semvec/pypi-packages huggingface dataset:
pytest-github-actions-annotate-failures 0.3.0
pytest plugin to annotate failed tests with a workflow command for GitHub Actions
License: MIT
Requires Python: >=3.8
Keywords: ansible, testing, molecule, plugin

# pytest-github-actions-annotate-failures
[Pytest](https://pypi.org/project/pytest/) plugin to annotate failed tests with a [workflow command for GitHub Actions](https://help.github.com/en/actions/reference/workflow-commands-for-github-actions)

## Usage
Just install and run pytest with this plugin in your workflow. For example,

```yaml
name: test

on:
  push:

jobs:
  test:
    runs-on: ubuntu-latest

    steps:
    - uses: actions/checkout@v3

    - uses: actions/setup-python@v4
      with:
        python-version: 3.8

    - name: Install dependencies
      run: |
        python -m pip install --upgrade pip
        pip install -r requirements.txt

    - name: Install plugin
      run: pip install pytest-github-actions-annotate-failures

    - run: pytest
```

If your test is running in a Docker container, you have to install this plugin and manually set `GITHUB_ACTIONS` environment variable to `true` inside of Docker container. (For example, `docker-compose run --rm -e GITHUB_ACTIONS=true app -- pytest`)

If your tests are run from a subdirectory of the git repository, you have to set the `PYTEST_RUN_PATH` environment variable to the path of that directory relative to the repository root in order for GitHub to identify the files with errors correctly.

### Warning annotations

This plugin also supports warning annotations when used with Pytest 6.0+. To disable warning annotations, pass `--exclude-warning-annotations` to pytest.

## Screenshot
[![Image from Gyazo](https://i.gyazo.com/b578304465dd1b755ceb0e04692a57d9.png)](https://gyazo.com/b578304465dd1b755ceb0e04692a57d9)
yollama 2.1.0
helper functions for Ollama with Langchain
License: MIT

# Yollama

## what is this ?

This repo holds the code for helper functions for Ollama with Langchain.

## prerequisites

This has been tested on a Ubuntu 24 server. You'll need:

- Ollama installed
- the Ollama models you'll need to install are:

```python
default_llm_model_name = "ajindal/llama3.1-storm:8b"
default_long_context_llm_model_name = "mistral-nemo"
default_reasoning_llm_model_name = "gemma2:9b"
default_sql_llm_model_name = "qwen2.5-coder"
default_tools_llm_model_name = "qwen2.5"
```

The `num_ctx` parameter is the size of the context window used to generate the next token, here we use values for a 20GB VRAM GPU.

- Python 3 with `venv` and `pip` installed

## basic usage

`pip install yollama`

### `get_llm()`

1st parameter is the use case (`Literal["default", "long-context", "reasoning", "sql", "tools"]`), which determines the model and context size.

2nd parameter is a boolean to determine if the LLM should return a JSON object or a string.

```python
from yollama import get_llm

llm = get_llm()

# invoke default model (ajindal/llama3.1-storm:8b) in JSON mode
print(llm.invoke({"input_query": some_user_input}))
```

```python
# invoke Mistral-Nemo model in text mode with some lengthy context
print(get_llm("long-context", False).invoke({"document": some_big_text}))
```

```python
# invoke Gemma2 model in text mode, to get answers requiring more reasoning abilities
print(get_llm("reasoning", False).invoke({"question": """Owen left a tray of lemon cakes unattended in the staff room for an hour and one of the cakes went missing. Three people are suspects, and here are the facts:
Person A is allergic to citrus but was seen in the area
Person B loves lemon cake but wasn't near the location
Person C has stolen food before and similar incidents increased after they arrived
Who is most likely responsible and why?"""}))
```

```python
# invoke Qwen2.5-Coder model in text mode, to get a raw SQL query
print(get_llm("sql", False).invoke({"query": "create a SQL query to get the latest headlines"}))
```

## philosophy

### fully local models

I've decided to let go of OpenAI to:

- preserve my privacy
- save money

As local models are getting better and better, I'm confident that I'll be able to have a greater experience in the future, even with my small 20GB VRAM GPU. 🤔 Of course, it's not fast, but hey everything comes with tradeoffs right? 💪

## contributions guidelines

🤝 I haven't made up my mind on contribution guidelines yet. I guess we'll update them as you contribute!