Most interesting practices RAG with Milvus vector database, half 2
Retrieval Augmented Expertise (RAG) is a useful strategy for using your private info in an AI-powered Chatbot. On this weblog submit, I’ll stroll by way of three key strategies to get primarily essentially the most out of RAG and contemplate each approach to look out among the finest combos.
For readers who merely have to know the TL;DR conclusion: primarily essentially the most RAG accuracy enchancment received right here from exploring completely completely different chunking strategies.
- 89% Enchancment by altering the Chunking Approach 📦
- 20% Enchancment by altering the Embedding Model 🤖
- 6% Enchancment by altering the LLM Model 🧪
Let’s dive into each approach and uncover the best-performers for a real-world RAG utility using RAG component evaluations! 🚀📚
I’ll use Milvus documentation public internet pages as a result of the docs info and Ragas as a result of the evaluation methodology. See my earlier blog about how to use RAGAS. The rest of this weblog is organized as follows:
- Textual content material Chunking Strategies
- Embedding Fashions
- LLM (Generative) Fashions
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