geopolitex-analyzer 2025.12.21165107

A new package that processes news headlines or short text inputs related to geopolitical and defense topics, such as reports on military capabilities or international competition. It uses an LLM to ge

geopolitex-analyzer

geopolitex-analyzer is a lightweight Python package that turns short news headlines or text snippets about geopolitics, defense, and international competition into structured, LLM‑generated summaries. The output always follows a predefined pattern (subject, comparative context, implications) and is validated with robust regex matching to guarantee consistency and accuracy.

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Features

• One‑line summarisation for geopolitical and defense‑related texts.
• Pattern‑based validation using llmatch to ensure the LLM output conforms to the expected format.
• Pluggable LLM backend – defaults to ChatLLM7 but works with any LangChain‑compatible chat model (OpenAI, Anthropic, Google, etc.).
• Simple API: just pass the raw text and get a list of structured strings back.

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Installation

pip install geopolitex_analyzer

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Quick Start

from geopolitex_analyzer import geopolitex_analyzer

# Simple usage with the default ChatLLM7 (requires an API key)
summary = geopolitex_analyzer(
    user_input="China launches a new hypersonic missile system, threatening regional stability."
)

print(summary)
# -> ['Subject: China...', 'Comparative Context: ...', 'Implications: ...']

Using a custom LangChain LLM

You can provide any LangChain chat model that implements BaseChatModel. This is useful if you prefer OpenAI, Anthropic, Google Gemini, etc.

OpenAI example

from langchain_openai import ChatOpenAI
from geopolitex_analyzer import geopolitex_analyzer

my_llm = ChatOpenAI(model="gpt-4o-mini")
summary = geopolitex_analyzer(
    user_input="Russia expands its naval presence in the Arctic.",
    llm=my_llm,
)

print(summary)

Anthropic example

from langchain_anthropic import ChatAnthropic
from geopolitex_analyzer import geopolitex_analyzer

my_llm = ChatAnthropic(model="claude-3-haiku-20240307")
summary = geopolitex_analyzer(
    user_input="India announces a new defense budget focusing on AI‑driven weaponry.",
    llm=my_llm,
)

print(summary)

Google Gemini example

from langchain_google_genai import ChatGoogleGenerativeAI
from geopolitex_analyzer import geopolitex_analyzer

my_llm = ChatGoogleGenerativeAI(model="gemini-1.5-flash")
summary = geopolitex_analyzer(
    user_input="EU sanctions target new Russian aerospace firms.",
    llm=my_llm,
)

print(summary)

Supplying an API key for the default LLM7 backend

If you rely on the built‑in ChatLLM7 model, provide an API key either via the LLM7_API_KEY environment variable or directly:

from geopolitex_analyzer import geopolitex_analyzer

summary = geopolitex_analyzer(
    user_input="Turkey acquires advanced drone technology from South Korea.",
    api_key="your_llm7_api_key_here",
)

print(summary)

A free API key can be obtained by registering at https://token.llm7.io/. The free tier’s rate limits are sufficient for most typical use cases.

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Function Reference

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

Parameter	Type	Description
user_input	str	Raw headline or short text describing a geopolitical/defense topic.
api_key	Optional[str]	API key for the default ChatLLM7. If omitted, the function looks for the LLM7_API_KEY env var.
llm	Optional[BaseChatModel]	Any LangChain chat model. If provided, it overrides the default ChatLLM7.

Return value: A list of strings that match the defined output pattern (e.g., subject, comparative context, implications). If the LLM response does not satisfy the pattern, a RuntimeError is raised.

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Dependencies

• llmatch_messages – pattern‑matching helper used to enforce output format.
• langchain-core – core interfaces for LLMs (BaseChatModel, message types).
• langchain-llm7 – wrapper for the LLM7 service (automatically installed).
• geopolitex-analyzer’s own prompts.py (contains the system / human prompts and the regex pattern).

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Contributing & Support

• Bug reports & feature requests: https://github.com/chigwell/geopolitex_analyzer/issues
• Pull requests: Contributions are welcome—please follow the standard GitHub workflow.

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Author

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

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License

MIT License – see the LICENSE file in the repository for details.

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Happy analysing!