Chunking Strategies in RAG - Optimising Data for Advanced AI Responses

---

---

AI Summary

Summary: Chunking Strategies in RAG Applications

• Introduction to Chunking
  • Data is divided into chunks.
  • Chunks are converted to embeddings.
  • Embeddings are stored in a vector database.
• Process Overview
  • User asks a question.
  • Relevant chunks are searched in the database.
  • Information is used as context for the language model to provide an answer.
• Importance of Chunking
  • High-quality chunks lead to accurate answers.
  • Fixed size chunking can break words and produce irrelevant results.
  • Dividing text by commas or full stops may miss related content.
• Chunking Strategies
  • Character Splitting: Dividing text into fixed-size chunks.
  • Recursive Character Splitting: Using characters like new lines as splitters.
  • Document Based Chunking: Splitting based on markdown, Python, or JavaScript documents.
  • Semantic Chunking: Using embeddings to determine related sentences and group them.
  • Agentic Chunking: Using a language model to make chunks stand on their own and group related chunks.
• Implementation Steps
  • Install necessary packages and set up the environment.
  • Create app.py and define functions for different chunking methods.
  • Run the code to see how text is split and grouped.
• Optimization with Agentic Chunking
  • Proportion Based Chunking: Chunks can stand alone with complete meaning.
  • Grouping Chunks: Group related chunks to maintain context and improve responses.
• Conclusion
  • The video demonstrates various chunking strategies to optimize RAG applications.
  • Encourages viewers to like, share, and subscribe for more AI-related content.