IMPORTANT: No additional bug fixes or documentation updates will be released for this version. For the latest information, see the current release documentation.

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Fingerprint analyzer

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Fingerprint analyzer

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The fingerprint analyzer implements a fingerprinting algorithm which is used by the OpenRefine project to assist in clustering.

Input text is lowercased, normalized to remove extended characters, sorted, deduplicated and concatenated into a single token. If a stopword list is configured, stop words will also be removed.

Example output

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resp = client.indices.analyze(
    analyzer="fingerprint",
    text="Yes yes, Gödel said this sentence is consistent and.",
)
print(resp)

response = client.indices.analyze(
  body: {
    analyzer: 'fingerprint',
    text: 'Yes yes, Gödel said this sentence is consistent and.'
  }
)
puts response

const response = await client.indices.analyze({
  analyzer: "fingerprint",
  text: "Yes yes, Gödel said this sentence is consistent and.",
});
console.log(response);

POST _analyze
{
  "analyzer": "fingerprint",
  "text": "Yes yes, Gödel said this sentence is consistent and."
}

The above sentence would produce the following single term:

[ and consistent godel is said sentence this yes ]

Configuration

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The fingerprint analyzer accepts the following parameters:

`separator`	The character to use to concatenate the terms. Defaults to a space.
`max_output_size`	The maximum token size to emit. Defaults to `255`. Tokens larger than this size will be discarded.
`stopwords`	A pre-defined stop words list like `_english_` or an array containing a list of stop words. Defaults to `_none_`.
`stopwords_path`	The path to a file containing stop words.

See the Stop Token Filter for more information about stop word configuration.

Example configuration

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In this example, we configure the fingerprint analyzer to use the pre-defined list of English stop words:

resp = client.indices.create(
    index="my-index-000001",
    settings={
        "analysis": {
            "analyzer": {
                "my_fingerprint_analyzer": {
                    "type": "fingerprint",
                    "stopwords": "_english_"
                }
            }
        }
    },
)
print(resp)

resp1 = client.indices.analyze(
    index="my-index-000001",
    analyzer="my_fingerprint_analyzer",
    text="Yes yes, Gödel said this sentence is consistent and.",
)
print(resp1)

response = client.indices.create(
  index: 'my-index-000001',
  body: {
    settings: {
      analysis: {
        analyzer: {
          my_fingerprint_analyzer: {
            type: 'fingerprint',
            stopwords: '_english_'
          }
        }
      }
    }
  }
)
puts response

response = client.indices.analyze(
  index: 'my-index-000001',
  body: {
    analyzer: 'my_fingerprint_analyzer',
    text: 'Yes yes, Gödel said this sentence is consistent and.'
  }
)
puts response

const response = await client.indices.create({
  index: "my-index-000001",
  settings: {
    analysis: {
      analyzer: {
        my_fingerprint_analyzer: {
          type: "fingerprint",
          stopwords: "_english_",
        },
      },
    },
  },
});
console.log(response);

const response1 = await client.indices.analyze({
  index: "my-index-000001",
  analyzer: "my_fingerprint_analyzer",
  text: "Yes yes, Gödel said this sentence is consistent and.",
});
console.log(response1);

PUT my-index-000001
{
  "settings": {
    "analysis": {
      "analyzer": {
        "my_fingerprint_analyzer": {
          "type": "fingerprint",
          "stopwords": "_english_"
        }
      }
    }
  }
}

POST my-index-000001/_analyze
{
  "analyzer": "my_fingerprint_analyzer",
  "text": "Yes yes, Gödel said this sentence is consistent and."
}

The above example produces the following term:

[ consistent godel said sentence yes ]

Definition

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The fingerprint tokenizer consists of:

Tokenizer

Standard Tokenizer

Token Filters (in order)

If you need to customize the fingerprint analyzer beyond the configuration parameters then you need to recreate it as a custom analyzer and modify it, usually by adding token filters. This would recreate the built-in fingerprint analyzer and you can use it as a starting point for further customization:

resp = client.indices.create(
    index="fingerprint_example",
    settings={
        "analysis": {
            "analyzer": {
                "rebuilt_fingerprint": {
                    "tokenizer": "standard",
                    "filter": [
                        "lowercase",
                        "asciifolding",
                        "fingerprint"
                    ]
                }
            }
        }
    },
)
print(resp)

response = client.indices.create(
  index: 'fingerprint_example',
  body: {
    settings: {
      analysis: {
        analyzer: {
          rebuilt_fingerprint: {
            tokenizer: 'standard',
            filter: [
              'lowercase',
              'asciifolding',
              'fingerprint'
            ]
          }
        }
      }
    }
  }
)
puts response

const response = await client.indices.create({
  index: "fingerprint_example",
  settings: {
    analysis: {
      analyzer: {
        rebuilt_fingerprint: {
          tokenizer: "standard",
          filter: ["lowercase", "asciifolding", "fingerprint"],
        },
      },
    },
  },
});
console.log(response);

PUT /fingerprint_example
{
  "settings": {
    "analysis": {
      "analyzer": {
        "rebuilt_fingerprint": {
          "tokenizer": "standard",
          "filter": [
            "lowercase",
            "asciifolding",
            "fingerprint"
          ]
        }
      }
    }
  }
}

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