How to use haystack with Django in python: an example of the full-text search framework

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haystack: A framework for full-text retrieval

whoosh: written in pure Python Full-text search engine

jieba: a free Chinese word segmentation package

First install These three packages

pip install django-haystack
pip install whoosh
pip install jieba

1. Modify the settings.py file and install the application haystack ,

2. Configure the search engine in the settings.py file

HAYSTACK_CONNECTIONS = {
 'default': {
  # 使用whoosh引擎
  'ENGINE': 'haystack.backends.whoosh_cn_backend.WhooshEngine',
  # 索引文件路径
  'PATH': os.path.join(BASE_DIR, 'whoosh_index'),
 }
}
# 当添加、修改、删除数据时，自动生成索引
HAYSTACK_SIGNAL_PROCESSOR = 'haystack.signals.RealtimeSignalProcessor'

3. Create the "search/indexes/blog/" directory under the templates directory Create a file blog_text.txt using the name of the blog application
#Specify the attributes of the index

{{ object.title }}
{{ object.text}}
{{ object.keywords }}

#4. Create search_indexes

from haystack import indexes
from models import Post #指定对于某个类的某些数据建立索引
class GoodsInfoIndex(indexes.SearchIndex, indexes.Indexable): 
 text = indexes.CharField(document=True, use_template=True)
 def get_model(self):  
 return Post #搜索的模型类
 def index_queryset(self, using=None):  
  return self.get_model().objects.all()

# under the application that needs to be searched ##5.

1. Modify the haystack file

2. Find the haystack directory under the virtual environment py_django. This directory will vary depending on the python environment you use, and the path will vary. Same.

3. site-packages/haystack/backends/ Create a file named ChineseAnalyzer.py and write the following code for Chinese word segmentation

import jieba
from whoosh.analysis import Tokenizer, Token
 class ChineseTokenizer(Tokenizer):
 def __call__(self, value, positions=False, chars=False,
     keeporiginal=False, removestops=True,
     start_pos=0, start_char=0, mode='', **kwargs):
  t = Token(positions, chars, removestops=removestops, mode=mode,
     **kwargs)
  seglist = jieba.cut(value, cut_all=True)
  for w in seglist:
   t.original = t.text = w
   t.boost = 1.0
   if positions:
    t.pos = start_pos + value.find(w)
   if chars:
    t.startchar = start_char + value.find(w)
    t.endchar = start_char + value.find(w) + len(w)
   yield t
 def ChineseAnalyzer():
 return ChineseTokenizer()

6.

1. Copy the whoosh_backend.py file and change it to the following name

whoosh_cn_backend.py

Import the Chinese word segmentation module into the copied file

from .ChineseAnalyzer import ChineseAnalyzer

2. Change the word analysis class to Chinese

Find analyzer=StemmingAnalyzer() and change it to analyzer=ChineseAnalyzer()

7 . The last step is to create initial index data

python manage.py rebuild_index

8. Create a search template and create a search.html template in templates/indexes/

Search results are paginated. , the context passed by the view to the template is as follows

query: search keyword

page: page object of the current page

paginator: paging paginator object

9. Import the module into your own application view

from haystack.generic_views import SearchView

Define a class to override the get_context_data method so that you can add it to the template Pass custom context.

class GoodsSearchView(SearchView):
　　def get_context_data(self, *args, **kwargs):
　　　　context = super().get_context_data(*args, **kwargs)
　　　　context['iscart']=1
　　　　context['qwjs']=2
　　　　return context

Add this url to the urls file of the application and use the class as a view method. as_view()

url('^search/$', views.BlogSearchView.as_view())

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