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vlt Launches "reproduce": A New Tool Challenging the Limits of Package Provenance
vlt's new "reproduce" tool verifies npm packages against their source code, outperforming traditional provenance adoption in the JavaScript ecosystem.
@machinat/dialogflow
Advanced tools
This module implement the [`IntentRecognizer`](https://machinat.com/api/modules/core_base_intentrecognizer.html) interface with [DialogFlow ES](https://cloud.google.com/dialogflow/es/docs) API.
This module implement the IntentRecognizer
interface with DialogFlow ES API.
npm install @machinat/core @machinat/dialogflow
# or with yarn
yarn add @machinat/core @machinat/dialogflow
Check the Recognizing Intent document and the API references.
You can use this module in two different modes:
In this mode, the DialogFlow project is managed by the package. You maintain the training data along with the codes, and delegate the configuring procedures to the package.
First you need to create a GCP project and a service account to access the API. Follow this guide and set the GOOGLE_APPLICATION_CREDENTIALS
environment variable when running your app.
Then add @machinat/dialogflow
module like this:
import Machinat from '@machinat/core';
import DialogFlow from '@machinat/dialogflow';
const app = Machinat.createApp({
modules: [
DialogFlow.initModule({
projectId: '_YOUR_DIALOGFLOW_PROJECT_ID_',
recognitionData: {
defaultLanguage: 'en',
languages: ['en'],
intents: {
greeting: {
trainingPhrases: {
en: ['hello', 'hi']
}
}
},
},
}),
],
});
Finally you have to call DialogFlowRecognizer.train()
method every time you update the intents data. Like:
// cli/updateDialogFlow.js
import Machinat from '@machinat/core';
import DialogFlow from '@machinat/dialogflow';
const app = Machinat.createApp({/* ... */});
app
.start()
.then(() => {
const [recognizer] = app.useServices([DialogFlow.Recognizer]);
return recognizer.train();
});
It's recommended to run this every time you deploy a new version of your app. It'll not retrain if the intents data remain the same, so it's really cheap to call.
In this mode, you have to manage the DialogFlow project on your own. The package only works as a client for detecting intents.
First you have to prepare a ready-to-use DialogFlow agent. You can follow this guide to create one and add the intents in the DialogFlow console.
Next follow this section to create a service account and set the GOOGLE_APPLICATION_CREDENTIALS
environment variable.
Then add @machinat/dialogflow
module like this:
import Machinat from '@machinat/core';
import DialogFlow from '@machinat/dialogflow';
const app = Machinat.createApp({
modules: [
DialogFlow.initModule({
projectId: '_YOUR_DIALOGFLOW_PROJECT_ID_',
// prevent package to edit the project
manualMode: true,
// detect intent with the default agent in dev
useDefaultAgent: process.env.NODE_ENV !== 'production',
// specify the environment to be used on production
environment: 'production',
recognitionData: {
defaultLanguage: 'en',
languages: ['en'],
intents: {},
},
}),
],
});
If you call DialogFlowRecognizer.train()
method under manual mode, it creates a snapshot version on the environment. You can do it every time you deploy a new version of app, so the DialogFlow agent would have a revertible version along with the version of app.
FAQs
This module implement the [`IntentRecognizer`](https://machinat.com/api/modules/core_base_intentrecognizer.html) interface with [DialogFlow ES](https://cloud.google.com/dialogflow/es/docs) API.
The npm package @machinat/dialogflow receives a total of 2 weekly downloads. As such, @machinat/dialogflow popularity was classified as not popular.
We found that @machinat/dialogflow demonstrated a not healthy version release cadence and project activity because the last version was released a year ago. It has 2 open source maintainers collaborating on the project.
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