vidconcept-sum 2025.12.21193338

vidconcept-sum generates structured, factual summaries of scientific/educational concepts from video titles or descriptions using an LLM.

vidconcept-sum

vidconcept-sum is a lightweight Python package that turns a video title or short text description into a structured, factual summary of the scientific or educational concept being presented. It leverages an LLM (by default ChatLLM7) to interpret the topic and returns a concise explanation, key terms, and contextual information, all formatted according to a strict regex pattern.

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Features

• One‑line API – just pass a string, get a list of structured summary elements.
• LLM‑agnostic – works out‑of‑the‑box with ChatLLM7, but you can plug any LangChain‑compatible chat model.
• Safety‑first – the prompt enforces factual, non‑speculative answers and validates output against a regular expression.
• Easy installation – available on PyPI.

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Installation

pip install vidconcept_sum

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Quick Start

from vidconcept_sum import vidconcept_sum

# Simple call using the default ChatLLM7 model (requires an API key)
summary = vidconcept_sum(
    user_input="A short intro to quantum entanglement in YouTube video titles."
)

print(summary)

Parameters

Name	Type	Description
user_input	str	The video title or short description to be summarised.
llm (optional)	BaseChatModel	Any LangChain chat model instance. If omitted, the default ChatLLM7 will be instantiated.
api_key (optional)	str	API key for LLM7. If not supplied, the function looks for the environment variable LLM7_API_KEY.

The function returns a List[str] containing the extracted summary data.

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Using a Custom LLM

You can replace the default ChatLLM7 with any LangChain‑compatible chat model. Below are examples for OpenAI, Anthropic, and Google Generative AI.

OpenAI

from langchain_openai import ChatOpenAI
from vidconcept_sum import vidconcept_sum

llm = ChatOpenAI(model="gpt-4o-mini")
response = vidconcept_sum(
    user_input="Explaining the basics of photosynthesis.",
    llm=llm
)
print(response)

Anthropic

from langchain_anthropic import ChatAnthropic
from vidconcept_sum import vidconcept_sum

llm = ChatAnthropic(model="claude-3-haiku-20240307")
response = vidconcept_sum(
    user_input="A video about black holes and event horizons.",
    llm=llm
)
print(response)

Google Generative AI

from langchain_google_genai import ChatGoogleGenerativeAI
from vidconcept_sum import vidconcept_sum

llm = ChatGoogleGenerativeAI(model="gemini-1.5-flash")
response = vidconcept_sum(
    user_input="Understanding the theory of relativity in simple terms.",
    llm=llm
)
print(response)

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API Key & Rate Limits

• LLM7 free tier provides generous rate limits that cover typical usage of this package.
• If you need higher limits, supply your own API key:
  • Via environment variable: export LLM7_API_KEY="your_key_here"
  • Or directly in the call: vidconcept_sum(..., api_key="your_key_here")
• Obtain a free API key by registering at https://token.llm7.io/.

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Contributing & Support

If you encounter any issues or have feature requests, please open an issue on GitHub:

https://github.com/chigwell/vidconcept-sum/issues

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License

This project is licensed under the MIT License.

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Author

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

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Enjoy using vidconcept-sum to instantly extract clear, educational concepts from video titles!