semvec/pypi-packages huggingface dataset:
UVEC 1.1.2
User defined VEhiCle models [UVEC]
Requires Python: >=3.9

![Tests](https://github.com/StemVibrations/vehicle_models/actions/workflows/workflow.yml/badge.svg)
[![codecov](https://codecov.io/gh/StemVibrations/vehicle_models/graph/badge.svg?token=0SCPA6LAGT)](https://codecov.io/gh/StemVibrations/vehicle_models)


# User defined VEhiCle models [UVEC]

This repository contains the User defined VEhiCle models [UVEC] for STEM.

At the moment, the following vehicle models are available:

* `ten_dof_vehicle_2D`: 2-dimensional 10 degrees-of-freedom vehicle model (based on [Zhai and Sun, 1994](https://www.tandfonline.com/doi/abs/10.1080/00423119308969544))

* `two_dof_vehicle_2D`: 2-dimensional 2 degrees-of-freedom vehicle model (based on [Lou and Zeng, 2004](https://onlinelibrary.wiley.com/doi/epdf/10.1002/nme.1207))
chuk-mcp-s3-bucket-manager 0.1.1
MCP S3 Bucket Manager
Requires Python: >=3.11

# CHUK MCP S3 Bucket Manager

## Overview

The CHUK MCP S3 Bucket Manager is a Python-based tool for managing Amazon S3 buckets using the MCP (Model Context Protocol) runtime. This utility provides a simple interface for listing, creating, and deleting S3 buckets.

## Features

- List all S3 buckets
- Create new S3 buckets
- Delete existing S3 buckets
- Optional force deletion of buckets (clearing all objects)
- Supports custom AWS endpoint and region configuration

## Prerequisites

- Python 3.11+
- AWS credentials configured
- Required dependencies (installed automatically via pip)

## Installation

You can install the package directly from the repository:

```bash
pip install git+https://github.com/chrishayuk/chuk-mcp-s3-bucket-manager.git
```

## Configuration

### Environment Variables

- `AWS_ENDPOINT_URL_S3`: Optional custom S3 endpoint URL
- `AWS_REGION`: AWS region (defaults to us-east-1)

### Configuration File

The project uses a `config.yaml` file to configure the MCP server settings:

```yaml
host:
  name: "chuk-mcp-s3-bucket-manager"
  log_level: "INFO"

server:
  type: "stdio"

mcp_servers:
  s3_bucket_manager:
    enabled: true
    location: "."
    tools:
      enabled: true
      module: "chuk_mcp_s3_bucket_manager.tools"
```

## Usage

### Command-Line Interface

```bash
# List buckets
chuk-mcp-s3-bucket-manager list_buckets

# Create a new bucket
chuk-mcp-s3-bucket-manager create_bucket --bucket_name my-new-bucket

# Delete a bucket
chuk-mcp-s3-bucket-manager delete_bucket --bucket_name my-bucket

# Force delete a bucket (remove all objects first)
chuk-mcp-s3-bucket-manager delete_bucket --bucket_name my-bucket --force
```

### Programmatic Usage

```python
from chuk_mcp_s3_bucket_manager import tools

# List buckets
buckets = tools.list_buckets()

# Create a bucket
result = tools.create_bucket("my-new-bucket")

# Delete a bucket
result = tools.delete_bucket("my-bucket", force=False)
```

## Development

### Setup

1. Clone the repository
2. Create a virtual environment
3. Install development dependencies:

```bash
pip install -e .[dev]
```

### Running Tests

```bash
pytest tests/
```

## Dependencies

- boto3
- chuk-mcp-runtime
- pydantic
- PyYAML

## License

[Specify your license here]

## Contributing

Contributions are welcome! Please submit pull requests or open issues on the GitHub repository.

## Support

For issues or questions, please file an issue on the GitHub issue tracker.
magicsandbox-streaming 0.1.0
Streaming utilities for Magic Sandbox
Requires Python: >=3.7

# magicsandbox_streaming

Python streaming utilities for [Magic Sandbox](https://magicsandbox.ai).

## Getting Started

`pip install magicsandbox_streaming`

## Usage

See the [Magic Sandbox docs](https://magicsandbox.ai/?_app=magicsandbox.Docs#streaming-json) for example usage.
muopdb-python-client 0.0.9
Python client for MuopDB
Requires Python: <3.13,>=3.7

# MuopDB Python Client

## Installation
First, install the dependencies:
```
pip install -r requirements.txt
```

## Usage

This is a Python client for muopDB.

To generate Python code from proto files to support gRPC protocol:

```
python -m grpc_tools.protoc \
  --proto_path=muopdb_python_client/protos \
  --python_out=muopdb_python_client/protos \
  --grpc_python_out=muopdb_python_client/protos \
  muopdb.proto
```

Then run the following command to patch the import errors in the generated files:
```
sed -i '' 's/^import muopdb_pb2/from . import muopdb_pb2/' muopdb_python_client/protos/muopdb_pb2_grpc.py
```

## Testing

To run the tests:
- Unit tests:
```
pytest -v -s --cov=muopdb_python_client tests/unit
```
- Integration tests:
```
pytest -v -s --cov=muopdb_python_client tests/integration
```

## Grpc Debugging

Check the available grpc services and methods from a specific grpc server (e.g. localhost:9002) using grpcurl:
```
brew install grpcurl
grpcurl -plaintext localhost:9002 list
```

To view the grpc services and methods in a GUI:
```
brew install grpcui
grpcui -plaintext localhost:9002
```

References:
- [gRPC in Python](https://grpc.io/docs/languages/python/basics)


# Vectorizer Usage

The MuopDB Python client supports multiple embedding providers out of the box. You can select which provider to use by setting environment variables appropriately. Below is a quick reference on how to configure each provider.

## 1. Supported Providers

- **OpenAI**  
- **Gemini** (Google GenAI)  
- **Claude** (via `voyageai` client)  
- **Ollama** (Local LLM embeddings, e.g., `ollama` or fallback via HTTP)

## 2. Environment Variables

1. **`VECTOR_SERVICE`**  
   - **Description**: Chooses which provider to use.  
   - **Allowed values**: `openai`, `gemini`, `claude`, `ollama`.  
   - **Default**: `openai`.  
   - **Example**:
     ```bash
     export VECTOR_SERVICE=gemini
     ```
2. **API Keys**  
   - **OpenAI**: `OPENAI_API_KEY`  
     - Typically looks like `sk-...`  
   - **Gemini**: `GEMINI_API_KEY`  
   - **Claude**: `CLAUDE_API_KEY`  
     - If you’re using `voyageai`, set `VOYAGE_API_KEY` (some distributions also require `CLAUDE_API_KEY`).  
   - **Ollama**: `OLLAMA_API_KEY`  
     - May not be strictly required if running Ollama locally without authentication.

**Example** (bash):
```bash
export VECTOR_SERVICE=openai
export OPENAI_API_KEY="sk-1234..."