wit-api is a node package that allows you to easily configure, train and consume your NLP through Wit.ai HTTP API.
Note: This is the version 1.x.y, it has no dependency, and it uses promises.
Note: Version 1.0.0 is stable, and will recieve only additional utils methods
npm i wit-api --saveconst Wit = require('./src/Wit')
async function run () {
try {
// Instantiation
const wit = new Wit('<YOUR-SERVER-ACCESS-TOKEN>')
// Syncronize with remote
await wit.sync()
} catch (e) {
console.error(e)
}
}
run()Additionally, you can pass options to the instantiation, like:
new Wit('<YOUR-SERVER-ACCESS-TOKEN>', { debug: true, timeout: 30000, version: '20170307'})
- debug: will display more insights about the http requests
- timeout: in milliseconds, indicate when the http request times-out
- version: indicate what version of wit.ai to use, this lib ain't work for versions prior to 20170307
async function run () {
try {
const wit = new Wit('<YOUR-SERVER-ACCESS-TOKEN>')
await wit.sync()
// Adding a new entity
await wit.entities.add('cloth')
// The newly added entity is sync-ed in the `wit` object
// and can be accessed through `wit.entities.cloth`
// For script reusability, it is recomended check the entity existance before adding it
if (!wit.entities.size) {
await wit.entities.add('size')
}
// Update an entity
await wit.entities.cloth.update({ doc: 'An item from the store', lookups: ['keywords'] })
// We can also manipulate the entities values and expression through the `.update` function
// Also additional options are present, like metadata
await wit.entities.size.update({
doc: 'Size',
lookups: ['free-text', 'keywords'],
values: [
{value: 'extra small', expressions: ['36'], metadata: 'XS'},
{value: 'small', expressions: ['37', '38'], metadata: 'S'},
{value: 'medium', expressions: ['39', '40'], metadata: 'M'},
{value: 'large', expressions: ['41', '42'], metadata: 'L'},
{value: 'extra large', expressions: ['43', '44'], metadata: 'XL'}
]
})
// Deleting an entity
await wit.entities.cloth.destroy()
} catch (e) {
console.error(e)
}
}
run()// This goes insde an `async function`
await wit.services.sample.train([
{
text: 'I would like to have a jean please.',
entities: [
{
entity: 'intent',
value: 'order'
},
{
entity: 'product',
value 'pant',
start: '22',
end: '26'
}
]
}
])
// wit.service.sample.train can take multiples training sample
// You can reverse a training by using .forget
await wit.services.sample.forget([
{text: 'I would like to have a jean please.'}
])This is actually where we use the api for understanding
All the guess methods retuen a Guess Object
// this goes inside an `async function`
// Text:
const guess = await wit.services.guess.message('I want to try a coton black shirt, preferably size 43')
console.log(guess)
/* example of a result:
{ _text: 'I want to try a coton black shirt, preferably size 43',
entities:
{ product: [ { confidence: 1, value: 'shirt', type: 'value' } ],
shirtTextile: [ { confidence: 1, value: 'coton', type: 'value' } ],
color:
[ { metadata: '#000000',
confidence: 1,
value: 'black',
type: 'value' } ],
size: [ { confidence: 1, value: 'XL', type: 'value' } ],
intent: [ { confidence: 0.72456770482335, value: 'try' } ] },
msg_id: '1AY90y2ewdQhotazg'
}
*/
// Language:
await wit.services.guess.language('je m\'appel foobar')
// You can also have both language guessing and text guessing by using
await wit.services.guess.all('I want to try a coton black shirt, preferably size 43')
// Audio:
await wit.services.guess.speech('path/to/an/audio/file.wav')Both .message and .speech can take additional options, in like the following
const options = {n: 5}
await wit.services.guess.message(text, options)More about these options at https://wit.ai/docs/http/20170307#get__message_link
Once the guessing service was called, it returns a Guess Object, theses objects have utils methods.
For instance to return only relevant guesses (with confidance above 0.95)
const guess = await wit.services.guess.all('I want to try a coton black shirt, preferably size 43', {n: 5})
console.log(guess.bestGuesses(.95))
/*
would print:
{
"shirtTextile": {"confidence": 1, "value": "coton", "type": "value"},
"color": {"metadata": "#000000", "confidence": 1, "value": "black", "type": "value"},
"product": {"confidence": 1, "value": "shirt", "type": "value"},
"size": {"confidence": 1, "value": "XL", "type": "value"},
"intent": {"confidence": 0.98808266775175, "value": "request"},
"language": {"locale": "en_XX", "confidence": 1}
}
*/
// Additionally you can filter and softfilter guessing results using:
guess.filter(.95)
guess.softFilter(1)