semvec/pypi-packages huggingface dataset:
mongo-easy 0.1.1
A simple and intuitive MongoDB helper library for everyone
Requires Python: >=3.6

# Mongo Easy 🧠🍃

**Mongo Easy** is a beginner-friendly Python library that makes working with MongoDB super simple and readable — even for non-coders. Whether you're building a script, a CLI tool, or just learning databases, Mongo Easy helps you do CRUD operations with minimal setup and code.

---

## 🔥 Features

- ⚡ One-line `connect()` to MongoDB
- 📁 Simple `save()`, `find()`, `update()`, and `delete()` functions
- 🧰 Optional collection aliases (`alias("users", "u")`)
- 🧪 Easy-to-read code — perfect for learners or quick scripting
- 💻 Built-in CLI to run MongoDB operations from your terminal
- 📤 CSV/JSON import/export
- 🧪 Test suite included
- 🧩 Custom utils for common DB tasks

---

## 🚀 Installation

```bash
pip install mongo-easy
```

Or clone for development:

```bash
git clone https://github.com/prakhardoneria/mongo_easy.git
cd mongo_easy
pip install -e .
```

---

## 🛠️ Quick Start

```python
from mongo_easy.core.connection import connect
from mongo_easy.core.crud import save, find

connect()  # connect to local MongoDB

# Insert a document
save("users", {"name": "Alice", "age": 30})

# Query documents
users = find("users", {"age": {"$gte": 18}})
print(users)
```

---

## 🧪 CLI Usage

```bash
# List all collections
mongo-easy list

# Insert a user
mongo-easy insert users --data '{"name": "Bob", "age": 25}'

# Find users
mongo-easy find users --filter '{"age": {"$gt": 20}}'
```

---

## 🧩 Modules

- `core`: Core CRUD operations + MongoDB connection
- `cli`: Command-line interface helpers
- `io`: CSV/JSON import/export
- `aliasing`: Define shortcuts for collection names
- `utils`: Common utility functions for MongoDB operations

---

## 🧰 Example

```python
from mongo_easy import connect, save, find

connect("mongodb://localhost:27017", db_name="school")

save("students", {"name": "John", "grade": "A"})
students = find("students", {"grade": "A"})

for s in students:
    print(s)
```

---

## 📁 Project Structure

```
mongo_easy/
├── core/
├── cli/
├── io/
├── aliasing/
├── utils/
├── tests/
├── examples/
└── docs/
```

---

## 📄 Docs

- [Getting Started](docs/getting_started.md)
- [Function Reference](docs/functions_reference.md)
- [CLI Guide](docs/cli_guide.md)
- [FAQ](docs/faq.md)

---

## 🧪 Testing

```bash
pytest
```

Use `mongomock` for offline tests (optional).

---

## 🤝 Contributing

Pull requests are welcome! Check out the [Contributing Guide](CONTRIBUTING.md) (or create one!) to get started.

---

## 📝 License

MIT License. Use it freely and star it if you love it ⭐

---

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## 🙌 Author

Made with ❤️ by [Prakhar Doneria](https://github.com/prakhardoneria)
meltingface 0.1.3
Python SDK for meltingface.eu
Requires Python: >=3.12

# Melting Face 🫠 

A Python SDK that allows you to load prompts hosted on [meltingface.eu](https://meltingface.eu). The library handles:

- Simple **prompt** objects (`Prompt`)  
- **Fetching** public and private prompts from the MeltingFace hub  
- **Caching** to avoid repeated downloads  

## Installation

```bash
pip install meltingface
```

## Basic Usage

### Loading a Public Prompt

```python
from meltingface import Prompt

# Load a prompt from the public hub
prompt = Prompt.from_hub("owner/repo", version="0.1.0")

print(prompt.text)   # -> "Hello from my prompt!"
print(prompt.version) # -> "v1"
```

### Loading a Private Prompt

Private prompts require authentication with an API key:

```python
from meltingface import Prompt

# Option 1: Pass the API key directly
prompt = Prompt.from_hub("owner/private-repo", api_key="your-api-key")

# Option 2: Set the MELTINGFACE_API_KEY environment variable
# export MELTINGFACE_API_KEY="your-api-key"
prompt = Prompt.from_hub("owner/private-repo")
```

> **Coming Soon:** API key support for accessing private prompts will be available on meltingface.eu as a paid feature. This will allow you to create, manage, and use private prompts programmatically in your applications.
>
> **Premium Subscription Benefits:**
> - Generate API keys to access your private prompts programmatically
> - Use version-controlled prompts in programmatically
> - Manage team access to shared prompts
>
> Stay tuned for our official launch with multiple subscription tiers for individuals and teams.

## API Reference

- **`repo_id`** (`"owner/repo"`) is how you reference your prompt's repository on meltingface.eu
- **`version`** is optional; if not provided, the library defaults to `"latest"`
- **`api_key`** is required for private prompts; can be passed directly or via the MELTINGFACE_API_KEY environment variable
- **`cache_dir`** lets you specify a custom cache location (defaults to ~/.meltingface/prompts)
- **`force_download`** when set to True, bypasses the cache and fetches the prompt again
ldmud-tracing 0.0.2
Python tracing package for LDMud

# Python Tracing package for LDMud

Python package to provide tracing information to LPC.

This package provides the following types:
 * `profile_result`
 * `trace_result`
 * `trace_cursor`

This package contains the following efuns:
 * `profile_result profile_call(mixed& result, closure fun, mixed arg, ...)`
 * `trace_result trace_call(struct trace_call_options opts, mixed& result, closure fun, mixed arg, ...)`

## Usage

### Install from the python package index

The efun package can be downloaded from the python package index:

```
pip3 install --user ldmud-tracing
```

### Build & install the package yourself

You can build the package yourself.

First clone the repository
```
git clone https://github.com/ldmud/python-tracing.git
```

Install the package
```
cd python-tracing
python3 setup.py install --user
```

### Automatically load the modules at startup

Also install the [LDMud Python efuns](https://github.com/ldmud/python-efuns) and use its
[startup.py](https://github.com/ldmud/python-efuns/blob/master/startup.py) as the Python startup script for LDMud.
It will automatically detect the installed Python efuns and load them.

### Manually load the modules at startup

Add the following lines to your startup script:
```
import ldmud_tracing.profile

ldmud_tracing.profile.register()
```

## Profiling

The `profile_call` efun evaluates the given closure, any extra arguments will
be passed to the closure. The result will be assigned to the first argument
which needs to be passed as a reference.

The efun will return a `profile_result` object. This object provides evaluation
cost and elapsed time information for each executed LPC code line. A complete
list of functions is available in the efun documentation.

## Tracing

The `trace_call` efun evaluates the given closure, any extra arguments will
be passed to the closure. The result will be assigned to the first argument
which needs to be passed as a reference.

The efun will return a `trace_result` object. This object provides two
functions: `begin()` and `end()`, which will return a `trace_cursor`
object. The `trace_cursor` object contains the information for a specific
point in time (`get_object()`, `get_program_name()`, `get_file_name()`,
`get_line_number()`, `get_eval_cost()`, `get_time()` and `get_variables()`),
and allows to move through the trace (`step_into()`, `step_over()` and
`step_out()`).

Have fun!