govaitextextract

Extract structured information from government or organizational tech/AI initiatives

A Python package that processes text inputs (e.g., news headlines, announcements) to extract structured data about government or organizational technology and AI initiatives. It uses a language model to identify key details like entity, initiative name, focus, and more, returning results in a consistent format (JSON/XML-compatible).

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📦 Installation

pip install govaitextextract

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🚀 Usage

Basic Usage (Default LLM: ChatLLM7)

from govaitextextract import govaitextextract

user_input = "The Ministry of Digital Transformation announces a new AI project with 50 specialists."
response = govaitextextract(user_input)
print(response)

Custom LLM Integration

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

Using OpenAI:

from langchain_openai import ChatOpenAI
from govaitextextract import govaitextextract

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

Using Anthropic:

from langchain_anthropic import ChatAnthropic
from govaitextextract import govaitextextract

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

Using Google Generative AI:

from langchain_google_genai import ChatGoogleGenerativeAI
from govaitextextract import govaitextextract

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

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🔧 Parameters

Parameter	Type	Description
user_input	str	The input text (e.g., news headline) to process.
api_key	Optional[str]	LLM7 API key (defaults to LLM7_API_KEY env var).
llm	Optional[BaseChatModel]	Custom LangChain LLM (e.g., ChatOpenAI). Falls back to ChatLLM7.

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🔑 API Key & Rate Limits

• Default LLM: ChatLLM7 (from langchain_llm7).
• Free Tier: Sufficient for most use cases.
• Custom Key: Pass via api_key or LLM7_API_KEY env var.
• Get a Key: Register at LLM7.

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📜 Output Format

The function returns structured data (e.g., JSON-like) extracted from the input text, such as:

{
    "entity": "Ministry of Digital Transformation",
    "initiative_name": "New AI Project",
    "specialists": 50,
    "focus": "AI"
}

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📝 Notes

• Uses regex validation for consistency.
• Error handling included for LLM failures.
• Extensible for custom patterns via pattern in prompts.

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📢 Issues & Support

Report bugs or feature requests at: GitHub Issues

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👤 Author

Eugene Evstafev 📧 hi@euegne.plus 🔗 GitHub: chigwell

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