decentralized-summarizer 2025.12.21120929

A new package that processes user input to generate structured summaries of decentralized protocol concepts, such as Polyproto, by extracting key features and benefits. It uses pattern matching to ens

decentralized-summarizer

decentralized-summarizer is a Python package that turns user input about decentralized protocols (e.g., Polyproto) into clear, structured summaries. It extracts key features and benefits while avoiding technical jargon, making the output accessible to non‑expert audiences. The package leverages pattern matching to guarantee that the generated summaries follow a predefined format.

Features

• Simple API – One function call to generate a summary.
• LLM‑agnostic – Uses ChatLLM7 by default; you can plug any LangChain‑compatible LLM.
• Formatted output – Enforces a regex pattern, ensuring consistent structure.
• No jargon – Tailored prompts keep language clear and approachable.

Installation

pip install decentralized_summarizer

Quick Start

from decentralized_summarizer import decentralized_summarizer

# Simple usage with the default ChatLLM7
summary = decentralized_summarizer(
    user_input="Explain the main advantages of Polyproto in a decentralized network."
)

print(summary)  # => List of formatted summary strings

Function Signature

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

Parameter	Type	Description
user_input	str	The raw text you want to summarise.
api_key	Optional[str]	API key for ChatLLM7. If omitted, the function reads LLM7_API_KEY from the environment or uses a placeholder "None" (which works for the free tier).
llm	Optional[BaseChatModel]	A LangChain LLM instance. If not provided, ChatLLM7 from langchain_llm7 is used.

Using a Custom LLM

You can pass any LangChain‑compatible chat model (OpenAI, Anthropic, Google, etc.):

OpenAI

from langchain_openai import ChatOpenAI
from decentralized_summarizer import decentralized_summarizer

my_llm = ChatOpenAI(model="gpt-4o-mini")
summary = decentralized_summarizer(
    user_input="What are the security benefits of a decentralized exchange?",
    llm=my_llm,
)

Anthropic

from langchain_anthropic import ChatAnthropic
from decentralized_summarizer import decentralized_summarizer

my_llm = ChatAnthropic(model="claude-3-opus-20240229")
summary = decentralized_summarizer(
    user_input="Describe how decentralised governance works.",
    llm=my_llm,
)

Google Gemini

from langchain_google_genai import ChatGoogleGenerativeAI
from decentralized_summarizer import decentralized_summarizer

my_llm = ChatGoogleGenerativeAI(model="gemini-1.5-flash")
summary = decentralized_summarizer(
    user_input="Summarise the key points of the Polkadot parachain model.",
    llm=my_llm,
)

API Key & Rate Limits

• The package defaults to ChatLLM7 from the langchain_llm7 package.
• The free tier of ChatLLM7 provides sufficient rate limits for typical usage.
• For higher limits, supply your own API key:
  • Environment variable: export LLM7_API_KEY="your_key"
  • Direct argument: api_key="your_key"

Obtain a free API key by registering at: https://token.llm7.io/

Contributing & Support

• Issues: https://github.com/chigwell/decentralized_summarizer/issues
• Pull requests: Contributions are welcome—please follow standard GitHub workflow.

Author

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

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The package uses ChatLLM7 from langchain_llm7 by default. Feel free to replace it with any other LangChain chat model that matches the BaseChatModel interface.