NgramField根据查询项的子字符串返回resutls [英] NgramField returning resutls based on substring of the query term

问题描述

我有一个仓库模型，其索引如下

 类WarehouseIndex（SearchIndex，可索引）：
 $
 SearchIndex存储Model Warehouse索引的类

 text = CharField（document = True，use_template = True）
 search_auto = NgramField（）
 .... 
 
 def get_model（self）：
 return WareHouse 
  

在我的shell中，我运行以下sqs查询。

 >>> sqs = SearchQuerySet（）。models（WareHouse）
>>> sqs.filter（customers = 3）.filter（search_auto ='pondicherry'）
  

的结果没有确切的术语 pondicherry 它还提供了一些匹配如 ich ， che ， ndi 等。

我甚至尝试使用 __ exact 和确切的但都返回相同的结果？

编辑：索引映射，索引设置

如何避免这种情况，并提供仅限于 pondicherry ？

解决方案

正如@Val在上面的答案中所说的那样，错误是因为search_analyzer和indexed_analyzer是一样的，导致了这个问题， >

我们都知道 haystack 在设置基本弹性搜索配置方面非常不灵活，我all elasticstack ，而在我的setting.py中，将后端更改为 elasticsearch_backend 作为建议，并另外添加以下2个配置

 ＃elasticslack设置
 ELASTICSEARCH_DEFAULT_ANALYZER ='雪球'
 ELASTICSEARCH_DEFAULT_NGRAM_SEARCH_ANALYZER ='标准'
  

这似乎解决了我的问题。 >

I have a Warehouse Model which is getting index as follows

class WarehouseIndex(SearchIndex, Indexable):
    """
    SearchIndex Class that stored indexes for Model Warehouse
    """
    text = CharField(document=True, use_template=True)
    search_auto = NgramField()
    ....

   def get_model(self):
        return WareHouse

In my shell I am running the following sqs query.

>>> sqs = SearchQuerySet().models(WareHouse)
>>> sqs.filter(customers=3).filter(search_auto='pondicherry')

This returns result consisting of results that do not have exact term pondicherry it also provides me some results that match terms like ich, che, ndi, etc.

I have even tried using __exact and Exact but all return the same result?

EDIT: Index mapping, Index Setting

How can I avoid this and provide result only of term pondicherry?

解决方案

As @Val has stated in the above answer, the error was because search_analyzer and indexed_analyzer are same which caused the issue,

As we all know haystack is very inflexible in setting up the basic elasticsearch configuration, I installed elasticstack and in my setting.py changed the backend to it's elasticsearch_backend as suggest and additionally added the following 2 configurations

# elasticslack setting
ELASTICSEARCH_DEFAULT_ANALYZER = 'snowball'
ELASTICSEARCH_DEFAULT_NGRAM_SEARCH_ANALYZER = 'standard'   

this seemed to solve my problem.

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