diff --git a/docs/troubleshooting/rag.mdx b/docs/troubleshooting/rag.mdx
index 0090076..4602698 100644
--- a/docs/troubleshooting/rag.mdx
+++ b/docs/troubleshooting/rag.mdx
@@ -78,6 +78,25 @@ Bad embeddings = bad retrieval. If the vector representation of your content is
 
 ---
 
+### 5. ❌ 400: 'NoneType' object has no attribute 'encode'
+
+This error indicates a misconfigured or missing embedding model. When Open WebUI tries to create embeddings but doesn’t have a valid model loaded, it can’t process the text—and the result is this cryptic error.
+
+💥 Cause:
+- Your embedding model isn’t set up properly.
+- It might not have downloaded completely.
+- Or if you're using an external embedding model, it may not be accessible.
+
+✅ Solution:
+
+- Go to: **Admin Settings > Documents > Embedding Model**
+- Save the embedding model again—even if it's already selected. This forces a recheck/download.
+- If you're using a remote/external embedding tool, make sure it's running and accessible to Open WebUI.
+
+📌 Tip: After fixing the configuration, try re-embedding a document and verify no error is shown in the logs.
+
+---
+
 ## 🧪 Pro Tip: Test with GPT-4o or GPT-4
 
 If you’re not sure whether the issue is with retrieval, token limits, or embedding—try using GPT-4o temporarily (e.g., via OpenAI API). If the results suddenly become more accurate, it's a strong signal that your local model’s context limit (2048 by default in Ollama) is the bottleneck.