I want to be able to search and suggest through long texts.
Below is my input string:
Clinical Support Specialist Medical Staff
If I search for clin or supp or spe or med or st it should give the results as the above string.
Also searches could be like clinical sup or specialist medi
Below is the mappings I create for the field:
description: {
type: 'completion',
analyzer: 'simple',
preserve_separators: true,
preserve_position_increments: true,
contexts: {
name: 'company',
type: 'category',
path: 'company',
}
}
And below is the search body:
descSuggestor: {
prefix: searchTerm,
completion: {
field: 'description'
}
}
Your question does not specify the elastic search version, or the environment you are trying to write your search query. However, you would be able to do that with regular expression in Kibana. For example, in the Dev tools of Kibana, you could write something like:
GET utilization_aggregation_2018/_search
{
"query": {
"regexp" : {"name": "supp.*"}
}
}
Hope this helps!
Related
I've added individual meta to every page following the Nuxt documentation but whenever I share my links on social media, the meta just show this '## build setup'. Another issue is the same metadata is showing for every page. I read you need to put "hids" to have individual page meta but nothing seems to be working?
Index Meta:
<script>
export default {
head: {
title: 'Animal Crossing Portal | The Best Tier Lists for Animal Crossing',
meta: [
{ property: 'og:description', hid: 'og:description', name: 'og:description', content: 'Vote monthly in Animal Crossing Tier Lists for New Horizons & Pocket Camp! Including Villager Tier Lists, Sanrio, Gyroids & more at Animal Crossing Portal!' },
{ name: 'twitter:title', hid: 'twitter:title', content: 'Animal Crossing Portal | The Best Tier Lists for Animal Crossing' },
{ name: 'twitter:description', hid: 'twitter:description', content: 'Vote monthly in Animal Crossing Tier Lists for New Horizons & Pocket Camp! Including Villager Tier Lists, Sanrio, Gyroids & more at Animal Crossing Portal!' },
{ name: 'twitter:card', hid: 'twitter:card', content: 'summary_large_image' },
{ name: 'twitter:image:src', hid: 'twitter:image:src', content: 'https://www.animalcrossingportal.com/images/meta.jpg' },
{ property: 'og:title', hid: 'og:title', name: 'og:title', content: 'Animal Crossing Portal | The Best Tier Lists for Animal Crossing' },
{ property: 'og:type', hid: 'og:type', content: 'website' },
{ property: 'og:site_name', hid: 'og:site_name', content: 'Animal Crossing Portal' },
{ property: 'og:url', hid: 'og:url', content: 'https://www.animalcrossingportal.com/' },
{ property: 'og:image', hid: 'og:image', content: 'https://www.animalcrossingportal.com/images/meta.jpg' }
],
link: [
{
rel: 'canonical',
href: 'https://www.animalcrossingportal.com/'
}
]
}
}
</script>
My nuxt.config.js file has:
head: {
meta: [
{ name: 'viewport', content: 'width=device-width, initial-scale=1' }
],
link: [
{ rel: 'icon', type: 'image/x-icon', href: '/favicon.ico' }
]
}
The meta was actually lying inside of a README.md file, removing it from there fixed OP's issue!
I thought it was the readme file, but after a few hours of digging (issue resurfaced), turns out it was actually an empty build tag in the nuxt config file which was making Nuxt try SSR. Removing the empty tag 100% fixed it.
I was running into the same issue recently. After having a look into the source code view of the rendered page (Ctrl + U in the browser), a quick search yielded that the meta-field og:description was not populated correctly. Correction of that was straightforward.
Thus, a look on your rendered page source code might reveal the missing/wrongly populated field (e.g. "description").
I'm following the tutorial instruction: https://docs.mongodb.com/manual/core/index-text/
This is the sample data:
db.stores.insert(
[
{ _id: 1, name: "Java Hut", description: "Coffee and cakes" },
{ _id: 2, name: "Burger Buns", description: "Gourmet hamburgers" },
{ _id: 3, name: "Coffee Shop", description: "Just coffee" },
{ _id: 4, name: "Clothes Clothes Clothes", description: "Discount clothing" },
{ _id: 5, name: "Java Shopping", description: "Indonesian goods" }
]
)
Case 1: db.stores.find( { $text: { $search: "java coffee shop" } } ) => FOUND
Case 2: db.stores.find( { $text: { $search: "java" } } ) => FOUND
Case 3: db.stores.find( { $text: { $search: "coff" } } ) => NOT FOUND
I'm expecting case 3 is FOUND because the query is matches a part of java coffee shop
Case 3 will not work with $text operator and reason is how Mongo Creates Text Indexes.
Mongo takes text indexed fields values and creates separate indexes for each unique word in string and not character(!).
so this means, that in your case for 1 object:
field name will have 2 indexes:
java
hut
field description will have 3 indexes:
coffee
and
cakes
$text operator compare $search values with this indexes and that's why "coff" will not work.
If you strongly want to take advantages of indexes you have to use $text operator, but it does not give you all flexibility, just like you want.
solution:
You Can simply use $regex with case sensitiveness option (i) and optimize your query with skip and limit.
If you want to return all documents and collection is large, $regex will cause performance issue
you can also check this article https://medium.com/coding-in-depth/full-text-search-part-1-how-to-create-mongodb-full-and-partial-text-search-c09c0bae17a3 and maybe use wildcard indexes for that, but i do not know is it a good practice or not.
I can able to search the case by company name
var mySearch = search.create({
type: search.Type.SUPPORT_CASE,
columns: [{
name: 'title'
}, {
name: 'company'
}],
filters: [{
name: 'company',
operator: 'is',
values: 'Test'
}]
});
return mySearch.run({
ld: mySearch.id
}).getRange({
start: 0,
end: 1000
});
But I am not able to search case by company id.
companyId is 115
Below are not working
i)
filters: [{
name: 'company',
operator: 'is',
values: 115
}]
ii)
filters: [{
name: 'companyid',
operator: 'is',
values: 115
}]
According to the Case schema company is a Text filter, meaning you would have to provide it with the precise Name of the company, not the internal ID.
Instead you may want to use the customer.internalid joined filter to provide the internal ID. Also, Internal ID fields are nearly always Select fields, meaning they do not accept the is operator, but instead require the anyof or noneof operator.
You can find the valid operators by field type on the Help page titled Search Operators
First, you can try this :
var supportcaseSearchObj = search.create({
type: "supportcase",
filters:
[
["company.internalid","anyof","100"]
],
columns:
[
search.createColumn({
name: "casenumber",
sort: search.Sort.ASC
}),
"title",
"company",
"contact",
"stage",
"status",
"profile",
"startdate",
"createddate",
"category",
"assigned",
"priority"
]
});
Second : how did I get this ? The answer is hint that will make your life easier :
Install the "NetSuite Saved Search Code Export" chrome plugin.
In Netsuite UI, create your saved search (it is always easier that doing it in code).
After saving the search, open it again for edition.
At the top right corner (near list, search menu in the netsuite page), you will see a link "Export as script" : click on it and you will get your code ;)
If you can not install the chrome plugin :
In Netsuite UI, create your saved search (it is always easier that doing it in code).
In your code, load your saved search
Add a log.debug to show the [loadedesearchVar].filters
You can then copy what you will see in the log to use it as your search filters.
Good luck!
I have inserted following values in my events collection
db.events.insert(
[
{ _id: 1, name: "Amusement Ride", description: "Fun" },
{ _id: 2, name: "Walk in Mangroves", description: "Adventure" },
{ _id: 3, name: "Walking in Cypress", description: "Adventure" },
{ _id: 4, name: "Trek at Tikona", description: "Adventure" },
{ _id: 5, name: "Trekking at Tikona", description: "Adventure" }
]
)
I've also created a index in a following way:
db.events.createIndex( { name: "text" } )
Now when I execute the following query (Search - Walk):
db.events.find({
'$text': {
'$search': 'Walk'
},
})
I get these results:
{ _id: 2, name: "Walk in Mangroves", description: "Adventure" },
{ _id: 3, name: "Walking in Cypress", description: "Adventure" }
But when I search Trek:
db.events.find({
'$text': {
'$search': 'Trek'
},
})
I get only one result:
{ _id: 4, name: "Trek at Tikona", description: "Adventure" }
So my question is why it dint resulted:
{ _id: 4, name: "Trek at Tikona", description: "Adventure" },
{ _id: 5, name: "Trekking at Tikona", description: "Adventure" }
When I searched walk it resulted the documents containing both walk and walking. But when I searched for Trek it only resulted the document including trek where it should have resulted both trek and trekking
MongoDB text search uses the Snowball stemming library to reduce words to an expected root form (or stem) based on common language rules. Algorithmic stemming provides a quick reduction, but languages have exceptions (such as irregular or contradicting verb conjugation patterns) that can affect accuracy. The Snowball introduction includes a good overview of some of the limitations of algorithmic stemming.
Your example of walking stems to walk and matches as expected.
However, your example of trekking stems to trekk so does not match your search keyword of trek.
You can confirm this by explaining your query and reviewing the parsedTextQuery information which shows the stemmed search terms used:
db.events.find({$text: {$search: 'Trekking'} }).explain().queryPlanner.winningPlan.parsedTextQuery
{
"terms" : [
"trekk"
],
"negatedTerms" : [ ],
"phrases" : [ ],
"negatedPhrases" : [ ]
}
You can also check expected Snowball stemming using the online Snowball Demo or by finding a Snowball library for your preferred programming language.
To work around exceptions that might commonly affect your use case, you could consider adding another field to your text index with keywords to influence the search results. For this example, you would add trek as a keyword so that the event described as trekking also matches in your search results.
There are other approaches for more accurate inflection which are generally referred to as lemmatization. Lemmatization algorithms are more complex and start heading into the domain of natural language processing. There are many open source (and commercial) toolkits that you may be able to leverage if you want to implement more advanced text search in your application, but these are outside the current scope of the MongoDB text search feature.
We have a catalog of products stored in ElasticSearch.
Each document looks like this:
{
'family': 'products family'
'category': 'products category'
'name': 'product name'
'description': 'product description'
}
We are trying to build a query that will get the fuzzy match for a search term and will score the results by the following order of fields:
family
category
name
description
Is there a way to do it?
A simple approach would be to use multi-match query giving each field an appropriate boost.
{
"query": {
"multi_match": {
"query": "produce",
"fields": ["family^4","category^3","name^2","description"],
"fuzziness" : "AUTO",
"rewrite" : "constant_score_auto"
}
}
}
All documents which match on the same field would get the same score.
You can change this behavior by tweaking rewrite parameter
Article gives further insight to this.