semvec/pypi-packages huggingface dataset:
openapi2oms 0.1.0
A tool to convert an OpenAPI spec to an OMS spec

# openapi2oms
This tool aims to convert an OpenAPI 3 spec to its corresponding OMS
equivalent. Not all features are supported, and some assumptions have
been made. Please read the caveats section to understand these
assumptions. 

## Storyscript
```coffee
result = openapi2oms convert spec: openApiSpec properties: {"serverIndex": 0}
```  

## Caveats
### Assumptions
1. If there are multiple content types available for a given path, the content type
   `application/json` shall be preferred if available. If `application/json` is not
   available, then an appropriate content type will be chosen arbitrarily
2. OMS doesn't support multiple responses. As such, the following order of response
   codes are considered as successful operations: `200, 201, 202, 2XX, 204, default`.
   Furthermore, since multiple content types are not supported, `application/json`
   will be used if available. If `application/json` is not available, then an
   appropriate content type will be chosen arbitrarily


### todos 
related:
1. [ ] https://github.com/microservices/openmicroservices.org/issues/50
2. [x] https://github.com/microservices/openmicroservices.org/pull/96

## License
MIT License
elasticsearch-tool 0.9.9
some like orm for elasticsearch, elasticsearch的辅助工具, 提供类似ORM的操作方式, 此版本提高了稳定性,解决了出现的bug
Requires Python: >=3.6

# ht_es

#### 介绍
python es的再封装

主要借鉴SQLAlchemy的方式

#### 使用说明

简单使用

```python
"""
搜索测试工具,数据填充脚本
"""
import random, time
from elasticsearch_tool.elements.document import Doc, Fields
from elasticsearch_tool.base_element.operate import datetime_tool, NOT, OR
from datetime import datetime

from elasticsearch_tool.config import Config


# 配置es服务器地址信息
Config.set_host('localhost', port='9200')


words = ['单词', '词汇', '检索', '我了', '艾克', '维护费', '没理解', '接是', '咯怕', '那么', '行风', '奶茶店', '全网通', '雨天',
         '末尾', '已收到', '就好撒大家看法', '是你们', '你们', '舞女', '不', '容易', '一样', '是你的', '玩儿一天']


def insert_indices():
    text = ''
    word = []
    id = int(time.time() * 1000)
    num = random.randint(000, 999)
    for _ in range(100):
        n = random.randint(0, len(words) - 1)
        text += words[n]
    for _ in range(5):
        n = random.randint(0, len(words) - 1)
        word.append(words[n])

    return {
        'text': text,
        'word': word,
        'id': id,
        'num': num,
        'date': datetime.today(),
        'has_go': num % 2 == 1,
        'height': time.time()
    }


class DocTry(Doc):
    # 配置文档的索引值,类型值,以及文档id的字段
    __indices__ = 'fifth'
    __types__ = 'docs'
    __pk__ = 'id'

    # 设定文档字段和类型
    text = Fields.String
    word = Fields.List
    id = Fields.Integer
    num = Fields.Integer
    date = Fields.Datetime
    has_go = Fields.Boolean
    height = Fields.Float


if __name__ == '__main__':

    for _ in range(15):
        time.sleep(0.01)
        data = insert_indices()
        doc = DocTry()
        for k, v in data.items():
            doc.__setattr__(k, v)

        top = doc.text > 'str'
        nop = doc.num > 20

        doc.save()

    DocTry().search(text='检索').all()
    all_re = DocTry().filter(DocTry.num > 50,
                             DocTry.text=='单词',
                             DocTry.text!='一样',
                             OR(DocTry.text == '知识',
                                DocTry.id > 100,
                                DocTry.word.in_(['你们', '那么', '已收到', '一样']),),
                             NOT(DocTry.has_go == False)).all()
    for result in all_re:
        print(result.query_to_dict())
```

更多使用方法,请见: 

#### 参与贡献

联系642641850@qq.com
condor-ir 1.2.2
A latent semantic search engine implementation
Keywords: lsa,search,search engine,semantics

=========
condor-ir
=========

.. image:: https://travis-ci.org/odarbelaeze/condor-ir.svg?branch=master
    :target: https://travis-ci.org/odarbelaeze/condor-ir

.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.495722.svg
   :target: https://doi.org/10.5281/zenodo.495722


This is a program to work with examples of Latent Semantic Analysis search
engines, a.k.a., `LSA <https://en.wikipedia.org/wiki/Latent_semantic_analysis>`_.
The program is set up so that it understands froac xml documents on input
as well as plain text records from isi web of knowledge.

You can find more information about froac repositories at
http://froac.manizales.unal.edu.co/froac/ http://froac.manizales.unal.edu.co/froac/
and about isi web of knowledge text files at
`the thomson reuters website <http://images.webofknowledge.com/WOK46/help/WOK/h_ml_options.html>`_

Installing the condor-ir package
----------------------------------

The second thing you will need is to download the program from its pypi
repository,

.. code-block:: bash

  pip install -U condor-ir

the ``-U`` parameter will upgrade the package to the latest version, a very
recommendable step for a unstable package.

For specific databases support you can install their appropriate extra package:

.. code-block:: bash

  pip install -U condor-ir[mysql]
  pip install -U condor-ir[postgres]

Furthermore, we require a bit of the ``nltk`` data package for the stems and
stop word removal to work.

.. code-block:: bash

  python -m nltk.downloader snowball_data stopwords

Finally, in order to prepare the database or reset the database in preparation
for a new version of `condor-ir` you can run the database preparation script,

.. code-block:: bash

  condor utils preparedb

If you need to specify a database other than the default you can do so through
environment variables:

.. code-block:: bash

  export CONDOR_DB_URL="mysql://localhost/condor"
  condor utils preparedb # will now work on mysql://localhost/condor 

CLI Interface
-------------

After installing the program you will have three basic commands at your
disposal, for handling bibliography sets, term document matrices and engines,
the CLI interface gives you most CRUD operations in a hierachical manner.

``condor`` triggers the main program and you can get top level help by running
``condor --help``.

``condor bibliography`` namespaces the bibliography set related commands, you can
list and get help about those using ``condor bibliography --help``.

``condor model`` is a short cut that offers the ``condor model create``
sub command, that creates both a term document matrix and an *lsa* search
engine, get help on *models* using ``condor model --help``.

``condor query <string...>`` this non crud command search a bibliography set
using a previously created search engine, the search engine can be targeted
figure out how using ``condor query --help``.

Feel free to check detailed descriptions of these commands using their
``--help`` flag.