AI Data Extraction with Scrapfly API on New Scheduled Tasks from AWS API

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New Scheduled Tasks from the AWS API

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AI Data Extraction with the Scrapfly API

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Getting Started#

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

Select this integration
Configure the New Scheduled Tasks trigger
1. Connect your AWS account
2. Select a AWS Region
3. Optional- Configure Secret
Configure the AI Data Extraction action
1. Connect your Scrapfly account
2. Configure Body
3. Select a Content Type
4. Configure URL
5. Optional- Configure Charset
6. Optional- Configure Extraction Template
7. Optional- Configure Extraction Prompt
8. Optional- Configure Extraction Model
9. Optional- Configure Webhook Name
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 Scheduled Tasks on AWS

Description:Creates a Step Function State Machine to publish a message to an SNS topic at a specific timestamp. The SNS topic delivers the message to this Pipedream source, and the source emits it as a new event.

Version:0.4.2

Key:aws-new-scheduled-tasks

View on GitHub

AWS Overview#

The AWS API unlocks endless possibilities for automation with Pipedream. With this powerful combo, you can manage your AWS services and resources, automate deployment workflows, process data, and react to events across your AWS infrastructure. Pipedream offers a serverless platform for creating workflows triggered by various events that can execute AWS SDK functions, making it an efficient tool to integrate, automate, and orchestrate tasks across AWS services and other apps.

Trigger Code#

import base from "../common/scheduled.mjs";
import { toSingleLineString } from "../../common/utils.mjs";

export default {
  ...base,
  key: "aws-new-scheduled-tasks",
  name: "New Scheduled Tasks",
  description: toSingleLineString(`
    Creates a Step Function State Machine to publish a message
    to an SNS topic at a specific timestamp. The SNS topic delivers
    the message to this Pipedream source, and the source emits it as a new event.
  `),
  version: "0.4.2",
  type: "source",
  dedupe: "unique", // Dedupe on SNS message ID
  methods: {
    ...base.methods,
    getStateMachineDefinition() {
      const { PD_COMPONENT: componentId } = process.env;
      const topicArn = this.getTopicArn();
      return {
        Comment: `Task Scheduler for Pipedream component ${componentId}`,
        StartAt: "Wait",
        States: {
          Wait: {
            Comment: "Wait until specified timestamp",
            Type: "Wait",
            TimestampPath: "$.timestamp",
            Next: "SendMessageToSNS",
          },
          SendMessageToSNS: {
            Type: "Task",
            Resource: "arn:aws:states:::sns:publish",
            Parameters: {
              "TopicArn": topicArn,
              "Message.$": "$",
            },
            End: true,
          },
        },
      };
    },
    getStateMachinePermissions() {
      const topicArn = this.getTopicArn();
      const document = {
        Version: "2012-10-17",
        Statement: [
          {
            Effect: "Allow",
            Action: [
              "sns:Publish",
            ],
            Resource: [
              topicArn,
            ],
          },
        ],
      };
      const name = "publish-messages-to-pipedream-sns-topic";
      return {
        document,
        name,
      };
    },
  },
};

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
AWS	`aws`	`app`	This component uses the AWS app.
N/A	`db`	`$.service.db`	This component uses `$.service.db` to maintain state between executions.
N/A	`http`	`$.interface.http`	This component uses `$.interface.http` to generate a unique URL when the component is first instantiated. Each request to the URL will trigger the `run()` method of the component.
AWS Region	`region`	`string`	Select a value from the drop down menu.
Secret	`secret`	`string`	Optional but recommended: if you enter a secret here, you must pass this value in the `secret` parameter of each HTTP POST request

Trigger Authentication#

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

Follow the AWS Instructions for creating an IAM user with an associated access and secret key.

As a best practice, attach the minimum set of IAM permissions necessary to perform the specific task in Pipedream. If your workflow only needs to perform a single API call, you should create a user and associate an IAM group / policy with permission to do only that task. You can create as many linked AWS accounts in Pipedream as you'd like.

Enter your access and secret key below.

About AWS#

Amazon Web Services (AWS) offers reliable, scalable, and inexpensive cloud computing services.

Action#

AI Data Extraction on Scrapfly

Description:Automate content extraction from any text-based source using AI, LLM, and custom parsing. [See the documentation](https://scrapfly.io/docs/extraction-api/getting-started)

Version:0.0.1

Key:scrapfly-ai-data-extraction

View on GitHub

Action Code#

import { ConfigurationError } from "@pipedream/platform";
import fs from "fs";
import { checkTmp } from "../../common/utils.mjs";
import scrapfly from "../../scrapfly.app.mjs";

export default {
  key: "scrapfly-ai-data-extraction",
  name: "AI Data Extraction",
  description: "Automate content extraction from any text-based source using AI, LLM, and custom parsing. [See the documentation](https://scrapfly.io/docs/extraction-api/getting-started)",
  version: "0.0.1",
  type: "action",
  props: {
    scrapfly,
    body: {
      propDefinition: [
        scrapfly,
        "body",
      ],
    },
    contentType: {
      propDefinition: [
        scrapfly,
        "contentType",
      ],
    },
    url: {
      propDefinition: [
        scrapfly,
        "url",
      ],
    },
    charset: {
      type: "string",
      label: "Charset",
      description: "Charset of the document pass in the body. If you are not sure, you can use the `auto` value and we will try to detect it. Bad charset can lead to bad extraction, so it's important to set it correctly. **The most common charset is `utf-8` for text document and `ascii` for binary**. The symptom of a bad charset is that the text is not correctly displayed (accent, special characters, etc).",
      default: "auto",
      optional: true,
    },
    extractionTemplate: {
      type: "string",
      label: "Extraction Template",
      description: "Define an extraction template to get structured data. Use an ephemeral template (declared on the fly on the API call) or a stored template (declared in the dashboard) by using the template name.",
      optional: true,
    },
    extractionPrompt: {
      type: "string",
      label: "Extraction Prompt",
      description: "Instruction to extract data or ask a question on the scraped content with an LLM (Large Language Model). [Must be url encoded](https://scrapfly.io/web-scraping-tools/urlencode).",
      optional: true,
    },
    extractionModel: {
      type: "string",
      label: "Extraction Model",
      description: "AI Extraction to auto parse document to get structured data. E.g., `product`, `review`, `real-estate`, `article`.",
      optional: true,
    },
    webhookName: {
      type: "string",
      label: "Webhook Name",
      description: "Queue you scrape request and redirect API response to a provided webhook endpoint. You can create a webhook endpoint from your `dashboard`, it takes the name of the webhook. Webhooks are scoped to the given project/env.",
      optional: true,
    },
  },
  async run({ $ }) {
    if (!this.extractionTemplate && !this.extractionPrompt && !this.extractionModel) {
      throw new ConfigurationError("You must provide at least **Extraction Template**, **Extraction Prompt** or **Extraction Model**");
    }
    const response = await this.scrapfly.automateContentExtraction({
      $,
      headers: {
        "content-type": this.contentType,
      },
      maxBodyLength: Infinity,
      params: {
        url: this.url,
        charset: this.charset,
        extraction_template: this.extractionTemplate,
        extraction_prompt: this.extractionPrompt,
        extraction_model: this.extractionModel,
        webhook_name: this.webhookName,
      },
      data: fs.readFileSync(checkTmp(this.body)).toString(),
    });

    $.export("$summary", "Successfully extracted content");
    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
Scrapfly	`scrapfly`	`app`	This component uses the Scrapfly app.
Body	`body`	`string`	The request body must contain the content of the page you want to extract data from. The content must be in the format specified by the `content-type` header or via the `content_type` HTTP parameter. Provide a file from `/tmp`. To upload a file to `/tmp` folder, please follow the doc here
Content Type	`contentType`	`string`	Select a value from the drop down menu:`application/jsonapplication/jsonldapplication/xmltext/plaintext/htmltext/markdowntext/csvapplication/xhtml+xml`
URL	`url`	`string`	This URL is used to transform any relative URLs in the document into absolute URLs automatically. It can be either the base URL or the exact URL of the document. Must be url encoded.
Charset	`charset`	`string`	Charset of the document pass in the body. If you are not sure, you can use the `auto` value and we will try to detect it. Bad charset can lead to bad extraction, so it's important to set it correctly. The most common charset is `utf-8` for text document and `ascii` for binary. The symptom of a bad charset is that the text is not correctly displayed (accent, special characters, etc).
Extraction Template	`extractionTemplate`	`string`	Define an extraction template to get structured data. Use an ephemeral template (declared on the fly on the API call) or a stored template (declared in the dashboard) by using the template name.
Extraction Prompt	`extractionPrompt`	`string`	Instruction to extract data or ask a question on the scraped content with an LLM (Large Language Model). Must be url encoded.
Extraction Model	`extractionModel`	`string`	AI Extraction to auto parse document to get structured data. E.g., `product`, `review`, `real-estate`, `article`.
Webhook Name	`webhookName`	`string`	Queue you scrape request and redirect API response to a provided webhook endpoint. You can create a webhook endpoint from your `dashboard`, it takes the name of the webhook. Webhooks are scoped to the given project/env.

Action Authentication#

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

About Scrapfly#

Scrapfly Web Scraping API for developer

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Other Popular Integrations#

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Pipedream makes it easy to connect APIs for Scrapfly, AWS and 2,400+ other apps remarkably fast.

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

Developers ♥ Pipedream

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