semvec/pypi-packages huggingface dataset:
py-agent-tools 0.1.8.post19
Agent Switcher
Requires Python: >=3.10

# py-agent-tools

A Python package providing tools for working with various AI agents and APIs.

## Installation

```bash
pip install py-agent-tools
```

## Usage

- How to use model switcher with local credentials:

```python
# 服务起来的时候需要加载所有可用的凭证
switcher = ModelSwitcher(allowed_providers=['laozhang'])
await switcher.start_all_pools()

# 通过 model_provider 和 model_name 获取凭证池
model_provider = 'laozhang'
model_name = 'o4-mini'
credential_pool = switcher.get_credential_pool(model_provider, model_name)

# 根据 model_provider 和凭证池创建 agent, 可以设置系统提示词, 模型参数, 以及 http 请求的超时时间
agent= switcher.get_agent_cls(model_provider).create(
    system_prompt='system',
    credential_pool=credential_pool,
    model_settings={'temperature': 0.0},
    timeout=300,
)

# 给定提示词，agent 运行推理并存储结果于 runner 对象里, 支持传入后处理函数, 支持查看 usage 等信息
runner = await agent.run('hello')
print(runner.result)

# 服务关闭的时候清除所有凭证
switcher.stop_all_pools()
```

- TODO: How to use model switcher with remote credential api:

## Features

- Support for multiple AI agent providers
- Unified interface for different APIs
- Credential management
- Model switching capabilities

## Requirements

- Python >= 3.10

## 添加新的凭证

### 非微调模型

- 参考 `*_agent.py`

### 微调模型

- 参考 `*_ft_agent.py`
renumics-spotlight 1.7.2
Visualize and maintain datasets to develop and understand data-driven algorithms.
Requires Python: <4.0,>=3.9
Keywords: data curation, machine learning, data science, visualization, pandas, ai

# Renumics Spotlight

> Spotlight helps you to **identify critical data segments and model failure modes**. It enables you to build and maintain reliable machine learning models by **curating a high-quality datasets**.

## Introduction

Spotlight is built on the idea that you can only truly **understand unstructured datasets** if you can **interactively explore** them. Its core principle is to identify and fix critical data segments by leveraging **data enrichments** (e.g. features, embeddings, uncertainties). We are building Spotlight for cross-functional teams that want to be in **control of their data and data curation processes**. Currently, Spotlight supports many use cases based on image, audio, video and time series data.

## Quickstart

Get started by installing Spotlight and loading your first dataset.

#### What you'll need

-   [Python](https://www.python.org/downloads/) version 3.9-3.13

#### Install Spotlight via [pip](https://packaging.python.org/en/latest/key_projects/#pip)

```bash
pip install renumics-spotlight
```

> We recommend installing Spotlight and everything you need to work on your data in a separate [virtual environment](https://docs.python.org/3/tutorial/venv.html)

#### Load a dataset and start exploring

```python
import pandas as pd
from renumics import spotlight

df = pd.read_csv("https://spotlight.renumics.com/data/mnist/mnist-tiny.csv")
spotlight.show(df, dtype={"image": spotlight.Image, "embedding": spotlight.Embedding})
```

> `pd.read_csv` loads a sample csv file as a pandas [DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html).

> `spotlight.show` opens up spotlight in the browser with the pandas dataframe ready for you to explore. The `dtype` argument specifies custom column types for the browser viewer.

#### Load a [Hugging Face](https://huggingface.co/) dataset

```python
import datasets
from renumics import spotlight

dataset = datasets.load_dataset("olivierdehaene/xkcd", split="train")
df = dataset.to_pandas()
spotlight.show(df, dtype={"image_url": spotlight.Image})
```

> The `datasets` package can be installed via pip.
tftree-manager 0.0.1
TF 树梳理与变换管理：YAML 外参加载/保存、图路径与环检测、变换矩阵计算
Requires Python: >=3.10

# tftree-manager

TF 树梳理与变换管理：从 YAML 目录加载/保存外参与顺序，构建无向图、路径查找、环检测、变换矩阵计算。

## 安装

```bash
pip install tftree-manager
```

## 功能特性

- 从 `yaml_dir/extrinsics/*.yaml` 与 `order_manifest.yaml` 加载/保存外参与顺序
- 无向图构建、BFS 路径查找、有向环检测
- 沿路径累积 4x4 变换矩阵、从根节点计算到所有可达节点
- 更新变换、求逆、重排顺序并写回 YAML
- 可选 Flask API 服务与命令行入口

## 快速开始

```python
from tftree_manager import TFTreeManager

manager = TFTreeManager("/path/to/yaml_dir")
manager.load_from_yaml()

# 所有坐标系
frames = manager.get_all_frames()

# 两节点间变换矩阵
T = manager.get_transform("lidar", "camera")

# 从根节点计算到所有节点
transforms = manager.compute_all_transforms_from_root("world")

# 检测环
cycles = manager.detect_cycles()

# 保存
manager.save_to_yaml()
```

## 命令行

```bash
# 以 YAML 目录为参数，加载并打印树与环信息
tftree-manager /path/to/yaml_dir

# 启动 Flask API 服务（默认 0.0.0.0:5000）
tftree-manager-server
```

环境变量 `TFTREE_DATA_DIR` 可指定 API 默认数据目录（默认 `./data`）。

## 依赖

- Python >= 3.10
- numpy >= 1.20.0
- PyYAML >= 5.4.0
- Flask >= 2.0.0（API 与 server 入口需要）

## License

MIT