diff --git a/examples/lightrag_yangdx.py b/examples/lightrag_yangdx.py
deleted file mode 100644
index 2691deac..00000000
--- a/examples/lightrag_yangdx.py
+++ /dev/null
@@ -1,82 +0,0 @@
-import os
-# import asyncio
-# import inspect
-import logging
-from dotenv import load_dotenv
-from lightrag import LightRAG, QueryParam
-from lightrag.llm import openai_complete_if_cache, ollama_embedding
-from lightrag.utils import EmbeddingFunc
-
-load_dotenv()
-
-WORKING_DIR = "./examples/output"
-
-logging.basicConfig(format="%(levelname)s:%(message)s", level=logging.INFO)
-
-async def llm_model_func(
-    prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs
-) -> str:
-    return await openai_complete_if_cache(
-        "deepseek-chat",
-        prompt,
-        system_prompt=system_prompt,
-        history_messages=history_messages,
-        api_key=os.getenv("DEEPSEEK_API_KEY"),
-        base_url=os.getenv("DEEPSEEK_ENDPOINT"),
-        **kwargs,
-    )
-
-if not os.path.exists(WORKING_DIR):
-    os.mkdir(WORKING_DIR)
-
-rag = LightRAG(
-    working_dir=WORKING_DIR,
-    llm_model_func=llm_model_func,
-    embedding_func=EmbeddingFunc(
-        embedding_dim=1024,
-        max_token_size=8192,
-        func=lambda texts: ollama_embedding(
-            texts, embed_model="bge-m3:latest", host="http://m4.lan.znipower.com:11434"
-        ),
-    ),
-)
-
-with open("./examples/input/book.txt", "r", encoding="utf-8") as f:
-    rag.insert(f.read())
-
-# Perform naive search
-print(
-    rag.query("What are the top themes in this story?", param=QueryParam(mode="naive"))
-)
-
-# Perform local search
-print(
-    rag.query("What are the top themes in this story?", param=QueryParam(mode="local"))
-)
-
-# Perform global search
-print(
-    rag.query("What are the top themes in this story?", param=QueryParam(mode="global"))
-)
-
-# Perform hybrid search
-print(
-    rag.query("What are the top themes in this story?", param=QueryParam(mode="hybrid"))
-)
-
-# # stream response
-# resp = rag.query(
-#     "What are the top themes in this story?",
-#     param=QueryParam(mode="hybrid", stream=True),
-# )
-
-
-# async def print_stream(stream):
-#     async for chunk in stream:
-#         print(chunk, end="", flush=True)
-
-
-# if inspect.isasyncgen(resp):
-#     asyncio.run(print_stream(resp))
-# else:
-#     print(resp)