tech_insights_extractor is a lightweight Python package that lets you quickly turn unstructured text about recent technological advancements into structured, concise insights.
It uses a language‑model‑based prompt‐engineering approach combined with regular‑expression validation to:
- Summarise key innovations
- Identify the nature of breakthroughs
- Output the information in a consistent, easy‑to‑consume format
The package comes with a default LLM implementation (ChatLLM7 from langchain_llm7), but you can inject any LangChain BaseChatModel (OpenAI, Anthropic, Google Gemini, etc.) for customized behaviour.
pip install tech_insights_extractorfrom tech_insights_extractor import tech_insights_extractor
text = """
Recent research has unveiled a novel quantum‑error correction code that reduces surface‑code overhead by 30%. ...
"""
# Using the default ChatLLM7
insights = tech_insights_extractor(user_input=text)
print(insights)
# ['Summary: ...', 'Key Innovation: ...', 'Category: Quantum Computing']from tech_insights_extractor import tech_insights_extractor| Parameter | Type | Description |
|---|---|---|
user_input |
str |
The raw text you want to analyse. |
llm |
Optional[BaseChatModel] |
A LangChain chat model to send prompts to. If omitted, the package falls back to its built‑in ChatLLM7. |
api_key |
Optional[str] |
The API key for LLM7. If omitted, the package checks the LLM7_API_KEY environment variable, and finally defaults to "None" (free tier). |
If you do not supply an llm, tech_insights_extractor will automatically instantiate a ChatLLM7 with the provided or environment key.
# No LLM arg – uses ChatLLM7 internally
insights = tech_insights_extractor(user_input=text)You can drop in any LangChain BaseChatModel. Examples below:
from langchain_openai import ChatOpenAI
from tech_insights_extractor import tech_insights_extractor
llm = ChatOpenAI(model="gpt-4o-mini")
insights = tech_insights_extractor(user_input=text, llm=llm)from langchain_anthropic import ChatAnthropic
from tech_insights_extractor import tech_insights_extractor
llm = ChatAnthropic()
insights = tech_insights_extractor(user_input=text, llm=llm)from langchain_google_genai import ChatGoogleGenerativeAI
from tech_insights_extractor import tech_insights_extractor
llm = ChatGoogleGenerativeAI(model="gemini-1.5-pro")
insights = tech_insights_extractor(user_input=text, llm=llm)The free tier of LLM7 offers generous limits for most projects.
If you require higher throughput or want to avoid the default key:
- Register for an API key at https://token.llm7.io/.
- Pass it directly:
insights = tech_insights_extractor(user_input=text, api_key="your_api_key_here")or export it as an environment variable:
export LLM7_API_KEY="your_api_key_here"Please report bugs or feature requests at: https://github.com/chigwell/tech-insights-extractor/issues
- Author: Eugene Evstafev
- Email: hi@euegne.plus
- GitHub: @chigwell
The project is open source under the MIT License. Feel free to fork, modify, and contribute!
Happy extracting!