fix demo

examples/lightrag_ollama_neo4j_milvus_mongo_demo.py

@@ -2,6 +2,11 @@ import os
 from lightrag import LightRAG, QueryParam
 from lightrag.llm.ollama import ollama_model_complete, ollama_embed
 from lightrag.utils import EmbeddingFunc
+import asyncio
+import nest_asyncio
+
+nest_asyncio.apply()
+from lightrag.kg.shared_storage import initialize_pipeline_status
 
 # WorkingDir
 ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
@@ -27,30 +32,59 @@ os.environ["MILVUS_USER"] = "root"
 os.environ["MILVUS_PASSWORD"] = "root"
 os.environ["MILVUS_DB_NAME"] = "lightrag"
 
-
-rag = LightRAG(
-    working_dir=WORKING_DIR,
-    llm_model_func=ollama_model_complete,
-    llm_model_name="qwen2.5:14b",
-    llm_model_max_async=4,
-    llm_model_max_token_size=32768,
-    llm_model_kwargs={"host": "http://127.0.0.1:11434", "options": {"num_ctx": 32768}},
-    embedding_func=EmbeddingFunc(
-        embedding_dim=1024,
-        max_token_size=8192,
-        func=lambda texts: ollama_embed(
-            texts=texts, embed_model="bge-m3:latest", host="http://127.0.0.1:11434"
+async def initialize_rag():
+    rag = LightRAG(
+        working_dir=WORKING_DIR,
+        llm_model_func=ollama_model_complete,
+        llm_model_name="qwen2.5:14b",
+        llm_model_max_async=4,
+        llm_model_max_token_size=32768,
+        llm_model_kwargs={"host": "http://127.0.0.1:11434", "options": {"num_ctx": 32768}},
+        embedding_func=EmbeddingFunc(
+            embedding_dim=1024,
+            max_token_size=8192,
+            func=lambda texts: ollama_embed(
+                texts=texts, embed_model="bge-m3:latest", host="http://127.0.0.1:11434"
+            ),
         ),
-    ),
-    kv_storage="MongoKVStorage",
-    graph_storage="Neo4JStorage",
-    vector_storage="MilvusVectorDBStorage",
-)
+        kv_storage="MongoKVStorage",
+        graph_storage="Neo4JStorage",
+        vector_storage="MilvusVectorDBStorage",
+    )
 
-file = "./book.txt"
-with open(file, "r") as f:
-    rag.insert(f.read())
+    await rag.initialize_storages()
+    await initialize_pipeline_status()
+    
+    return rag
 
-print(
-    rag.query("What are the top themes in this story?", param=QueryParam(mode="hybrid"))
-)
+def main():
+    # Initialize RAG instance
+    rag = asyncio.run(initialize_rag())
+
+    # Insert example text
+    with open("./book.txt", "r", encoding="utf-8") as f:
+        rag.insert(f.read())
+
+    # Test different query modes
+    print("\nNaive Search:")
+    print(
+        rag.query("What are the top themes in this story?", param=QueryParam(mode="naive"))
+    )
+
+    print("\nLocal Search:")
+    print(
+        rag.query("What are the top themes in this story?", param=QueryParam(mode="local"))
+    )
+
+    print("\nGlobal Search:")
+    print(
+        rag.query("What are the top themes in this story?", param=QueryParam(mode="global"))
+    )
+
+    print("\nHybrid Search:")
+    print(
+        rag.query("What are the top themes in this story?", param=QueryParam(mode="hybrid"))
+    )
+
+if __name__ == "__main__":
+    main()