I am working on autocomplete suggestion on document field that has array of type string. My document is like below;
{
"title": "Product1",
"sales": "6",
"rating": "0.0",
"cost": "45.00",
"tags": [
"blog",
"magazine",
"responsive",
"two columns",
"wordpress"
],
"category": "wordpress",
"description": "Product1 Description",
"createDate": "2013-12-19"
}
{
"title": "Product1",
"sales": "6",
"rating": "0.0",
"cost": "45.00",
"tags": [
"blog",
"paypal",
"responsive",
"skrill",
"wordland"
],
"category": "wordpress",
"description": "Product1 Description",
"createDate": "2013-12-19"
}
I am performing autocomplete search on tags field. My query is like;
query: {
query_string: {
query: "word*",
fields: ["tags"]
}
},
facets: {
tags: {
terms: {
field: "tags"
}
}
}
When user type "word" I want to display "wordland" and "wordpress". However, I couldn't manage to do that.
Could you please help on this?
Thanks
Autocomplete can be achieved by changing match queries to prefix queries. While match queries work on token (indexed) to token (search query tokens) match, prefix queries (as their name suggests) match all the tokens starting with search tokens, hence the number of documents (results) matched is high.
In the previous articles, we look into Prefix Queries and Edge NGram Tokenizer to generate search-as-you-type suggestions. Suggesters are an advanced solution in Elasticsearch to return similar looking terms based on your text input. Movie, song or job titles have a widely known or popular order.
Usually, the search query is executed when the user presses the enter key or clicks the search button. Search-as-you-type feature allows search queries to be executed on every keystroke.
“Did you mean” is a very important feature in search engines because they help the user by displaying a suggested term so that he can make a more accurate search. To create a “did you mean” we are going to use the Phrase suggester because through it we will be able to suggest sentence corrections and not just terms.
Have you tried completion suggest? One way to solve your problem is as follows:
1) Create the index:
curl -XPUT "http://localhost:9200/test_index/"
2) Create the mapping, using the completion suggester type:
curl -XPUT "http://localhost:9200/test_index/product/_mapping" -d'
{
"product": {
"properties": {
"category": {
"type": "string"
},
"cost": {
"type": "string"
},
"createDate": {
"type": "date",
"format": "dateOptionalTime"
},
"description": {
"type": "string"
},
"rating": {
"type": "string"
},
"sales": {
"type": "string"
},
"tags": {
"type": "string"
},
"title": {
"type": "string"
},
"suggest": {
"type": "completion",
"index_analyzer": "simple",
"search_analyzer": "simple",
"payloads": false
}
}
}
}'
3) Add your documents:
curl -XPUT "http://localhost:9200/test_index/product/1" -d'
{
"title": "Product1",
"sales": "6",
"rating": "0.0",
"cost": "45.00",
"tags": [
"blog",
"magazine",
"responsive",
"two columns",
"wordpress"
],
"suggest": {
"input": [
"blog",
"magazine",
"responsive",
"two columns",
"wordpress"
]
},
"category": "wordpress",
"description": "Product1 Description",
"createDate": "2013-12-19"
}'
curl -XPUT "http://localhost:9200/test_index/product/2" -d'
{
"title": "Product2",
"sales": "6",
"rating": "0.0",
"cost": "45.00",
"tags": [
"blog",
"paypal",
"responsive",
"skrill",
"wordland"
],
"suggest": {
"input": [
"blog",
"paypal",
"responsive",
"skrill",
"wordland"
]
},
"category": "wordpress",
"description": "Product1 Description",
"createDate": "2013-12-19"
}'
4) And then query using the _suggest endpoint:
curl -XPOST "http://localhost:9200/test_index/_suggest" -d'
{
"product_suggest":{
"text":"word",
"completion": {
"field" : "suggest"
}
}
}'
and you will get the results back that you expected:
{
"_shards": {
"total": 2,
"successful": 2,
"failed": 0
},
"product_suggest": [
{
"text": "word",
"offset": 0,
"length": 4,
"options": [
{
"text": "wordland",
"score": 1
},
{
"text": "wordpress",
"score": 1
}
]
}
]
}
This solution could be refined a bit, of course, particularly by pruning some duplicate data, but this should point you in the right direction.
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