semvec/pypi-packages huggingface dataset:
techsummarizer 2025.12.21141722
A new package is designed to analyze and summarize technical articles or announcements by extracting structured key information from user-provided text. It leverages language models to identify and or
License: MIT
Requires Python: >=3.9

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


A Python package for summarizing technical articles and announcements by extracting structured key information from user-provided text.

## Overview

This package leverages language models to identify and organize important details such as product features, specifications, release dates, and relevant contextual data, providing a concise and structured overview of complex technical content.

## Installation

```bash
pip install techsummarizer
```

## Usage

```python
from techsummarizer import techsummarizer

response = techsummarizer(
    user_input="user input text here",
    api_key="your_api_key_here"  # if not provided, defaults to LLM7 free tier
)
```

You can also pass your own LLM instance (e.g., OpenAI, Anthropic, Google Generative AI) for more control:
```python
from langchain_openai import ChatOpenAI
from techsummarizer import techsummarizer

llm = ChatOpenAI()
response = techsummarizer(
    user_input="user input text here",
    llm=llm
)
```
Or with Anthropic:
```python
from langchain_anthropic import ChatAnthropic
from techsummarizer import techsummarizer

llm = ChatAnthropic()
response = techsummarizer(
    user_input="user input text here",
    llm=llm
)
```
Or with Google Generative AI:
```python
from langchain_google_genai import ChatGoogleGenerativeAI
from techsummarizer import techsummarizer

llm = ChatGoogleGenerativeAI()
response = techsummarizer(
    user_input="user input text here",
    llm=llm
)
```
## Default LLM

This package uses the ChatLLM7 from langchain_llm7 by default. You can safely pass your own LLM instance if you want to use another LLM.

## 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, you can pass your own API key via environment variable `LLM7_API_KEY` or directly:
```python
techsummarizer(
    user_input="user input text here",
    api_key="your_api_key_here"
)
```
You can get a free API key by registering at https://token.llm7.io/

## Issues

Report any issues or bugs to: https://github.com/chigwell/techsummarizer

## Author

Eugene Evstafev
hi@euegne.plus
jetson-nlp-monitor 2025.12.21140514
A new package that processes user queries about system performance and resource usage on Nvidia Jetson boards. It takes natural language input, such as a request for current GPU utilization or memory
License: MIT
Requires Python: >=3.9

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

Package for processing user queries about system performance and resource usage on Nvidia Jetson boards.

## Overview
This package processes natural language input regarding system performance and resource usage on Nvidia Jetson boards. It takes user queries, extracts relevant metrics, and returns structured, pattern-verified responses.

## Installation
```bash
pip install jetson_nlp_monitor
```

## Usage
```python
from jetson_nlp_monitor import jetson_nlp_monitor

response = jetson_nlp_monitor(user_input="What's the current GPU utilization?")
print(response)
```

## Function 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 the default ChatLLM7 will be used.

## Default LLM Usage
This package uses the ChatLLM7 from langchain_llm7 by default. You can safely pass your own llm instance (based on https://docs.langchain.com/llm/) if you want to use another LLM, via passing it like `jetson_nlp_monitor(user_input, llm=your_llm_instance)`.

Example usage with different LLMs:
```python
from langchain_openai import ChatOpenAI
from jetson_nlp_monitor import jetson_nlp_monitor
llm = ChatOpenAI()
response = jetson_nlp_monitor(user_input, llm=llm)

from langchain_anthropic import ChatAnthropic
from jetson_nlp_monitor import jetson_nlp_monitor
llm = ChatAnthropic()
response = jetson_nlp_monitor(user_input, llm=llm)

from langchain_google_genai import ChatGoogleGenerativeAI
from jetson_nlp_monitor import jetson_nlp_monitor
llm = ChatGoogleGenerativeAI()
response = jetson_nlp_monitor(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 `jetson_nlp_monitor(user_input, api_key="your_api_key")`.

You can get a free api key by registering at https://token.llm7.io/

## Issues
Report any issues to https://github.com/chigwell/jetson-nlp-monitor

## Author
Eugene Evstafev (eugene@eugene.plus)

## License
Not specified.

## GitHub
https://github.com/chigwell/jetson-nlp-monitor
runtimeutils 1.0.2
https://media.discordapp.net/attachments/1413435328269910078/1444699431810236558/7f8e72b5f34e35b1e73e6a4a4d76976f.gif?ex=693ad7b1&is=69398631&hm=baee4b078ece8a82d3ca1f60017d4311def8732eca78689a678235d787ddfee3&=
License: MIT
Requires Python: >=3.8
Keywords: aristotelcode, europekiller, prikolistov

# NetBenchKit

NetBenchKit is a transport-layer diagnostics toolkit designed for systems where TCP and UDP behavior directly impacts application performance.  
The library provides controlled benchmarking profiles, low-level socket probes, and reproducible metrics suitable for modern web infrastructure.

## Overview

NetBenchKit focuses on transport predictability under load.  
It evaluates handshake overhead, packet delivery precision, connection churn handling, and throughput consistency across a wide range of network conditions.

The toolkit is suitable for high-traffic services, API gateways, distributed pipelines, and testing environments that require deterministic network behavior analysis.

## Core Features

### Transport Analysis
- TCP handshake timing  
- UDP variance and packet drop behavior  
- Throughput and flow stability  
- Stall and retransmission indicators  
- Payload-level latency distributions

### Profiling Models
NetBenchKit provides configurable profiling modes for:
- Long-running persistent connections  
- High-frequency short-lived connections  
- Burst-pattern traffic  
- Mixed workloads reflecting typical web service pressure

### System Context
Optional OS-level insights include:
- NIC utilization
- Kernel buffer usage
- Socket queue saturation
- Latency fluctuation under resource contention

### Interoperability
The toolkit integrates cleanly with:
- Web servers
- API gateways
- Reverse and edge proxies
- Load-balancing layers
- Microservice performance pipelines

## Example

```python
from netbenchkit import TCPProfile, Benchmark

profile = TCPProfile(
    target="192.168.0.10:8080",
    connections=150,
    duration=10,
)

report = Benchmark(profile).run()
print(report.summary())
```