The `term` suggester provides a very convenient API to access word alternatives on 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 already ranked
/ selected suggestions are required in order to present to the end-user.
Inside ElasticSearch we have the ability to access way more statistics and information quickly
to make better decision which token alternative to pick or if to pick an alternative at all.
This `phrase` suggester adds some logic on top of the `term` suggester to select entire
corrected phrases instead of individual tokens weighted based on a *ngram-langugage models*. In practice it
will be able to make better decision about which tokens to pick based on co-occurence and frequencies.
The current implementation is kept quite general and leaves room for future improvements.
# API Example
The `phrase` request is defined along side the query part in the json request:
```json
curl -s -XPOST 'localhost:9200/_search' -d {
"suggest" : {
"text" : "Xor the Got-Jewel",
"simple_phrase" : {
"phrase" : {
"analyzer" : "body",
"field" : "bigram",
"size" : 1,
"real_word_error_likelihood" : 0.95,
"max_errors" : 0.5,
"gram_size" : 2,
"direct_generator" : [ {
"field" : "body",
"suggest_mode" : "always",
"min_word_len" : 1
} ]
}
}
}
}
```
The response contains suggested sored by the most likely spell correction first. In this case we got the expected correction
`xorr the god jewel` first while the second correction is less conservative where only one of the errors is corrected. Note, the request
is executed with `max_errors` set to `0.5` so 50% of the terms can contain misspellings (See parameter descriptions below).
```json
{
"took" : 37,
"timed_out" : false,
"_shards" : {
"total" : 5,
"successful" : 5,
"failed" : 0
},
"hits" : {
"total" : 2938,
"max_score" : 0.0,
"hits" : [ ]
},
"suggest" : {
"simple_phrase" : [ {
"text" : "Xor the Got-Jewel",
"offset" : 0,
"length" : 17,
"options" : [ {
"text" : "xorr the god jewel",
"score" : 0.17877324
}, {
"text" : "xor the god jewel",
"score" : 0.14231323
} ]
} ]
}
}
````
# Phrase suggest API
## Basic parameters
* `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.
* `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`.
* `real_word_error_likelihood` - the likelihood of a term being a misspelled even if the term exists in the dictionary. The default it `0.95` corresponding to 5% or 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 that at most 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 a number `>=1` as an absolut number of query terms. The default is set to `1.0` which corresponds to that only corrections with at most 1 misspelled term are returned.
* `separator` - the separator that is used to separate terms in the bigram field. If not set the whitespce 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 analyse to suggest text with. Defaults to the search analyzer of the suggest field passed via `field`.
* `shard_size` - Sets the maximum number of suggested term to be retrieved from each individual shard. During the reduce phase the only the top N suggestions are returned based on the `size` option. Defaults to `5`.
* `text` - Sets the text / query to provide suggestions for.
## 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).
* `laplace` - the default model that uses an additive smoothing model where a constant (typically `1.0` or smaller) is added to all counts to balance weights, The default `alpha` is `0.5`.
* `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`.
* `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.
## 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 in combination with the candidates from the other terms to 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 are called per term in the original text.
## Direct Generators
The direct generators support the following parameters:
* `field` - The field to fetch the candidate suggestions from. This is an required option that either needs to be set globally or per suggestion.
* `analyzer` - The analyzer to analyse the suggest text with. Defaults to the search analyzer of the suggest field.
* `size` - The maximum corrections to be returned per suggest text token.
* `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 suggest terms in the suggest text that aren't 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.
* `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 result in an bad request error being thrown. Defaults to 2.
* `min_prefix` - 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_query_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 spell corrections on the shard level. Can improve accuracy at the cost of performance. Defaults to 5.
* `threshold_frequency` - 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_query_frequency` - The maximum threshold in number of documents a sugges 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 an 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 from being spellchecked. High frequency terms are usually spelled correctly on top of this 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. (optional)
* post_filter - a filter (analyzer) that is applied to each of the generated tokens before they are passed to the actual phrase scorer. (optional)
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.
```json
curl -s -XPOST 'localhost:9200/_search' -d {
"suggest" : {
"text" : "Xor the Got-Jewel",
"simple_phrase" : {
"phrase" : {
"analyzer" : "body",
"field" : "bigram",
"size" : 4,
"real_word_error_likelihood" : 0.95,
"confidence" : 2.0,
"gram_size" : 2,
"direct_generator" : [ {
"field" : "body",
"suggest_mode" : "always",
"min_word_len" : 1
}, {
"field" : "reverse",
"suggest_mode" : "always",
"min_word_len" : 1,
"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 term `usq` which is a synonym for `america` which allows to present `captain america` to the user if this phrase scores high enough.
Closes#2709
The sorting by nested field support has the following parameters on top of the already existing sort options:
nested_path - Defines the on what nested object to sort. The actual sort field must be a direct field inside this nested object. The default is to use the most immediate inherited nested object from the sort field.
nested_filter - A filter the inner objects inside the nested path should match with in order for its field values to be taken into account by sorting. Common case is to repeat the query / filter inside the nested filter or query. By default no nested_filter is active.
Either the highest (max) or lowest (min) inner object is picked for during sorting depending on the sort_mode being used. The sort_mode options avg and sum can still be used for number based fields inside nested objects. All the values for the sort field are taken into account for each nested object.
Closes#2662
SpatialPrefixTree#recursiveGetNodes uses an optimization that prevents
recursion into the deepest tree level if a parent node in the penultimate
level covers all its children. This produces a bug if the optimization
happens both at indexing and at query/filter time.
This patch fixes the bug by disabling the optimization at indexing time
(to avoid adding overhead for query-heavy workloads).
See LUCENE-4770 for reference
the main goal of the facet refactoring is to allow for two modes of facet execution, collector based, that get callbacks as hist match, and post based, which iterates over all the relevant hits
it also includes a some simplification of the facet implementation
The `min` and `max` sort modes are supported for all field types. Either the lowest value or the highest value is picked. In addition to that number based fields also support `sum` and `avg` as sort mode. If `sum` sort mode is used then all the values for a field and belonging to a document are added together and the result of that is used as sort value. If the `avg` sort mode is used then the average of all values for the sort field belonging to that document is used as sort value.
Relates to #2634
The rescore feature allows te rescore a document returned by a query based
on a secondary algorithm. Rescoring is commonly used if a scoring algorithm
is too costly to be executed across the entire document set but efficient enough
to be executed on the Top-K documents scored by a faster retrieval method. Rescoring
can help to improve precision by reordering a larger Top-K window than actually
returned to the user. Typically is it executed on a window between 100 and 500 documents
while the actual result window requested by the user remains the same.
# Query Rescorer
The `query` rescorer executes a secondary query only on the Top-K results of the actual
user query and rescores the documents based on a linear combination of the user query's score
and the score of the `rescore_query`. This allows to execute any exposed query as a
`rescore_query` and supports a `query_weight` as well as a `rescore_query_weight` to weight the
factors of the linear combination.
# Rescore API
The `rescore` request is defined along side the query part in the json request:
```json
curl -s -XPOST 'localhost:9200/_search' -d {
"query" : {
"match" : {
"field1" : {
"query" : "the quick brown",
"type" : "boolean",
"operator" : "OR"
}
}
},
"rescore" : {
"window_size" : 50,
"query" : {
"rescore_query" : {
"match" : {
"field1" : {
"query" : "the quick brown",
"type" : "phrase",
"slop" : 2
}
}
},
"query_weight" : 0.7,
"rescore_query_weight" : 1.2
}
}
}
```
Each `rescore` request is executed on a per-shard basis within the same roundtrip. Currently the rescore API
has only one implementation (the `query` rescorer) which modifies the result set in-place. Future developments
could include dedicated rescore results if needed by the implemenation ie. a pair-wise reranker.
*Note:* Only regualr queries are rescored, if the search type is set to `scan` or `count` rescorers are not executed.
Closes#2640
Shay Banon
Simon Willnauer
Drew Raines
Martijn van Groningen
Igor Motov
Lukas Vlcek
David Pilato
Ilya Nazarov
Jeffrey Gerard
morsegel
uboness
Lee Hinman
Florian Schilling