Ask Question with DocsBot AI API on Failed Task in Schema from Snowflake API

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Trigger workflow on

Failed Task in Schema from the Snowflake API

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Getting Started#

This integration creates a workflow with a Snowflake trigger and DocsBot AI 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 Ask Question action
1. Connect your DocsBot AI account
2. Select a Team ID
3. Select a Bot ID
4. Configure Question
5. Optional- Configure Full Source
6. Optional- Select a Format
7. Optional- Configure Chat History
8. Optional- Configure Metadata
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#

Ask Question on DocsBot AI

Description:Ask a question to a specific bot in a specific team. [See the documentation](https://docsbot.ai/documentation/developer/chat-api)

Version:0.0.1

Key:docsbot_ai-ask-question

View on GitHub

DocsBot AI Overview#

DocsBot AI API transforms natural language into structured data, enabling the extraction and analysis of information from unstructured text. In Pipedream, you can leverage this API to automate workflows that require processing of text for sentiment analysis, summarization, or data extraction. With Pipedream's serverless platform, these capabilities can be integrated into diverse systems without writing complex infrastructure code.

Action Code#

import docsbotAi from "../../docsbot_ai.app.mjs";

export default {
  key: "docsbot_ai-ask-question",
  name: "Ask Question",
  description: "Ask a question to a specific bot in a specific team. [See the documentation](https://docsbot.ai/documentation/developer/chat-api)",
  version: "0.0.1",
  type: "action",
  props: {
    docsbotAi,
    teamId: {
      propDefinition: [
        docsbotAi,
        "teamId",
      ],
    },
    botId: {
      propDefinition: [
        docsbotAi,
        "botId",
        ({ teamId }) => ({
          teamId,
        }),
      ],
    },
    question: {
      propDefinition: [
        docsbotAi,
        "question",
      ],
    },
    fullSource: {
      propDefinition: [
        docsbotAi,
        "fullSource",
      ],
    },
    format: {
      propDefinition: [
        docsbotAi,
        "format",
      ],
    },
    history: {
      propDefinition: [
        docsbotAi,
        "history",
      ],
    },
    metadata: {
      propDefinition: [
        docsbotAi,
        "metadata",
      ],
    },
  },
  async run({ $ }) {
    const response = await this.docsbotAi.askQuestion({
      $,
      teamId: this.teamId,
      botId: this.botId,
      data: {
        question: this.question,
        full_source: this.fullSource,
        format: this.format,
        history: this.history,
        metadata: this.metadata,
      },
    });
    $.export("$summary", "Successfully asked question");
    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
DocsBot AI	`docsbotAi`	`app`	This component uses the DocsBot AI app.
Team ID	`teamId`	`string`	Select a value from the drop down menu.
Bot ID	`botId`	`string`	Select a value from the drop down menu.
Question	`question`	`string`	The question to ask the bot (2 to 2000 characters)
Full Source	`fullSource`	`boolean`	Whether the full source content should be returned
Format	`format`	`string`	Select a value from the drop down menu:`markdowntext`
Chat History	`history`	`string[]`	The chat history array
Metadata	`metadata`	`object`	A user identification object with arbitrary metadata about the the user. Will be saved to the question history log. Keys `referrer`, `email`, and `name` are shown in question history logs