go-concise-summary 2025.12.21141123

A new package would take a technical description or code snippet related to concurrency in Go and generate a structured summary of the concept, such as a fair, cancelable semaphore. It would extract k

go-concise-summary

A lightweight helper library that takes a technical description or code snippet about Go concurrency concepts and returns a structured, concise summary. It is especially useful for extracting key details such as purpose, usage, and implementation notes from a fair, cancelable semaphore or other concurrency patterns.

The package is available on PyPI as go_concise_summary and works as a drop‑in function that can use the default ChatLLM7 model or any LangChain compatible LLM.

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Installation

pip install go_concise_summary

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Basic Usage

from go_concise_summary import go_concise_summary

user_text = """
A fair, cancelable semaphore in Go can be implemented using a counting semaphore
pattern with a context for cancellation. Its purpose is to limit concurrent
access to a resource while ensuring that waiting goroutines are notified when
the semaphore is released or when the context is cancelled.
"""

summary = go_concise_summary(
    user_input=user_text
)

print(summary)
# Output will be a list of strings matching the expected regex pattern.

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Function Signature

def go_concise_summary(
    user_input: str,
    api_key: Optional[str] = None,
    llm: Optional[BaseChatModel] = None
) -> List[str]

Argument	Type	Description
user_input	str	The Go concurrency description or code that you want summarized.
api_key	Optional[str]	API key for the default ChatLLM7 service. If omitted, the function will look for an environment variable LLM7_API_KEY.
llm	Optional[BaseChatModel]	A LangChain LLM instance. If supplied, the function will use this LLM instead of the default ChatLLM7.

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Overriding the LLM

The library ships with a bundled ChatLLM7 instance, but you can pass any LangChain LLM that implements BaseChatModel.

OpenAI

from langchain_openai import ChatOpenAI
from go_concise_summary import go_concise_summary

llm = ChatOpenAI()
summary = go_concise_summary(user_input=text, llm=llm)

Anthropic

from langchain_anthropic import ChatAnthropic
from go_concise_summary import go_concise_summary

llm = ChatAnthropic()
summary = go_concise_summary(user_input=text, llm=llm)

Google Gemini

from langchain_google_genai import ChatGoogleGenerativeAI
from go_concise_summary import go_concise_summary

llm = ChatGoogleGenerativeAI()
summary = go_concise_summary(user_input=text, llm=llm)

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Using a Custom LLM7 API Key

If you require higher rate limits than the default free tier, create or export an LLM7 API key:

export LLM7_API_KEY="your-api-key-here"

or pass it directly:

summary = go_concise_summary(user_input=text, api_key="your_api_key_here")

Free LLM7 API keys can be obtained at https://token.llm7.io/.

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How It Works

1. Prompting – The function composes a system prompt (system_prompt) and a user prompt (human_prompt) that instruct the model to produce a concise, structured summary of the Go concurrency snippet.
2. LLM Call – It sends the prompts to the configured LLM.
3. Regex Extraction – Using the pre‑defined pattern, the response is validated and formatted. The extracted information is returned as a List[str].

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Issues & Support

Found a bug or have a feature request? Open an issue here: https://github.com/chigwell/go-concise-summary/issues

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Author

• Eugene Evstafev
• Email: [e-mail]
• GitHub: @chigwell