← Hugging Face

Document Question Answering with Hugging Face API

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Document Question Answering with the Hugging Face API
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Getting Started

Create a workflow to Document Question Answering with the Hugging Face API. When you configure and deploy the workflow, it will run on Pipedream's servers 24x7 for free.

  1. Configure the Document Question Answering action
    1. Connect your Hugging Face account
    2. Select a Model ID
    3. Configure Image URL
    4. Configure Question
  2. Select a trigger to run your workflow on HTTP requests, schedules or app events
  3. Deploy the workflow
  4. Send a test event to validate your setup
  5. Turn on the trigger

Integrations

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Document Question Answering with Hugging Face API on New Submission (Instant) from Jotform API
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Document Question Answering with Hugging Face API on New Custom Webhook Events from Stripe API
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Document Question Answering with Hugging Face API on New Scheduled Tasks from Pipedream API
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Details

This is a pre-built, source-available component from Pipedream's GitHub repo. The component is 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.

Document Question Answering on Hugging Face
Description: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](https://huggingface.co/docs/huggingface.js/inference/classes/HfInference#questionanswer).
Version:0.0.1
Key:hugging_face-document-question-answering

Code

import app from "../../hugging_face.app.mjs";
import tag from "../common/tag.mjs";

export default {
  key: "hugging_face-document-question-answering",
  name: "Document Question Answering",
  description: "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](https://huggingface.co/docs/huggingface.js/inference/classes/HfInference#questionanswer).",
  type: "action",
  version: "0.0.1",
  props: {
    app,
    modelId: {
      propDefinition: [
        app,
        "modelId",
        () => ({
          tagFilter: tag.DOCUMENT_QUESTION_ANSWERING,
        }),
      ],
    },
    imageUrl: {
      description: "The image url to use for answering the question.",
      propDefinition: [
        app,
        "imageUrl",
      ],
    },
    question: {
      type: "string",
      label: "Question",
      description: "The question to ask.",
    },
  },
  async run({ $: step }) {
    const {
      modelId,
      imageUrl,
      question,
    } = this;

    const image = await this.app.getBinaryFromUrl(imageUrl);

    const response = await this.app.inference({
      step,
      modelId,
      data: {
        inputs: {
          image,
          question,
        },
      },
    });

    step.export("$summary", `Successfully answered question with score ${response[0].score}`);

    return response;
  },
};

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.
LabelPropTypeDescription
Hugging FaceappappThis component uses the Hugging Face app.
Model IDmodelIdstringSelect a value from the drop down menu.
Image URLimageUrlstring

The image url to use for answering the question.

Questionquestionstring

The question to ask.

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”

About Hugging Face

Build, train and deploy state of the art models powered by the reference open source in machine learning.

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Text Summarization with the Hugging Face API

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