llm-event-digest 2025.12.21181028

A new package that processes news headlines or short text inputs to generate structured summaries of events, such as service disruptions or incidents. It uses an LLM to extract key details like the co

LLM Event Digest

LLM Event Digest is a Python package designed to process news headlines or short text inputs and generate structured summaries of events, such as service disruptions or incidents. Utilizing a language model, it extracts key details like the involved company, the nature of the disruption, and the cause, ensuring outputs conform to a predefined format for consistency and reliability. This tool is ideal for automated news monitoring, alert systems, or data aggregation where structured, error-free information extraction from text is required.

Installation

Install the package via pip:

pip install llm_event_digest

Usage

Here's an example of how to use the package in Python:

from llm_event_digest import llm_event_digest

response = llm_event_digest(
    user_input="The internet service in downtown was down for 3 hours caused by a fiber cut.",
    api_key="your-llm7-api-key"  # Optional, if not set in environment variables
)
print(response)

Parameters

• user_input (str): The text input (news headline or short description) to process.
• llm (Optional[BaseChatModel]): An optional LangChain language model instance. If not provided, the default ChatLLM7 is used.
• api_key (Optional[str]): API key for LLM7. If not provided, it looks for the LLM7_API_KEY environment variable.

Supported LLMs

The package uses ChatLLM7 from langchain_llm7 by default.

You can also pass your own LLM instance, such as:

from langchain_openai import ChatOpenAI

llm = ChatOpenAI()
response = llm_event_digest(
    user_input="Network outage in the city center.",
    llm=llm
)

Or:

from langchain_anthropic import ChatAnthropic

llm = ChatAnthropic()
response = llm_event_digest(
    user_input="Server downtime due to maintenance.",
    llm=llm
)

And:

from langchain_google_genai import ChatGoogleGenerativeAI

llm = ChatGoogleGenerativeAI()
response = llm_event_digest(
    user_input="Scheduled power outage.",
    llm=llm
)

Rate Limits

Default rate limits for LLM7 free tier are suitable for most use cases. For higher usage, obtain an API key from https://token.llm7.io/ and pass it via environment variable LLM7_API_KEY or directly in the function call.

Support and Issues

If you encounter any issues or have questions, please open an issue on the GitHub repository: https://github.com/chigwell/llm-event-digest/issues

Author

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