semvec/pypi-packages huggingface dataset:
perf-metric-xtr 2025.12.21195642
perf-metric-xtr extracts key performance metrics from tech announcements and outputs standardized summaries.
License: MIT
Requires Python: >=3.9

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

Extract and structure key performance metrics from technology announcements.

## Overview
A new package that takes raw text input about product launches and returns a standardized output highlighting specific performance improvements.

## Installation
```bash
pip install perf_metric_xtr
```

## Example of Usage
```python
from perf_metric_xtr import perf_metric_xtr

response = perf_metric_xtr(user_input="Moore Threads unveils next-gen gaming GPU with 15x performance, 50x ray tracing")
print(response)
```

## Parameters

- `user_input` (str): the user input text to process
- `llm` (Optional[BaseChatModel]): the langchain llm instance to use, if not provided the default ChatLLM7 will be used
- `api_key` (Optional[str]): the api key for llm7, if not provided Also, you can safely pass your own llm instance (based on https://docs.langchain.com/docs/custom-forwards-llms) if you want to use another LLM, via passing it like `perf_metric_xtr(user_input, llm=your_llm_instance)`, for example to use the openai https://docs$langchain_openai
```python
from langchain_openai import ChatOpenAI
from perf_metric_xtr import perf_metric_xtr

llm = ChatOpenAI()
response = perf_metric_xtr(user_input, llm=llm)
```
or for example to use the anthropic https://docs.langchain.com/forward/docs/anthropic
```python
from langchain_anthropic import ChatAnthropic
from perf_metric_xtr import perf_metric_xtr

llm = ChatAnthropic()
response = perf_metric_xtr(user_input, llm=llm)
```
or google https://docs.langchain.com/docs/google-genai-forward
```python
from langchain_google_genai import ChatGoogleGenerativeAI
from perf_metric_xtr import perf_metric_xtr

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

## Rate Limits
The default rate limits for LLM7 free tier are sufficient for most use cases of this package. If you want higher rate limits for LLM7 you can pass your own api_key via environment variable `LLM7_API_KEY` or via passing it directly like `perf_metric_xtr(user_input, api_key="your_api_key")`. You can get a free api key by registering at https://token.llm7.io/

## GitHub Issues
https://github.com/chigwell/perf-metric-xtr

## Author
Eugene Evstafev
hi@euegne.plus
data-rental-share 2025.12.21194601
data-rental-share facilitates secure, temporary, and structured data sharing with defined rental terms, usage permissions, and expiration dates for transparency and manageability.
License: MIT
Requires Python: >=3.9

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

A package for securely and temporarily sharing structured data with others on a rental basis.

## Overview
A command-line tool that enables users to easily and securely share text-based data descriptions or access requests, ensuring that shared information is formatted consistently and access terms are clearly defined.

## Installation
```bash
pip install data_rental_share
```

## Usage
```python
from data_rental_share import data_rental_share

response = data_rental_share(user_input="Your text-based data description or access request")
```
You can pass an optional `llm` parameter to use a custom LLM instance:
```python
from langchain_openai import ChatOpenAI
from data_rental_share import data_rental_share

llm = ChatOpenAI()
response = data_rental_share(user_input="Your text-based data description or access request", llm=llm)
```
You can also pass an optional `api_key` parameter to use a custom LLM7 API key:
```python
from data_rental_share import data_rental_share

response = data_rental_share(user_input="Your text-based data description or access request", api_key="your_api_key")
```

## Defaults
This package uses the ChatLLM7 from langchain_llm7 by default. You can safely pass your own LLM instance (based on https://docs.langchain.dev/en/latest/provider.html) by passing it like `data_rental_share(user_input, llm=their_llm_instance)`.

## Rate Limits
The default rate limits for LLM7 free tier are sufficient for most use cases of this package. If you need higher rate limits for LLM7, you can pass your own API key via environment variable LLM7_API_KEY or via passing it directly like `data_rental_share(user_input, api_key="your_api_key")`. You can get a free API key by registering at https://token.llm7.io/.

## Documentation
For more information about the llm7 API, please visit https://docs.llm7.io/.

## GitHub Issues
If you encounter an issue, please report it at https://github.com/chigwell/data-rental-share/issues

## Author
Eugene Evstafev
hi@euegne.plus
chigwell