with Microsoft Power BI and Google Dialogflow?
Emits a new event when a dataset refresh operation has completed. See the documentation
Emits an event when a dataset refresh operation has failed in Power BI. See the documentation
Emit new event when a new dataset refresh operation is created. See the documentation
Adds new data rows to the specified table within the specified dataset from My workspace. See the documentation
Cancels a refresh operation for a specified dataset in Power BI. See the documentation
Batch create entities, See REST docs and client API docs
Creates a new Push Dataset in Power BI. See the documentation
The Microsoft Power BI API allows you to interact with your Power BI assets programmatically. With this API, you can embed your reports and dashboards into applications, manage Power BI datasets, push data into datasets for real-time dashboard updates, and automate your reporting workflows. On Pipedream, you can use this API to create intricate workflows that react to various triggers, like webhooks or schedules, and perform actions like refreshing datasets, posting to datasets, and managing Power BI assets.
import { axios } from "@pipedream/platform"
export default defineComponent({
props: {
microsoft_power_bi: {
type: "app",
app: "microsoft_power_bi",
}
},
async run({steps, $}) {
return await axios($, {
url: `https://api.powerbi.com/v1.0/myorg/availableFeatures`,
headers: {
Authorization: `Bearer ${this.microsoft_power_bi.$auth.oauth_access_token}`,
},
})
},
})
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.
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.`)
}
},
})