Open jesus-seijas-sp opened 3 years ago
intech
commented
3 years ago If I understand correctly, then this is an analogue of story in rasa, the name is more logical.
They have interactive training that creates a story out of dialogue that can be used for tests too. It is very comfortable.
I like the idea of a single syntax - json better, but for some reason in nlp.js everything is divided into json, md, and a kind of pipeline, which is not very convenient.
syntithenai
commented
3 years ago Hi, I've been working on something similar.
I've implemented something like RASA core with rules, stories and forms based on your neural network library so it can run in a browser. https://github.com/syntithenai/voicedialogjs
I'm using it in https://opennludata.org/ which is a web UI for annotating intent data and importing/exporting Jovo/RASA/Mycroft intents/responses/entities. When I saw your neural network library I figured I could use that for the stories part of Rasa style core routing and I've added features to the UI to manage stories, forms, rules, actions and apis (written in javascript to run in a browser) that can be exported as a single JSON file containing everything needed to run the bot in the browser.
Published skills are available as a standalone chat application served from github eg. https://opennludata.org/static/skills/Syntithenai-music%20player.html
I think the combination of forms (slot filling) with machine learning based stories is a winner in building a bot framework.
I ponder what other conversational structures might have a place and the idea of goals and goal completion would be a good addition.
Food for thought
Again thanks.
Steve
On Tue, Nov 10, 2020 at 2:16 AM Jesús Seijas notifications@github.com wrote:
Hello!
For the version 5 we are working on making a chatbot orchestration. You can see an explanation and example in this comment: #713 (comment) https://github.com/axa-group/nlp.js/issues/713#issuecomment-724072580
You can see there the commands that are already implemented, but we want to start a dialog to get feedback about which commands can be implemented.
Also, we developed a connector for building bots using CDD (Conversation Driven Development), you can see an example here:
https://github.com/axa-group/nlp.js/blob/master/packages/bot/test/bot.test.js#L54 This Unit Tests is able to run the bot and test it using an scenario described in the file scenario01.dlt, with this content:
user> hello bot> What's your name? user> John bot> (Converting name to uppercases...) bot> Hello JOHN bot> This is the help bot> This is the second help bot> Bye user
So, feel free to comment what features you wish related with conversation orchestration.
Thank you!
— You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub https://github.com/axa-group/nlp.js/issues/715, or unsubscribe https://github.com/notifications/unsubscribe-auth/ABA5YTTMSCDIMG2U54Z3IOLSPABUVANCNFSM4TPPB2TA .
-- Steve Ryan Software Engineer stever@syntithenai.com phone (mobile) 0457057526 skype : irishflute 260 Spring Creek Road Buckajo NSW 2550 Australia
ericzon
commented
3 years ago From my point of view, one of the most interesting purposes for the orchestration should:
Something like this, having in mind these assumptions:
getWeatherCondition async code attached to bot with registerAction or similar, where getWeatherAnswer value could be accessed and also other data like user coordinates to perform an async request to weather API
getWeatherCard function that can receive parameters and access to context to generate the json of an adaptive card
plot1.dlg
dialog main
nlp
dialog askWeatherDialog
say do you want to know the weather?
ask getWeatherAnswer
[if getWeatherCondition is true] run acceptGetWeather
[else] run declineGetWeather
dialog acceptGetWeather
read weather.dlg
run byeDialog
dialog declineGetWeather
say well, If you have any request, just tell me
run main
dialog byeDialog
say bye bye :)weather.dlg
dialog acceptGetWeather
say well, the weather in your coordinates is
card getWeatherCard
jesus-seijas-sp
commented
3 years ago Hello, a little update of last version 4.16, and what it includes:
You have an example here: https://github.com/jesus-seijas-sp/nlpjs-examples/blob/master/04.bot
Comments If a line starts with # then is a comment. Example
# this is a commentImport another .dlg If a line starts with import then will import other .dlgs. You can provide several separated by space:
import something.dlg ./others/other.dlglanguage If a line starts with language it sets a locale for the code until another language command is found.
# this sets the language to english
language enintents
You can add new intents using dlg. They will be added to the language of the last language command For each intent you can define the utterances, tests and answers
language en
intent agent.acquaintance
utterances
- say about you
- describe yourself
- tell me about yourself
- who are you
- I want to know more about you
answers
- I'm a virtual agent
- Think of me as a virtual agententity In the same way that you can define intents, you can define entities. There are two different kind of entities that you can define: enum and regex.
entity hero
- spiderman: spiderman, spider-man
- ironman: ironman, iron-man
- thor: thor
entity email
regex /\\b(\\w[-._\\w]*\\w@\\w[-._\\w]*\\w\\.\\w{2,3})\\b/giThis code will create two entities: hero and email. Email is a regex entity. Hero is an enum entity with three options: spiderman, ironman and thor. Spiderman is identified when the text "spiderman" or "spider-man" is found.
dialog This will create a new dialog with a pipeline of commands. Example:
# Script for a simple turn conversation
import corpus.dlg
dialog main
nlp
dialog hellodialog
[!user_name] run greet
run bye
dialog greet
say Hello user!
say Tell me your name
ask user_name
call uppers user_name
[user_name !== 'ADMIN'] say Hello {{ user_name }}
[user_name === 'ADMIN'] say You unblocked admin mode
dialog bye
say Bye user say This is the command for the chatbot to say something to the user.
say Hello, I'm a chatbotask This wait for input of the user and store it into a variable. Important: this will not say anything to the user, just wait the input and store it. If you want to say something to the user, use say.
say What's your name?
ask nameThis will store the input from the user in the name variable
run Executes another dialog by name. The dialog execution is an stack that stores also the last position of each dialog on the stack. Example:
dialog main
run greet
run help
run bye
dialog greet
say hello user
dialog help
say I'm a bot
say You can ask me questions
dialog bye
say bye usernlp This command execute the input from user to the nlp and retrieve the answer. Important thing here: the answre from nlp can be a message to the user or starts by /. If the answer starts by / then it means that is to execute a dialog, so it acts as "run /dialogname". In the example if you go to the corpus you'll find this:
https://github.com/jesus-seijas-sp/nlpjs-examples/blob/master/04.bot/corpus.dlg#L502
intent greetings.hello
utterances
- hello
- hi
- howdy
answers
- /hellodialogThat means that when someone says 'Hello' to the bot, the dialog '/hellodialog' is executed:
dialog hellodialog
[!user_name] run greet
run byeinc It will increment a variable by it's name. You can provide the increment or not, by default is 1. If the variable does not exists, then initialize it to 0 before inc.
# This will add 1 to count
inc count
# This will add 3 to count
inc count 3dec It will decrement a variable by it's name. You can provide the decrement or not, by default is 1. If the variable does not exists, then initialize it to 0 before dec.
# This will substract 1 from count
dec count
# This will substract 3 from count
dec count 3set This will set a value to a variable. You can provide an expression:
# this will set count to (count*2+1)
set count count * 2 + 1conditions You can add conditions before each command so the command will be execute if the condition is solved to truthy. Just add the condition between square brackets:
# Script for a simple turn conversation
import corpus.dlg
dialog main
nlp
dialog hellodialog
[!user_name] run greet
run bye
dialog greet
say Hello user!
say Tell me your name
ask user_name
call uppers user_name
[user_name !== 'ADMIN'] say Hello {{ user_name }}
[user_name === 'ADMIN'] say You unblocked admin mode
dialog bye
say Bye userThe [!user_name] run greet means that the dialog greet will be executed only if the variable user_name does not exists. When the user_name is ADMIN then the user will receive the message "You unblocked admin mode" otherwise the user will receive "Hello {{ user_name }}"
String templating When you see "Hello {{ user_name }}" that means that the part {{ user_name }} will be replaced with the variable user_name from the context.
call This is used to call functions so you can code by yourself actions for the chatbot. In the example you'll find:
call uppers user_nameThat means that the bot will try to find the function uppers and call it with parameter "user_name". Important: user_name will not be replaced with the user name from context, user_name will be provided exactly as is, an string with value "user_name". The function uppers has this code:
const uppers = (session, context, params) => {
if (params) {
const variableName = typeof params === 'string' ? params : params[0];
if (variableName) {
context[variableName] = (context[variableName] || '').toUpperCase();
}
}
};The way to register an action is calling bot.registerAction with the name of the action and the function to be executed:
bot.registerAction('uppers', uppers)The signature of each action is:
function action(session, context, params)So the action will receive the session object, the context object and the parameters.
jesus-seijas-sp
commented
3 years ago New features added:
suggest Car|Bus|Bicyclecard card02Example:
# Script for a simple turn conversation
import corpus-ner.json
import card01.json card02.json card03.json
dialog main
nlp
dialog demo01
suggest Car|Bus|Bicycle
say Please enter your mode of transport.
dialog demo02
card card02card02.json content:
{
"name": "card02",
"type": "message",
"attachments": [
{
"contentType": "application/vnd.microsoft.card.adaptive",
"content": {
"$schema": "http://adaptivecards.io/schemas/adaptive-card.json",
"version": "1.0",
"type": "AdaptiveCard",
"speak": "Your flight is confirmed for you and 3 other passengers from San Francisco to Amsterdam on Friday, October 10 8:30 AM",
"body": [
{
"type": "TextBlock",
"text": "Passengers",
"weight": "bolder",
"isSubtle": false
},
{
"type": "TextBlock",
"text": "Sarah Hum",
"separator": true
},
{
"type": "TextBlock",
"text": "Jeremy Goldberg",
"spacing": "none"
},
{
"type": "TextBlock",
"text": "Evan Litvak",
"spacing": "none"
},
{
"type": "TextBlock",
"text": "2 Stops",
"weight": "bolder",
"spacing": "medium"
},
{
"type": "TextBlock",
"text": "Fri, October 10 8:30 AM",
"weight": "bolder",
"spacing": "none"
},
{
"type": "ColumnSet",
"separator": true,
"columns": [
{
"type": "Column",
"width": 1,
"items": [
{
"type": "TextBlock",
"text": "San Francisco",
"isSubtle": true
},
{
"type": "TextBlock",
"size": "extraLarge",
"color": "accent",
"text": "SFO",
"spacing": "none"
}
]
},
{
"type": "Column",
"width": "auto",
"items": [
{
"type": "TextBlock",
"text": " "
},
{
"type": "Image",
"url": "http://adaptivecards.io/content/airplane.png",
"size": "small",
"spacing": "none"
}
]
},
{
"type": "Column",
"width": 1,
"items": [
{
"type": "TextBlock",
"horizontalAlignment": "right",
"text": "Amsterdam",
"isSubtle": true
},
{
"type": "TextBlock",
"horizontalAlignment": "right",
"size": "extraLarge",
"color": "accent",
"text": "AMS",
"spacing": "none"
}
]
}
]
},
{
"type": "TextBlock",
"text": "Non-Stop",
"weight": "bolder",
"spacing": "medium"
},
{
"type": "TextBlock",
"text": "Fri, October 18 9:50 PM",
"weight": "bolder",
"spacing": "none"
},
{
"type": "ColumnSet",
"separator": true,
"columns": [
{
"type": "Column",
"width": 1,
"items": [
{
"type": "TextBlock",
"text": "Amsterdam",
"isSubtle": true
},
{
"type": "TextBlock",
"size": "extraLarge",
"color": "accent",
"text": "AMS",
"spacing": "none"
}
]
},
{
"type": "Column",
"width": "auto",
"items": [
{
"type": "TextBlock",
"text": " "
},
{
"type": "Image",
"url": "http://adaptivecards.io/content/airplane.png",
"size": "small",
"spacing": "none"
}
]
},
{
"type": "Column",
"width": 1,
"items": [
{
"type": "TextBlock",
"horizontalAlignment": "right",
"text": "San Francisco",
"isSubtle": true
},
{
"type": "TextBlock",
"horizontalAlignment": "right",
"size": "extraLarge",
"color": "accent",
"text": "SFO",
"spacing": "none"
}
]
}
]
},
{
"type": "ColumnSet",
"spacing": "medium",
"columns": [
{
"type": "Column",
"width": "1",
"items": [
{
"type": "TextBlock",
"text": "Total",
"size": "medium",
"isSubtle": true
}
]
},
{
"type": "Column",
"width": 1,
"items": [
{
"type": "TextBlock",
"horizontalAlignment": "right",
"text": "$4,032.54",
"size": "medium",
"weight": "bolder"
}
]
}
]
}
]
}
}
]
}
torloneg
commented
3 years ago is it possible to send a card as a template ?
jesus-seijas-sp
commented
3 years ago @torloneg Yes, you can include templating inside the card that will be replaced with context information. When you're sending a card, all the card object nodes are visited and passed through a template, so if you put something like "Hello {{ name }}" the "{{ name }}" part will be replaced by the value of context.name.
torloneg
commented
3 years ago I can generate a card at runtime in onIntent function ?
jesus-seijas-sp
commented
3 years ago No, not on an onIntent because onIntent is part of the NLP, not part of the bot orchestration. But you can do it on a "call", as explained in this same thread, is used to build actions by yourself.
But ok, step by step. 1) In your code you can build your card dinamically, but here is an example as constant:
const card = {
"name": "card04",
"type": "message",
"attachments": [
{
"contentType": "application/vnd.microsoft.card.adaptive",
"content": {
"$schema": "http://adaptivecards.io/schemas/adaptive-card.json",
"version": "1.0",
"type": "AdaptiveCard",
"speak": "Your flight is confirmed for you and 3 other passengers from San Francisco to Amsterdam on Friday, October 10 8:30 AM",
"body": [
{
"type": "TextBlock",
"text": "Passengers",
"weight": "bolder",
"isSubtle": false
},
{
"type": "TextBlock",
"text": "Sarah Hum",
"separator": true
},
{
"type": "TextBlock",
"text": "Jeremy Goldberg",
"spacing": "none"
},
{
"type": "TextBlock",
"text": "Evan Litvak",
"spacing": "none"
},
{
"type": "TextBlock",
"text": "2 Stops",
"weight": "bolder",
"spacing": "medium"
},
{
"type": "TextBlock",
"text": "Fri, October 10 8:30 AM",
"weight": "bolder",
"spacing": "none"
},
{
"type": "Input.Text",
"placeholder": "Placeholder text",
"id": "inputText"
},
{
"type": "Input.Date",
"id": "inputDate"
},
{
"type": "Input.Time",
"id": "inputTime"
},
{
"type": "ActionSet",
"actions": [
{
"type": "Action.Submit",
"title": "Send!"
}
]
}
]
}
}
]
}2) Create the code for an action. An action receives the session, the context, and the parameters. The session contains the method "sendCard" that receives the card and the context.
const sendCard = (session, context, params) => {
session.sendCard(card, context);
};3) Register the action to the bot with a name. That way you'll be able to call this action from the script of the bot using 'call'
bot.registerAction('sendCard', sendCard);4) Define a dialog that calls this action:
dialog dialogCard
call sendCardOk, now we have the dialog card, that will call the action sendCard, that sends the card. But, how to call this dialog from an intent?
5) Create an intent where the answer is '/dialogCard'
6) Run the bot, trigger the intent.
rohit-32
commented
3 years ago Hello, I am using nlpjs in a react-native project, is it possible to achieve bot orchestration in React-Native. if not, what would be the right way to do it.
FYI I would prefer to do it code than in script format
Thank you
Hello!
For the version 5 we are working on making a chatbot orchestration. You can see an explanation and example in this comment: https://github.com/axa-group/nlp.js/issues/713#issuecomment-724072580
You can see there the commands that are already implemented, but we want to start a dialog to get feedback about which commands can be implemented.
Also, we developed a connector for building bots using CDD (Conversation Driven Development), you can see an example here: https://github.com/axa-group/nlp.js/blob/master/packages/bot/test/bot.test.js#L54 This Unit Tests is able to run the bot and test it using an scenario described in the file scenario01.dlt, with this content:
So, feel free to comment what features you wish related with conversation orchestration.
Thank you!