Classify Text with Cohere API on New Fine Tuning Job Created from OpenAI (ChatGPT) API

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New Fine Tuning Job Created from the OpenAI (ChatGPT) API

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Classify Text with the Cohere API

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Getting Started#

This integration creates a workflow with a OpenAI (ChatGPT) trigger and Cohere action. When you configure and deploy the workflow, it will run on Pipedream's servers 24x7 for free.

Select this integration
Configure the New Fine Tuning Job Created trigger
1. Connect your OpenAI (ChatGPT) account
2. Configure timer
Configure the Classify Text action
1. Connect your Cohere account
2. Configure Inputs
3. Optional- Select a Model
4. Optional- Configure Preset
5. Optional- Select a Truncate
6. Configure Number of Examples
Deploy the workflow
Send a test event to validate your setup
Turn on the trigger

Details#

This integration uses pre-built, source-available components from Pipedream's GitHub repo. These components are developed by Pipedream and the community, and verified and maintained by Pipedream.

To contribute an update to an existing component or create a new component, create a PR on GitHub. If you're new to Pipedream component development, you can start with quickstarts for trigger span and action development, and then review the component API reference.

Trigger#

New Fine Tuning Job Created on OpenAI (ChatGPT)

Description:Emit new event when a new fine-tuning job is created in OpenAI. [See the documentation](https://platform.openai.com/docs/api-reference/fine-tuning/list)

Version:0.0.10

Key:openai-new-fine-tuning-job-created

View on GitHub

OpenAI (ChatGPT) Overview#

OpenAI provides a suite of powerful AI models through its API, enabling developers to integrate advanced natural language processing and generative capabilities into their applications. Here’s an overview of the services offered by OpenAI's API:

Use Python or Node.js code to make fully authenticated API requests with your OpenAI account:

Trigger Code#

import common from "../common/common.mjs";
import sampleEmit from "./test-event.mjs";

export default {
  ...common,
  key: "openai-new-fine-tuning-job-created",
  name: "New Fine Tuning Job Created",
  description: "Emit new event when a new fine-tuning job is created in OpenAI. [See the documentation](https://platform.openai.com/docs/api-reference/fine-tuning/list)",
  version: "0.0.10",
  type: "source",
  dedupe: "unique",
  methods: {
    ...common.methods,
    getData() {
      return this.openai.listFineTuningJobs();
    },
    getMeta(item) {
      return {
        id: item.id,
        summary: `New Fine Tuning Job: ${item.id}`,
        ts: item.created_at * 1000,
      };
    },
  },
  sampleEmit,
};

Trigger Configuration#

This component may be configured based on the props defined in the component code. Pipedream automatically prompts for input values in the UI and CLI.

Label	Prop	Type	Description
OpenAI (ChatGPT)	`openai`	`app`	This component uses the OpenAI (ChatGPT) app.
N/A	`db`	`$.service.db`	This component uses `$.service.db` to maintain state between executions.
	`timer`	`$.interface.timer`

Trigger Authentication#

OpenAI (ChatGPT) uses API keys for authentication. When you connect your OpenAI (ChatGPT) account, Pipedream securely stores the keys so you can easily authenticate to OpenAI (ChatGPT) APIs in both code and no-code steps.

About OpenAI (ChatGPT)#

OpenAI is an AI research and deployment company with the mission to ensure that artificial general intelligence benefits all of humanity. They are the makers of popular models like ChatGPT, DALL-E, and Whisper.

Action#

Classify Text on Cohere

Description:This action makes a prediction about which label fits the specified text inputs best. [See the documentation](https://docs.cohere.com/reference/classify-1)

Version:0.1.0

Key:cohere_platform-classify-text

View on GitHub

Cohere Overview#

The Cohere API enables the development of apps with advanced natural language understanding capabilities. Utilizing machine learning, it can help with tasks like text generation, summarization, sentiment analysis, and more. On Pipedream, you can seamlessly integrate Cohere's features into serverless workflows, triggering actions based on text input, processing large volumes of data, or even enhancing chatbots with more human-like responses.

Action Code#

import app from "../../cohere_platform.app.mjs";
import { clearObj } from "../../common/utils.mjs";

export default {
  key: "cohere_platform-classify-text",
  name: "Classify Text",
  version: "0.1.0",
  description: "This action makes a prediction about which label fits the specified text inputs best. [See the documentation](https://docs.cohere.com/reference/classify-1)",
  type: "action",
  props: {
    app,
    inputs: {
      type: "string[]",
      label: "Inputs",
      description: "Represents a list of queries to be classified, each entry must not be empty. The maximum is 96 inputs.",
    },
    model: {
      propDefinition: [
        app,
        "classifyModel",
      ],
    },
    preset: {
      propDefinition: [
        app,
        "preset",
      ],
    },
    truncate: {
      propDefinition: [
        app,
        "truncate",
      ],
    },
    numExamples: {
      type: "integer",
      label: "Number of Examples",
      description: "To make a prediction, Classify uses provided examples of text + label pairs. Specify the number of examples to provide. Each example is a text string and its associated label/class. At least 2 unique labels must be provided and each label should have at least 2 different examples.",
      default: 4,
      reloadProps: true,
    },
  },
  additionalProps() {
    const props = {};

    for (let i = 0; i < this.numExamples; i++) {
      props[`text_${i}`] = {
        type: "string",
        label: `Example ${i + 1} Text`,
        description: "Text string of the example",
      };
      props[`label_${i}`] = {
        type: "string",
        label: `Example ${i + 1} Label`,
        description: "Label of the example. Each unique label requires at least 2 examples associated with it.",
      };
    }

    return props;
  },
  methods: {
    getExamples() {
      const examples = [];
      for (let i = 0; i < this.numExamples; i++) {
        examples.push({
          text: this[`text_${i}`],
          label: this[`label_${i}`],
        });
      }
      return examples;
    },
    classifyText(data) {
      return this.app.client().classify(data);
    },
  },
  async run({ $ }) {
    const {
      classifyText,
      inputs,
      model,
      preset,
      truncate,
      getExamples,
    } = this;

    const response = await classifyText(clearObj({
      inputs,
      model,
      preset,
      truncate,
      examples: getExamples(),
    }));

    $.export("$summary", `The text was successfully classified with ID \`${response.id}\``);
    return response;
  },
};

Action Configuration#

This component may be configured based on the props defined in the component code. Pipedream automatically prompts for input values in the UI.

Label	Prop	Type	Description
Cohere	`app`	`app`	This component uses the Cohere app.
Inputs	`inputs`	`string[]`	Represents a list of queries to be classified, each entry must not be empty. The maximum is 96 inputs.
Model	`model`	`string`	Select a value from the drop down menu:`embed-multilingual-v2.0embed-english-light-v2.0embed-english-v2.0`
Preset	`preset`	`string`	The ID of a custom playground preset. You can create presets in the playground. If you use a preset, all other parameters become optional, and any included parameters will override the preset's parameters.
Truncate	`truncate`	`string`	Select a value from the drop down menu:`NONESTARTEND`
Number of Examples	`numExamples`	`integer`	To make a prediction, Classify uses provided examples of text + label pairs. Specify the number of examples to provide. Each example is a text string and its associated label/class. At least 2 unique labels must be provided and each label should have at least 2 different examples.