semvec/pypi-packages huggingface dataset:
llm-api-engine 0.1.2
Unified LLM API provider and engine library
License: MIT
Requires Python: >=3.8
Keywords: llm, api, openai, deepseek, ollama, ai, language-model, chatgpt

# LLM Engine

Unified LLM API provider and engine library for Python.

## Features

- Support for multiple LLM providers (OpenAI, DeepSeek, Ollama, Custom)
- Both synchronous and asynchronous API support
- Unified configuration via `providers.yml`
- Environment variable resolution
- Automatic retry logic
- Streaming support
- Token estimation and resource management

## Installation

Install from PyPI:

```bash
pip install llm-engine
```

For local development:

```bash
cd llm-engine
pip install -e .
```

Or install with development dependencies:

```bash
pip install -e ".[dev]"
```

## Configuration

Create a `providers.yml` file:

```yaml
providers:
  deepseek:
    base_url: "https://api.deepseek.com/v1"
    api_key: ${DEEPSEEK_API_KEY}
    default_model: "deepseek-chat"
    models:
      - name: "deepseek-chat"
        context_length: 128000
        functions:
          json_output: true
```

## Usage

### Async Usage

```python
from llm_engine import LLMConfig, LLMProvider, LLMEngine

config = LLMConfig(
    provider=LLMProvider.DEEPSEEK,
    model_name="deepseek-chat",
    api_key="your-api-key",
)

engine = LLMEngine(config)
response = await engine.generate("Hello, world!")
```

### Sync Usage

```python
from llm_engine import LLMConfig, LLMProvider
from llm_engine.providers.openai_compatible import OpenAICompatibleProvider

config = LLMConfig(
    provider=LLMProvider.DEEPSEEK,
    model_name="deepseek-chat",
    api_key="your-api-key",
)

provider = OpenAICompatibleProvider(config)
response = provider.call("Hello, world!")
```

## License

MIT
secretverify 0.7.2
Terminal-based tool to quickly validate leaked secrets across multiple providers.
License: MIT
Requires Python: >=3.10

# SecretVerify
A terminal-based tool to quickly validate leaked secrets across multiple providers.

## Installation

### PyPI
```bash
pip install secretverify
```

### From source
```bash
git clone https://github.com/markgraziano-twlo/secretverify.git
cd secretverify
pip install .
```

### Homebrew (macOS)
```bash
brew tap markgraziano-twlo/tap
brew install secretverify
```

## Usage

Run the interactive CLI:
```bash
secretverify
```

1. **Select** the secret type from the numbered list.
2. **Enter** the required credentials (token, key, domain/subdomain).
3. **View** the full HTTP response and a clear status:
   - ✅ Secret successfully rotated.
   - ⚠️ Secret is still live.

## Example

```bash
$ secretverify
Select a secret type to validate:
  1. GitHub Personal Access Token
  2. AWS Access Key & Secret
  …
Enter number: 1
Enter token: ghp_…
{HTTP 200 OK JSON}
✅ Secret successfully rotated.
```

## Contributing
Feel free to add new providers or improve error handling.
irlab-pysdk 0.0.1
Defensive package registration.
License: MIT
Requires Python: >=3.6

# irlab-pysdk

**NOTICE: Defensive Package Registration**

To provide internal services and prevent new enterprise users from accidentally installing the wrong package, this package is treated as a placeholder package and a friendly reminder is given during installation.

## Contact

For inquiries about this package, please contact the insiders.
stdplus-ext 0.0.1
The stdplus activation function
License: MIT
Requires Python: >=3.6

# stdplus

`stdplus(x)` is a smooth activation function for computing **Gaussian standard deviations** in variational models. It is designed as a numerically stable alternative to `softplus` / `exp`, and is introduced in *Chapter 6* of Yujia Yan’s PhD dissertation (University of Rochester, 2025).  
It resembles a **1-centered softplus** (i.e., equals 1 at `x = 0`) while providing a clean analytic derivative and a stable evaluation recipe. 


## Definition 

stdplus function is defined as a function y = f(x) such that
$$y^2- 2 \log(y) - 1 = x^2$$

This is proposed in Chapter 6 of Yujia Yan's dissertation: Slashed Normal Parameterization for Approximate Normal Posterior Distributions


## Why stdplus?

In the **Slashed Normal / KL-amplitude** parameterization, Gaussian parameters are expressed as a complex “KL amplitude” `ψ = a + bi`, and the standard deviation is computed with:

- `μ = sqrt(2) * a`
- `σ = stdplus(sqrt(2) * b)` 

This yields an exact closed-form KL term equal to `||ψ||_2^2` (enabling direct, interpretable rate control). 

## Reference

- Yujia Yan, *Structured Analysis and Generation in Music, Audio, and Beyond*, PhD Dissertation, University of Rochester, 2025. (See Chapter 6, Sec. 6.6 “More on stdplus Function”.) 


## Install 
pip3 install -e .