I have the following elasticsearch settings:
"settings": {
"index":{
"analysis":{
"analyzer":{
"analyzer_keyword":{
"tokenizer":"keyword",
"filter":["lowercase", "asciifolding"]
}
}
}
}
}
The above works fine for the following keywords:
Beyoncé
Céline Dion
The above data is stored in elasticsearch as beyonce and celine dion respectively.
I can search for Celine or Celine Dion without the accent and I get the same results. However, the moment I search for Céline, I don't get any results. How can I configure elasticsearch to search for partial keywords with the accent?
The query body looks like:
{
"track_scores": true,
"query": {
"bool": {
"must": [
{
"multi_match": {
"fields": ["name"],
"type": "phrase",
"query": "Céline"
}
}
]
}
}
}
and the mapping is
"mappings" : {
"artist" : {
"properties" : {
"name" : {
"type" : "string",
"fields" : {
"orig" : {
"type" : "string",
"index" : "not_analyzed"
},
"simple" : {
"type" : "string",
"analyzer" : "analyzer_keyword"
}
},
}
I would suggest this mapping and then go from there:
{
"settings": {
"index": {
"analysis": {
"analyzer": {
"analyzer_keyword": {
"tokenizer": "whitespace",
"filter": [
"lowercase",
"asciifolding"
]
}
}
}
}
},
"mappings": {
"test": {
"properties": {
"name": {
"type": "string",
"analyzer": "analyzer_keyword"
}
}
}
}
}
Confirm that the same analyzer is getting used at query time. Here are some possible reasons why that might not be happening:
you specify a separate analyzer at query time on purpose that is not performing similar analysis
you are using a term or terms query for which no analyzer is applied (See Term Query and the section title "Why doesn’t the term query match my document?")
you are using a query_string query (E.g. see Simple Query String Query) - I have found that if you specify multiple fields with different analyzers and so I have needed to separate the fields into separate queries and specify the analyzer parameter (working with version 2.0)
Related
I am using Elastic Search 6.This is query
PUT /semtesttest
{
"settings": {
"index" : {
"analysis" : {
"filter": {
"my_stop": {
"type": "stop",
"stopwords_path": "analysis1/stopwords.csv"
},
"synonym" : {
"type" : "synonym",
"synonyms_path" : "analysis1/synonym.txt"
}
},
"analyzer" : {
"my_analyzer" : {
"tokenizer" : "standard",
"filter" : ["synonym","my_stop"]
}
}
}
}
},
"mappings": {
"all_questions": {
"dynamic": "strict",
"properties": {
"kbaid":{
"type": "integer"
},
"answer":{
"type": "text"
},
"question": {
"type": "text",
"analyzer": "my_analyzer"
}
}
}
}
}
PUT /semtesttest/all_questions/1
{
"question":"this is hippie"
}
GET /semtesttest/all_questions/_search
{
"query":{
"fuzzy":{"question":{"value":"hippie","fuzziness":2}}
}
}
GET /semtesttest/all_questions/_search
{
"query":{
"fuzzy":{"question":{"value":"this is","fuzziness":2}}
}
}
in synonym.txt it is
this, that, money => sainai
in stopwords.csv it is
hello
how
are
you
The first get ('hippie') return empty
only the second get ('this is') return results
what is the problem? It looks like the stop word "this is" is filtered in the first query, but I have specified my stop words explicitly?
fuzzy is a term query. It is not going to analyze the input, so your query was looking for the exact term this is (applying some fuzzy fun).
So you either want to build a query off those two terms, or use a full text query instead. If fuzziness is important, I think the only full text query is match:
GET /semtesttest/all_questions/_search?pretty
{
"query":{
"match":{"question":{"query":"this is","fuzziness":2}}
}
}
If match phrases is important, you may want to look at this answer and work with span queries.
This might also help you so you can see how your analyzer is being used:
GET /semtesttest/_analyze?analyzer=my_analyzer&field=question&text=this is
I am trying to search again content using prefix and if I search for diode I get results that differ from Diode. How do I get ES to return result where both diode and Diode return the same results? This is the mappings and settings I am using in ES.
"settings":{
"analysis": {
"analyzer": {
"lowercasespaceanalyzer": {
"type": "custom",
"tokenizer": "whitespace",
"filter": [
"lowercase"
]
}
}
}
},
"mappings": {
"articles": {
"properties": {
"title": {
"type": "text"
},
"url": {
"type": "keyword",
"index": "true"
},
"imageurl": {
"type": "keyword",
"index": "true"
},
"content": {
"type": "text",
"analyzer" : "lowercasespaceanalyzer",
"search_analyzer":"whitespace"
},
"description": {
"type": "text"
},
"relatedcontentwords": {
"type": "text"
},
"cmskeywords": {
"type": "text"
},
"partnumbers": {
"type": "keyword",
"index": "true"
},
"pubdate": {
"type": "date"
}
}
}
}
here is an example of the query I use
POST _search
{
"query": {
"bool" : {
"must" : {
"prefix" : { "content" : "capacitance" }
}
}
}
}
it happens because you use two different analyzers at search time and at indexing time.
So when you input query "Diod" at search time because you use "whitespace" analyzer your query is interpreted as "Diod".
However, because you use "lowercasespaceanalyzer" at index time "Diod" will be indexed as "diod". Just use the same analyzer both at search and index time, or analyzer that lowercases your strings because default "whitespace" analyzer doesn't https://www.elastic.co/guide/en/elasticsearch/reference/current/analysis-whitespace-analyzer.html
There will be no term of Diode in your index. So if you want to get same results, you should let your query context analyzed by same analyzer.
You can use Query string query like
"query_string" : {
"default_field" : "content",
"query" : "Diode",
"analyzer" : "lowercasespaceanalyzer"
}
UPDATE
You can analyze your context before query.
AnalyzeResponse resp = client.admin().indices()
.prepareAnalyze(index, text)
.setAnalyzer("lowercasespaceanalyzer")
.get();
String analyzedContext = resp.getTokens().get(0);
...
Then use analyzedContext as new query context.
We have added the hunspell filter to our elastic search instance. Nothing fancy...
{
"index" : {
"analysis" : {
"tokenizer" : {
"comma" : {
"type" : "pattern",
"pattern" : ","
}
},
"filter": {
"en_GB": {
"type": "hunspell",
"language": "en_GB"
}
},
"analyzer" : {
"comma" : {
"type" : "custom",
"tokenizer" : "comma"
},
"en_GB": {
"filter": [
"lowercase",
"en_GB"
],
"tokenizer": "standard"
}
}
}
}
}
Now though we seem to have lost the built in facility to do exact match queries using quotation marks. So searching for "lace" will also do an equal score search for "lacy" for example. I understand this is kind of the point of including hunspell but I would like to be able to force exact matches by using quotes
I am doing boolean queries for this by the way. Along the lines of (in java)
"bool" : {
"must" : {
"query_string" : {
"query" : "\"lace\"",
"fields" :
...
or (postman direct to 9200 ...
{
"query" : {
"query_string" : {
"query" : "\"lace\"",
"fields" :
....
Is this possible ? I'm guessing this might be something we would do in the tokaniser but I'm not quite sure where to start...?
You will not be able to handle this tokenizer level, but you can tweak configurations at mapping level to use multi-fields, you can keep a copy of the same field which will not be analyzed and later use this in query to support your usecase.
You can update your mappings like following
"mappings": {
"desc": {
"properties": {
"labels": {
"type": "string",
"analyzer": "en_GB",
"fields": {
"raw": {
"type": "keyword"
}
}
}
}
}
}
Furthur modify your query to search on raw field instead of analyzed field.
{
"query": {
"bool": {
"must": [{
"query_string": {
"default_field": "labels.raw",
"query": "lace"
}
}]
}
}
}
Hope this helps
Thanks
I've indexed a list of terms and now I want to query for some of them
Say that I've indexed 'dog food','red dog','dog','food','cats'
How do I create an exact bidirectional match query. ie: I want when search for 'dog' to get only the term dog and not the other terms (because they don't match back).
One primitive solution I thought of is indexing the terms with their length (Words-wise) and then when searching query with lengh X restrict it to the terms of length X. but it seems over complicated.
Create a custom analyzer to lowercase and normalize your search terms. So that would be your index:
{
"settings" : {
"analysis" : {
"analyzer" : {
"my_analyzer_keyword" : {
"type" : "custom",
"tokenizer" : "keyword",
"filter" : [
"asciifolding",
"lowercase"
]
}
}
}
},
"mappings" : {
"your_type" : {
"properties" : {
"name" : {
"type" : "string",
"analyzer" : "my_analyzer_keyword"
}
}
}
}
}
So if you have indexed 'dog' and users types in Dog or dog or DOG, it will match only dog, 'dog food' won't be brought back.
Just set your field's index property to not_analyzed and your query should use term filter to search for text.
As per Evaldas' suggestion, find below a more complete solution, that also keeps the original value indexed with standard analyzer but uses a sub-field with a lowercased version of the terms:
PUT /test
{
"settings": {
"analysis": {
"analyzer": {
"my_keyword_lowercase_analyzer": {
"type": "custom",
"filter": [
"lowercase"
],
"tokenizer": "keyword"
}
}
}
},
"mappings": {
"asset": {
"properties": {
"name": {
"type": "string",
"fields": {
"case_ignore": {
"type": "string",
"analyzer": "my_keyword_lowercase_analyzer"
}
}
}
}
}
}
}
POST /test/asset/1
{
"name":"dog"
}
POST /test/asset/2
{
"name":"dog food"
}
POST /test/asset/3
{
"name":"red dog"
}
GET /test/asset/_search
{
"query": {
"match": {
"name.case_ignore": "Dog"
}
}
}
I want to use elasticsearch for multi-word searches, where all the fields are checked in a document with the assigned analyzers.
So if I have a mapping:
{
"settings": {
"analysis": {
"analyzer": {
"folding": {
"tokenizer": "standard",
"filter": [ "lowercase", "asciifolding" ]
}
}
}
},
"mappings" : {
"typeName" :{
"date_detection": false,
"properties" : {
"stringfield" : {
"type" : "string",
"index" : "folding"
},
"numberfield" : {
"type" : "multi_field",
"fields" : {
"numberfield" : {"type" : "double"},
"untouched" : {"type" : "string", "index" : "not_analyzed"}
}
},
"datefield" : {
"type" : "multi_field",
"fields" : {
"datefield" : {"type" : "date", "format": "dd/MM/yyyy||yyyy-MM-dd"},
"untouched" : {"type" : "string", "index" : "not_analyzed"}
}
}
}
}
}
}
As you see I have different types of fields, but I do know the structure.
What I want to do is starting a search with a string to check all fields using the analyzers too.
For example if the query string is:
John Smith 2014-10-02 300.00
I want to search for "John", "Smith", "2014-10-02" and "300.00" in all the fields, calculating the relevance score as well. The better solution is the one that have more field matches in a single document.
So far I was able to search in all the fields by using multi_field, but in that case I was not able to parse 300.00, since 300 was stored in the string part of multi_field.
If I was searching in "_all" field, then no analyzer was used.
How should I modify my mapping or my queries to be able to do a multi-word search, where dates and numbers are recognized in the multi-word query string?
Now when I do a search, error occurs, since the whole string cannot be parsed as a number or a date. And if I use the string representation of the multi_search then 300.00 will not be a result, since the string representation is 300.
(what I would like is similar to google search, where dates, numbers and strings are recognized in a multi-word query)
Any ideas?
Thanks!
Using whitespace as filter in analyzer and then applying this analyzer as search_analyzer to fields in mapping will split query in parts and each of them would be applied to index to find the best matching. And using ngram for index_analyzer would very improve results.
I am using following setup for query:
"query": {
"multi_match": {
"query": "sample query",
"fuzziness": "AUTO",
"fields": [
"title",
"subtitle",
]
}
}
And for mappings and settings:
{
"settings" : {
"analysis": {
"analyzer": {
"autocomplete": {
"type": "custom",
"tokenizer": "whitespace",
"filter": [
"standard",
"lowercase",
"ngram"
]
}
},
"filter": {
"ngram": {
"type": "ngram",
"min_gram": 2,
"max_gram": 15
}
}
},
"mappings": {
"title": {
"type": "string",
"search_analyzer": "whitespace",
"index_analyzer": "autocomplete"
},
"subtitle": {
"type": "string"
}
}
}
See following answer and article for more details.