diff --git a/examples/lightrag_api_openai_compatible_demo.py b/examples/lightrag_api_openai_compatible_demo.py
index 2cd262bb..7e1f608a 100644
--- a/examples/lightrag_api_openai_compatible_demo.py
+++ b/examples/lightrag_api_openai_compatible_demo.py
@@ -21,19 +21,18 @@ print(f"WORKING_DIR: {WORKING_DIR}")
 if not os.path.exists(WORKING_DIR):
     os.mkdir(WORKING_DIR)
 
+
 # LLM model function
 
 
 async def llm_model_func(
-    prompt, system_prompt=None, history_messages=[], **kwargs
+        prompt, system_prompt=None, history_messages=[], **kwargs
 ) -> str:
     return await openai_complete_if_cache(
-        "gpt-4o-mini",
+        os.environ.get("LLM_MODEL", "gpt-4o-mini"),
         prompt,
         system_prompt=system_prompt,
         history_messages=history_messages,
-        api_key="YOUR_API_KEY",
-        base_url="YourURL/v1",
         **kwargs,
     )
 
@@ -44,21 +43,28 @@ async def llm_model_func(
 async def embedding_func(texts: list[str]) -> np.ndarray:
     return await openai_embedding(
         texts,
-        model="text-embedding-3-large",
-        api_key="YOUR_API_KEY",
-        base_url="YourURL/v1",
+        model=os.environ.get("EMBEDDING_MODEL", "text-embedding-3-large"),
     )
 
 
+async def get_embedding_dim():
+    test_text = ["This is a test sentence."]
+    embedding = await embedding_func(test_text)
+    embedding_dim = embedding.shape[1]
+    print(f"{embedding_dim=}")
+    return embedding_dim
+
+
 # Initialize RAG instance
 rag = LightRAG(
     working_dir=WORKING_DIR,
     llm_model_func=llm_model_func,
-    embedding_func=EmbeddingFunc(
-        embedding_dim=3072, max_token_size=8192, func=embedding_func
-    ),
+    embedding_func=EmbeddingFunc(embedding_dim=asyncio.run(get_embedding_dim()),
+                                 max_token_size=8192,
+                                 func=embedding_func),
 )
 
+
 # Data models