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指纹分析器

fingerprint 分析器实现了一种指纹算法，该算法被 OpenRefine 项目用于辅助聚类。

输入文本会被转换为小写，标准化以删除扩展字符，排序，去重，并连接成一个单独的词元。如果配置了停用词列表，停用词也会被删除。

示例输出

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."
}

上面的句子会产生以下单个词元

[ and consistent godel is said sentence this yes ]

配置

编辑

fingerprint 分析器接受以下参数：

`separator`	用于连接词元的字符。默认为空格。
`max_output_size`	要发出的最大词元大小。默认为 `255`。大于此大小的词元将被丢弃。
`stopwords`	预定义的停用词列表，如 `_english_` 或包含停用词列表的数组。默认为 `_none_`。
`stopwords_path`	包含停用词的文件的路径。

有关停用词配置的更多信息，请参阅停用词词元过滤器。

示例配置

编辑

在此示例中，我们将 fingerprint 分析器配置为使用预定义的英语停用词列表

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."
}

上面的示例产生以下词元

[ consistent godel said sentence yes ]

定义

编辑

fingerprint 分词器包括：

分词器

标准分词器

词元过滤器（按顺序）

如果需要自定义 fingerprint 分析器，使其超出配置参数的范围，则需要将其重新创建为 custom 分析器并进行修改，通常是通过添加词元过滤器。这将重新创建内置的 fingerprint 分析器，您可以将其用作进一步自定义的起点

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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