semvec/pypi-packages huggingface dataset:
fgo-api-types 2026.2.13.6.41.41
Provide Pydantic types from FGO API
Requires Python: <4.0,>=3.6

# Types for FGO Game Data API

This is a package containing the Pydantic definitions of the objects returned by https://api.atlasacademy.io/rapidoc.

Example usage:
```
from fgo_api_types.enums import Trait
from fgo_api_types.gameenums import SvtType
from fgo_api_types.nice import NiceServant

r = httpx.get("https://api.atlasacademy.io/nice/NA/servant/200")
fujino = NiceServant.parse_raw(r.content)

assert Trait.genderFemale in fujino.traits
```
hydra-utils 0.3.4
Utilities for hydra-core library
Requires Python: <3.14,>=3.10
Keywords: hydra, hydra-core, hydra-utils

# hydra-utils

[![test](https://github.com/rcmdnk/hydra-utils/actions/workflows/test.yml/badge.svg)](https://github.com/rcmdnk/hydra-utils/actions/workflows/test.yml)
[![test coverage](https://img.shields.io/badge/coverage-check%20here-blue.svg)](https://github.com/rcmdnk/hydra-utils/tree/coverage)

Wrapper for ([hydra-core](https://pypi.org/project/hydra-core/) to add flexible configuration treatment.

## Requirement

- Python 3.13, 3.12, 3.11, 3.10

## Installation

```bash
pip install hydra-utils
```

## Usage

```python
@hydra_utils.hydra_wrapper(
    app_name='my_app',
    app_version='0.1.0',
    app_file=__file__,
    config_path='conf',
    config_name='config',
    version_base='1.2',
    verbose=1,
)
def main(conf: dict[Any, Any]) -> None: ...


if __name__ == '__main__':
    main()
```

- `app_name` and `app_version` are used to log information of current version.
  - It is useful if app_version is set to `__version__` of the app.
- `app_file` is used to check the file place's git status to log the git commit hash and diff from the last commit.
- The argument which the main function takes is `dict[Any, Any]` instead of `DictConfig`.
- Configuration file can be passed by the first argument or `conf_file=...` at the command line, which will be merged to the configuration file specified by conf_path and conf_name.
- Configuration file can have `include` keyword which has the list of configuration files to include.
  - Included files are merged to the main configuration file.
  - It is resolved at the place of `include`. If the main file has other configurations after the `include`, they will overwrite the included configurations.
- `n_jobs` is fixed to the number of CPUs.
  - It is used to set the number of jobs for parallel processing.
  - If `n_jobs` is set to 0 or 1, it is set to 1.
  - If `n_jobs` is set to -1, it is set to the number of logical cores.
  - If `n_jobs` is set to None, it is set to the number of physical cores.
  - If `n_jobs` is set to a negative number, it is set to the number of logical cores + 1 + n_jobs, i.e. -1 is the same as the number of logical cores.

It is recommended to define `hydra.job.name` in your default configuration file (`<conf_pth>/<conf_name>.yaml`):

```
hydra:
  job:
    name: my_custom_job_name
```

Otherwise the job name is `utils` as hydra detect the file where `hydra.main` is executed.

There is also wrapper function for `to_absolute_path`.

```python
from hydra_utils import to_absolute_path
```

- It returns empty string if the input is empty.
- It returns the input as is if the input includes `:` (e.g., `http://`, `s3://`, ...).

Based on [rcmdnk/python-template](https://github.com/rcmdnk/python-template), v0.1.2
py-easy-hikvision 1.0.0
一个简化海康威视ISecureCenter平台API使用的Python工具包，提供设备管理、事件订阅等功能的便捷封装。
Requires Python: >=3.6

# py-easy-hikvision

一个简化海康威视ISecureCenter平台API使用的Python工具包，提供设备管理、事件订阅等功能的便捷封装。

## 功能特性

- **认证管理**：支持海康威视ISecureCenter平台的AK/SK认证
- **签名生成**：自动生成符合平台要求的请求签名
- **请求头构建**：自动构建符合API规范的请求头
- **同步与异步**：同时支持同步和异步操作
- **响应处理**：统一的响应处理机制，自动验证响应结果
- **类型提示**：完整的类型注解，提供良好的IDE支持

## 安装

### 使用pip安装

```bash
pip install py-easy-hikvision
```

### 从源码安装

```bash
git clone https://gitee.com/guolei19850528/py_easy_hikvision.git
cd py_easy_hikvision
pip install -e .
```

## 依赖

- httpx
- jsonschema
- jsonpath-ng

## 快速开始

### 基本使用

```python
from py_easy_hikvision.isc import ISecureCenter

# 初始化ISecureCenter实例
isc = ISecureCenter(
    host="https://your-isc-server:1443",  # ISecureCenter平台地址
    ak="YOUR_AK",  # Access Key
    sk="YOUR_SK"   # Secret Key
)

# 发送API请求
response = isc.request(
    url="/artemis/api/resource/v1/person/condition/personInfo",
    json={
        "paramName": "phoneNo",
        "paramValue": [
            "13800000000"
        ]
    },
)

print(f"请求结果: {response.validated}")
if response.validated:
    print(f"返回数据: {response.value}")
else:
    print(f"错误信息: {response.json}")
```

### 异步使用

```python
import asyncio
from py_easy_hikvision.isc import ISecureCenter

async def main():
    # 初始化ISecureCenter实例
    isc = ISecureCenter(
        host="https://your-isc-server:1443",
        ak="YOUR_AK",
        sk="YOUR_SK"
    )
    
    # 异步发送API请求
    response = await isc.async_request(
        url="/artemis/api/resource/v1/person/condition/personInfo",
        json={
            "paramName": "phoneNo",
            "paramValue": [
                "13800000000"
            ]
        },
    )
    
    print(f"请求结果: {response.validated}")
    if response.validated:
        print(f"返回数据: {response.value}")
    else:
        print(f"错误信息: {response.json}")

asyncio.run(main())
```

## API文档

### ISecureCenter类

#### 初始化参数
- `host`：ISecureCenter平台的基础URL
- `ak`：Access Key，用于身份认证
- `sk`：Secret Key，用于签名生成
- `client_kwargs`：传递给httpx.Client的额外参数

#### 方法
- `timestamp()`：生成当前时间戳（毫秒）
- `nonce()`：生成随机的UUID字符串
- `signature(string)`：生成请求签名
- `headers(method, path, headers)`：生成符合ISecureCenter API规范的请求头
- `client(**kwargs)`：创建同步客户端实例
- `async_client(**kwargs)`：创建异步客户端实例
- `request(client=None, **kwargs)`：同步请求方法
- `async_request(client=None, **kwargs)`：异步请求方法

### 响应处理

所有API调用都会返回`RequestResponse`对象，包含以下属性：
- `validated`：响应是否有效
- `value`：从响应中提取的值
- `json`：响应的JSON数据
- `response`：原始的httpx.Response对象

## 测试

运行测试用例：

```bash
python -m unittest test_isc.py
```

## 项目结构

```
py-easy-hikvision/
├── py_easy_hikvision/
│   ├── __init__.py        # 模块初始化
│   └── isc/
│       └── __init__.py    # ISecureCenter API客户端实现
├── test_isc.py            # ISecureCenter测试
├── setup.py               # 安装配置
├── requirements.txt       # 依赖声明
├── LICENSE                # 许可证
└── README.md              # 项目文档
```

## 许可证

MIT License

## 贡献

欢迎提交Issue和Pull Request！

## 联系方式

- 作者：guolei
- 邮箱：174000902@qq.com
- 项目地址：https://gitee.com/guolei19850528/py_easy_hikvision
stackply 0.0.1
The Reliability Layer for Agentic AI. Orchestration, observability, and guardrails for autonomous AI agents.
Requires Python: >=3.8
Keywords: ai, agents, agentic, ops, observability, orchestration, guardrails, llm

# Stackply

**The Reliability Layer for Agentic AI.**

Stackply is an orchestration and observability infrastructure for autonomous AI agents. We help developers build reliable, production-grade agentic workflows that don't break.

## What is Stackply?

We are solving the "Black Box" problem of Agent Ops:
- **Orchestration:** Chain LLMs and tools with deterministic control flow.
- **Observability:** Trace agent reasoning steps (Thought -> Action -> Observation) in real-time.
- **Guardrails:** Prevent hallucinations and unauthorized actions at the infrastructure layer.

## Installation

```bash
pip install stackply
```

## Status

Stackply is currently in active development. Early access is coming soon.

Visit [stackply.com](https://stackply.com) to join the waitlist.

## License

MIT
mce-ragas-adapter 0.1.3
RAGAS integration adapter for Metrics Computation Engine
Requires Python: >=3.11

# MCE Ragas Adapter

A Python adapter library that integrates [Ragas](https://github.com/explodinggradients/ragas) metrics as third-party plugins for the [Metric Computation Engine (MCE)](https://github.com/agntcy/telemetry-hub/tree/main/metrics_computation_engine). This adapter enables seamless use of Ragas's LLM evaluation metrics within the MCE framework for evaluating agentic applications.

## Installation

Install via MCE extras:
```bash
pip install "metrics-computation-engine[ragas]"
```

## Prerequisites

- [Metric Computation Engine (MCE)](https://github.com/agntcy/telemetry-hub/tree/main/metrics_computation_engine) installed
- Agentic applications instrumented with [AGNTCY's observe SDK](https://github.com/agntcy/observe)

## Supported Ragas Metrics

The following Ragas metrics are supported by this adapter (use with the `ragas.` prefix in service payloads):

| Metric Name | Description |
| :---------: | :---------- |
| **TopicAdherenceScore** | Evaluates whether the model response adheres to the intended topic |

For requests to support additional Ragas metrics, please file an issue in our repo: [agntcy/telemetry-hub](https://github.com/agntcy/telemetry-hub).

## Usage

### Basic Usage

```python
import asyncio
from mce_ragas_adapter.adapter import RagasMetricAdapter
from metrics_computation_engine.models.requests import LLMJudgeConfig
from metrics_computation_engine.registry import MetricRegistry

# Initialize LLM configuration
llm_config = LLMJudgeConfig(
    LLM_BASE_MODEL_URL="https://api.openai.com/v1",
    LLM_MODEL_NAME="gpt-4o",
    LLM_API_KEY="your-api-key-here"
)

# Create registry and register Ragas metrics
registry = MetricRegistry()

# Method 1: Direct registration with metric name
registry.register_metric(RagasMetricAdapter, "TopicAdherenceScore")

# Method 2: Using get_metric_class helper with prefix
from metrics_computation_engine.util import get_metric_class
metric, metric_name = get_metric_class("ragas.TopicAdherenceScore")
registry.register_metric(metric, metric_name)
```

### Using with MCE REST API

When using the MCE as a service, include Ragas metrics in your API request:

```json
{
  "metrics": [
    "ragas.TopicAdherenceScore"
  ],
  "llm_judge_config": {
    "LLM_API_KEY": "your-api-key",
    "LLM_MODEL_NAME": "gpt-4o",
    "LLM_BASE_MODEL_URL": "https://api.openai.com/v1"
  },
  "data_fetching_infos": {
    "batch_config": {
      "time_range": { "start": "2024-01-01T00:00:00Z", "end": "2024-12-31T23:59:59Z" }
    },
    "session_ids": []
  }
}
```

## Contributing

Contributions are welcome! Please follow these steps to contribute:

1. Fork the repository.
2. Create a new branch (`git checkout -b feature-branch`).
3. Commit your changes (`git commit -am 'Add new feature'`).
4. Push to the branch (`git push origin feature-branch`).
5. Create a new Pull Request.
mce-opik-adapter 0.1.4
Opik integration adapter for Metrics Computation Engine
Requires Python: >=3.8

# MCE Opik Adapter

A Python adapter library that integrates [Opik](https://github.com/comet-ml/opik) metrics as third-party plugins for the [Metric Computation Engine (MCE)](https://github.com/agntcy/telemetry-hub/tree/main/metrics_computation_engine). This adapter enables seamless use of Opik's LLM evaluation metrics within the MCE framework for evaluating agentic applications.

## Installation

Install via MCE extras:
```bash
pip install "metrics-computation-engine[opik]"
```

## Prerequisites

- [Metric Computation Engine (MCE)](https://github.com/agntcy/telemetry-hub/tree/main/metrics_computation_engine) installed
- Agentic applications instrumented with [AGNTCY's observe SDK](https://github.com/agntcy/observe)

## Supported Opik Metrics

The following Opik metrics are supported by this adapter (use with the `opik.` prefix in service payloads):

| Metric Name | Description |
| :---------: | :---------- |
| **Hallucination** | Evaluates whether the model response contains hallucinations |
| **Sentiment** | Measures the sentiment of a model response |

For requests to support additional Opik metrics, please file an issue in our repo: [agntcy/telemetry-hub](https://github.com/agntcy/telemetry-hub).

## Usage

### Basic Usage

```python
import asyncio
from mce_opik_adapter.adapter import OpikMetricAdapter
from metrics_computation_engine.models.requests import LLMJudgeConfig
from metrics_computation_engine.registry import MetricRegistry

# Initialize LLM configuration
llm_config = LLMJudgeConfig(
    LLM_BASE_MODEL_URL="https://api.openai.com/v1",
    LLM_MODEL_NAME="gpt-4o",
    LLM_API_KEY="your-api-key-here"
)

# Create registry and register Opik metrics
registry = MetricRegistry()

# Method 1: Direct registration with metric name
registry.register_metric(OpikMetricAdapter, "Hallucination")

# Method 2: Using get_metric_class helper with prefix
from metrics_computation_engine.util import get_metric_class
metric, metric_name = get_metric_class("opik.Hallucination")
registry.register_metric(metric, metric_name)
```

### Using with MCE REST API

When using the MCE as a service, include Opik metrics in your API request:

```json
{
  "metrics": [
    "opik.Hallucination",
    "opik.Sentiment"
  ],
  "llm_judge_config": {
    "LLM_API_KEY": "your-api-key",
    "LLM_MODEL_NAME": "gpt-4o",
    "LLM_BASE_MODEL_URL": "https://api.openai.com/v1"
  },
  "data_fetching_infos": {
    "batch_config": {
      "time_range": { "start": "2024-01-01T00:00:00Z", "end": "2024-12-31T23:59:59Z" }
    },
    "session_ids": []
  }
}
```

## Contributing

Contributions are welcome! Please follow these steps to contribute:

1. Fork the repository.
2. Create a new branch (`git checkout -b feature-branch`).
3. Commit your changes (`git commit -am 'Add new feature'`).
4. Push to the branch (`git push origin feature-branch`).
5. Create a new Pull Request.