drone-capability-parser 2025.12.21140812

A new package processes textual descriptions of drone designs to extract structured summaries of their operational capabilities. It focuses on identifying and categorizing key features such as locomot

Drone Capability Parser

drone-capability-parser is a lightweight Python package that extracts structured summaries of a drone’s operational capabilities from free‑form textual descriptions.
It automatically identifies locomotion methods (e.g., wheeled movement, flight) and unified actuation systems, delivering a clean, standardized output for engineers and designers.

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Installation

pip install drone_capability_parser

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Quick Start

from drone_capability_parser import drone_capability_parser

# Basic usage – let the library create the default LLM7 client for you
text = """
The new Explorer drone can roll on its six wheeled chassis at up to 15 km/h,
while its rotors enable vertical take‑off and hover for 30 minutes.
All motion is coordinated through a unified actuation controller.
"""
caps = drone_capability_parser(user_input=text)

print(caps)
# -> ['locomotion: wheeled, speed: 15 km/h', 'locomotion: flight, hover_time: 30 min', ...]

Function signature

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

Parameter	Type	Description
user_input	str	The free‑form description of the drone design you want to parse.
llm	Optional[BaseChatModel]	A LangChain LLM instance. If omitted, the function creates a ChatLLM7 client automatically.
api_key	Optional[str]	API key for the LLM7 service. If omitted, the function reads the LLM7_API_KEY environment variable, or falls back to a placeholder "None" (which triggers the default free‑tier limits).

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Using a Custom LLM

You can plug any LangChain‑compatible chat model that inherits from BaseChatModel.
Below are a few examples:

OpenAI (langchain-openai)

from langchain_openai import ChatOpenAI
from drone_capability_parser import drone_capability_parser

llm = ChatOpenAI(model="gpt-4o-mini")
response = drone_capability_parser(user_input=text, llm=llm)

Anthropic (langchain-anthropic)

from langchain_anthropic import ChatAnthropic
from drone_capability_parser import drone_capability_parser

llm = ChatAnthropic(model="claude-3-haiku-20240307")
response = drone_capability_parser(user_input=text, llm=llm)

Google Generative AI (langchain-google-genai)

from langchain_google_genai import ChatGoogleGenerativeAI
from drone_capability_parser import drone_capability_parser

llm = ChatGoogleGenerativeAI(model="gemini-1.5-flash")
response = drone_capability_parser(user_input=text, llm=llm)

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Default LLM – ChatLLM7

If you do not provide an llm instance, drone_capability_parser uses ChatLLM7 from the langchain_llm7 package:

https://pypi.org/project/langchain-llm7/

The free tier of LLM7 offers generous rate limits that satisfy most development and testing scenarios.
To increase limits, supply a personal API key:

export LLM7_API_KEY="your_api_key_here"

or directly:

response = drone_capability_parser(user_input=text, api_key="your_api_key_here")

You can obtain a free API key by registering at:

https://token.llm7.io/

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Contributing & Support

• Issues: Please open any bugs or feature requests on the GitHub issue tracker:
  https://github....
• Author: Eugene Evstafev – [e-mail]
• GitHub: chigwell

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License

This project is licensed under the MIT License. See the LICENSE file for details.