diff --git a/README.md b/README.md
index 90c3ec04..f1aeed99 100644
--- a/README.md
+++ b/README.md
@@ -695,7 +695,7 @@ Output the results in the following structure:
 ```
 </details>
 
- ### Batch Eval
+### Batch Eval
 To evaluate the performance of two RAG systems on high-level queries, LightRAG uses the following prompt, with the specific code available in `example/batch_eval.py`.
 
 <details>
@@ -746,6 +746,7 @@ Output your evaluation in the following JSON format:
 </details>
 
 ### Overall Performance Table
+
 |                      | **Agriculture**         |                       | **CS**                |                       | **Legal**             |                       | **Mix**               |                       |
 |----------------------|-------------------------|-----------------------|-----------------------|-----------------------|-----------------------|-----------------------|-----------------------|-----------------------|
 |                      | NaiveRAG                | **LightRAG**          | NaiveRAG              | **LightRAG**          | NaiveRAG              | **LightRAG**          | NaiveRAG              | **LightRAG**          |
diff --git a/examples/lightrag_yangdx.py b/examples/lightrag_yangdx.py
new file mode 100644
index 00000000..162900c4
--- /dev/null
+++ b/examples/lightrag_yangdx.py
@@ -0,0 +1,70 @@
+import asyncio
+import os
+import inspect
+import logging
+from lightrag import LightRAG, QueryParam
+from lightrag.llm import ollama_model_complete, ollama_embedding
+from lightrag.utils import EmbeddingFunc
+
+WORKING_DIR = "./dickens"
+
+logging.basicConfig(format="%(levelname)s:%(message)s", level=logging.INFO)
+
+if not os.path.exists(WORKING_DIR):
+    os.mkdir(WORKING_DIR)
+
+rag = LightRAG(
+    working_dir=WORKING_DIR,
+    llm_model_func=ollama_model_complete,
+    llm_model_name="gemma2:2b",
+    llm_model_max_async=4,
+    llm_model_max_token_size=32768,
+    llm_model_kwargs={"host": "http://localhost:11434", "options": {"num_ctx": 32768}},
+    embedding_func=EmbeddingFunc(
+        embedding_dim=768,
+        max_token_size=8192,
+        func=lambda texts: ollama_embedding(
+            texts, embed_model="nomic-embed-text", host="http://localhost:11434"
+        ),
+    ),
+)
+
+with open("./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)