Spell check Ngram for elastic Search not working with rails - ruby-on-rails

Question

I have used in my model to include spell check such that if the user inputs data like "Rentaal" then it should fetch the correct data as "Rental"
document.rb code
require 'elasticsearch/model'
class Document < ApplicationRecord
include Elasticsearch::Model
include Elasticsearch::Model::Callbacks
belongs_to :user
Document.import force: true
def self.search(query)
__elasticsearch__.search({
query: {
multi_match: {
query: query,
fields: ['name^10', 'service']
}
}
})
end
settings index: {
"number_of_shards": 1,
analysis: {
analyzer: {
edge_ngram_analyzer: { type: "custom", tokenizer: "standard", filter:
["lowercase", "edge_ngram_filter", "stop", "kstem" ] },
}
},
filter: {
edge_ngram_filter: { type: "edgeNGram", min_gram: "3", max_gram:
"20" }
}
} do
mapping do
indexes :name, type: "string", analyzer: "edge_ngram_analyzer"
indexes :service, type: "string", analyzer: "edge_ngram_analyzer"
end
end
end
search controller code:
def search
if params[:query].nil?
#documents = []
else
#documents = Document.search params[:query]
end
end
However, if I enter Rentaal or any misspelled word, it does not display anything.
In my console
#documents.results.to_a
gives an empty array.
What am I doing wrong here? Let me know if more data is required.

Answer 1

Try to add fuzziness in your multi_match query:
{
"query": {
"multi_match": {
"query": "Rentaal",
"fields": ["name^10", "service"],
"fuzziness": "AUTO"
}
}
}
Explanation
Kstem filter is used for reducing words to their root forms and it does not work as you expected here - it would handle corectly phrases like Renta or Rent, but not the misspelling you provided.
You can check how stemming works with following query:
curl -X POST \
'http://localhost:9200/my_index/_analyze?pretty=true' \
-d '{
"analyzer" : "edge_ngram_analyzer",
"text" : ["rentaal"]
}'
As a result I see:
{
"tokens": [
{
"token": "ren"
},
{
"token": "rent"
},
{
"token": "renta"
},
{
"token": "rentaa"
},
{
"token": "rentaal"
}
]
}
So typical misspelling will be handled much better with applying fuzziness.