Classify Text with Cohere API on New Reaction Added (Instant) from Slack API

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New Reaction Added (Instant) from the Slack API

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Classify Text with the Cohere API

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Getting Started#

This integration creates a workflow with a Slack 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 Reaction Added (Instant) trigger
1. Connect your Slack account
2. Optional- Select one or more Channels
3. Configure slackApphook
4. Optional- Configure Ignore Bots
5. Optional- Select one or more Icon (emoji)
6. Optional- Configure Include User Data
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 Reaction Added (Instant) on Slack

Description:Emit new event when a member has added an emoji reaction to a message

Version:1.2.0

Key:slack_v2-new-reaction-added

View on GitHub

Slack Overview#

The Pipedream app for Slack enables you to build event-driven workflows that interact with the Slack API. Once you authorize the app's access to your workspace, you can use Pipedream workflows to perform common Slack actions or write your own code against the Slack API.

The Pipedream app for Slack is not a typical app. You don't interact with it directly as a bot, and it doesn't add custom functionality to your workspace out of the box. It makes it easier to automate anything you'd typically use the Slack API for, using Pipedream workflows.

Automate posting updates to your team channels
Create a bot to answer common questions
Integrate with your existing tools and services
And much more!

Trigger Code#

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

export default {
  ...common,
  key: "slack_v2-new-reaction-added",
  name: "New Reaction Added (Instant)",
  version: "1.2.0",
  description: "Emit new event when a member has added an emoji reaction to a message",
  type: "source",
  dedupe: "unique",
  props: {
    ...common.props,
    conversations: {
      propDefinition: [
        common.props.slack,
        "conversation",
      ],
      type: "string[]",
      label: "Channels",
      description: "Select one or more channels to monitor for new messages.",
      optional: true,
    },
    // eslint-disable-next-line pipedream/props-description,pipedream/props-label
    slackApphook: {
      type: "$.interface.apphook",
      appProp: "slack",
      async eventNames() {
        if (this.conversations?.length) {
          const conversations = [];
          for (const conversation of this.conversations) {
            conversations.push(`reaction_added:${conversation}`);
          }
          return conversations;
        }

        return [
          "reaction_added",
        ];
      },
    },
    ignoreBot: {
      propDefinition: [
        common.props.slack,
        "ignoreBot",
      ],
    },
    iconEmoji: {
      propDefinition: [
        common.props.slack,
        "icon_emoji",
      ],
      description: "Select one or more emojis to use as a filter. E.g. `fire, email`",
      type: "string[]",
      optional: true,
    },
    includeUserData: {
      label: "Include User Data",
      description: "Include user object in the response. Default `false`",
      type: "boolean",
      optional: true,
      default: false,
    },
  },
  methods: {
    ...common.methods,
    getSummary() {
      return "New reaction added";
    },
    async processEvent(event) {
      let iconEmojiParsed = [];

      try {
        iconEmojiParsed = typeof this.iconEmoji === "string" ?
          JSON.parse(this.iconEmoji) :
          this.iconEmoji;
      } catch (error) {
        iconEmojiParsed = this.iconEmoji.replace(/\s+/g, "").split(",");
      }

      if (
        ((this.ignoreBot) && (event.subtype == "bot_message" || event.bot_id)) ||
        (iconEmojiParsed?.length > 0 && !iconEmojiParsed.includes(event.reaction))
      ) {
        return;
      }

      if (this.includeUserData) {
        const promises = [
          this.slack.usersInfo({
            user: event.user,
          }),
        ];
        if (event.item_user) {
          promises.push(this.slack.usersInfo({
            user: event.item_user,
          }));
        }
        const responses = await Promise.all(promises);
        event.userInfo = responses[0].user;
        if (responses[1]) {
          event.itemUserInfo = responses[1].user;
        }
      }

      try {
        event.message = await this.getMessage({
          channel: event.item.channel,
          event_ts: event.item.ts,
        });
      } catch (err) {
        console.log("Error fetching message:", err);
      }

      return event;
    },
  },
  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
Slack	`slack`	`app`	This component uses the Slack app.
N/A	`db`	`$.service.db`	This component uses `$.service.db` to maintain state between executions.
Channels	`conversations`	`string[]`	Select a value from the drop down menu.
	`slackApphook`	`$.interface.apphook`
Ignore Bots	`ignoreBot`	`boolean`	Ignore messages from bots
Icon (emoji)	`iconEmoji`	`string[]`	Select a value from the drop down menu.
Include User Data	`includeUserData`	`boolean`	Include user object in the response. Default `false`

Trigger Authentication#

Slack uses OAuth authentication. When you connect your Slack account, Pipedream will open a popup window where you can sign into Slack and grant Pipedream permission to connect to your account. Pipedream securely stores and automatically refreshes the OAuth tokens so you can easily authenticate any Slack API.

Pipedream requests the following authorization scopes when you connect your account:

chat:writechat:write.customizechat:write.publicfiles:read

About Slack#

Slack is the AI-powered platform for work bringing all of your conversations, apps, and customers together in one place. Around the world, Slack is helping businesses of all sizes grow and send productivity through the roof.

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:0.1.1

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: "0.1.1",
  annotations: {
    destructiveHint: false,
    openWorldHint: true,
    readOnlyHint: false,
  },
  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.

Classify Text with Cohere API on New Reaction Added (Instant) from Slack API

Pipedream makes it easy to connect APIs for Cohere, Slack and 2,900+ other apps remarkably fast.

Trusted by 1,000,000+ developers from startups to Fortune 500 companies

Developers ♥ Pipedream

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