relnote-extractor 2025.12.22080754

Relnote-extractor parses raw update texts, extracting version, date, features, fixes, and formats into a clean, concise summary.

Relnote Extractor

Extract and structure release notes from software update announcements

Overview

This package is designed to extract and structure release notes from software update announcements. It takes raw text containing software release information as input and processes it to identify and format key details such as version numbers, release dates, new features, bug fixes, and other relevant updates.

Features

• Extracts and structures release notes from software update announcements
• Supports multiple language models via langchain library
• Can use default ChatLLM7 from langchain_llm7 library or user-provided LLM instance
• Allows users to pass their own API key for higher rate limits (LLM7 free tier sufficient for most use cases)
• Easy to use and integrate into existing workflows

Installation

pip install relnote_extractor

Example Usage

from relnote_extractor import relnote_extractor

user_input = """
Release Notes:

* Fixed issue with XYZ
* Added feature ABC
* Updated to version 1.2.3
"""

response = relnote_extractor(user_input)
print(response)

Input Parameters

• user_input: str - the user input text to process
• llm: Optional[BaseChatModel] - the langchain LLM instance to use (default: ChatLLM7 from langchain_llm7)
• api_key: Optional[str] - the API key for LLM7 (default: None)

Using a Different LLM

You can safely pass your own LLM instance based on https://docs.langchain.com/llm.html if you want to use another LLM. For example, to use the OpenAI LLM:

from langchain_openai import ChatOpenAI
from relnote_extractor import relnote_extractor

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

Similarly, you can use the Anthropic or Google Generative AI LLMs:

from langchain_anthropic import ChatAnthropic
from relnote_extractor import relnote_extractor

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

from langchain_google_genai import ChatGoogleGenerativeAI
from relnote_extractor import relnote_extractor

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

Rate Limits

The default rate limits for LLM7 free tier are sufficient for most use cases of this package. If you need higher rate limits, you can pass your own api_key via environment variable LLM7_API_KEY or via passing it directly like relnote_extractor(user_input, api_key="your_api_key"). You can get a free API key by registering at https://token.llm7.io/

GitHub Issues

https://github.com/chigwell/relnote-extractor/issues

Author

Eugene Evstafev ([e-mail])