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).
📦 Installation
pip install govaitextextract
🚀 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)
🔧 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. |
🔑 API Key & Rate Limits
- Default LLM:
ChatLLM7(from langchain_llm7). - Free Tier: Sufficient for most use cases.
- Custom Key: Pass via
api_keyorLLM7_API_KEYenv var. - Get a Key: Register at LLM7.
📜 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"
}
📝 Notes
- Uses regex validation for consistency.
- Error handling included for LLM failures.
- Extensible for custom patterns via
patternin prompts.
📢 Issues & Support
Report bugs or feature requests at: GitHub Issues
👤 Author
Eugene Evstafev 📧 hi@euegne.plus 🔗 GitHub: chigwell
Metadata
Release files for govaitextextract 2025.12.21194243
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| govaitextextract-2025.12.21194243.tar.gz | 4.3 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| govaitextextract-2025.12.21194243-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 9.2 kB
Release files / govaitextextract-2025.12.21194243.tar.gz
| Download URL | govaitextextract-2025.12.21194243.tar.gz |
|---|---|
| Size | 4.3 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
f3d3ab4501be245f387687da3f8597c33f0fd754aa868ad85492217f1c4d5580
|
|
BLAKE2b-256 checksum How to use checksums |
420aa359b3f41c8a451857fc9b6c4461895e38923d44fa8820e9d0025d021f4f
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.12.1
|
Release files / govaitextextract-2025.12.21194243-py3-none-any.whl
| Download URL | govaitextextract-2025.12.21194243-py3-none-any.whl |
|---|---|
| Size | 4.9 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
40e8535d4514d5b67a78882b48b2c66f28b897636858877d0bea4a5ef1a53801
|
|
BLAKE2b-256 checksum How to use checksums |
03a80f8d3e5a5e619949323172b175eec65f104a1d7753720c0684a4990ec24e
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.12.1
|