semvec/pypi-packages huggingface dataset:
mindflow-synth 2025.12.21184613
mindflow-synth extracts and structures insights on deep focus and flow from text, providing consistent, actionable summaries.
License: MIT
Requires Python: >=3.9

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

Package designed to extract and structure key insights from text about cognitive processes, such as deep focus and flow states.

## Overview
The MindFlow Synth package takes text input describing psychological or neurological concepts and returns a structured summary that highlights the main principles, triggers, and benefits of achieving deep focus. It leverages advanced language models to parse and organize information, providing a reliable and repeatable way to distill complex ideas into actionable insights.

## Installation
```
pip install mindflow_synth
```

## Usage
```python
from mindflow_synth import mindflow_synth

user_input = "Text about cognitive processes..."
response = mindflow_synth(user_input)
print(response)
```

## Function Signature
```python
def mindflow_synth(
    user_input: str,
    api_key: Optional[str] = None,
    llm: Optional[BaseChatModel] = None
) -> List[str]
```
- `user_input`: str - the user input text to process
- `api_key`: Optional[str] - the api key for llm7, if not provided the default ChatLLM7 will be used
- `llm`: Optional[BaseChatModel] - the langchain llm instance to use, if not provided the default ChatLLM7 will be used

## Default LLM
The package uses the ChatLLM7 from `langchain_llm7` by default. You can safely pass your own `llm` instance (based on `langchain`) if you want to use another LLM, for example:
```python
from langchain_openai import ChatOpenAI
from mindflow_synth import mindflow_synth
llm = ChatOpenAI()
response = mindflow_synth(... llm=llm)
```
or for example to use the anthropic:
```python
from langchain_anthropic import ChatAnthropic
from mindflow_synth import mindflow_synth
llm = ChatAnthropic()
response = mindflow_synth(... llm=llm)
```
or google:
```python
from langchain_google_genai import ChatGoogleGenerativeAI
from mindflow_synth import mindflow_synth
llm = ChatGoogleGenerativeAI()
response = mindflow_synth(... llm=llm)
```

## LLM7 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 `mindflow_synth(... api_key="your_api_key")`. You can get a free API key by registering at https://token.llm7.io/

## Issues
For any issues or feature requests, please submit a pull request to https://github.com/chigwell/mindflow-synth

## Author
Eugene Evstafev (hi@euegne.plus)
text-theme-scribe 2025.12.21184024
text-theme-scribe extracts recurring themes from text and returns a structured list of key concepts via LLM, ensuring consistent formatting.
License: MIT
Requires Python: >=3.9

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


A package that analyzes user-provided text to identify and extract recurring themes or topics, then summarizes them into a structured list of key concepts.

## Installation

To install the package, use pip:
```bash
pip install text-theme_scribe
```

## Usage

To use the package, import the `text_theme_scribe` function and call it with a string input:
```python
from text_theme_scribe import text_theme_scribe

user_input = "This is a sample text with multiple themes..."
response = text_theme_scribe(user_input)
print(response)
```

## Parameters

* `user_input`: str, the user input text to process
* `llm`: Optional[BaseChatModel], the langchain LLM instance to use (default: ChatLLM7 from langchain_llm7 https://pypi.org/project/langchain_llm7/)
* `api_key`: Optional[str], the API key for llm7 (default: environment variable LLM7_API_KEY)

You can pass a custom LLM instance by passing it as the `llm` parameter. For example:
```python
from text_theme_scribe import text_theme_scribe
from langchain_openai import ChatOpenAI

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

You can also pass a custom API key by setting the `LLM7_API_KEY` environment variable or by passing it directly:
```python
import os
os.environ["LLM7_API_KEY"] = "your_api_key"
response = text_theme_scribe(user_input)
```

If you want to use a different LLM, you can use a different API key:
```python
response = text_theme_scribe(user_input, api_key="your_api_key")
```

## Default LLM and API Key

By default, the package uses the ChatLLM7 from langchain_llm7 https://pypi.org/project/langchain_llm7/ and the free tier API key. If you want to use a different LLM or has higher rate limits, you can pass a custom LLM instance or API key.

## 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 by passing it directly.

## Author

**Eugene Evstafev**

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

## GitHub Issues

If you find any issues or have any questions, please open an issue on our GitHub repository: https://github.com/chigwell/text-theme_scribe
voltengine 0.1.2
Utility-grade electricity billing engine supporting prepaid, LT and HT consumers
License: MIT
Requires Python: >=3.8
Keywords: electricity, billing, prepaid, smart-meter, utility, amisps

# ⚡ VoltEngine
**The Universal Utility Billing Framework**

VoltEngine is an industry-ready Python library designed to automate complex electricity billing cycles. From simple domestic slab rates (LT) to complex industrial demand charges (HT), VoltEngine provides a modular math-heavy core to ensure zero-error invoicing.

## 🎯 Domain Coverage
- **LT Postpaid:** Residential/Commercial slab-based billing.
- **LT Prepaid:** Real-time credit-to-energy conversion logic.
- **HT (High Tension):** Industrial billing featuring Demand Charges, Power Factor (PF) adjustments, and Time-of-Day (ToD) tariffs.

## ⚙️ Core Logic Flow
The engine processes data through a structured pipeline to ensure compliance with regulatory standards:

1. **Input Layer:** Consumes KWh (Active), KVAh (Apparent), and MD (Maximum Demand).
2. **Tariff Resolver:** Matches consumer type to specific state/utility rate cards.
3. **Calculation Engine:** Processes fixed charges, energy charges, and penalties.
4. **Taxation Module:** Applies Duty, GST, and Surcharges.
5. **Output Layer:** Generates a structured JSON objects ready for PDF invoicing or API consumption.



## 🛠️ Tech Stack
- **Engine:** Python 3.10+ (Logic & Math)
- **Validation:** Pydantic (Data integrity)
- **API Layer:** FastAPI (High-speed integration)
- **UI/Simulator:** Streamlit (For real-time billing simulation)
cogni-summary 2025.12.21183907
cogni-summary efficiently generates structured non-technical summaries for productivity and cognitive science content.
License: MIT
Requires Python: >=3.9

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

Extract key insights, definitions, and actionable takeaways from any text input using a large language model.

## Installation
```bash
pip install cogni_summary
```

## Usage
```python
from cogni_summary import cogni_summary

response = cogni_summary(user_input="Your text here")
```

You can pass additional parameters to customize the model:

- `user_input`: The text to be summarized (required).
- `llm`: The LangChain LLM instance to use. If not provided, the default ChatLLM7 will be used.
- `api_key`: The API key for LLM7. If not provided, the default rate limits for LLM7 free tier will be used.

You can safely pass your own LLM instance (e.g., OpenAI, Anthropic, Google) using the corresponding LangChain library. For example, to use the OpenAI LLM:
```python
from langchain_openai import ChatOpenAI
from cogni_summary import cogni_summary

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

Alternatively, to use the Anthropic LLM:
```python
from langchain_anthropic import ChatAnthropic
from cogni_summary import cogni_summary

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

Or to use the Google LLM:
```python
from langchain_google_genai import ChatGoogleGenerativeAI
from cogni_summary import cogni_summary

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

If you need higher rate limits for LLM7, you can pass your own API key as an environment variable `LLM7_API_KEY` or directly as the `api_key` parameter.

Get a free API key by registering at <https://token.llm7.io/>.

## Documentation

For more information about the LangChain libraries used in this package, please refer to:

- [LangChain LLM7 documentation](https://docs.langchain.com/docs/tutorials/llm7/)
- [LangChain OpenAI documentation](https://docs.langchain.com/docs/tutorials/openai/)
- [LangChain Anthropic documentation](https://docs.langchain.com/docs/tutorials/anthropic/)
- [LangChain Google GenAI documentation](https://docs.langchain.com/docs/tutorials/google-genai/)

## Contributing

Please submit any issues or pull requests to our GitHub repository: <https://github.com/chigwell/cogni-summary>

## Author

Eugene Evstafev (<hi@eugene.plus>)