text-parsematch processes text input with pattern matching and retries to ensure structured, validated output for data extraction and content categorization.

A new package facilitates extracting a concise, structured summary from user-provided news headlines or brief texts by utilizing pattern matching and LLM interactions. This tool aims to help researche

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

A new package that takes user-provided text input and returns structured, validated output using pattern matching to ensure consistent formatting. It processes text extracted from various sources like

This project analyzes Netflix's content library using SQL. It explores content type distribution, rating trends, country-wise content availability, and genre classification to extract meaningful insights from Netflix data for better analysis.

📝 Extract clear, concise summaries from news headlines and brief texts for faster insights in research and reporting on complex issues.

📝 Transform text into structured, validated output using pattern matching for consistent formatting—ideal for summaries, feedback categorization, and reports.

🔍 Process text inputs effortlessly with this Python package, returning structured and validated outputs using pattern matching and retries.

📝 Summarize video titles or descriptions into factual summaries of scientific concepts using this lightweight Python package.