我用字段title_auto创建了一个索引:
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')
flex 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
}
}
}
}
}
我尝试进行自动完成搜索，但是可以返回太多不相关的结果:
qs = SearchQuerySet().models(Game).autocomplete(title_auto=search_phrase)要么
qs = SearchQuerySet().models(Game).filter(title_auto=search_phrase)它们都产生相同的输出。
如果search_phrase为“monopoly”，则第一个结果的标题中包含“Monopoly”，但是，由于只有2个相关项，因此返回51。其他与“Monopoly”无关。
所以我的问题是-如何更改结果的相关性？
最佳答案
由于我还没有看到完整的映射，因此很难确定，但是我怀疑问题是分析器(其中之一)同时用于索引和搜索。因此，当您为文档建立索引时，会创建并索引许多ngram术语。如果您搜索并且对搜索文本也进行了相同的分析，则会生成许多搜索词。由于最小的ngram是单个字母，因此几乎所有查询都将匹配许多文档。
我们在博客文章http://blog.qbox.io/multi-field-partial-word-autocomplete-in-elasticsearch-using-ngrams上写了一篇关于将ngrams用于自动完成的文章，您可能会觉得有帮助。但是，我将给您提供一个更简单的示例来说明我的意思。我对干草堆不是很熟悉，所以我可能无法为您提供帮助，但是我可以在Elasticsearch中用ngrams解释问题。
首先，我将建立一个使用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"}
并运行一个简单的
match搜索"poly":POST /test_index/_search
{
"query": {
"match": {
"title": "poly"
}
}
}
它返回所有五个文档:
{
"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"
}
}
]
}
}
这是因为搜索项
"poly"被标记为术语"p"，"o"，"l"和"y"，由于每个文档中的"title"字段被标记为单字母术语，因此它们与每个文档匹配。如果我们改用此映射重建索引(相同的分析器和文档):
"mappings": {
"doc": {
"properties": {
"title": {
"type": "string",
"index_analyzer": "nGram_analyzer",
"search_analyzer": "standard"
}
}
}
}
该查询将返回我们期望的结果:
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"
}
}
]
}
}
边缘ngram的工作原理类似，除了仅使用单词开头的术语。
这是我用于此示例的代码:
http://sense.qbox.io/gist/b24cbc531b483650c085a42963a49d6a23fa5579
关于django - django-haystack自动完成返回的结果太宽，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/29008725/