Integrate the Google Dialogflow API with the Google PaLM API

Setup the Google Dialogflow API trigger to run a workflow which integrates with the Google PaLM API. Pipedream's integration platform allows you to integrate Google Dialogflow and Google PaLM remarkably fast. Free for developers.

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Popular Google Dialogflow and Google PaLM Actions#

Create Context with the Google Dialogflow API

Creates a context, See REST docs and client API

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Chat with the Google PaLM API

Chat using Google PaLM. See the docs here

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Create Entities with the Google Dialogflow API

Batch create entities, See REST docs and client API docs

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Generate Embeddings with the Google PaLM API

Generate embeddings using Google PaLM. See the docs here

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Create Entity Type with the Google Dialogflow API

Creates an Entity Type, See REST docs and client API docs

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Overview of Google Dialogflow#

Google Dialogflow API empowers you to create conversational interfaces for websites, apps, and messaging platforms. Think chatbots that can engage in human-like dialogue, provide customer support, guide through sales processes, or control smart home devices with voice commands. With Pipedream's integration capabilities, you can create automated workflows that trigger actions in other apps based on Dialogflow's processed input, enabling seamless interaction across a plethora of services.

Connect Google Dialogflow#

module.exports = defineComponent({
  props: {
    google_dialogflow: {
      type: "app",
      app: "google_dialogflow",
    }
  },
  async run({steps, $}) {
    // Example code from the Dialogflow Node.js library:
    // https://github.com/googleapis/nodejs-dialogflow
    const dialogflow = require('dialogflow')
    const uuid = require('uuid')
    
    // A unique identifier for the given session
    const sessionId = uuid.v4()
    
    const key = JSON.parse(this.google_dialogflow.$auth.key_json)
     
    // Creates a session client from a Google service account key.
    const sessionClient = new dialogflow.SessionsClient({
      projectId: key.project_id,
      credentials: {
        client_email: key.client_email,
        private_key: key.private_key,
      }
    })
    const sessionPath = sessionClient.sessionPath(key.project_id, sessionId)
    
    // The text query request.
    const request = {
      session: sessionPath,
      queryInput: {
        text: {
          // The query to send to the dialogflow agent
          text: 'hello',
          // The language used by the client (en-US)
          languageCode: 'en-US',
        },
      },
    }
    
    // Send request and log result
    const responses = await sessionClient.detectIntent(request)
    console.log('Detected intent')
    const result = responses[0].queryResult
    console.log(`Query: ${result.queryText}`)
    console.log(`Response: ${result.fulfillmentText}`)
    if (result.intent) {
      console.log(`Intent: ${result.intent.displayName}`)
    } else {
      console.log(`No intent matched.`)
    }
  },
})

Overview of Google PaLM#

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.

Connect Google PaLM#

import { v1beta2 } from "@google-ai/generativelanguage";
import { GoogleAuth } from "google-auth-library";

export default defineComponent({
  props: {
    google_palm_api: {
      type: "app",
      app: "google_palm_api",
    }
  },
  async run({ steps, $ }) {
    const client = new v1beta2.TextServiceClient({
      authClient: new GoogleAuth().fromAPIKey(this.google_palm_api.$auth.palm_api_key),
    });

    const text = "Repeat after me: one, two,";
    const model = "models/text-bison-001";

    return await client
      .generateText({
        model,
        prompt: {
          text,
        },
      })
  },
})

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