semvec/pypi-packages huggingface dataset:
neomate 0.1.1
Lightweight Neo4j ORM with type validation and relationship management
Requires Python: >=3.7
Keywords: orm, database, graph database, type validation, relationship management

# NeoMate

A lightweight and intuitive ORM for Neo4j in Python, providing type validation, relationship management, and elegant query building.

## Features

- Type validation system using dataclasses
- Transaction management with automatic rollback on errors
- Relationship creation and management
- Flexible node querying with AND/OR conditions
- Colored logging for better debugging
- Bulk node creation support
- Bidirectional relationship support
- Custom type validation

## Installation

```bash
pip install neomate
```

## Quick Start

```python
from neomate import NeoMate, Types
from neo4j import GraphDatabase

# Define your model
class Person:
    __nodename__ = "Person"  # Optional: customize node type name
    
    name = Types(str, is_must_have=True)
    age = Types(int, default=0)
    hobbies = Types(list, is_must_have=False)

# Connect to Neo4j
uri = "neo4j://localhost:7687"
driver = GraphDatabase.driver(uri, auth=("neo4j", "password"))
session = driver.session()

# Initialize NeoMate
neo = NeoMate(session)

# Create a person
person = Person()
person.name = "Alex"
person.hobbies = ["programming", "music"]

# Save to database
neo.create_node(person)

# Query nodes
results = neo.get_node("Person", name="Alex")
```

## Type Validation

The `Types` class supports:
- Basic Python types (str, int, list, etc.)
- Custom classes
- Optional fields with `is_must_have=False`
- Default values
- Bidirectional relationships

```python
class User:
    name = Types(str, is_must_have=True)
    friend = Types("User", relate_name="FRIEND", bidiractional=True)
```

## Transaction Management

All database operations are automatically wrapped in transactions:

```python
# Automatic transaction management
neo.add_node("Person", name="John")

# Multiple nodes in single transaction
nodes = [
    {"name": "Alice", "age": 25},
    {"name": "Bob", "age": 30}
]
neo.add_nodes("Person", nodes)
```

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## License

This project is licensed under the MIT License - see the LICENSE file for details.

## Credits

Created by Alex
ayonix 0.3.7
A modern CLI program for API testing
Requires Python: >=3.6

# Ayonix

A CLI program for API testing.

## Features

- Send HTTP requests to specified URLs
- Supports GET, POST, PUT, DELETE, and other HTTP methods
- Include headers and data in requests
- Measure and display response time
- Calculate average response time over multiple requests
- Display response status code, headers, and content

## Installation

You can install the package using `pip`:
```bash
pip install ayonix
```

## Usage

> [!IMPORTANT]
> The flag [`-d` `--data`] is deprecated and fully out of support, has been renamed to [`-b` `--body`]


The usage name for Ayonix is `call`:
```bash
call "https://jsonplaceholder.typicode.com/posts" --method POST -H '{"Content-Type": "application/json"}' -b '{"title": "foo", "body": "bar", "userId": 1}'
```

The following table shows the available flags that Ayonix allows for:

# CLI Flags and Arguments

| **Flag/Argument**         | **Type**       | **Default**  | **Description**                                                                 |
|---------------------------|----------------|--------------|---------------------------------------------------------------------------------|
| `url`                     | `str`          | Required     | URL to send the request to.                                                    |
| `--method`                | `str`          | `GET`        | HTTP method to use (e.g., GET, POST, PUT, DELETE).                             |
| `-H`, `--header`          | `JSON string`  | None         | Headers to include in the request as a JSON string.                            |
| `-b`, `--body`            | `JSON string`  | None         | Body data to include in the request as a JSON string.                          |
| `--only-content`          | `flag`         | `False`      | Only print the content of the response.                                        |
| `--only-res-code`         | `flag`         | `False`      | Only print the response code.                                                  |
| `-m`, `--metrics`         | `flag`         | `False`      | Print metrics of the response (e.g., time, headers, cookies).                  |
| `-a`, `--average-response-time` | `int`   | None         | Calculate and print the average response time (number of requests specified).  |
mcp-scholarly 0.1.0
A MCP server to search for accurate academic articles.
Requires Python: >=3.11

# mcp-scholarly MCP server

A MCP server to search for accurate academic articles. More scholarly vendors will be added soon.

![demo1.jpeg](examples/demo1.png)

![image](https://github.com/user-attachments/assets/13202184-bc12-4530-b7c1-2ee698f3e1cc)


<a href="https://glama.ai/mcp/servers/aq05b2p0ql"><img width="380" height="200" src="https://glama.ai/mcp/servers/aq05b2p0ql/badge" alt="Scholarly Server MCP server" /></a>

## Components

### Tools

The server implements one tool:
- search-arxiv: Search arxiv for articles related to the given keyword.
  - Takes "keyword" as required string arguments

## Quickstart

### Install

#### Claude Desktop

On MacOS: `~/Library/Application\ Support/Claude/claude_desktop_config.json`
On Windows: `%APPDATA%/Claude/claude_desktop_config.json`

<details>
  <summary>Development/Unpublished Servers Configuration</summary>
  ```
  "mcpServers": {
    "mcp-scholarly": {
      "command": "uv",
      "args": [
        "--directory",
        "/Users/adityakarnam/PycharmProjects/mcp-scholarly/mcp-scholarly",
        "run",
        "mcp-scholarly"
      ]
    }
  }
  ```
</details>

<details>
  <summary>Published Servers Configuration</summary>
  ```
  "mcpServers": {
    "mcp-scholarly": {
      "command": "uvx",
      "args": [
        "mcp-scholarly"
      ]
    }
  }
  ```
</details>

or if you are using Docker

<details>
  <summary>Published Docker Servers Configuration</summary>
  ```
  "mcpServers": {
    "mcp-scholarly": {
      "command": "docker",
      "args": [
        "run", "--rm", "-i",
        "mcp/scholarly"
      ]
    }
  }
  ```
</details>


## Development

### Building and Publishing

To prepare the package for distribution:

1. Sync dependencies and update lockfile:
```bash
uv sync
```

2. Build package distributions:
```bash
uv build
```

This will create source and wheel distributions in the `dist/` directory.

3. Publish to PyPI:
```bash
uv publish
```

Note: You'll need to set PyPI credentials via environment variables or command flags:
- Token: `--token` or `UV_PUBLISH_TOKEN`
- Or username/password: `--username`/`UV_PUBLISH_USERNAME` and `--password`/`UV_PUBLISH_PASSWORD`

### Debugging

Since MCP servers run over stdio, debugging can be challenging. For the best debugging
experience, we strongly recommend using the [MCP Inspector](https://github.com/modelcontextprotocol/inspector).


You can launch the MCP Inspector via [`npm`](https://docs.npmjs.com/downloading-and-installing-node-js-and-npm) with this command:

```bash
npx @modelcontextprotocol/inspector uv --directory /Users/adityakarnam/PycharmProjects/mcp-scholarly/mcp-scholarly run mcp-scholarly
```


Upon launching, the Inspector will display a URL that you can access in your browser to begin debugging.