histextractor 2025.12.21165426

A new package that extracts and structures key historical facts and insights from textual descriptions of historical events, such as the Lend-Lease program during World War II. The package takes a tex

histextractor

histextractor is a lightweight Python package that extracts and structures key historical facts from natural‑language descriptions of historical events.
Given a text passage such as a summary of the Lend‑Lease program during World War II, the package returns a list of structured strings containing the main participants, key actions, outcomes, and notable controversies. The output follows a consistent format, making it ideal for educational tools, research databases, or historical analysis platforms.

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📦 Installation

pip install histextractor

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🚀 Quick Start

from histextractor import histextractor

user_input = """
The Lend‑Lease program was a U.S. policy during World War II that supplied Allied nations
with military aid. It involved the United States sending vehicles, weapons, and supplies
to the United Kingdom, the Soviet Union, China, and others. This assistance was crucial
in sustaining Allied forces before the U.S. formally entered the war.
"""

# Use the default ChatLLM7
response = histextractor(user_input)

for idx, item in enumerate(response, 1):
    print(f"{idx}. {item}")

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📚 How histextractor Works

1. LLM Choice

   • By default, the package uses ChatLLM7 from langchain_llm7.
   • You can overwrite this by passing any langchain compatible BaseChatModel.

2. API Key

   • If you do not specify an api_key, the function will look for the environment variable LLM7_API_KEY.
   • If that is also missing, it will fall back to the string "None", which still triggers the credentials that the free tier of LLM7 provides.

3. Output Format

   • The function returns a List[str].
   • Each entry in the list follows the regex pattern defined in pattern.py, ensuring consistent structure.

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🔌 Custom LLM Examples

Using OpenAI

from langchain_openai import ChatOpenAI
from histextractor import histextractor

llm = ChatOpenAI()
response = histextractor(user_input, llm=llm)

Using Anthropic

from langchain_anthropic import ChatAnthropic
from histextractor import histextractor

llm = ChatAnthropic()
response = histextractor(user_input, llm=llm)

Using Google Gemini

from langchain_google_genai import ChatGoogleGenerativeAI
from histextractor import histextractor

llm = ChatGoogleGenerativeAI()
response = histextractor(user_input, llm=llm)

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⚙️ Rate Limits & API Keys

• LLM7 Free Tier rate limits are sufficient for most use cases.
• For higher limits, provide your own API key via:
  • Environment variable: export LLM7_API_KEY=your_key_here
  • Parameter: histextractor(user_input, api_key="your_key_here")
• Obtain a free API key at: https://token.llm7.io/

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🐛 Issues & Feedback

Please file issues at: https://github.com/chigwell/histextractor/issues

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👤 Author

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

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