lvmthin‑helper

lvmthin_helper is a lightweight Python package that helps system administrators and storage engineers manage LVM Thin Provisioning configurations.
Given a natural‑language description of storage requirements, current LVM setup or a specific thin‑provisioning problem, the package uses LLM7 and the llmatch‑messages protocol to return structured, actionable advice or exact configuration snippets.

Key features:

• Zero‑config: Uses the free tier of LLM7 by default; falls back to an optional custom LLM from LangChain.
• Pattern‑based safety: The LLM response is validated against a pre‑defined regular‑expression to guarantee consistent, parseable output.
• Extensible: Pass your own LangChain BaseChatModel (e.g. OpenAI, Anthropic, Google Gemini) if you prefer a different provider.

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Installation

pip install lvmthin_helper

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Basic Usage

from lvmthin_helper import lvmthin_helper

# Example user input – a description of the problem or requirement
user_input = """
I have two VG’s: vg_data (thin pool tp_data) and vg_backup (thin pool tp_backup).
I need to move a 120 GiB thin logical volume from vg_data to vg_backup, preserving data.
"""

# Call the helper – this will automatically use the free LLM7 tier
response = lvmthin_helper(user_input=user_input)

# response is a list of strings with step‑by‑step guidance / commands
print("\n".join(response))

Parameters

Parameter	Type	Description
user_input	str	The natural‑language description of your storage need or issue.
llm	Optional[BaseChatModel]	A LangChain-compatible LLM instance. If omitted the default ChatLLM7 is used.
api_key	Optional[str]	API key for LLM7. If omitted it is read from the environment variable LLM7_API_KEY.

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Using a Different LLM Provider

OpenAI

from langchain_openai import ChatOpenAI
from lvmthin_helper import lvmthin_helper

llm = ChatOpenAI(model="gpt-4o-mini", temperature=0.2)
response = lvmthin_helper(user_input="Move a thin LV from VG A to VG B.", llm=llm)

Anthropic

from langchain_anthropic import ChatAnthropic
from lvmthin_helper import lvmthin_helper

llm = ChatAnthropic(model="claude-3-haiku-20240307", temperature=0.2)
response = lvmthin_helper(user_input="Resize a thin LV to 200 GiB.", llm=llm)

Google Gemini

from langchain_google_genai import ChatGoogleGenerativeAI
from lvmthin_helper import lvmthin_helper

llm = ChatGoogleGenerativeAI(model="gemini-1.5-pro-001")
response = lvmthin_helper(user_input="Check thin pool usage statistics.", llm=llm)

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Rate Limits & API Key

• The free tier of LLM7 is sufficient for most typical use cases of this helper.
• If you need higher limits, obtain a key at https://token.llm7.io/ and either:
  • Export it: export LLM7_API_KEY="your_key_here"
  • Pass it directly: lvmthin_helper(user_input, api_key="your_key_here")

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License

MIT License – feel free to use, modify, and distribute.

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Contributing & Issues

Bug reports, feature requests, and pull requests are welcome!
Please open an issue at: https://github.com/chigwell/lvmthin-helper/issues

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Author

Eugene Evstafev
📧 hi@euegne.plus
GitHub: https://github.com/chigwell