semvec/pypi-packages huggingface dataset:
doc-track 0.1.4
Command that helps keeping track of piece of code marked as 'documented'
Requires Python: >=3.7

# doc-track

doc-track is a tool that allows developpers to make CI fail when a piece of code marked as "is documented" is added / modified / deleted.

# Installation
```bash
pip install doc-track
```

# Usage
```bash
doc-track check
```

# options available
`--version-from` # Git version of comparison used

`--version-to` # Git version to compare the first version to

`--path` # Path where comparison is checked

`--tags` # Pair list of start / end tag: `... --tags "# start","# end" "#start","#end"...`

`--config` # to specify config file, default .doctrack.yml

`--fail-status` # Return code in case code documented is modified, default 0

`--show-result` # To enable showing result in error output, default True

`--skip-blank-lines` # To skip blank lines changes of documented code, default True

# Config
You can add a configuration file that must respect the .yml format.

All options listed above are also available in the config file.

Here is an example of a typical usage for a python project's CI/CD:

```yaml
# .doctrack.yml

version_from: master
version_to: HEAD
path: .

tags:
  - ["# doc", "# enddoc"]

fail_status: 1
show_result: True
skip_blank_lines: True
```

# Mark code as documented

To mark code as documented, wrap it with both start tag and end tag.

Tag must be the only text on the line:
```python
class A:
    # doc
    def fct():
        return 22
    # enddoc
```
End tag must be different from start tag


# Warning
Do not allow user you do not trust to execute this code.
Since subprocesses are run it could lead to security breach.
pybragi 0.0.21.post2
pybragi is web server framework for fast&simple usage
Requires Python: >=3.8


# pybragi is web server framework
- tronado http&websocket impl
  - flask way
  - fastapi way
- service-discovery and service-register
  - mongo way
  - consul way
- graceful shutdown
- prometheus metrics
- benchmark tools for openai_api and http_api
- kafka producer and consumer
  - sliding window 



## commandline

```bash
# simple server
python -m pybragi.base.base_handler --port=1122      

# system info     eth0 ipv4/disk/memory/gpu
python -m pybragi.base.ps

```

```bash
# http prof
python -m pybragi.bench.http_prof --url http://10.121.0.11:18000/healthcheck --num=100 --qps=40
python -m pybragi.bench.http_prof --url http://10.121.0.11:19000/v1/chat/completions --method=POST -H "Content-Type: application/json" --body='{"model": "Qwen3-235B-A22B", "messages": [{"role": "user", "content": "你好"}], "stream": false}' --num=10 --qps=3


# llm openai
python -m pybragi.bench.llm_openai --qps=2 --url=http://localhost:30000 --api-key=aaabbb --jsonl-file=https://xxx.com/queue-2025-05-12.jsonl --num=10 --model=qwen3_235b_0419_25k  --metrics-dir='/home/cxj/self/py3_server/output'

python -m pybragi.bench.llm_openai --show-metrics-pkl='/output/llm_openai_2025-05-12T15-48-27.pkl'


| 统计类型 | prompt_len | output_len | 端到端 | ttft | itl |
|---------|-----------|-----------|-------|------|-----|
| 平均值 | 8172.300 | 235.500 | 5170.644 | 306.232 | 95.763 |
| 中位数 | 8327.000 | 237.500 | 5211.785 | 305.749 | 89.392 |
| 99分位 | 9312.820 | 270.640 | 6036.196 | 339.780 | 125.736 |
| 最大值 | 9331.000 | 271.000 | 6069.074 | 340.317 | 127.126 |



# server dao_server_discovery

python -m pybragi.server.dao_server_discovery --mongo-url "mongodb://aa:bb@127.0.0.1:3717/?authSource=llm&retryWrites=true" --mongo-db=llm --action=show_type --model-type=openai

# --mongo-db=llm --action=show --model-type=openai # openai同步流式调用

# --mongo-db=llm --action=show_type_online # api+回调
# --mongo-db=llm --action=show_type_online --model-type=openai # openai同步流式调用

# --mongo-db=llm --action=show_type # api+回调
# --mongo-db=llm --action=show_type --model-type=openai # openai同步流式调用

# --mongo-db=llm --action=show_all_online # 完全所有
# --mongo-db=llm --action=show_all # 完全所有



```
topsis-nandini 0.1.11
TOPSIS implementation for multi-criteria decision making

# 📊 TOPSIS — A Python Package for Multi-Criteria Decision Making

[![License](https://img.shields.io/badge/License-MIT-blue.svg)](LICENSE)
[![Python](https://img.shields.io/badge/Python-3.6%2B-blue)](https://www.python.org/downloads/)

> This package implements the **TOPSIS (Technique for Order Preference by Similarity to Ideal Solution)** algorithm — a widely used multi-criteria decision analysis method.

---

## 📌 What is TOPSIS?

**TOPSIS** is an algorithm that helps in ranking and selecting from a set of alternatives based on multiple criteria.  
It is based on the concept that the chosen alternative should have the **shortest distance from the ideal solution** and the **farthest from the negative-ideal solution**.

It is especially useful in areas like:
- Decision-making systems
- Recommendation engines
- Business and financial analysis
- Supply chain and logistics

---

## 🚀 Features

- Simple and clean API
- Accepts CSV or pandas DataFrame input
- Supports weighted and normalized decision matrices
- Command-line interface support
- Generates final rankings of alternatives
---

## 📦 Installation

Install the package directly from [PyPI](https://pypi.org):

```bash
pip install topsis-nandini==0.1.11
```

## 🛠️ Usage

```bash
# 1. Command Line Interface
Once installed, you can use `topsis` as a CLI tool:
Topsis-nandini input.csv "0.3,0.5,0.2" "+,+,-" output.csv

This will:
Read data from input.csv
Apply weights 0.3,0.5,0.2
Apply impacts +,+,-
Write ranked output to output.csv


# 2. Use inside Python script
from topsis_package.topsis import topsis
topsis("data.csv", "0.4,0.3,0.3", "+,-,+", "result.csv")
```

## 📁 Project Structure
```bash
topsis_nandini/
│
├── topsis
│ ├── __init__.py 
│ ├── __main__.py 
│ └── topsis.py 
├── README.md 
├── LICENSE 
├── setup.py 
└── pyproject.toml 
```
## 📄 License
This project is licensed under the MIT License - see the LICENSE file for details.
text2kgh-prompts 0.1.6
Add your description here
Requires Python: >=3.10

uv pip install -e .



main 

继续维持和改进单一文件prompt效果

Arch1 规则

1 单一文章独立生成

2 将生成后的内容进行融合

3 使用程序来整合和实现 大模型配合识别