Metadata-Version: 2.4
Name: pySunshower
Version: 0.0.1
Summary: pySunshower is a Python library for evaluating AI agents in a declarative way
License-Expression: MIT
License-File: LICENSE
Author: Vic Fernandez III
Author-email: victor@deathlabs.io
Requires-Python: >=3.12
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Dist: langchain (>=1.2.0,<2.0.0)
Requires-Dist: langchain-community (>=0.4.1,<0.5.0)
Requires-Dist: langchain-openai (>=1.1.6,<2.0.0)
Requires-Dist: langgraph (>=1.0.5,<2.0.0)
Requires-Dist: pyyaml (>=6.0.3,<7.0.0)
Requires-Dist: ray (>=2.53.0,<3.0.0)
Project-URL: Homepage, https://github.com/deathlabs/sunshower
Project-URL: Issues, https://github.com/deathlabs/sunshower/issues
Description-Content-Type: text/markdown

# pySunshower
`pySunshower` is a Python library for evaluating AI agents in a declarative way.

## Installation
The instructions below explain how to install `pySunshower`.

**Step 1.** Clone this repository and change directories to it.
```bash
git https://github.com/deathlabs/sunshower.git
cd sunshower
```

**Step 2.** Create a Python virtual environment.
```bash
python -m venv .venv
```

**Step 3.** Activate the Python virtual environment you just created. This step assumes you're working in a Linux-based environment. If you're using another operating system, adjust accordingly. 
```bash
source .venv/bin/activate
```

**Step 4.** Install the Python dependencies.
```bash
pip install -r requirements
```

## Usage
The instructions below provide an example of using `pySunshower` (they assume you are using VS Code in a Linux-based environment and have already completed the [Installation](#installation) instructions above).

**Step 1.** Add `ipykernel`, `ipywidgets`, `langchain`, and `matplotlib` to your Python virtual environment.
```bash
pip install ipykernel ipywidgets langchain matplotlib
```

**Step 2.** Create a file called `.env` and added your Large Language Model (LLM) provider API keys to it. 

**Step 3.** Create a file called `experiments.yaml` using `example.yaml` for reference. 

**Step 4.** Open the provided Jupyter Notebook.

**Step 5.** Click "Select Kernel" and then click "Python Environments..." when prompted.

**Step 6.** Select your Python virtual environment.

**Step 7.** Click "Run All" and review the results at the bottom of the Jupyter Notebook.

