Elasticsearch case insesitive wildcard search with spaced words

Question

field priorityName is of search_as_you_type dataType.

My use case is like I want to search the document with the following words:---

1. "let's" -> should give both the results
2. "DOING" -> should give both the results
3. "are you" -> should give both the results
4. "Are You" -> should give both the results
5. "you do" (short of you doing)-> should give both the results
6. "re you" -> should give both the results

Out of 6, only the first 5 are giving me the desired result using multi_match. how can I have the 6th use case where we can have incomplete word not starting with the first characters.

Sampple docs

        "_index": "priority",
        "_type": "_doc",
        "_id": "vaCI_HAB31AaC-t5TO9H",
        "_score": 1,
        "_source": { - 
          "priorityName": "What are you doing along Let's Go out"
        }
      },
      { - 
        "_index": "priority",
        "_type": "_doc",
        "_id": "vqCQ_HAB31AaC-t5wO8m",
        "_score": 1,
        "_source": { - 
          "priorityName": "what are you doing along let's go for shopping"
        }
      }
    ]
  }

Answer 1

For last search re you, you need infix tokens and by default its not included in the search_as_you_type datatype. I would suggest you to create a custom analyzer which will create infix tokens and allow you to match all your 6 queries.

I have already created a custom analyzer and test it with your sample documents and all 6 queries are giving both the sample results.

Index mapping

POST /infix-index

{
    "settings": {
        "max_ngram_diff": 50,
        "analysis": {
            "filter": {
                "autocomplete_filter": {
                    "type": "ngram",
                    "min_gram": 1,
                    "max_gram": 8
                }
            },
            "analyzer": {
                "autocomplete_analyzer": {
                    "type": "custom",
                    "tokenizer": "whitespace",
                    "filter": [
                        "lowercase",
                        "autocomplete_filter"
                    ]
                },
                "lowercase_analyzer": {
                    "type": "custom",
                    "tokenizer": "whitespace",
                    "filter": [
                        "lowercase"
                    ]
                }
            }
        }
    },
    "mappings": {
        "properties": {
            "priorityName": {
                "type": "text",
                "analyzer": "autocomplete_analyzer",
                "search_analyzer": "standard" --> note this
            }
        }
    }
}

Index your sample docs

{
  "priorityName" : "What are you doing along Let's Go out"
}

{
  "priorityName" : "what are you doing along let's go for shopping"
}

Search query for last re you

{
    "query": {
        "match" : {
            "priorityName" : "re you"
        }
    }
}

And result

"hits": [
      {
        "_index": "ngram",
        "_type": "_doc",
        "_id": "1",
        "_score": 1.4652853,
        "_source": {
          "priorityName": "What are you doing along Let's Go out"
        }
      },
      {
        "_index": "ngram",
        "_type": "_doc",
        "_id": "2",
        "_score": 1.4509768,
        "_source": {
          "priorityName": "what are you doing along let's go for shopping"
        }
      }

Other queries also returned me both the documents but not including them to shorten the length of this answer.

Note: Below are some important links to understand the answer in detail.

https://www.elastic.co/guide/en/elasticsearch/reference/current/search-analyzer.html

https://www.elastic.co/guide/en/elasticsearch/reference/current/analysis-ngram-tokenizer.html