Get term vector information
Get information and statistics about terms in the fields of a particular document.
You can retrieve term vectors for documents stored in the index or for artificial documents passed in the body of the request.
You can specify the fields you are interested in through the fields
parameter or by adding the fields to the request body.
For example:
GET /my-index-000001/_termvectors/1?fields=message
Fields can be specified using wildcards, similar to the multi match query.
Term vectors are real-time by default, not near real-time.
This can be changed by setting realtime
parameter to false
.
You can request three types of values: term information, term statistics, and field statistics. By default, all term information and field statistics are returned for all fields but term statistics are excluded.
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 computed on the fly if possible. Additionally, term vectors could be computed for documents not even existing in the index, but instead provided by the user.
Start and end offsets assume UTF-16 encoding is being used. If you want to use these offsets in order to get the original text that produced this token, you should make sure that the string you are taking a sub-string of is also encoded using UTF-16.
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.
By default, when requesting term vectors of artificial documents, a shard to get the statistics from is randomly selected.
Use routing
only to hit a particular shard.
Path parameters
-
The name of the index that contains the document.
-
A unique identifier for the document.
Query parameters
-
fields string | array[string]
A comma-separated list or wildcard expressions of fields to include in the statistics. It is used as the default list unless a specific field list is provided in the
completion_fields
orfielddata_fields
parameters. -
field_statistics boolean
If
true
, the response includes:- The document count (how many documents contain this field).
- The sum of document frequencies (the sum of document frequencies for all terms in this field).
- The sum of total term frequencies (the sum of total term frequencies of each term in this field).
-
offsets boolean
If
true
, the response includes term offsets. -
payloads boolean
If
true
, the response includes term payloads. -
positions boolean
If
true
, the response includes term positions. -
preference string
The node or shard the operation should be performed on. It is random by default.
-
realtime boolean
If true, the request is real-time as opposed to near-real-time.
-
routing string
A custom value that is used to route operations to a specific shard.
-
term_statistics boolean
If
true
, the response includes:- The total term frequency (how often a term occurs in all documents).
- The 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.
-
version number
If
true
, returns the document version as part of a hit. -
version_type string
The version type.
Values are
internal
,external
,external_gte
, orforce
.
Body
-
doc object
An artificial document (a document not present in the index) for which you want to retrieve term vectors.
Additional properties are allowed.
-
filter object
Additional properties are allowed.
-
per_field_analyzer object
Override the default per-field analyzer. This is useful in order to generate term vectors in any fashion, especially when using artificial documents. When providing an analyzer for a field that already stores term vectors, the term vectors will be regenerated.
curl \
--request POST http://api.example.com/{index}/_termvectors/{id} \
--header "Content-Type: application/json" \
--data '"{\n \"fields\" : [\"text\"],\n \"offsets\" : true,\n \"payloads\" : true,\n \"positions\" : true,\n \"term_statistics\" : true,\n \"field_statistics\" : true\n}"'
{
"fields" : ["text"],
"offsets" : true,
"payloads" : true,
"positions" : true,
"term_statistics" : true,
"field_statistics" : true
}
{
"doc" : {
"fullname" : "John Doe",
"text" : "test test test"
},
"fields": ["fullname"],
"per_field_analyzer" : {
"fullname": "keyword"
}
}
{
"doc": {
"plot": "When wealthy industrialist Tony Stark is forced to build an armored suit after a life-threatening incident, he ultimately decides to use its technology to fight against evil."
},
"term_statistics": true,
"field_statistics": true,
"positions": false,
"offsets": false,
"filter": {
"max_num_terms": 3,
"min_term_freq": 1,
"min_doc_freq": 1
}
}
{
"fields" : ["text", "some_field_without_term_vectors"],
"offsets" : true,
"positions" : true,
"term_statistics" : true,
"field_statistics" : true
}
{
"doc" : {
"fullname" : "John Doe",
"text" : "test test test"
}
}
{
"_index": "my-index-000001",
"_id": "1",
"_version": 1,
"found": true,
"took": 6,
"term_vectors": {
"text": {
"field_statistics": {
"sum_doc_freq": 4,
"doc_count": 2,
"sum_ttf": 6
},
"terms": {
"test": {
"doc_freq": 2,
"ttf": 4,
"term_freq": 3,
"tokens": [
{
"position": 0,
"start_offset": 0,
"end_offset": 4,
"payload": "d29yZA=="
},
{
"position": 1,
"start_offset": 5,
"end_offset": 9,
"payload": "d29yZA=="
},
{
"position": 2,
"start_offset": 10,
"end_offset": 14,
"payload": "d29yZA=="
}
]
}
}
}
}
}
{
"_index": "my-index-000001",
"_version": 0,
"found": true,
"took": 6,
"term_vectors": {
"fullname": {
"field_statistics": {
"sum_doc_freq": 2,
"doc_count": 4,
"sum_ttf": 4
},
"terms": {
"John Doe": {
"term_freq": 1,
"tokens": [
{
"position": 0,
"start_offset": 0,
"end_offset": 8
}
]
}
}
}
}
}
{
"_index": "imdb",
"_version": 0,
"found": true,
"term_vectors": {
"plot": {
"field_statistics": {
"sum_doc_freq": 3384269,
"doc_count": 176214,
"sum_ttf": 3753460
},
"terms": {
"armored": {
"doc_freq": 27,
"ttf": 27,
"term_freq": 1,
"score": 9.74725
},
"industrialist": {
"doc_freq": 88,
"ttf": 88,
"term_freq": 1,
"score": 8.590818
},
"stark": {
"doc_freq": 44,
"ttf": 47,
"term_freq": 1,
"score": 9.272792
}
}
}
}
}