pre-v7-unix-summarizer 2025.12.21164816

A new package that processes text input related to early Unix history (pre-V7) and returns structured, verified summaries using pattern matching and LLM interactions. It takes user-provided text (e.g.

pre‑v7‑unix‑summarizer

A lightweight Python package that processes text about early Unix history (pre‑V7) and returns concise, structured summaries. The summaries are generated via a language model and are forced to match a predefined XML‑like pattern, making them easy to parse and validate.

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Installation

pip install pre_v7_unix_summarizer

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Quick Start

from pre_v7_unix_summarizer import pre_v7_unix_summarizer

# Simple call – the default ChatLLM7 will be used
summary = pre_v7_unix_summarizer(
    user_input="The early days of Unix started at AT&T Bell Labs in the late 1960s..."
)

print(summary)   # -> List of strings that match the output pattern

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Parameters

Name	Type	Description
user_input	str	Raw text containing historical Unix information that you want to summarize.
llm	Optional[BaseChatModel]	A LangChain LLM instance. If omitted, the package creates a default ChatLLM7 instance.
api_key	Optional[str]	API key for the LLM7 service. If not supplied, the function looks for the environment variable LLM7_API_KEY. If that is also missing, a placeholder key "None" is used.

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Under the Hood

• Default LLM – ChatLLM7 from the langchain_llm7 package (see https://pypi.org/project/langchain-llm7/).
• Pattern Matching – The response is validated against a regular expression defined in prompts.pattern using llmatch. Only data that matches the pattern is returned.

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Using a Custom LLM

You can provide any LangChain‑compatible chat model. Below are a few examples.

OpenAI

from langchain_openai import ChatOpenAI
from pre_v7_unix_summarizer import pre_v7_unix_summarizer

llm = ChatOpenAI()
summary = pre_v7_unix_summarizer(
    user_input="Your Unix text here...",
    llm=llm
)

Anthropic

from langchain_anthropic import ChatAnthropic
from pre_v7_unix_summarizer import pre_v7_unix_summarizer

llm = ChatAnthropic()
summary = pre_v7_unix_summarizer(
    user_input="Your Unix text here...",
    llm=llm
)

Google Generative AI

from langchain_google_genai import ChatGoogleGenerativeAI
from pre_v7_unix_summarizer import pre_v7_unix_summarizer

llm = ChatGoogleGenerativeAI()
summary = pre_v7_unix_summarizer(
    user_input="Your Unix text here...",
    llm=llm
)

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API Key & Rate Limits

• LLM7 Free Tier – The default rate limits are sufficient for most research and hobbyist use cases.
• Higher Limits – Provide your own API key either through the LLM7_API_KEY environment variable or by passing api_key="YOUR_KEY" directly to the function.
• Get a Free Key – Register at https://token.llm7.io/ to obtain an API key.

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Contributing & Support

If you encounter any issues or have feature requests, please open an issue on GitHub:

https://github....

We welcome contributions, bug reports, and suggestions.

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License

This project is licensed under the MIT License.

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Author

Eugene Evstafev – [e-mail]
GitHub: chigwell