fix demo

examples/lightrag_ollama_gremlin_demo.py

@@ -12,6 +12,7 @@ import os
 from lightrag import LightRAG, QueryParam
 from lightrag.llm.ollama import ollama_embed, ollama_model_complete
 from lightrag.utils import EmbeddingFunc
+from lightrag.kg.shared_storage import initialize_pipeline_status
 
 WORKING_DIR = "./dickens_gremlin"
 
@@ -31,59 +32,72 @@ os.environ["GREMLIN_TRAVERSE_SOURCE"] = "g"
 os.environ["GREMLIN_USER"] = ""
 os.environ["GREMLIN_PASSWORD"] = ""
 
-rag = LightRAG(
-    working_dir=WORKING_DIR,
-    llm_model_func=ollama_model_complete,
-    llm_model_name="llama3.1:8b",
-    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_embed(
-            texts, embed_model="nomic-embed-text", host="http://localhost:11434"
+async def initialize_rag():
+    rag = LightRAG(
+        working_dir=WORKING_DIR,
+        llm_model_func=ollama_model_complete,
+        llm_model_name="llama3.1:8b",
+        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_embed(
+                texts, embed_model="nomic-embed-text", host="http://localhost:11434"
+            ),
         ),
-    ),
-    graph_storage="GremlinStorage",
-)
-
-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),
-)
+        graph_storage="GremlinStorage",
+    )
 
+    await rag.initialize_storages()
+    await initialize_pipeline_status()
+    
+    return rag
 
 async def print_stream(stream):
     async for chunk in stream:
         print(chunk, end="", flush=True)
 
+def main():
+    # Initialize RAG instance
+    rag = asyncio.run(initialize_rag())
 
-if inspect.isasyncgen(resp):
-    asyncio.run(print_stream(resp))
-else:
-    print(resp)
+    # 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"))
+    )
+
+    # stream response
+    resp = rag.query(
+        "What are the top themes in this story?",
+        param=QueryParam(mode="hybrid", stream=True),
+    )
+
+    if inspect.isasyncgen(resp):
+        asyncio.run(print_stream(resp))
+    else:
+        print(resp)
+
+if __name__ == "__main__":
+    main()