fix demo

examples/lightrag_zhipu_demo.py

@@ -1,10 +1,12 @@
 import os
 import logging
+import asyncio
 
 
 from lightrag import LightRAG, QueryParam
 from lightrag.llm.zhipu import zhipu_complete, zhipu_embedding
 from lightrag.utils import EmbeddingFunc
+from lightrag.kg.shared_storage import initialize_pipeline_status
 
 WORKING_DIR = "./dickens"
 
@@ -17,39 +19,51 @@ api_key = os.environ.get("ZHIPUAI_API_KEY")
 if api_key is None:
     raise Exception("Please set ZHIPU_API_KEY in your environment")
 
+async def initialize_rag():
+    rag = LightRAG(
+        working_dir=WORKING_DIR,
+        llm_model_func=zhipu_complete,
+        llm_model_name="glm-4-flashx",  # Using the most cost/performance balance model, but you can change it here.
+        llm_model_max_async=4,
+        llm_model_max_token_size=32768,
+        embedding_func=EmbeddingFunc(
+            embedding_dim=2048,  # Zhipu embedding-3 dimension
+            max_token_size=8192,
+            func=lambda texts: zhipu_embedding(texts),
+        ),
+    )
 
-rag = LightRAG(
-    working_dir=WORKING_DIR,
-    llm_model_func=zhipu_complete,
-    llm_model_name="glm-4-flashx",  # Using the most cost/performance balance model, but you can change it here.
-    llm_model_max_async=4,
-    llm_model_max_token_size=32768,
-    embedding_func=EmbeddingFunc(
-        embedding_dim=2048,  # Zhipu embedding-3 dimension
-        max_token_size=8192,
-        func=lambda texts: zhipu_embedding(texts),
-    ),
-)
+    await rag.initialize_storages()
+    await initialize_pipeline_status()
 
-with open("./book.txt", "r", encoding="utf-8") as f:
-    rag.insert(f.read())
+    return rag
 
-# Perform naive search
-print(
-    rag.query("What are the top themes in this story?", param=QueryParam(mode="naive"))
-)
+def main():
+    # Initialize RAG instance
+    rag = asyncio.run(initialize_rag())
 
-# Perform local search
-print(
-    rag.query("What are the top themes in this story?", param=QueryParam(mode="local"))
-)
+    with open("./book.txt", "r", encoding="utf-8") as f:
+        rag.insert(f.read())
 
-# Perform global search
-print(
-    rag.query("What are the top themes in this story?", param=QueryParam(mode="global"))
-)
+    # Perform naive search
+    print(
+        rag.query("What are the top themes in this story?", param=QueryParam(mode="naive"))
+    )
 
-# Perform hybrid search
-print(
-    rag.query("What are the top themes in this story?", param=QueryParam(mode="hybrid"))
-)
+    # 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"))
+    )
+
+if __name__ == "__main__":
+    main()