satellite-insight-extractor 2025.12.21080509

A new package designed to streamline the extraction of structured insights from brief, domain-specific reports. It receives a short text input describing recent events—such as satellite incidents—and

Satellite Insight Extractor

A Python package designed to extract structured insights from brief, domain-specific reports about satellite incidents and other aerospace events. It provides a concise, structured summary highlighting key aspects like event type, involved entities, and immediate implications.

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📦 Installation

Install the package via pip:

pip install satellite_insight_extractor

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🚀 Features

• Extracts structured insights from short text reports
• Supports customizable LLM backends (LLM7 by default)
• Regex-validated output for consistent formatting
• Lightweight and efficient for aerospace/satellite monitoring

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🔧 Usage

Basic Usage (Default LLM7)

from satellite_insight_extractor import satellite_insight_extractor

# Example input: A short report about a satellite incident
user_input = """
Satellite X-912 experienced a partial solar array failure during orbital maneuver.
Ground control is investigating potential debris impact.
"""

# Extract insights (uses default LLM7)
response = satellite_insight_extractor(user_input)
print(response)

Custom LLM Integration

You can replace the default LLM7 with any LangChain-compatible model:

Using OpenAI

from langchain_openai import ChatOpenAI
from satellite_insight_extractor import satellite_insight_extractor

llm = ChatOpenAI()
response = satellite_insight_extractor(
    user_input="Your satellite report here...",
    llm=llm
)

Using Anthropic

from langchain_anthropic import ChatAnthropic
from satellite_insight_extractor import satellite_insight_extractor

llm = ChatAnthropic()
response = satellite_insight_extractor(
    user_input="Your satellite report here...",
    llm=llm
)

Using Google Vertex AI

from langchain_google_genai import ChatGoogleGenerativeAI
from satellite_insight_extractor import satellite_insight_extractor

llm = ChatGoogleGenerativeAI()
response = satellite_insight_extractor(
    user_input="Your satellite report here...",
    llm=llm
)

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🔑 API Key Configuration

The package uses LLM7 as the default LLM provider. You can configure it in two ways:

1. Environment Variable (recommended for security):

   export LLM7_API_KEY="your_api_key_here"
   

2. Direct Parameter:

   from satellite_insight_extractor import satellite_insight_extractor
   
   response = satellite_insight_extractor(
       user_input="Your report...",
       api_key="your_api_key_here"
   )
   

Get a Free LLM7 API Key

Register at https://token.llm7.io/ for your free API key.

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📝 Function Signature

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

Parameters:

• user_input (str): The input text describing satellite/aerospace events.
• api_key (Optional[str]): LLM7 API key (default: os.getenv("LLM7_API_KEY")).
• llm (Optional[BaseChatModel]): Custom LangChain LLM instance (default: ChatLLM7).

Returns:

• List[str]: Structured insights matching the predefined regex pattern.

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📌 Output Format

The output is a regex-validated list of structured insights, typically including:

• Event type (e.g., "solar array failure")
• Involved entities (e.g., "Satellite X-912")
• Immediate implications (e.g., "orbital maneuver disrupted")

Example output:

[
    "Event: Solar Array Failure",
    "Entity: Satellite X-912",
    "Location: Low Earth Orbit",
    "Status: Under Investigation"
]

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🔄 Default LLM7 Rate Limits

The free tier of LLM7 is sufficient for most use cases. For higher rate limits, upgrade your plan or use a custom LLM.

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📖 Documentation & Support

• GitHub Issues: https://github.com/chigwell/satellite-insight-extractor/issues
• Author: Eugene Evstafev (LinkedIn)
• Email: [e-mail]

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📜 License

This project is licensed under the MIT License.

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