[[phrase-suggester]]
==== Phrase Suggester

The `term` suggester provides a very convenient API to access word
alternatives on a per token basis within a certain string distance. The API
allows accessing each token in the stream individually while
suggest-selection is left to the API consumer. Yet, often pre-selected
suggestions are required in order to present to the end-user. The
`phrase` suggester adds additional logic on top of the `term` suggester
to select entire corrected phrases instead of individual tokens weighted
based on `ngram-language` models. In practice this suggester will be
able to make better decisions about which tokens to pick based on
co-occurrence and frequencies.

===== API Example

In general the `phrase` suggester requires special mapping up front to work.
The `phrase` suggester examples on this page need the following mapping to
work. The `reverse` analyzer is used only in the last example.

[source,console]
--------------------------------------------------
PUT test
{
  "settings": {
    "index": {
      "number_of_shards": 1,
      "analysis": {
        "analyzer": {
          "trigram": {
            "type": "custom",
            "tokenizer": "standard",
            "filter": ["lowercase","shingle"]
          },
          "reverse": {
            "type": "custom",
            "tokenizer": "standard",
            "filter": ["lowercase","reverse"]
          }
        },
        "filter": {
          "shingle": {
            "type": "shingle",
            "min_shingle_size": 2,
            "max_shingle_size": 3
          }
        }
      }
    }
  },
  "mappings": {
    "properties": {
      "title": {
        "type": "text",
        "fields": {
          "trigram": {
            "type": "text",
            "analyzer": "trigram"
          },
          "reverse": {
            "type": "text",
            "analyzer": "reverse"
          }
        }
      }
    }
  }
}
POST test/_doc?refresh=true
{"title": "noble warriors"}
POST test/_doc?refresh=true
{"title": "nobel prize"}
--------------------------------------------------
// TESTSETUP

Once you have the analyzers and mappings set up you can use the `phrase`
suggester in the same spot you'd use the `term` suggester:

[source,console]
--------------------------------------------------
POST test/_search
{
  "suggest": {
    "text": "noble prize",
    "simple_phrase": {
      "phrase": {
        "field": "title.trigram",
        "size": 1,
        "gram_size": 3,
        "direct_generator": [ {
          "field": "title.trigram",
          "suggest_mode": "always"
        } ],
        "highlight": {
          "pre_tag": "<em>",
          "post_tag": "</em>"
        }
      }
    }
  }
}
--------------------------------------------------

The response contains suggestions scored by the most likely spelling correction first. In this case we received the expected correction "nobel prize".

[source,console-result]
--------------------------------------------------
{
  "_shards": ...
  "hits": ...
  "timed_out": false,
  "took": 3,
  "suggest": {
    "simple_phrase" : [
      {
        "text" : "noble prize",
        "offset" : 0,
        "length" : 11,
        "options" : [ {
          "text" : "nobel prize",
          "highlighted": "<em>nobel</em> prize",
          "score" : 0.48614594
        }]
      }
    ]
  }
}
--------------------------------------------------
// TESTRESPONSE[s/"_shards": .../"_shards": "$body._shards",/]
// TESTRESPONSE[s/"hits": .../"hits": "$body.hits",/]
// TESTRESPONSE[s/"took": 3,/"took": "$body.took",/]

===== Basic Phrase suggest API parameters

[horizontal]
`field`::
    The name of the field used to do n-gram lookups for the
    language model, the suggester will use this field to gain statistics to
    score corrections. This field is mandatory.

`gram_size`::
    Sets max size of the n-grams (shingles) in the `field`.
    If the field doesn't contain n-grams (shingles), this should be omitted
    or set to `1`. Note that Elasticsearch tries to detect the gram size
    based on the specified `field`. If the field uses a `shingle` filter, the
    `gram_size` is set to the `max_shingle_size` if not explicitly set.

`real_word_error_likelihood`::
    The likelihood of a term being
    misspelled even if the term exists in the dictionary. The default is
    `0.95`, meaning 5% of the real words are misspelled.


`confidence`::
    The confidence level defines a factor applied to the
    input phrases score which is used as a threshold for other suggest
    candidates. Only candidates that score higher than the threshold will be
    included in the result. For instance a confidence level of `1.0` will
    only return suggestions that score higher than the input phrase. If set
    to `0.0` the top N candidates are returned. The default is `1.0`.

`max_errors`::
    The maximum percentage of the terms 
    considered to be misspellings in order to form a correction. This method
    accepts a float value in the range `[0..1)` as a fraction of the actual
    query terms or a number `>=1` as an absolute number of query terms. The
    default is set to `1.0`, meaning only corrections with
    at most one misspelled term are returned. Note that setting this too high
    can negatively impact performance. Low values like `1` or `2` are recommended;
    otherwise the time spend in suggest calls might exceed the time spend in
    query execution.

`separator`::
    The separator that is used to separate terms in the
    bigram field. If not set the whitespace character is used as a
    separator.

`size`::
    The number of candidates that are generated for each
    individual query term. Low numbers like `3` or `5` typically produce good
    results. Raising this can bring up terms with higher edit distances. The
    default is `5`.

`analyzer`::
    Sets the analyzer to analyze to suggest text with.
    Defaults to the search analyzer of the suggest field passed via `field`.

`shard_size`::
    Sets the maximum number of suggested terms to be
    retrieved from each individual shard. During the reduce phase, only the
    top N suggestions are returned based on the `size` option. Defaults to
    `5`.

`text`::
    Sets the text / query to provide suggestions for.

`highlight`::
    Sets up suggestion highlighting. If not provided then
    no `highlighted` field is returned. If provided must
    contain exactly `pre_tag` and `post_tag`, which are
    wrapped around the changed tokens. If multiple tokens
    in a row are changed the entire phrase of changed tokens
    is wrapped rather than each token.

`collate`::
    Checks each suggestion against the specified `query` to prune suggestions
    for which no matching docs exist in the index. The collate query for a
    suggestion is run only on the local shard from which the suggestion has
    been generated from. The `query` must be specified and it can be templated.
    See <<search-template>>.
    The current suggestion is automatically made available as the `{{suggestion}}`
    variable, which should be used in your query. You can still specify
    your own template `params` -- the `suggestion` value will be added to the
    variables you specify. Additionally, you can specify a `prune` to control
    if all phrase suggestions will be returned; when set to `true` the suggestions
    will have an additional option `collate_match`, which will be `true` if
    matching documents for the phrase was found, `false` otherwise.
    The default value for `prune` is `false`.

[source,console]
--------------------------------------------------
POST test/_search
{
  "suggest": {
    "text" : "noble prize",
    "simple_phrase" : {
      "phrase" : {
        "field" :  "title.trigram",
        "size" :   1,
        "direct_generator" : [ {
          "field" :            "title.trigram",
          "suggest_mode" :     "always",
          "min_word_length" :  1
        } ],
        "collate": {
          "query": { <1>
            "source" : {
              "match": {
                "{{field_name}}" : "{{suggestion}}" <2>
              }
            }
          },
          "params": {"field_name" : "title"}, <3>
          "prune": true <4>
        }
      }
    }
  }
}
--------------------------------------------------

<1> This query will be run once for every suggestion.
<2> The `{{suggestion}}` variable will be replaced by the text
    of each suggestion.
<3> An additional `field_name` variable has been specified in
    `params` and is used by the `match` query.
<4> All suggestions will be returned with an extra `collate_match`
    option indicating whether the generated phrase matched any
    document.

===== Smoothing Models

The `phrase` suggester supports multiple smoothing models to balance
weight between infrequent grams (grams (shingles) are not existing in
the index) and frequent grams (appear at least once in the index). The
smoothing model can be selected by setting the `smoothing` parameter
to one of the following options. Each smoothing model supports specific
properties that can be configured.

[horizontal]
`stupid_backoff`::
    A simple backoff model that backs off to lower
    order n-gram models if the higher order count is `0` and discounts the
    lower order n-gram model by a constant factor. The default `discount` is
    `0.4`. Stupid Backoff is the default model.

`laplace`::
    A smoothing model that uses an additive smoothing where a
    constant (typically `1.0` or smaller) is added to all counts to balance
    weights. The default `alpha` is `0.5`.

`linear_interpolation`::
    A smoothing model that takes the weighted
    mean of the unigrams, bigrams, and trigrams based on user supplied
    weights (lambdas). Linear Interpolation doesn't have any default values.
    All parameters (`trigram_lambda`, `bigram_lambda`, `unigram_lambda`)
    must be supplied.

[source,console]
--------------------------------------------------
POST test/_search
{
  "suggest": {
    "text" : "obel prize",
    "simple_phrase" : {
      "phrase" : {
        "field" : "title.trigram",
        "size" : 1,
        "smoothing" : {
          "laplace" : {
            "alpha" : 0.7
          }
        }
      }
    }
  }
}
--------------------------------------------------

===== Candidate Generators

The `phrase` suggester uses candidate generators to produce a list of
possible terms per term in the given text. A single candidate generator
is similar to a `term` suggester called for each individual term in the
text. The output of the generators is subsequently scored in combination
with the candidates from the other terms for suggestion candidates.

Currently only one type of candidate generator is supported, the
`direct_generator`. The Phrase suggest API accepts a list of generators
under the key `direct_generator`; each of the generators in the list is
called per term in the original text.

===== Direct Generators

The direct generators support the following parameters:

[horizontal]
`field`::
    The field to fetch the candidate suggestions from. This is
    a required option that either needs to be set globally or per
    suggestion.

`size`::
    The maximum corrections to be returned per suggest text token.

`suggest_mode`::
    The suggest mode controls what suggestions are included on the suggestions
    generated on each shard. All values other than `always` can be thought of
    as an optimization to generate fewer suggestions to test on each shard and
    are not rechecked when combining the suggestions generated on each
    shard. Thus `missing` will generate suggestions for terms on shards that do
    not contain them even if other shards do contain them. Those should be
    filtered out using `confidence`. Three possible values can be specified:
    ** `missing`: Only generate suggestions for terms that are not in the
                 shard. This is the default.
    ** `popular`: Only suggest terms that occur in more docs on the shard than
                 the original term.
    ** `always`: Suggest any matching suggestions based on terms in the
                 suggest text.

`max_edits`::
    The maximum edit distance candidate suggestions can have
    in order to be considered as a suggestion. Can only be a value between 1
    and 2. Any other value results in a bad request error being thrown.
    Defaults to 2.

`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.

`min_word_length`::
    The minimum length a suggest text term must have in
    order to be included. Defaults to 4.

`max_inspections`::
    A factor that is used to multiply with the
    `shards_size` in order to inspect more candidate spelling corrections on
    the shard level. Can improve accuracy at the cost of performance.
    Defaults to 5.

`min_doc_freq`::
    The minimal threshold in number of documents a
    suggestion should appear in. This can be specified as an absolute number
    or as a relative percentage of number of documents. This can improve
    quality by only suggesting high frequency terms. Defaults to 0f and is
    not enabled. If a value higher than 1 is specified, then the number
    cannot be fractional. The shard level document frequencies are used for
    this option.

`max_term_freq`::
    The maximum threshold in number of documents in which a
    suggest text token can exist in order to be included. Can be a relative
    percentage number (e.g., 0.4) or an absolute number to represent document
    frequencies. If a value higher than 1 is specified, then fractional can
    not be specified. Defaults to 0.01f. This can be used to exclude high
    frequency terms -- which are usually spelled correctly -- from being spellchecked. This also improves the spellcheck
    performance. The shard level document frequencies are used for this
    option.

`pre_filter`::
    A filter (analyzer) that is applied to each of the
    tokens passed to this candidate generator. This filter is applied to the
    original token before candidates are generated.

`post_filter`::
    A filter (analyzer) that is applied to each of the
    generated tokens before they are passed to the actual phrase scorer.

The following example shows a `phrase` suggest call with two generators:
the first one is using a field containing ordinary indexed terms, and the
second one uses a field that uses terms indexed with a `reverse` filter
(tokens are index in reverse order). This is used to overcome the limitation
of the direct generators to require a constant prefix to provide
high-performance suggestions. The `pre_filter` and `post_filter` options
accept ordinary analyzer names.

[source,console]
--------------------------------------------------
POST test/_search
{
  "suggest": {
    "text" : "obel prize",
    "simple_phrase" : {
      "phrase" : {
        "field" : "title.trigram",
        "size" : 1,
        "direct_generator" : [ {
          "field" : "title.trigram",
          "suggest_mode" : "always"
        }, {
          "field" : "title.reverse",
          "suggest_mode" : "always",
          "pre_filter" : "reverse",
          "post_filter" : "reverse"
        } ]
      }
    }
  }
}
--------------------------------------------------

`pre_filter` and `post_filter` can also be used to inject synonyms after
candidates are generated. For instance for the query `captain usq` we
might generate a candidate `usa` for the term `usq`, which is a synonym for
`america`. This allows us to present `captain america` to the user if this
phrase scores high enough.
