semvec/pypi-packages huggingface dataset:
docling-litellm-picture-description 0.1.1
Docling plugin for LiteLLM picture description
Requires Python: >=3.11

# Docling LiteLLM Picture Description Plugin

This plugin allows you to use the [LiteLLM](https://github.com/BerriAI/litellm) library for generating picture descriptions in your Docling pipeline. This plugin will allow you to use Vision LLM from any external providers supported by LiteLLM, such as OpenAI, Azure OpenAI, Vertex AI, and xAI.

## Installation

```bash
pip install docling-litellm-picture-description
```

## Usage

```python
from docling.datamodel.base_models import InputFormat
from docling.datamodel.pipeline_options import PdfPipelineOptions
from docling_litellm_picture_description.datamodel.pipeline_options import PictureDescriptionLiteLLMOptions

picture_description_options = PictureDescriptionApiOptions(
    model=self.model_name,
    prompt="Describe the picture in detail.",
    scale=1.0,
    timeout=120,
)
pipeline_options = PdfPipelineOptions(
    enable_remote_services=True,
    allow_external_plugins=True,
    do_picture_description=True,
    picture_description_options=picture_description_options,
    generate_picture_images=True,
)
converter = DocumentConverter(
    format_options={
        InputFormat.IMAGE: PdfFormatOption(
            pipeline_options=pipeline_options,
        ),
        InputFormat.PDF: PdfFormatOption(
            pipeline_options=pipeline_options,
        )
    }
)
doc = converter.convert(file).document
markdown_text = doc.export_to_markdown()
```
electrum-fp 0.1.1
Metal-aware molecular fingerprinting tools
Requires Python: >=3.7
Keywords: cheminformatics, fingerprint, electrum, coordination

# electrum-fp

**Metal-aware molecular fingerprinting tools for transition metal complexes.**  
`electrum-fp` provides vector representations that combine ligand topology with metal identity, enabling downstream tasks like classification, regression, and visualization.

## Installation

```bash
pip install electrum-fp
```

## Usage

```python
from electrum_fp.electrum import calculate_fingerprint

ligands = "Cc1c(C)c(C)c(c2cccc3cccnc32)c1C.Cl.Cl"        # Ligand SMILES
metal = "Rh"                                             # Corresponding metal

fps = calculate_fingerprint(ligands, metal, radius=2, n_bits=512)
print(fps) 
```
tjhlp-checker 0.1.3
This project is a code inspection tool for teaching purposes in the "High-level Language Programming" course at Tongji University, used to check whether C/C++ code uses syntax features that violate assignment requirements.
Requires Python: >=3.13

# TJHLP-CHECKER 同济高程代码合规检查

[![Continuous Integration](https://github.com/Maoyao233/tjhlp-checker/actions/workflows/continuous-integration.yml/badge.svg)](https://github.com/Maoyao233/tjhlp-checker/actions/workflows/continuous-integration.yml)  [![cov](https://Maoyao233.github.io/tjhlp-checker/badges/coverage.svg)](https://github.com/Maoyao233/tjhlp-checker/actions)  [![PyPI - Version](https://img.shields.io/pypi/v/tjhlp-checker)](https://pypi.org/project/tjhlp-checker/)

## 简介

*高级语言程序设计* 是为同济大学信息类大一学生开设的专业入门课，使用 C/C++ 教学。由于教学需求，课程对作业中允许使用的语言特性做出了一定的限制。本项目基于 [libclang](https://clang.llvm.org/doxygen/group__CINDEX.html) 的 [Python binding](https://pypi.org/project/libclang/) 实现，提供 AST 级别的准确检测工具。

## 使用

`tjhlp-checker`既可以用库的形式引入，也可以直接作为 CLI 工具使用。

### 安装

```bash
pip install tjhlp-checker
# 若需直接在命令行使用，则改为：
# pip install tjhlp-checker[cli]
```

### 作为库引入

```Python
import sys

from tjhlp-checker import load_config, find_all_violations

if __name__ == '__main__':
    """
    Usage: python main.py <cpp file> <config file>
    """
    with open(sys.argv[2], 'rb') as conf:
        violations = find_all_violations(
            sys.argv[1],
            load_config(conf)
        )
    print(violations)
```

### 直接在命令行使用

```bash
pip install tjhlp-checker[cli]
tjhlp-checker --config-file=<PATH TO CONFIG FILE> <FILE>
```

配置文件使用 TOML 格式。由于本项目使用 [Pydantic](https://docs.pydantic.dev/latest/) 验证配置文件格式，因此具体配置项可以直接参考 [src/tjhlp_checker/config.py](src/tjhlp_checker/config.py)。

## 构建

本项目使用 [uv](https://docs.astral.sh/uv/) 进行项目管理。

```bash
git clone https://github.com/Maoyao233/tjhlp-checker && cd tjhlp-checker
uv sync --all-extras --dev
uv build
```

### 使用 Docker

也可以直接使用 Docker:

```bash
docker build -t tjhlp-checker .
docker run -it tjhlp-checker
```