semvec/pypi-packages huggingface dataset:
vebboard 0.1.0
Интерактивная библиотека для создания дашбордов из различных источников данных.
Requires Python: >=3.7

# vebboard

**vebboard** — Python-библиотека для создания дашбордов из файлов и баз данных (CSV, Excel, JSON, SQLite, Google Sheets и др.).

## Возможности

- Простое создание дашбордов с графиками.
- Импорт данных из CSV, Excel, JSON, Parquet, SQLite, Google Sheets.
- Экспорт дашборда в HTML.
- Сохранение/загрузка конфигурации в JSON.
- Поддержка тем и кастомного оформления.

## Установка

```
pip install vebboard
```

## Пример использования

```python
from vebboard.core.dashboard import Dashboard
import pandas as pd

df = pd.DataFrame({
    'day': ['Mon', 'Tue', 'Wed'],
    'revenue': [100, 200, 150]
})

dashboard = Dashboard(\"Мой дашборд\")
dashboard.add_chart(df, chart_type='bar', x='day', y='revenue')
dashboard.show()
```
pypidoctor 0.1.3
模型query理解能力
Requires Python: >=3.10

# PypiDoctor
项目级别

## 常规操作

### 导出环境
```
uv export --format requirements-txt > requirements.txt
```
### 更新文档
```
mkdocs serve # 预览
mkdocs gh-deploy -d ../.temp # 同步到github网站
```
### 发布
```
uv build
uv publish
```
### 运行测试并同步到测试服务
```
bash run_test.sh
```
gllm-rag-binary 0.0.2
A library containing RAG pipeline builders and presets for Gen AI applications.
Requires Python: <3.13,>=3.11

# GLLM RAG

## Description

A library containing RAG pipeline builders and presets for Gen AI applications.

## Installation

1. Python v3.11 or above:

You can install Python using [Miniconda](https://docs.anaconda.com/free/miniconda/index.html).

2. Make sure you're in the `base` conda environment:
```bash
conda activate
```

3. [Poetry](https://python-poetry.org/docs/) v1.8.1 or above:

You can install Poetry using cURL (you need Python to install Poetry):
```bash
curl -sSL https://install.python-poetry.org | python3 -
```

4. Install the library using Poetry:
```bash
# Latest
poetry add "git+ssh://git@github.com/GDP-ADMIN/gen-ai-internal.git#subdirectory=libs/gllm-rag"

# Specific version
poetry add "git+ssh://git@github.com/GDP-ADMIN/gen-ai-internal.git@gllm_rag-v0.0.1-beta.1#subdirectory=libs/gllm-rag"

# Specific Branch Name
poetry add "git+ssh://git@github.com/GDP-ADMIN/gen-ai-internal.git@<BRANCH NAME>#subdirectory=libs/gllm-rag"
```

5. At this step, you can deactivate Miniconda environment as Poetry will create and manage its own virtual environment for you.
```bash
conda deactivate
```

## Managing Dependencies
1. Go to root folder of `gllm-rag` module, e.g. `cd libs/gllm-rag`.
2. Run `poetry shell` to create a virtual environment.
3. Run `poetry lock` to create a lock file if you haven't done it yet.
4. Run `poetry install` to install the `gllm-rag` requirements for the first time.
5. Run `poetry update` if you update any dependency module version at `pyproject.toml`.


## Contributing
Please refer to this [Python Style Guide](https://docs.google.com/document/d/1uRggCrHnVfDPBnG641FyQBwUwLoFw0kTzNqRm92vUwM/edit?usp=sharing)
to get information about code style, documentation standard, and SCA that you need to use when contributing to this project

1. Activate `pre-commit` hooks using `pre-commit install`
2. Run `poetry shell` to create a virtual environment.
3. Run `poetry lock` to create a lock file if you haven't done it yet.
4. Run `poetry install` to install the `gllm-rag` requirements for the first time.
5. Run `which python` to get the path to be referenced at Visual Studio Code interpreter path (`Ctrl`+`Shift`+`P` or `Cmd`+`Shift`+`P`)
6. Try running the unit test to see if it's working:
```bash
poetry run pytest -s tests/unit/
```
novikovtv-parser-fns 1.0.7
This is the fns parser
Requires Python: <3.12,>=3.11

# Parser FNS

## Пример работы

```python
import asyncio

from novikovtv_parser_fns.parser.models.search import SearchResult
from novikovtv_parser_fns.parser.nalog import NalogParser


async def main():
    parser = NalogParser()
    res: list[SearchResult] = await parser.search("Тест")


if __name__ == '__main__':
    asyncio.run(main())
```