Nuxt Chatgpt

ChatGPT integration for Nuxt 3.

About the module

This user-friendly module boasts of an easy integration process that enables seamless implementation into any Nuxt 3 project. With type-safe integration, you can integrate ChatGPT into your Nuxt 3 project without breaking a sweat. Enjoy easy access to the chat, and chatCompletion methods through the useChatgpt() composable. Additionally, the module guarantees security as requests are routed through a Nitro Server, thus preventing the exposure of your API Key. The module use openai library version 4.0.0 behind the scene.

Features

💪 Easy implementation into any Nuxt 3 project.
👉 Type-safe integration of Chatgpt into your Nuxt 3 project.
🕹️ Provides a useChatgpt() composable that grants easy access to the chat, and chatCompletion methods.
🔥 Ensures security by routing requests through a Nitro Server, preventing the API Key from being exposed.
🧱 It is lightweight and performs well.

Getting Started

Add nuxt-chatgpt dependency to your project

npm
```
npm install --save-dev nuxt-chatgpt
```
pnpm
```
pnpm add -D nuxt-chatgpt
```
yarn
```
yarn add --dev nuxt-chatgpt
```

Add nuxt-chatgpt to the modules section of nuxt.config.ts

export default defineNuxtConfig({
  modules: ["nuxt-chatgpt"],

  // entirely optional
  chatgpt: {
    apiKey: 'Your apiKey here goes here'
  },
})

That's it! You can now use Nuxt Chatgpt in your Nuxt app 🔥

Usage & Examples

To access the chat, and chatCompletion methods in the nuxt-chatgpt module, you can use the useChatgpt() composable, which provides easy access to them. The chat, and chatCompletion methods requires three parameters:

Name	Type	Default	Description
message	`String`	available only for `chat()`	A string representing the text message that you want to send to the GPT model for processing.
messages	`Array`	available only for `chatCompletion()`	An array of objects that contains `role` and `content`
model	`String`	`text-davinci-003` for `chat()` and `gpt-3.5-turbo` for `chatCompletion()`	Represent certain model for different types of natural language processing tasks.
options	`Object`	`{ temperature: 0.5, max_tokens: 2048, top_p: 1 frequency_penalty: 0, presence_penalty: 0 }`	An optional object that specifies any additional options you want to pass to the API request, such as the number of responses to generate, and the maximum length of each response.

Available models:

text-davinci-002
text-davinci-003
gpt-3.5-turbo
gpt-3.5-turbo-0301
gpt-3.5-turbo-1106
gpt-4
gpt-4-1106-preview
gpt-4-0314
gpt-4-0613
gpt-4-32k
gpt-4-32k-0314
gpt-4-32k-0613

You need to join waitlist to use gpt-4 models within chatCompletion method

Simple `chat` usage

In the following example, the model is unspecified, and the text-davinci-003 model will be used by default.

const { chat } = useChatgpt()

const data = ref('')
const inputData = ref('')

async function sendMessage() {
  try {
    const response = await chat(inputData.value)
    data.value = response
  } catch(error) {
    alert(`Join the waiting list if you want to use GPT-4 models: ${error}`)
  }
}

<template>
  <div>
    <input v-model="inputData">
    <button
      @click="sendMessage"
      v-text="'Send'"
    />
    <div>{{ data }}</div>
  </div>
</template>

Usage of `chat` with different model

const { chat } = useChatgpt()

const data = ref('')
const inputData = ref('')

async function sendMessage() {
  try {
    const response = await chat(inputData.value, 'gpt-3.5-turbo')
    data.value = response
  } catch(error) {
    alert(`Join the waiting list if you want to use GPT-4 models: ${error}`)
  }
}

<template>
  <div>
    <input v-model="inputData">
    <button
      @click="sendMessage"
      v-text="'Send'"
    />
    <div>{{ data }}</div>
  </div>
</template>

Simple `chatCompletion` usage

In the following example, the model is unspecified, and the gpt-3.5-turbo model will be used by default.

const { chatCompletion } = useChatgpt()

const chatTree = ref([])
const inputData = ref('')

async function sendMessage() {
  try {
    const message = {
      role: 'user',
      content: `${inputData.value}`,
    }

    chatTree.value.push(message)

    const response = await chatCompletion(chatTree.value)
    
    const responseMessage = {
      role: response[0].message.role,
      content: response[0].message.content
    }
    
    chatTree.value.push(responseMessage)
  } catch(error) {
    alert(`Join the waiting list if you want to use GPT-4 models: ${error}`)
  }
}

<template>
  <div>
    <input v-model="inputData">
    <button
      @click="sendMessage"
      v-text="'Send'"
    />
    <div>
      <div
        v-for="chat in chatTree"
        :key="chat"
      >
        <strong>{{ chat.role }} :</strong>
        <div>{{ chat.content }} </div>
      </div>
    </div>
  </div>
</template>

Usage of `chatCompletion` with different model

const { chatCompletion } = useChatgpt()

const chatTree = ref([])
const inputData = ref('')

async function sendMessage() {
  try {
    const message = {
      role: 'user',
      content: `${inputData.value}`,
    }

    chatTree.value.push(message)

    const response = await chatCompletion(chatTree.value, 'gpt-3.5-turbo-0301')
    
    const responseMessage = {
      role: response[0].message.role,
      content: response[0].message.content
    }
    
    chatTree.value.push(responseMessage)
  } catch(error) {
    alert(`Join the waiting list if you want to use GPT-4 models: ${error}`)
  }
}

<template>
  <div>
    <input v-model="inputData">
    <button
      @click="sendMessage"
      v-text="'Send'"
    />
    <div>
      <div
        v-for="chat in chatTree"
        :key="chat"
      >
        <strong>{{ chat.role }} :</strong>
        <div>{{ chat.content }} </div>
      </div>
    </div>
  </div>
</template>

chat vs chatCompletion

The chat method allows the user to send a prompt to the OpenAI API and receive a response. You can use this endpoint to build conversational interfaces that can interact with users in a natural way. For example, you could use the chat method to build a chatbot that can answer customer service questions or provide information about a product or service.

The chatCompletion method is similar to the chat method, but it provides additional functionality for generating longer, more complex responses. Specifically, the chatCompletion method allows you to provide a conversation history as input, which the API can use to generate a response that is consistent with the context of the conversation. This makes it possible to build chatbots that can engage in longer, more natural conversations with users.

Module Options

Name	Type	Default	Description
apiKey	`String`	`xxxxxx`	Your apiKey here goes here
isEnabled	`Boolean`	`true`	Enable or disable the module. `True` by default.

Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

Fork the Project
Create your Feature Branch (git checkout -b feature/AmazingFeature)
Commit your Changes (git commit -m 'Add some AmazingFeature')
Push to the Branch (git push origin feature/AmazingFeature)
Open a Pull Request

# Install dependencies
npm install

# Generate type stubs
npm run dev:prepare

# Develop with the playground
npm run dev

# Build the playground
npm run dev:build

# Run ESLint
npm run lint

# Run Vitest
npm run test
npm run test:watch

# Release new version
npm run release