I'm using the code below and it does not give auto-suggestion as curd when i type "cu"
But it does match the document with yogurt which is correct.
How can I get both auto-complete for synonym words and document match for the same?
PUT products
{
"settings": {
"index": {
"analysis": {
"analyzer": {
"synonym_analyzer": {
"tokenizer": "standard",
"filter": [
"lowercase",
"synonym_graph"
]
}
},
"filter": {
"synonym_graph": {
"type": "synonym_graph",
"synonyms": [
"yogurt, curd, dahi"
]
}
}
}
}
}
}
PUT products/_mapping
{
"properties": {
"description": {
"type": "text",
"analyzer": "synonym_analyzer"
}
}
}
POST products/_doc
{
"description": "yogurt"
}
GET products/_search
{
"query": {
"match": {
"description": "cu"
}
}
}
When you provide a list of synonyms in a synonym_graph filter it simply means that ES will treat any of the synonyms interchangeably. But when they're analyzed via the standard analyzer, only full-word tokens will be produced:
POST products/_analyze?filter_path=tokens.token
{
"text": "yogurt",
"field": "description"
}
yielding:
{
"tokens" : [
{
"token" : "curd"
},
{
"token" : "dahi"
},
{
"token" : "yogurt"
}
]
}
As such, a regular match_query won't cut it here because the standard analyzer hasn't provided it with enough context in terms of matchable substrings (n-grams).
In the meantime you can replace match with match_phrase_prefix which does exactly what you're after -- match an ordered sequence of characters while taking into account the synonyms:
GET products/_search
{
"query": {
"match_phrase_prefix": {
"description": "cu"
}
}
}
But that, as the query name suggests, is only going to work for prefixes. If you fancy an autocomplete that suggests terms regardless of where the substring matches occur, have a look at my other answer where I talk about leveraging n-grams.
Related
I'm using elasticsearch 7.14 and I want to perform a query using a custom analyzer. This is the index:
PUT /my-index-001
{
"settings": {
"index": {
"number_of_shards": 3,
"number_of_replicas": 0
},
"analysis": {
"analyzer": {
"alphanumeric_only_analyzer": {
"type": "custom",
"tokenizer": "standard",
"char_filter": [
"alphanumeric_only_filter"
],
"filter": [
"lowercase"
]
}
},
"char_filter": {
"alphanumeric_only_filter": {
"type": "pattern_replace",
"pattern": "[^A-Za-z0-9]",
"replacement": ""
}
}
}
},
"mappings": {
"properties": {
"myField": {
"type": "text",
"analyzer": "alphanumeric_only_analyzer",
"search_analyzer": "alphanumeric_only_analyzer"
}
}
}
}
And 2 documents to test the queries:
POST /my-index-001/_doc
{
"myField":"asd-9887"
}
POST /my-index-001/_doc
{
"myField":"asd 9887"
}
Checking the analyzer, it works as expected, resulting the token "asd9887"
POST my-index-001/_analyze
{
"analyzer": "alphanumeric_only_analyzer",
"text": "aSd 9887"
}
Since everything is there and looks fine, let's start querying:
Query1: This finds both documents:
GET /my-index-001/_search
{
"query": {
"term": {
"myField": "asd9887"
}
}
}
Query2: This doesn't find any document
GET /my-index-001/_search
{
"query": {
"term": {
"myField": "asd 9887"
}
}
}
Query3: This finds both documents, but I had to inform which analyser to use:
GET /my-index-001/_search
{
"query": {
"match": {
"myField": {
"query": "asd 9887",
"analyzer": "alphanumeric_only_analyzer"
}
}
}
}
Why should I be required to do it this way, since I created the mapping informing search_analyzer as alphanumeric_only_analyzer?
There is a way to make Query2 work as is? I don't want my users having to know analyzer names, as well as I want them to be able to find both documents when querying any value that, after analyzed, matches the analyzed document value.
Use match query instead of term query
The term query does not analyze the search term. The term query only searches for the exact term you provide. So it is searching for "asd 9887" in your tokens.
Match query analyzes search term using same analyzer as field resulting in creation of same tokens. So "asd 9887" is converted to "asd9887" while searching
I have the following string "Word1 Word2 StopWord1 StopWord2 Word3 Word4".
When I query for this string using ["bool"]["must"]["match"], I would like to return all text that matches "Word1Word2" and/or "Word3Word4".
I have created an analyzer that I would like to use for indexing and searching.
Using analyze API, I have confirmed that indexing is being done correctly. The shingles returned are "Word1Word2" and "Word3Word4"
I want to query so that text matching "Word1Word2" and/or "Word3Word4" are returned. How can I do this dynamically - meaning, I don't know up front how many shingles will be generated, so I don't know how many match_phrase to code up in a query.
"should":[
{ "match_phrase" : {"content": phrases[0]}},
{ "match_phrase" : {"content": phrases[1]}}
]
To query for shingles(and unigrams), you could set up your mappings to handle them cleanly in separate fields. In the example below, the field "shingles" will be used to analyze and retrieve shingles, while the implicit field will be used to handle unigrams.
PUT /my_index
{
"settings": {
"number_of_shards": 1,
"analysis": {
"filter": {
"my_shingle_filter": {
"type": "shingle",
"min_shingle_size": 2,
"max_shingle_size": 2,
"output_unigrams": false
}
},
"analyzer": {
"my_shingle_analyzer": {
"type": "custom",
"tokenizer": "standard",
"filter": [
"lowercase",
"my_shingle_filter"
]
}
}
}
}
}
PUT /my_index/_mapping/my_type
{
"my_type": {
"properties": {
"title": {
"type": "string",
"fields": {
"shingles": {
"type": "string",
"analyzer": "my_shingle_analyzer"
}
}
}
}
}
}
GET /my_index/my_type/_search
{
"query": {
"bool": {
"must": {
"match": {
"title": "<your query string>"
}
},
"should": {
"match": {
"title.shingles": "<your query string"
}
}
}
}
}
Ref. Elasticsearch: The Definitive Guide....
For example, I have some titles data in Elasticsearch likes this,
gamexxx_nightmare,
gamexxx_little_guy
Then I input
game => search out gamexxx_nightmare and gamexxx_little_guy
little guy => search out gamexxx_little_guy ?
first I think I will use a wildcard to make game match gamexxx, the second it is fulltext search?
How to combine them in one DSL??
While Jaspreet's answer is right but doesn't combine both the requirements in one query DSL as asked by OP in his question How to combine them in one DSL??.
It's an enhancement to Jaspreet's solution as I am also not using the wild-card and even avoiding the n-gram analyzer which is too costly(increases the index size) and requires re-indexing if requirement changes.
One Search query to combine both the requirement can be done as below:
Index mapping
{
"settings": {
"analysis": {
"analyzer": {
"my_analyzer": {
"tokenizer": "standard",
"char_filter": [
"replace_underscore" -->note this
]
}
},
"char_filter": {
"replace_underscore": {
"type": "mapping",
"mappings": [
"_ => \\u0020"
]
}
}
}
},
"mappings": {
"properties": {
"title": {
"type": "text",
"analyzer" : "my_analyzer"
}
}
}
}
Index your sample docs
{
"title" : "gamexxx_little_guy"
}
And
{
"title" : "gamexxx_nightmare"
}
Single Search query
{
"query": {
"bool": {
"must": [ --> note this
{
"bool": {
"must": [
{
"prefix": {
"title": {
"value": "game"
}
}
}
]
}
},
{
"bool": {
"must": [
{
"match": {
"title": {
"query": "little guy"
}
}
}
]
}
}
]
}
}
}
Result
{
"_index": "so-46873023",
"_type": "_doc",
"_id": "2",
"_score": 2.2814486,
"_source": {
"title": "gamexxx_little_guy"
}
}
Important points:
The first part of the query is prefix query, which would match the game in both the documents. (This would avoid costly regex).
The second part is allowing the full-text search, to enable this, I used custom analyzer which replaces the _ with whitespace, so you don't need expensive (n-grams in index) and simple match query would fetch the results.
Above query, returns result matching both the criteria, you can change the high level, bool clause to should from must if, you want to return matching any criteria.
NGrams have better performance than wildcards. For wild card all documents have to be scanned to see which match the pattern. Ngrams break a text in small tokens. Ex Quick Foxes will stored as [ Qui, uic, ick, Fox, oxe, xes ] depending on min_gram and max_gram size.
PUT my_index
{
"settings": {
"analysis": {
"analyzer": {
"my_analyzer": {
"tokenizer": "my_tokenizer"
}
},
"tokenizer": {
"my_tokenizer": {
"type": "ngram",
"min_gram": 3,
"max_gram": 3,
"token_chars": [
"letter",
"digit"
]
}
}
}
},
"mappings": {
"properties": {
"text":{
"type": "text",
"analyzer": "my_analyzer"
}
}
}
}
Query
GET my_index/_search
{
"query": {
"match": {
"text": "little guy"
}
}
}
If you want to go with wildcard only then you can search on not_analyzed string. This will handle spaces between words
"wildcard": {
"text.keyword": {
"value": "*gamexxx*"
}
}
I have a problem index which has multiple fields e.g tags (comma separated string of tags), author, tester. I am creating a global search where problems can be searched by all these fields at once.
I am using boolean query
e.g
{
"query": {
"bool": {
"must": [{
"match": {
"author": "author_username"
}
},
{
"match": {
"tester": "tester_username"
}
},
{
"match": {
"tags": "<tag1,tag2>"
}
}
]
}
}
}
Without Analyzer I am able to get the results but it uses space as separator e.g python 3 is getting searched as python or 3.
But I wanted to search Python 3 as single query. So, I have created an analyzer for tags so that every comma-separated tag is considered as one, not by standard whitespace.
{
"settings": {
"analysis": {
"analyzer": {
"my_analyzer": {
"tokenizer": "my_tokenizer"
}
},
"tokenizer": {
"my_tokenizer": {
"type": "pattern",
"pattern": ","
}
}
}
},
"mappings": {
"properties": {
"tags": {
"type": "text",
"analyzer": "my_analyzer",
"search_analyzer": "standard"
}
}
}
}
But now I am not getting any results. Please let me know what I am missing here. I am not able to find the use of analyzer in compound queries in the documentation: https://www.elastic.co/guide/en/elasticsearch/reference/current/compound-queries.html
Adding an example:
{
"query": {
"bool": {
"must": [{
"match": {
"author": "test1"
}
},
{
"match": {
"tester": "test2"
}
},
{
"match": {
"tags": "test3, abc 4"
}
}
]
}
}
}
Results should match all the fields but for the tags field there should be a union of tags and query should be comma-separated not by space. i.e query should match test and abc 4 but above query searching for test, abc and 4.
You need to either remove search_analyzer from your mapping or pass my_analyzer in match query
GET tags/_search
{
"query": {
"bool": {
"must": [
{
"match": {
"tags": {
"query": "python 3",
"analyzer": "my_analyzer" --> by default search analyzer is used
}
}
}
]
}
}
}
By default, queries will use the analyzer defined in the field mapping, but this can be overridden with the search_analyzer setting.
I have an Elasticsearch instance full of documents containing movie and series titles.
When I run this:
{
"query": {
"bool": {
"must": [
{
"multi_match": {
"fields": [
"Name^2",
"SeriesName^1.5",
"Description"
],
"fuzziness": "AUTO",
"prefix_length": 2,
"query": "game"
}
}
]
}
}
}
... I get titles like "The big game", "Hunger games", "War game", etc.
However, I would like to get titles starting with "game" BEFORE titles just containing "game".
When a user searches for "game", they expect titles like "Game of Thrones" and "Game change", before "The imitation game".
How can I make this more precise? Thank you!
Try something like below :
{ "query": {
"prefix" : { "Name" : "game" }
}
}
Please refer the documentation for the same Elasticsearch Documentation
To do this your field/property have to be tokenized as a keyword, see query below. One can also add an additional lowercase filter in mapping for your field/property.
{
"settings": {
"index": {
"analysis": {
"analyzer": {
"analyzer_startswith": {
"tokenizer": "keyword",
"filter": "lowercase"
}
}
}
}
},
"mappings": {
"test_index": {
"properties": {
"Name": {
"search_analyzer": "analyzer_startswith",
"index_analyzer": "analyzer_startswith",
"type": "string"
}
}
}
}
}