How to find the best chunking method for your RAG app
In this tutorial, Jacky from Timescale's AI team reveals the importance of proper document chunking in RAG applications and demonstrates how to optimize these chunking strategies using pgai Vectorizer and SQL.
Learn to balance context and detail in your large language models and enhance your RAG system's accuracy without managing complex pipelines. Follow along as Jacky walks through setting up your environment on Timescale Cloud, experimenting with different chunking strategies, and creating an interactive RAG app. Discover four key chunking methods: recursive character text splitting, simple character splitting, metadata-rich chunks, and HTML document chunking. Finally, compare these strategies to find the best fit for your application needs.
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📚 𝗖𝗵𝗮𝗽𝘁𝗲𝗿𝘀
00:00 The Dirty Secret of RAG Applications
00:29 Introduction to pgai Vectorizer
00:46 Setting Up Your RAG Application
01:41 Creating and Configuring the Vectorizer
04:33 Exploring Different Chunking Strategies
05:24 Implementing Recursive Character Text Splitter
06:46 Simple Character Text Splitter
07:34 Metadata-Rich Chunking
08:50 HTML Content Chunking
09:38 Generating RAG Responses
10:17 Comparing Chunking Strategies
11:15 Conclusion and Next Steps