Maybe I am going down the wrong route, but I am trying to set up Elasticsearch to use Partial Phrase matching to return parts of words from any order of a sentence.
Eg. I have the following input
test name
tester name
name test
namey mcname face
test
And I hope to do a search for "test name" (or "name test"), and I hope all of these return (hopefully sorted in order of score). I can do partial searches, and also can do out of order searches, but not able to combine the 2. I am sure this would be a very common issue.
Below is my Settings
{
"myIndex": {
"settings": {
"index": {
"analysis": {
"filter": {
"mynGram": {
"type": "nGram",
"min_gram": "2",
"max_gram": "5"
}
},
"analyzer": {
"custom_analyser": {
"filter": [
"lowercase",
"mynGram"
],
"type": "custom",
"tokenizer": "my_tokenizer"
}
},
"tokenizer": {
"my_tokenizer": {
"type": "nGram",
"min_gram": "2",
"max_gram": "5"
}
}
}
}
}
}
}
My mapping
{
"myIndex": {
"mappings": {
"myIndex": {
"properties": {
"name": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword"
}
},
"analyzer": "custom_analyser"
}
}
}
}
}
}
And my query
{
"query": {
"bool": {
"must": [{
"match_phrase": {
"name": {
"query": "test name",
"slop": 5
}
}
}]
}
}
}
Any help would be greatly appreciated.
Thanks in advance
not sure if you found your solution - I bet you did because this is such an old post, but I was on the hunt for the same thing and found this: Query-Time Search-as-you-type
Look up slop.
Related
Suppose there is the following mapping with Edge NGram Tokenizer:
{
"settings": {
"analysis": {
"analyzer": {
"autocomplete_analyzer": {
"tokenizer": "autocomplete_tokenizer",
"filter": [
"standard"
]
},
"autocomplete_search": {
"tokenizer": "whitespace"
}
},
"tokenizer": {
"autocomplete_tokenizer": {
"type": "edge_ngram",
"min_gram": 1,
"max_gram": 10,
"token_chars": [
"letter",
"symbol"
]
}
}
}
},
"mappings": {
"tag": {
"properties": {
"id": {
"type": "long"
},
"name": {
"type": "text",
"analyzer": "autocomplete_analyzer",
"search_analyzer": "autocomplete_search"
}
}
}
}
}
And the following documents are indexed:
POST /tag/tag/_bulk
{"index":{}}
{"name" : "HITS FIND SOME"}
{"index":{}}
{"name" : "TRENDING HI"}
{"index":{}}
{"name" : "HITS OTHER"}
Then searching
{
"query": {
"match": {
"name": {
"query": "HI"
}
}
}
}
yields all with the same score, or TRENDING - HI with a score higher than one of the others.
How can it be configured, to show with a higher score the entries that actually start with the searcher n-gram? In this case, HITS FIND SOME and HITS OTHER to have a higher score than TRENDING HI; at the same time TRENDING HI should be in the results.
Highlighter is also used, so the given solution shouldn't mess it up.
The highlighter used in query is:
"highlight": {
"pre_tags": [
"<"
],
"post_tags": [
">"
],
"fields": {
"name": {}
}
}
Using this with match_phrase_prefix messes up the highlighting, yielding <H><I><T><S> FIND SOME when searching only for H.
You must understand how elasticsearch/lucene analyzes your data and calculate the search score.
1. Analyze API
https://www.elastic.co/guide/en/elasticsearch/reference/current/_testing_analyzers.html this will show you what elasticsearch will store, in your case:
T / TR / TRE /.... TRENDING / / H / HI
2. Score
https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-bool-query.html
The bool query is often used to build complex query where you need a particular use case. Use must to filter document, then should to score. A common use case is to use different analyzers on a same field (by using the keyword fields in the mapping, you can analyze a same field differently).
3. dont mess highlight
According the doc: https://www.elastic.co/guide/en/elasticsearch/reference/current/search-request-highlighting.html#specify-highlight-query
You can add an extra query:
{
"query": {
"bool": {
"must" : [
{
"match": {
"name": "HI"
}
}
],
"should": [
{
"prefix": {
"name": "HI"
}
}
]
}
},
"highlight": {
"pre_tags": [
"<"
],
"post_tags": [
">"
],
"fields": {
"name": {
"highlight_query": {
"match": {
"name": "HI"
}
}
}
}
}
}
In this particular case you could add a match_phrase_prefix term to your query, which does prefix match on the last term in the text:
{
"query": {
"bool": {
"should": [
{
"match": {
"name": "HI"
}
},
{
"match_phrase_prefix": {
"name": "HI"
}
}
]
}
}
}
The match term will match on all three results, but the match_phrase_prefix won't match on TRENDING HI. As a result, you'll get all three items in the results, but TRENDING HI will appear with a lower score.
Quoting the docs:
The match_phrase_prefix query is a poor-man’s autocomplete[...] For better solutions for search-as-you-type see the completion suggester and Index-Time Search-as-You-Type.
On a side note, if you're introducing that bool query, you'll probably want to look at the minimum_should_match option, depending on the results you want.
A possible solution for this problem is to use multifields. They allow for indexing of the same data from your source document in different ways. In your case you could index the name field as default text, then as ngrams and also as edgengrams. Then the query would have to be a bool query comparing with all those different fields.
The final score of documents is composed of the match value for each one. Those matches are also called signals, signalling that there is a match between the query and the document. The document with most signals matching gets the highest score.
In your case all documents would match the ngram HI. But only the HITS FIND SOME and the HITS OTHER document would get the edgengram additional score. This would give those two documents a boost and put them on top. The complication with this is that you have to make sure that the edgengram doesn't split on whitespaces, because then the HI at the end would get the same score as in the beginning of the document.
Here is an example mapping and query for your case:
PUT /tag/
{
"settings": {
"analysis": {
"analyzer": {
"edge_analyzer": {
"tokenizer": "edge_tokenizer"
},
"kw_analyzer": {
"tokenizer": "kw_tokenizer"
},
"ngram_analyzer": {
"tokenizer": "ngram_tokenizer"
},
"autocomplete_analyzer": {
"tokenizer": "autocomplete_tokenizer",
"filter": [
"standard"
]
},
"autocomplete_search": {
"tokenizer": "whitespace"
}
},
"tokenizer": {
"kw_tokenizer": {
"type": "keyword"
},
"edge_tokenizer": {
"type": "edge_ngram",
"min_gram": 2,
"max_gram": 10
},
"ngram_tokenizer": {
"type": "ngram",
"min_gram": 2,
"max_gram": 10,
"token_chars": [
"letter",
"digit"
]
},
"autocomplete_tokenizer": {
"type": "edge_ngram",
"min_gram": 1,
"max_gram": 10,
"token_chars": [
"letter",
"symbol"
]
}
}
}
},
"mappings": {
"tag": {
"properties": {
"id": {
"type": "long"
},
"name": {
"type": "text",
"fields": {
"edge": {
"type": "text",
"analyzer": "edge_analyzer"
},
"ngram": {
"type": "text",
"analyzer": "ngram_analyzer"
}
}
}
}
}
}
}
And a query:
POST /tag/_search
{
"query": {
"bool": {
"should": [
{
"function_score": {
"query": {
"match": {
"name.edge": {
"query": "HI"
}
}
},
"boost": "5",
"boost_mode": "multiply"
}
},
{
"match": {
"name.ngram": {
"query": "HI"
}
}
},
{
"match": {
"name": {
"query": "HI"
}
}
}
]
}
}
}
Ive been around the houses with this for the past few days trying things in various orders but cant figure out why its not working.
I am trying to create an index in Elasticsearch with an analyzer which is the same as the "standard" analyzer but retains upper case characters when records are stored.
I create my analyzer and index as follows:
PUT /upper
{
"settings": {
"index" : {
"analysis" : {
"analyzer": {
"rebuilt_standard": {
"tokenizer": "standard",
"filter": [
"standard"
]
}
}
}
}
},
"mappings": {
"doc": {
"properties": {
"title": {
"type": "text",
"analyzer": "rebuilt_standard"
}
}
}
}
}
Then add two records to test like this...
POST /upper/doc
{
"text" : "TEST"
}
Add a second record...
POST /upper/doc
{
"text" : "test"
}
Using /upper/_settings gives the following:
{
"upper": {
"settings": {
"index": {
"number_of_shards": "5",
"provided_name": "upper",
"creation_date": "1537788581060",
"analysis": {
"analyzer": {
"rebuilt_standard": {
"filter": [
"standard"
],
"tokenizer": "standard"
}
}
},
"number_of_replicas": "1",
"uuid": "s4oDgdsFTxOwsdRuPAWEkg",
"version": {
"created": "6030299"
}
}
}
}
}
But when I search with the following query I still get two matches! Both the upper and lower cases which must mean the analyser is not applied when I store the records.
Search like so...
GET /upper/_search
{
"query": {
"term": {
"text": {
"value": "test"
}
}
}
}
Thanks in advance!
first thing first you set your analyzer on the title field instead of upon the text field (since your search is on the text property, and since you are indexing doc with only text property)
"properties": {
"title": {
"type": "text",
"analyzer": "rebuilt_standard"
}
}
try
"properties": {
"text": {
"type": "text",
"analyzer": "rebuilt_standard"
}
}
and keep us posted ;)
Let be a set index/type named customers/customer.
Each document of this set has a zip-code as property.
Basically, a zip-code can be like:
String-String (ex : 8907-1009)
String String (ex : 211-20)
String (ex : 30200)
I'd like to set my index analyzer to get as many documents as possible that could match. Currently, I work like that :
PUT /customers/
{
"mappings":{
"customer":{
"properties":{
"zip-code": {
"type":"string"
"index":"not_analyzed"
}
some string properties ...
}
}
}
When I search a document I'm using that request :
GET /customers/customer/_search
{
"query":{
"prefix":{
"zip-code":"211-20"
}
}
}
That works if you want to search rigourously. But for instance if the zip-code is "200 30", then searching with "200-30" will not give any results.
I'd like to give orders to my index analyser in order to don't have this problem.
Can someone help me ?
Thanks.
P.S. If you want more information, please let me know ;)
As soon as you want to find variations you don't want to use not_analyzed.
Let's try this with a different mapping:
PUT zip
{
"settings": {
"number_of_shards": 1,
"analysis": {
"analyzer": {
"zip_code": {
"tokenizer": "standard",
"filter": [ ]
}
}
}
},
"mappings": {
"_doc": {
"properties": {
"zip": {
"type": "text",
"analyzer": "zip_code"
}
}
}
}
}
We're using the standard tokenizer; strings will be broken up at whitespaces and punctuation marks (including dashes) into tokens. You can see the actual tokens if you run the following query:
POST zip/_analyze
{
"analyzer": "zip_code",
"text": ["8907-1009", "211-20", "30200"]
}
Add your examples:
POST zip/_doc
{
"zip": "8907-1009"
}
POST zip/_doc
{
"zip": "211-20"
}
POST zip/_doc
{
"zip": "30200"
}
Now the query seems to work fine:
GET zip/_search
{
"query": {
"match": {
"zip": "211-20"
}
}
}
This will also work if you just search for "211". However, this might be too lenient, since it will also find "20", "20-211", "211-10",...
What you probably want is a phrase search where all the tokens in your query need to be in the field and also in the right order:
GET zip/_search
{
"query": {
"match_phrase": {
"zip": "211"
}
}
}
Addition:
If the ZIP codes have a hierarchical meaning (if you have "211-20" you want this to be found when searching for "211", but not when searching for "20"), you can use the path_hierarchy tokenizer.
So changing the mapping to this:
PUT zip
{
"settings": {
"number_of_shards": 1,
"analysis": {
"analyzer": {
"zip_code": {
"tokenizer": "zip_tokenizer",
"filter": [ ]
}
},
"tokenizer": {
"zip_tokenizer": {
"type": "path_hierarchy",
"delimiter": "-"
}
}
}
},
"mappings": {
"_doc": {
"properties": {
"zip": {
"type": "text",
"analyzer": "zip_code"
}
}
}
}
}
Using the same 3 documents from above you can use the match query now:
GET zip/_search
{
"query": {
"match": {
"zip": "1009"
}
}
}
"1009" won't find anything, but "8907" or "8907-1009" will.
If you want to also find "1009", but with a lower score, you'll have to analyze the zip code with both variations I have shown (combine the 2 versions of the mapping):
PUT zip
{
"settings": {
"number_of_shards": 1,
"analysis": {
"analyzer": {
"zip_hierarchical": {
"tokenizer": "zip_tokenizer",
"filter": [ ]
},
"zip_standard": {
"tokenizer": "standard",
"filter": [ ]
}
},
"tokenizer": {
"zip_tokenizer": {
"type": "path_hierarchy",
"delimiter": "-"
}
}
}
},
"mappings": {
"_doc": {
"properties": {
"zip": {
"type": "text",
"analyzer": "zip_standard",
"fields": {
"hierarchical": {
"type": "text",
"analyzer": "zip_hierarchical"
}
}
}
}
}
}
}
Add a document with the inverse order to properly test it:
POST zip/_doc
{
"zip": "1009-111"
}
Then search both fields, but boost the one with the hierarchical tokenizer by 3:
GET zip/_search
{
"query": {
"multi_match" : {
"query" : "1009",
"fields" : [ "zip", "zip.hierarchical^3" ]
}
}
}
Then you can see that "1009-111" has a much higher score than "8907-1009".
Create an index:
Here I an using edge_ngram
PUT my_index
{
"settings": {
"analysis": {
"analyzer": {
"my_analyzer": {
"tokenizer": "my_tokenizer"
}
},
"tokenizer": {
"my_tokenizer": {
"type": "edge_ngram",
"min_gram": 3,
"max_gram": 3,
"token_chars": [
"letter",
"digit"
]
}
}
}
},
"mappings": {
"my_type": {
"properties": {
"city": {
"type": "keyword",
"fields": {
"raw": {
"type": "text",
"analyzer": "my_analyzer"
}
}
}
}
}
}
}
POST my_index/my_type/1
{
"text": "2 #Quick Foxes lived and died"
}
POST my_index/my_type/2
{
"text": "2 #Quick Foxes lived died"
}
Now when we search
GET my_index/my_type/_search
{
"query": {
"query_string": {
"default_operator" : "AND",
"query" : "f* d*",
"fields": ["text.raw"]
}
}
}
Only ID 2 should list. But nothing returns.
when you try this
GET my_index/my_type/_search
{
"query": {
"query_string": {
"default_operator" : "AND",
"query" : "f* d*",
"fields": ["text"]
}
}
}
It will return both.
If we have an index with huge data and if we wanted to search wildcards, how we will do it?
single keyword will work, but if we add phrases like which i mentioned in the example, it won't give you any proper result.
To generate a regex expression you can follow these websites:-
Generate regex expression here- http://buildregex.com/
and test your string with expression generated from here https://regex101.com/
I have a document which looks like this
Name
Thomy tyson Olando Magua
Using ngram i was able to acheive the wildcard search so that if i type in omy tyson it can return me the above document pretty much similar to this sql query
select name from table where name like '%omy tyson%'
PUT sample
{
"settings": {
"analysis": {
"analyzer": {
"my_ngram_analyzer": {
"tokenizer": "my_ngram_tokenizer"
}
},
"tokenizer": {
"my_ngram_tokenizer": {
"type": "nGram",
"min_gram": "2",
"max_gram": "15"
}
}
}
},
"mappings": {
"typename": {
"properties": {
"name": {
"type": "string",
"fields": {
"search": {
"type": "string",
"analyzer": "my_ngram_analyzer"
}
}
}
}
}
}
}
PUT sample/typename/2
{
"name": "Thomy tyson Olando Magua"
}
{
"query": {
"bool": {
"should": [
{
"term": {
"name.search": "omy tyson"
}
}
]
}
}
}
Is there a way in elastic search where i can perform wildcard search on 2 different words separated by other words like
select name from table where name like '%omy Magua%'
So in this case i would like to perform partial search on first and fourth word.
Any feedback would be helpfull