From 5180f41224f3423674791d393d291a1215b15312 Mon Sep 17 00:00:00 2001
From: JessYan <15711968+JessYanCoding@users.noreply.github.com>
Date: Wed, 4 Mar 2026 12:41:11 +0800
Subject: [PATCH] Update Llamaindex concepts url

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 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
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@@ -350,7 +350,7 @@ With RAG, LLMs retrieve contextual documents from a database to improve the accu
 * **Evaluation**: We need to evaluate both the document retrieval (context precision and recall) and the generation stages (faithfulness and answer relevancy). It can be simplified with tools [Ragas](https://github.com/explodinggradients/ragas/tree/main) and [DeepEval](https://github.com/confident-ai/deepeval) (assessing quality).
 
 📚 **References**:
-* [Llamaindex - High-level concepts](https://docs.llamaindex.ai/en/stable/getting_started/concepts.html): Main concepts to know when building RAG pipelines.
+* [Llamaindex - High-level concepts](https://developers.llamaindex.ai/python/framework/getting_started/concepts/): Main concepts to know when building RAG pipelines.
 * [Model Context Protocol](https://modelcontextprotocol.io/introduction): Introduction to MCP with motivate, architecture, and quick starts.
 * [Pinecone - Retrieval Augmentation](https://www.pinecone.io/learn/series/langchain/langchain-retrieval-augmentation/): Overview of the retrieval augmentation process. 
 * [LangChain - Q&A with RAG](https://python.langchain.com/docs/tutorials/rag/): Step-by-step tutorial to build a typical RAG pipeline.