techchallenge-summarizer 2025.12.21164129

A new package that processes user-provided text input to generate structured summaries of technical challenges, such as the difficulty of implementing resumable LLM streaming. It uses a system prompt

TechChallenge Summarizer

A small utility that turns a free‑form description of a technical challenge (e.g., “how to implement resumable LLM streaming”) into a clean, concise summary ready for documentation or issue‑tracking systems.
It works by sending a system prompt to an LLM (by default ChatLLM7), then extracts the answer with a regular‑expression pattern, guaranteeing a predictable output format.

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Installation

pip install techchallenge_summarizer

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Quick Start

from techchallenge_summarizer import techchallenge_summarizer

# Minimal call – uses the default ChatLLM7 and the API key from the environment.
summary = techchallenge_summarizer(
    user_input="I need a way to keep an LLM stream alive across network interruptions, ..."
)

print(summary)   # → List of extracted summary strings

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API Reference

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

Parameter	Type	Description
user_input	str	The raw text describing the technical challenge.
llm	Optional[BaseChatModel]	A LangChain chat model instance. If omitted, a ChatLLM7 instance is created automatically.
api_key	Optional[str]	API key for the LLM7 service. If omitted, the function reads LLM7_API_KEY from the environment.

The function returns a list of extracted summary strings. If the LLM call fails, a RuntimeError is raised with the underlying error message.

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Using a Custom LLM

You can pass any LangChain‑compatible chat model (e.g., OpenAI, Anthropic, Google) instead of the default ChatLLM7.

OpenAI

from langchain_openai import ChatOpenAI
from techchallenge_summarizer import techchallenge_summarizer

my_llm = ChatOpenAI(model="gpt-4o-mini")
summary = techchallenge_summarizer(
    user_input="My challenge is ...",
    llm=my_llm,
)

Anthropic

from langchain_anthropic import ChatAnthropic
from techchallenge_summarizer import techchallenge_summarizer

my_llm = ChatAnthropic(model="claude-3-haiku-20240307")
summary = techchallenge_summarizer(
    user_input="I want to ...",
    llm=my_llm,
)

Google Generative AI

from langchain_google_genai import ChatGoogleGenerativeAI
from techchallenge_summarizer import techchallenge_summarizer

my_llm = ChatGoogleGenerativeAI(model="gemini-1.5-flash")
summary = techchallenge_summarizer(
    user_input="The problem is ...",
    llm=my_llm,
)

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Configuration & Rate Limits

• Default LLM – ChatLLM7 from the langchain-llm7 package (see https://pypi.org/project/langchain-llm7).
• Free‑tier limits – Sufficient for typical documentation or issue‑tracking usage.
• Higher limits – Provide your own API key via the LLM7_API_KEY environment variable or directly with the api_key argument.
• Getting a free key – Register at https://token.llm7.io/.

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Contributing & Support

• Bug reports & feature requests – Open an issue on GitHub: https://github.com/chigwell/techchallenge-summarizer/issues
• Pull requests – Contributions are welcome; please follow the standard GitHub workflow.

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License

This project is licensed under the MIT License.

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Author

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