Returns information and statistics on terms in the fields of a particular
document. The document could be stored in the index or artificially provided
by the user. Term vectors are realtime by default, not near
realtime. This can be changed by setting realtime parameter to false.
GET /twitter/_termvectors/1
Optionally, you can specify the fields for which the information is retrieved either with a parameter in the url
GET /twitter/_termvectors/1?fields=message
or by adding the requested fields in the request body (see example below). Fields can also be specified with wildcards in similar way to the multi match query
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.
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.
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.
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)
Terms Filtering
With the parameter filter, the terms returned could also be filtered based
on their tf-idf scores. This could be useful in order find out a good
characteristic vector of a document. This feature works in a similar manner to
the second phase of the
More Like This Query. See example 5
for usage.
The following sub-parameters are supported:
|
|
Maximum number of terms that must be returned per field. Defaults to |
|
|
Ignore words with less than this frequency in the source doc. Defaults to |
|
|
Ignore words with more than this frequency in the source doc. Defaults to unbounded. |
|
|
Ignore terms which do not occur in at least this many docs. Defaults to |
|
|
Ignore words which occur in more than this many docs. Defaults to unbounded. |
|
|
The minimum word length below which words will be ignored. Defaults to |
|
|
The maximum word length above which words will be ignored. Defaults to unbounded ( |
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.
Example: Returning stored term vectors
First, we create an index that stores term vectors, payloads etc. :
PUT /twitter
{ "mappings": {
"properties": {
"text": {
"type": "text",
"term_vector": "with_positions_offsets_payloads",
"store" : true,
"analyzer" : "fulltext_analyzer"
},
"fullname": {
"type": "text",
"term_vector": "with_positions_offsets_payloads",
"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:
PUT /twitter/_doc/1
{
"fullname" : "John Doe",
"text" : "twitter test test test "
}
PUT /twitter/_doc/2
{
"fullname" : "Jane Doe",
"text" : "Another twitter test ..."
}The following request returns all information and statistics for field
text in document 1 (John Doe):
GET /twitter/_termvectors/1
{
"fields" : ["text"],
"offsets" : true,
"payloads" : true,
"positions" : true,
"term_statistics" : true,
"field_statistics" : true
}Response:
{
"_id": "1",
"_index": "twitter",
"_type": "_doc",
"_version": 1,
"found": true,
"took": 6,
"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
}
}
}
}
}Example: Generating term vectors on the fly
Term vectors which are not explicitly stored in the index are automatically
computed on the fly. The following request returns all information and statistics for the
fields in document 1, even though the terms haven’t been explicitly stored in the index.
Note that for the field text, the terms are not re-generated.
GET /twitter/_termvectors/1
{
"fields" : ["text", "some_field_without_term_vectors"],
"offsets" : true,
"positions" : true,
"term_statistics" : true,
"field_statistics" : true
}Example: Artificial documents
Term vectors can also be generated for artificial documents,
that is for documents not present in the index. For example, the following request would
return the same results as in example 1. The mapping used is determined by the index.
If dynamic mapping is turned on (default), the document fields not in the original mapping will be dynamically created.
GET /twitter/_termvectors
{
"doc" : {
"fullname" : "John Doe",
"text" : "twitter test test test"
}
}Per-field analyzer
Additionally, a different analyzer than the one at the field may be provided
by using the per_field_analyzer parameter. 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 re-generated.
GET /twitter/_termvectors
{
"doc" : {
"fullname" : "John Doe",
"text" : "twitter test test test"
},
"fields": ["fullname"],
"per_field_analyzer" : {
"fullname": "keyword"
}
}Response:
{
"_index": "twitter",
"_type": "_doc",
"_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
}
]
}
}
}
}
}Example: Terms filtering
Finally, the terms returned could be filtered based on their tf-idf scores. In the example below we obtain the three most "interesting" keywords from the artificial document having the given "plot" field value. Notice that the keyword "Tony" or any stop words are not part of the response, as their tf-idf must be too low.
GET /imdb/_termvectors
{
"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
}
}Response:
{
"_index": "imdb",
"_type": "_doc",
"_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
}
}
}
}
}