insightminer 2025.12.21123144

A new package designed to extract and structure key insights from opinionated articles or social media posts. The package takes a text input, such as a blog post or a tweet, and uses llmatch-messages

insightminer

insightminer is a lightweight Python package that extracts and structures key insights from opinionated articles, blog posts, tweets, and other short texts. It leverages the llmatch‑messages library together with a language model (LLM) to parse free‑form input into a predefined, regex‑validated structure, making downstream analysis (sentiment, trend tracking, etc.) straightforward and consistent.

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✨ Features

• One‑function interface – Call insightminer() with your text and get a list of extracted fields.
• Built‑in LLM – Uses ChatLLM7 from the langchain_llm7 package by default (free tier suitable for most workloads).
• Pluggable LLM – Pass any LangChain‑compatible chat model (OpenAI, Anthropic, Google, etc.) if you prefer a different provider.
• Regex‑driven pattern matching – Guarantees that the output conforms to the pattern you define.
• Simple installation – Available on PyPI.

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📦 Installation

pip install insightminer

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🚀 Quick Start

from insightminer import insightminer

user_input = "I Wouldn't Want John Solomon's New CMO Job at Mozilla"
results = insightminer(user_input)

print(results)
# Example output: ['John Solomon', 'new CMO job', 'Mozilla']

Parameters

Parameter	Type	Description
user_input	str	The text to be processed (e.g., a blog post title, tweet, etc.).
llm	Optional[BaseChatModel]	A LangChain chat model instance. If omitted, the default ChatLLM7 is used.
api_key	Optional[str]	API key for LLM7. If omitted, the function reads the LLM7_API_KEY environment variable or falls back to the default key ("None").

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🔧 Using a Custom LLM

You can safely replace the default ChatLLM7 with any LangChain‑compatible chat model.

OpenAI

from langchain_openai import ChatOpenAI
from insightminer import insightminer

llm = ChatOpenAI()
response = insightminer("Your text here", llm=llm)
print(response)

Anthropic

from langchain_anthropic import ChatAnthropic
from insightminer import insightminer

llm = ChatAnthropic()
response = insightminer("Your text here", llm=llm)
print(response)

Google Generative AI

from langchain_google_genai import ChatGoogleGenerativeAI
from insightminer import insightminer

llm = ChatGoogleGenerativeAI()
response = insightminer("Your text here", llm=llm)
print(response)

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🔑 LLM7 API Key

• Default behaviour: The function looks for an environment variable LLM7_API_KEY.
• Providing manually: Pass api_key="YOUR_KEY" when calling insightminer.
• Free tier: Sufficient for typical usage of this package.
• Get a key: Register for free at https://token.llm7.io/.

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📚 Documentation & References

• ChatLLM7 package: https://pypi.org/project/langchain-llm7/
• LangChain LLM documentation: https://python.langchain.com/docs/

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🐞 Issues & Contributions

If you encounter bugs or have feature requests, please open an issue:

https://github....

Pull requests are welcome! Follow the standard GitHub workflow and ensure tests (if any) pass before submitting.

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✉️ Author

Eugene Evstafev – [e-mail]
GitHub: chigwell

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📄 License

This project is licensed under the MIT License. See the LICENSE file for details.