Create Social Media Post with TextCortex API on Failed Task in Schema from Snowflake API

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Failed Task in Schema from the Snowflake API

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Create Social Media Post with the TextCortex API

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Getting Started#

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

Select this integration
Configure the Failed Task in Schema trigger
1. Connect your Snowflake account
2. Configure timer
3. Select a Database
4. Select one or more Schema
5. Optional- Configure Task Name
Configure the Create Social Media Post action
1. Connect your TextCortex account
2. Configure Context
3. Optional- Configure Keywords
4. Optional- Configure Max Tokens
5. Select a Mode
6. Optional- Select a Model
7. Optional- Configure N
8. Optional- Select a Source Language
9. Optional- Select a Target Language
10. Optional- Configure Temperature
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#

Failed Task in Schema on Snowflake

Description:Emit new events when a task fails in a database schema

Version:0.1.2

Key:snowflake-failed-task-in-schema

View on GitHub

Snowflake Overview#

Snowflake offers a cloud database and related tools to help developers create robust, secure, and scalable data warehouses. See Snowflake's Key Concepts & Architecture.

Getting Started

1. Create a user, role and warehouse in Snowflake

Snowflake recommends you create a new user, role, and warehouse when you integrate a third-party tool like Pipedream. This way, you can control permissions via the user / role, and separate Pipedream compute and costs with the warehouse. You can do this directly in the Snowflake UI.

We recommend you create a read-only account if you only need to query Snowflake. If you need to insert data into Snowflake, add permissions on the appropriate objects after you create your user.

2. Enter those details in Pipedream

Visit https://pipedream.com/accounts. Click the button to Connect an App. Enter the required Snowflake account data.

You'll only need to connect your account once in Pipedream. You can connect this account to multiple workflows to run queries against Snowflake, insert data, and more.

3. Build your first workflow

Visit https://pipedream.com/new to build your first workflow. Pipedream workflows let you connect Snowflake with 1,000+ other apps. You can trigger workflows on Snowflake queries, sending results to Slack, Google Sheets, or any app that exposes an API. Or you can accept data from another app, transform it with Python, Node.js, Go or Bash code, and insert it into Snowflake.

Learn more at Pipedream University.

Trigger Code#

import common from "../common.mjs";

export default {
  ...common,
  props: {
    ...common.props,
    timer: {
      description: "Watch for failed tasks on this schedule",
      type: "$.interface.timer",
      default: {
        intervalSeconds: 15 * 60,
      },
    },
    database: {
      propDefinition: [
        common.props.snowflake,
        "database",
      ],
    },
    schemas: {
      propDefinition: [
        common.props.snowflake,
        "schema",
        (configuredProps) => ({
          database: configuredProps.database,
        }),
      ],
      type: "string[]",
    },
    taskName: {
      type: "string",
      label: "Task Name",
      description: "The name of the task to watch for failures. It allows you to use Regex to match multiple tasks.",
      optional: true,
    },
  },
  type: "source",
  key: "snowflake-failed-task-in-schema",
  // eslint-disable-next-line
  name: "Failed Task in Schema",
  description: "Emit new events when a task fails in a database schema",
  version: "0.1.2",
  async run() {
    await this.emitFailedTasks({
      database: this.database,
      schemas: this.schemas,
      taskName: this.taskName,
    });
  },
};

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
Snowflake	`snowflake`	`app`	This component uses the Snowflake app.
N/A	`db`	`$.service.db`	This component uses `$.service.db` to maintain state between executions.
	`timer`	`$.interface.timer`	Watch for failed tasks on this schedule
Database	`database`	`string`	Select a value from the drop down menu.
Schema	`schemas`	`string[]`	Select a value from the drop down menu.
Task Name	`taskName`	`string`	The name of the task to watch for failures. It allows you to use Regex to match multiple tasks.

Trigger Authentication#

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

We recommend you create a read-only account if you only need to query Snowflake. If you need to insert data into Snowflake, add permissions on the appropriate objects after you create your user.

About Snowflake#

A data warehouse built for the cloud

Action#

Create Social Media Post on TextCortex

Description:Create a social media post. [See the documentation](https://docs.textcortex.com/api/paths/texts-social-media-posts/post)

Version:0.0.1

Key:textcortex-create-social-media-post

View on GitHub

TextCortex Overview#

The TextCortex API enables automated creation of human-like text, making it a powerful tool for a variety of text generation tasks like drafting emails, creating content, or generating product descriptions. With Pipedream, you can integrate TextCortex into serverless workflows to harness AI writing assistance, trigger content creation based on events, or enhance data with generated text. The API's capability enhances automation and can be combined with other apps for more complex tasks.

Action Code#

import app from "../../textcortex.app.mjs";
import constants from "../../common/constants.mjs";

export default {
  key: "textcortex-create-social-media-post",
  name: "Create Social Media Post",
  description: "Create a social media post. [See the documentation](https://docs.textcortex.com/api/paths/texts-social-media-posts/post)",
  type: "action",
  version: "0.0.1",
  props: {
    app,
    context: {
      type: "string",
      label: "Context",
      description: "The context of the social media post.",
    },
    keywords: {
      type: "string[]",
      label: "Keywords",
      description: "Keywords to be included in the post.",
      optional: true,
    },
    maxTokens: {
      propDefinition: [
        app,
        "maxTokens",
      ],
    },
    mode: {
      type: "string",
      label: "Mode",
      description: "The platform, e.g. `twitter` to generate a Tweet. Allowed values: `twitter`, `linkedin`",
      options: Object.values(constants.SOCIAL_MEDIA_MODE),
    },
    model: {
      propDefinition: [
        app,
        "model",
      ],
    },
    n: {
      propDefinition: [
        app,
        "n",
      ],
    },
    sourceLang: {
      propDefinition: [
        app,
        "sourceLang",
      ],
    },
    targetLang: {
      propDefinition: [
        app,
        "targetLang",
      ],
    },
    temperature: {
      propDefinition: [
        app,
        "temperature",
      ],
    },
  },
  methods: {
    createSocialMediaPost(args = {}) {
      return this.app.post({
        path: "/texts/social-media-posts",
        ...args,
      });
    },
  },
  async run({ $: step }) {
    const {
      createSocialMediaPost,
      context,
      keywords,
      maxTokens,
      mode,
      model,
      n,
      sourceLang,
      targetLang,
      temperature,
    } = this;

    const response = await createSocialMediaPost({
      step,
      data: {
        context,
        keywords,
        max_tokens: maxTokens,
        mode,
        model,
        n,
        source_lang: sourceLang,
        target_lang: targetLang,
        temperature,
      },
    });

    step.export("$summary", `Successfully created social media post with status \`${response.status}\``);

    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
TextCortex	`app`	`app`	This component uses the TextCortex app.
Context	`context`	`string`	The context of the social media post.
Keywords	`keywords`	`string[]`	Keywords to be included in the post.
Max Tokens	`maxTokens`	`integer`	The maximum number of tokens to generate.
Mode	`mode`	`string`	Select a value from the drop down menu:`twitterlinkedin`
Model	`model`	`string`	Select a value from the drop down menu:`velox-1aecus-1alta-1sophos-1chat-sophos-1`
N	`n`	`integer`	The number of outputs to generate.
Source Language	`sourceLang`	`string`	Select a value from the drop down menu:`{ "label": "English (Default)", "value": "en" }{ "label": "The API will attempt to detect the language of the text and translate it.", "value": "auto" }{ "label": "Bulgarian", "value": "bg" }{ "label": "Czech", "value": "cs" }{ "label": "Danish", "value": "da" }{ "label": "German", "value": "de" }{ "label": "Greek", "value": "el" }{ "label": "Spanish", "value": "es" }{ "label": "Estonian", "value": "et" }{ "label": "Finnish", "value": "fi" }{ "label": "French", "value": "fr" }{ "label": "Hungarian", "value": "hu" }{ "label": "Indonesian", "value": "id" }{ "label": "Italian", "value": "it" }{ "label": "Japanese", "value": "ja" }{ "label": "Korean", "value": "ko" }{ "label": "Lithuanian", "value": "lt" }{ "label": "Latvian", "value": "lv" }{ "label": "Norwegian", "value": "nb" }{ "label": "Dutch", "value": "nl" }{ "label": "Polish", "value": "pl" }{ "label": "Portuguese (all Portuguese varieties mixed)", "value": "pt" }{ "label": "Romanian", "value": "ro" }{ "label": "Russian", "value": "ru" }{ "label": "Slovak", "value": "sk" }{ "label": "Slovenian", "value": "sl" }{ "label": "Swedish", "value": "sv" }{ "label": "Turkish", "value": "tr" }{ "label": "Ukrainian", "value": "uk" }{ "label": "Chinese", "value": "zh" }`
Target Language	`targetLang`	`string`	Select a value from the drop down menu:`{ "label": "English (Default)", "value": "en" }{ "label": "The API will attempt to detect the language of the text and translate it.", "value": "auto" }{ "label": "Bulgarian", "value": "bg" }{ "label": "Czech", "value": "cs" }{ "label": "Danish", "value": "da" }{ "label": "German", "value": "de" }{ "label": "Greek", "value": "el" }{ "label": "Spanish", "value": "es" }{ "label": "Estonian", "value": "et" }{ "label": "Finnish", "value": "fi" }{ "label": "French", "value": "fr" }{ "label": "Hungarian", "value": "hu" }{ "label": "Indonesian", "value": "id" }{ "label": "Italian", "value": "it" }{ "label": "Japanese", "value": "ja" }{ "label": "Korean", "value": "ko" }{ "label": "Lithuanian", "value": "lt" }{ "label": "Latvian", "value": "lv" }{ "label": "Norwegian", "value": "nb" }{ "label": "Dutch", "value": "nl" }{ "label": "Polish", "value": "pl" }{ "label": "Portuguese (all Portuguese varieties mixed)", "value": "pt" }{ "label": "Romanian", "value": "ro" }{ "label": "Russian", "value": "ru" }{ "label": "Slovak", "value": "sk" }{ "label": "Slovenian", "value": "sl" }{ "label": "Swedish", "value": "sv" }{ "label": "Turkish", "value": "tr" }{ "label": "Ukrainian", "value": "uk" }{ "label": "Chinese", "value": "zh" }`
Temperature	`temperature`	`string`	The sampling temperature to be used in text generation. The higher the temperature, the higher the risk of the output to sound "made up".