Elasticsearch：使用文档中的自定义分数字段进行影响力评分

Z时代
2024-01-10
分类：问答

我有一组通过NLP算法从文本中提取的单词，以及每个文档中每个单词的相关分数。

例如：

document 1: {  "vocab": [ {"wtag":"James Bond", "rscore": 2.14 }, 
                          {"wtag":"world", "rscore": 0.86 }, 
                          ...., 
                          {"wtag":"somemore", "rscore": 3.15 }
                        ] 
            }
document 2: {  "vocab": [ {"wtag":"hiii", "rscore": 1.34 }, 
                          {"wtag":"world", "rscore": 0.94 },
                          ...., 
                          {"wtag":"somemore", "rscore": 3.23 } 
                        ] 
            }

我希望每个文档中rscore的match

wtag都可以影响_scoreES给它的给定值，或者乘以或加到上_score，以影响_score结果文档的最终（依次，顺序）。有什么办法可以做到这一点？

回答：

解决此问题的另一种方法是使用嵌套文档：

首先设置映射以创建vocab一个嵌套文档，这意味着每个wtag/ rscore文档将在内部作为单独的文档建立索引：

curl -XPUT "http://localhost:9200/myindex/" -d'
{
  "settings": {"number_of_shards": 1}, 
  "mappings": {
    "mytype": {
      "properties": {
        "vocab": {
          "type": "nested",
          "fields": {
            "wtag": {
              "type": "string"
            },
            "rscore": {
              "type": "float"
            }
          }
        }
      }
    }
  }
}'

然后索引您的文档：

curl -XPUT "http://localhost:9200/myindex/mytype/1" -d'
{
  "vocab": [
    {
      "wtag": "James Bond",
      "rscore": 2.14
    },
    {
      "wtag": "world",
      "rscore": 0.86
    },
    {
      "wtag": "somemore",
      "rscore": 3.15
    }
  ]
}'
curl -XPUT "http://localhost:9200/myindex/mytype/2" -d'
{
  "vocab": [
    {
      "wtag": "hiii",
      "rscore": 1.34
    },
    {
      "wtag": "world",
      "rscore": 0.94
    },
    {
      "wtag": "somemore",
      "rscore": 3.23
    }
  ]
}'

并运行nested查询以匹配所有嵌套文档，并rscore为每个与之匹配的嵌套文档求和：

curl -XGET "http://localhost:9200/myindex/mytype/_search" -d'
{
  "query": {
    "nested": {
      "path": "vocab",
      "score_mode": "sum",
      "query": {
        "function_score": {
          "query": {
            "match": {
              "vocab.wtag": "james bond world"
            }
          },
          "script_score": {
            "script": "doc[\"rscore\"].value"
          }
        }
      }
    }
  }
}'