semvec/pypi-packages huggingface dataset:
bus-mgmt-dolt-mcp-server 0.1.23
Bus Management Dolt MCP Server
Requires Python: >=3.11

Run these

```bash
uv venv
source .venv/bin/activate
uv pip install -r requirement.txt
```

Then to run the MCP Inspector and your MCP server code: 

```bash
mcp dev bus_mgmt_dolt_mcp_server/bus_mgmt_dolt_mcp_server.py
```

Or you can do this:

```bash
uv run bus_mgmt_dolt_mcp_server/bus_mgmt_dolt_mcp_server.py
```

To run the test code do this:

```bash
uv run test_client.py
```
py-bls-api 0.1.2
A Python wrapper for the U.S. Bureau of Labor Statistics (BLS) API
Requires Python: >=3.6

# py-bls-api

A Python wrapper for the Bureau of Labor Statistics (BLS) API, providing easy access to select surveys, metadata, data previews, series metadata, popular series IDs, and time series data.

## Features

- List available BLS surveys (`get_surveys`)
- Fetch survey metadata (`get_survey_metadata`)
- Preview data structure for a survey (`get_data_preview`)
- Retrieve series ID metadata as a DataFrame (`get_seriesid_metadata`)
- Get popular series IDs for a survey (`get_popular_seriesids`)
- Download time series data (`get_bls_data`)

## Installation

Install directly from PyPI:

```bash
pip install py-bls-api
```

Or install from GitHub (latest development version):

```bash
pip install git+https://github.com/coding-with-chris/py-bls-api.git
```

## Quick Start

First, import the package:

```python
from py_bls_api import (
    get_surveys,
    get_survey_metadata,
    get_data_preview,
    get_seriesid_metadata,
    get_popular_seriesids,
    get_bls_data
)
```

### 1. List all surveys

```python
surveys = get_surveys()
print(surveys)
# e.g., {'IP': 'Industrial Production', 'CU': 'Consumer Price Index', ...}
```

### 2. Fetch survey metadata

```python
meta = get_survey_metadata("CU")
print(meta.keys())
# e.g., ['survey_name', 'survey_abbreviation', 'series', 'data_preview']
```

### 3. Preview data for a survey

```python
df_preview = get_data_preview("IP")
print(df_preview.head())
```

### 4. Retrieve series metadata

```python
df_series_meta = get_seriesid_metadata("CU")
print(df_series_meta.head())
```

### 5. Get popular series IDs

```python
# Requires a BLS API registration key
api_key = "YOUR_API_KEY_HERE"
popular_ids = get_popular_seriesids("CU", api_key)
print(popular_ids)
```

### 6. Download time series data

```python
series_ids = ["CUUR0000SA0"]  # e.g., CPI-All Urban Consumers
start_year = 2020
end_year = 2024
api_key = "YOUR_API_KEY_HERE"

# Returns a tuple: (data_df, log_df)
data_df, log_df = get_bls_data(series_ids, start_year, end_year, api_key)
print(data_df.head())
print(log_df)
```

## Configuration

By default, metadata files are fetched from GitHub Pages:

```
https://coding-with-chris.github.io/py-bls-api/metadata/
```

## Requirements

- Python 3.6+
- pandas >= 1.3.0
- requests >= 2.25.0

## Contributing

Not looking for contributions at this time. However, feedback and suggestions are always welcome!  
Please feel free to open an issue if you find bugs or have ideas for improvements.

## License

This project is released under a Non-Commercial, No-Derivatives license. See [LICENSE](LICENSE) for details.
bus-mgmt-mcp-server 0.1.14
Bus Management MCP Server
Requires Python: >=3.11

Run these

```bash
uv venv
source .venv/bin/activate
uv pip install -r requirement.txt
```

Then to run the MCP Inspector and your MCP server code: 

```bash
mcp dev bus_mgmt_mcp_server/bus_mgmt_mcp_server.py
```

Or you can do this:

```bash
uv run bus_mgmt_mcp_server/bus_mgmt_mcp_server.py
```

To run the test code do this:

```bash
uv run test_client.py
```
pydantic-rdf 0.2.0
Bridge between Pydantic V2 models and RDF graphs
Requires Python: >=3.11
Keywords: graph, linked data, pydantic, rdf, semantic web, serialization

<div align="center">

# PydanticRDF

[<img alt="github" src="https://img.shields.io/badge/github-Omegaice/pydantic--rdf-8da0cb?style=for-the-badge&logo=github" height="20">](https://github.com/Omegaice/pydantic-rdf)
[<img alt="PyPI" src="https://img.shields.io/pypi/v/pydantic-rdf?style=for-the-badge&color=1E88E5&logo=python" height="20">](https://pypi.org/project/pydantic-rdf)
[<img alt="Python" src="https://img.shields.io/pypi/pyversions/pydantic-rdf?style=for-the-badge&logo=python" height="20">](https://pypi.org/project/pydantic-rdf)
[<img alt="docs" src="https://img.shields.io/badge/docs-pydantic--rdf-blue?style=for-the-badge&logo=readthedocs" height="20">](https://omegaice.github.io/pydantic-rdf)
[<img alt="license" src="https://img.shields.io/github/license/Omegaice/pydantic-rdf?style=for-the-badge&color=green" height="20">](https://github.com/Omegaice/pydantic-rdf/blob/master/LICENSE)

</div>

A Python library that bridges Pydantic V2 models and RDF graphs, enabling seamless bidirectional conversion between typed data models and semantic web data. PydanticRDF combines Pydantic's powerful validation with RDFLib's graph capabilities to simplify working with linked data.

## Features

- ✅ **Type Safety**: Define data models with Pydantic V2 and map them to RDF graphs
- 🔄 **Serialization**: Convert Pydantic models to RDF triples with customizable predicates
- 📥 **Deserialization**: Parse RDF data into validated Pydantic models
- 🧩 **Complex Structures**: Support for nested models, lists, and circular references
- 📊 **JSON Schema**: Generate valid JSON schemas from RDF models with proper URI handling

## Installation

```bash
pip install pydantic-rdf
```

## Quick Example

```python
from rdflib import Namespace
from pydantic_rdf import BaseRdfModel, WithPredicate
from typing import Annotated

# Define a model
EX = Namespace("http://example.org/")

class Person(BaseRdfModel):
    rdf_type = EX.Person
    _rdf_namespace = EX
    
    name: str
    age: int
    email: Annotated[str, WithPredicate(EX.emailAddress)]

# Create and serialize
person = Person(uri=EX.person1, name="John Doe", age=30, email="john@example.com")
graph = person.model_dump_rdf()

# The resulting RDF graph looks like this:
# @prefix ex: <http://example.org/> .
# @prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
#
# ex:person1 a ex:Person ;
#     ex:age 30 ;
#     ex:emailAddress "john@example.com" ;
#     ex:name "John Doe" .

# Deserialize
loaded_person = Person.parse_graph(graph, EX.person1)
```

## Requirements

- Python 3.11+
- pydantic >= 2.11.3
- rdflib >= 7.1.4

## Documentation

For complete documentation, visit [https://omegaice.github.io/pydantic-rdf/](https://omegaice.github.io/pydantic-rdf/)

## License

MIT