Analyze Text Sentiment with Google Vertex AI API on New Deleted S3 File from AWS API

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New Deleted S3 File from the AWS 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 AWS 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 Deleted S3 File trigger
1. Connect your AWS account
2. Select a AWS Region
3. Select a S3 Bucket Name
4. Configure Ignore Delete Markers
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 Deleted S3 File on AWS

Description:Emit new event when a file is deleted from a S3 bucket

Version:0.1.2

Key:aws-s3-deleted-file

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/s3.mjs";

export default {
  ...base,
  type: "source",
  key: "aws-s3-deleted-file",
  name: "New Deleted S3 File",
  description: "Emit new event when a file is deleted from a S3 bucket",
  version: "0.1.2",
  dedupe: "unique",
  props: {
    ...base.props,
    ignoreDeleteMarkers: {
      type: "boolean",
      label: "Ignore Delete Markers",
      description: "When ignoring delete markers this will only emit events for permanently deleted files",
      default: false,
    },
  },
  methods: {
    ...base.methods,
    getEvents() {
      return [
        this.ignoreDeleteMarkers
          ? "s3:ObjectRemoved:Delete"
          : "s3:ObjectRemoved:*",
      ];
    },
    generateMeta(data) {
      const { "x-amz-request-id": id } = data.responseElements;
      const { key } = data.s3.object;
      const { eventTime: isoTimestamp } = data;
      return {
        id,
        summary: `Deleted file: '${key}'`,
        ts: Date.parse(isoTimestamp),
      };
    },
  },
};

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.
S3 Bucket Name	`bucket`	`string`	Select a value from the drop down menu.
Ignore Delete Markers	`ignoreDeleteMarkers`	`boolean`	When ignoring delete markers this will only emit events for permanently deleted files

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#

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

About Google Vertex AI#

Vertex AI offers everything you need to build and use generative AI—from AI solutions, to Search and Conversation, to 130+ foundation models, to a unified AI platform.

More Ways to Connect Google Vertex AI + AWS#

Other Popular Integrations#

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Analyze Text Sentiment with Google Vertex AI API on New Deleted S3 File from AWS API

Pipedream makes it easy to connect APIs for Google Vertex AI, 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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