textsummarizer-llm 2025.12.20201642

A new package that processes user-provided text inputs (such as headlines, descriptions, or extracted content from media) and returns structured, pattern-validated summaries using an LLM. It leverages

textsummarizer-llm

Overview

textsummarizer_llm provides a simple, pattern‑validated summarization utility powered by a language model (LLM).
Given a raw text input (e.g., a headline, article excerpt, or description), the package:

• Sends the text to a chat LLM using LangChain messages.
• Enforces a predefined output format via a regular‑expression pattern (llmatch).
• Returns a list of extracted, structured summaries.

Typical use‑cases include content moderation, topic tagging, and automated summarization where a consistent response format is required.

Installation

pip install textsummarizer_llm

Quick Start

from textsummarizer_llm import textsummarizer_llm

# Simple call – uses the default ChatLLM7 internally
summary = textsummarizer_llm(
    user_input="OpenAI just released GPT‑4 Turbo, offering faster inference and lower cost."
)

print(summary)   # → ['...structured summary according to the defined pattern...']

Advanced Usage – Plugging Your Own LLM

You can pass any LangChain‑compatible chat model (e.g., OpenAI, Anthropic, Google) to the function.

OpenAI

from langchain_openai import ChatOpenAI
from textsummarizer_llm import textsummarizer_llm

my_llm = ChatOpenAI(model="gpt-4o-mini")
result = textsummarizer_llm(
    user_input="A new study shows that daily meditation improves mental health.",
    llm=my_llm
)
print(result)

Anthropic

from langchain_anthropic import ChatAnthropic
from textsummarizer_llm import textsummarizer_llm

anthropic_llm = ChatAnthropic(model="claude-3-sonnet-20240229")
result = textsummarizer_llm(
    user_input="The city council approved a new bike‑lane network.",
    llm=anthropic_llm
)

Google Generative AI

from langchain_google_genai import ChatGoogleGenerativeAI
from textsummarizer_llm import textsummarizer_llm

google_llm = ChatGoogleGenerativeAI(model="gemini-1.5-flash")
result = textsummarizer_llm(
    user_input="Tesla announced a new battery technology with higher energy density.",
    llm=google_llm
)

API Reference

def textsummarizer_llm(
    user_input: str,
    api_key: Optional[str] = None,
    llm: Optional[BaseChatModel] = None
) -> List[str]:
    """
    Summarize `user_input` while ensuring the output matches a predefined regex pattern.

    Parameters
    ----------
    user_input: str
        The raw text that needs to be processed and summarized.
    api_key: Optional[str]
        API key for the default `ChatLLM7`. If omitted, the function first looks for the
        `LLM7_API_KEY` environment variable, then falls back to a placeholder key.
    llm: Optional[BaseChatModel]
        A LangChain chat model instance. If not provided, `ChatLLM7` from
        `langchain_llm7` is instantiated automatically.

    Returns
    -------
    List[str]
        A list of extracted summary strings that conform to the regex pattern.
    """

Authentication & Rate Limits

The default LLM is ChatLLM7 from the langchain_llm7 package.
Free‑tier limits are sufficient for typical development and small‑scale usage.
If you require higher limits, provide your own API key:

export LLM7_API_KEY="your-llm7-api-key"

or directly:

summary = textsummarizer_llm(
    user_input="...", 
    api_key="your-llm7-api-key"
)

You can obtain a free key at https://token.llm7.io/.

Contributing

Contributions are welcome! Please open issues or pull requests on the GitHub repository.

License

This project is licensed under the MIT License.

Author

Eugene Evstafev – [e-mail]
GitHub: chigwell

Issues

Report bugs or request features via the issue tracker:
https://github.com/chigwell/textsummarizer-llm/issues