A tool to download, transcribe, summarize, and chat with media files like videos, audio, documents, web articles, and books, all locally and automated.
The article explores how smaller language models like the Meta 1 Billion model can be used for efficient summarization and indexing of large documents, improving the performance and scalability of Retrieval-Augmented Generation (RAG) systems.
This project creates bulleted notes summaries of books and other long texts using Python and language models, splitting documents into chunks for more granular summaries and question-based analyses.
A tool to transcribe and summarize videos from multiple sources using AI models in Google Colab or locally.
"Generate 5 essential questions that, when answered, capture the main points and core meaning of the text. Focus on questions that:
Address the central theme or argument
Identify key supporting ideas
Highlight important facts or evidence
Reveal the author's purpose or perspective
Explore any significant implications or conclusions
Phrase the questions to encourage comprehensive yet concise answers. Present only the questions, numbered and without any additional text."
Address the central theme or argument
Identify key supporting ideas
Highlight important facts or evidence
Reveal the author's purpose or perspective
Explore any significant implications or conclusions
Phrase the questions to encourage comprehensive yet concise answers. Present only the questions, numbered and without any additional text."
The article explains semantic text chunking, a technique for automatically grouping similar pieces of text to be used in pre-processing stages for Retrieval Augmented Generation (RAG) or similar applications. It uses visualizations to understand the chunking process and explores extensions involving clustering and LLM-powered labeling.
Distilling key points after >2 years of experience and from AI developers’ own tutorials, hands-on and with examples.
