Techsummarizer

A Python package for summarizing technical articles and announcements by extracting structured key information from user-provided text.

Overview

This package leverages language models to identify and organize important details such as product features, specifications, release dates, and relevant contextual data, providing a concise and structured overview of complex technical content.

Installation

pip install techsummarizer

Usage

from techsummarizer import techsummarizer

response = techsummarizer(
    user_input="user input text here",
    api_key="your_api_key_here"  # if not provided, defaults to LLM7 free tier
)

You can also pass your own LLM instance (e.g., OpenAI, Anthropic, Google Generative AI) for more control:

from langchain_openai import ChatOpenAI
from techsummarizer import techsummarizer

llm = ChatOpenAI()
response = techsummarizer(
    user_input="user input text here",
    llm=llm
)

Or with Anthropic:

from langchain_anthropic import ChatAnthropic
from techsummarizer import techsummarizer

llm = ChatAnthropic()
response = techsummarizer(
    user_input="user input text here",
    llm=llm
)

Or with Google Generative AI:

from langchain_google_genai import ChatGoogleGenerativeAI
from techsummarizer import techsummarizer

llm = ChatGoogleGenerativeAI()
response = techsummarizer(
    user_input="user input text here",
    llm=llm
)

Default LLM

This package uses the ChatLLM7 from langchain_llm7 by default. You can safely pass your own LLM instance if you want to use another 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 directly:

techsummarizer(
    user_input="user input text here",
    api_key="your_api_key_here"
)

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

Issues

Report any issues or bugs to: https://github.com/chigwell/techsummarizer

Author

Eugene Evstafev hi@euegne.plus