How to use Jieba to implement word segmentation search function in Django?

Django efficient word segmentation search solution

In full-text search, it is crucial to accurately match user input. For example, if a user searches for "PPT template file", the ideal result should contain matching terms such as "PPT file", "PPT", "PPT template", "file", and "template". This article discusses how to implement such word segmentation search function under the Django framework.

The core of Django word segmentation search is to choose the right Python word segmentation library. This article uses the popular Chinese word participle tool Jieba. The installation method is as follows:

 pip install jieba

After the installation is complete, you can import and use Jieba for word segmentation in the Django project:

 import jieba

text = 'PPT template file'

# Precision pattern participle jieba.lcut(text) # Output: ['PPT', 'Template', 'File']

# Search engine pattern participle (more suitable for search)
jieba.lcut_for_search(text) # Output: ['PPT', 'Template', 'File']

jieba.lcut_for_search() method is more suitable for search scenarios, it returns a more comprehensive keyword combination. For example, for "PPT template files", it may return more fine-grained keywords to improve search recall.

Next, the word participle results need to be compared with the text in the database. You can use Django's ORM or other database query methods to build query conditions based on word segmentation results, such as using contains or icontains operators for fuzzy matching, or using full-text indexing techniques (such as full-text indexing of PostgreSQL) to improve search efficiency. The matching results can be highlighted using the template engine to improve the user experience.

By combining Jieba word segmentation library and Django's database query functions, an efficient and accurate word segmentation search system can be built, which significantly improves search efficiency and user experience.

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