Question

我创建了一个带有字段title_auto的索引：

class GameIndex(indexes.SearchIndex, indexes.Indexable):
    text = indexes.CharField(document=True, model_attr='title')
    title = indexes.CharField(model_attr='title')
    title_auto = indexes.NgramField(model_attr='title')

弹性搜索设置如下所示：

ELASTICSEARCH_INDEX_SETTINGS = {
    'settings': {
        "analysis": {
            "analyzer": {
                "ngram_analyzer": {
                    "type": "custom",
                    "tokenizer": "lowercase",
                    "filter": ["haystack_ngram"],
                    "token_chars": ["letter", "digit"]
                },
                "edgengram_analyzer": {
                    "type": "custom",
                    "tokenizer": "lowercase",
                    "filter": ["haystack_edgengram"]
                }
            },
            "tokenizer": {
                "haystack_ngram_tokenizer": {
                    "type": "nGram",
                    "min_gram": 1,
                    "max_gram": 15,
                },
                "haystack_edgengram_tokenizer": {
                    "type": "edgeNGram",
                    "min_gram": 1,
                    "max_gram": 15,
                    "side": "front"
                }
            },
            "filter": {
                "haystack_ngram": {
                    "type": "nGram",
                    "min_gram": 1,
                    "max_gram": 15
                },
                "haystack_edgengram": {
                    "type": "edgeNGram",
                    "min_gram": 1,
                    "max_gram": 15
                }
            }
        }
    }
}

我尝试进行自动填充搜索，但是有效，但会返回太多不相关的结果：

qs = SearchQuerySet().models(Game).autocomplete(title_auto=search_phrase)

OR

qs = SearchQuerySet().models(Game).filter(title_auto=search_phrase)

它们都产生相同的输出。

如果search_phrase是＆＃34;垄断＆＃34;，首先结果包含＆＃34; Monopoly＆＃34;然而，在他们的头衔中，因为只有2个相关的项目，它返回51.其他的与＆＃34; Monopoly＆＃34;一点都不。

所以我的问题是 - 如何更改结果的相关性？

Answer 1

由于我还没有看到您的完整映射，因此很难确定，但我怀疑问题在于分析器（其中之一）正用于索引和搜索。因此，当您索引文档时，会创建许多ngram术语并将其编入索引。如果您搜索并且您的搜索文本也以相同的方式进行分析，则会生成大量搜索字词。由于你的最小ngram是一个字母，几乎任何查询都会匹配很多文档。

我们写了一篇关于使用ngrams进行自动填充的博客文章，您可能会发现这些内容很有帮助，http://blog.qbox.io/multi-field-partial-word-autocomplete-in-elasticsearch-using-ngrams。但是我会给你一个更简单的例子来说明我的意思。我对大海捞针不太熟悉，所以我可能无法帮助你，但我可以用Elasticsearch中的ngrams来解释这个问题。

首先，我设置一个使用ngram分析器进行索引和搜索的索引：

PUT /test_index
{
   "settings": {
       "number_of_shards": 1,
      "analysis": {
         "filter": {
            "nGram_filter": {
               "type": "nGram",
               "min_gram": 1,
               "max_gram": 15,
               "token_chars": [
                  "letter",
                  "digit",
                  "punctuation",
                  "symbol"
               ]
            }
         },
         "analyzer": {
            "nGram_analyzer": {
               "type": "custom",
               "tokenizer": "whitespace",
               "filter": [
                  "lowercase",
                  "asciifolding",
                  "nGram_filter"
               ]
            }
         }
      }
   },
   "mappings": {
        "doc": {
            "properties": {
                "title": {
                    "type": "string", 
                    "analyzer": "nGram_analyzer"
                }
            }
        }
   }
}

并添加一些文档：

PUT /test_index/_bulk
{"index":{"_index":"test_index","_type":"doc","_id":1}}
{"title":"monopoly"}
{"index":{"_index":"test_index","_type":"doc","_id":2}}
{"title":"oligopoly"}
{"index":{"_index":"test_index","_type":"doc","_id":3}}
{"title":"plutocracy"}
{"index":{"_index":"test_index","_type":"doc","_id":4}}
{"title":"theocracy"}
{"index":{"_index":"test_index","_type":"doc","_id":5}}
{"title":"democracy"}

并对match进行简单的"poly"搜索：

POST /test_index/_search
{
    "query": {
        "match": {
           "title": "poly"
        }
    }
}

它返回所有五个文件：

{
   "took": 3,
   "timed_out": false,
   "_shards": {
      "total": 1,
      "successful": 1,
      "failed": 0
   },
   "hits": {
      "total": 5,
      "max_score": 4.729521,
      "hits": [
         {
            "_index": "test_index",
            "_type": "doc",
            "_id": "2",
            "_score": 4.729521,
            "_source": {
               "title": "oligopoly"
            }
         },
         {
            "_index": "test_index",
            "_type": "doc",
            "_id": "1",
            "_score": 4.3608603,
            "_source": {
               "title": "monopoly"
            }
         },
         {
            "_index": "test_index",
            "_type": "doc",
            "_id": "3",
            "_score": 1.0197333,
            "_source": {
               "title": "plutocracy"
            }
         },
         {
            "_index": "test_index",
            "_type": "doc",
            "_id": "4",
            "_score": 0.31496215,
            "_source": {
               "title": "theocracy"
            }
         },
         {
            "_index": "test_index",
            "_type": "doc",
            "_id": "5",
            "_score": 0.31496215,
            "_source": {
               "title": "democracy"
            }
         }
      ]
   }
}

这是因为搜索字词"poly"被标记为"p"，"o"，"l"和"y"这两个词，因为"title"每个文档中的1}}字段被标记为单个字母的术语，匹配每个文档。

如果我们使用此映射重建索引（相同的分析器和文档）：

"mappings": {
  "doc": {
     "properties": {
        "title": {
           "type": "string",
           "index_analyzer": "nGram_analyzer",
           "search_analyzer": "standard"
        }
     }
  }
}

查询将返回我们期望的内容：

POST /test_index/_search
{
    "query": {
        "match": {
           "title": "poly"
        }
    }
}
...
{
   "took": 1,
   "timed_out": false,
   "_shards": {
      "total": 1,
      "successful": 1,
      "failed": 0
   },
   "hits": {
      "total": 2,
      "max_score": 1.5108256,
      "hits": [
         {
            "_index": "test_index",
            "_type": "doc",
            "_id": "1",
            "_score": 1.5108256,
            "_source": {
               "title": "monopoly"
            }
         },
         {
            "_index": "test_index",
            "_type": "doc",
            "_id": "2",
            "_score": 1.5108256,
            "_source": {
               "title": "oligopoly"
            }
         }
      ]
   }
}

Edge ngrams的工作方式类似，只是只使用从单词开头开始的术语。

以下是我在此示例中使用的代码：

http://sense.qbox.io/gist/b24cbc531b483650c085a42963a49d6a23fa5579

Answer 2

不幸的是，此时似乎没有办法（除了实现自定义后端）分别通过Django-Haystack配置搜索分析器和索引分析器。如果Django-Haystack自动完成返回的结果太宽，您可以利用每个搜索结果提供的分数值来优化输出。

if search_query != "":
# Use autocomplete query or filter
# with results_filtered being a SearchQuerySet()
    results_filtered = results_filtered.filter(text=search_query)

#Remove objects with a low score
for result in results_filtered:
    if result.score < SEARCH_SCORE_THRESHOLD:
        results_filtered = results_filtered.exclude(id=result.id)

在不必定义自己的后端和方案构建的情况下，它对我来说非常合理。

django-haystack autocomplete返回太宽的结果

2 个答案: