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return term vectors and some statistics for a document #3114

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brwe opened this Issue May 29, 2013 · 1 comment

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commented May 29, 2013

This feature seems to be useful as can be seen by typing "term vectors elasticsearch" in google.

Here is how it should work:

Returns information and statistics on terms in the fields of a particular document as stored in the index.

    curl -XGET 'http://localhost:9200/twitter/tweet/1/_termvector?pretty=true'

Tree 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.

Optionally, you can specify the fields for which the information is retrieved either with a parameter in the url

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).

YOU MUST ENABLE TERM VECTOR STORING FOR USING THE API

See mapping doc and the example below on how to do that.

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 on how to configure your index to store term vectors.

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)

Behavior

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.

Example

First, we create an index that stores term vectors, payloads etc. :

    curl -s -XPUT 'http://localhost:9200/twitter/' -d '{
        "mappings": {
            "tweet": {
                "properties": {
                    "text": {
                                "type": "string",
                                "term_vector": "with_positions_offsets_payloads",
                                "store" : "yes",
                                "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:

    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):

     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
            }'

Equivalently, all parameters can be passed as URI parameters:
curl -GET 'http://localhost:9200/twitter/tweet/1/_termvector?pretty=true&fields=text&offsets=true&payloads=true&positions=true&term_statistics=true&field_statistics=true'

Response:

  {
    "_index" : "twitter",
    "_type" : "tweet",
    "_id" : "1",
    "_version" : 1,
    "exists" : true,
    "term_vectors" : {
      "text" : {
        "field_statistics" : {
          "sum_doc_freq" : 6,
          "doc_count" : 2,
          "sum_ttf" : 8
        },
        "terms" : {
          "test" : {
            "doc_freq" : 2,
            "ttf" : 4,
            "term_freq" : 3,
            "pos" : [ 1, 2, 3 ],
            "start" : [ 8, 13, 18 ],
            "end" : [ 12, 17, 22 ],
            "payload" : [ "d29yZA==", "d29yZA==", "d29yZA==" ]
          },
          "twitter" : {
            "doc_freq" : 2,
            "ttf" : 2,
            "term_freq" : 1,
            "pos" : [ 0 ],
            "start" : [ 0 ],
            "end" : [ 7 ],
            "payload" : [ "d29yZA==" ]
          }
        }
      }
    }
  }

This is similar to Issue #2691

brwe added a commit to brwe/elasticsearch that referenced this issue Jun 7, 2013

term vector request
================================

Returns information and statistics on terms in the fields of a particular document as stored in the index.

        curl -XGET 'http://localhost:9200/twitter/tweet/1/_termvector?pretty=true'

Tree 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.

Optionally, you can specify the fields for which the information is retrieved either with a parameter in the url

	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).

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](http://www.elasticsearch.org/guide/reference/mapping/core-types/) on how to configure your index to store term vectors.

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)

Behavior
-------------------------

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.

Example
-------------------------

First, we create an index that stores term vectors, payloads etc. :

    curl -s -XPUT 'http://localhost:9200/twitter/' -d '{
        "mappings": {
            "tweet": {
                "properties": {
                    "text": {
                                "type": "string",
                                "term_vector": "with_positions_offsets_payloads",
                                "store" : "yes",
                                "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:

    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):

     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
            }'
Equivalently, all parameters can be passed as URI parameters:
     curl -GET 'http://localhost:9200/twitter/tweet/1/_termvector?pretty=true&fields=text&offsets=true&payloads=true&positions=true&term_statistics=true&field_statistics=true'

Response:

  {
    "_index" : "twitter",
    "_type" : "tweet",
    "_id" : "1",
    "_version" : 1,
    "exists" : true,
    "term_vectors" : {
      "text" : {
        "field_statistics" : {
          "sum_doc_freq" : 6,
          "doc_count" : 2,
          "sum_ttf" : 8
        },
        "terms" : {
          "test" : {
            "doc_freq" : 2,
            "ttf" : 4,
            "term_freq" : 3,
            "pos" : [ 1, 2, 3 ],
            "start" : [ 8, 13, 18 ],
            "end" : [ 12, 17, 22 ],
            "payload" : [ "d29yZA==", "d29yZA==", "d29yZA==" ]
          },
          "twitter" : {
            "doc_freq" : 2,
            "ttf" : 2,
            "term_freq" : 1,
            "pos" : [ 0 ],
            "start" : [ 0 ],
            "end" : [ 7 ],
            "payload" : [ "d29yZA==" ]
          }
        }
      }
    }
  }

Further changes:
-------------------------

XContentBuilder
new method
public XContentBuilder field(XContentBuilderString name, int offset, int length, int... value)
to put an integer array.

IndicesAnalysisService
make token filter for saving payloads available in elasticsearch

AbstractFieldMapper/TypeParser
make term vector options string available and also fix the parsing of this string:
with_positions_payloads is actually allowed as can be seen in TermVectorsConsumerPerFields.

Closes elastic#3114

@brwe brwe closed this in 11d08ac Jun 10, 2013

@ghost ghost assigned brwe Jun 10, 2013

lmenezes pushed a commit to lmenezes/elasticsearch that referenced this issue Nov 3, 2013

Multi term vector request
--------------------------

This feature allows to retrieve [term vectors](elastic#3114) for a list of documents. The json request has exactly the same [format](elastic#3484) as the ```_termvectors``` endpoint

It use it, call

```
curl -XGET 'http://localhost:9200/index/type/_mtermvectors' -d '{
    "fields": [
        "field1",
        "field2",
        ...
    ],
    "ids": [
        "docId1",
        "docId2",
        ...
    ],
    "offsets": false|true,
    "payloads": false|true,
    "positions": false|true,
    "term_statistics": false|true,
    "field_statistics": false|true
}'

```

The return format is an array, each entry of which conatins the term vector response for one document:

```
{
   "docs": [
      {
         "_index": "index",
         "_type": "type",
         "_id": "docId1",
         "_version": 1,
         "exists": true,
         "term_vectors": {
         	...
         }
      },
      {
         "_index": "index",
         "_type": "type",
         "_id": "docId2",
         "_version": 1,
         "exists": true,
         "term_vectors": {
         ...
         }
      }
   ]
}
```

Note that, like term vectors, the mult term vectors request will silenty skip over documents that have no term vectors stored in the index and will simply return an empty response in this case.

Closes elastic#3536
@GraphGrailAi

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commented Oct 4, 2016

Could anyone point me on how to enable termvectors with django-haystack (Python lib that works with Elasticsearch) https://django-haystack.readthedocs.io
No such data in docs

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