** Installation To install flask-msearch:
#+BEGIN_SRC shell pip install flask-msearch
pip install whoosh blinker
pip install elasticsearch==6.3.1 #+END_SRC
Or alternatively, you can download the repository and install manually by doing: #+BEGIN_SRC sehll git clone https://github.com/honmaple/flask-msearch cd flask-msearch python setup.py install #+END_SRC
** Quickstart #+BEGIN_SRC python from flask_msearch import Search [...] search = Search() search.init_app(app)
# models.py
class Post(db.Model):
__tablename__ = 'post'
__searchable__ = ['title', 'content']
# views.py
@app.route("/search")
def w_search():
keyword = request.args.get('keyword')
results = Post.query.msearch(keyword,fields=['title'],limit=20).filter(...)
# or
results = Post.query.filter(...).msearch(keyword,fields=['title'],limit=20).filter(...)
# elasticsearch
keyword = "title:book AND content:read"
# more syntax please visit https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-query-string-query.html
results = Post.query.msearch(keyword,limit=20).filter(...)
return ''
#+END_SRC
** Config
#+BEGIN_SRC python # when backend is elasticsearch, MSEARCH_INDEX_NAME is unused # flask-msearch will use table name as elasticsearch index name unless set msearch_index MSEARCH_INDEX_NAME = 'msearch' # simple,whoosh,elaticsearch, default is simple MSEARCH_BACKEND = 'whoosh' # table's primary key if you don't like to use id, or set msearch_primary_key for special model MSEARCH_PRIMARY_KEY = 'id' # auto create or update index MSEARCH_ENABLE = True # logger level, default is logging.WARNING MSEARCH_LOGGER = logging.DEBUG # SQLALCHEMY_TRACK_MODIFICATIONS must be set to True when msearch auto index is enabled SQLALCHEMY_TRACK_MODIFICATIONS = True # when backend is elasticsearch ELASTICSEARCH = {"hosts": ["127.0.0.1:9200"]} #+END_SRC
** Usage #+BEGIN_SRC python from flask_msearch import Search [...] search = Search() search.init_app(app)
class Post(db.Model):
__tablename__ = 'basic_posts'
__searchable__ = ['title', 'content']
id = db.Column(db.Integer, primary_key=True)
title = db.Column(db.String(49))
content = db.Column(db.Text)
def __repr__(self):
return '<Post:{}>'.format(self.title)
#+END_SRC
if raise sqlalchemy ValueError,please pass db param to Search #+BEGIN_SRC python db = SQLalchemy() search = Search(db=db) #+END_SRC
*** Create_index #+BEGIN_SRC sh search.create_index() search.create_index(Post) #+END_SRC
*** Update_index #+BEGIN_SRC python search.update_index() search.update_index(Post) # or search.create_index(update=True) search.create_index(Post, update=True) #+END_SRC
*** Delete_index #+BEGIN_SRC python search.delete_index() search.delete_index(Post) # or search.create_index(delete=True) search.create_index(Post, delete=True) #+END_SRC
*** Custom Analyzer only for whoosh backend #+BEGIN_SRC python from jieba.analyse import ChineseAnalyzer search = Search(analyzer=ChineseAnalyzer()) #+END_SRC
or use =__msearch_analyzer__= for special model
#+BEGIN_SRC python
class Post(db.Model):
__tablename__ = 'post'
__searchable__ = ['title', 'content', 'tag.name']
__msearch_analyzer__ = ChineseAnalyzer()
#+END_SRC
*** Custom index name If you want to set special index name for some model. #+BEGIN_SRC python class Post(db.Model): tablename = 'post' searchable = ['title', 'content', 'tag.name'] msearch_index = "post111" #+END_SRC
*** Custom schema #+BEGIN_SRC python from whoosh.fields import ID
class Post(db.Model):
__tablename__ = 'post'
__searchable__ = ['title', 'content', 'tag.name']
__msearch_schema__ = {'title': ID(stored=True, unique=True), 'content': 'text'}
#+END_SRC
*Note:* if you use =hybrid_property=, default field type is =Text= unless set special =__msearch_schema__=
*** Custom parser #+begin_src python from whoosh.qparser import MultifieldParser
class Post(db.Model):
__tablename__ = 'post'
__searchable__ = ['title', 'content']
def _parser(fieldnames, schema, group, **kwargs):
return MultifieldParser(fieldnames, schema, group=group, **kwargs)
__msearch_parser__ = _parser
#+end_src
*Note:* Only for =MSEARCH_BACKEND= is =whoosh=
*** Custom index signal flask-msearch uses flask signal to update index by default, if you want to use other asynchronous tools such as celey to update index, please set special =MSEARCH_INDEX_SIGNAL= #+begin_src python # app.py app.config["MSEARCH_INDEX_SIGNAL"] = celery_signal # or use string as variable app.config["MSEARCH_INDEX_SIGNAL"] = "modulename.tasks.celery_signal" search = Search(app)
# tasks.py
from flask_msearch.signal import default_signal
@celery.task(bind=True)
def celery_signal_task(self, backend, sender, changes):
default_signal(backend, sender, changes)
return str(self.request.id)
def celery_signal(backend, sender, changes):
return celery_signal_task.delay(backend, sender, changes)
#+end_src
** Relate index(Experimental) for example #+BEGIN_SRC python class Tag(db.Model): tablename = 'tag'
id = db.Column(db.Integer, primary_key=True)
name = db.Column(db.String(49))
class Post(db.Model):
__tablename__ = 'post'
__searchable__ = ['title', 'content', 'tag.name']
id = db.Column(db.Integer, primary_key=True)
title = db.Column(db.String(49))
content = db.Column(db.Text)
# one to one
tag_id = db.Column(db.Integer, db.ForeignKey('tag.id'))
tag = db.relationship(
Tag, backref=db.backref(
'post', uselist=False), uselist=False)
def __repr__(self):
return '<Post:{}>'.format(self.title)
#+END_SRC
You must add msearch_FUN to Tag model,or the tag.name can't auto update. #+BEGIN_SRC python class Tag.... ...... def msearch_post_tag(self, delete=False): from sqlalchemy import text sql = text('select id from post where tag_id=' + str(self.id)) return { 'attrs': [{ 'id': str(i[0]), 'tag.name': self.name } for i in db.engine.execute(sql)], '_index': Post } #+END_SRC


职场AI,就用扣子
AI办公助手,复杂任务高效处理。办公效率低?扣子空间AI助手支持播客生成、PPT制作、网页开发及报告写作,覆盖科研、商业、舆情等领域的专家Agent 7x24小时响应,生活工作无缝切换,提升50%效率!


多风格AI绘画神器
堆友平台由阿里巴巴设计团队创建,作为一款AI驱动的设计工具,专为设计师提供一站式增长服务。功能覆盖海量3D素材、AI绘画、实时渲染以及专业抠图,显著提升设计品质和效率。平台不仅提供工具,还是一个促进创意交流和个人发展的空间,界面友好,适合所有级别的设计师和创意工作者。


零代码AI应用开发平台
零代码AI应用开发平台,用户只需一句话简单描述需求,AI能自动生成小程序、APP或H5网页应用,无需编写代码。


免费创建高清无水印Sora视频
Vora是一个免费创建高清无水印Sora视频的AI工具


最适合小白的AI自动化工作流平台
无需编码,轻松生成可复用、可变现的AI自动化工作流

大模型驱动的Excel数据处理工具
基于大模型交互的表格处理系统,允许用户通过对话方式完成数据整理和可视化分析。系统采用机器学习算法解析用户指令,自动执行排序、公式计算和数据透视等操作,支持多种文件格式导入导出。数据处理响应速度保持在0.8秒以内,支持超过100万行数据的即时分析。


AI辅助编程,代码自动修复
Trae是一种自适应的集成开发环境(IDE),通过自动化和多元协作改变开发流程。利用Trae,团队能够更快速、精确地编写和部署代码,从而提高编程效率和项目交付速度。Trae具备上下文感知和代码自动完成功能,是提升开发效率的理想工具。


AI论文写作指导平台
AIWritePaper论文写作是一站式AI论文写作辅助工具,简化了选题、文献检索至论文撰写的整个过程。通过简单设定,平台可快速生成高质量论文大纲和全文,配合图表、参考文献等一应俱全,同时提供开题报告和答辩PPT等增值服务,保障数据安全,有效提升写作效率和论文质量。


AI一键生成PPT,就用博思AIPPT!
博思AIPPT,新一代的AI生成PPT平台,支持智能生成PPT、AI美化PPT、文本&链接生成PPT、导入Word/PDF/Markdown文档生成PPT等,内置海量精美PPT模板,涵盖商务、教育、科技等不同风格,同时针对每个页面提供多种版式,一键自适应切换,完美适配各种办公场景。


AI赋能电商视觉革命,一站式智能商拍平台
潮际好麦深耕服装行业,是国内AI试衣效果最好的软件。使 用先进AIGC能力为电商卖家批量提供优质的、低成本的商拍图。合作品牌有Shein、Lazada、安踏、百丽等65个国内外头部品牌,以及国内10万+淘宝、天猫、京东等主流平台的品牌商家,为卖家节省将近85%的出图成本,提升约3倍出图效率,让品牌能够快速上架。