Document Question Answering with Hugging Face API on New Message In Channel from Slack Bot API

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New Message In Channel from the Slack Bot API

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Document Question Answering with the Hugging Face API

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Getting Started#

This integration creates a workflow with a Slack Bot 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 New Message In Channel trigger
1. Connect your Slack Bot account
2. Configure Polling schedule
3. Select a Channel ID
Configure the Document Question Answering action
1. Connect your Hugging Face account
2. Select a Model ID
3. Configure Image URL
4. Configure Question
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 Message In Channel on Slack Bot

Description:Emit new event when a new message is posted to a public, private or group channel (Bot)

Version:0.0.4

Key:slack_bot-new-message-in-channel

View on GitHub

Slack Bot Overview#

The Slack Bot API allows you to build rich, interactive bots for Slack workspaces. These bots can respond to messages, post updates, and interact with users in various ways. With the Slack Bot API on Pipedream, developers can create automated workflows that trigger on specific events in Slack, such as new messages or reactions, and then perform defined actions, like sending data to other apps or processing the information within Pipedream's serverless platform. This tight integration with Pipedream enables both simple and complex automations, leveraging Pipedream's ability to connect with numerous apps and its powerful built-in code steps.

Trigger Code#

import common from "../common/base.mjs";
import constants from "../../common/constants.mjs";
import utils from "../../common/utils.mjs";

export default {
  ...common,
  key: "slack_bot-new-message-in-channel",
  name: "New Message In Channel",
  version: "0.0.4",
  description: "Emit new event when a new message is posted to a public, private or group channel (Bot)",
  type: "source",
  dedupe: "unique",
  props: {
    ...common.props,
    channelId: {
      propDefinition: [
        common.props.app,
        "channelId",
        () => ({
          types: [
            constants.CHANNEL_TYPE.PUBLIC,
            constants.CHANNEL_TYPE.PRIVATE,
            constants.CHANNEL_TYPE.MPIM,
          ],
        }),
      ],
      description: `Select the channel's ID. ${utils.CONVERSATION_PERMISSION_MESSAGE}`,
    },
  },
  methods: {
    ...common.methods,
    getResourceName() {
      return constants.RESOURCE_NAME.MESSAGES;
    },
    getResourceFn() {
      return this.app.conversationsHistory;
    },
    getResourceFnArgs() {
      return {
        channel: this.channelId,
        limit: constants.LIMIT,
        oldest: this.getLastTimestamp(),
        inclusive: false,
      };
    },
    generateMeta(resource) {
      return {
        id: resource.ts,
        ts: resource.ts,
        summary: `New Message Timestamp ${resource.ts}`,
      };
    },
  },
};

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
Slack Bot	`app`	`app`	This component uses the Slack Bot app.
N/A	`db`	`$.service.db`	This component uses `$.service.db` to maintain state between executions.
Polling schedule	`timer`	`$.interface.timer`	How often to poll the Slack API
Channel ID	`channelId`	`string`	Select a value from the drop down menu.

Trigger Authentication#

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

In the settings for your Slack app, visit the OAuth & Permissions section on the left. You'll see a token labeled Bot User OAuth Access Token. Copy and paste that below.

See Slack's docs on Bot User Tokens for more info.

About Slack Bot#

Interact with Slack with your own bot user

Action#

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

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 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;
  },
};

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.
Image URL	`imageUrl`	`string`	The image url to use for answering the question.
Question	`question`	`string`	The question to ask.

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”

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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Popular Triggers#

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