private-concept 2025.12.21082713

A new package that enables users to provide simple text inputs about innovative, privacy-focused services—such as a phone company that doesn't collect personal data—and receive structured summaries or

private_concept

A Python package for processing and structuring innovative, privacy-focused service ideas into clear, concise summaries using pattern matching and large language models (LLMs).

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📌 Overview

private_concept helps users document privacy-focused concepts (e.g., a phone service that doesn’t collect personal data) by converting raw text inputs into structured, well-formatted summaries. It leverages LLM7 (by default) or any LangChain-compatible LLM to extract and refine key details, ensuring clarity and consistency.

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🚀 Installation

Install via pip:

pip install private_concept

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🔧 Usage

Basic Usage (Default LLM: LLM7)

from private_concept import private_concept

response = private_concept(
    user_input="A phone company that never collects user data, ensuring full privacy."
)
print(response)

Custom LLM Integration

You can replace the default LLM with any LangChain-compatible model (e.g., OpenAI, Anthropic, Google Generative AI):

Using OpenAI

from langchain_openai import ChatOpenAI
from private_concept import private_concept

llm = ChatOpenAI()
response = private_concept(user_input="My privacy-first app idea...", llm=llm)

Using Anthropic

from langchain_anthropic import ChatAnthropic
from private_concept import private_concept

llm = ChatAnthropic()
response = private_concept(user_input="A service that anonymizes all user interactions.", llm=llm)

Using Google Generative AI

from langchain_google_genai import ChatGoogleGenerativeAI
from private_concept import private_concept

llm = ChatGoogleGenerativeAI()
response = private_concept(user_input="A decentralized messaging platform.", llm=llm)

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🔑 API Key Configuration

• Default: Uses LLM7_API_KEY from environment variables.
• Manual Override: Pass the key directly:

  from private_concept import private_concept
  response = private_concept(user_input="...", api_key="your_llm7_api_key")
  

• Get a Free Key: Register at LLM7

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📝 Parameters

Parameter	Type	Description
user_input	str	Raw text describing the privacy-focused concept.
api_key	Optional[str]	LLM7 API key (defaults to LLM7_API_KEY env var).
llm	Optional[BaseChatModel]	Custom LangChain LLM (e.g., ChatOpenAI, ChatAnthropic).

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📊 Default LLM: LLM7

The package defaults to LLM7 (via langchain_llm7), a lightweight and efficient LLM. Free-tier rate limits are sufficient for most use cases. For higher limits, use your own API key.

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🔄 Pattern Matching

The package enforces structured output via regex patterns, ensuring responses are consistent and easy to parse.

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📜 License

MIT License. See LICENSE for details.

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📢 Support & Issues

For bugs or feature requests, open an issue on GitHub.

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👤 Author

Eugene Evstafev (LinkedIn) | [e-mail]