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.

Installation

pip install pre_v7_unix_summarizer

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

Parameters

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.

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
)

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.

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.

License

This project is licensed under the MIT License.

Author

Eugene Evstafev – hi@euegne.plus
GitHub: chigwell