Search-as-you-type field type

The search_as_you_type field type is a text-like field that is optimized to provide out-of-the-box support for queries that serve an as-you-type completion use case. It creates a series of subfields that are analyzed to index terms that can be efficiently matched by a query that partially matches the entire indexed text value. Both prefix completion (i.e matching terms starting at the beginning of the input) and infix completion (i.e. matching terms at any position within the input) are supported.

When adding a field of this type to a mapping

resp = client.indices.create(
    index="my-index-000001",
    mappings={
        "properties": {
            "my_field": {
                "type": "search_as_you_type"
            }
        }
    },
)
print(resp)

response = client.indices.create(
  index: 'my-index-000001',
  body: {
    mappings: {
      properties: {
        my_field: {
          type: 'search_as_you_type'
        }
      }
    }
  }
)
puts response

const response = await client.indices.create({
  index: "my-index-000001",
  mappings: {
    properties: {
      my_field: {
        type: "search_as_you_type",
      },
    },
  },
});
console.log(response);

PUT my-index-000001
{
  "mappings": {
    "properties": {
      "my_field": {
        "type": "search_as_you_type"
      }
    }
  }
}

This creates the following fields

The size of shingles in subfields can be configured with the max_shingle_size mapping parameter. The default is 3, and valid values for this parameter are integer values 2 - 4 inclusive. Shingle subfields will be created for each shingle size from 2 up to and including the max_shingle_size. The my_field._index_prefix subfield will always use the analyzer from the shingle subfield with the max_shingle_size when constructing its own analyzer.

Increasing the max_shingle_size will improve matches for queries with more consecutive terms, at the cost of larger index size. The default max_shingle_size should usually be sufficient.

The same input text is indexed into each of these fields automatically, with their differing analysis chains, when an indexed document has a value for the root field my_field.

resp = client.index(
    index="my-index-000001",
    id="1",
    refresh=True,
    document={
        "my_field": "quick brown fox jump lazy dog"
    },
)
print(resp)

response = client.index(
  index: 'my-index-000001',
  id: 1,
  refresh: true,
  body: {
    my_field: 'quick brown fox jump lazy dog'
  }
)
puts response

const response = await client.index({
  index: "my-index-000001",
  id: 1,
  refresh: "true",
  document: {
    my_field: "quick brown fox jump lazy dog",
  },
});
console.log(response);

PUT my-index-000001/_doc/1?refresh
{
  "my_field": "quick brown fox jump lazy dog"
}

The most efficient way of querying to serve a search-as-you-type use case is usually a multi_match query of type bool_prefix that targets the root search_as_you_type field and its shingle subfields. This can match the query terms in any order, but will score documents higher if they contain the terms in order in a shingle subfield.

resp = client.search(
    index="my-index-000001",
    query={
        "multi_match": {
            "query": "brown f",
            "type": "bool_prefix",
            "fields": [
                "my_field",
                "my_field._2gram",
                "my_field._3gram"
            ]
        }
    },
    highlight={
        "fields": {
            "my_field": {
                "matched_fields": [
                    "my_field._index_prefix"
                ]
            }
        }
    },
)
print(resp)

GET my-index-000001/_search
{
  "query": {
    "multi_match": {
      "query": "brown f",
      "type": "bool_prefix",
      "fields": [
        "my_field",
        "my_field._2gram",
        "my_field._3gram"
      ]
    }
  },
  "highlight": {
    "fields": {
      "my_field": {
        "matched_fields": ["my_field._index_prefix"] 
      }
    }
  }
}

{
  "took" : 44,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 1,
      "relation" : "eq"
    },
    "max_score" : 0.8630463,
    "hits" : [
      {
        "_index" : "my-index-000001",
        "_id" : "1",
        "_score" : 0.8630463,
        "_source" : {
          "my_field" : "quick brown fox jump lazy dog"
        },
        "highlight": {
          "my_field": [
            "quick <em>brown fox jump lazy</em> dog"
          ]
        }
      }
    ]
  }
}

To search for documents that strictly match the query terms in order, or to search using other properties of phrase queries, use a match_phrase_prefix query on the root field. A match_phrase query can also be used if the last term should be matched exactly, and not as a prefix. Using phrase queries may be less efficient than using the match_bool_prefix query.

resp = client.search(
    index="my-index-000001",
    query={
        "match_phrase_prefix": {
            "my_field": "brown f"
        }
    },
)
print(resp)

response = client.search(
  index: 'my-index-000001',
  body: {
    query: {
      match_phrase_prefix: {
        my_field: 'brown f'
      }
    }
  }
)
puts response

const response = await client.search({
  index: "my-index-000001",
  query: {
    match_phrase_prefix: {
      my_field: "brown f",
    },
  },
});
console.log(response);

GET my-index-000001/_search
{
  "query": {
    "match_phrase_prefix": {
      "my_field": "brown f"
    }
  }
}