tech-insights-extractor 2025.12.21230046

tech-insights-extractor summarizes tech breakthroughs, extracts key innovations, and categorizes advances from user text using LLM prompts.

tech-insights-extractor

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.

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Table of Contents

• Installation
• Quick Start
• Usage
  • Parameters
  • Default LLM
  • Custom LLMs
• Rate Limits & API Key
• Troubleshooting & Issues
• License & Contact

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Installation

pip install tech_insights_extractor

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Quick Start

from 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']

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Usage

from tech_insights_extractor import tech_insights_extractor

Parameters

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).

Default LLM

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)

Custom LLMs

You can drop in any LangChain BaseChatModel. Examples below:

OpenAI

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)

Anthropic

from langchain_anthropic import ChatAnthropic
from tech_insights_extractor import tech_insights_extractor

llm = ChatAnthropic()
insights = tech_insights_extractor(user_input=text, llm=llm)

Google Gemini

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)

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Rate Limits & API Key

The free tier of LLM7 offers generous limits for most projects.
If you require higher throughput or want to avoid the default key:

1. Register for an API key at https://token.llm7.io/.
2. 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"

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Troubleshooting & Issues

Please report bugs or feature requests at: https://github.com/chigwell/tech-insights-extractor/issues

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License & Contact

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

The project is open source under the MIT License. Feel free to fork, modify, and contribute!

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Happy extracting!