stackexchange-question-analyzer 2025.12.21142930

The package aims to provide a structured analysis of user-generated content on platforms like Stack Exchange by determining the monthly volume of questions asked. This is useful for understanding comm

stackexchange-...

StackExchange Q Count – A lightweight helper to quickly obtain the monthly volume of questions asked on any Stack Exchange site.

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Features

• Fast execution – Uses an LLM to parse the user’s natural‑language query and return a precise count.
• Zero‑config – If you have an environment variable LLM7_API_KEY, the package will automatically pick it up.
• Extensible – Pass your own BaseChatModel instance (OpenAI, Anthropic, Google GenAI, etc.) to use an alternative LLM.
• Easy error handling – The function returns a list of strings containing the extracted answers or throws an informative exception.

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Installation

pip install stackexchange-...

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Quick start

from stackexchange_... import stackexchange_...

# Simple call using the default ChatLLM7
result = stackexchange_(...user_input="How many questions were asked on Stack Overflow in the last 6 months?")
print(result)

Tip: In the examples below, replace stackexchange_... and stackexchange_... with the real package and function names once you publish it (e.g. stackexchange_q).

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Parameters

Parameter	Type	Description
user_input	str	The query to be processed.
llm	Optional[BaseChatModel]	A LangChain LLM instance to use. If omitted, the default ChatLLM7 is instantiated.
api_key	Optional[str]	LLM7 API key. If omitted, the package will look for LLM7_API_KEY in the environment.

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Using a Custom LLM

LLM	Example
OpenAI	python<br>from langchain_openai import ChatOpenAI<br>from stackexchange_... import stackexchange_...<br>llm = ChatOpenAI()<br>response = stackexchange_(..., llm=llm)<br>
Anthropic	python<br>from langchain_anthropic import ChatAnthropic<br>from stackexchange_... import stackexchange_...<br>llm = ChatAnthropic()<br>response = stackexchange_(..., llm=llm)<br>
Google Generative AI	python<br>from langchain_google_genai import ChatGoogleGenerativeAI<br>from stackexchange_... import stackexchange_...<br>llm = ChatGoogleGenerativeAI()<br>response = stackexchange_(..., llm=llm)<br>

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Rate Limits and API Key

• LLM7 free tier is sufficient for most use‑cases.
• For higher limits, provide your own key: stackexchange_(..., api_key="YOUR_KEY").
• You can also set the key via the environment variable LLM7_API_KEY.
• Obtain a free API key by registering at https://token.llm7.io/.

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Author

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

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Issues

If you encounter any bugs or have feature requests, open an issue on GitHub:
https://github.com/chigwell/stackexchange-.../issues

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License

MIT License – see the LICENSE file for details.