5 Chunking Strategies for better RAG | Better Retrieval | Text Splitting Methods | RAG
Unlock the full potential of Retrieval-Augmented Generation (RAG) with this all-in-one, detailed guide covering five essential chunking strategies! 🌟 In this single, comprehensive video, I provide an in-depth explanation of each chunking strategy, including:
Fixed Size Chunking: Overview, working algorithm, and implementation with LangChain's CharacterTextSplitter, plus when and why to use it.
Recursive Chunking: Definition, working algorithm, implementation using LangChain's RecursiveCharacterTextSplitter, and real-world scenarios.
Semantic Chunking: Explanation, custom implementation, vector embeddings, Hugging Face embedding models, and practical use cases.
Document Specific Chunking: Detailed walkthrough, implementation for Python code and markdown text, LangChain's MarkdownHeaderTextSplitter, and usage scenarios.
Agentic Chunking: Overview, working algorithm, custom implementation with LLaMA 3 70B instruct model, prompt writing, and example scenarios.
This video is packed with ample examples, practical insights, and real-world applications for each chunking strategy. Whether you're a beginner or a seasoned AI professional, this comprehensive guide will enhance your understanding and application of RAG techniques.
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