logistics-headline-extractor 2025.12.21191245

Extracts key information from unstructured news headlines into structured domain-specific summaries for business, logistics, and transportation sectors.

Logistics Headline Extractor

A Python package for transforming unstructured news headlines or short text snippets into structured, domain-specific summaries. Ideal for business, logistics, and transportation sectors, this tool extracts key details (e.g., entity, action, reason, location, and impact) and outputs them in a standardized JSON-like format.

Features

• Extracts structured information from noisy text inputs
• Outputs results in a consistent JSON-like format
• Supports custom LLM backends via LangChain
• Easy integration with existing workflows

Installation

Install the package via pip:

pip install logistics_headline_extractor

Usage

Basic Example

from logistics_headline_extractor import logistics_headline_extractor

user_input = "Waymo temporarily suspends service in SF amid power outage"
result = logistics_headline_extractor(user_input=user_input)
print(result)

Using a Custom LLM

You can use any LangChain-compatible LLM by passing it to the llm parameter:

OpenAI Example

from langchain_openai import ChatOpenAI
from logistics_headline_extractor import logistics_headline_extractor

llm = ChatOpenAI()
user_input = "Waymo temporarily suspends service in SF amid power outage"
result = logistics_headline_extractor(user_input=user_input, llm=llm)

Anthropic Example

from langchain_anthropic import ChatAnthropic
from logistics_headline_extractor import logistics_headline_extractor

llm = ChatAnthropic()
user_input = "Waymo temporarily suspends service in SF amid power outage"
result = logistics_headline_extractor(user_input=user_input, llm=llm)

Google Generative AI Example

from langchain_google_genai import ChatGoogleGenerativeAI
from logistics_headline_extractor import logistics_headline_extractor

llm = ChatGoogleGenerativeAI()
user_input = "Waymo temporarily suspends service in SF amid power outage"
result = logistics_headline_extractor(user_input=user_input, llm=llm)

Using Custom API Key

For LLM7 (default provider), you can provide your API key:

from logistics_headline_extractor import logistics_headline_extractor

user_input = "Waymo temporarily suspends service in SF amid power outage"
result = logistics_headline_extractor(user_input=user_input, api_key="your_api_key_here")

Or set it as an environment variable:

export LLM7_API_KEY="your_api_key_here"

Parameters

• user_input (str): The text input to process
• llm (Optional[BaseChatModel]): LangChain LLM instance (defaults to ChatLLM7)
• api_key (Optional[str]): API key for LLM7 (if using default provider)

Default LLM Provider

This package uses ChatLLM7 by default. The free tier rate limits are sufficient for most use cases. For higher rate limits, you can:

1. Get a free API key by registering at https://token.llm7.io/
2. Pass your API key via the api_key parameter or LLM7_API_KEY environment variable
3. Use a different LLM provider by passing a custom LangChain LLM instance

Output Format

The package returns a list of strings matching the pattern:

{"entity": "...", "action": "...", "reason": "...", "location": "...", "temporal": "..."}

Error Handling

If the LLM call fails, the function will raise a RuntimeError with details about the failure.

Support

For issues and feature requests, please create an issue on our GitHub repository.

Author

Eugene Evstafev
Email: [e-mail]
GitHub: chigwell