elasticsearch - Elasticsearch:带 “function_score”的 “boost_mode”忽略:“replace”函数得分

Question

我正在尝试使用function_score中定义的不同功能修改普通查询的分数。

为了找出由我的函数计算出的分数，我将“boost_mode”指定为“replace”。但是，这会使所有分数保持不变:全部等于1。

考虑以下查询:

{
  "query": {
    "function_score": {
      "query": {
        "terms": {
          "name": ["men", "women"]
        }
      },
      "score_mode": "avg",
      "functions": [
        {
          "filter": {
            "terms": {
              "name": ["men","man"]
            }
          },
          "weight": 2
        }
      ],
      "boost_mode": "replace"
    }
  },
  "explain": true,
  "from": 0
}

我希望在这里获得不同的分数，具体取决于“姓名”字段包含“男人”还是“男人”。此类文件肯定存在于索引中。

此外，如果我指定“explain”:true，则说明中显示的分数与hit的_score字段中显示的分数不同:

{  
    "_shard":0,
    "_node":"ro26nlDuTfiTaIlIgHqg4g",
    "_index":"products10",
    "_type":"product_basic",
    "_id":"0c25fc90433481aac0cce62dd1a21e06",
    "_score":1,
    "_source":{  
        "category":[  
            "Chicago Blues",
            "Blues",
            "Styles",
            "Digital Music"
        ],
        "site_name":"www.amazon.com",
        "name":"Who's That Women?",
        "url":"http://www.amazon.com/dp/B001125F8I/",
        "price":0.99,
        "reviews":[  

        ],
        "breadcrumb":"Digital Music",
        "in_stock":true,
        "features":[  

        ],
        "pic_urls":[  
            "http://ecx.images-amazon.com/images/I/51CvgPMwtsL.jpg",
            "http://ecx.images-amazon.com/images/I/51CvgPMwtsL.jpg"
        ],
        "name_semantic_core":[  
            "Women ?",
            "?"
        ],
        "category_path":"/Chicago Blues/Blues/Styles/",
        "visit_datetime":"2014-11-04T11:50:34.169779",
        "detected_category":"Digital Music"
    },
    "_explanation":{  
        "value":1.2249949,
        "description":"function score, no filter match, product of:",
        "details":[  
            {  
                "value":1.2249949,
                "description":"product of:",
                "details":[  
                    {  
                        "value":2.4499898,
                        "description":"sum of:",
                        "details":[  
                            {  
                                "value":2.4499898,
                                "description":"weight(name:women in 6181332) [PerFieldSimilarity], result of:",
                                "details":[  
                                    {  
                                        "value":2.4499898,
                                        "description":"score(doc=6181332,freq=1.0), product of:",
                                        "details":[  
                                            {  
                                                "value":0.67790973,
                                                "description":"queryWeight, product of:",
                                                "details":[  
                                                    {  
                                                        "value":7.228071,
                                                        "description":"idf(docFreq=238699, maxDocs=120967660)"
                                                    },
                                                    {  
                                                        "value":0.09378847,
                                                        "description":"queryNorm"
                                                    }
                                                ]
                                            },
                                            {  
                                                "value":3.6140356,
                                                "description":"fieldWeight in 6181332, product of:",
                                                "details":[  
                                                    {  
                                                        "value":1,
                                                        "description":"tf(freq=1.0), with freq of:",
                                                        "details":[  
                                                            {  
                                                                "value":1,
                                                                "description":"termFreq=1.0"
                                                            }
                                                        ]
                                                    },
                                                    {  
                                                        "value":7.228071,
                                                        "description":"idf(docFreq=238699, maxDocs=120967660)"
                                                    },
                                                    {  
                                                        "value":0.5,
                                                        "description":"fieldNorm(doc=6181332)"
                                                    }
                                                ]
                                            }
                                        ]
                                    }
                                ]
                            }
                        ]
                    },
                    {  
                        "value":0.5,
                        "description":"coord(1/2)"
                    }
                ]
            },
            {  
                "value":1,
                "description":"queryBoost"
            }
        ]
    }
}

此处的说明显示“值”:1.2249949，而“_score”为1。

我究竟做错了什么？在结合原始查询分数之前，如何获得使用functinon_score函数计算的实际分数？

更新:如果找到匹配的产品，这就是我得到的。出于某种原因，得分为1，而得分应为2:

Answer 1

在您的示例中，该函数与任何文档都不匹配:function score, no filter match,。另外，在使用replace时，从documentation发生以下情况:only function score is used, the query score is ignored。因此，情况如下:过滤器不匹配-因此不计算任何得分-replace将使查询分数被忽略并使用过滤器中的分数(由于不存在而不存在比赛)。

当功能不匹配时，该功能的默认值为1。您可以使用"boost_mode": "sum"进行检查。我的意见是，这就是您看到1分数的原因。

关于avg行为，这在我看来并不好，而且很可能是一个错误。我在这里报告了:https://github.com/elastic/elasticsearch/issues/13732