I'm trying to make an app to reserve meeting rooms in my office by Google Home and Dialogflow.
Here's my plan:
Me: "OK Google, is there any available room now?"
Google Home: "Room 1 is available until 16:00."
Me: "Book it."
Google Home: "Booked Room 1."
The current problem is how to make Google Home's response stateful. In my plan, when I say "Book it", Google Home has to remember Room 1. But I don't know how to make it happen.
I read documents of conversation API, but I haven't understand it's possible to preserve variables or states within the same conversation Id.
https://developers.google.com/actions/reference/v1/conversation
Does anyone know about that?
It is absolutely possible, and even fairly easy, to preserve state as part of the conversation your users have with your Action. Dialogflow makes it particularly easy with what they call context, and it uses this in a few ways:
As part of your fulfillment, you can set a Context, the lifetime (number of steps in the conversation) that context will be good for, and any parameter/value pairs for this context. Using your example, when you have the Action replying with the room number and time, you might set a "pending_request" context with the pair {"room": "1"} and {"time": "2017-11-15T16:00Z"} and a lifetime of 5.
You can indicate as part of the Intent what Contexts must be set for that Intent to be selected in the conversation. So asking "Who is available?" while the "pending_request" context is active might trigger an Intent that looks who is available at that time that can meet at that room (perhaps because they're in the same building). But if the context is not active, it might trigger an Intent to see who is available right now that you can call (even if they're in a different building).
The parameters that you set in the Context are available to you in the Intent that is called. So you'll be able to find out what room and time have been set in the fulfillment of the Intent.
If you don't renew the Context, it will vanish after the selected number of exchanges. This means that after you inquire about the room, you could inquire if you have any appointments today (a question unrelated to the room or the time) and the phrase "Book it" would still have the context available to it.
If you're using the node.js client library from Google, you can use app.getContext() and app.setContext(). If you're doing it in JSON, you need to provide the Context information directly in the response.
Google also provides a more general app.data object that you can set properties on with the node.js client library, and these properties are retained during a conversation (although not between conversations). It uses Contexts behind the scenes, although it isn't quite as powerful as Contexts are since you can't use it as part of Intent matching.
(As an aside - the link you provided was to version 1 of the API. That version has been deprecated and will be turned off in May 2018. It was also for the Actions API rather than Dialogflow. The equivalent documentation is at https://developers.google.com/actions/reference/rest/conversation-webhook, but that probably isn't what you want if you're using Dialogflow.)
Related
body: TabBarView(
children: [
Home_G_HX(),
Physical(),
Text("1"),
],
),
Home_G_HX and Physical have many widgets so home screen loading becomes slow
The most effective method to Create a Chatbot Using DialogFlow ?
What is DialogFlow ?
DialogFlow is an improvement stage made by Google that can assist us with making Chatbots. It depends on NLP (Natural Language Processing) which offers our chatbots the likelihood to be extremely strong.
What is the ChatBot ?
A chatBot is a keen program that can cooperate with individuals like a human and serves them in the particular space where it has been made. The chatbot examines the expectation of the client and investigates the reaction that will be more adjusted.
Presently you understand what DialogFlow and chatbot are, we should perceive how we can make a chatbot utilizing Dialogflow.
Note: You ought to have a google account and login in to the Dialogflow stage prior to following these means.
In this article, we will make a chatbot that can serve clients who will believe should do a booking for a room in a Hotel.
Stage 1. Make an Agent
An Agent is a smart program inside the chatbot, that program interfaces with the clients or clients.
To make an Agent, go to the left segment of your screen and snap on the main button underneath the Dialogflow logo and go down to the make new specialist button.
From that point onward, the new screen will be stacked, and you will be request to indicate the name of the Agent, the language that it ought to be talk and the time region. As far as I might be concerned, I type reservation-bot for the name and the rest, I leave the default values. From that point onward, you should tap on the CREATE button and DialogFlow will make a specialist for your chatbot.
Stage 2. Make plans
Purposes is use by the chatbot to comprehend what the clients or clients need. It's inside the goals that we ought to give to the chatbot the instances of expressions that the clients might ask and a few reactions that the chatbot ought to use to pay all due respects to the clients. We should demonstrate the way that we can make it happen.
Note: When we make another specialist, it accompanies two defaults aims named Default Fallback Intent and Default Welcome Intent
For make another Intent, click on the Create Intent button
From that point onward, you should give the name of your goal. Then, at that point, go to the Training Phrases segment and snap on add preparing phrases. This part concerns the way where you ought to give the case of the expression which addresses the various inquiries that clients might pose to the chatbot. we prescribe giving numerous guides to make your chatbot extremely strong.
For this model, you could accept similar expressions as me.
We have added a few expressions that clients might ask to our chatbot, for your own chatbot, go ahead and add one more expression to work on the force of your chatbot
In this picture, we can see that two articulations are overlined. DialogFlow has recognized these articulations as a substance, truth be told. DialogFlow perceives three kinds of elements like frameworks substances, engineer elements, and meeting substances. this evening and today are perceived as frameworks elements, it alludes to the date or timeframe, this kind of substance is now set in Dialogflow. Later we will make our own elements which will perceive by DialogFlow as Developer substances. For more data, look at this documentation
Presently, we should characterize a Responses that the specialist might use to pay all due respects to clients. Go down to the Response segment and snap on the Add reaction button, and add a few reactions explanations.
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You can see that inside these reactions models there are a few articulations that beginning with the $ image, these articulations are considered as factors that will contain the qualities that clients will make reference to in their inquiries, and that DialogFlow will have perceived as a specific substance. On the picture above, we have three factors, for example, $time-period, $date-time, and $reservation-type. $time-period and $date-time are frameworks substances factors and $reservation-type is a Developer element variable, and that implies $reservation-type ought to be made by the engineer, before that DialogFLow might remember it. After added a few reactions that the specialist ought to utilize, click on the Save button, we will return from this point forward.
Stage 3. Making of substances
In all actuality, substances are catchphrases that assist the Agent with perceiving what the client needs. To make it, simply follow me.
Click on the Entities button
production of substances
After click on the Create Entity button
formation of element
Later, determine the name of the element (you ought to give reservation-type as name of your substance, since you have use it as factor when you gave a few reactions to the specialist). Then, add a substance bed-room and a few equivalents like beneath.
try to check the case Define equivalent words previously, and afterward click on Save button.
The job of equivalent words is that, when clients ought to discuss bed-room, bed or room, all of this ought to allude to the bed-room.
Do likewise with the element reservation-activity and save it.
formation of reservation-activity substance
Presently, we have two elements fit to be utilized.
Stage 4. add our substances inside preparing phrases articulations
back to the booking aim interface and go to the preparation phrases segment.
At the point when you are there, select an articulation, and inside this articulation select the word bed-room like this
Then, at that point, research for #reservation-type
What's more, click on this, and the shade of bed-room will change.
Do exactly the same thing to all the bed-room inside all articulations.
For the words booking, reservation, and save, do exactly the same things however rather than research #reservation-type you will explore #reservation-activity.
adding developper elements inside our preparation expression articulations
Stage 5. Meaning of boundaries and activities
It's not needed, yet now and again, it will be vital to commit the client to provide for the chatbot, some data.
Go down to the Actions and boundaries segment, consistently inside the booking expectation interface. you ought to have this picture underneath.
activity and boundaries
For our chatbot, we need that clients give the booking type and the date of the reservation. Make a point to really look at it.
activities and boundaries
From that point forward, we ought to indicate the brief text that the Agent ought to show to the client when they haven't determined the necessary boundaries. You want to tap on the Define prompts… space on the ideal locations of this segment, in the wake of characterizing brief text, close the container discourse.
for the date-time boundary
characterize brief text for date time boundary
for the booking type boundary
After this, save the plan.
Presently you can test your chatbot.
test segment
You can test your chatbot here.
Stage 6. Coordination on the web stage
reconciliation
Click on the reconciliations button
You can incorporate your chatbot within numerous stages, as Facebook courier, WhatsApp, wire, etc.
For this article, we will pick the Web Demo
coordination demo
click on the connection, and test it once more.
You can use DialogFlow from Google, Luis/Bot-service from Azure etc for cloud based solutions or Rasa-Ai for simpler on-prem solutions. So to get started, build a simple Flutter app that has a text box where you can type something. Once you type, let the content flow to server via an explicit submit button for instance and let the NodeJos server ( or any server ) return you a random message. This is your first phase. You then need to replace the request-response scheme with a chatbot.
An example of a bot using Azure system can be found here: https://github.com/pawanit17/EventBright
Another example that uses socket.io based communication to the clients can be found here:
https://github.com/pawanit17/LetsChat-A-Simple-WebSockets-Chatting-App
Experiment with them.
With v1 of the measurement protocol, you could use these parameters to add custom dimensions or change medium, source or refer for a page view:
https://ssl.google-analytics.com/collect?v=1&tid=UA-xxxxxxxx&cid=[custom-id]&t=pageview&dp=[Url of pageview]&dh=[hostname of pageview]&cm=[new-medium]&cs=[new-source]&dr=[new-referer]&cd1=[custom-dimension-1]&cd2=[custom-dimension-2]
How is it done in measurement protocol v2?
I couldn't find any documentation about the page-view-event in V2 (for example it's just not mentioned here
https://developers.google.com/analytics/devguides/collection/protocol/ga4/reference/events), even the event-builder (https://ga-dev-tools.web.app/ga4/event-builder/) doesn't support a simple page-view.
So, all I got so far is this:
$data = '
{ "client_id": "'.[custom-id].'",
"events": [
{
"name": "page_view",
"params": {
"page_location": "'.[Url of pageview].'"
}
}
]
}
';
So, what are possible parameters for a page-view-event?
Ok, a few things here right away that you should know if you're playing with MP:
Measurement protocol is a poor name. It implies there's more than one protocol for data gathering. There's none. There is just only one protocol for tracking.
MP2 still largely MP1. Google tries to pose GA4 as a new product, but it's just our old good GA UA with a simplified backend and overengineered front-end that tries to deliver the level of quality Site Catalyst/Omniture/Adobe Analytics have been delivering for a decade. MP is largely the same. dr, cm, cs and a lot of other fields are still there. cds aren't there anymore cuz they're replaced with eps and ups, but more about that a bit later.
GA4 uses this big marketing claim that the new analytics is so wonderfully event-based, unlike the old one. When I dug into why they keep claiming it everywhere, I realized that the only difference is that pageviews are now events. Not much difference really. But yes, a pageview is just an event named page_view. We'll talk about it a bit more later.
Custom dimensions are no more. Now they're called event properties and user properties. The same thing really, Google just tries to make it less obvious that there are no more session level custom dimensions. Or product-level CDs. Though the product level is seemingly on their roadmap.
Make sure you're using the correct measurement id. They made it a lot harder to find it in GA4. It's no longer just the property id visible in the property list, unfortunately.
GA's real-time reports don't include all dimensions, especially if those dimensions are involved in advanced metrics/dimensions calculations. Do not use real time reports for inspecting the content of your events. It's not meant for debugging. It's a vanity report. Still helpful to check the volume of events when you're sending a bunch and expect to see them in GA. Google even has a warning here:
Like the DebugView report, the Realtime report performs limited attribution analysis to ensure responsive reporting. We recommend that you refer to the Acquisition reports for the most accurate attribution information.
Finally, what I often do instead of reading the so-still-unfinished-and-not-really-helpful documentation on MP2, is either use a library like this.
Or, since 1 is the case, I would just implement a moniker tracking in my test GTM, then see what and how it sends to where in the Network debugger and simply reimplement it on my side exactly how GTM does it. No magic involved. Here is how my GTM tag would look like:
With a trigger on any click or any page load. After all is done, I publish the lib. Then I would inject this GTM's code in a local site, or in my test site, or however else you want to test it. And trigger the tag that you need to mimic with MP.
I use this wonderful extension to show all events that fire and their details right in my console.
Now this is how the above tag looks on my test site through the extension:
It's pretty useful.
How do I know that page_referrer is used as dr instead of ep in GTM? Here is the list of the fields that will never be seen as ep. But Google doesn't care enough to map them properly to what these fields are called in MP, so you either have to test, or know, or google it elsewhere.
Finally, here is how the network request looks like:
I published the tag to prod (I keep a test site in prod), so you can go and look at it. Or just find a site that uses GA4 and see its network requests. How does google know that this is a pageview? by the event name: en=page_view
Of course, you do the same with medium and source. Judging from the documentation I've linked to above, the medium and source look like campaign_source and campaign_medium in GTM. GTM maps them accordingly to cs and cm fields. And that's how you know these are the correct mp fields. Give GA time to process these and check on them in a few days.
Good, now this is applicable to the enhanced ecommerce hits too, it's just that they have more variables and data structures in them typically.
Finally, if you want to simulate batch events, you can just make a few tags fire in rapid succession and GTM will neatly pack them in one network request if they fit. You can then digest how the packing is done through the same methods as we do here and simulate.
I'm creating my own service and in the endpoint test fails here and this error is shown:
"returns at least three items"
This error comes from the trigger part.
Can somebody share a sample value of output with three items in it. Please help
IFTTT Expects you to send at least 3 result items, to skip just clone the same object twice with different ids.
From the FAQ section;
My service fails the returns at least three items endpoint test. Why does IFTTT require three items? We require three items during the
testing phase to make sure your API behaves like a timeline of events,
not a state engine.
This requirement might seem strange when you think of your integration
with IFTTT as something that is entirely realtime in nature, like “IF
Button Pressed, THEN Turn On Lights”— what good would come from
anything but the current state of the button?
But what about the Applet “IF Button Pressed, THEN Log to
Spreadsheet”? In this case it would be important to store and return
multiple event items because there is no guarantee that we’ll call
your API (even with the Realtime API) at the moment the event occurs.
By keeping and returning a list of events, IFTTT users are more
assured they won’t miss a thing.
I'm designing a ticket booking API. Right now booking a ticket resolves into POST /users/{id}/tickets but each /events/{id} has a maximum of available tickets. How do I properly design a check?
I've come up with two ways:
1) having an availibleTickets: field into the /events/{id} that gets checked and possibly updated each time I POST a new ticket.
2) having a maxTickets: field into /events/{id} and check the length of GET /events/{id}/tickets array, compare it to maxTickets
Anyway I have to perform a GET request inside the POST handler but it doesn't look right to me, do you have any suggestions?
How would you desing a ticketing system for a Web page? The same steps you apply to a Web page also apply to REST as it is just a generalization of the same interaction flow used on the Web.
Usually, on the Web you have a link you can see an event you can order tickets for. On this page you have a link to order tickets for that particular show. Depending on the system you use, you might see a layout of the event venue in the form of buttons or images to click if there is a certain seat order where available seats are marked as green and ones that are already booked as red or whatever color scheme you use. A click on a seat will trigger some reservation logic on the server that returns almost the same page as before but this time with the seat marked as orange to indicate a reservation. Next you click the available seat next to that seat to reserve a further seat. This story continues until you either have enough seats marked as reserved or no available seats are available and you have no options left as to either cancel the reservation, proceed to the order step or unreserve seats you marked as reserved beforehand. Once you are satisfied with your choice, you will find an order or submit button or link where you turn your reservation into a booking. This might involve some further steps like entering your contact and/or billing information. Though this is in principle how I'd design such a system for the Web.
As you might see, this turns out into some kind of state machine where the server tells you all of the options you have available at this current state of the process. This is exactly what Asbjørn Ulsberg mentiones when talking about affordance and state machines. From the blueprint of the venue and the respective seats on that blueprint, which are actually buttons or images you might click, you knew what these widges are for and you somehow know what will happen when you click on one of the seats. This is what affordance is all about. By seeing it you know what you can do with it.
The interaction concept outlined above should be taken and translated to REST. As a client you don't need to know the structure of the URI, all you need to know is what seats are available and what happens when you click certain links. This is usually done in REST through link relation names that give the mentioned link some semantical context to the current state of the resource the client just fetched. Such link-relations may seem like a-priori knowledge needed by the client, which is a bit anti-REST, as REST tries to decouple clients from servers to allow the latter one to evolve freely without risking clients to break, though as link-relations should be standardized, or should be based on extensions, such as dublin-core or other microformats. Buidling up on standards will either lead to broad acceptance and support by different clients or on mechanisms to plug-in such knowledge into a client later on. This in general avoids so-called out-of-band information or process flows that force you to lookup up the manual on how to use that system.
The approach outlined above would utilize an own reservation resource that is uniquely created on "entering" the reservation, which is kept till the order ticket step is invoked. This reservation resource keeps track of the reserved seats the user has chosen so far. Whether the system considers reserved seats by other users as taken or not is an implementation detail. It is ok to either use a first-come system or a more polite one that guarantees the reserver his seats until some grace-period has passed and the user didn't order them. This gives you a good impression that such resources can be volatile and just be part of a certain process.
In regards whether to use GET, POST or other HTTP methods, a Web page that sends you to a reservation page will show you a form containing all of the seats of the venue. As HTML does only support GET or POST, the latter one is the most appropriate thing. In a REST or HTTP API you might use PUT though. A server might already have assigned you a certain, unique "reservation" link that you can just invoke with PUT. If the reservation resource does not exist yet, it will be created for you, if it did, the whole content will just be updated. Especially when you dealing with reservations and money flows you want to use idempotent methods such as PUT.
I hope I could give you some ideas on how you might design your reservation system by letting a server teach a client everything it needs to know to proceed through its task.
It's inside the post method (server-side) that you must check if tickets are available before book the event.
you can create a specific route to know how many tickets is available if needed. the client could call it before book an event. Or give the availibleTickets in the get /events/{id}
Imagine 10 client trying to buy the last ticket at the same time, if the security is not in the post method, you'll book 9 imaginary tickets
While developing and testing the conversation, IBM Watson Assistant identifies multiple intents and respond to the one with highest confidence level. Sometimes I want it to respond to second intent not the first one because it is more relevant to the current conversation context. For example, if the dialogue contains nodes to handle making transfer or payment, during the transfer scenario the user can say execute which will match both execute transfer and execute payment. So I want Watson to always respond to execute transfer which is the current context even if it identifies execute payment with higher confidence.
So users ask generic questions assuming that the bot is aware about the current context and will reply accordingly.
For example, assume that I'm developing a FAQ bot to answer inquires about 2 programs Loyalty and Saving. For simplicity I'll assume there are 4 intents
(Loyality-Define - which has examples related to what is the loyalty program)
(Loyality-Join - which has examples related to how to join loyalty program)
(Saving-Define - which has examples related to what is the saving program)
(Saving-Join - which has examples related to how to join saving program)
so users can start the conversation by utterance like "tell me about the loyalty program". then they will ask "how to join" (without mentioning the program assuming that the bot is aware). In that case Watson will identify 2 intents (Loyalty-Join, Saving-Join) and Saving-Join intent may have a higher confidence.
so I need to intercept the dialogue (may be be creating a parent node to check the context and based on that will filter-about the wrong intents).
I couldn't find a way to write code in the dialogue to check the context and modify the intents array so I want to ask about the best practice to do that.
You can't edit the intents object, so it makes what you want to do tricky but not impossible.
In your answer node, add a context variable like $topic. You fill this with a term that will denote the topic.
Then if the users question is not answered, you can check for the topic context and add that to a new context variable. This new variable is then picked up by the application layer to re-ask the question.
Example:
User: tell me about the loyalty program
WA-> Found #Loyality-Define
Set $topic to "loyalty"
Return answer.
User: how to join
WA-> No intent found.
$topic is not blank.
Set $reask to "$topic !! how to join"
APP-> $reask is set.
Ask question "loyalty !! how to join"
Clear $reask and $topic
WA-> Found #Loyalty-join
$topic set to "loyalty"
Return answer
Now in the last situation, if even with the loaded question it is not found, clearing the $topic stops it looping.
The other thing to be aware is that if a user changes topic you must either set the topic or clear it. To prevent it picking the old topics.
NOTE: The question was changed so it is technically a different question. Leaving previous answer below
You can use the intents[] object to analyse the returning the results.
So you can check the confidence difference between the first intent and second intent. If they fall inside a certain range, then you can take action.
Example condition:
intents[0] > 0.24 && intents.[1] - intents[0] > 0.05
This checks if two intents are within 5% of each other. The threshold of 0.24 is to ignore the second intent as it will likely fall below 0.2 which normally means the intent should not be actioned on.
You may want to play with this threshold.
Just to explain why you do this. Look at these two charts. The first one it's clear there is only one question asked. The second chart shows that the two intents are close together.
To take actual action, it's best to have a closed folder (condition = false). In that folder you look for matching intents[1]. This will lower the complexity within the dialog.
If you want something more complex, you can do k-means at the application layer. Then pass back the second intent at the application layer to have the dialog logic take action. There is an example here.
Watson Assistant Plus also does this automatically with the Disambiguation feature.
You can train Watson Assistant to respond accordingly. In the tool where you work on the skill click on the User conversations page in the navigation bar. In the message overview you would need to identify those that have been answered incorrectly and then specify the correct intent. Watson Assistant will pick that up, retrain and then hopefully answer correctly.
In addition, you could revisit how you define the intents. Are the examples like the real user messages? Could you provide more variations? What are the conflicts that make Watson Assistant pick the one, but not the other intent?
Added:
If you want Watson Assistant to "know" about the context, you could extract the current intent and store it as topic in a context variable. Then, if the "join" intent is detected, switch to the dialog node based on intent "join" and the specific topic. For that I would recommend to either have only one intent for "join program" or if really needed, put details about the specifics into the intent. Likely there is not much difference and you end up with just one intent.