I'm working in a chatbot with EN and ES locales.
The default locale is ES.
I want to add small talk using prebuilt agents. But here is the problem: Once I import the agent, this agent only appear with ES locale. It means that when I export the intents to my chatbot, this intents only has ES responses.
I want to get both, ES and EN responses.
The question is, how can I import this prebuilt agents with both languages?
Yes, you can have responses in several languages within one Dialogflow agent, however, you'll need to have duplicate intents (one intent in each language). There's a Dialogflow multi-locale sample available up on Github for further reference on this. Also, you can check out the official documentation.
Related
I need to build a chatbot . so should I build one in Python or should I use dialogueFlow
I tried building with DialogFLow , but at times I get stuck in identifying the langauge of the user
There are number of chatbot platforms and NLP engines from which you can try from.
Few of them are : Dialogflow, Amazon Lex, LUIS, Chatfuel, Botsify, Beep-boop, Motion.ai, QnA maker, Recast.ai, Octane.ai etc.
It all comes to your requirement and trying out what suits you better.
My Google Action delivers information to college students. For example: Who is the Title IX Coordinator?
To answer this question, we need to know the college the student attends. There are 2700+ colleges in the U.S. Many have the same name or similar sounding names.
So, #college-name is an entity in DialogFlow. Is there a way to import all 2700+ college names into DialogFlow as potential values for #college-name?
Also, is there a way to use a listbox with DialogFlow / Actions on Google with Google Assistant to ensure the correct college is identified?
Dialogflow has the ability to import entities from a file either in a CSV or JSON format.
There isn't a listbox visual widget, although you can use a List with similar names or Suggestion Chips to narrow down their search.
In addition to importing entity values from a file you can also push them to Dialogflow programmatically via the Dialogflow REST API. This API manages the agent itself and is thus different from the Dialogflow Webhook, which calls your fulfillment service.
The specific endpoint you would use to update entity values is projects.agent.entityTypes.entities. Dialogflow also offers SDKs for Python, Node.js and other languages. This is probably the best option if you have a large number of values, as it allows you to setup some kind of pipeline from your data source to Dialogflow and schedule it to update the entity on a regular basis (i.e. with an AWS Lambda function or a cron job that runs once a day).
This post follow this one where I explain one of my problems. Currently, I have to found a way to publish and maintain a high number of agents. I am not limited to Dialogflow.
I need some integrations like the google assistant (text and vocal), facebook messenger, telegram and if possible others like Slack, Twitter, Twillio, Alexa...
Okay, so I have already produced some agents with Dialogflow to understand the technology. I also read some pages of the actions-on-google documentation and I did'nt found anything on this subject. So basically I have to implement this:
Deploy around X agents through differents integrations instanciations. I mean I really need X facebook contacts, X google assistant apps, etc.
Maintain one code-base but have the ability to add localized-features like the name of the chatbot, currency or just block some intents (for Dialogflow example but in a more generic way, dialogs triggers).
It is just possible ? I am thinking about a web UI that can handle some facilities like the deployment, the monitoring and the maintenance. I am wondering if it's not overkill and if a more easier solution than mine exists already.
It isn't currently possible to create agents automatically, although Dialogflow's V2 API provides a mechanism to update agents via JSON once they have been created; see the restore and import endpoints.
I have some experience building chat and voice agents for other platforms, but I’m not using API.AI to understand natural language and parse intents. Do I have to replace my existing solution with API.AI?
Not at all. The advantages of using API.AI in creating a Conversation Action include Natural Language Understanding and grammar expansion, form filling, intent matching, and more.
That said, the Actions on Google platform includes a CLI, client library, and Web Simulator, all of which can be used to develop an Action entirely independent of API.AI. To do this you’ll need to build your own Action Package, which describes your Action and expected user grammars, and an endpoint to serve Assistant’s requests and provide responses to your users queries. The CLI can be used to deploy your Action Package directly to Google, and you can host your endpoint on any hosting service you wish. Google recommends App Engine on Google Cloud Platform.
I found this explanation from the official page most helpful.
API.AI
Use this option for most use cases. Understanding and parsing natural, human language is a very hard task, and API.AI does all that for you. API.AI also wraps the functionality of the Actions SDK into an easy-to-use web IDE that has conveniences such as generating and deploys action packages for you.
It also lets you build conversational experiences once and deploy to many other platforms other than Actions on Google.
ACTIONS SDK
Use this option if you have simple actions that have very short conversations with limited user input variability. These type of actions typically don't require robust language understanding and typically accomplish one quick use case.
In addition, if you already have an NLU that you want to use and just want to receive raw text and pass it to your own NLU, you will also need to use the Actions SDK.
Finally, the Actions SDK doesn't provide modern conveniences of an IDE, so you have to manually create action packages with a text editor and deploy them to your Google Developer project with a command-line utility.
Google is pushing aggressively everybody to API.AI. The only SDK they have (Node.js) no longer supports expected events for instance. Of course, you don't need to rely on their SDK (you can talk to the API directly) but they may change the API too. So proceed with caution.
We are in the early stage of overhauling a multi-brand website built using a custom developed java mvc framework to enable web 2.0 features. Built-in features we are looking at are: i18n, sso, content search and indexing, personalization, mashup support, ajax support, rich media content storage and management support, friendly to search engine optimizations, bookmarkable URLs, support for social networking sites, support for page composition and decoration using templates.
A combination of these features are supported by many portal and cms software.
Any insights will be very helpful in using a portal/cms combination to address this requirements!
This is a follow-up on this post focusing on the portal/cms angle
we are developing the same sort of thing, we are using Umbraco, open source, by far the best opensource we have come across
Joomla comes to mind. The ability to skin and implement templates is a core strength of the product. You can create channels of content as well as enable varying levels of user customization via roles.
Another nice feature is that you can export your changes to your template. that way you can port your changes easily from QA to a customer site.
Finally, there is a very active community of extension developers with customizations, as well as numerous template designers.
If you require a Portal that does integrate with your CRM such as Salesforce and yet allows you to build a Mobile-Optimized branded portal for Customers, Partners or any other groups of users you can check out Magentrix:
www.magentrix.com