sergeliatko · August 16, 2024 11:43 · sergeliatko · Aug 16, 2024
diff --git a/outline.txt b/outline.txt
 Semantic Chunking - 3 Methods for Better RAG
 	 Preface: Introduction to Semantic Chunkers in RAG
 		 Introduction to Semantic Chunkers for Text Modality in Retrieval-Augmented Generation (RAG).
 		 Introduction to Three Types of Semantic Chunkers.
 		 Introduction to Semantic Chunkers Library and Usage of Chunker’s Intro Notebook in Python via Colab.
 	 Prerequisites
 		 Prerequisites Installation: Semantic Chunkers and Hugging Face Datasets.
 		 Data Testing for Chunking Methods: Impact on Latency and Quality of Results.
 	 Data Setup
 		 Introduction to Dataset and Structure of AI Archive Papers.
 		 Limitation on Text Due to Resource-Intensive Chunker.
 		 Requirement of Embedding Model for Semantic Chunking.
 		 Use of OpenAI's Text-Embedding-Ada-002 Model and API Key Requirements.
 	 1. Statistical Semantic Chunking
 		 Introduction to the Statistical Chunking Method and Its Advantages.
 		 Explanation of Statistical Chunker Functionality and Similarity Threshold Calculation.
 		 Overview of Initial Document Chunking Results and Preliminary Assessment.
 	 2. Consecutive Semantic Chunking
 		 Recommendation Order for Consecutive Chunking Method.
 		 Score Threshold Requirements for Various Text-Embedding Models.
 		 User Input and Performance Adjustment for Chunker Threshold.
 		 Explanation of Consecutive Chunker Functionality.
 	 3. Cumulative Semantic Chunking
 		 Cumulative Chunker Method: Step-by-Step Embedding Process and Similarity Comparison.
 		 Higher Time and Cost Due to Increased Embeddings Creation.
 		 Comparison of Noise Resistance and Performance of Chunkers.
 		 Performance Analysis and Threshold Adjustment of the Chunker.
 		 Threshold Adjustment for Improved Performance Over Consecutive Chunker.
 	 Multi-modal Chunking
 		 Introduction to Modalities Handled by Different Chunkers.
 		 Statistical Chunker Limitation to Text Modality.
 		 Capabilities and Future Demonstration of the Consecutive Chunker for Video Handling.
 		 Text-Focused Nature of the Cumulative Chunker.
 	 Conclusion and Sign-off for Semantic Chunkers Presentation !
	Semantic Chunking - 3 Methods for Better RAG
	Preface: Introduction to Semantic Chunkers in RAG
	Introduction to Semantic Chunkers for Text Modality in Retrieval-Augmented Generation (RAG).
	Introduction to Three Types of Semantic Chunkers.
	Introduction to Semantic Chunkers Library and Usage of Chunker’s Intro Notebook in Python via Colab.
	Prerequisites
	Prerequisites Installation: Semantic Chunkers and Hugging Face Datasets.
	Data Testing for Chunking Methods: Impact on Latency and Quality of Results.
	Data Setup
	Introduction to Dataset and Structure of AI Archive Papers.
	Limitation on Text Due to Resource-Intensive Chunker.
	Requirement of Embedding Model for Semantic Chunking.
	Use of OpenAI's Text-Embedding-Ada-002 Model and API Key Requirements.
	1. Statistical Semantic Chunking
	Introduction to the Statistical Chunking Method and Its Advantages.
	Explanation of Statistical Chunker Functionality and Similarity Threshold Calculation.
	Overview of Initial Document Chunking Results and Preliminary Assessment.
	2. Consecutive Semantic Chunking
	Recommendation Order for Consecutive Chunking Method.
	Score Threshold Requirements for Various Text-Embedding Models.
	User Input and Performance Adjustment for Chunker Threshold.
	Explanation of Consecutive Chunker Functionality.
	3. Cumulative Semantic Chunking
	Cumulative Chunker Method: Step-by-Step Embedding Process and Similarity Comparison.
	Higher Time and Cost Due to Increased Embeddings Creation.
	Comparison of Noise Resistance and Performance of Chunkers.
	Performance Analysis and Threshold Adjustment of the Chunker.
	Threshold Adjustment for Improved Performance Over Consecutive Chunker.
	Multi-modal Chunking
	Introduction to Modalities Handled by Different Chunkers.
	Statistical Chunker Limitation to Text Modality.
	Capabilities and Future Demonstration of the Consecutive Chunker for Video Handling.
	Text-Focused Nature of the Cumulative Chunker.
	Conclusion and Sign-off for Semantic Chunkers Presentation !