semvec/pypi-packages huggingface dataset:
prob-extract-ai-odds-parser-market-insight-llm-predictive-text-miner 2025.12.20202202
A new package that takes a short textual description of a market or prediction (e.g., a headline about an AI spotting a mispriced odds disparity) and returns a structured summary containing: the event
License: MIT
Requires Python: >=3.9

# prob-extract-a...
[![PyPI version](https://badge.fury.io/py/prob-extract-a.svg)](https://badge.fury.io/py/prob-extract-a)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Downloads](https://_visitor_count_readme deployed gh visites )))](https://github.com/chigwell/prob-extract-a/versions)
[![LinkedIn](https://img.shields.io/badge/LinkedIn-Eugene_Evstafev-blue.svg)](https://www.linkedin.com/in/euegne/)
# Overview
 prob-extract-a... is a Python package that takes a short textual description of a market or prediction and returns a structured summary containing:
 - event name
 - predicted probability reported by the AI
 - reference probability from the market
 - percentage difference
 - confidence score

The package uses the `llmatch-messages` utility to send a system prompt telling the LLM to format its answer in a predefined XML-like tag block, then validates and extracts the fields with a regex, retrying if the format is incorrect.
# Installation
```bash
pip install prob_extract_a...
```
# Usage

```python
from prob_extract_a import prob_extract_a...

response = prob_extract_a...(
    user_input="AI spots mispriced odds disparity",
    llm=None,
    api_key="your_llm7_api_key_here"
)
```
You can safely pass your own `llm` instance (based on `langchain` ) if you want to use another LLM:
```python
from langchain_openai import ChatOpenAI
from prob_extract_a import prob_extract_a...

llm = ChatOpenAI()
response = prob_extract_a...(
    user_input="AI spots mispriced odds disparity",
    llm=llm
)
```
Similarly for `anthropic` or `google` APIs.
# 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 like `prob_extract_a... api_key="your_api_key_here")`.
Get a free API key by registering at https://token.llm7.io/
# GitHub Issues
https://github.com/chigwell/prob-extract-a/issues
# Author
Eugene Evstafev
# Email
hi@euegne.plus
newsstructure 2025.12.20202005
A new package that takes a news headline or short text snippet as input and returns a structured summary of the key event, actors, and context, using an LLM to generate the content and pattern matchin
License: MIT
Requires Python: >=3.9

# newsstructure

[![PyPI version](https://img.shields.io/pypi/v/newsstructure.svg)](https://pypi.org/project/newsstructure/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Downloads](https://static.pepy.tech/badge/newsstructure)](https://pepy.tech/project/newsstructure)
[![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/in/eugene-evstafev/)

A Python package that takes a news headline or short text snippet as input and returns a structured summary of the key event, actors, and context. It uses an LLM to generate the content and pattern matching to ensure the output follows a consistent format. Ideal for news aggregation, automated reporting, and content tagging.

## Installation

```bash
pip install newsstructure
```

## Usage

### Basic Example

```python
from newsstructure import newsstructure

user_input = "Apple announces new iPhone with advanced AI features"
result = newsstructure(user_input)
print(result)
```

### Using a Custom LLM

You can pass your own LangChain-compatible LLM instance. For example, to use OpenAI:

```python
from langchain_openai import ChatOpenAI
from newsstructure import newsstructure

llm = ChatOpenAI()
user_input = "Tesla stock surges after earnings report"
response = newsstructure(user_input, llm=llm)
```

To use Anthropic:

```python
from langchain_anthropic import ChatAnthropic
from newsstructure import newsstructure

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

To use Google Generative AI:

```python
from langchain_google_genai import ChatGoogleGenerativeAI
from newsstructure import newsstructure

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

### Using a Custom API Key

By default, the package uses the free tier of LLM7 (via [langchain_llm7](https://pypi.org/project/langchain-llm7/)). You can provide your own API key either via environment variable or directly:

```python
import os
os.environ["LLM7_API_KEY"] = "your_api_key_here"

# or

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

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

## Parameters

- `user_input` (str): The text input (e.g., news headline or snippet) to process.
- `llm` (Optional[BaseChatModel]): A LangChain LLM instance. If not provided, defaults to `ChatLLM7`.
- `api_key` (Optional[str]): API key for LLM7. If not provided, the package will try to read from the `LLM7_API_KEY` environment variable.

## License

This project is licensed under the MIT License.

## Issues

Report issues or feature requests on [GitHub](https://github.com/chigwell/newsstructure/issues).

## Author

Eugene Evstafev  
Email: hi@euegne.plus  
GitHub: [chigwell](https://github.com/chigwell)
ai-video-insights 2025.12.20201730
Extract relevant technical insights and knowledge from video descriptions into structured data points, allowing users to gain a deeper understanding of AI advancements and supercomputer capabilities.
License: MIT
Requires Python: >=3.9

# A I Video Insights
**Extract Relevant Technical Insights from Video Descriptions**
===========================================================

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

This package allows users to gain a deeper understanding of AI advancements and supercomputer capabilities by extracting key information from video descriptions.

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

```bash
pip install ai_video_insights
```

**Usage**
-------

```python
from ai_video_insights import ai_video_insights

response = ai_video_insights(
    user_input="Video description here...",
    llm=None  # Optional langchain llm instance, defaults to ChatLLM7
)
```

**Input Parameters**
-------------------

* `user_input`: `str` - The user input text to process.
* `llm`: `Optional[BaseChatModel]` - The langchain llm instance to use, defaults to ChatLLM7.
* `api_key`: `Optional[str]` - The api key for llm7, defaults to None.

**LLM Support**
------------

This package uses the ChatLLM7 from `langchain_llm7` by default. You can safely pass your own llm instance (based on the langchain `BaseChatModel`) if you want to use another LLM.

**Examples**
---------

```python
# Using ChatLLM7 by default
response = ai_video_insights(user_input="Example video description.")

# Using a custom language model instance
from langchain_openai import ChatOpenAI
llm = ChatOpenAI()
response = ai_video_insights(user_input="Example video description.", llm=llm)

# Using an anthropic language model instance
from langchain_anthropic import ChatAnthropic
llm = ChatAnthropic()
response = ai_video_insights(user_input="Example video description.", llm=llm)

# Using a google language model instance
from langchain_google_genai import ChatGoogleGenerativeAI
llm = ChatGoogleGenerativeAI()
response = ai_video_insights(user_input="Example video description.", llm=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 via passing it directly.

**API Key Registration**
-----------------------

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

**Contributing**
--------------

This package is hosted on GitHub at [https://github.com/chigwell/ai-video-insights](https://github.com/chigwell/ai-video-insights).

**License**
---------

MIT License

**Author**
---------

Eugene Evstafev (eugene.evstafev@euegne.plus)
fraudincident-extractor 2025.12.20201423
A new package is designed to analyze user-submitted incident descriptions related to financial frauds, scams, or cybersecurity breaches. It processes the input text to extract structured details such 
License: MIT
Requires Python: >=3.9

# Fraud Incident Extractor

[![PyPI version](https://badge.fury.io/py/fraudincident_extractor.svg)](https://pypi.org/project/fraudincident_extractor/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Downloads](https://static.pepy.tech/badge/fraudincident-extractor)](https://pepy.tech/project/fraudincident-extractor)
[![LinkedIn](https://img.shields.io/badge/LinkedIn-@chigwell-0e76a8.svg)](https://www.linkedin.com/in/eugene-evstafev/)

## Overview

A package designed to analyze user-submitted incident descriptions related to financial frauds, scams, or cybersecurity breaches. It processes the input text to extract structured details such as involved parties, amounts lost, scam types, and brief summaries.

## Installation

```bash
pip install fraudincident_extractor
```

## Usage

```python
from fraudincident_extractor import fraudincident_extractor

user_input = "I lost $100 to a phishing scam. The scammer called me and asked for my bank details."

response = fraudincident_extractor(
    user_input=user_input,
    api_key="your_api_key",
    llm=ChatAnthropic()
)

print(response)
```

You can also use your own LLM instance from [langchain](https://docs.langchain.com/) by passing it like this:
```python
from langchain_openai import ChatOpenAI
from fraudincident_extractor import fraudincident_extractor

llm = ChatOpenAI()
response = fraudincident_extractor(user_input=user_input, llm=llm)
```
or use anthropic:
```python
from langchain_anthropic import ChatAnthropic
from fraudincident_extractor import fraudincident_extractor

llm = ChatAnthropic()
response = fraudincident_extractor(user_input=user_input, llm=llm)
```
or googl:
```python
from langchain_google_genai import ChatGoogleGenerativeAI
from fraudincident_extractor import fraudincident_extractor

llm = ChatGoogleGenerativeAI()
response = fraudincident_extractor(user_input=user_input, llm=llm)
```
You can get a free API key for LLM7 by registering at [https://token.llm7.io](https://token.llm7.io). If you want to use your own API key, you can pass it directly like this:
```python
fraudincident_extractor(user_input=user_input, api_key="your_api_key")
```
You can also set the API key as an environment variable `LLM7_API_KEY`.

## Contribution and Issues

If you encounter any issues or want to contribute to the package, please submit an issue to the GitHub repository: https://github.com/chigwell/fraud-incident-extractor

## Author

Eugene Evstafev (chigwell)
hi@euegne.plus