text-snippet-summarizer 2025.12.21113112

A new package facilitates extracting a concise, structured summary from user-provided news headlines or brief texts by utilizing pattern matching and LLM interactions. This tool aims to help researche

Text-Snippet-Summarizer

Extract structured, concise summaries from news headlines and brief texts using pattern matching and LLM interactions.

This package helps researchers, journalists, and analysts quickly categorize and understand complex domain-specific issues (e.g., environmental crises, economic policies, geopolitics) by summarizing raw textual snippets. It avoids processing full documents, multimedia, or URLs, focusing on rapid, structured insights for decision-making or reporting.

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🚀 Features

• Pattern-based extraction for structured summaries
• LLM-powered summarization with configurable models
• Lightweight – works with short text snippets
• Flexible LLM integration (supports OpenAI, Anthropic, Google, etc.)
• Default LLM7 integration (free tier sufficient for most use cases)

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📦 Installation

pip install text_snippet_summarizer

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🔧 Usage

Basic Usage (Default LLM7)

from text_snippet_summarizer import text_snippet_summarizer

user_input = "Climate change impacts: rising temperatures, extreme weather events, and biodiversity loss."
response = text_snippet_summarizer(user_input)
print(response)

Custom LLM Integration

Using OpenAI

from langchain_openai import ChatOpenAI
from text_snippet_summarizer import text_snippet_summarizer

llm = ChatOpenAI()
response = text_snippet_summarizer(user_input, llm=llm)

Using Anthropic

from langchain_anthropic import ChatAnthropic
from text_snippet_summarizer import text_snippet_summarizer

llm = ChatAnthropic()
response = text_snippet_summarizer(user_input, llm=llm)

Using Google Generative AI

from langchain_google_genai import ChatGoogleGenerativeAI
from text_snippet_summarizer import text_snippet_summarizer

llm = ChatGoogleGenerativeAI()
response = text_snippet_summarizer(user_input, llm=llm)

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🔑 API Key Configuration

• Default: Uses LLM7_API_KEY from environment variables.
• Manual override: Pass api_key directly:

  response = text_snippet_summarizer(user_input, api_key="your_api_key_here")
  

• Get a free LLM7 API key: Register here

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📝 Function Signature

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

• user_input: Raw text snippet to summarize.
• api_key (optional): LLM7 API key (defaults to LLM7_API_KEY env var).
• llm (optional): Custom LangChain LLM (e.g., ChatOpenAI, ChatAnthropic).

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📌 Notes

• Rate Limits: LLM7 free tier is sufficient for most use cases.
• Output: Returns a list of structured summary points matching predefined patterns.
• Dependencies: Uses langchain_llm7 (default) or any BaseChatModel from LangChain.

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📢 Issues & Support

Report bugs or feature requests: 🔗 GitHub Issues

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👤 Author

Eugene Evstafev (@chigwell) 📧 [e-mail]

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