Summarix

Summarix is a Python package designed to process user-inputted text statements or stories and extract structured summaries or insights using a reliable language model with pattern matching and retries. It simplifies transforming plain text prompts into organized, actionable data by leveraging the capabilities of a pattern-aware conversation framework. This ensures consistent interpretation and mapping of user inputs into predefined data formats, avoiding ambiguities and enhancing automation in knowledge extraction or storytelling analysis.

Features

• Uses advanced language models from langchain (by default ChatLLM7)
• Pattern matching with regex for precise output extraction
• Supports custom language model integration
• Handles retries and error management seamlessly
• Simplifies conversion of complex text inputs into structured data

Installation

Install the package via pip:

pip install summarix

Usage

Import the main function and use it with your input text:

from summarix import summarix

response = summarix(user_input="Your text here")

Parameters

• user_input (str): The text statement or story to process.
• llm (Optional[BaseChatModel]): A custom langchain language model instance. Defaults to using ChatLLM7.
• api_key (Optional[str]): API key for the LLM7 service. If not provided, it will look for the environment variable LLM7_API_KEY or use the default free tier.

Supporting Different Language Models

You can pass your own language model instance to utilize other providers supported by langchain, e.g., OpenAI, Anthropic, Google Generative AI.

Example using OpenAI:

from langchain_openai import ChatOpenAI
from summarix import summarix

llm = ChatOpenAI()
response = summarix(user_input="Analyze this story", llm=llm)

Example using Anthropic:

from langchain_anthropic import ChatAnthropic
from summarix import summarix

llm = ChatAnthropic()
response = summarix(user_input="Describe the scenario", llm=llm)

Example using Google Generative AI:

from langchain_google_genai import ChatGoogleGenerativeAI
from summarix import summarix

llm = ChatGoogleGenerativeAI()
response = summarix(user_input="Generate insights", llm=llm)

Rate Limits & API Keys

The default rate limits for LLM7's free tier are sufficient for most use cases. For higher limits, you can obtain a free API key at https://token.llm7.io/ and provide it via environment variable LLM7_API_KEY or directly in the function call:

response = summarix(user_input="Task", api_key="your_api_key")

Support

For issues or feature requests, please visit the GitHub repository:

https://github.com/chigwell/summarix/issues

Author

Eugene Evstafev

Email: hi@eugene.plus
GitHub: chigwell