diff --git a/README-zh.md b/README-zh.md
index 3d0e6301..478d67ab 100644
--- a/README-zh.md
+++ b/README-zh.md
@@ -445,6 +445,10 @@ async def initialize_rag():
     return rag
 ```
 
+> **关于嵌入函数封装的重要说明：**
+>
+> `EmbeddingFunc` 不能嵌套封装。已经被 `@wrap_embedding_func_with_attrs` 装饰过的嵌入函数（如 `openai_embed`、`ollama_embed` 等）不能再次使用 `EmbeddingFunc()` 封装。这就是为什么在创建自定义嵌入函数时，我们调用 `xxx_embed.func`（底层未封装的函数）而不是直接调用 `xxx_embed`。
+
 </details>
 
 <details>
@@ -550,6 +554,10 @@ rag = LightRAG(
 )
 ```
 
+> **关于嵌入函数封装的重要说明：**
+>
+> `EmbeddingFunc` 不能嵌套封装。已经被 `@wrap_embedding_func_with_attrs` 装饰过的嵌入函数（如 `openai_embed`、`ollama_embed` 等）不能再次使用 `EmbeddingFunc()` 封装。这就是为什么在创建自定义嵌入函数时，我们调用 `xxx_embed.func`（底层未封装的函数）而不是直接调用 `xxx_embed`。
+
 * **低RAM GPU**
 
 为了在低RAM GPU上运行此实验，您应该选择小型模型并调整上下文窗口（增加上下文会增加内存消耗）。例如，在6Gb RAM的改装挖矿GPU上运行这个ollama示例需要将上下文大小设置为26k，同时使用`gemma2:2b`。它能够在`book.txt`中找到197个实体和19个关系。
diff --git a/README.md b/README.md
index 6ab85dc4..68d61eee 100644
--- a/README.md
+++ b/README.md
@@ -441,6 +441,10 @@ async def initialize_rag():
     return rag
 ```
 
+> **Important Note on Embedding Function Wrapping:**
+>
+> `EmbeddingFunc` cannot be nested. Functions that have been decorated with `@wrap_embedding_func_with_attrs` (such as `openai_embed`, `ollama_embed`, etc.) cannot be wrapped again using `EmbeddingFunc()`. This is why we call `xxx_embed.func` (the underlying unwrapped function) instead of `xxx_embed` directly when creating custom embedding functions.
+
 </details>
 
 <details>
@@ -548,6 +552,10 @@ rag = LightRAG(
 )
 ```
 
+> **Important Note on Embedding Function Wrapping:**
+>
+> `EmbeddingFunc` cannot be nested. Functions that have been decorated with `@wrap_embedding_func_with_attrs` (such as `openai_embed`, `ollama_embed`, etc.) cannot be wrapped again using `EmbeddingFunc()`. This is why we call `xxx_embed.func` (the underlying unwrapped function) instead of `xxx_embed` directly when creating custom embedding functions.
+
 * **Low RAM GPUs**
 
 In order to run this experiment on low RAM GPU you should select small model and tune context window (increasing context increase memory consumption). For example, running this ollama example on repurposed mining GPU with 6Gb of RAM required to set context size to 26k while using `gemma2:2b`. It was able to find 197 entities and 19 relations on `book.txt`.