Elasticsearch Elasticsearch Term Vectors

By Opster Team

Updated: Aug 24, 2023

| 2 min read

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Elasticsearch Term Vectors provide a detailed view of the terms in a specific document field. They are a crucial component in Elasticsearch’s text analysis process, offering insights into term frequency, term positions, and term payloads. This article will delve into the intricacies of Term Vectors, their use cases, and how to effectively implement them in your Elasticsearch operations.

The anatomy of Term Vectors

Term Vectors are essentially a breakdown of the terms within a text field. They provide the following information:

1. Term Frequency (tf): This is the number of times a term appears in a field. It is a crucial factor in determining the relevance of a term within a document.

2. Document Frequency (df): This is the number of documents in the index that contain the term. It helps in understanding the commonality of a term.

3. Term Positions: This is the position or order in which a term appears in the field. It is particularly useful in phrase queries and proximity searches.

4. Term Payloads: These are additional metadata or information associated with a term, such as synonyms or stemmed forms.

Enabling Term Vectors

By default, Term Vectors are not enabled in Elasticsearch due to the additional storage requirements. However, they must be enabled at the field level during index creation. Here’s how you can enable Term Vectors during index creation:

PUT /my_index
  "mappings": {
    "properties": {
      "my_field": {
        "type": "text",
        "term_vector": "yes"

In the above example, the `term_vector` parameter is set to `yes`, enabling Term Vectors for `my_field`. Other possible values for `term_vector` include `no`, `with_positions`, `with_offsets`, `with_positions_offsets`, `with_positions_payloads`, and `with_positions_offsets_payloads`.

Retrieving Term Vectors

Once Term Vectors are enabled, you can retrieve them using the `_termvectors` API. Here’s an example:

GET /my_index/_termvectors/1
  "fields" : ["my_field"],
  "offsets" : true,
  "positions" : true,
  "term_statistics" : true,
  "field_statistics" : true

In this request, `offsets`, `positions`, `term_statistics`, and `field_statistics` are set to `true`, requesting all available information for `my_field` in the document with the ID `1`.

Use cases of Term Vectors

Term Vectors are particularly useful in text analysis and natural language processing tasks. Here are a few specific use cases:

1. Information Retrieval: Term Vectors can be used to calculate the relevance of a document to a query, using measures like Term Frequency-Inverse Document Frequency (TF-IDF).

2. Text Classification: Term Vectors can be used to build feature vectors for machine learning algorithms in text classification tasks.

3. Text Similarity: By comparing Term Vectors, you can measure the similarity between two documents.

4. Keyword Extraction: Term Vectors can be used to identify important keywords in a document based on term frequency.

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