Analyze Text Sentiment with Google Vertex AI API on New Scraping Completed from WebScraper.IO API

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New Scraping Completed from the WebScraper.IO API

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Analyze Text Sentiment with the Google Vertex AI API

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Getting Started#

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

Select this integration
Configure the New Scraping Completed trigger
1. Connect your WebScraper.IO account
2. Configure timer
Configure the Analyze Text Sentiment action
1. Connect your Google Vertex AI account
2. Select a Project ID
3. Configure Text
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 Scraping Completed on WebScraper.IO

Description:Emit new event when a page scraping job has completed. [See the docs here](https://webscraper.io/documentation/web-scraper-cloud/api)

Version:0.0.1

Key:webscraper_io-new-scraping-completed

View on GitHub

WebScraper.IO Overview#

The WebScraper.IO API allows you to programmatically perform web scraping tasks, extracting structured data from websites. With the API, you can automate the gathering of web content for analysis, monitoring, and integration with other data sources. In Pipedream, you can leverage this API to build workflows that process, analyze, and act on the data you scrape without writing code for backend infrastructure.

Trigger Code#

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

export default {
  ...common,
  key: "webscraper_io-new-scraping-completed",
  name: "New Scraping Completed",
  description: "Emit new event when a page scraping job has completed. [See the docs here](https://webscraper.io/documentation/web-scraper-cloud/api)",
  version: "0.0.1",
  type: "source",
  dedupe: "unique",
  methods: {
    ...common.methods,
    async emitHistoricalEvents({ limit }) {
      const jobs = await this.getScrapingJobs();
      if (!(jobs?.length > 0)) {
        return;
      }
      jobs.reverse().slice(0, limit)
        .forEach((job) => this.emitEvent(job));
    },
    isRelevant(job, previousIds) {
      return job.status === "finished" && !previousIds[job.id];
    },
    emitEvent(job) {
      const meta = this.generateMeta(job);
      this.$emit(job, meta);
    },
    generateMeta(job) {
      return {
        id: job.id,
        summary: job.sitemap_name,
        ts: job.time_created,
      };
    },
    async getScrapingJobs() {
      const jobs = [];
      const previousIds = this._getPreviousIds();

      const results = await this.webscraper.paginate(this.webscraper.getScrapingJobs);
      for (const job of results) {
        if (this.isRelevant(job, previousIds)) {
          previousIds[job.id] = true;
          jobs.push(job);
        }
      }

      this._setPreviousIds(previousIds);

      return jobs;
    },
  },
  async run() {
    const jobs = await this.getScrapingJobs();
    jobs.forEach((job) => this.emitEvent(job));
  },
};

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
WebScraper.IO	`webscraper`	`app`	This component uses the WebScraper.IO app.
N/A	`db`	`$.service.db`	This component uses `$.service.db` to maintain state between executions.
	`timer`	`$.interface.timer`

Trigger Authentication#

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

" To retrieve your API token,

Navigate to your Web Scraper account and sign in
Go to “Account Info” > “API settings”"

About WebScraper.IO#

Making web data extraction easy and accessible for everyone.

Action#

Analyze Text Sentiment on Google Vertex AI

Description:Analyzes a specified text for its underlying sentiment. [See the documentation](https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.publishers.models/generateContent)

Version:0.0.1

Key:google_vertex_ai-analyze-text-sentiment

View on GitHub

Google Vertex AI Overview#

With the Google Vertex AI API, you can tap into a robust suite of AI tools offered by Google Cloud to build, deploy, and scale machine learning models. Whether you're processing data, training custom models, or using pre-trained ones, Vertex AI provides a unified platform for AI development. In Pipedream, you can create serverless workflows that interact with Vertex AI, allowing you to automate tasks like model training, prediction, and resource management without provisioning your own infrastructure.

Action Code#

import vertexAi from "../../google_vertex_ai.app.mjs";

export default {
  key: "google_vertex_ai-analyze-text-sentiment",
  name: "Analyze Text Sentiment",
  description: "Analyzes a specified text for its underlying sentiment. [See the documentation](https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.publishers.models/generateContent)",
  version: "0.0.1",
  type: "action",
  props: {
    vertexAi,
    projectId: {
      propDefinition: [
        vertexAi,
        "projectId",
      ],
    },
    text: {
      type: "string",
      label: "Text",
      description: "The content to analyze for sentiment",
    },
  },
  async run({ $ }) {
    const response = await this.vertexAi.generateContent({
      $,
      projectId: this.projectId,
      model: "gemini-1.0-pro",
      data: {
        contents: [
          {
            role: "user",
            parts: [
              {
                text: `${this.text}\nClassify the sentiment of the message:\n`,
              },
            ],
          },
        ],
      },
    });
    $.export("$summary", "Successfully analyzed text sentiment");
    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
Google Vertex AI	`vertexAi`	`app`	This component uses the Google Vertex AI app.
Project ID	`projectId`	`string`	Select a value from the drop down menu.
Text	`text`	`string`	The content to analyze for sentiment

Action Authentication#

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

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

emailprofilehttps://www.googleapis.com/auth/cloud-platform