phenomenon-interpreter 2025.12.21230657

phenomenon-interpreter analyzes textual descriptions of natural events to generate structured summaries or classifications automatically

phenomenon-interpreter

A Python package for interpreting user-submitted text about natural or scientific phenomena, extracting structured insights, and classifying events based on textual input.

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📌 Overview

phenomenon_interpreter is designed to analyze free-form descriptions of phenomena (e.g., solar storms, earthquakes, or other natural events) and generate structured summaries or classifications. It leverages large language models (LLMs) to extract domain-specific insights from unstructured text, enabling automated analysis without requiring multimedia processing.

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🚀 Installation

Install the package via pip:

pip install phenomenon_interpreter

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🔧 Usage

Basic Usage (Default LLM: ChatLLM7)

from phenomenon_interpreter import phenomenon_interpreter

user_input = "A massive solar storm caused radio blackouts in Australia today."
response = phenomenon_interpreter(user_input)
print(response)  # Structured output based on the input

Custom LLM Integration

You can replace the default ChatLLM7 with any LangChain-compatible LLM (e.g., OpenAI, Anthropic, Google Generative AI). Example:

Using OpenAI:

from langchain_openai import ChatOpenAI
from phenomenon_interpreter import phenomenon_interpreter

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

Using Anthropic:

from langchain_anthropic import ChatAnthropic
from phenomenon_interpreter import phenomenon_interpreter

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

Using Google Generative AI:

from langchain_google_genai import ChatGoogleGenerativeAI
from phenomenon_interpreter import phenomenon_interpreter

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

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🔑 API Key Configuration

By default, the package uses ChatLLM7 with an API key fetched from the environment variable LLM7_API_KEY. You can:

1. Set it via environment variable:

   export LLM7_API_KEY="your_api_key_here"
   

2. Pass it directly:

   from phenomenon_interpreter import phenomenon_interpreter
   response = phenomenon_interpreter(user_input, api_key="your_api_key_here")
   

Get a free API key from LLM7.

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📝 Parameters

Parameter	Type	Description
user_input	str	The text describing the phenomenon to analyze.
api_key	Optional[str]	LLM7 API key (default: fetched from LLM7_API_KEY environment variable).
llm	Optional[BaseChatModel]	Custom LLM instance (e.g., ChatOpenAI, ChatAnthropic). Default: ChatLLM7.

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📊 Output

The function returns a list of structured insights extracted from the input text, formatted to match predefined patterns (e.g., impact classification, event nature).

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🔄 Rate Limits

• LLM7 Free Tier: Sufficient for most use cases.
• Custom API Key: For higher rate limits, provide your own api_key or set LLM7_API_KEY.

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📜 License

MIT License.

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📢 Support & Issues

For bugs or feature requests, open an issue on GitHub.

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👤 Author

• Eugene Evstafev (GitHub)
• Email: [e-mail]

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