historical-scam-summary

Extract structured, domain‑specific summaries from a single sentence or title.
The package turns unstructured text (e.g., “How Scams Worked In The 1800s (2015)”) into a concise, standardized output that lists key historical scams, their mechanisms, and societal impact—all without any additional manual parsing.

Features

• Uses llmatch-messages to enforce an output regex and recover only the relevant information.
• Defaults to the free tier of ChatLLM7 via the langchain_llm7 wrapper.
• Works with any LangChain LLM instance – OpenAI, Anthropic, Google Generative AI, or custom models.
• Returns a List[str] – one string per extracted entity (scam, scheme, etc.).

Installation

pip install historical_scam_summary

Quick Start

from historical_scam_summary import historical_scam_summary

# Minimal usage – relies on ChatLLM7 (free tier)
response = historical_scam_summary(user_input="How Scams Worked In The 1800s (2015)")
print(response)   # e.g. ["Confidence trick (3800s): ...", "..."]

Custom LLM

If you prefer another provider, instantiate the desired LangChain model and pass it to the function:

OpenAI

from langchain_openai import ChatOpenAI
from historical_scam_summary import historical_scam_summary

llm = ChatOpenAI()          # <-- provide your own API key via env var or param
response = historical_scam_summary(user_input="Masonic scams of 1920s", llm=llm)

Anthropic

from langchain_anthropic import ChatAnthropic
from historical_scam_summary import historical_scam_summary

llm = ChatAnthropic()
response = historical_scam_summary(user_input="Pyramid schemes in the 1980s", llm=llm)

Google Generative AI

from langchain_google_genai import ChatGoogleGenerativeAI
from historical_scam_summary import historical_scam_summary

llm = ChatGoogleGenerativeAI()   # set `api_key` via environment
response = historical_scam_summary(user_input="Early Ponzi schemes", llm=llm)

Configuration

Note
The default free tier of ChatLLM7 is sufficient for most use cases. If you require higher rate limits, supply your own key.

Getting an LLM7 API Key

Register for a free key at https://token.llm7.io/.
You can then provide it via:

export LLM7_API_KEY="YOUR_KEY"

or directly in code:

historical_scam_summary(user_input="...", api_key="YOUR_KEY")

Issues & Contribution

• Open an issue or submit a pull request via the GitHub repo:
  https://github.com/chigwell/historical_scam_summary
• Contributions are welcome! Please follow the standard PR process.

Author

Eugene Evstafev
hi@euegne.plus
GitHub: chigwell

License

MIT © 2025 Eugene Evstafev