semvec/pypi-packages huggingface dataset:
uoapi 0.2.6
A robust Python client for interacting with the Newcastle Urban Observatory API (v1.1). This package provides a comprehensive interface for accessing and analyzing urban sensor data with built-in caching, configuration management, and data analysis capabilities.
License: MIT
Requires Python: <4.0,>=3.10

# Urban Observatory API Client

A robust Python client for interacting with the Newcastle Urban Observatory API (v1.1). This package provides a comprehensive interface for accessing and analyzing urban sensor data with built-in caching, configuration management, and data analysis capabilities. Package documentation, installation instructions and examples can be found at [https://carrowmw.github.io/uoapi/](https://carrowmw.github.io/uoapi/)   

## Installation

### Using pip
```bash
pip install uoapi
```

### Using Docker
Pull and run the Docker image:
```bash
docker pull carrowmw/uoapi:latest
docker run carrowmw/uoapi:latest
```

## Quick Start
```python
from uoapi import api_client
# Initialize the client
client = api_client.APIClient()
# Get sensor data
data = client.get_raw_sensor_data(
    theme="Traffic",
    last_n_days=7
)
# Analyze the data
analysis = client.analyze_json(theme="Traffic")
```

## Key Features
- Flexible Configuration: Support for YAML-based configuration and runtime parameter updates
- Intelligent Caching: Built-in caching system with customizable retention periods
- Comprehensive Data Access:
  - Sensor metadata retrieval
  - Raw sensor data access
  - Theme and variable information
  - Spatial and temporal filtering
- Error Handling: Robust error handling and logging
- Data Analysis Tools: JSON structure analysis and data formatting utilities
- DataFrame Export: Easy conversion of metadata to Pandas DataFrames

## Use Cases
- Urban data analysis
- Environmental monitoring
- Research and data collection
- Smart city applications
- Sensor network analysis

## Technical Highlights
- Type-hinted for modern Python development
- Decorator-based caching system
- Dataclass-driven configuration
- Pandas integration for data manipulation
- Comprehensive logging system
- RESTful API interaction with request retry handling
The package is designed for researchers, data scientists, and developers working with urban sensor data, providing a clean and intuitive interface to the Urban Observatory's extensive sensor network.
promptbook 0.5.0
PromptBook is a powerful and flexible prompt management library designed for AI-driven workflows
License: MIT
Requires Python: >=3.7

# 📚 PromptBook

## 📝 Overview
PromptBook is a powerful and flexible prompt management library designed for AI-driven workflows. It allows users to create, manage, and execute prompts efficiently, including pre-prompts and post-prompts. The library integrates seamlessly with **Ollama**, enabling contextual AI conversations across multiple prompts.

## 🚀 Features
- **Create & Manage Prompt Books**: Organize prompts in structured prompt books.
- **Pre & Post-Prompt Execution**: Automatically process dependent prompts before and after the main prompt.
- **Variable Substitution**: Dynamically replace placeholders with user-defined or default values.
- **Ollama Integration**: Run prompts in AI models while maintaining context across multiple prompts.
- **Git-Based Prompt Cloning**: Clone prompt books from Git repositories for shared collaboration.
- **Customizable Configuration**: Modify API settings, debugging options, and more via `config.yaml`.

## 📥 Installation
```sh
pip install promptbook
```

## 🔧 Configuration
After installation, a `config.yaml` file can be created in the project root to customize settings:

```yaml
ollama:
  model: "llama3:2b"
  stream: true
  debug: false
  api_url: "http://localhost:11434/api/chat"
  tags_url: "http://localhost:11434/api/tags"
```

## 📖 Usage
### 1️⃣ Running a Prompt
To execute a prompt from a prompt book:
```sh
promptbook run test-prompt
```

### 2️⃣ Creating a New Prompt Book
To create a new prompt book:
```sh
promptbook create-book
```
The CLI will guide you through setting up the book structure and an initial prompt file.

### 3️⃣ Cloning a Prompt Book from GitHub
```sh
promptbook clone https://github.com/username/promptbook-repo.git
```

### 4️⃣ Debugging API Calls
Enable debug mode in `config.yaml`:
```yaml
debug: true
```

### 5️⃣ Running a Prompt with Ollama
Each item in a prompt list will be executed sequentially while maintaining conversation context.
```sh
promptbook run my-prompt
```
Example output:
```
💬 Sending: What is the capital of France?
🧠 Ollama Response: The capital of France is Paris.
```

## 📂 File Structure
```
my-promptbook/
├── prompt-book.yaml  # Book metadata
├── prompts/
│   ├── test-prompt.prompt
│   ├── another-prompt.prompt
```

## 🛠 Development & Contribution
1. Clone the repository:
   ```sh
   git clone https://github.com/yourusername/promptbook.git
   cd promptbook
   ```
2. Install dependencies:
   ```sh
   pip install -e .
   ```
3. Run tests:
   ```sh
   pytest tests/
   ```

## 📜 License
This project is licensed under the **MIT License**.

## ✨ Credits
Developed by [Sharfuddin Shawon](https://shawon.me).
PromptLang 0.1.0
A lightweight language for dynamic prompt processing with inline data transformations and caching.
License: MIT
Requires Python: >=3.6

# PromptLang

PromptLang is a lightweight and efficient language designed for dynamic prompt processing with inline data transformations and caching.

## Features
- **Data Retrieval**: Access values dynamically using `{user.name}`.
- **Function Calls**: Invoke functions with `{fn:do_something user.patient_id}`.
- **Mandatory Fields**: Ensure essential data is provided using `{mandatory user.name}`.
- **Logical OR Operation**: Handle fallback values with `{user.patient_id | user.name}`.

## Example Usage
```python
from promptlang import PromptLang

def get_diagnosis_details(id):
   return "some serious thing"

data = {"user": {"name": "Alice", "patient_id": 1234}}
cache = {}
pl = PromptLang(data, cache)

prompt = """
Please check the diagnosis is aligned with symptoms?
User's name is {user.name} and Discharge Details are {fn:get_diagnosis_details user.patient_id}
"""
print(pl.generate_prompt(prompt))
```

## Installation
To install PromptLang, use:
```sh
pip install promptlang
```

## License
This project is licensed under the MIT License.
backstage-simple-client 0.1.4
A simple client for Backstage developer portal
License: MIT
Requires Python: >=3.11

# Backstage Simple Client

A simple client for the [Backstage](https://backstage.io/) developer portal.

## Table of Contents

- [Installation](#installation)
- [Usage](#usage)
- [Examples](#examples)
- [Contributing](#contributing)
- [License](#license)

## Installation

To install the Backstage Simple Client, you can use `pip`:

```sh
pip install backstage-simple-client
```

## Usage

Here's an example of how to use the Backstage Simple Client:

```python
from backstage_simple_client.client import BackstageClient
from backstage_simple_client.filters import Filter, FullSearchFilter

# Initialize the client
base_url = "https://example.com"
token = "your-token-here"

with BackstageClient(base_url, token=token) as client:
    # Search entities with a filter
    entities = client.search_entities(
        filter=Filter(metadata_namespace='development')
    )

# Print the results
for entity in entities.items:
    print(entity.metadata.name)
```

## Examples

#### Multiple filters

```
entities = client.search_entities(
    filter = [
        Filter(kind='component', metadata_namespace='development'),
        Filter(kind='user')
    ]
)
```

#### Full-text search

```
entities = client.search_entities(
    full_text_filter = FullSearchFilter(term = "foobar", fields = ["metadata.name"])
)
```

#### Selecting fields, while applying limits and ordering

```
entities = client.search_entities(
    filter = Filter(kind='component', metadata_namespace='development'),
    fields = ['kind', 'metadata.name'],
    limit = 10,
    order_field = 'kind,asc'
)
```

#### Using pagination

```
pages = client.search_entities(
        filter   = Filter(kind='user')
        paginate = True
)
for page in pages:
    for entity in page.items:
        print(entity.metadata.name)
```

#### Getting entity by uid

```
entity = client.get_entities(uid="0442f73b-c3e9-4a94-a790-0981809b7267")
```

#### Getting entity (with its ancestry) by name 

```
entity = client.get_entities(kind="User", name="jdoe")
entities = client.get_entities(kind="User", name="jdoe", ancestry=True)
```

#### Geting entities by refs
        
```
entities = client.get_entities(refs=["user:default/jdoe", "user:default/gijoe"], fields=["metadata.name", "metadata.namespace", "metadata.uid"])
```

## Contributing

Contributions are welcome! Please open an issue or submit a pull request on GitHub.

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.
yt-finder 1.1.0
Perform YouTube video searches without the API. Fork of the youtube-search library.
License: MIT

# yt_finder - Fork of youtube-search

Python function for searching for youtube videos to avoid using their heavily rate-limited API

To avoid using the API, this uses the form on the youtube homepage and scrapes the resulting page.

## Example Usage

#### For more examples, see the [examples](examples) folder

For a basic search (and all of the current functionality), you can use the search tool as follows:

```pip install yt-search```

```python
from yt_finder import YoutubeSearch
import asyncio

async def main():
    search = YoutubeSearch("python", max_results=5, language="en", region="US")
    videos = await search.search()
    for video in videos:
        print("=" * 20)
        print(f"Title: {video.title}")
        print(f"URL: {video.yt_url}")
        print("=" * 20)

if __name__ == "__main__":
    asyncio.run(main())
```
PromptBook 0.2.0
A prompt management library for AI-driven workflows
License: MIT
Requires Python: >=3.8

# 📚 PromptBook

## 📝 Overview
PromptBook is a powerful and flexible prompt management library designed for AI-driven workflows. It allows users to create, manage, and execute prompts efficiently, including pre-prompts and post-prompts. The library integrates seamlessly with **Ollama**, enabling contextual AI conversations across multiple prompts.

## 🚀 Features
- **Create & Manage Prompt Books**: Organize prompts in structured prompt books.
- **Pre & Post-Prompt Execution**: Automatically process dependent prompts before and after the main prompt.
- **Variable Substitution**: Dynamically replace placeholders with user-defined or default values.
- **Ollama Integration**: Run prompts in AI models while maintaining context across multiple prompts.
- **Git-Based Prompt Cloning**: Clone prompt books from Git repositories for shared collaboration.
- **Customizable Configuration**: Modify API settings, debugging options, and more via `config.yaml`.

## 📥 Installation
```sh
pip install promptbook
```

## 🔧 Configuration
After installation, a `config.yaml` file can be created in the project root to customize settings:

```yaml
ollama:
  model: "llama3:2b"
  stream: true
  debug: false
  api_url: "http://localhost:11434/api/chat"
  tags_url: "http://localhost:11434/api/tags"
```

## 📖 Usage
### 1️⃣ Running a Prompt
To execute a prompt from a prompt book:
```sh
promptbook run test-prompt
```

### 2️⃣ Creating a New Prompt Book
To create a new prompt book:
```sh
promptbook create-book
```
The CLI will guide you through setting up the book structure and an initial prompt file.

### 3️⃣ Cloning a Prompt Book from GitHub
```sh
promptbook clone https://github.com/username/promptbook-repo.git
```

### 4️⃣ Debugging API Calls
Enable debug mode in `config.yaml`:
```yaml
debug: true
```

### 5️⃣ Running a Prompt with Ollama
Each item in a prompt list will be executed sequentially while maintaining conversation context.
```sh
promptbook run my-prompt
```
Example output:
```
💬 Sending: What is the capital of France?
🧠 Ollama Response: The capital of France is Paris.
```

## 📂 File Structure
```
my-promptbook/
├── prompt-book.yaml  # Book metadata
├── prompts/
│   ├── test-prompt.prompt
│   ├── another-prompt.prompt
```

## 🛠 Development & Contribution
1. Clone the repository:
   ```sh
   git clone https://github.com/yourusername/promptbook.git
   cd promptbook
   ```
2. Install dependencies:
   ```sh
   pip install -e .
   ```
3. Run tests:
   ```sh
   pytest tests/
   ```

## 📜 License
This project is licensed under the **MIT License**.

## ✨ Credits
Developed by [Sharfuddin Shawon](https://shawon.me).