← HTTP / Webhook + Google PaLM integrations

Generate Embeddings with Google PaLM API on New event when the content of the URL changes. from HTTP / Webhook API

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New event when the content of the URL changes. from the HTTP / Webhook API
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Generate Embeddings with the Google PaLM API
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Getting Started

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

  1. Select this integration
  2. Configure the New event when the content of the URL changes. trigger
    1. Connect your HTTP / Webhook account
    2. Configure Watching timer
    3. Configure HTTP Request Configuration
    4. Configure Emit body only
    5. Configure Emit as array
  3. Configure the Generate Embeddings action
    1. Connect your Google PaLM account
    2. Configure Text
  4. Deploy the workflow
  5. Send a test event to validate your setup
  6. 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

Description:Emit new event when the content of the URL changes.
Version:0.0.2
Key:http-watch-url

HTTP / Webhook Overview

Build, test, and send HTTP requests without code using your Pipedream workflows. The HTTP / Webhook action is a tool to build HTTP requests with a Postman-like graphical interface.

An interface for configuring an HTTP request within Pipedream's workflow system. The current selection is a GET request with fields for the request URL, authorization type (set to 'None' with a note explaining "This request does not use authorization"), parameters, headers (with a count of 1, though the detail is not visible), and body. Below the main configuration area is an option to "Include Response Headers," and a button labeled "Configure to test." The overall layout suggests a user-friendly, no-code approach to setting up custom HTTP requests.

Point and click HTTP requests

Define the target URL, HTTP verb, headers, query parameters, and payload body without writing custom code.

A screenshot of Pipedream's HTTP Request Configuration interface with a GET request type selected. The request URL is set to 'https://api.openai.com/v1/models'. The 'Auth' tab is highlighted, indicating that authentication is required for this request. In the headers section, there are two headers configured: 'User-Agent' is set to 'pipedream/1', and 'Authorization' is set to 'Bearer {{openai_api_key}}', showing how the OpenAI account's API key is dynamically inserted into the headers to handle authentication automatically.

Here's an example workflow that uses the HTTP / Webhook action to send an authenticated API request to OpenAI.

Focus on integrating, not authenticating

This action can also use your connected accounts with third-party APIs. Selecting an integrated app will automatically update the request’s headers to authenticate with the app properly, and even inject your token dynamically.

This GIF depicts the process of selecting an application within Pipedream's HTTP Request Builder. A user hovers the cursor over the 'Auth' tab and clicks on a dropdown menu labeled 'Authorization Type', then scrolls through a list of applications to choose from for authorization purposes. The interface provides a streamlined and intuitive method for users to authenticate their HTTP requests by selecting the relevant app in the configuration settings.

Pipedream integrates with thousands of APIs, but if you can’t find a Pipedream integration simply use Environment Variables in your request headers to authenticate with.

Compatible with no code actions or Node.js and Python

The HTTP/Webhook action exports HTTP response data for use in subsequent workflow steps, enabling easy data transformation, further API calls, database storage, and more.

Response data is available for both coded (Node.js, Python) and no-code steps within your workflow.

An image showing the Pipedream interface where the HTTP Webhook action has returned response data as a step export. The interface highlights a structured view of the returned data with collapsible sections. We can see 'steps.custom_request1' expanded to show 'return_value' which is an object containing a 'list'. Inside the list, an item 'data' is expanded to reveal an element with an 'id' of 'whisper-1', indicating a model created by and owned by 'openai-internal'. Options to 'Copy Path' and 'Copy Value' are available for easy access to the data points.

Trigger Code

import http from "../../http.app.mjs";
import hash from "object-hash";
import { DEFAULT_POLLING_SOURCE_TIMER_INTERVAL } from "@pipedream/platform";

export default {
  key: "http-watch-url",
  name: "New event when the content of the URL changes.",
  description: "Emit new event when the content of the URL changes.",
  version: "0.0.2",
  type: "source",
  dedupe: "unique",
  props: {
    http,
    timer: {
      type: "$.interface.timer",
      label: "Watching timer",
      description: "How often to watch the URL.",
      default: {
        intervalSeconds: DEFAULT_POLLING_SOURCE_TIMER_INTERVAL,
      },
    },
    httpRequest: {
      type: "http_request",
      label: "HTTP Request Configuration",
      description: "HTTP Request Configuration",
      default: {
        method: "GET",
      },
    },
    emitBodyOnly: {
      label: "Emit body only",
      description: "If set as true the emitted item will contain only the response body, otherwise, it will include the request status code.",
      type: "boolean",
      default: true,
    },
    emitAsArray: {
      label: "Emit as array",
      description: "If the request responds with an array, this source will emit changes individually for each item of the array.",
      type: "boolean",
      default: false,
    },
  },
  methods: {
    getMeta(data, status) {
      if (this.emitBodyOnly) {
        return data;
      }
      return {
        data,
        status,
      };
    },
    emitArray(event, response) {
      if (!Array.isArray(response.data)) {
        return this.emitAny(event, response);
      }
      for (const item of response.data) {
        const meta = this.getMeta(item, response.status);
        this.emit(event, meta);
      }
    },
    emitAny(event, response) {
      const meta = this.getMeta(response.data, response.status);
      this.emit(event, meta);
    },
    emit(event, meta) {
      const ts = event.timestamp || Math.round(Date.now() / 1000);
      this.$emit(meta, {
        id: hash(meta),
        summary: `Requested at ${ts}`,
        ts,
      });
    },
  },
  async run(event) {
    const response = await this.httpRequest.execute();
    if (this.emitAsArray) {
      this.emitArray(event, response);
    } else {
      this.emitAny(event, response);
    }
  },
};

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.
LabelPropTypeDescription
HTTP / WebhookhttpappThis component uses the HTTP / Webhook app.
Watching timertimer$.interface.timer

How often to watch the URL.

HTTP Request ConfigurationhttpRequesthttp_request

HTTP Request Configuration

Emit body onlyemitBodyOnlyboolean

If set as true the emitted item will contain only the response body, otherwise, it will include the request status code.

Emit as arrayemitAsArrayboolean

If the request responds with an array, this source will emit changes individually for each item of the array.

Trigger Authentication

The HTTP / Webhook API does not require authentication.

About HTTP / Webhook

Get a unique URL where you can send HTTP or webhook requests

Action

Description:Generate embeddings using Google PaLM. [See the docs here](https://developers.generativeai.google/api/python/google/generativeai/generate_embeddings)
Version:0.0.1
Key:google_palm_api-generate-embeddings

Google PaLM Overview

The Google PaLM API is a cutting-edge language model that allows developers to integrate advanced natural language understanding into their applications. On Pipedream, you can harness this power to create serverless workflows that react to various triggers and perform actions based on the insights and outputs from PaLM. Whether it's generating content, summarizing text, or understanding user intent, PaLM's capabilities can be integrated into Pipedream workflows to automate complex tasks involving language.

Action Code

import palm from "../../google_palm_api.app.mjs";

export default {
  key: "google_palm_api-generate-embeddings",
  name: "Generate Embeddings",
  description: "Generate embeddings using Google PaLM. [See the docs here](https://developers.generativeai.google/api/python/google/generativeai/generate_embeddings)",
  version: "0.0.1",
  type: "action",
  props: {
    palm,
    text: {
      type: "string",
      label: "Text",
      description: "The text that will be used to generate embeddings",
    },
  },
  async run({ $ }) {
    const response = await this.palm.generateEmbeddings(this.text);
    $.export("$summary", "Successfully generated embeddings");
    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.

LabelPropTypeDescription
Google PaLMpalmappThis component uses the Google PaLM app.
Texttextstring

The text that will be used to generate embeddings

Action Authentication

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

Generate an API key or join the PaLM waitlist here, then enter your API key below.

About Google PaLM

Build generative AI applications with Google's PaLM 2 model.

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