[TRI-1430] : Improve the OpenAI integration documentation
This commit is contained in:
Rutam21
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---
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title: OpenAI tasks
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sidebarTitle: Tasks
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---
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Tasks are executed after the job is triggered and are the main building blocks of a job. You can string together as many tasks as you want.
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---
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## All tasks
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### `createCompletion`
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Generates text completions as per given prompt. [Official OpenAI Docs](https://platform.openai.com/docs/api-reference/chat)
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```ts example.ts
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run: async (payload, io, ctx) => {
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// This code demonstrates using OpenAI's text completion with the "davinci" model.
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// It generates text based on the given prompt.
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await io.openai.createCompletion("completion", {
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model: "davinci",
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prompt: "Once upon a time",
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});
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},
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```
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### `backgroundCreateCompletion`
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Generates text completions in the background. [Official OpenAI Docs](https://platform.openai.com/docs/api-reference/chat)
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```ts example.ts
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run: async (payload, io, ctx) => {
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// This code showcases background text completion using the "gpt-3.5-turbo" model.
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// It generates text based on the provided programming task and logs the result.
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const programmingTask = `Create a function that checks if a string is a palindrome.`;
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const response = await io.openai.backgroundCreateCompletion("background-completion", {
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model: "gpt-3.5-turbo",
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prompt: `Coding task: ${programmingTask}\n\n`,
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});
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await io.logger.info("codeSnippet", response.choices[0]?.text);
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},
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```
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### `createChatCompletion`
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Generates text completions in a conversational context. [Official OpenAI Docs](https://platform.openai.com/docs/api-reference/chat)
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```ts example.ts
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run: async (payload, io, ctx) => {
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// This code demonstrates chat completion with the "gpt-3.5-turbo" model.
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// It simulates a conversation by providing messages and receiving a chat response.
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await io.openai.createChatCompletion("chat-completion", {
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model: "gpt-3.5-turbo",
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messages: [
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{
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role: "user",
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content: "Create a good programming joke about background jobs",
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},
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],
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});
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},
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```
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### `backgroundCreateChatCompletion`
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Generates text completions in a conversational context in the background. [Official OpenAI Docs](https://platform.openai.com/docs/api-reference/chat)
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```ts example.ts
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run: async (payload, io, ctx) => {
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// This code showcases background chat completion using the "gpt-3.5-turbo" model.
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// It simulates a conversation with a user message and logs the response choices.
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const response = await io.openai.backgroundCreateChatCompletion("background-chat-completion", {
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model: "gpt-3.5-turbo",
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messages: [
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{
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role: "user",
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content: "Create a good programming joke about background jobs",
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},
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],
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});
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await io.logger.info("choices", response.choices);
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},
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```
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### `retrieveModel`
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Retrieves a specific model by ID. [Official OpenAI Docs](https://platform.openai.com/docs/api-reference/models)
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```ts example.ts
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run: async (payload, io, ctx) => {
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// In this code snippet, we retrieve detailed information about a specific OpenAI model.
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// Specify the ID of the model you want to retrieve. Replace 'your_model_id' with the actual model ID.
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const modelIdToRetrieve = "your_model_id";
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try {
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// Retrieve the model information using the OpenAI API
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const retrievedModel = await io.openai.retrieveModel("get-model", {
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model: modelIdToRetrieve,
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});
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// Log the detailed model information
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await io.logger.info("retrievedModel", retrievedModel);
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} catch (error) {
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// Handle errors, such as if the model with the provided ID does not exist.
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await io.logger.error("Error retrieving model:", error.message);
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}
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},
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```
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### `listModels`
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Lists the available models. [Official OpenAI Docs](https://platform.openai.com/docs/api-reference/models)
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```ts example.ts
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run: async (payload, io, ctx) => {
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// This code lists available models without retrieving detailed information.
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const models = await io.openai.listModels("list-models");
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},
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```
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### `createEdit`
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Edits a given text prompt. [Official OpenAI Docs](https://platform.openai.com/docs/api-reference/edits)
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```ts example.ts
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run: async (payload, io, ctx) => {
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// This code snippet demonstrates using the OpenAI API to create an edit task.
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// Specify the task parameters:
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const editTaskParams = {
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model: "text-davinci-edit-001", // Replace with the desired model
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input: "Thsi is ridddled with erors", // Replace with the input text
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instruction: "Fix the spelling errors", // Replace with the editing instruction
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};
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try {
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// Create an edit task using the OpenAI API
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const editResponse = await io.openai.createEdit("edit", editTaskParams);
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// Log the response
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await io.logger.info("editResponse", editResponse);
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} catch (error) {
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// Handle any potential errors that may occur during the API request.
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await io.logger.error("Error creating edit task:", error.message);
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}
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},
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```
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### `createImage`
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Generates images from textual descriptions. [Official OpenAI Docs](https://platform.openai.com/docs/api-reference/images)
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```ts example.ts
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run: async (payload, io, ctx) => {
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const imageResults = await io.openai.createImage("image", {
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prompt: "A hedgehog wearing a party hat",
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n: 2,
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size: "256x256",
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response_format: "url",
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});
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```
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### `createImageEdit`
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Creates an edited or extended image given an original image and a prompt. [Official OpenAI Docs](https://platform.openai.com/docs/api-reference/images)
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```ts example.ts
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run: async (payload, io, ctx) => {
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// Specify the parameters for the image edit
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const imageEditParams = {
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style: "data:image/png;base64,base64_encoded_style_image",
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content: "data:image/png;base64,base64_encoded_content_image",
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};
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// Create the image edit using the OpenAI API
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const imageEditResponse = await io.openai.createImageEdit(imageEditParams);
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// Log the response
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await io.logger.info("imageEditResponse", imageEditResponse);
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},
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```
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### `createImageVariation`
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Creates a variation of a given image. [Official OpenAI Docs](https://platform.openai.com/docs/api-reference/images)
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```ts example.ts
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run: async (payload, io, ctx) => {
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// Specify the parameters for creating an image variation
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const imageVariationParams = {
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image: "data:image/png;base64,base64_encoded_image",
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variation: "brightness(1.2) contrast(0.8) rotate(45deg)",
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};
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// Create the image variation using the OpenAI API
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const imageVariationResponse = await io.openai.createImageVariation(imageVariationParams);
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// Log the response
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await io.logger.info("imageVariationResponse", imageVariationResponse);
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},
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```
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### `createEmbedding`
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Generates embeddings for a given text. [Official OpenAI Docs](https://platform.openai.com/docs/api-reference/embeddings/object)
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```ts example.ts
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run: async (payload, io, ctx) => {
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// This code snippet demonstrates using the OpenAI API to create a text embedding.
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// Specify the task parameters:
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const embeddingTaskParams = {
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model: "text-embedding-ada-002", // Replace with the desired model
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input: "The food was delicious and the waiter...", // Replace with the input text
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};
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try {
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// Create a text embedding using the OpenAI API
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const embeddingResponse = await io.openai.createEmbedding("embedding", embeddingTaskParams);
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// Log the response
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await io.logger.info("embeddingResponse", embeddingResponse);
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} catch (error) {
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// Handle any potential errors that may occur during the API request.
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await io.logger.error("Error creating text embedding:", error.message);
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}
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},
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```
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### `createFile`
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Uploads a file to the OpenAI API. [Official OpenAI Docs](https://platform.openai.com/docs/api-reference/files/object)
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```ts example.ts
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run: async (payload, io, ctx) => {
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// Specify the parameters for creating a file
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const fileParams = {
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name: "example.txt",
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content: "This is the content of the file.",
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};
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// Create the file using the OpenAI API
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const fileResponse = await io.openai.createFile(fileParams);
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// Log the response
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await io.logger.info("fileResponse", fileResponse);
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},
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```
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### `listFiles`
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Lists the uploaded files. [Official OpenAI Docs](https://platform.openai.com/docs/api-reference/files/object)
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```ts example.ts
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run: async (payload, io, ctx) => {
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// List the files available in your OpenAI account
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const fileListResponse = await io.openai.listFiles();
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// Log the list of files
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await io.logger.info("fileListResponse", fileListResponse);
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},
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```
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### `createFineTuneFile`
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Uploads a file for fine-tuning a model. [Official OpenAI Docs](https://platform.openai.com/docs/api-reference/fine-tuning)
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```ts example.ts
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run: async (payload, io, ctx) => {
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// Specify the parameters for creating a fine-tune file
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const fineTuneFileParams = {
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model: "text-davinci-002",
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prompt: "Translate English to French: 'Hello, world.'",
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language: "en",
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description: "Fine-tune file for translation task",
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};
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// Create the fine-tune file using the OpenAI API
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const fineTuneFileResponse = await io.openai.createFineTuneFile(fineTuneFileParams);
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// Log the response
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await io.logger.info("fineTuneFileResponse", fineTuneFileResponse);
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},
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```
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### `createFineTune`
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Fine-tunes a model on a given task. [Official OpenAI Docs](https://platform.openai.com/docs/api-reference/fine-tuning)
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```ts example.ts
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run: async (payload, io, ctx) => {
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// Specify the parameters for creating a fine-tune task
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const fineTuneParams = {
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model: "text-davinci-002",
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dataset: "your_dataset_id",
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description: "Fine-tune task for custom dataset",
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};
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// Create the fine-tune task using the OpenAI API
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const fineTuneResponse = await io.openai.createFineTune(fineTuneParams);
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// Log the response
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await io.logger.info("fineTuneResponse", fineTuneResponse);
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},
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```
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### `listFineTunes`
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Lists the available fine-tunes. [Official OpenAI Docs](https://platform.openai.com/docs/api-reference/fine-tuning)
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```ts example.ts
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run: async (payload, io, ctx) => {
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// List the fine-tunes available in your OpenAI account
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const fineTunesListResponse = await io.openai.listFineTunes();
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// Log the list of fine-tunes
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await io.logger.info("fineTunesListResponse", fineTunesListResponse);
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},
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```
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### `retrieveFineTune`
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Retrieves a specific fine-tune by ID. [Official OpenAI Docs](https://platform.openai.com/docs/api-reference/fine-tuning)
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```ts example.ts
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run: async (payload, io, ctx) => {
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// Specify the ID of the fine-tune you want to retrieve
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const fineTuneId = "your_fine_tune_id"; // Replace with the actual fine-tune ID
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// Retrieve the fine-tune using the OpenAI API
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const retrievedFineTune = await io.openai.retrieveFineTune(fineTuneId);
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// Log the retrieved fine-tune
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await io.logger.info("retrievedFineTune", retrievedFineTune);
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},
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```
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### `cancelFineTune`
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Cancels a specific fine-tune by ID. [Official OpenAI Docs](https://platform.openai.com/docs/api-reference/fine-tuning)
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```ts example.ts
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run: async (payload, io, ctx) => {
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// Specify the ID of the fine-tune you want to cancel
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const fineTuneIdToCancel = "your_fine_tune_id"; // Replace with the actual fine-tune ID
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// Cancel the specified fine-tune using the OpenAI API
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const cancellationResponse = await io.openai.cancelFineTune(fineTuneIdToCancel);
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// Log the cancellation response
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await io.logger.info("cancellationResponse", cancellationResponse);
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},
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```
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### `createFineTuningJob`
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Creates a job that fine-tunes a specified model from a given dataset. [Official OpenAI Docs](https://platform.openai.com/docs/api-reference/fine-tuning)
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```ts example.ts
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run: async (payload, io, ctx) => {
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// Specify the parameters for creating a fine-tuning job
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const fineTuningJobParams = {
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fineTuneId: "your_fine_tune_id", // Replace with the actual fine-tune ID
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datasetId: "your_dataset_id", // Replace with the ID of your dataset
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model: "text-davinci-002", // Replace with the model for fine-tuning
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n_examples: 100, // Replace with the number of examples
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};
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// Create the fine-tuning job using the OpenAI API
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const fineTuningJobResponse = await io.openai.createFineTuningJob(fineTuningJobParams);
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// Log the response
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await io.logger.info("fineTuningJobResponse", fineTuningJobResponse);
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},
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```
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### `retrieveFineTuningJob`
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Get info about a fine-tuning job. [Official OpenAI Docs](https://platform.openai.com/docs/api-reference/fine-tuning)
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```ts example.ts
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run: async (payload, io, ctx) => {
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// Specify the ID of the fine-tuning job you want to retrieve
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const fineTuningJobId = "your_fine_tuning_job_id"; // Replace with the actual job ID
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// Retrieve the fine-tuning job using the OpenAI API
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const retrievedJob = await io.openai.retrieveFineTuningJob(fineTuningJobId);
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// Log the retrieved job
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await io.logger.info("retrievedJob", retrievedJob);
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},
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```
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### `cancelFineTuningJob`
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Cancel a fine-tuning job. [Official OpenAI Docs](https://platform.openai.com/docs/api-reference/fine-tuning)
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```ts example.ts
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run: async (payload, io, ctx) => {
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// Specify the ID of the fine-tuning job you want to cancel
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const fineTuningJobIdToCancel = "your_fine_tuning_job_id"; // Replace with the actual job ID
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// Cancel the specified fine-tuning job using the OpenAI API
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const cancellationResponse = await io.openai.cancelFineTuningJob(fineTuningJobIdToCancel);
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// Log the cancellation response
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await io.logger.info("cancellationResponse", cancellationResponse);
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},
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```
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### `listFineTuningJobEvents`
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List events for a fine-tuning job. [Official OpenAI Docs](https://platform.openai.com/docs/api-reference/fine-tuning)
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```ts example.ts
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run: async (payload, io, ctx) => {
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// Specify the ID of the fine-tuning job for which you want to list events
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const fineTuningJobId = "your_fine_tuning_job_id"; // Replace with the actual job ID
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// List events for the specified fine-tuning job using the OpenAI API
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const eventsListResponse = await io.openai.listFineTuningJobEvents(fineTuningJobId);
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// Log the list of events
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await io.logger.info("eventsListResponse", eventsListResponse);
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},
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```
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### `listFineTuningJobs`
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List fine tuning jobs. [Official OpenAI Docs](https://platform.openai.com/docs/api-reference/fine-tuning)
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```ts example.ts
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run: async (payload, io, ctx) => {
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// List the fine-tuning jobs available in your OpenAI account
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const jobsListResponse = await io.openai.listFineTuningJobs();
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// Log the list of fine-tuning jobs
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await io.logger.info("jobsListResponse", jobsListResponse);
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},
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```
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## Example usage
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In this example we'll create a task that generates a random joke using OpenAI GPT 3.5 .
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```ts example.ts
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import { TriggerClient, eventTrigger } from "@trigger.dev/sdk";
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import { OpenAI } from "@trigger.dev/openai";
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import { z } from "zod";
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// Initialize a TriggerClient with the ID "jobs-showcase"
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const client = new TriggerClient({ id: "jobs-showcase" });
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// Create an instance of the OpenAI client and provide the OpenAI API key from environment variables
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const openai = new OpenAI({
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id: "openai",
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apiKey: process.env.OPENAI_API_KEY!, // Replace with your actual OpenAI API key
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});
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// Define a job that uses OpenAI GPT-3.5 Turbo to tell jokes
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client.defineJob({
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id: "openai-tell-me-a-joke",
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name: "OpenAI: tell me a joke",
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version: "1.0.0",
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trigger: eventTrigger({
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name: "openai.tasks", // Define the trigger event name
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schema: z.object({
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jokePrompt: z.string(), // Expect a joke prompt as input
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}),
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}),
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integrations: {
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openai, // Use the OpenAI integration for this job
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},
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run: async (payload, io, ctx) => {
|
||||
// Retrieve information about the GPT-3.5 Turbo model
|
||||
await io.openai.retrieveModel("get-model", {
|
||||
model: "gpt-3.5-turbo",
|
||||
});
|
||||
|
||||
// List available models (optional, for reference)
|
||||
const models = await io.openai.listModels("list-models");
|
||||
|
||||
// Generate a joke in the background using the chat conversation format
|
||||
const jokeResult = await io.openai.backgroundCreateChatCompletion(
|
||||
"background-chat-completion",
|
||||
{
|
||||
model: "gpt-3.5-turbo",
|
||||
messages: [
|
||||
{
|
||||
role: "user",
|
||||
content: payload.jokePrompt, // User-provided joke prompt
|
||||
},
|
||||
],
|
||||
}
|
||||
);
|
||||
|
||||
// Return the generated joke as the result
|
||||
return {
|
||||
joke: jokeResult.choices[0]?.message?.content,
|
||||
};
|
||||
},
|
||||
});
|
||||
|
||||
// These lines are specific to the Express framework and can be removed if not needed
|
||||
import { createExpressServer } from "@trigger.dev/express";
|
||||
createExpressServer(client);
|
||||
```
|
||||
@@ -1,17 +1,23 @@
|
||||
---
|
||||
title: OpenAI
|
||||
title: OpenAI overview & authentication
|
||||
sidebarTitle: Overview & authentication
|
||||
---
|
||||
|
||||
## Overview
|
||||
|
||||
Trigger.dev has a seamless integration with OpenAI, enabling developers to harness the power of AI
|
||||
language models in their serverless applications. With Trigger.dev's background tasks, long-running
|
||||
OpenAI completions become possible, even within the constraints of serverless timeouts.
|
||||
|
||||
<Snippet file="integration-getting-started.mdx" />
|
||||
<Card
|
||||
title="Jobs Showcase - OpenAI"
|
||||
icon="rocket"
|
||||
href="https://trigger.dev/showcase?tags=&integrations=openai"
|
||||
>
|
||||
Check out pre-built OpenAI jobs in our showcase.
|
||||
</Card>
|
||||
|
||||
## Installation
|
||||
|
||||
To get started with the OpenAI integration on Trigger.dev, you need to install the `@trigger.dev/openai` package.
|
||||
You can do this using npm, pnpm, or yarn:
|
||||
## Installing the OpenAI packages
|
||||
|
||||
<CodeGroup>
|
||||
|
||||
@@ -43,276 +49,12 @@ const openai = new OpenAI({
|
||||
});
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
||||
Include the OpenAI integration in your Trigger.dev job:
|
||||
|
||||
```ts
|
||||
client.defineJob({
|
||||
id: "openai-tasks",
|
||||
name: "OpenAI Tasks",
|
||||
version: "0.0.1",
|
||||
trigger: eventTrigger({
|
||||
name: "openai.tasks",
|
||||
schema: z.object({}),
|
||||
}),
|
||||
integrations: {
|
||||
openai,
|
||||
},
|
||||
run: async (payload, io, ctx) => {
|
||||
// Now you can access the OpenAI tasks through the io object
|
||||
await io.openai.createCompletion("completion", {
|
||||
model: "davinci",
|
||||
prompt: "Once upon a time",
|
||||
});
|
||||
},
|
||||
});
|
||||
```
|
||||
|
||||
## Tasks
|
||||
|
||||
Tasks that are marked as "long-running" can last longer than your serverless timeout – they are performed on one of our background workers.
|
||||
Once you have set up a OpenAI client, you can use it to create tasks.
|
||||
|
||||
| Function Name | Description | Long-running? |
|
||||
| -------------------------------- | ------------------------------------------------------------------------- | ------------- |
|
||||
| `createCompletion` | Generates text completions given a prompt. |
|
||||
| `backgroundCreateCompletion` | Generates text completions in the background. | ✔ |
|
||||
| `createChatCompletion` | Generates text completions in a conversational context. |
|
||||
| `backgroundCreateChatCompletion` | Generates text completions in a conversational context in the background. | ✔ |
|
||||
| `retrieveModel` | Retrieves a specific model by ID. |
|
||||
| `listModels` | Lists the available models. |
|
||||
| `createEdit` | Edits a given text prompt. |
|
||||
| `createImage` | Generates images from textual descriptions. |
|
||||
| `createImageEdit` | Creates an edited or extended image given an original image and a prompt |
|
||||
| `createImageVariation` | Creates a variation of a given image. |
|
||||
| `createEmbedding` | Generates embeddings for a given text. |
|
||||
| `createFile` | Uploads a file to the OpenAI API. |
|
||||
| `listFiles` | Lists the uploaded files. |
|
||||
| `createFineTuneFile` | Uploads a file for fine-tuning a model. |
|
||||
| `createFineTune` | Fine-tunes a model on a given task. |
|
||||
| `listFineTunes` | Lists the available fine-tunes. |
|
||||
| `retrieveFineTune` | Retrieves a specific fine-tune by ID. |
|
||||
| `cancelFineTune` | Cancels a specific fine-tune by ID. |
|
||||
| `createFineTuningJob` | Creates a job that fine-tunes a specified model from a given dataset. |
|
||||
| `retrieveFineTuningJob` | Get info about a fine-tuning job. |
|
||||
| `cancelFineTuningJob` | Cancel a fine-tuning job |
|
||||
| `listFineTuningJobEvents` | List events for a fine-tuning job |
|
||||
| `listFineTuningJobs` | List fine tuning jobs |
|
||||
|
||||
## Examples
|
||||
|
||||
### Generate a joke
|
||||
|
||||
Here's an example of how to use the OpenAI integration in a Trigger.dev job.
|
||||
In this example, we'll create a background task to generate a programming joke.
|
||||
|
||||
```ts
|
||||
client.defineJob({
|
||||
id: "openai-tasks",
|
||||
name: "OpenAI Tasks",
|
||||
version: "0.0.1",
|
||||
trigger: eventTrigger({
|
||||
name: "openai.tasks",
|
||||
schema: z.object({}),
|
||||
}),
|
||||
integrations: {
|
||||
openai,
|
||||
},
|
||||
run: async (payload, io, ctx) => {
|
||||
const response = await io.openai.backgroundCreateChatCompletion("background-chat-completion", {
|
||||
model: "gpt-3.5-turbo",
|
||||
messages: [
|
||||
{
|
||||
role: "user",
|
||||
content: "Create a good programming joke about background jobs",
|
||||
},
|
||||
],
|
||||
});
|
||||
|
||||
await io.logger.info("choices", response.choices);
|
||||
},
|
||||
});
|
||||
```
|
||||
|
||||
### Generate Code Snippets
|
||||
|
||||
In this example, we'll leverage Trigger.dev's background task to generate code snippets for
|
||||
a given programming task:
|
||||
|
||||
```ts
|
||||
client.defineJob({
|
||||
id: "openai-tasks",
|
||||
name: "OpenAI Tasks",
|
||||
version: "0.0.1",
|
||||
trigger: eventTrigger({
|
||||
name: "openai.tasks",
|
||||
schema: z.object({}),
|
||||
}),
|
||||
integrations: {
|
||||
openai,
|
||||
},
|
||||
run: async (payload, io, ctx) => {
|
||||
const programmingTask = `Create a function that checks if a string is a palindrome.`;
|
||||
|
||||
const response = await io.openai.backgroundCreateCompletion("background-completion", {
|
||||
model: "gpt-3.5-turbo",
|
||||
prompt: `Coding task: ${programmingTask}\n\n`,
|
||||
});
|
||||
|
||||
await io.logger.info("codeSnippet", response.choices[0]?.text);
|
||||
},
|
||||
});
|
||||
```
|
||||
|
||||
### Summarize Text
|
||||
|
||||
We'll use Trigger.dev's background task to summarize a lengthy article:
|
||||
|
||||
```ts
|
||||
client.defineJob({
|
||||
id: "openai-tasks",
|
||||
name: "OpenAI Tasks",
|
||||
version: "0.0.1",
|
||||
trigger: eventTrigger({
|
||||
name: "openai.tasks",
|
||||
schema: z.object({}),
|
||||
}),
|
||||
integrations: {
|
||||
openai,
|
||||
},
|
||||
run: async (payload, io, ctx) => {
|
||||
const articleToSummarize = `Lorem ipsum. olor sit amet, consectetur adipiscing elit.
|
||||
Sed nec aliquet sapien. Pellentesque vitae nisi id purus luctus tincidunt.
|
||||
Proin condimentum malesuada turpis, eget tincidunt mauris viverra in.`;
|
||||
|
||||
const response = await io.openai.backgroundCreateCompletion("background-completion", {
|
||||
model: "gpt-3.5-turbo",
|
||||
prompt: `Please summarize the following article:\n\n${articleToSummarize}`,
|
||||
});
|
||||
|
||||
await io.logger.info("summary", response.choices[0]?.text);
|
||||
},
|
||||
});
|
||||
```
|
||||
|
||||
### Draft Email Response
|
||||
|
||||
we'll use Trigger.dev's background task to draft an email response based on a given email content:
|
||||
|
||||
```ts
|
||||
client.defineJob({
|
||||
id: "openai-tasks",
|
||||
name: "OpenAI Tasks",
|
||||
version: "0.0.1",
|
||||
trigger: eventTrigger({
|
||||
name: "openai.tasks",
|
||||
schema: z.object({}),
|
||||
}),
|
||||
integrations: {
|
||||
openai,
|
||||
},
|
||||
run: async (payload, io, ctx) => {
|
||||
const emailContent = `Dear John,
|
||||
|
||||
Thank you for your inquiry. We appreciate your interest in our products.
|
||||
I have reviewed your request, and I'm pleased to inform you that we can
|
||||
accommodate your requirements. Please find the attached proposal for your
|
||||
reference. If you have any further questions, feel free to ask.
|
||||
|
||||
Best regards,
|
||||
Jane Doe`;
|
||||
|
||||
const response = await io.openai.backgroundCreateChatCompletion("background-chat-completion", {
|
||||
model: "gpt-3.5-turbo",
|
||||
messages: [
|
||||
{
|
||||
role: "user",
|
||||
content: emailContent,
|
||||
},
|
||||
{
|
||||
role: "assistant",
|
||||
content: "Draft a suitable response to the email above.",
|
||||
},
|
||||
],
|
||||
});
|
||||
|
||||
await io.logger.info("draftedEmailResponse", response.choices[0]?.text);
|
||||
},
|
||||
});
|
||||
```
|
||||
|
||||
### Chatbot Counseling Session
|
||||
|
||||
This job represents a simulated AI counseling session. Leveraging OpenAI's ability to understand context and generate human-like text, it forms empathetic responses to user inputs. Such a system could be part of a mental wellness app.
|
||||
|
||||
```ts
|
||||
client.defineJob({
|
||||
id: "openai-chatbot-counseling",
|
||||
name: "Chatbot Counseling Session",
|
||||
version: "0.0.1",
|
||||
trigger: eventTrigger({
|
||||
name: "openai.startCounselingSession",
|
||||
schema: z.object({}),
|
||||
}),
|
||||
integrations: {
|
||||
openai,
|
||||
},
|
||||
run: async (payload, io, ctx) => {
|
||||
const response = await io.openai.backgroundCreateChatCompletion(
|
||||
"background-counseling-chat-completion",
|
||||
{
|
||||
model: "gpt-3.5-turbo",
|
||||
messages: [
|
||||
{
|
||||
role: "system",
|
||||
content: "You are a helpful and empathetic AI counselor.",
|
||||
},
|
||||
{
|
||||
role: "user",
|
||||
content: "I've been feeling really stressed out lately.",
|
||||
},
|
||||
],
|
||||
}
|
||||
);
|
||||
await io.logger.info("counseling session", response.choices);
|
||||
},
|
||||
});
|
||||
```
|
||||
|
||||
### AI Roleplay Game Session
|
||||
|
||||
This job creates a fantasy AI role-playing game. It could be fun for interactive storytelling or game development contexts.
|
||||
|
||||
```ts
|
||||
client.defineJob({
|
||||
id: "openai-roleplay-game-session",
|
||||
name: "AI Roleplay Game Session",
|
||||
version: "0.0.1",
|
||||
trigger: eventTrigger({
|
||||
name: "openai.startRoleplayGameSession",
|
||||
schema: z.object({}),
|
||||
}),
|
||||
integrations: {
|
||||
openai,
|
||||
},
|
||||
run: async (payload, io, ctx) => {
|
||||
const response = await io.openai.backgroundCreateChatCompletion(
|
||||
"background-roleplay-game-session-chat-completion",
|
||||
{
|
||||
model: "gpt-3.5-turbo",
|
||||
messages: [
|
||||
{
|
||||
role: "system",
|
||||
content: "You are an intelligent guide in a fantasy role-playing game.",
|
||||
},
|
||||
{
|
||||
role: "user",
|
||||
content: "I embark on a quest for the enchanted crown. What's the first step?",
|
||||
},
|
||||
],
|
||||
}
|
||||
);
|
||||
await io.logger.info("roleplay game session", response.choices);
|
||||
},
|
||||
});
|
||||
```
|
||||
<CardGroup>
|
||||
<Card title="Tasks" icon="sparkles" href="/integrations/apis/openai-tasks">
|
||||
Perform different AI-powered tasks using OpenAI.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
||||
+7
-1
@@ -255,7 +255,13 @@
|
||||
]
|
||||
},
|
||||
"integrations/apis/linear",
|
||||
"integrations/apis/openai",
|
||||
{
|
||||
"group": "OpenAI",
|
||||
"pages": [
|
||||
"integrations/apis/openai",
|
||||
"integrations/apis/openai-tasks"
|
||||
]
|
||||
},
|
||||
{
|
||||
"group": "Plain",
|
||||
"pages": [
|
||||
|
||||
Reference in New Issue
Block a user