semvec/pypi-packages huggingface dataset:
gitlabbot 0.3.6
Make comment on gitlab using `flux-local` diff outputs.
Requires Python: >=3.12

# GitlabBot

## Flux-local

### Bot User

Setup your bot account and generate a PAT token.
Then, add the bot to your project with the reporter permission.
Finally , store the pat token in the `GITLAB_BOT_PAT` CI/CD variable.


### CI
You can use the following content to setup GitLab CI to post the diff comment.
```yaml
---
.flux-local-diff:
  stage: flux-local
  image:
    name: ghcr.io/alexsaphir/gitlabbot:v0.3.6
  parallel:
    matrix:
      - FLUX_RESOURCE:
          - hr
          - ks
  variables:
    GIT_DEPTH: "0"  # Tells git to fetch all the branches of the project, required by the analysis task
    CONFIG_FILE: '.gitlabbot.yaml'
  script:
    - |
      DIFF="dyff between --omit-header --ignore-order-changes -o gitlab" \
      flux-local diff $FLUX_RESOURCE -A \
      --path $CLUSTER_PATH \
      --branch-orig main \
      --strip-attrs "helm.sh/chart,checksum/config,app.kubernetes.io/version,chart" \
      --output-file out.diff
    - |
      gitlab-comment --diff_file out.diff \
        --flux_resource $FLUX_RESOURCE \
        --diff_mode dyff \
        --comment_mode $COMMENT_MODE
  rules:
    - if: $CI_PIPELINE_SOURCE == "merge_request_event"

flux-local-diff:
  extends: .flux-local-diff
  variables:
    CLUSTER_PATH: cluster
    COMMENT_MODE: recreate
```
r4sdk 0.1.5
Re4ctoR Randomness SDK Client
Requires Python: >=3.7

# r4sdk

**r4sdk** is a lightweight Python client for the [Re4ctoR](https://github.com/pipavlo82/r4-monorepo) high-entropy randomness API.

Supports secure random byte fetching via HTTP with API Key authentication.  
Ideal for cryptographic, simulation, and research use.

---

## 🚀 Features

- 🔒 API key authentication
- ⚡ Fast local access (`localhost` by default)
- 📦 Clean Python interface

---

## 📦 Installation

Install from PyPI:

```bash
pip install r4sdk
Or install locally:

pip install .
🧪 Usage
from r4sdk import R4Client

client = R4Client(api_key="demo", host="http://localhost:8082")
print(client.get_random(16).hex())
🔧 CLI Quick Test
echo 'from r4sdk import R4Client; c=R4Client(api_key="demo", host="http://localhost:8082"); print(c.get_random(16).hex())' | python3

📁 Files
r4sdk/__init__.py — core SDK client

test_sdk.py — standalone test

setup.py — packaging

🛡 License
MIT © Pavlo Tvardovskyi
pangea-openai 0.3.0
A wrapper around the OpenAI Python library that wraps the Responses API with Pangea AI Guard
Requires Python: >=3.10

# Pangea + OpenAI Python API library

A wrapper around the OpenAI Python library that wraps the [Responses API](https://platform.openai.com/docs/api-reference/responses)
with Pangea AI Guard. Supports Python v3.10 and greater.

## Installation

```bash
pip install -U pangea-openai
```

## Usage

```python
import os
from pangea_openai import PangeaOpenAI

client = PangeaOpenAI(
    api_key=os.environ.get("OPENAI_API_KEY"),
    # Pangea options
    pangea_api_key=os.environ.get("PANGEA_API_KEY"),
    pangea_input_recipe="pangea_prompt_guard",
    pangea_output_recipe="pangea_llm_response_guard",
)

response = client.responses.create(
    model="gpt-4o",
    instructions="You are a coding assistant that talks like a pirate.",
    input="How do I check if a Python object is an instance of a class?",
)

print(response.output_text)
```

## Microsoft Azure OpenAI

To use this library with [Azure OpenAI](https://learn.microsoft.com/azure/ai-services/openai/overview),
use the `PangeaAzureOpenAI` class instead of the `PangeaOpenAI` class.

```python
from pangea_openai import PangeaAzureOpenAI

client = PangeaAzureOpenAI(
    # https://learn.microsoft.com/azure/ai-services/openai/reference#rest-api-versioning
    api_version="2023-07-01-preview",
    # https://learn.microsoft.com/azure/cognitive-services/openai/how-to/create-resource?pivots=web-portal#create-a-resource
    azure_endpoint="https://example-endpoint.openai.azure.com",
    # Pangea options
    pangea_api_key=os.environ.get("PANGEA_API_KEY"),
    pangea_input_recipe="pangea_prompt_guard",
    pangea_output_recipe="pangea_llm_response_guard",
)

completion = client.chat.completions.create(
    model="deployment-name",  # e.g. gpt-35-instant
    messages=[
        {
            "role": "user",
            "content": "How do I output all files in a directory using Python?",
        },
    ],
)
print(completion.to_json())
```