# 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
- Install packages like swarms
## 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:
```bash
git clone https://github.com/baronservices/weatherman_agent.git
cd weatherman_agent
```
### Setting Up the Environment
Create a Python virtual environment to manage dependencies:
```bash
python -m venv venv
source venv/bin/activate # On Windows use `venv\Scripts\activate`
```
### Installing Dependencies
Install the necessary Python packages via pip:
```bash
pip install -r requirements.txt
```
## Usage
To start querying the Baron Weather API using the autonomous agents, run:
```bash
python main.py
```
## API
```bash
python3 api.py
```
### Llama3
```python
from swarms 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)
```
# Documentation
- [Llama3Hosted](docs/llama3_hosted.md)
## 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](mailto:kye@swarms.world) - [GitHub Profile](https://github.com/baronservices)
# Todo
- [x] Implement the parser and the function calling mapping to execute the functions
- [ ] Then, implement the API server wrapping the hiearchical swarm
- [ ] Then, Deploy on the server 24/7
- [ ] Temperature and forecast of tomorrow
# Requirements
- Simple
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"description": "# Baron Weather\n\n## Overview\nBaron 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.\n\n## Features\nBaron Weather includes the following key features:\n- **Real-time Weather Data Access**: Instantly fetch and analyze weather conditions using the Baron API.\n- **Autonomous Agents**: A swarm system for handling multiple concurrent API queries efficiently.\n- **Data Visualization**: Tools for visualizing complex meteorological data for easier interpretation.\n\n\n## Prerequisites\nBefore you begin, ensure you have met the following requirements:\n- Python 3.10 or newer\n- git installed on your machine\n- Install packages like swarms\n\n## Installation\n\nThere are 2 methods, git cloning which allows you to modify the codebase or pip install for simple usage:\n\n### Pip \n`pip3 install -U weather-swarm`\n\n### Cloning the Repository\nTo get started with Baron Weather, clone the repository to your local machine using:\n\n```bash\ngit clone https://github.com/baronservices/weatherman_agent.git\ncd weatherman_agent\n```\n\n### Setting Up the Environment\nCreate a Python virtual environment to manage dependencies:\n\n```bash\npython -m venv venv\nsource venv/bin/activate # On Windows use `venv\\Scripts\\activate`\n```\n\n### Installing Dependencies\nInstall the necessary Python packages via pip:\n\n```bash\npip install -r requirements.txt\n```\n\n## Usage\nTo start querying the Baron Weather API using the autonomous agents, run:\n\n```bash\npython main.py\n```\n\n## API\n\n```bash\npython3 api.py\n```\n\n\n### Llama3\n\n```python\nfrom swarms import llama3Hosted\n\n\n# Example usage\nllama3 = llama3Hosted(\n model=\"meta-llama/Meta-Llama-3-8B-Instruct\",\n temperature=0.8,\n max_tokens=1000,\n system_prompt=\"You are a helpful assistant.\",\n)\n\ncompletion_generator = llama3.run(\n \"create an essay on how to bake chicken\"\n)\n\nprint(completion_generator)\n\n```\n\n# Documentation\n- [Llama3Hosted](docs/llama3_hosted.md)\n\n## Contributing\nContributions to Baron Weather are welcome and appreciated. Here's how you can contribute:\n\n1. Fork the Project\n2. Create your Feature Branch (`git checkout -b feature/YourAmazingFeature`)\n3. Commit your Changes (`git commit -m 'Add some YourAmazingFeature'`)\n4. Push to the Branch (`git push origin feature/YourAmazingFeature`)\n5. Open a Pull Request\n\n\n## Tests\nTo run tests run the following:\n\n`pytest`\n\n## Contact\nProject Maintainer - [Kye Gomez](mailto:kye@swarms.world) - [GitHub Profile](https://github.com/baronservices)\n\n\n# Todo\n- [x] Implement the parser and the function calling mapping to execute the functions\n- [ ] Then, implement the API server wrapping the hiearchical swarm\n- [ ] Then, Deploy on the server 24/7\n- [ ] Temperature and forecast of tomorrow\n\n\n# Requirements\n- Simple ",
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