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349b7a3a8b
This adds a `per_field_analyzer` parameter to the Term Vectors API, which allows to override the default analyzer at the field. If the field already stores term vectors, then they will be re-generated. Since the MLT Query uses the Term Vectors API under its hood, this commits also adds the same ability to the MLT Query, thereby allowing users to fine grain how each field item should be processed and analyzed. Closes #7801
330 lines
9.6 KiB
Plaintext
330 lines
9.6 KiB
Plaintext
[[docs-termvectors]]
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== Term Vectors
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Returns information and statistics on terms in the fields of a particular
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document. The document could be stored in the index or artificially provided
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by the user coming[1.4.0]. Term vectors are now <<realtime,realtime>>, as opposed to
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previously near realtime coming[1.5.0]. The functionality is disabled by setting
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`realtime` parameter to `false`.
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[source,js]
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--------------------------------------------------
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curl -XGET 'http://localhost:9200/twitter/tweet/1/_termvector?pretty=true'
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--------------------------------------------------
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Optionally, you can specify the fields for which the information is
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retrieved either with a parameter in the url
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[source,js]
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--------------------------------------------------
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curl -XGET 'http://localhost:9200/twitter/tweet/1/_termvector?fields=text,...'
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--------------------------------------------------
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or by adding the requested fields in the request body (see
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example below). Fields can also be specified with wildcards
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in similar way to the <<query-dsl-multi-match-query,multi match query>>
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[float]
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=== Return values
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Three types of values can be requested: _term information_, _term statistics_
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and _field statistics_. By default, all term information and field
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statistics are returned for all fields but no term statistics.
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[float]
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==== Term information
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* term frequency in the field (always returned)
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* term positions (`positions` : true)
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* start and end offsets (`offsets` : true)
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* term payloads (`payloads` : true), as base64 encoded bytes
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If the requested information wasn't stored in the index, it will be
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computed on the fly if possible. Additionally, term vectors could be computed
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for documents not even existing in the index, but instead provided by the user.
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[WARNING]
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======
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Start and end offsets assume UTF-16 encoding is being used. If you want to use
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these offsets in order to get the original text that produced this token, you
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should make sure that the string you are taking a sub-string of is also encoded
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using UTF-16.
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======
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[float]
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==== Term statistics
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Setting `term_statistics` to `true` (default is `false`) will
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return
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* total term frequency (how often a term occurs in all documents) +
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* document frequency (the number of documents containing the current
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term)
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By default these values are not returned since term statistics can
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have a serious performance impact.
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[float]
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==== Field statistics
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Setting `field_statistics` to `false` (default is `true`) will
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omit :
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* document count (how many documents contain this field)
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* sum of document frequencies (the sum of document frequencies for all
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terms in this field)
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* sum of total term frequencies (the sum of total term frequencies of
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each term in this field)
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[float]
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=== Behaviour
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The term and field statistics are not accurate. Deleted documents
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are not taken into account. The information is only retrieved for the
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shard the requested document resides in. The term and field statistics
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are therefore only useful as relative measures whereas the absolute
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numbers have no meaning in this context. By default, when requesting
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term vectors of artificial documents, a shard to get the statistics from
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is randomly selected. Use `routing` only to hit a particular shard.
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[float]
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=== Example 1
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First, we create an index that stores term vectors, payloads etc. :
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[source,js]
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--------------------------------------------------
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curl -s -XPUT 'http://localhost:9200/twitter/' -d '{
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"mappings": {
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"tweet": {
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"properties": {
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"text": {
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"type": "string",
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"term_vector": "with_positions_offsets_payloads",
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"store" : true,
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"index_analyzer" : "fulltext_analyzer"
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},
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"fullname": {
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"type": "string",
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"term_vector": "with_positions_offsets_payloads",
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"index_analyzer" : "fulltext_analyzer"
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}
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}
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}
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},
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"settings" : {
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"index" : {
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"number_of_shards" : 1,
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"number_of_replicas" : 0
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},
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"analysis": {
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"analyzer": {
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"fulltext_analyzer": {
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"type": "custom",
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"tokenizer": "whitespace",
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"filter": [
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"lowercase",
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"type_as_payload"
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]
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}
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}
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}
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}
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}'
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--------------------------------------------------
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Second, we add some documents:
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[source,js]
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--------------------------------------------------
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curl -XPUT 'http://localhost:9200/twitter/tweet/1?pretty=true' -d '{
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"fullname" : "John Doe",
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"text" : "twitter test test test "
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}'
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curl -XPUT 'http://localhost:9200/twitter/tweet/2?pretty=true' -d '{
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"fullname" : "Jane Doe",
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"text" : "Another twitter test ..."
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}'
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--------------------------------------------------
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The following request returns all information and statistics for field
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`text` in document `1` (John Doe):
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[source,js]
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--------------------------------------------------
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curl -XGET 'http://localhost:9200/twitter/tweet/1/_termvector?pretty=true' -d '{
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"fields" : ["text"],
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"offsets" : true,
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"payloads" : true,
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"positions" : true,
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"term_statistics" : true,
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"field_statistics" : true
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}'
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--------------------------------------------------
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Response:
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[source,js]
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--------------------------------------------------
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{
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"_id": "1",
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"_index": "twitter",
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"_type": "tweet",
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"_version": 1,
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"found": true,
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"term_vectors": {
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"text": {
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"field_statistics": {
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"doc_count": 2,
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"sum_doc_freq": 6,
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"sum_ttf": 8
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},
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"terms": {
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"test": {
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"doc_freq": 2,
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"term_freq": 3,
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"tokens": [
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{
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"end_offset": 12,
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"payload": "d29yZA==",
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"position": 1,
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"start_offset": 8
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},
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{
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"end_offset": 17,
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"payload": "d29yZA==",
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"position": 2,
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"start_offset": 13
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},
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{
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"end_offset": 22,
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"payload": "d29yZA==",
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"position": 3,
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"start_offset": 18
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}
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],
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"ttf": 4
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},
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"twitter": {
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"doc_freq": 2,
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"term_freq": 1,
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"tokens": [
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{
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"end_offset": 7,
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"payload": "d29yZA==",
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"position": 0,
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"start_offset": 0
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}
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],
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"ttf": 2
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}
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}
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}
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}
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}
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--------------------------------------------------
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[float]
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=== Example 2
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Term vectors which are not explicitly stored in the index are automatically
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computed on the fly. The following request returns all information and statistics for the
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fields in document `1`, even though the terms haven't been explicitly stored in the index.
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Note that for the field `text`, the terms are not re-generated.
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[source,js]
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--------------------------------------------------
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curl -XGET 'http://localhost:9200/twitter/tweet/1/_termvector?pretty=true' -d '{
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"fields" : ["text", "some_field_without_term_vectors"],
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"offsets" : true,
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"positions" : true,
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"term_statistics" : true,
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"field_statistics" : true
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}'
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--------------------------------------------------
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[float]
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=== Example 3
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Term vectors can also be generated for artificial documents,
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that is for documents not present in the index. The syntax is similar to the
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<<search-percolate,percolator>> API. For example, the following request would
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return the same results as in example 1. The mapping used is determined by the
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`index` and `type`.
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[WARNING]
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======
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If dynamic mapping is turned on (default), the document fields not in the original
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mapping will be dynamically created.
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======
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[source,js]
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--------------------------------------------------
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curl -XGET 'http://localhost:9200/twitter/tweet/_termvector' -d '{
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"doc" : {
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"fullname" : "John Doe",
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"text" : "twitter test test test"
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}
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}'
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--------------------------------------------------
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[float]
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[[docs-termvectors-per-field-analyzer]]
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=== Example 4 coming[1.5.0]
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Additionally, a different analyzer than the one at the field may be provided
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by using the `per_field_analyzer` parameter. This is useful in order to
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generate term vectors in any fashion, especially when using artificial
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documents. When providing an analyzer for a field that already stores term
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vectors, the term vectors will be re-generated.
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|
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[source,js]
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--------------------------------------------------
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curl -XGET 'http://localhost:9200/twitter/tweet/_termvector' -d '{
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"doc" : {
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"fullname" : "John Doe",
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"text" : "twitter test test test"
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},
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"fields": ["fullname"],
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"per_field_analyzer" : {
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"fullname": "keyword"
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}
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}'
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--------------------------------------------------
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Response:
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[source,js]
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--------------------------------------------------
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{
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"_index": "twitter",
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"_type": "tweet",
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"_version": 0,
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"found": true,
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"term_vectors": {
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"fullname": {
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"field_statistics": {
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"sum_doc_freq": 1,
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"doc_count": 1,
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"sum_ttf": 1
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},
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"terms": {
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"John Doe": {
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"term_freq": 1,
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"tokens": [
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{
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"position": 0,
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"start_offset": 0,
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"end_offset": 8
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}
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]
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}
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}
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}
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}
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}
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--------------------------------------------------
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