Video Text Summarizer

A Python package designed to analyze and summarize video content by processing pre-extracted textual information such as transcripts, subtitles, and descriptions. It leverages structured pattern matching to extract key topics, themes, and insights from lengthy videos, providing concise summaries without processing multimedia directly.

📌 Overview

This package helps users quickly grasp the essence of video content by summarizing textual data (transcripts, subtitles, etc.) using advanced language models. It is ideal for environments where only text data is available, enabling efficient content discovery and knowledge extraction.

📦 Installation

Install the package via pip:

pip install video_text_summarizer

🚀 Usage

Basic Usage

from video_text_summarizer import video_text_summarizer

# Summarize text using the default LLM7 model
response = video_text_summarizer(
    user_input="Your video transcript or text here..."
)
print(response)

Custom LLM Usage

You can replace the default ChatLLM7 with any other LangChain-compatible LLM (e.g., OpenAI, Anthropic, Google Generative AI):

Using OpenAI

from langchain_openai import ChatOpenAI
from video_text_summarizer import video_text_summarizer

llm = ChatOpenAI()
response = video_text_summarizer(
    user_input="Your video transcript or text here...",
    llm=llm
)
print(response)

Using Anthropic

from langchain_anthropic import ChatAnthropic
from video_text_summarizer import video_text_summarizer

llm = ChatAnthropic()
response = video_text_summarizer(
    user_input="Your video transcript or text here...",
    llm=llm
)
print(response)

Using Google Generative AI

from langchain_google_genai import ChatGoogleGenerativeAI
from video_text_summarizer import video_text_summarizer

llm = ChatGoogleGenerativeAI()
response = video_text_summarizer(
    user_input="Your video transcript or text here...",
    llm=llm
)
print(response)

🔧 Parameters

🔑 API Key & Rate Limits

• Default LLM: Uses ChatLLM7 from langchain_llm7.
• Free Tier: Sufficient for most use cases (check LLM7 docs for limits).
• Custom API Key: Pass via api_key parameter or set LLM7_API_KEY environment variable.
• Get API Key: Register at LLM7 Token for free.

📝 License

MIT License (see LICENSE for details).

📢 Support & Issues

For bugs, feature requests, or support, open an issue on GitHub.

👤 Author

Eugene Evstafev 📧 hi@euegne.plus 🔗 GitHub: chigwell