Text Classification with Hugging Face API on Job Run Completed (Instant) from dbt Cloud API

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Job Run Completed (Instant) from the dbt Cloud API

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Text Classification with the Hugging Face API

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Getting Started#

This integration creates a workflow with a dbt Cloud trigger and Hugging Face action. When you configure and deploy the workflow, it will run on Pipedream's servers 24x7 for free.

Select this integration
Configure the Job Run Completed (Instant) trigger
1. Connect your dbt Cloud account
2. Select a Account
Configure the Text Classification action
1. Connect your Hugging Face account
2. Select a Model ID
3. Configure Text
4. Optional- Configure Additional Fields
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#

Job Run Completed (Instant) on dbt Cloud

Description:Emit new event when a job run has completed.

Version:0.0.2

Key:dbt-job-run-completed

View on GitHub

dbt Cloud Overview#

The dbt Cloud API allows users to initiate jobs, check on their status, and interact with dbt Cloud programmatically. On Pipedream, you can harness this functionality to automate workflows, such as triggering dbt runs, monitoring your data transformation jobs, and integrating dbt Cloud with other data services. By leveraging Pipedream's serverless platform, you can create custom workflows that act on dbt Cloud events or use the dbt Cloud API to manage your data transformation processes seamlessly.

Trigger Code#

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

export default {
  ...common,
  key: "dbt-job-run-completed",
  name: "Job Run Completed (Instant)",
  description: "Emit new event when a job run has completed.",
  version: "0.0.2",
  type: "source",
  dedupe: "unique",
  methods: {
    ...common.methods,
    getEventTypes() {
      return [
        "job.run.completed",
      ];
    },
    generateMeta(body) {
      return {
        id: body.eventId,
        summary: `Job "${body.data.jobName}"" completed`,
        ts: Date.parse(body.data.runFinishedAt),
      };
    },
  },
  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
dbt Cloud	`dbt`	`app`	This component uses the dbt Cloud app.
N/A	`db`	`$.service.db`	This component uses `$.service.db` to maintain state between executions.
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.
Account	`accountId`	`string`	Select a value from the drop down menu.

Trigger Authentication#

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

About dbt Cloud#

dbt is an intuitive, collaborative platform that lets you reliably transform data using SQL and Python code.

Action#

Text Classification on Hugging Face

Description:Usually used for sentiment-analysis this will output the likelihood of classes of an input. This action allows you to classify text into categories. [See the docs](https://huggingface.co/docs/huggingface.js/inference/classes/HfInference#textclassification).

Version:0.0.2

Key:hugging_face-text-classification

View on GitHub

Hugging Face Overview#

The Hugging Face API provides access to a vast range of machine learning models, primarily for natural language processing (NLP) tasks like text classification, translation, summarization, and question answering. It lets you leverage pre-trained models and fine-tune them on your data. Using the API within Pipedream, you can automate workflows that involve language processing, integrate AI insights into your apps, or respond to events with AI-generated content.

Action Code#

import { ConfigurationError } from "@pipedream/platform";
import app from "../../hugging_face.app.mjs";
import utils from "../../common/utils.mjs";
import tag from "../common/tag.mjs";

export default {
  key: "hugging_face-text-classification",
  name: "Text Classification",
  description: "Usually used for sentiment-analysis this will output the likelihood of classes of an input. This action allows you to classify text into categories. [See the docs](https://huggingface.co/docs/huggingface.js/inference/classes/HfInference#textclassification).",
  type: "action",
  version: "0.0.2",
  annotations: {
    destructiveHint: false,
    openWorldHint: true,
    readOnlyHint: false,
  },
  props: {
    app,
    modelId: {
      propDefinition: [
        app,
        "modelId",
        () => ({
          tagFilter: tag.TEXT_CLASSIFICATION,
        }),
      ],
    },
    text: {
      type: "string",
      label: "Text",
      description: "The text to use for classification.",
    },
    additionalFields: {
      type: "object",
      label: "Additional Fields",
      description: "Additional fields to include in the request in case the model requires them. E.g you can set `parameters` as a key and `{\"candidate_labels\":[\"love\",\"like\"]}` as a value. Or Just something like `my_field` as a key and `my_value` as a value.",
      optional: true,
    },
  },
  async run({ $: step }) {
    const {
      modelId,
      text,
      additionalFields,
    } = this;

    if (additionalFields && typeof(additionalFields) !== "object" && additionalFields !== null) {
      throw new ConfigurationError("Additional Fields property must be an object.");
    }

    const response = await this.app.inference({
      step,
      modelId,
      data: {
        inputs: text,
        ...utils.parseFields(additionalFields),
      },
    });

    step.export("$summary", "Successfully classified text");

    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
Hugging Face	`app`	`app`	This component uses the Hugging Face app.
Model ID	`modelId`	`string`	Select a value from the drop down menu.
Text	`text`	`string`	The text to use for classification.
Additional Fields	`additionalFields`	`object`	Additional fields to include in the request in case the model requires them. E.g you can set `parameters` as a key and `{"candidate_labels":["love","like"]}` as a value. Or Just something like `my_field` as a key and `my_value` as a value.

Action Authentication#

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

To retrieve your Access Tokens,

Navigate to your Hugging Face account and sign in
Go to “Settings” > “Access Tokens”

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Popular Triggers#

Job Run Completed (Instant) from the dbt Cloud API

Emit new event when a job run has completed.

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Job Run Errored (Instant) from the dbt Cloud API

Emit new event when a job run has errored.

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Job Run Started (Instant) from the dbt Cloud API

Emit new event when a job run has started.

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New Job Run Event (Instant) from the dbt Cloud API

Emit new event when a job run has started, errored, or completed.

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Popular Actions#

Get Environment with the dbt Cloud API

Retrieve information about an environment. See the documentation

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Get Run with the dbt Cloud API

Retrieve information about a run. See the documentation

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Get Run Artifact with the dbt Cloud API

Retrieve information about a run artifact. See the documentation

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Trigger Job Run with the dbt Cloud API

Trigger a specified job to begin running. See the documentation

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Document Question Answering with the Hugging Face API

Want to have a nice know-it-all bot that can answer any question?. This action allows you to ask a question and get an answer from a trained model. See the docs

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