I'm using Elasticsearch Phrase Suggester for correcting user's misspellings. everything is working as I expected unless user enters a query which it's first letter is misspelled. At this situation phrase suggester returns nothing or returns unexpected results.
My query for suggestion:
{
"suggest": {
"text": "user_query",
"simple_phrase": {
"phrase": {
"field": "title.phrase",,
"collate": {
"query": {
"inlile" : {
"bool": {
"should": [
{ "match": {"title": "{{suggestion}}"}},
{ "match": {"participants": "{{suggestion}}"}}
]
}
}
}
}
}
}
}
}
Example when first letter is misspelled:
"simple_phrase" : [
{
"text" : "گاشانچی",
"offset" : 0,
"length" : 11,
"options" : [ {
"text" : "گارانتی",
"score" : 0.00253151
}]
}
]
Example when fifth letter is misspelled:
"simple_phrase" : [
{
"text" : "کاشاوچی",
"offset" : 0,
"length" : 11,
"options" : [ {
"text" : "کاشانچی",
"score" : 0.1121
},
{
"text" : "کاشانجی",
"score" : 0.0021
},
{
"text" : "کاشنچی",
"score" : 0.0020
}]
}
]
I expect that these two misspelled queries have same suggestions(my expected suggestions are second one). what is wrong?
P.S: I'm using this feature for Persian language.
I have solution for your problem, only need to add some fields in your schema.
P.S: I don't have that much expertise in elasticsearch but I have solved same problem using solr, you can implement same way in elasticSearch too
Create new ngram field and copy all you title name in ngram field.
When you fire any query for missspell word and you get blank result then split
the word and again fire the same query you will get results as expected.
Example : Suppose user searching for word Akshay but type it as Skshay, then
create query in below way you will get results as expected hopefully.
I am here giving you solr example same way you can achieve it using
elasticsearch.
**(ngram:"skshay" OR ngram:"sk" OR ngram:"ks" OR ngram:"sh" OR ngram:"ha" ngram:"ay")**
We have split the word sequence wise and fire query on field ngram.
Hope it will help you.
From Elasticsearch doc:
https://www.elastic.co/guide/en/elasticsearch/reference/6.8/search-suggesters-phrase.html
prefix_length
The number of minimal prefix characters that must match in order be a
candidate suggestions. Defaults to 1. Increasing this number improves
spellcheck performance. Usually misspellings don’t occur in the
beginning of terms. (Old name "prefix_len" is deprecated)
So by default phrase-suggester assumes that the first character is correct because the default value for prefix_length is 1.
Note: setting this value to 0 is not a good way because this will have performance implications.
You need to use the reverse analyzer
I explained it in this post so please go and check my answer
Elasticsearch spell check suggestions even if first letter missed
And regarding the duplicates, you can use
skip_duplicates
Whether duplicate suggestions should be filtered out (defaults to
false).
Related
I know that Elastic have "keyword" type in order to find something with exact matching. Ex:
"address": { "type": "keyword"}
That's cool. exact matching works!
but I would like to have both "exact matching" and "sub-string" matching. So I decided to create the following mapping:
"address": { "type": "text" , "index": true }
Problem
If I have "text" type, how can I search exact matching string? (not sub-string). I've tried several ways but does not works:
GET testing_index/_search
{
"query" : {
"constant_score" : {
"filter" : {
"term" : {
"address" : "washington"
}
}
}
}
}
or
GET testing_index/_search
{
"query": {
"match": {
"address" : "washington"
}
}
}
I need just something universal mapping:
to find exact string
to find sub-strings
I hope elastic can do this.
By default, text fields use the default analyzer, which drops most punctuation, breaks up text into individual words, and lower cases them. For instance, the standard analyzer would turn the string “Quick Brown Fox!” into the terms [quick, brown, fox]. As you can imagine, this makes it difficult to write an exact match query against the text field. For your use case, I suggest one of 2 options:
store as keyword, and accomplish sub-string-like matching using wildcard or fuzzy queries. Wildcard queries, in particular queries with a leading wildcard, are notoriously slow, so proceed with caution.
store the field twice: one as keyword and one as text. Obvious downside here is bloating the size of the index.
For more background, see the "Term Query" Elasticsearch documentation, and in particular the section on "Why doesn’t the term query match my document?": https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-term-query.html
I'm using text suggester of elasticsearch. My index contains a document which has a filed name and its value is crick
{
"suggest": {
"my-suggest" : {
"text" : "crick",
"term" : {
"field" : "name",
"sort": "score"
}
}
}
}
it return no match, it only returns a value if there is a misspelled
if I pass the exact text it return nothing any idea !!
You are not using suggest_mode
The suggest mode controls what suggestions are included or controls for what suggest text terms, suggestions should be suggested. Three possible values can be specified:
missing: Only provide suggestions for suggest text terms that are not in the index. This is the default.
popular: Only suggest suggestions that occur in more docs then the original suggest text term.
always: Suggest any matching suggestions based on terms in the suggest text.
Since you haven't mentioned suggest_mode it is picking missing by default.
use this settings
{
"suggest": {
"my-suggest" : {
"text" : "crick",
"term" : {
"field" : "name",
"sort": "score",
"suggest_mode": "always"
}
}
}
}
I'm using a completion suggester in Elasticsearch on a single field. The type contains documents of several users. Is there a way to limit the returned suggestions to documents that match a specific query?
I'm currently using this query:
{
"name" : {
"text" : "Peter",
"completion" : {
"field" : "name_suggest"
}
}
}
Is there a way to combine this query with a different one, e.g.
{
"query":{
"term" : {
"user_id" : "590c5bd2819c3e225c990b48"
}
}
}
Have a look at the context suggester, which is just a specialized completion suggester with filtering capabilities - however this is still not a regular query filter, just keep that in mind.
You can specify both the query and the suggester in your query, like this:
{
"query":{
"term" : {
"user_id" : "590c5bd2819c3e225c990b48"
}
},
"suggest": {
"name" : {
"text" : "Peter",
"completion" : {
"field" : "name_suggest"
}
}
}
}
I have a similar use case, and I've posted my question on elastic search forum, see here
From what I've read so far, I don't think with completion suggester you can limit documents. They essentially create a finite state transducer (prefix tree) at index time, this makes it fast but you lose the flexibility of filtering on additional fields. I don't think context suggester would work in your case (let me know if i am wrong), because the cardinality of user_id is very high.
I think edge-ngrams partial matching is more flexible and might actually work in your use case.
Let me know what you end up implementing.
Having spent ages reading the docs and various websites. I don't understand how one is supposed to use the phrase suggester to influence the results of a query. My understanding was that running the following query and suggester, the results from the suggester would be used for the query.
POST test/test/_search
{
"query": {
"multi_match": {
"query": "anti-inefffective",
"fields": ["*#value"]
}
},
"highlight" : {
"fields" : {
"*#value" : {
"pre_tags" : ["<mark>"],
"post_tags" : ["</mark>"]
}
}
},
"suggest" : {
"text" : "anti-inefffective"",
"simple_phrase" : {
"phrase" : {
"analyzer" : "default",
"field" : "_all",
"size" : 1,
"real_word_error_likelihood" : 0.95,
"max_errors" : 0.5,
"gram_size" : 2,
"direct_generator" : [ {
"field" : "_all",
"suggest_mode" : "always",
"min_word_length" : 1
} ],
"highlight": {
"pre_tag": "<em>",
"post_tag": "</em>"
}
}
}
}
}
How can I get the results of the suggester to be used for the query term all within a json request? All the examples I've seen have the phrase suggester executed after the query which seems bizarre to me. The only way I can see to do this would be to run a phrase suggester query then extract the value and then add it programatically to a query and then run the query with the suggested text.
In other words I would like to be able to do what Google does, if you type "cancerous tummour" in Google it returns results for "cancerous tumour" but gives you the option to use the incorrect phrase but the corrected phrase is used automatically for the query.
You should take a look at the collate+query option of the Phrase Suggester when used together with the confidence parameter.
The phrase suggester workflow looks like this:
Suggests candidate terms for cancerous and tummour based on
the parameters passed to the candidate generator section.
Generates a number of 'mad-lib' phrase suggestions using the term
candidates, combining the word-frequency of the phrase terms to
generate a score for each suggestion.
With the collate/match option, actually runs each candidate
inside a query template (defined by you, the query author) so
that queries w/zero-results can be discarded.
To emulate the Google functionality you describe, when you run the user's query you'd also:
Use the phrase suggester to generate the #1 "size": 1, top-scoring, collated/non-zero results phrase suggestion for the original user input query.
With the default "confidence": 1.0 the phrase suggester will only give you a phrase suggestion the suggester considers to be of higher confidence compared to the original user input query.
When you see the (higher-confidence) suggestion come back alongside the original query result, your client could decide to take the suggestion and execute the suggested query in place of the original query (while preserving the original query-text to display as a fallback search option).
Short answer: There's no option to automatically use the top suggestion within Elasticsearch as the query text. But you could build that in your search client using the functionality currently provided by the phrase suggester.
I am facing a strange issue in elasticsearch query. I don't know much about elasticsearch. My query is:
{
"query":
{
"bool":
{
"must":
[
{
"text":
{
"countryCode2":"DE"
}
}
],
"must_not":[],
"should":[]
}
},"from":0,"size":1,"sort":[],"facets":{}
}
The issues is for "DE". It is giving me results but for "BE" or "IN" it returns empty result.
You are indexing using the default mapping, which by default removes english stopwords. The country codes "IN", "BE", and many more are stopwords which don't even get indexed, therefore it's not possible to have matching documents, nor get back those country codes when faceting on that field.
The solution is to reindex after having submitted your own mapping for the country code field:
{
"your_type_name" : {
"country" : {
"type" : "string", "index" : "not_analyzed"
}
}
}
If you already tried to do this but nothing changed, the mapping didn't get submitted properly. I would suggest to double check that its json structure is correct and that you can actually get it back using the get mapping api.
As this is a common problem the defaults are probably going to change in the future to be less intrusive and avoid applying any language dependent text analysis.