semvec/pypi-packages huggingface dataset:
boto3-pydantic-partnercentral-selling 1.0.2
Pydantic models for AWS Partnercentral_selling
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, partnercentral_selling

# boto3-pydantic-partnercentral_selling

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Partnercentral_selling.
systemhealthai 0.1.0
An SRE AI agent to analyze, and report back on health of a node
Requires Python: <4.0,>=3.11

### AI SRE for system health triaging

As an SRE, Cloud engineer, you may have to constantly look at logs, metrics, traces to troubleshoot and triage issues to figure out why particular systems may be having issues. SystemHealthAI ( SHAI ) is an AI agent which will act as an AI SRE, to look at different data sources like prometheus, elasticsearch, cloudwatch, splunk and help triage issues and provide insights into why the system or systems might be acting up. 


### SHAI Architecture

![SHAI Architecture](./static/shai.svg)


### Show Your Support ⭐  
If you find SHAI useful, please consider giving it a **STAR** ! ⭐  


### Quick start 

### Pre-Reqs

- Install `uv` to run mcp servers
- OpenAI Api Key
- Datasource url for prometheus have a prometheus url ready to use
- pip or poetry  

#### Using pip 

```
pip install systemhealthai

```

#### From Source using poetry

```
git clone git@github.com:ajinkyakadam/systemhealthai.git
cd systemhealthai
poetry install -e . 
```

### Setup 


### Using SHAI

```
shai nodeA --model "openai:o4-mini"
```

The above command instructs shai to use the `o4-mini` model and triage the nodeA server. 
Please replace the nodeA with an actual hostname that you would like to find information for.


## Roadmap 

### Datasource support

| Data Source   | Status         | Description                                      |
|---------------|----------------|--------------------------------------------------|
| Prometheus    | ✅             | Find node metrics to correlate and triage health issues          |
| Grafana Loki  | 🟡             | search loki logs  |
| Elasticsearch | 🟡             | search elasticsearch logs for system issues        |
| Splunk        | 🟡             | search splunk logs for system issues |


### LLM Provider Support

| Provider       | Status         | Description                                      |
|----------------|----------------|--------------------------------------------------|
| OpenAI         | ✅             | Integrate with OpenAI models for advanced insights and triaging |
| Claude         | 🟡             | Support for Claude models to assist in system health analysis  |
| Hugging Face   | 🟡             | Utilize Hugging Face models         |
| Local LLMs     | 🟡             | Deploy and use local LLMs for on-premise triaging solutions     |


### How to Contribute 
Contributions are welcome, be it bug reports, feature requests, or PRs! 
- Open a github issue to report issues, or suggest features
- Open a pull request with improvements
- Share your experience and how it has been useful to you or your organization. 


### Citation 
If you use shai in your work, blogs, projects, please do cite:

```
@software{systemhealthai,
  author = {Kadam, Ajinkya},
  title = {SHAI: An AI SRE for triaging system health issues},
  year = {2025},
  publisher = {GitHub},
  url = {https://github.com/ajinkyakadam/systemhealthai}
}
```

### License 
MIT
peegeem 0.0.2
PGMs that are fun to play with
Requires Python: >=3.8


# peegeem

> Probabilistic graphical models that are fun to work with <br>

This simple library is meant as a demo that shows what might be possible when you combine domain specific languages with bespoke UI tools. You might end up with a domain specific environment as a result!

![CleanShot 2025-05-02 at 16 23 22](https://github.com/user-attachments/assets/9618de8a-1f0b-49da-9055-0cbf124258ee)

## Install

You can install via pip.

```
uv pip install peegeem
```

## Usage 

This is how you might use this experiment. 

```python
from peegeem import DAG

# Define the DAG for the PGM, nodes is a list of column names, edges is a list of tuples
dag = DAG(nodes, edges, dataframe)

# Get variables out
outcome, smoker, age = dag.get_variables()

# Use variables to construct a probablistic query
P(outcome | (smoker == "Yes") & (age > 40))

# Latex utility, why not?
P.to_latex(outcome | (smoker == "Yes") & (age > 40))
```

The goal is to have an API that really closely mimics the math notation, so stuff like this:

$$ P(\\text{outcome} \\mid do(\\text{smoker}=\\texttt{Yes}), \\text{age}>40) $$

That means that we indeed also have a `do` function, though this needs more extensive testing. 

```python 
# You can also get crazy fancy 
P(A & B | C & do(D))
```

## Demo 

For a solid demo, download and run [this notebook](https://github.com/koaning/peegeem/blob/main/nbs/__init__.py) locally.

You can run the notebook in a sandboxed environment with uv. Navigate to the `nbs` directory and run:

```
uvx marimo edit --sandbox __init__.py
```
boto3-pydantic-panorama 1.0.2
Pydantic models for AWS Panorama
Requires Python: <4.0,>=3.9
Keywords: aws, boto3, pydantic, models, panorama

# boto3-pydantic-panorama

This package was automatically generated by aws-resource-validator repository.

For more information, visit:
- Package homepage: https://coreoxide.github.io/aws_resource_validator/
- GitHub repository: https://github.com/CoreOxide/aws_resource_validator
- PyPI project: https://pypi.org/project/aws-resource-validator/

Pydantic models for AWS Panorama.