semvec/pypi-packages huggingface dataset:
ner-agent 0.4.1
A simple NER agent
License: MIT
Requires Python: <4,>=3.11

# ner-agent

A simple, language-agnostic Named Entity Recognition (NER) agent powered by LLMs.

## Features

- __Multilingual__: Supports English, Chinese, Japanese, Korean, French, German, Russian, Spanish, and more.
- __Entity Types__: Recognizes PERSON, NORP (nationalities, religions, political groups, languages), LOCATION, DATETIME, NUMERIC, and PROPER_NOUN (events, works, organizations, products, etc.).
- __Easy Integration__: Use as a Python library with a simple async API.
- __Tested__: Includes comprehensive test cases for multiple languages and entity types.

## Installation

```bash
pip install ner-agent
```

Or clone and install locally:

```bash
git clone https://github.com/allen2c/ner-agent.git
cd ner-agent
pip install .
```

__Python 3.11+ required.__

## Usage

```python
import asyncio
from ner_agent import NerAgent

async def main():
    agent = NerAgent()
    text = "Elon Musk visited Tesla's Gigafactory in Austin on March 15, 2024, announcing a 20% increase."
    result = await agent.run(text)
    for entity in result.entities:
        print(entity)

asyncio.run(main())
```

__Output:__

```plaintext
name='PERSON' value='Elon Musk' start=0 end=9
name='PROPER_NOUN' value='Tesla' start=18 end=23
name='LOCATION' value='Gigafactory' start=26 end=37
...
```

## Entity Types

- `PERSON`: People, including fictional characters.
- `NORP`: Nationalities, religious groups, political groups, languages.
- `LOCATION`: Geopolitical entities, facilities, places.
- `DATETIME`: Dates, times, periods, ages.
- `NUMERIC`: Numbers, money, quantities, percentages, ordinals/cardinals.
- `PROPER_NOUN`: Named events, works, laws, products, organizations, companies, etc.

## Testing

To run the tests:

```bash
pytest
```

## Configuration

- By default, uses OpenAI-compatible LLMs via [openai-agents](https://pypi.org/project/openai-agents/).
- You can configure the model and OpenAI client (see `tests/conftest.py` for examples).

## License

MIT License

---

For more details, see [ner_agent/__init__.py](ner_agent/__init__.py) and the [tests](tests/test_ner_agent_run.py).

If you need more advanced usage or want to contribute, please check the [GitHub repository](https://github.com/allen2c/ner-agent).
x2case 1.2.3
Xmind to jira test case
License: MIT
Requires Python: >=3.11
Keywords: Xmind, testcase, jira, xmindparser

# X2Case

> **X2Case** , `Xmind` 文件转 `jira` CSV 文件。 支持新版 xmind zen。

### 一、安装 Install

```
pip3 install x2case
```

### 二、使用方式 Usage

#### 1. to jira csv

```
import json
import logging

from x2case.func import XmindZenParser
from x2case.jira import xmind_to_jira_csv_file

logging.basicConfig(level=logging.INFO)

xmind_file = 'docs/jira_demo.xmind'
print('Start to convert XMind file: %s' % xmind_file)

# 1、testcases import file
# (1) jira
csv_file = xmind_to_jira_csv_file(xmind_file)
print(f'Convert XMind file to zentao csv file successfully: {xmind_file}')
```

#### 2. to testcases json file

```
parser = XmindZenParser(xmind_file)
# (1) testsuite
testsuite_json_file = parser.xmind_2_suite_json_file()
print('Convert XMind file to testsuite json file successfully: %s' % testsuite_json_file)
# (2) testcase
testcase_json_file = parser.xmind_2_case_json_file()
print('Convert XMind file to testcase json file successfully: %s' % testcase_json_file)
```

#### 3、test dict/json data

```
# (1) testsuite

test_suite = parser.get_xmind_testsuite_list()
print('Convert XMind to test suits dict data:\n%s' %
      json.dumps(test_suite, indent=2, separators=(',', ': '), ensure_ascii=False))
# (2) testcase
testcases = parser.get_xmind_testcase_list()
print('Convert Xmind to testcases dict data:\n%s' %
      json.dumps(testcases, indent=4, separators=(',', ': '), ensure_ascii=False))

print('Finished conversion, Congratulations!')

```

### 三、致谢

**X2Case** 工具的产生，受益于以下两个开源项目，并在此基础上扩展、优化，受益匪浅，感恩！

- 1、**[XMindParser](https://github.com/tobyqin/xmindparser)**：Parse xmind file to programmable data type (e.g. json,
  xml). Python 3.x required. Now it supports XmindZen file type as well.
- 2、**[Xmind2Testcase](https://github.com/zhuifengshen/xmind2testcase)**：XMind2TestCase 工具，提供了一个高效测试用例设计的解决方案！

（如果本项目对你有帮助的话，也欢迎 _**[star](https://github.com/Allenzzz/x2case)**_ ）

### LICENSE

```
MIT License
```