We can upload CSV or JSON file for entities at Dialogflow.
But I want to make an array for entities in inline editor of Dialogflow.
For example,
function hello(agent) {
//[I want to make entities array in the function with API call]
}
How can make that array? Is this correct?
add.entities({
"entries": [{
"synonyms": ["apple", "red apple"],
"value": "apple"
},
{
"value": "banana"
}
],
"name": "fruit"
})
Thank you so much.
Related
I would like to do a query matches against two properties of the same item in a sub-collection.
Example:
[
{
"name": "Person 1",
"contacts": [
{ "type": "email", "value": "person.1#xpto.org" },
{ "type": "phone", "value": "555-12345" },
]
}
]
I would like to be able to search by emails than contain xpto.org but,
doing something like the following doesn't work:
search.ismatchscoring('email','contacts/type,','full','all') and search.ismatchscoring('/.*xpto.org/','contacts/value,','full','all')
instead, it will consider the condition in the context of the main object and objects like the following will also match:
[
{
"name": "Person 1",
"contacts": [
{ "type": "email", "value": "555-12345" },
{ "type": "phone", "value": "person.1#xpto.org" },
]
}
]
Is there any way around this without having an additional field that concatenates type and value?
Just saw the official doc. At this moment, there's no support for correlated search:
This happens because each clause applies to all values of its field in
the entire document, so there's no concept of a "current sub-document
https://learn.microsoft.com/en-us/azure/search/search-howto-complex-data-types
and https://learn.microsoft.com/en-us/azure/search/search-query-understand-collection-filters
The solution I've implemented was creating different collections per contact type.
This way I'm able to search directly in, lets say, the email collection without the need for correlated search. It might not be the solution for all cases but it works well in this case.
I have an content type entry in Contentful that has fields like this:
"fields": {
"title": "How It Works",
"slug": "how-it-works",
"countries": [
{
"sys": {
"type": "Link",
"linkType": "Entry",
"id": "3S5dbLRGjS2k8QSWqsKK86"
}
},
{
"sys": {
"type": "Link",
"linkType": "Entry",
"id": "wHfipcJS6WUSaKae0uOw8"
}
}
],
"content": [
{
"sys": {
"type": "Link",
"linkType": "Entry",
"id": "72R0oUMi3uUGMEa80kkSSA"
}
}
]
}
I'd like to run a query that would only return entries if they contain a particular country.
I played around with this query:
https://cdn.contentful.com/spaces/aoeuaoeuao/entries?content_type=contentPage&fields.countries=3S5dbLRGjS2k8QSWqsKK86
However get this error:
The equals operator cannot be used on fields.countries.en-AU because it has type Object.
I'm playing around with postman, but will be using the .NET API.
Is it possible to search for entities, and filter on arrays that contain Objects?
Still learning the API, so I'm guessing it should be pretty straight forward.
Update:
I looked at the request the Contentful Web CMS makes, as this functionality is possible there. They use query params like this:
filters.0.key=fields.countries.sys.id&filters.0.val=3S5dbLRGjS2k8QSWqsKK86
However, this did not work in the delivery API, and might only be an internal query format.
Figured this out. I used the following URL:
https://cdn.contentful.com/spaces/aoeuaoeua/entries?content_type=contentPage&fields.countries.sys.id=wHfipcJS6WUSaKae0uOw8
Note the query parameter fields.countries.sys.id
I have a word for example ABC SSS. I need this to be recognised as one entity. At the same time this ABC SSS phrase precedes a lot of other words that need to be recognised as one entity which are not interchangeable. For example ABC SSS word. How can I train LUIS to be able to do this. I tried ABC SSS as a phrase feature but then LUIS doesn't recognise ABC SSS word as an entity. Currently, I marked ABC SSS as a feature phrase and word as a separate feature phrase. This is not ideal. Thanks for your help.
You'll want to create composite entities, not use phrase lists for this.
Here's a screenshot of the entities creation page on LUIS. I've created three simple entities and one composite entity which takes the other three entities:
Here are some snippets from a response I got from LUIS on a query. This first bit indicates the actual query and matched intent.
"query": "order large pepperoni pizza",
"topScoringIntent": {
"intent": "OrderPizza",
"score": 0.9999995
},
Under the entities list you'll find your simple and composite entities together, like the following.
{
"entity": "large",
"type": "PizzaSize",
"startIndex": 6,
"endIndex": 10,
"score": 0.9186653
},
{
"entity": "large",
"type": "Pizza", // This is the composite entity!
"startIndex": 6,
"endIndex": 10,
"score": 0.940835536
}
And here is the list for composite entities:
"compositeEntities": [
{
"parentType": "Pizza",
"value": "large",
"children": [
{
"type": "PizzaSize",
"value": "large"
}
]
},
{
"parentType": "Pizza",
"value": "pepperoni",
"children": [
{
"type": "PizzaTopping",
"value": "pepperoni"
}
]
},
{
"parentType": "Pizza",
"value": "pizza",
"children": []
}
]
Composite Entities are ideal for this case:
Set "ABC SSS" as an entity 1, but then tag "ABC SSS" plus those other words into a composite entity 2. This should be enough to both capture "ABC SSS" as entity 1 and the whole sentence as entity 2 in the case those other phrases appear.
Also, you can also tag those other words as Entities by themselves if you want to capture them while you are on it.
I understand that I can search by tag so I can find all photos that have been tagged to contain the term "dog" for example, but is there a way to get the photo and all its related tags (dog,puppy,backyard,etc.)?
If you use the endpoint GET /photos/:id you are able to retrieve the categories of the photo.
"categories": [
{
"id": 4,
"title": "Nature",
"photo_count": 24783,
"links": {
"self": "https://api.unsplash.com/categories/4",
"photos": "https://api.unsplash.com/categories/4/photos"
}
},
// and so on
],
I have the web-form builder for science events. The event moderator creates registration form with arbitrary amount of boolean, integer, enum and text fields.
Created form is used for:
register a new member to event;
search through registered members.
What is the best search tool for second task (to search memebers of event)? Is ElasticSearch well for this task?
I wrote a post about how to index arbitrary data into Elasticsearch and then to search it by specific fields and values. All this, without blowing up your index mapping.
The post is here: http://smnh.me/indexing-and-searching-arbitrary-json-data-using-elasticsearch/
In short, you will need to do the following steps to get what you want:
Create a special index described in the post.
Flatten the data you want to index using the flattenData function:
https://gist.github.com/smnh/30f96028511e1440b7b02ea559858af4.
Create a document with the original and flattened data and index it into Elasticsearch:
{
"data": { ... },
"flatData": [ ... ]
}
Optional: use Elasticsearch aggregations to find which fields and types have been indexed.
Execute queries on the flatData object to find what you need.
Example
Basing on your original question, let's assume that the first event moderator created a form with following fields to register members for the science event:
name string
age long
sex long - 0 for male, 1 for female
In addition to this data, the related event probably has some sort of id, let's call it eventId. So the final document could look like this:
{
"eventId": "2T73ZT1R463DJNWE36IA8FEN",
"name": "Bob",
"age": 22,
"sex": 0
}
Now, before we index this document, we will flatten it using the flattenData function:
flattenData(document);
This will produce the following array:
[
{
"key": "eventId",
"type": "string",
"key_type": "eventId.string",
"value_string": "2T73ZT1R463DJNWE36IA8FEN"
},
{
"key": "name",
"type": "string",
"key_type": "name.string",
"value_string": "Bob"
},
{
"key": "age",
"type": "long",
"key_type": "age.long",
"value_long": 22
},
{
"key": "sex",
"type": "long",
"key_type": "sex.long",
"value_long": 0
}
]
Then we will wrap this data in a document as I've showed before and index it.
Then, the second event moderator, creates another form having a new field, field with same name and type, and also a field with same name but with different type:
name string
city string
sex string - "male" or "female"
This event moderator decided that instead of having 0 and 1 for male and female, his form will allow choosing between two strings - "male" and "female".
Let's try to flatten the data submitted by this form:
flattenData({
"eventId": "F1BU9GGK5IX3ZWOLGCE3I5ML",
"name": "Alice",
"city": "New York",
"sex": "female"
});
This will produce the following data:
[
{
"key": "eventId",
"type": "string",
"key_type": "eventId.string",
"value_string": "F1BU9GGK5IX3ZWOLGCE3I5ML"
},
{
"key": "name",
"type": "string",
"key_type": "name.string",
"value_string": "Alice"
},
{
"key": "city",
"type": "string",
"key_type": "city.string",
"value_string": "New York"
},
{
"key": "sex",
"type": "string",
"key_type": "sex.string",
"value_string": "female"
}
]
Then, after wrapping the flattened data in a document and indexing it into Elasticsearch we can execute complicated queries.
For example, to find members named "Bob" registered for the event with ID 2T73ZT1R463DJNWE36IA8FEN we can execute the following query:
{
"query": {
"bool": {
"must": [
{
"nested": {
"path": "flatData",
"query": {
"bool": {
"must": [
{"term": {"flatData.key": "eventId"}},
{"match": {"flatData.value_string.keyword": "2T73ZT1R463DJNWE36IA8FEN"}}
]
}
}
}
},
{
"nested": {
"path": "flatData",
"query": {
"bool": {
"must": [
{"term": {"flatData.key": "name"}},
{"match": {"flatData.value_string": "bob"}}
]
}
}
}
}
]
}
}
}
ElasticSearch automatically detects the field content in order to index it correctly, even if the mapping hasn't been defined previously. So, yes : ElasticSearch suits well these cases.
However, you may want to fine tune this behavior, or maybe the default mapping applied by ElasticSearch doesn't correspond to what you need : in this case, take a look at the default mapping or, for even further control, the dynamic templates feature.
If you let your end users decide the keys you store things in, you'll have an ever-growing mapping and cluster state, which is problematic.
This case and a suggested solution is covered in this article on common problems with Elasticsearch.
Essentially, you want to have everything that can possibly be user-defined as a value. Using nested documents, you can have a key-field and differently mapped value fields to achieve pretty much the same.