weather-swarm 0.0.4

Weather Swarm - Pytorch

Baron Weather

Overview

Baron Weather is a sophisticated toolset designed to enable real-time querying of weather data using the Baron API. It utilizes a swarm of autonomous agents to handle concurrent data requests, optimizing for efficiency and accuracy in weather data retrieval and analysis.

Features

Baron Weather includes the following key features:

• Real-time Weather Data Access: Instantly fetch and analyze weather conditions using the Baron API.
• Autonomous Agents: A swarm system for handling multiple concurrent API queries efficiently.
• Data Visualization: Tools for visualizing complex meteorological data for easier interpretation.

Prerequisites

Before you begin, ensure you have met the following requirements:

• Python 3.10 or newer
• git installed on your machine

Installation

There are 2 methods, git cloning which allows you to modify the codebase or pip install for simple usage:

Pip

pip3 install -U weather-swarm

Cloning the Repository

To get started with Baron Weather, clone the repository to your local machine using:

git clone https://github.com/baronservices/weatherman_agent.git
cd weatherman_agent

Setting Up the Environment

Create a Python virtual environment to manage dependencies:

python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`

Installing Dependencies

Install the necessary Python packages via pip:

pip install -r requirements.txt

Usage

To start querying the Baron Weather API using the autonomous agents, run:

python main.py

Llama3

from weather_swarm.llama import llama3Hosted


# Example usage
llama3 = llama3Hosted(
    model="meta-llama/Meta-Llama-3-8B-Instruct",
    temperature=0.8,
    max_tokens=1000,
    system_prompt="You are a helpful assistant.",
)

completion_generator = llama3.run(
    "create an essay on how to bake chicken"
)

print(completion_generator)

Contributing

Contributions to Baron Weather are welcome and appreciated. Here's how you can contribute:

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/YourAmazingFeature)
3. Commit your Changes (git commit -m 'Add some YourAmazingFeature')
4. Push to the Branch (git push origin feature/YourAmazingFeature)
5. Open a Pull Request

Tests

To run tests run the following:

pytest

Contact

Project Maintainer - Kye Gomez - GitHub Profile

Acknowledgements

Todo

• Make API server functional
• Make Dockerfile
• Create a team of specialized agents for different types of weather tools.
• Create documentation for llama3,
• Create dockerfile
• Create documentation for the agents and how to use them