eventlytics-summarizer 2025.12.21101526

A new package is designed to facilitate structured and reliable interactions with language models to generate concise summaries and extract key information from user-provided texts related to specific

eventlytics-summarizer

eventlytics-summarizer is a tiny Python package that wraps a language‑model call with pattern‑matching and retry logic, making it easy to get concise, structured summaries and key‑point extractions from free‑form event descriptions.
It is especially suited for incident reports, news snippets, or any narrative that needs to be turned into a predictable, machine‑readable format.

Installation

pip install eventlytics_summarizer

Quick start

from eventlytics_summarizer import eventlytics_summarizer

text = """
Gloria Ramirez, a 31‑year‑old patient at the emergency department, suddenly began
seizing and emitting a strange, oily odor.  Shortly after, several staff members
lost consciousness.  The incident was later attributed to a toxic reaction to a
medication administered to Ms. Ramirez.
"""

summary = eventlytics_summarizer(user_input=text)
print(summary)
# -> ['...']   # a list of strings that match the defined output pattern

API

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

Parameter	Type	Description
user_input	str	The raw narrative you want to summarize / extract.
llm	BaseChatModel (optional)	Any LangChain‑compatible chat model. If omitted, the package creates a ChatLLM7 instance automatically.
api_key	str (optional)	API key for LLM7. If not supplied, the function looks for the LLM7_API_KEY environment variable, falling back to a placeholder "None" (which triggers the default free‑tier limits).

Default LLM (LLM7)

If you do not provide your own model, eventlytics_summarizer uses ChatLLM7 from the langchain_llm7 package:

from langchain_llm7 import ChatLLM7

The free tier of LLM7 provides enough calls for typical usage. To increase limits, supply your own API key:

summary = eventlytics_summarizer(user_input=text, api_key="YOUR_LLM7_API_KEY")

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

Using a custom LLM

You can plug any LangChain chat model that implements BaseChatModel. Below are a few common examples.

OpenAI (ChatGPT)

from langchain_openai import ChatOpenAI
from eventlytics_summarizer import eventlytics_summarizer

llm = ChatOpenAI(model="gpt-4o-mini")  # or any other OpenAI model
summary = eventlytics_summarizer(user_input=text, llm=llm)

Anthropic (Claude)

from langchain_anthropic import ChatAnthropic
from eventlytics_summarizer import eventlytics_summarizer

llm = ChatAnthropic(model="claude-3-haiku-20240307")
summary = eventlytics_summarizer(user_input=text, llm=llm)

Google Gemini

from langchain_google_genai import ChatGoogleGenerativeAI
from eventlytics_summarizer import eventlytics_summarizer

llm = ChatGoogleGenerativeAI(model="gemini-1.5-flash")
summary = eventlytics_summarizer(user_input=text, llm=llm)

How it works

1. Prompt construction – a system prompt guides the model to produce concise, domain‑appropriate output, while a human prompt injects your user_input.
2. Pattern matching – the expected output format is described by a regular expression (eventlytics_summarizer.prompts.pattern). The response is validated against this regex.
3. Retry / error handling – llmatch automatically retries on mismatches and surfaces a clear RuntimeError if the model cannot satisfy the pattern.

Contributing

Contributions are welcome! Please open an issue or submit a pull request on GitHub.

License

This project is licensed under the MIT License.

Author

Eugene Evstafev – [e-mail]
GitHub: chigwell

Bugs & feature requests

If you encounter any problems or have ideas for improvement, please file an issue at:
https://github.com/chigwell/eventlytics-summarizer/issues