I use Elasticsearch to search with autocompletion with an ngram filter. I need to boost a result if it starts with the search keyword.
My query is simple :
"query": {
"match": {
"query": "re",
"operator": "and"
}
}
And this is my results :
Restaurants
Couture et retouches
Restauration rapide
But I want them like this :
Restaurants
Restauration rapide
Couture et retouches
How can I boost a result starting with the keyword?
In case it can helps, here is my mapping :
{
"settings": {
"analysis": {
"analyzer": {
"partialAnalyzer": {
"type": "custom",
"tokenizer": "ngram_tokenizer",
"filter": ["asciifolding", "lowercase"]
},
"searchAnalyzer": {
"type": "custom",
"tokenizer": "standard",
"filter": ["asciifolding", "lowercase"]
}
},
"tokenizer": {
"ngram_tokenizer": {
"type": "edge_ngram",
"min_gram": "1",
"max_gram": "15",
"token_chars": [ "letter", "digit" ]
}
}
}
},
"mappings": {
"place": {
"properties": {
"name": {
"type": "string",
"index_analyzer": "partialAnalyzer",
"search_analyzer": "searchAnalyzer",
"term_vector": "with_positions_offsets"
}
}
}
}
}
Regards,
How about this idea, not 100% sure of it as it depends on the data I think:
create a sub-field in your name field that should be analyzed with keyword analyzer (pretty much staying as is)
change the query to be a bool with shoulds
one should is the query you have now
the other should is a match with phrase_prefix on the sub-field.
The mapping:
{
"settings": {
"analysis": {
"analyzer": {
"partialAnalyzer": {
"type": "custom",
"tokenizer": "ngram_tokenizer",
"filter": [
"asciifolding",
"lowercase"
]
},
"searchAnalyzer": {
"type": "custom",
"tokenizer": "standard",
"filter": [
"asciifolding",
"lowercase"
]
},
"keyword_lowercase": {
"type": "custom",
"tokenizer": "keyword",
"filter": [
"asciifolding",
"lowercase"
]
}
},
"tokenizer": {
"ngram_tokenizer": {
"type": "edge_ngram",
"min_gram": "1",
"max_gram": "15",
"token_chars": [
"letter",
"digit"
]
}
}
}
},
"mappings": {
"place": {
"properties": {
"name": {
"type": "string",
"index_analyzer": "partialAnalyzer",
"search_analyzer": "searchAnalyzer",
"term_vector": "with_positions_offsets",
"fields": {
"as_is": {
"type": "string",
"analyzer": "keyword_lowercase"
}
}
}
}
}
}
}
The query:
{
"query": {
"bool": {
"should": [
{
"match": {
"name": {
"query": "re",
"operator": "and"
}
}
},
{
"match": {
"name.as_is": {
"query": "re",
"type": "phrase_prefix"
}
}
}
]
}
}
}
Related
I'm trying to make a search request that retrieves the results only when less than
5 words are between requested tokens.
{
"settings": {
"index": {
"analysis": {
"filter": {
"stopWords": {
"type": "stop",
"stopwords": [
"_english_"
]
}
},
"normalizer": {
"lowercaseNormalizer": {
"filter": [
"lowercase",
"asciifolding"
],
"type": "custom",
"char_filter": []
}
},
"analyzer": {
"autoCompleteAnalyzer": {
"filter": [
"lowercase"
],
"type": "custom",
"tokenizer": "autoCompleteTokenizer"
},
"autoCompleteSearchAnalyzer": {
"type": "custom",
"tokenizer": "lowercase"
},
"charGroupAnalyzer": {
"filter": [
"lowercase"
],
"type": "custom",
"tokenizer": "charGroupTokenizer"
}
},
"tokenizer": {
"charGroupTokenizer": {
"type": "char_group",
"max_token_length": "20",
"tokenize_on_chars": [
"whitespace",
"-",
"\n"
]
},
"autoCompleteTokenizer": {
"token_chars": [
"letter"
],
"min_gram": "3",
"type": "edge_ngram",
"max_gram": "20"
}
}
}
}
}
}
The settings:
{
"mappings": {
"_doc": {
"properties": {
"description": {
"properties": {
"name": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 64
}
},
"analyzer": "autoCompleteAnalyzer",
"search_analyzer": "autoCompleteSearchAnalyzer"
},
"text": {
"type": "text",
"analyzer": "charGroupAnalyzer"
}
}
}
}
}
}
}
And make a bool request with request:
{
"query": {
"bool": {
"must": [
{
"multi_match": {
"fields": [
"description.name"
],
"operator": "and",
"query": "rounded elephant",
"fuzziness": 1
}
},
{
"match_phrase": {
"description.text": {
"analyzer": "charGroupAnalyzer",
"query": "rounded elephant",
"slop": 5,
"boost": 20
}
}
}
]
}
}
}
I expect the request to retrieve documents, where description contains:
... rounded very interesting elephant ...
This works good, when i use the complete words, like rounded elephant.
But, whe i enter prefixed words, like round eleph it fails.
But it's obvious that the description.name and description.text have different tokenizers (name contains ngram tokens, but text contain word tokens), so i get completely wrong results.
How can I configure mappings and search, to be able to use ngrams with distance between tokens?
I'm trying to get synonyms working for my existing setup. Currently I have this settings:
PUT city
{
"settings": {
"analysis": {
"analyzer": {
"autocomplete": {
"tokenizer": "autocomplete",
"filter": [
"lowercase",
"my_synonym_filter",
"german_normalization",
"my_ascii_folding"
]
},
"autocomplete_search": {
"tokenizer": "lowercase",
"filter": [
"lowercase",
"my_synonym_filter",
"german_normalization",
"my_ascii_folding"
]
}
},
"filter": {
"my_ascii_folding": {
"type": "asciifolding",
"preserve_original": true
},
"my_synonym_filter": {
"type": "synonym",
"ignore_case": "true",
"synonyms": [
"sankt, st => sankt"
]
}
},
"tokenizer": {
"autocomplete": {
"type": "edge_ngram",
"min_gram": 1,
"max_gram": 15,
"token_chars": [
"letter",
"digit",
"symbol"
]
}
}
}
},
"mappings": {
"city": {
"properties": {
"name": {
"type": "text",
"analyzer": "autocomplete",
"search_analyzer": "autocomplete_search"
}
}
}
}
}
In this City Index I have documents like that:
St. Wolfgang or Sankt Wolfgang and so on. For me St. and Sankt are synonyms. So if I search for Sankt both of the documents should appear.
I created a new Filter and added the filter to my autocomplete analyzer:
"my_synonym_filter": {
"type": "synonym",
"ignore_case": "true",
"synonyms": [
"sankt, st."
]
}
So good for now. But the issues I faced are following:
Its clear that the dot after st is not analyzed and not searchable at the moment. But For the synonym the dot is important.
The second issue is if I search for sankt the synonym is st which gives me all documents which starts with st like Stuttgart. So this happens also because the dot is not used.
Do you have any idea how I can achieve the stuff? If you need any more information, please let me know.
Update:
After discussions I did this changes in my settings:
changed edge_ngram tokenizer to a standard tokenizer.
added an edgeNGram filter and added this filter to my analyzer.
deleted the filter german_normalization and my_ascii_folding from my analyzer to simplify the tests.
PUT city
{
"settings": {
"analysis": {
"analyzer": {
"autocomplete": {
"tokenizer": "autocomplete",
"filter": [
"lowercase",
"my_synonym_filter",
"edge_filter"
]
},
"autocomplete_search": {
"tokenizer": "autocomplete",
"filter": [
"my_synonym_filter",
"lowercase"
]
}
},
"filter": {
"edge_filter": {
"type": "edgeNGram",
"min_gram": 1,
"max_gram": 15
},
"my_synonym_filter": {
"type": "synonym",
"ignore_case": "true",
"synonyms": [
"sankt, st => sankt"
]
}
},
"tokenizer": {
"autocomplete": {
"type": "standard"
}
}
}
},
"mappings": {
"city": {
"properties": {
"name": {
"type": "text",
"analyzer": "autocomplete",
"search_analyzer": "autocomplete_search"
}
}
}
}
}
I added these 3 documents to the index:
"name":"Sankt Wolfgang",
"name":"Stuttgart",
"name":"St. Wolfgang"
Query String - Result
st -> "St. Wolfgang", "Stuttgart"
st. -> "St. Wolfgang", "Sankt Wolfgang"
sankt -> "St. Wolfgang", "Sankt Wolfgang"
This works pretty well for me. The main point here is to make sure to
put the synonym filter after the lowercase one
put the edge-n-gram filter at the end
use the edge-n-gram only at indexing time
So we create the index:
PUT city
{
"settings": {
"analysis": {
"analyzer": {
"autocomplete": {
"tokenizer": "standard",
"filter": [
"lowercase",
"my_synonym_filter",
"edge_filter"
]
},
"autocomplete_search": {
"tokenizer": "standard",
"filter": [
"lowercase",
"my_synonym_filter"
]
}
},
"filter": {
"edge_filter": {
"type": "edgeNGram",
"min_gram": 1,
"max_gram": 15
},
"my_synonym_filter": {
"type": "synonym",
"ignore_case": "true",
"synonyms": [
"sankt, st. => sankt"
]
}
}
}
},
"mappings": {
"city": {
"properties": {
"name": {
"type": "text",
"analyzer": "autocomplete",
"search_analyzer": "autocomplete_search"
}
}
}
}
}
Then we index data:
PUT city/city/1
{
"name":"St. Wolfgang"
}
PUT city/city/2
{
"name":"Stuttgart"
}
PUT city/city/3
{
"name":"Sankt Wolfgang"
}
Finally searching for either st or sankt will only return documents 1 and 3 but not 2
POST city/_search?q=name:st
POST city/_search?q=name:sankt
Settings:
{
"settings": {
"analysis": {
"analyzer": {
"idx_analyzer_ngram": {
"type": "custom",
"filter": [
"lowercase",
"asciifolding",
"edgengram_filter_1_32"
],
"tokenizer": "ngram_alltokenchar_tokenizer_1_32"
},
"ngrm_srch_analyzer": {
"filter": [
"lowercase"
],
"type": "custom",
"tokenizer": "keyword"
}
},
"tokenizer": {
"ngram_alltokenchar_tokenizer_1_32": {
"token_chars": [
"letter",
"whitespace",
"punctuation",
"symbol",
"digit"
],
"min_gram": "1",
"type": "nGram",
"max_gram": "32"
}
}
}
}
}
Mappings:
{
"properties": {
"TITLE": {
"type": "string",
"fields": {
"untouched": {
"index": "not_analyzed",
"type": "string"
},
"ngramanalyzed": {
"search_analyzer": "ngrm_srch_analyzer",
"index_analyzer": "idx_analyzer_ngram",
"type": "string",
"term_vector": "with_positions_offsets"
}
}
}
}
}
Query:
{
"query": {
"filtered": {
"query": {
"query_string": {
"query": "have some ha",
"fields": [
"TITLE.ngramanalyzed"
],
"default_operator": "and"
}
}
}
},
"highlight": {
"fields": {
"TITLE.ngramanalyzed": {}
}
}
}
I have document indexed with TITLE have some happy meal. When I search have some, I am able to get proper highlights.
<em>have</em> <em>some</em> happy meal
As i type more have some ha, the highlight results are not as expected.
<em>ha</em>ve <em>some</em> <em>ha</em>ppy meal
The have word gets partially highlighted as ha.
I would expect it to highlight the longest matching token, because with an ngrams with min size = 1, this gives me a highlight of 1 or more char while there should be another matching token of 4 or 5 chars (for example: have should also be highlighted along with ha being highlighted.
I am not able to find any solution for the same. Please suggest.
I am trying to use synonym analyzer at query time and not getting expected results. Can someone throw some light on this?
Here is my mapping for the index:
{
"jobs_user_profile_v2": {
"mappings": {
"profile": {
"_all": {
"enabled": false
},
"_ttl": {
"enabled": true
},
"properties": {
"rsa": {
"type": "nested",
"properties": {
"answer": {
"type": "string",
"index_analyzer": "autocomplete",
"search_analyzer": "synonym",
"position_offset_gap": 100
},
"answerId": {
"type": "long"
},
"answerOriginal": {
"type": "string",
"index": "not_analyzed"
},
"createdAt": {
"type": "long"
},
"label": {
"type": "string",
"index": "not_analyzed"
},
"labelOriginal": {
"type": "string",
"index": "not_analyzed"
},
"question": {
"type": "string",
"index": "not_analyzed"
},
"questionId": {
"type": "long"
},
"questionOriginal": {
"type": "string"
},
"source": {
"type": "integer"
},
"updatedAt": {
"type": "long"
}
}
}
}
}
}
}
}
The field to focus on is rsa.answer, which is the field I am querying.
My synonym mapping:
Beautician,Stylist,Make up artist,Massage therapist,Therapist,Spa,Hair Dresser,Salon,Beauty Parlour,Parlor => Beautician
Carpenter,Wood Worker,Furniture Carpenter => Carpenter
Cashier,Store Manager,Store Incharge,Purchase Executive,Billing Executive,Billing Boy => Cashier
Content Writer,Writer,Translator,Writing,Copywriter,Content Creation,Script Writer,Freelance Writer,Freelance Content Writer => Content Writer
My Search Query:
http://{{domain}}/jobs_user_profile_v2/_search
{
"query": {
"nested":{
"path": "rsa",
"query":{
"query_string": {
"query": "hair dresser",
"fields": ["answer"],
"analyzer" :"synonym"
}
},
"inner_hits": {
"explain": true
}
}
},
"explain" : true,
"sort" : [ {
"_score" : { }
} ]
}
It is showing proper Beautician and 'Cashierprofiles for search queryHair Dresserandbilling executivebut not showing anything forwood worker => carpenter` case.
My analyzer results:
http://{{domain}}/jobs_user_profile_v2/_analyze?analyzer=synonym&text=hair dresser
{
"tokens": [
{
"token": "beautician",
"start_offset": 0,
"end_offset": 12,
"type": "SYNONYM",
"position": 1
}
]
}
and for wood worker case
http://{{domain}}/jobs_user_profile_v2/_analyze?analyzer=synonym&text=wood worker
{
"tokens": [
{
"token": "carpenter",
"start_offset": 0,
"end_offset": 11,
"type": "SYNONYM",
"position": 1
}
]
}
It is also not working a few other cases.
My analyzer setting for index:
"analysis": {
"filter": {
"synonym": {
"ignore_case": "true",
"type": "synonym",
"synonyms_path": "synonym.txt"
},
"autocomplete_filter": {
"type": "edge_ngram",
"min_gram": "3",
"max_gram": "10"
}
},
"analyzer": {
"text_en_splitting_search": {
"type": "custom",
"filter": [
"stop",
"lowercase",
"porter_stem",
"word_delimiter"
],
"tokenizer": "whitespace"
},
"synonym": {
"filter": [
"stop",
"lowercase",
"synonym"
],
"type": "custom",
"tokenizer": "standard"
},
"autocomplete": {
"filter": [
"lowercase",
"autocomplete_filter"
],
"type": "custom",
"tokenizer": "standard"
},
"text_en_splitting": {
"filter": [
"lowercase",
"porter_stem",
"word_delimiter"
],
"type": "custom",
"tokenizer": "whitespace"
},
"text_general": {
"filter": [
"lowercase"
],
"type": "custom",
"tokenizer": "standard"
},
"edge_ngram_analyzer": {
"filter": [
"lowercase"
],
"type": "custom",
"tokenizer": "edge_ngram_tokenizer"
},
"autocomplete_analyzer": {
"filter": [
"lowercase"
],
"tokenizer": "whitespace"
}
},
"tokenizer": {
"edge_ngram_tokenizer": {
"token_chars": [
"letter",
"digit"
],
"min_gram": "2",
"type": "edgeNGram",
"max_gram": "10"
}
}
}
For the above case one multi-match is more ideal than query-string.
Multi-Match unlike query string does not tokenize the query terms before analyzing it . As a result multi-word synonyms may not work as expected.
Example:
{
"query": {
"nested": {
"path": "rsa",
"query": {
"multi_match": {
"query": "wood worker",
"fields": [
"rsa.answer"
],
"type" : "cross_fields",
"analyzer": "synonym"
}
}
}
}
}
If for some reason you prefer query-string then you would need to pass the entire query in double quotes to ensure it is not tokenized:
example :
post test/_search
{
"query": {
"nested": {
"path": "rsa",
"query": {
"query_string": {
"query": "\"wood worker\"",
"fields": [
"rsa.answer"
],
"analyzer": "synonym"
}
}
}
}
}
Referring to this post, I've created the following mapping:
POST music
{
"song": {
"settings": {
"analysis": {
"filter": {
"nGram_filter": {
"type": "nGram",
"min_gram": 2,
"max_gram": 20,
"token_chars": [
"letter",
"digit",
"punctuation",
"symbol"
]
},
"analyzer": {
"nGram_analyzer": {
"type": "custom",
"tokenizer": "whitespace",
"filter": [
"lowercase",
"asciifolding",
"nGram_filter"
]
},
"whitespace_analyzer": {
"type": "custom",
"tokenizer": "whitespace",
"filter": [
"lowercase",
"asciifolding"
]
}
}
}
}
},
"mappings": {
"song_field_1": {
"type": "string",
"index": "not_analyzed",
"index_analyzer": "nGram_analyzer",
"search_analyzer": "whitespace_analyzer"
}
}
}
}
Inserted the following document:
POST music/song
{
"song_field_1" : "Premeditiated fella"
}
And sent this query:
POST music/song/_search
{
"size": 10,
"query": {
"match": {
"_all": {
"query": "pre"
}
}
}
}
I expected to get the document as an autocomplete option, but didn't get any result.
You need to create your index like this:
POST music
{
"settings": {
"analysis": {
"filter": {
"nGram_filter": {
"type": "nGram",
"min_gram": 2,
"max_gram": 20,
"token_chars": [
"letter",
"digit",
"punctuation",
"symbol"
]
},
"analyzer": {
"nGram_analyzer": {
"type": "custom",
"tokenizer": "whitespace",
"filter": [
"lowercase",
"asciifolding",
"nGram_filter"
]
},
"whitespace_analyzer": {
"type": "custom",
"tokenizer": "whitespace",
"filter": [
"lowercase",
"asciifolding"
]
}
}
}
}
},
"mappings": {
"song": {
"properties": {
"song_field_1": {
"type": "string",
"index_analyzer": "nGram_analyzer",
"search_analyzer": "whitespace_analyzer"
}
}
}
}
}
So:
song goes inside mappings
No need for "index": "not_analyzed" since you're specifying analyzers