Extract structured summaries from infrastructure-as-code (IaC) discussions
A Python package that analyzes technical arguments in text discussions about IaC practices and extracts concise, structured summaries. Ideal for developers and architects who need quick insights from lengthy technical debates, forum posts, or documentation.
- Extracts core arguments from text discussions about IaC (e.g., Terraform, CloudFormation, Pulumi).
- Validates output against predefined patterns for consistency.
- Supports custom LLMs via LangChain for flexibility.
- Defaults to ChatLLM7 (from langchain_llm7) for simplicity.
pip install iac_summarizerfrom iac_summarizer import iac_summarizer
user_input = """
Discussion about drawbacks of generic multi-cloud Terraform modules:
'These modules lack specificity, leading to bloated configurations and harder maintenance...'
"""
response = iac_summarizer(user_input)
print(response)Replace the default LLM with your preferred provider:
from langchain_openai import ChatOpenAI
from iac_summarizer import iac_summarizer
llm = ChatOpenAI()
response = iac_summarizer(user_input, llm=llm)from langchain_anthropic import ChatAnthropic
from iac_summarizer import iac_summarizer
llm = ChatAnthropic()
response = iac_summarizer(user_input, llm=llm)from langchain_google_genai import ChatGoogleGenerativeAI
from iac_summarizer import iac_summarizer
llm = ChatGoogleGenerativeAI()
response = iac_summarizer(user_input, llm=llm)- Default: Uses
LLM7_API_KEYfrom environment variables. - Override: Pass directly via
api_keyparameter:response = iac_summarizer(user_input, api_key="your_llm7_api_key")
- Get a Free Key: Register at https://token.llm7.io/.
| Parameter | Type | Description |
|---|---|---|
user_input |
str |
Text to analyze (e.g., forum posts, articles, or comments). |
llm |
Optional[BaseChatModel] |
Custom LangChain LLM (e.g., ChatOpenAI, ChatAnthropic). Defaults to ChatLLM7. |
api_key |
Optional[str] |
LLM7 API key (falls back to LLM7_API_KEY env var). |
The function returns a list of structured summaries extracted from the input text, validated against predefined patterns.
- Default (LLM7 Free Tier): Sufficient for most use cases.
- Upgrade: Use your own API key or environment variable (
LLM7_API_KEY).
MIT
For bugs or feature requests, open an issue at: https://github.com/chigwell/iac-summarizer/issues
Eugene Evstafev 📧 hi@euegne.plus 🔗 GitHub: chigwell