AI Data Extraction with Scrapfly API on New or Updated Row from PostgreSQL API

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New or Updated Row from the PostgreSQL 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 PostgreSQL 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 or Updated Row trigger
1. Connect your PostgreSQL account
2. Configure Polling Interval
3. Select a Schema
4. Select a Table
5. Select a Identifier Column
6. Select a Timestamp Column
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 or Updated Row on PostgreSQL

Description:Emit new event when a row is added or modified. [See the documentation](https://node-postgres.com/features/queries)

Version:2.0.6

Key:postgresql-new-or-updated-row

View on GitHub

PostgreSQL Overview#

On Pipedream, you can leverage the PostgreSQL app to create workflows that automate database operations, synchronize data across platforms, and react to database events in real-time. Think handling new row entries, updating records from webhooks, or even compiling reports on a set schedule. Pipedream's serverless platform provides a powerful way to connect PostgreSQL with a variety of apps, enabling you to create tailored automation that fits your specific needs.

Trigger Code#

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

export default {
  ...common,
  name: "New or Updated Row",
  key: "postgresql-new-or-updated-row",
  description: "Emit new event when a row is added or modified. [See the documentation](https://node-postgres.com/features/queries)",
  version: "2.0.6",
  type: "source",
  dedupe: "unique",
  props: {
    ...common.props,
    schema: {
      propDefinition: [
        common.props.postgresql,
        "schema",
      ],
    },
    table: {
      propDefinition: [
        common.props.postgresql,
        "table",
        (c) => ({
          schema: c.schema,
        }),
      ],
    },
    identifierColumn: {
      label: "Identifier Column",
      propDefinition: [
        common.props.postgresql,
        "column",
        (c) => ({
          table: c.table,
          schema: c.schema,
        }),
      ],
      description: "The column to identify an unique row, commonly it's `id` or `uuid`.",
    },
    timestampColumn: {
      label: "Timestamp Column",
      propDefinition: [
        common.props.postgresql,
        "column",
        (c) => ({
          table: c.table,
          schema: c.schema,
        }),
      ],
      description: "A datetime column, such as 'date_updated' or 'last_modified' that is set to the current datetime when a row is updated.",
    },
  },
  hooks: {
    async deploy() {
      await this.initialRows(
        this.schema,
        this.table,
        this.timestampColumn,
        this.limit,
      );
    },
  },
  methods: {
    ...common.methods,
    generateMeta(row, column) {
      return {
        id: `${row[this.identifierColumn]}-${row[column]}`,
        summary: "Row Added/Updated",
        ts: row[column],
      };
    },
  },
  async run() {
    await this.newRows(this.schema, this.table, this.timestampColumn);
  },
};

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
PostgreSQL	`postgresql`	`app`	This component uses the PostgreSQL app.
N/A	`db`	`$.service.db`	This component uses `$.service.db` to maintain state between executions.
Polling Interval	`timer`	`$.interface.timer`	Pipedream will poll the API on this schedule
Schema	`schema`	`string`	Select a value from the drop down menu.
Table	`table`	`string`	Select a value from the drop down menu.
Identifier Column	`identifierColumn`	`string`	Select a value from the drop down menu.
Timestamp Column	`timestampColumn`	`string`	Select a value from the drop down menu.

Trigger Authentication#

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

Connecting to Restricted Databases

Either enable the shared static IP for this account below, or configure a VPC to deploy any workflow in your workspace to a private network with a dedicated static IP. Learn more in our docs.

SSL Setup

Configure SSL on your PostgreSQL database by providing the CA (Certificate Authority), and choosing between Full Verification, Verify Certificate Authority (CA), or Skip Verification. Skipping verification is not recommended as this has serious security implications.

About PostgreSQL#

PostgreSQL is a free and open-source relational database management system emphasizing extensibility and SQL compliance.

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.