Classify Text with Cohere API on New Submission (Instant) from Jotform API

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New Submission (Instant) from the Jotform API

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Classify Text with the Cohere API

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Getting Started#

This integration creates a workflow with a Jotform 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 Submission (Instant) trigger
1. Connect your Jotform account
2. Optional- Select a Team
3. Select a Form
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 Submission (Instant) on Jotform

Description:Emit new event when a form is submitted

Version:0.1.5

Key:jotform-new-submission

View on GitHub

Jotform Overview#

Jotform’s API is a powerhouse for automating form and survey data management. With Pipedream, harness this API to trigger workflows from new form submissions, manipulate and analyze your form data, and sync it across various platforms. Think streamlined data entry to CRMs, instant notifications for new leads or feedback, and timely data backups to cloud storage.

Trigger Code#

import jotform from "../../jotform.app.mjs";
import sampleEmit from "./test-event.mjs";

export default {
  key: "jotform-new-submission",
  name: "New Submission (Instant)",
  description: "Emit new event when a form is submitted",
  version: "0.1.5",
  type: "source",
  dedupe: "unique",
  props: {
    jotform,
    http: "$.interface.http",
    teamId: {
      propDefinition: [
        jotform,
        "teamId",
      ],
    },
    formId: {
      propDefinition: [
        jotform,
        "formId",
        (c) => ({
          teamId: c.teamId,
          excludeDeleted: true,
        }),
      ],
    },
  },
  hooks: {
    async deploy() {
      const { content: form } = await this.jotform.getForm(this.formId, this.teamId);
      const { content: submissions } = await this.jotform.getFormSubmissions({
        formId: this.formId,
        teamId: this.teamId,
        params: {
          limit: 25,
          orderby: "created_at",
        },
      });
      for (let submission of submissions.reverse()) {
        const meta = {
          id: submission.id,
          summary: form.title,
          ts: Date.now(),
        };
        this.$emit(submission, meta);
      }
    },
    async activate() {
      return (await this.jotform.createHook({
        endpoint: this.http.endpoint,
        formId: this.formId,
        teamId: this.teamId,
      }));
    },
    async deactivate() {
      return (await this.jotform.deleteHook({
        endpoint: this.http.endpoint,
        formId: this.formId,
        teamId: this.teamId,
      }));
    },
  },
  async run(event) {
    const { body } = event;
    let { content: submission } = await this.jotform.getFormSubmission({
      submissionId: body.submissionID,
      teamId: this.teamId,
    });

    // insert answers from the webhook event
    const rawRequest = JSON.parse(body.rawRequest);
    for (const key of Object.keys(rawRequest)) {
      const regex = /^q(\d+)_/;
      const match = key.match(regex);
      if (match && match[1]) {
        submission.answers[match[1]].answer = rawRequest[key];
      }
    }

    this.$emit(submission, {
      summary: body.formTitle || JSON.stringify(body),
      id: body.submissionID,
      ts: Date.now(),
    });
  },
  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
Jotform	`jotform`	`app`	This component uses the Jotform app.
N/A	`http`	`$.interface.http`	This component uses `$.interface.http` to generate a unique URL when the component is first instantiated. Each request to the URL will trigger the `run()` method of the component.
Team	`teamId`	`string`	Select a value from the drop down menu.
Form	`formId`	`string`	Select a value from the drop down menu.

Trigger Authentication#

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

About Jotform#

Jotform enables you to create online forms, collect responses directly in your email, and create fillable PDF forms.

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.

Action Authentication#

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

Generate and copy your API key from your Cohere Platform dashboard.

About Cohere#

Making NLP part of every developer's toolkit. Harness the power of language understanding. Join the developers and businesses who are using Cohere to generate, categorize and organize text at a scale that was previously unimaginable.

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