semvec/pypi-packages huggingface dataset:
text-digestor 2025.12.22080445
Transforms raw text into clean, readable articles by removing ads, nav, sidebars, preserving core narrative with structured output.
License: MIT
Requires Python: >=3.9

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


Transform raw text into a clean, readable article format with Text-Digestor.

## Overview

Text-Digestor is a package that extracts the main content from unstructured text, such as web content or documents, and processes it to remove unnecessary elements like advertisements, navigation links, and sidebars. It focuses on preserving the core narrative, making it ideal for applications that require distraction-free reading experiences.

## Features

- Extracts main content from unstructured text
- Removes unnecessary elements like advertisements, navigation links, and sidebars
- Preserves core narrative
- Well-organized and easy-to-consume output

## Installation

```bash
pip install text_digestor
```

## Example Usage

```python
from text_digestor import text_digestor

user_input = "Unstructured text to process..."
response = text_digestor(user_input)
print(response)
```

## Input Parameters

- `user_input`: `str`: the user input text to process
- `llm`: `Optional[BaseChatModel]`: the Langchain LLM instance to use, defaults to `ChatLLM7` from `langchain_llm7` if not provided
- `api_key`: `Optional[str]`: the API key for LLM7, defaults to an empty string (`api_key is None`) if not provided

Note that you can safely pass your own LLM instance if you want to use another LLM. For example, to use OpenAI's LLM, you can pass it like this:
```python
from langchain_openai import ChatOpenAI
from text_digestor import text_digestor

llm = ChatOpenAI()
response = text_digestor(user_input, llm=llm)
```
Similarly, you can use Anthropic's LLM:
```python
from langchain_anthropic import ChatAnthropic
from text_digestor import text_digestor

llm = ChatAnthropic()
response = text_digestor(user_input, llm=llm)
```
Or Google's LLM:
```python
from langchain_google_genai import ChatGoogleGenerativeAI
from text_digestor import text_digestor

llm = ChatGoogleGenerativeAI()
response = text_digestor(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 need higher rate limits for LLM7, you can pass your own API key via environment variable `LLM7_API_KEY` or directly like this:
```python
response = text_digestor(user_input, api_key="your_api_key")
```
You can get a free API key by registering at [Token.LLM7.IO](https://token.llm7.io/)

## Issues

Report any issues to our [GitHub Issues Page](https://github.com/chigwell/text-digestor)

## Author

Eugene Evstafev
[eugene.evstafev@hi@euegne.plus](mailto:hi@eugene.plus)

## License

MIT License
psi-fusion-insights 2025.12.22075804
psi-fusion-insights extracts structured data from PsiQuantum fusion tech texts, summarizing techniques, benefits, challenges, and uses.
License: MIT
Requires Python: >=3.9

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

Psi Fusion Insights is a Python package designed to extract and structure key insights from technical descriptions about PsiQuantum's fusion-based quantum computation.

## Overview
Users input text containing detailed explanations or summaries of PsiQuantum's quantum computing advancements, and the package processes this text to return a standardized, structured output. The structured response includes identified key components such as quantum fusion techniques, computational advantages, technical challenges, and potential applications.

## Installation
To install Psi Fusion Insights, run the following command:
```bash
pip install psi_fusion_insights
```

## Usage
To use the package, simply call the `psi_fusion_insights` function with the input text as a string:
```python
from psi_fusion_insights import psi_fusion_insights

user_input = "Your technical description here"
response = psi_fusion_insights(user_input)
print(response)
```
Alternatively, you can specify a `llm` instance to use, if not provided, the default `ChatLLM7` from `langchain_llm7` will be used:
```python
from langchain_llm7 import ChatLLM7
from psi_fusion_insights import psi_fusion_insights

llm = ChatLLM7()
response = psi_fusion_insights(user_input, llm=llm)
print(response)
```
If you want to use a different LLM, you can pass a `langchain` instance accordingly, for example:
```python
from langchain_openai import ChatOpenAI
from psi_fusion_insights import psi_fusion_insights

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

## Notes
* Psi Fusion Insights uses the `ChatLLM7` from `langchain_llm7` by default.
* If you want to use a different LLM, simply pass a `langchain` instance accordingly.
* 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 `psi_fusion_insights(user_input, api_key="your_api_key")`. You can get a free API key by registering at https://token.llm7.io/

## Author
Eugene Evstafev
hi@eugene.plus
https://github.com/chigwell

## Issues
For issues with the package, please submit a pull request or open an issue at: https://github.com/chigwell/psi-fusion-insights
vitae-parser 2025.12.22074807
vitae-parser extracts and structures biographical details from unstructured text using pattern matching.
License: MIT
Requires Python: >=3.9

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


A Python package for extracting and structuring biographical information from unstructured text about notable individuals.

## Overview

This package uses a combination of natural language processing (NLP) and machine learning to extract key details such as name, profession, notable facts, and dates from text sources. It's designed for researchers, biographers, and historians who need to quickly parse and organize information from text sources.

## Installation

You can install Vitae-Parse using pip:

```bash
pip install vitae_parser
```

## Usage

You can use the `vitae_parser` function to extract information from a text input. Here's an example:

```python
from vitae_parser import vitae_parser

user_input = """
John Doe is a renowned scientist who has published numerous papers on AI. He received his PhD in Computer Science from Stanford University in 2010.
"""

response = vitae_parser(user_input)
print(response)
```

The function takes in three parameters:

* `user_input`: The text input to process, which should describe a person's life, achievements, or significant events.
* `api_key`: The API key for LLM7, which is used by default if not provided. You can get a free API key by registering at https://token.llm7.io/
* `llm`: The LangChain LL&M instance to use. If not provided, the default `ChatLLM7` will be used. You can safely pass your own LLM instance if you want to use another LLM. For example, to use the OpenAI LLM:

```python
from langchain_openai import ChatOpenAI
from vitae_parser import vitae_parser

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

## Development

This package uses the `LangChain` library to integrate with LLM7. You can find the documentation for `LangChain` at https://docs.langchain.com/. If you want to use a different LLM, you can pass your own instance of the `BaseChatModel` class.

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.

## Contributing

Contributions are welcome! Please submit issues or pull requests through the GitHub repository: https://github.com/chigwell/vitae-parser.

## Author

Eugene Evstafev (eugene.evstafev@eugene.plus)

## License

This package is released under the MIT license.