semvec/pypi-packages huggingface dataset:
py-simple-search 0.0.1a0
This is the Python package py-simple-search.
License: MIT
Requires Python: >=3.8

# Welcome

This is the Python package py-simple-search. This package provides a simple search functionality for Python. It also provides a CLI tool to search things (TBD).

# Usage

```python
from py_simple_search import breadth_first_search
from itertools import product
import string


SEARCH_CHARS = string.ascii_uppercase + string.ascii_lowercase + string.digits + '+_.'


def validate(x):
    return get('https://www.__some_example_website__.com/?q=' + x).status_code == 200


def expand_search_space(x, validate_result):
    if not validate_result:
        yield from product([x], SEARCH_CHARS)


for result in breadth_first_search(validate, expand_search_space, multithreaded_workers=10):
    print(result)

```
noun-verb 2025.12.21234620
noun-verb is a package that reframes self-concept from fixed labels (nouns) to dynamic actions (verbs).
License: MIT
Requires Python: >=3.9

# noun-verb
[![PyPI version](https://badge.fury.io/py/noun-verb.svg)](https://badge.fury.io/py/noun-verb)
[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
[![Downloads](https://static.pepy.tech/badge/noun-verb)](https://pepy.tech/project/noun-verb)
[![LinkedIn](https://img.shields.io/badge/LinkedIn-blue)](https://www.linkedin.com/in/eugene-evstafev-716669181/)


A Python package that helps users reframe their self-perception from static identity labels (nouns) to dynamic actions and processes (verbs). It uses structured LLM interactions to process user-provided text reflections and returns a transformed perspective that emphasizes growth, change, and agency.

## Installation

```bash
pip install noun_verb
```

## Usage

Import and use the `noun_verb` function to process user input:

```python
from noun_verb import noun_verb

user_input = "I am a shy person."
result = noun_verb(user_input)
print(result)
```

### Parameters

- `user_input` (str): The user input text to process.
- `llm` (Optional[BaseChatModel]): A LangChain LLM instance. If not provided, the default `ChatLLM7` is used.
- `api_key` (Optional[str]): API key for LLM7. If not provided, the environment variable `LLM7_API_KEY` is used.

### Using a Custom LLM

You can use any LangChain-compatible LLM by passing it to the `llm` parameter:

#### OpenAI Example
```python
from langchain_openai import ChatOpenAI
from noun_verb import noun_verb

llm = ChatOpenAI()
response = noun_verb(user_input, llm=llm)
```

#### Anthropic Example
```python
from langchain_anthropic import ChatAnthropic
from noun_verb import noun_verb

llm = ChatAnthropic()
response = noun_verb(user_input, llm=llm)
```

#### Google Generative AI Example
```python
from langchain_google_genai import ChatGoogleGenerativeAI
from noun_verb import noun_verb

llm = ChatGoogleGenerativeAI()
response = noun_verb(user_input, llm=llm)
```

### Using a Custom API Key for LLM7

To use your own LLM7 API key:

```python
from noun_verb import noun_verb

response = noun_verb(user_input, api_key="your_api_key_here")
```

Or set the environment variable:
```bash
export LLM7_API_KEY="your_api_key_here"
```

You can get a free API key by registering at [https://token.llm7.io/](https://token.llm7.io/).

## Default LLM

By default, the package uses `ChatLLM7` from the `langchain_llm7` package (available at [PyPI](https://pypi.org/project/langchain-llm7/)). The free tier rate limits are sufficient for most use cases.

## Contributing

If you encounter any issues or have suggestions, please open an issue on [GitHub](https://github.com/chigwell/noun-verb/issues).

## Author

Eugene Evstafev  
Email: hi@euegne.plus  
GitHub: [chigwell](https://github.com/chigwell)
tech-summary 2025.12.21234318
tech-summary processes text to extract structured summaries of technical concepts, ensuring consistent and reliable output for developers, educators, and writers.
License: MIT
Requires Python: >=3.9

# tech-summary
[![PyPI version](https://badge.fury.io/py/tech-summary.svg)](https://badge.fury.io/py/tech-summary)
[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
[![Downloads](https://static.pepy.tech/badge/tech-summary)](https://pepy.tech/project/tech-summary)
[![LinkedIn](https://img.shields.io/badge/LinkedIn-blue)](https://www.linkedin.com/in/eugene-evstafev-716669181/)

Package to extract structured summaries of technical concepts from text input.

## Overview

This package uses pattern matching to ensure output consistency and reliability, avoiding unstructured or ambiguous responses. It's useful for developers, educators, or technical writers who need concise, formatted explanations without manual reformatting.

## Installation

```bash
pip install tech_summary
```

## Usage

```python
from tech_summary import tech_summary

user_input = "Compare garbage collection and move semantics in programming languages."
response = tech_summary(user_input)
print(response)
```

You can also pass a LangChain LLM instance to use:

```python
from langchain_llm7 import ChatLLM7
from tech_summary import tech_summary

llm = ChatLLM7()
response = tech_summary(user_input, llm=llm)
print(response)
```

You can also use another LLM instance (e.g. OpenAI, Anthropic, Google Generative AI) by passing your own instance:

```python
from langchain_openai import ChatOpenAI
from tech_summary import tech_summary

llm = ChatOpenAI()
response = tech_summary(user_input, llm=llm)
print(response)

from langchain_anthropic import ChatAnthropic
from tech_summary import tech_summary

llm = ChatAnthropic()
response = tech_summary(user_input, llm=llm)
print(response)

from langchain_google_genai import ChatGoogleGenerativeAI
from tech_summary import tech_summary

llm = ChatGoogleGenerativeAI()
response = tech_summary(user_input, llm=llm)
print(response)
```

## Configuration

You can configure the LLM7 API key by setting the `LLM7_API_KEY` environment variable or passing it directly to the `tech_summary` function:

```python
tech_summary(user_input, api_key="your_api_key")
```

If you haven't registered for an API key, you can get one for free at https://token.llm7.io/.

## GitHub

Raise issues at https://github.tech-summary.

## Author

Eugene Evstafev
hi@euegne.plus

## Changelog

This package is under development. See GitHub for updates.

## Acknowledgments

This package uses ChatLLM7 (https://pypi.org/project/langchain-llm7/) by default.
cpufreqizer 2025.12.21234021
cpufreqizer recommends optimal CPU scaling governors and kernel params based on workload, balancing power and performance.
License: MIT
Requires Python: >=3.9

# CPUFreqRizer
[![PyPI version](https://badge.fury.io/py/cpufreqizer.svg)](https://badge.fury.io/py/cpufreqizer)
[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
[![Downloads](https://static.pepy.tech/badge/cpufreqizer)](https://pepy.tech/project/cpufreqizer)
[![LinkedIn](https://img.shields.io/badge/LinkedIn-blue)](https://www.linkedin.com/in/eugene-evstafev-716669181/)

Balancing Power Efficiency and Performance with CPU Scaling Configuration
======================================

Overview
--------

CPUFreqRizer is a Python package that determines the optimal CPU scaling configuration for your system based on your workload. It takes user input, such as task type, expected load, and performance requirements, and generates a structured response with recommended CPU scaling policies.

## Installation

```bash
pip install cpufreqizer
```

## Overview of Functionality

CPUFreqRizer uses an LLM (Large Language Model) to generate a structured response with CPU scaling recommendations. The user provides details about their workload, and the package returns a list of recommended CPU scaling policies, including the appropriate governor and kernel parameters.

## Using the Package

```python
from cpufreqizer import cpufreqizer

response = cpufreqizer(user_input={"task_type": "cpu-intensive", "expected_load": 0.8, "performance_requirements": "high"})
```

### Default Behavior

By default, CPUFreqRizer uses the `llm7` LLM from the `langchain_llm7` package. This is a free-tier LLM with sufficient rate limits for most use cases. You can safely pass your own `llm` instance (based on [these docs](https://docs.languagedev.ai/)) if you want to use a different LLM. For example, to use the `openai` LLM:

```python
from langchain_openai import ChatOpenAI
from cpufreqizer import cpufreqizer

llm = ChatOpenAI()
response = cpufreqizer(user_input={"task_type": "cpu-intensive", "expected_load": 0.8, "performance_requirements": "high"}, llm=llm)
```

### Obtaining a Free API Key

To use a different LLM with a higher rate limit, you can obtain a free API key on the [llm7.io website](https://token.llm7.io/). You can then pass this API key via environment variable `LLM7_API_KEY` or directly to the `cpufreqizer` function:

```python
cpufreqizer(user_input={"task_type": "cpu-intensive", "expected_load": 0.8, "performance_requirements": "high"}, api_key="your_api_key")
```

## GitHub and Contact Information

You can find the source code for this package on [GitHub](https://github.com/chigwell/cpufreqizer).

If you have any questions or need help with using the package, please don't hesitate to reach out to me at [hi@euegne.plus](mailto:hi@euegne.plus).

## Citing the Package

If you use the `cpufreqizer` package in your research, please cite it as follows:

E. Evstafev, "CPUFreqRizer: A Python Package for Determining Optimal CPU Scaling Configuration"
fin-trend-analyzer 2025.12.21233059
fin-trend-analyzer analyzes financial news and sentiment to identify trends and risks in AI and tech stocks, providing structured insights for quick market assessment.
License: MIT
Requires Python: >=3.9

# Fin-Trend-Analyzer
[![PyPI version](https://badge.fury.io/py/fin-trend-analyzer.svg)](https://badge.fury.io/py/fin-trend-analyzer)
[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
[![Downloads](https://static.pepy.tech/badge/fin-trend-analyzer)](https://pepy.tech/project/fin-trend-analyzer)
[![LinkedIn](https://img.shields.io/badge/LinkedIn-blue)](https://www.linkedin.com/in/eugene-evstafev-716669181/)

Financial Trend Analyzer for AI and Technology Stocks
=====================================================

A Python package that analyzes financial news and sentiment to identify and summarize market trends and risks related to AI and technology stocks.

**Overview**
-----------

Fin-Trend-Analyzer is a linguistic tool that quickly assesses market perceptions and potential bubble indicators by processing text inputs from news articles, reports, or social media posts. It returns structured insights, such as key risk factors, sentiment scores, and trend summaries, using pattern matching to ensure consistent and reliable output.

**Installation**
------------

```bash
pip install fin_trend_analyzer
```

**Usage**
-----

```python
from fin_trend_analyzer import fin_trend_analyzer

response = fin_trend_analyzer(user_input="This week's earnings report was outstanding.")
```

**API Documentation**
-------------------

* `fin_trend_analyzer(user_input: str, api_key: Optional[str] = None, llm: Optional[BaseChatModel] = None) -> List[str]`:
  * `user_input`: The user input text to process.
  * `api_key`: The API key for LLM7, default is `None` and will use the environment variable `LLM7_API_KEY`.
  * `llm`: The langchain LLM instance to use, default is `ChatLLM7` from `langchain_llm7`.

  Example using a custom LLM instance (e.g., OpenAI, Anthropic, or Google):
  ```python
  from langchain_openai import ChatOpenAI
  from fin_trend_analyzer import fin_trend_analyzer

  llm = ChatOpenAI()
  response = fin_trend_analyzer(user_input="This week's earnings report was outstanding.", llm=llm)
  ```

**Rate Limits**
------------

The default rate limits for LLM7 free tier are sufficient for most use cases of this package. If higher rate limits are needed, pass your own API key via environment variable `LLM7_API_KEY` or via passing it directly:
```python
from fin_trend_analyzer import fin_trend_analyzer

response = fin_trend_analyzer(user_input="This week's earnings report was outstanding.", api_key="your_api_key")
```
You can get a free API key by registering at [https://token.llm7.io/](https://token.llm7.io/).

**Author**
------

Eugene Evstafev
* Email: hi@euegne.plus
* GitHub: https://github.com/chigwell