Elasticsearch multi-word, multi-field search with analyzers - elasticsearch

Question

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!

Answer 1

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.