semvec/pypi-packages huggingface dataset:
tmt 1.67.0
Test Management Tool
Requires Python: >=3.9
Keywords: metadata, testing


==================================================================
                    tmt - Test Management Tool
==================================================================

|docs| |deepwiki| |matrix| |fedora-pkg| |copr-build| |pypi-version| |license|

The ``tmt`` tool provides a user-friendly way to work with tests.
You can comfortably create new tests, safely and easily run tests
across different environments, review test results, debug test
code and enable tests in the CI using a consistent and concise
config.

The python module and command-line tool implement the Metadata
Specification which allows storing all needed test execution data
directly within a git repository. Together with the possibility to
reference remote repositories, it makes it easy to share test
coverage across projects and distros.

The Flexible Metadata Format ``fmf`` is used to store data in both
human and machine readable way close to the source code. Thanks to
inheritance and elasticity metadata are organized in the structure
efficiently, preventing unnecessary duplication.


| Check the `overview`__ section for `install`__ instructions and
  a couple of inspirative `examples`__.
| See the `guide`__ for a more detailed introduction to available
  features.
| Find detailed descriptions of the options in the
  `specification`__ and `plugins`__ section.

__ https://tmt.readthedocs.io/en/stable/overview.html
__ https://tmt.readthedocs.io/en/stable/overview.html#install
__ https://tmt.readthedocs.io/en/stable/overview.html#examples
__ https://tmt.readthedocs.io/en/stable/guide.html
__ https://tmt.readthedocs.io/en/stable/spec.html
__ https://tmt.readthedocs.io/en/stable/plugins/index.html

Use the ``tldr tmt`` command to quickly get started.

.. |docs| image:: https://img.shields.io/badge/Read%20the%20Docs-8CA1AF?logo=readthedocs&logoColor=fff
    :target: https://tmt.readthedocs.io/
    :alt: Documentation

.. |deepwiki| image:: https://deepwiki.com/badge.svg
    :target: https://deepwiki.com/teemtee/tmt
    :alt: Ask DeepWiki

.. |matrix| image:: https://img.shields.io/badge/Matrix-000?logo=matrix&logoColor=fff
    :target: https://matrix.to/#/#tmt:fedoraproject.org
    :alt: Matrix chat

.. |fedora-pkg| image:: https://img.shields.io/fedora/v/tmt?logo=fedora&logoColor=fff&color=fff&labelColor=51a2da
    :target: https://src.fedoraproject.org/rpms/tmt
    :alt: Fedora package

.. |copr-build| image:: https://img.shields.io/badge/dynamic/json?logo=fedora&color=blue&label=dev-build&query=builds.latest.state&url=https%3A%2F%2Fcopr.fedorainfracloud.org%2Fapi_3%2Fpackage%3Fownername%3D%40teemtee%26projectname%3Dlatest%26packagename%3Dtmt%26with_latest_build%3DTrue
    :target: https://copr.fedorainfracloud.org/coprs/g/teemtee/latest/
    :alt: Copr build status

.. |pypi-version| image:: https://img.shields.io/badge/PyPI-3775A9?logo=pypi&logoColor=fff
    :target: https://pypi.org/project/tmt/
    :alt: PyPI

.. |license| image:: https://img.shields.io/badge/license-MIT-blue.svg
    :target: https://opensource.org/licenses/MIT
    :alt: MIT License
sqlcrucible 0.3.3
Define a single model that works as both Pydantic and SQLAlchemy, with explicit conversion between the two
Requires Python: >=3.11

# SQLCrucible

Get the full power of Pydantic for your API and the full power of SQLAlchemy for your database, without compromising on either.

SQLCrucible lets you define a single model class that works as both a Pydantic model and a SQLAlchemy table, with explicit conversion between the two. Your Pydantic models stay pure Pydantic (working with FastAPI, validation, serialization), your SQLAlchemy models stay pure SQLAlchemy (supporting relationships, inheritance, Alembic), and conversion only happens when you ask for it.

## Key Features

- **Explicit conversion** — `to_sa_model()` and `from_sa_model()` make it clear when you're crossing the boundary between Pydantic and SQLAlchemy
- **Native SQLAlchemy** — use `mapped_column()`, `relationship()`, and `__mapper_args__` directly; if SQLAlchemy supports it, so does SQLCrucible
- **Escape hatches everywhere** — mix with plain SQLAlchemy models, attach entities to existing tables, customize generated models, or drop down to raw queries
- **Full inheritance support** — single table, joined table, and concrete table inheritance all work out of the box
- **Type stub generation** — generate `.pyi` stubs for IDE autocompletion and type checking of SQLAlchemy columns
- **Multiple frameworks** — works with Pydantic, dataclasses, and attrs

## Example

```python
from typing import Annotated
from uuid import UUID, uuid4

from pydantic import Field
from sqlalchemy import create_engine, select
from sqlalchemy.orm import Session, mapped_column

from sqlcrucible import SAType, SQLCrucibleBaseModel

class Artist(SQLCrucibleBaseModel):
    __sqlalchemy_params__ = {"__tablename__": "artist"}

    id: Annotated[UUID, mapped_column(primary_key=True)] = Field(default_factory=uuid4)
    name: str

engine = create_engine("sqlite:///:memory:")
SAType[Artist].__table__.metadata.create_all(engine)

artist = Artist(name="Bob Dylan")
with Session(engine) as session:
    session.add(artist.to_sa_model())
    session.commit()

with Session(engine) as session:
    sa_artist = session.scalar(
        select(SAType[Artist]).where(SAType[Artist].name == "Bob Dylan")
    )
    artist = Artist.from_sa_model(sa_artist)
```

## Installation

```bash
pip install sqlcrucible
```

## Documentation

Full documentation is available at [sqlcrucible.rdrj.uk](https://sqlcrucible.rdrj.uk/).

---

<p align="center">
  🏴󠁧󠁢󠁳󠁣󠁴󠁿 Made with haggis
</p>
ezmemory 0.1.5
AI Agent Memory System with embeddings and vector storage
Requires Python: >=3.9

# EzMemory

AI Agent Memory System with embeddings and vector storage

## Installation

```bash
pip install ezmemory
```

## Quick Start

After installation, launch the CLI:

```bash
ezmemory
```

The setup wizard will guide you through:
- Selecting a vector database (Qdrant, Pinecone, or Zilliz)
- Configuring your embedding provider (OpenAI, VoyageAI, OpenRouter, NVIDIA, or Gemini)
- Setting up your first collection

## Features

- Multiple embedding provider support
- Multiple vector database support
- Interactive CLI for easy configuration
- FastMCP server for agent integration
- HTTP API server
- Memory lifecycle management

## Documentation

For detailed configuration and usage, visit the [GitHub repository](https://github.com/EsshUwU/EzMemory).
generate-env-sample 0.1.2
A simple CLI tool to generate .env.sample files from existing .env files, preserving comments and empty lines.
Requires Python: >=3.11
Keywords: cli, env, sample, python, typer, environment-variables

# Generate-Env-Sample

![PyPI version](https://img.shields.io/pypi/v/generate-env-sample?color=blue&label=PyPI&logo=pypi)  


A simple CLI tool to generate `.env.sample` files from existing `.env` files. This tool reads your `.env` file, preserves comments and empty lines, and creates a sample file with keys but without values.

## Features

- Preserves comments and empty lines from the original `.env` file.
- Generates a clean `.env.sample` with keys set to empty values.
- Handles encoding issues, permissions, and file existence gracefully.
- Built with Python and Typer for a smooth CLI experience.

## Installation

You can install using one of the following methods:

- Using pipx:
  ```sh
  pipx install generate-env-sample
  ```
- Using uv:
  ```sh
  uv tool install generate-env-sample
  ```
- Run directly with pipx:
  ```sh
  pipx run generate-env-sample
  ```
- Run directly with uvx:
  ```sh
  uvx generate-env-sample
  ```

### Prerequisites
- Python 3.11 or higher

## Build Instructions

This project uses `uv` for management. To build the project, follow these steps:

1. Install `uv` by following the [uv documentation](https://docs.astral.sh/uv/getting-started/installation/).
   
2. Clone the repository:

   ```bash
   git clone https://github.com/heshinth/generate-env-sample.git
   cd generate-env-sample
   ```

3. Sync the project dependencies using uv sync:
   ```bash
   uv sync
   ```

## Usage

Run the tool from the command line:

```bash
generate-env-sample
```

### Options
- `--file`: Specify the `.env` file to read from (default: `.env`)
- `--sample`: Specify the output `.env.sample` file (default: `.env.sample`)

### Examples

1. Generate `.env.sample` from the default `.env`:
   ```bash
   generate-env-sample
   ```

2. Use a custom `.env` file:
   ```bash
   generate-env-sample --file myenv.env --sample myenv.sample
   ```

3. Get help:
   ```bash
   generate-env-sample --help
   ```

## Example Input/Output

Given a `.env` file like:
```
# Database configuration
DB_HOST=localhost
DB_PORT=5432

# API keys
API_KEY=your_secret_key
```

The generated `.env.sample` will be:
```
# Database configuration
DB_HOST=
DB_PORT=

# API keys
API_KEY=
```

## Contributing

Contributions are welcome! Please open an issue or submit a pull request on [GitHub](https://github.com/heshinth/generate-env-sample).

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.