布尔查询中的不同得分函数 [英] Different score functions in bool query

问题描述

我正在研究Elasticsearch的产品索引.我有一个包含多个匹配项的布尔查询，用于检索我想要的结果:

I am working in a products index on elasticsearch. I have a bool query with groups of multi-match for retrieving the results I want:

{
  "query": {
    "bool": {
      "should": [
        {
          "multi_match": {
            "query": "term",
            "fields": [
              "name^3.0",
              "name.fullName^10.0",
              "description",
              "description.fullDesc",
              "detail",
              "detail.fullDetail"
            ],
            "type": "cross_fields",
            "operator": "AND",
            "slop": 0,
            "prefix_length": 0,
            "max_expansions": 50,
            "zero_terms_query": "NONE",
            "auto_generate_synonyms_phrase_query": true,
            "fuzzy_transpositions": true,
            "boost": 10
          }
        },
        {
          "multi_match": {
            "query": "term",
            "fields": [
              "name.fullName",
              "description.fullDesc",
              "detail.fullDetail"
            ],
            "type": "cross_fields",
            "operator": "OR",
            "slop": 0,
            "prefix_length": 0,
            "max_expansions": 50,
            "zero_terms_query": "NONE",
            "auto_generate_synonyms_phrase_query": true,
            "fuzzy_transpositions": true,
            "boost": 6
          }
        }
      ],
      "adjust_pure_negative": true,
      "boost": 1
    }
  }
}

我需要分别对在多匹配查询中检索到的每组文档进行评分.我已经考虑过使用分数函数，但是它允许我为整个布尔查询定义一个分数函数，如下所示:

I need to score each group of documents retrieved in multi-match query separately. I've considered using score function but it allows me to define one score function for the whole bool query like this:

{
  "from": 0,
  "size": 500,
  "query": {
    "function_score": {
      "query": {
        "bool": {
          "should": [
            {
              "multi_match": {
                "query": "term",
                "fields": [
                  "name^3.0",
                  "name.fullName^10.0",
                  "description",
                  "description.fullDesc",
                  "detail",
                  "detail.fullDetail"
                ],
                "type": "cross_fields",
                "operator": "AND",
                "slop": 0,
                "prefix_length": 0,
                "max_expansions": 50,
                "zero_terms_query": "NONE",
                "auto_generate_synonyms_phrase_query": true,
                "fuzzy_transpositions": true,
                "boost": 10
              }
            },
            {
              "multi_match": {
                "query": "term",
                "fields": [
                  "name.fullName",
                  "description.fullDesc",
                  "detail.fullDetail"
                ],
                "type": "cross_fields",
                "operator": "OR",
                "slop": 0,
                "prefix_length": 0,
                "max_expansions": 50,
                "zero_terms_query": "NONE",
                "auto_generate_synonyms_phrase_query": true,
                "fuzzy_transpositions": true,
                "boost": 6
              }
            }
          ],
          "adjust_pure_negative": true,
          "boost": 1
        }
      },
      "boost": "5",
      "field_value_factor": {
        "field": "gain",
        "factor": 1.2,
        "modifier": "sqrt",
        "missing": 1
      },
      "boost_mode": "multiply"
    }
  },
  "version": true
}

我需要定义不同的得分函数(每个多重匹配查询一个)来独立对结果进行得分.

I need to define different score functions (one for each multi-match query) to score results independently.

推荐答案

您可以将每个 multi_match 查询包装在 function_score 查询中.然后，您可以轻松地为每个 multi_match 定义不同的功能.这就是查询的样子:

You can wrap each of the multi_match query in function_score query. Then you can easily define the different functions for each multi_match. This is how your query will look like:

{
  "from": 0,
  "size": 500,
  "query": {
    "bool": {
      "should": [
        {
          "function_score": {
            "query": {
              "multi_match": {
                "query": "term",
                "fields": [
                  "name^3.0",
                  "name.fullName^10.0",
                  "description",
                  "description.fullDesc",
                  "detail",
                  "detail.fullDetail"
                ],
                "type": "cross_fields",
                "operator": "AND",
                "slop": 0,
                "prefix_length": 0,
                "max_expansions": 50,
                "zero_terms_query": "NONE",
                "auto_generate_synonyms_phrase_query": true,
                "fuzzy_transpositions": true,
                "boost": 10
              }
            },
            "boost": "5",
            "field_value_factor": {
              "field": "gain",
              "factor": 1.2,
              "modifier": "sqrt",
              "missing": 1
            },
            "boost_mode": "multiply"
          }
        },
        {
          "function_score": {
            "query": {
              "multi_match": {
                "query": "term",
                "fields": [
                  "name.fullName",
                  "description.fullDesc",
                  "detail.fullDetail"
                ],
                "type": "cross_fields",
                "operator": "OR",
                "slop": 0,
                "prefix_length": 0,
                "max_expansions": 50,
                "zero_terms_query": "NONE",
                "auto_generate_synonyms_phrase_query": true,
                "fuzzy_transpositions": true,
                "boost": 6
              }
            },
            "boost": "5",
            "field_value_factor": {
              "field": "gain",
              "factor": 1.2,
              "modifier": "sqrt",
              "missing": 1
            },
            "boost_mode": "multiply"
          }
        }
      ],
      "adjust_pure_negative": true,
      "boost": 1
    }
  },
  "version": true
}

请注意，在上述查询中，我在两个 multi_match 查询中都使用了相同的得分逻辑.您可以根据需要对其进行修改.

Please note that in the above query I have used same score logic in both multi_match queries. You can modify them as per your needs.

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