OpenSearch/docs/reference/search/termvectors.asciidoc

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[[search-termvectors]]
== Term Vectors
added[1.00.Beta]
Returns information and statistics on terms in the fields of a
particular document as stored in the index.
[source,js]
--------------------------------------------------
curl -XGET 'http://localhost:9200/twitter/tweet/1/_termvector?pretty=true'
--------------------------------------------------
Optionally, you can specify the fields for which the information is
retrieved either with a parameter in the url
[source,js]
--------------------------------------------------
curl -XGET 'http://localhost:9200/twitter/tweet/1/_termvector?fields=text,...'
--------------------------------------------------
or adding by adding the requested fields in the request body (see
example below).
[float]
=== Return values
Three types of values can be requested: _term information_, _term statistics_
and _field statistics_. By default, all term information and field
statistics are returned for all fields but no term statistics.
[float]
==== Term information
* term frequency in the field (always returned)
* term positions (`positions` : true)
* start and end offsets (`offsets` : true)
* term payloads (`payloads` : true), as base64 encoded bytes
If the requested information wasn't stored in the index, it will be
omitted without further warning. See <<mapping-types,type mapping>>
for how to configure your index to store term vectors.
[float]
==== Term statistics
Setting `term_statistics` to `true` (default is `false`) will
return
* total term frequency (how often a term occurs in all documents) +
* document frequency (the number of documents containing the current
term)
By default these values are not returned since term statistics can
have a serious performance impact.
[float]
==== Field statistics
Setting `field_statistics` to `false` (default is `true`) will
omit :
* document count (how many documents contain this field)
* sum of document frequencies (the sum of document frequencies for all
terms in this field)
* sum of total term frequencies (the sum of total term frequencies of
each term in this field)
[float]
=== Behaviour
The term and field statistics are not accurate. Deleted documents
are not taken into account. The information is only retrieved for the
shard the requested document resides in. The term and field statistics
are therefore only useful as relative measures whereas the absolute
numbers have no meaning in this context.
[float]
=== Example
First, we create an index that stores term vectors, payloads etc. :
[source,js]
--------------------------------------------------
curl -s -XPUT 'http://localhost:9200/twitter/' -d '{
"mappings": {
"tweet": {
"properties": {
"text": {
"type": "string",
"term_vector": "with_positions_offsets_payloads",
"store" : true,
"index_analyzer" : "fulltext_analyzer"
},
"fullname": {
"type": "string",
"term_vector": "with_positions_offsets_payloads",
"index_analyzer" : "fulltext_analyzer"
}
}
}
},
"settings" : {
"index" : {
"number_of_shards" : 1,
"number_of_replicas" : 0
},
"analysis": {
"analyzer": {
"fulltext_analyzer": {
"type": "custom",
"tokenizer": "whitespace",
"filter": [
"lowercase",
"type_as_payload"
]
}
}
}
}
}'
--------------------------------------------------
Second, we add some documents:
[source,js]
--------------------------------------------------
curl -XPUT 'http://localhost:9200/twitter/tweet/1?pretty=true' -d '{
"fullname" : "John Doe",
"text" : "twitter test test test "
}'
curl -XPUT 'http://localhost:9200/twitter/tweet/2?pretty=true' -d '{
"fullname" : "Jane Doe",
"text" : "Another twitter test ..."
}'
--------------------------------------------------
The following request returns all information and statistics for field
`text` in document `1` (John Doe):
[source,js]
--------------------------------------------------
curl -XGET 'http://localhost:9200/twitter/tweet/1/_termvector?pretty=true' -d '{
"fields" : ["text"],
"offsets" : true,
"payloads" : true,
"positions" : true,
"term_statistics" : true,
"field_statistics" : true
}'
--------------------------------------------------
Response:
[source,js]
--------------------------------------------------
{
"_id": "1",
"_index": "twitter",
"_type": "tweet",
"_version": 1,
"exists": true,
"term_vectors": {
"text": {
"field_statistics": {
"doc_count": 2,
"sum_doc_freq": 6,
"sum_ttf": 8
},
"terms": {
"test": {
"doc_freq": 2,
"term_freq": 3,
"tokens": [
{
"end_offset": 12,
"payload": "d29yZA==",
"position": 1,
"start_offset": 8
},
{
"end_offset": 17,
"payload": "d29yZA==",
"position": 2,
"start_offset": 13
},
{
"end_offset": 22,
"payload": "d29yZA==",
"position": 3,
"start_offset": 18
}
],
"ttf": 4
},
"twitter": {
"doc_freq": 2,
"term_freq": 1,
"tokens": [
{
"end_offset": 7,
"payload": "d29yZA==",
"position": 0,
"start_offset": 0
}
],
"ttf": 2
}
}
}
}
}
--------------------------------------------------