Term vectors API examples
Elastic Stack
Term vectors provide information about the terms that were produced by the analysis process, including term frequencies, positions, offsets, and payloads. They're useful for applications like highlighting, more-like-this queries, and text analysis.
This page shows you examples of using the term vectors API.
First, create an index that stores term vectors, payloads, and so on:
PUT /my-index-000001 { "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"
]
}
}
}
}
}
Add some documents:
PUT /my-index-000001/_doc/1 {
"fullname" : "John Doe",
"text" : "test test test "
}
PUT /my-index-000001/_doc/2?refresh=wait_for
{
"fullname" : "Jane Doe",
"text" : "Another test ..."
}
The following request returns all information and statistics for field
text in document 1 (John Doe):
GET /my-index-000001/_termvectors/1 {
"fields" : ["text"],
"offsets" : true,
"payloads" : true,
"positions" : true,
"term_statistics" : true,
"field_statistics" : true
}
Response:
{
"_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=="
}
]
}
}
}
}
}
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 /my-index-000001/_termvectors/1 {
"fields" : ["text", "some_field_without_term_vectors"],
"offsets" : true,
"positions" : true,
"term_statistics" : true,
"field_statistics" : true
}
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 /my-index-000001/_termvectors {
"doc" : {
"fullname" : "John Doe",
"text" : "test test test"
}
}
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 /my-index-000001/_termvectors {
"doc" : {
"fullname" : "John Doe",
"text" : "test test test"
},
"fields": ["fullname"],
"per_field_analyzer" : {
"fullname": "keyword"
}
}
Response:
{
"_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
}
]
}
}
}
}
}
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",
"_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
}
}
}
}
}