fix demo

README.md

@@ -655,16 +655,19 @@ setup_logger("lightrag", level="INFO")
 
 # Note: Default settings use NetworkX
 # Initialize LightRAG with Neo4J implementation.
-rag = LightRAG(
-    working_dir=WORKING_DIR,
-    llm_model_func=gpt_4o_mini_complete,  # Use gpt_4o_mini_complete LLM model
-    graph_storage="Neo4JStorage", #<-----------override KG default
-)
+async def initialize_rag():
+    rag = LightRAG(
+        working_dir=WORKING_DIR,
+        llm_model_func=gpt_4o_mini_complete,  # Use gpt_4o_mini_complete LLM model
+        graph_storage="Neo4JStorage", #<-----------override KG default
+    )
 
-# Initialize database connections
-await rag.initialize_storages()
-# Initialize pipeline status for document processing
-await initialize_pipeline_status()
+    # Initialize database connections
+    await rag.initialize_storages()
+    # Initialize pipeline status for document processing
+    await initialize_pipeline_status()
+
+    return rag
 ```
 see test_neo4j.py for a working example.
 

examples/lightrag_azure_openai_demo.py

@@ -81,34 +81,46 @@ asyncio.run(test_funcs())
 
 embedding_dimension = 3072
 
-rag = LightRAG(
-    working_dir=WORKING_DIR,
-    llm_model_func=llm_model_func,
-    embedding_func=EmbeddingFunc(
-        embedding_dim=embedding_dimension,
-        max_token_size=8192,
-        func=embedding_func,
-    ),
-)
 
-rag.initialize_storages()
-initialize_pipeline_status()
+async def initialize_rag():
+    rag = LightRAG(
+        working_dir=WORKING_DIR,
+        llm_model_func=llm_model_func,
+        embedding_func=EmbeddingFunc(
+            embedding_dim=embedding_dimension,
+            max_token_size=8192,
+            func=embedding_func,
+        ),
+    )
 
-book1 = open("./book_1.txt", encoding="utf-8")
-book2 = open("./book_2.txt", encoding="utf-8")
+    await rag.initialize_storages()
+    await initialize_pipeline_status()
 
-rag.insert([book1.read(), book2.read()])
+    return rag
 
-query_text = "What are the main themes?"
 
-print("Result (Naive):")
-print(rag.query(query_text, param=QueryParam(mode="naive")))
+def main():
+    rag = asyncio.run(initialize_rag())
 
-print("\nResult (Local):")
-print(rag.query(query_text, param=QueryParam(mode="local")))
+    book1 = open("./book_1.txt", encoding="utf-8")
+    book2 = open("./book_2.txt", encoding="utf-8")
 
-print("\nResult (Global):")
-print(rag.query(query_text, param=QueryParam(mode="global")))
+    rag.insert([book1.read(), book2.read()])
 
-print("\nResult (Hybrid):")
-print(rag.query(query_text, param=QueryParam(mode="hybrid")))
+    query_text = "What are the main themes?"
+
+    print("Result (Naive):")
+    print(rag.query(query_text, param=QueryParam(mode="naive")))
+
+    print("\nResult (Local):")
+    print(rag.query(query_text, param=QueryParam(mode="local")))
+
+    print("\nResult (Global):")
+    print(rag.query(query_text, param=QueryParam(mode="global")))
+
+    print("\nResult (Hybrid):")
+    print(rag.query(query_text, param=QueryParam(mode="hybrid")))
+
+
+if __name__ == "__main__":
+    main()

examples/lightrag_bedrock_demo.py

@@ -53,3 +53,7 @@ def main():
                 "What are the top themes in this story?", param=QueryParam(mode=mode)
             )
         )
+
+
+if __name__ == "__main__":
+    main()

examples/lightrag_nvidia_demo.py

@@ -125,7 +125,7 @@ async def initialize_rag():
 async def main():
     try:
         # Initialize RAG instance
-        rag = asyncio.run(initialize_rag())
+        rag = await initialize_rag()
 
         # reading file
         with open("./book.txt", "r", encoding="utf-8") as f:

examples/lightrag_openai_compatible_demo.py

@@ -77,7 +77,7 @@ async def initialize_rag():
 async def main():
     try:
         # Initialize RAG instance
-        rag = asyncio.run(initialize_rag())
+        rag = await initialize_rag()
 
         with open("./book.txt", "r", encoding="utf-8") as f:
             await rag.ainsert(f.read())

examples/lightrag_openai_compatible_demo_embedding_cache.py

@@ -81,7 +81,7 @@ async def initialize_rag():
 async def main():
     try:
         # Initialize RAG instance
-        rag = asyncio.run(initialize_rag())
+        rag = await initialize_rag()
 
         with open("./book.txt", "r", encoding="utf-8") as f:
             await rag.ainsert(f.read())

examples/lightrag_oracle_demo.py

@@ -107,7 +107,7 @@ async def initialize_rag():
 async def main():
     try:
         # Initialize RAG instance
-        rag = asyncio.run(initialize_rag())
+        rag = await initialize_rag()
 
         # Extract and Insert into LightRAG storage
         with open(WORKING_DIR + "/docs.txt", "r", encoding="utf-8") as f:

examples/lightrag_tidb_demo.py

@@ -87,7 +87,7 @@ async def initialize_rag():
 async def main():
     try:
         # Initialize RAG instance
-        rag = asyncio.run(initialize_rag())
+        rag = await initialize_rag()
 
         with open("./book.txt", "r", encoding="utf-8") as f:
             rag.insert(f.read())

examples/lightrag_zhipu_postgres_demo.py

@@ -59,7 +59,7 @@ async def initialize_rag():
 
 async def main():
     # Initialize RAG instance
-    rag = asyncio.run(initialize_rag())
+    rag = await initialize_rag()
 
     # add embedding_func for graph database, it's deleted in commit 5661d76860436f7bf5aef2e50d9ee4a59660146c
     rag.chunk_entity_relation_graph.embedding_func = rag.embedding_func

examples/query_keyword_separation_example.py

@@ -102,7 +102,7 @@ async def initialize_rag():
 # Example function demonstrating the new query_with_separate_keyword_extraction usage
 async def run_example():
     # Initialize RAG instance
-    rag = asyncio.run(initialize_rag())
+    rag = await initialize_rag()
 
     book1 = open("./book_1.txt", encoding="utf-8")
     book2 = open("./book_2.txt", encoding="utf-8")