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 – hi@euegne.plus
• GitHub: chigwell

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License

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