, name }));
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after all this we can return the result as below:

var result = {
        "tags": selectedTagObjects
    cors(req,res, ()=>{
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In conclusion, using the Chat GPT API for generating topics from a given quote can be a useful tool in various applications, such as content tagging and topic identification. The code snippet above demonstrates how we can utilize this API in a Firebase Cloud Function and provide an endpoint to generate topics from a given quote. By simply sending a post request with the quote in the request body, we can get back a list of relevant topics generated by the API. This can be further improved and integrated with other systems to create a more comprehensive solution for content analysis and classification.

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