Wolf allows you, the developer, to define the bot conversation with ease. There is one configuration point, which is hot-loadable, enabling you to change the bot behavior while the node process is still running.
Wolf facilitates information gathering, either by asking a question or accepting a piece of information parsed by NLP. The library is also an abstraction layer that ensures stability, which means if the Botbuilder SDKv4 interface changes, the bot behavior can stay the same.
Please see Roadmap for more details and planned features. If you do not see a feature, please feel free to open an issue.
Wolf Core will be framework agnostic in v3, making it easy to integrate with backend services like express, bot framework, dialogflow, etc.
For now, wolf v2 is coupled with Microsoft Bot Builder v4.
- Functional: Wolf stages are pure functions. Side-effects are allowed but is defined and managed by you, the user.
- Stateless: Wolf stages are stateless meaning that the data is passed in on every function invocation, making hot-swappable possible, and testing extremely easy.
- Declaritive: You specify what you want (abilities and slots), and what to do after you have the information. Wolf will figure out how to get the information and complete the ability for you.
Developing intelligent chatbots often lead to complex dialog trees which results in prompting the user for many pieces of information. Most frameworks require you to keep track of the state yourself as well as hard-coding static dialog flows to gather these pieces of information. Development often turns into creating a complex state machine where you must check the current state to determine which prompt the chatbot should issue next.
Wolf aims to provide a highly flexible and convenient framework for enabling state driven dynamic prompt flow. Simply define all the slots to be filled (information required from the user, prompts to issue, and actions to take after the information is fulfilled) and Wolf will handle the rest to ensure all information is collected. Slot can be defined as dependencies on other slots if desired. A collection of slots are grouped by abilities which also can have dependencies on another to help drive dynamic conversation flow.
All functions from wolf-core are pure functions.
AlarmBot demo with hot-loading abilities and Redux DevTools to visualize bot state in development.
This library takes the guesswork out of complex conversation flows, and allows you to declaritively define your slots. However, it does not parse user intent or entities for you. Wolf takes in the result of NLP (which can be as simple as regex or as complex as a tensorflow-backed model), and determines the next slot or ability to complete.
In order for Wolf to accept your NLP, the result to follow a specific object shape. This shape is typed as NlpResult, and it is as follows:
{
intent: string,
entities: [
{
value: any, // normalized value
text: string, // raw value
name: string // entity name (should match slot name)
}
]
}Please note: NLP entity name should match slot name for Wolf to detect matches!
Slot: A slot is structure that represents any piece of information that is required from the user and obtained through conversation or a system. This can be the user's name, address, etc.. A slot structure has a few properties which allows Wolf to dynamically search for possible matches. Anatomy of a slot:
name: name of slot. should match an entity name from your NLPorder: optimal order to fill slot. (ascending order)query: string to prompt user to obtain information.validate: function to test if the information is valid before fulfilling.retry: string(s) to prompt user if validator does not pass.onFill: function that returns string to present to user on slot fulfill.
Here is an example of a slot from the alarm example:
name: 'alarmName',
query: () => { return 'What is the name of the alarm?'},
retry: (turnCount) => {
// array of retry phrases to send to user
const phrase = ['Please try a new name (attempt: 2)', 'Try harder.. (attempt: 3)']
if (turnCount > phrase.length - 1) {
return phrase[phrase.length - 1]
}
return phrase[turnCount]
},
validate: (value) => {
// validator that must pass before slot is fulfilled
if (value.toLowerCase() === 'wolf') {
return { valid: false, reason: `${value} can not be used.`}
}
return { valid: true, reason: null }
},
onFill: (value) => `ok! name is set to ${value}.`Ability: An ability is a logical unit that contains a collection of slots and runs a function when the slots are filled. An ability also has other features like kicking off another ability once it is completed
name: name of the ability should match an intent name from your NLPslots: collection of SlotsnextAbility?: a function that specifies the next ability to kick off and a message to let the user know.onComplete: function (sync or asynchronous) that runs upon all slots being filled.
Here is an example of an ability from the alarm example:
name: 'addAlarm',
slots: [
// .. see `alarmName` slot example above
],
onComplete: (convoState, submittedData) => {
return new Promise((resolve, reject) => {
const value = submittedData
const alarms = convoState.alarms || []
// add alarm to convoState
convoState.alarms = [
...alarms,
value
]
// demonstrate async supported
setTimeout(() => {
resolve(`Your ${value.alarmName} alarm is added!`)
}, 2000)
})
}Open a pre-existing Microsft Bot Framework v4 project directory and run:
npm install wolf-core
- Install
wolf-core. - Import Wolf into a pre-existing Microsft Bot Framework v4 bot.
import { wolfMiddleware, getMessages, createWolfStore, IncomingSlotData } from 'wolf-core'- Create an abilities definition (see example alarmBot abilities)
- Import the abilities definition
import abilities from './abilities'- Setup the Wolf middleware
// Wolf middleware
adapter.use(...wolfMiddleware(
conversationState,
(context) => nlp(context.activity.text),
(context) => abilities,
'listAbility',
createWolfStore()
))- Handle the output messages in the
server.post
server.post('/api/messages', (req, res) => {
adapter.processActivity(req, res, async (context) => {
try {
if (context.activity.type !== 'message') {
return
}
const messages = getMessages(context) // retrieve output messages from Wolf
await context.sendActivities(messages.messageActivityArray) // send messages to user
} catch (err) {
console.error(err.stack)
}
})
})- Have a pre-existing v4 bot running with Wolf (see above)
- Setup the dev tool server
/**
* Starting dev tools server
*/
const remotedev = require('remotedev-server')
const { composeWithDevTools } = require('remote-redux-devtools')
remotedev({ hostname: 'localhost', port: 8100 })
const composeEnhancers = composeWithDevTools({ realtime: true, port: 8100, latency: 0 })- Edit the fifth argument (createWolfStore) for the
wolfMiddleware
// Wolf middleware
adapter.use(...wolfMiddleware(
conversationState,
(context) => nlp(context.activity.text),
() => {
delete require.cache[require.resolve('./abilities')]
const abilities = require('./abilities')
return abilities.default ? abilities.default : abilities
},
'listAbility',
createWolfStore([], composeEnhancers) // replace createWolfStore()
))- Download Redux DevTools from the Chrome store.
- In the Chrome browser, click on the DevTools icon (top right) > 'Open Remote DevTools'
- Settings (bottom right) > tick 'Use custom (local) server' and fill in information > Submit
Host name: localhost, Port: 8100 // port edefined in step 2
- To view the state in a chart display, change 'Inspector' dropdown to 'Chart' option
- Run the bot and the websocket server should start with chart visuals.
Note: See alarmBot example with Redux Dev Tools enabled.
Testing a bot has never been easier with Wolf-Rive testing package. Any Wolf enabled v4 bot has the ability to utilize this testing package which allows users to write end-to-end testing of input and output conversation flows.
All example bots have their own /tests which utilize wolf-rive package. Please refer to examples and Wolf-Rive for full testing details.
See Wolf Core Concepts for more information about middleware usage.
See examples for full implementation.
- simpleBot - Basic example.
- alarmBot - Redux DevTools and hot-loading.
- profileBot - More complex example. SlotData push model, setting up api endpoint to accept slotdata by conversationId.
Please refer to Wolf Wiki for roadmap and contribution information.
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