fix linting

examples/lightrag_api_ollama_demo.py

@@ -36,7 +36,10 @@ async def init():
         llm_model_name="gemma2:9b",
         llm_model_max_async=4,
         llm_model_max_token_size=8192,
-        llm_model_kwargs={"host": "http://localhost:11434", "options": {"num_ctx": 8192}},
+        llm_model_kwargs={
+            "host": "http://localhost:11434",
+            "options": {"num_ctx": 8192},
+        },
         embedding_func=EmbeddingFunc(
             embedding_dim=768,
             max_token_size=8192,
@@ -64,6 +67,8 @@ async def lifespan(app: FastAPI):
 app = FastAPI(
     title="LightRAG API", description="API for RAG operations", lifespan=lifespan
 )
+
+
 # Data models
 class QueryRequest(BaseModel):
     query: str

examples/lightrag_api_openai_compatible_demo.py

@@ -75,7 +75,7 @@ async def get_embedding_dim():
 # Initialize RAG instance
 async def init():
     embedding_dimension = await get_embedding_dim()
-    
+
     rag = LightRAG(
         working_dir=WORKING_DIR,
         llm_model_func=llm_model_func,
@@ -88,9 +88,10 @@ async def init():
 
     await rag.initialize_storages()
     await initialize_pipeline_status()
-    
+
     return rag
 
+
 @asynccontextmanager
 async def lifespan(app: FastAPI):
     global rag

examples/lightrag_bedrock_demo.py

@@ -21,6 +21,7 @@ WORKING_DIR = "./dickens"
 if not os.path.exists(WORKING_DIR):
     os.mkdir(WORKING_DIR)
 
+
 async def initialize_rag():
     rag = LightRAG(
         working_dir=WORKING_DIR,
@@ -33,9 +34,10 @@ async def initialize_rag():
 
     await rag.initialize_storages()
     await initialize_pipeline_status()
-    
+
     return rag
 
+
 def main():
     rag = asyncio.run(initialize_rag())
 
@@ -47,5 +49,7 @@ def main():
         print(f"| {mode.capitalize()} |")
         print("+-" + "-" * len(mode) + "-+\n")
         print(
-            rag.query("What are the top themes in this story?", param=QueryParam(mode=mode))
+            rag.query(
+                "What are the top themes in this story?", param=QueryParam(mode=mode)
+            )
         )

examples/lightrag_gemini_demo.py

@@ -12,6 +12,7 @@ from lightrag.kg.shared_storage import initialize_pipeline_status
 
 import asyncio
 import nest_asyncio
+
 # Apply nest_asyncio to solve event loop issues
 nest_asyncio.apply()
 
@@ -79,9 +80,10 @@ async def initialize_rag():
 
     await rag.initialize_storages()
     await initialize_pipeline_status()
-    
+
     return rag
 
+
 def main():
     # Initialize RAG instance
     rag = asyncio.run(initialize_rag())
@@ -98,5 +100,6 @@ def main():
 
     print(response)
 
+
 if __name__ == "__main__":
     main()

examples/lightrag_hf_demo.py

@@ -16,6 +16,7 @@ WORKING_DIR = "./dickens"
 if not os.path.exists(WORKING_DIR):
     os.mkdir(WORKING_DIR)
 
+
 async def initialize_rag():
     rag = LightRAG(
         working_dir=WORKING_DIR,
@@ -41,6 +42,7 @@ async def initialize_rag():
 
     return rag
 
+
 def main():
     rag = asyncio.run(initialize_rag())
 
@@ -49,23 +51,32 @@ def main():
 
     # Perform naive search
     print(
-        rag.query("What are the top themes in this story?", param=QueryParam(mode="naive"))
+        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"))
+        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"))
+        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"))
+        rag.query(
+            "What are the top themes in this story?", param=QueryParam(mode="hybrid")
+        )
     )
 
+
 if __name__ == "__main__":
     main()

examples/lightrag_llamaindex_direct_demo.py

@@ -83,7 +83,7 @@ async def get_embedding_dim():
 
 async def initialize_rag():
     embedding_dimension = await get_embedding_dim()
-    
+
     rag = LightRAG(
         working_dir=WORKING_DIR,
         llm_model_func=llm_model_func,
@@ -96,7 +96,7 @@ async def initialize_rag():
 
     await rag.initialize_storages()
     await initialize_pipeline_status()
-    
+
     return rag
 
 
@@ -111,23 +111,32 @@ def main():
     # Test different query modes
     print("\nNaive Search:")
     print(
-        rag.query("What are the top themes in this story?", param=QueryParam(mode="naive"))
+        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"))
+        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"))
+        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"))
+        rag.query(
+            "What are the top themes in this story?", param=QueryParam(mode="hybrid")
+        )
     )
 
+
 if __name__ == "__main__":
     main()

examples/lightrag_llamaindex_litellm_demo.py

@@ -86,7 +86,7 @@ async def get_embedding_dim():
 
 async def initialize_rag():
     embedding_dimension = await get_embedding_dim()
-    
+
     rag = LightRAG(
         working_dir=WORKING_DIR,
         llm_model_func=llm_model_func,
@@ -99,7 +99,7 @@ async def initialize_rag():
 
     await rag.initialize_storages()
     await initialize_pipeline_status()
-    
+
     return rag
 
 
@@ -114,23 +114,32 @@ def main():
     # Test different query modes
     print("\nNaive Search:")
     print(
-        rag.query("What are the top themes in this story?", param=QueryParam(mode="naive"))
+        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"))
+        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"))
+        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"))
+        rag.query(
+            "What are the top themes in this story?", param=QueryParam(mode="hybrid")
+        )
     )
 
+
 if __name__ == "__main__":
     main()

examples/lightrag_lmdeploy_demo.py

@@ -41,6 +41,7 @@ async def lmdeploy_model_complete(
         **kwargs,
     )
 
+
 async def initialize_rag():
     rag = LightRAG(
         working_dir=WORKING_DIR,
@@ -63,9 +64,10 @@ async def initialize_rag():
 
     await rag.initialize_storages()
     await initialize_pipeline_status()
-    
+
     return rag
 
+
 def main():
     # Initialize RAG instance
     rag = asyncio.run(initialize_rag())
@@ -77,23 +79,32 @@ def main():
     # Test different query modes
     print("\nNaive Search:")
     print(
-        rag.query("What are the top themes in this story?", param=QueryParam(mode="naive"))
+        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"))
+        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"))
+        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"))
+        rag.query(
+            "What are the top themes in this story?", param=QueryParam(mode="hybrid")
+        )
     )
 
+
 if __name__ == "__main__":
-    main()
+    main()

examples/lightrag_nvidia_demo.py

@@ -97,6 +97,7 @@ async def test_funcs():
 
 # asyncio.run(test_funcs())
 
+
 async def initialize_rag():
     embedding_dimension = await get_embedding_dim()
     print(f"Detected embedding dimension: {embedding_dimension}")
@@ -117,8 +118,10 @@ async def initialize_rag():
 
     await rag.initialize_storages()
     await initialize_pipeline_status()
-    
+
     return rag
+
+
 async def main():
     try:
         # Initialize RAG instance

examples/lightrag_ollama_age_demo.py

@@ -27,6 +27,7 @@ os.environ["AGE_POSTGRES_HOST"] = "localhost"
 os.environ["AGE_POSTGRES_PORT"] = "5455"
 os.environ["AGE_GRAPH_NAME"] = "dickens"
 
+
 async def initialize_rag():
     rag = LightRAG(
         working_dir=WORKING_DIR,
@@ -34,7 +35,10 @@ async def initialize_rag():
         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}},
+        llm_model_kwargs={
+            "host": "http://localhost:11434",
+            "options": {"num_ctx": 32768},
+        },
         embedding_func=EmbeddingFunc(
             embedding_dim=768,
             max_token_size=8192,
@@ -47,13 +51,15 @@ async def initialize_rag():
 
     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())
@@ -65,22 +71,30 @@ def main():
     # Test different query modes
     print("\nNaive Search:")
     print(
-        rag.query("What are the top themes in this story?", param=QueryParam(mode="naive"))
+        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"))
+        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"))
+        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"))
+        rag.query(
+            "What are the top themes in this story?", param=QueryParam(mode="hybrid")
+        )
     )
 
     # stream response
@@ -94,5 +108,6 @@ def main():
     else:
         print(resp)
 
+
 if __name__ == "__main__":
     main()

examples/lightrag_ollama_demo.py

@@ -17,6 +17,7 @@ logging.basicConfig(format="%(levelname)s:%(message)s", level=logging.INFO)
 if not os.path.exists(WORKING_DIR):
     os.mkdir(WORKING_DIR)
 
+
 async def initialize_rag():
     rag = LightRAG(
         working_dir=WORKING_DIR,
@@ -24,7 +25,10 @@ async def initialize_rag():
         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}},
+        llm_model_kwargs={
+            "host": "http://localhost:11434",
+            "options": {"num_ctx": 32768},
+        },
         embedding_func=EmbeddingFunc(
             embedding_dim=768,
             max_token_size=8192,
@@ -36,13 +40,15 @@ async def initialize_rag():
 
     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())
@@ -54,22 +60,30 @@ def main():
     # Test different query modes
     print("\nNaive Search:")
     print(
-        rag.query("What are the top themes in this story?", param=QueryParam(mode="naive"))
+        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"))
+        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"))
+        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"))
+        rag.query(
+            "What are the top themes in this story?", param=QueryParam(mode="hybrid")
+        )
     )
 
     # stream response
@@ -83,5 +97,6 @@ def main():
     else:
         print(resp)
 
+
 if __name__ == "__main__":
     main()

examples/lightrag_ollama_gremlin_demo.py

@@ -32,6 +32,7 @@ os.environ["GREMLIN_TRAVERSE_SOURCE"] = "g"
 os.environ["GREMLIN_USER"] = ""
 os.environ["GREMLIN_PASSWORD"] = ""
 
+
 async def initialize_rag():
     rag = LightRAG(
         working_dir=WORKING_DIR,
@@ -39,7 +40,10 @@ async def initialize_rag():
         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}},
+        llm_model_kwargs={
+            "host": "http://localhost:11434",
+            "options": {"num_ctx": 32768},
+        },
         embedding_func=EmbeddingFunc(
             embedding_dim=768,
             max_token_size=8192,
@@ -52,13 +56,15 @@ async def initialize_rag():
 
     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())
@@ -70,22 +76,30 @@ def main():
     # Test different query modes
     print("\nNaive Search:")
     print(
-        rag.query("What are the top themes in this story?", param=QueryParam(mode="naive"))
+        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"))
+        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"))
+        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"))
+        rag.query(
+            "What are the top themes in this story?", param=QueryParam(mode="hybrid")
+        )
     )
 
     # stream response
@@ -99,5 +113,6 @@ def main():
     else:
         print(resp)
 
+
 if __name__ == "__main__":
     main()

examples/lightrag_ollama_neo4j_milvus_mongo_demo.py

@@ -32,6 +32,7 @@ os.environ["MILVUS_USER"] = "root"
 os.environ["MILVUS_PASSWORD"] = "root"
 os.environ["MILVUS_DB_NAME"] = "lightrag"
 
+
 async def initialize_rag():
     rag = LightRAG(
         working_dir=WORKING_DIR,
@@ -39,7 +40,10 @@ async def initialize_rag():
         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}},
+        llm_model_kwargs={
+            "host": "http://127.0.0.1:11434",
+            "options": {"num_ctx": 32768},
+        },
         embedding_func=EmbeddingFunc(
             embedding_dim=1024,
             max_token_size=8192,
@@ -54,9 +58,10 @@ async def initialize_rag():
 
     await rag.initialize_storages()
     await initialize_pipeline_status()
-    
+
     return rag
 
+
 def main():
     # Initialize RAG instance
     rag = asyncio.run(initialize_rag())
@@ -68,23 +73,32 @@ def main():
     # Test different query modes
     print("\nNaive Search:")
     print(
-        rag.query("What are the top themes in this story?", param=QueryParam(mode="naive"))
+        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"))
+        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"))
+        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"))
+        rag.query(
+            "What are the top themes in this story?", param=QueryParam(mode="hybrid")
+        )
     )
 
+
 if __name__ == "__main__":
     main()

examples/lightrag_openai_compatible_demo.py

@@ -53,6 +53,7 @@ async def test_funcs():
 
 # asyncio.run(test_funcs())
 
+
 async def initialize_rag():
     embedding_dimension = await get_embedding_dim()
     print(f"Detected embedding dimension: {embedding_dimension}")
@@ -71,6 +72,8 @@ async def initialize_rag():
     await initialize_pipeline_status()
 
     return rag
+
+
 async def main():
     try:
         # Initialize RAG instance

examples/lightrag_openai_compatible_demo_embedding_cache.py

@@ -53,6 +53,7 @@ async def test_funcs():
 
 # asyncio.run(test_funcs())
 
+
 async def initialize_rag():
     embedding_dimension = await get_embedding_dim()
     print(f"Detected embedding dimension: {embedding_dimension}")
@@ -76,6 +77,7 @@ async def initialize_rag():
 
     return rag
 
+
 async def main():
     try:
         # Initialize RAG instance

examples/lightrag_openai_compatible_stream_demo.py

@@ -15,6 +15,8 @@ if not os.path.exists(WORKING_DIR):
 print(f"WorkingDir: {WORKING_DIR}")
 
 api_key = "empty"
+
+
 async def initialize_rag():
     rag = LightRAG(
         working_dir=WORKING_DIR,
@@ -40,11 +42,13 @@ async def initialize_rag():
 
     return rag
 
+
 async def print_stream(stream):
     async for chunk in stream:
         if chunk:
             print(chunk, end="", flush=True)
 
+
 def main():
     # Initialize RAG instance
     rag = asyncio.run(initialize_rag())
@@ -63,6 +67,6 @@ def main():
     else:
         print(resp)
 
+
 if __name__ == "__main__":
     main()
-

examples/lightrag_openai_demo.py

@@ -9,6 +9,7 @@ WORKING_DIR = "./dickens"
 if not os.path.exists(WORKING_DIR):
     os.mkdir(WORKING_DIR)
 
+
 async def initialize_rag():
     rag = LightRAG(
         working_dir=WORKING_DIR,
@@ -22,6 +23,7 @@ async def initialize_rag():
 
     return rag
 
+
 def main():
     # Initialize RAG instance
     rag = asyncio.run(initialize_rag())
@@ -31,24 +33,32 @@ def main():
 
     # Perform naive search
     print(
-        rag.query("What are the top themes in this story?", param=QueryParam(mode="naive"))
+        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"))
+        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"))
+        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"))
+        rag.query(
+            "What are the top themes in this story?", param=QueryParam(mode="hybrid")
+        )
     )
 
+
 if __name__ == "__main__":
     main()
-

examples/lightrag_openai_mongodb_graph_demo.py

@@ -76,23 +76,32 @@ def main():
 
     # Perform naive search
     print(
-        rag.query("What are the top themes in this story?", param=QueryParam(mode="naive"))
+        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"))
+        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"))
+        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"))
+        rag.query(
+            "What are the top themes in this story?", param=QueryParam(mode="hybrid")
+        )
     )
 
+
 if __name__ == "__main__":
-    main()
+    main()

examples/lightrag_openai_neo4j_milvus_redis_demo.py

@@ -50,6 +50,8 @@ embedding_func = EmbeddingFunc(
         texts, embed_model="shaw/dmeta-embedding-zh", host="http://117.50.173.35:11434"
     ),
 )
+
+
 async def initialize_rag():
     rag = LightRAG(
         working_dir=WORKING_DIR,
@@ -79,23 +81,32 @@ def main():
 
     # Perform naive search
     print(
-        rag.query("What are the top themes in this story?", param=QueryParam(mode="naive"))
+        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"))
+        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"))
+        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"))
+        rag.query(
+            "What are the top themes in this story?", param=QueryParam(mode="hybrid")
+        )
     )
 
+
 if __name__ == "__main__":
     main()

examples/lightrag_oracle_demo.py

@@ -64,6 +64,7 @@ async def get_embedding_dim():
     embedding_dim = embedding.shape[1]
     return embedding_dim
 
+
 async def initialize_rag():
     # Detect embedding dimension
     embedding_dimension = await get_embedding_dim()
@@ -102,6 +103,7 @@ async def initialize_rag():
 
     return rag
 
+
 async def main():
     try:
         # Initialize RAG instance

examples/lightrag_siliconcloud_demo.py

@@ -47,6 +47,7 @@ async def test_funcs():
 
 asyncio.run(test_funcs())
 
+
 async def initialize_rag():
     rag = LightRAG(
         working_dir=WORKING_DIR,
@@ -71,24 +72,32 @@ def main():
 
     # Perform naive search
     print(
-        rag.query("What are the top themes in this story?", param=QueryParam(mode="naive"))
+        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"))
+        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"))
+        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"))
+        rag.query(
+            "What are the top themes in this story?", param=QueryParam(mode="hybrid")
+        )
     )
 
+
 if __name__ == "__main__":
     main()
-

examples/lightrag_tidb_demo.py

@@ -55,6 +55,7 @@ async def get_embedding_dim():
     embedding_dim = embedding.shape[1]
     return embedding_dim
 
+
 async def initialize_rag():
     # Detect embedding dimension
     embedding_dimension = await get_embedding_dim()
@@ -82,6 +83,7 @@ async def initialize_rag():
 
     return rag
 
+
 async def main():
     try:
         # Initialize RAG instance

examples/lightrag_zhipu_demo.py

@@ -19,6 +19,7 @@ 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,
@@ -38,6 +39,7 @@ async def initialize_rag():
 
     return rag
 
+
 def main():
     # Initialize RAG instance
     rag = asyncio.run(initialize_rag())
@@ -47,23 +49,32 @@ def main():
 
     # Perform naive search
     print(
-        rag.query("What are the top themes in this story?", param=QueryParam(mode="naive"))
+        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"))
+        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"))
+        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"))
+        rag.query(
+            "What are the top themes in this story?", param=QueryParam(mode="hybrid")
+        )
     )
 
+
 if __name__ == "__main__":
     main()

examples/lightrag_zhipu_postgres_demo.py

@@ -28,6 +28,7 @@ os.environ["POSTGRES_USER"] = "rag"
 os.environ["POSTGRES_PASSWORD"] = "rag"
 os.environ["POSTGRES_DATABASE"] = "rag"
 
+
 async def initialize_rag():
     rag = LightRAG(
         working_dir=WORKING_DIR,
@@ -55,8 +56,9 @@ async def initialize_rag():
 
     return rag
 
+
 async def main():
-        # Initialize RAG instance
+    # Initialize RAG instance
     rag = asyncio.run(initialize_rag())
 
     # add embedding_func for graph database, it's deleted in commit 5661d76860436f7bf5aef2e50d9ee4a59660146c

examples/query_keyword_separation_example.py

@@ -80,6 +80,8 @@ async def test_funcs():
 asyncio.run(test_funcs())
 
 embedding_dimension = 3072
+
+
 async def initialize_rag():
     rag = LightRAG(
         working_dir=WORKING_DIR,
@@ -101,7 +103,7 @@ async def initialize_rag():
 async def run_example():
     # Initialize RAG instance
     rag = asyncio.run(initialize_rag())
-    
+
     book1 = open("./book_1.txt", encoding="utf-8")
     book2 = open("./book_2.txt", encoding="utf-8")
 

examples/test.py

@@ -1,4 +1,5 @@
 import os
+import asyncio
 from lightrag import LightRAG, QueryParam
 from lightrag.llm.openai import gpt_4o_mini_complete
 from lightrag.kg.shared_storage import initialize_pipeline_status
@@ -13,6 +14,7 @@ WORKING_DIR = "./dickens"
 if not os.path.exists(WORKING_DIR):
     os.mkdir(WORKING_DIR)
 
+
 async def initialize_rag():
     rag = LightRAG(
         working_dir=WORKING_DIR,
@@ -35,23 +37,32 @@ def main():
 
     # Perform naive search
     print(
-        rag.query("What are the top themes in this story?", param=QueryParam(mode="naive"))
+        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"))
+        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"))
+        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"))
+        rag.query(
+            "What are the top themes in this story?", param=QueryParam(mode="hybrid")
+        )
     )
 
+
 if __name__ == "__main__":
-    main()
+    main()

examples/test_chromadb.py

@@ -112,12 +112,13 @@ async def initialize_rag():
             },
         )
 
-
     await rag.initialize_storages()
     await initialize_pipeline_status()
 
     return rag
 
+
+def main():
     # Initialize RAG instance
     rag = asyncio.run(initialize_rag())
 
@@ -126,23 +127,32 @@ async def initialize_rag():
 
     # Perform naive search
     print(
-        rag.query("What are the top themes in this story?", param=QueryParam(mode="naive"))
+        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"))
+        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"))
+        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"))
+        rag.query(
+            "What are the top themes in this story?", param=QueryParam(mode="hybrid")
+        )
     )
 
+
 if __name__ == "__main__":
     main()

examples/test_faiss.py

@@ -58,6 +58,7 @@ async def embedding_func(texts: list[str]) -> np.ndarray:
     embeddings = model.encode(texts, convert_to_numpy=True)
     return embeddings
 
+
 async def initialize_rag():
     rag = LightRAG(
         working_dir=WORKING_DIR,
@@ -78,8 +79,8 @@ async def initialize_rag():
 
     return rag
 
+
 def main():
-    
     # Initialize RAG instance
     rag = asyncio.run(initialize_rag())
     # Insert the custom chunks into LightRAG

examples/test_neo4j.py

@@ -15,6 +15,7 @@ WORKING_DIR = "./local_neo4jWorkDir"
 if not os.path.exists(WORKING_DIR):
     os.mkdir(WORKING_DIR)
 
+
 async def initialize_rag():
     rag = LightRAG(
         working_dir=WORKING_DIR,
@@ -29,6 +30,7 @@ async def initialize_rag():
 
     return rag
 
+
 def main():
     # Initialize RAG instance
     rag = asyncio.run(initialize_rag())
@@ -38,23 +40,32 @@ def main():
 
     # Perform naive search
     print(
-        rag.query("What are the top themes in this story?", param=QueryParam(mode="naive"))
+        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"))
+        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"))
+        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"))
+        rag.query(
+            "What are the top themes in this story?", param=QueryParam(mode="hybrid")
+        )
     )
 
+
 if __name__ == "__main__":
     main()

examples/vram_management_demo.py

@@ -14,6 +14,7 @@ TEXT_FILES_DIR = "/llm/mt"
 if not os.path.exists(WORKING_DIR):
     os.mkdir(WORKING_DIR)
 
+
 async def initialize_rag():
     # Initialize LightRAG
     rag = LightRAG(
@@ -31,6 +32,7 @@ async def initialize_rag():
 
     return rag
 
+
 # Read all .txt files from the TEXT_FILES_DIR directory
 texts = []
 for filename in os.listdir(TEXT_FILES_DIR):
@@ -82,7 +84,8 @@ def main():
     try:
         print(
             rag.query(
-                "What are the top themes in this story?", param=QueryParam(mode="global")
+                "What are the top themes in this story?",
+                param=QueryParam(mode="global"),
             )
         )
     except Exception as e:
@@ -91,18 +94,17 @@ def main():
     try:
         print(
             rag.query(
-                "What are the top themes in this story?", param=QueryParam(mode="hybrid")
+                "What are the top themes in this story?",
+                param=QueryParam(mode="hybrid"),
             )
         )
     except Exception as e:
         print(f"Error performing hybrid search: {e}")
 
-
     # Function to clear VRAM resources
     def clear_vram():
         os.system("sudo nvidia-smi --gpu-reset")
 
-
     # Regularly clear VRAM to prevent overflow
     clear_vram_interval = 3600  # Clear once every hour
     start_time = time.time()
@@ -114,5 +116,6 @@ def main():
             start_time = current_time
         time.sleep(60)  # Check the time every minute
 
+
 if __name__ == "__main__":
     main()

reproduce/Step_1.py

@@ -31,6 +31,7 @@ WORKING_DIR = f"../{cls}"
 if not os.path.exists(WORKING_DIR):
     os.mkdir(WORKING_DIR)
 
+
 async def initialize_rag():
     rag = LightRAG(working_dir=WORKING_DIR)
 
@@ -39,6 +40,7 @@ async def initialize_rag():
 
     return rag
 
+
 def main():
     # Initialize RAG instance
     rag = asyncio.run(initialize_rag())

reproduce/Step_1_openai_compatible.py

@@ -62,6 +62,7 @@ WORKING_DIR = f"../{cls}"
 if not os.path.exists(WORKING_DIR):
     os.mkdir(WORKING_DIR)
 
+
 async def initialize_rag():
     rag = LightRAG(
         working_dir=WORKING_DIR,
@@ -76,6 +77,7 @@ async def initialize_rag():
 
     return rag
 
+
 def main():
     # Initialize RAG instance
     rag = asyncio.run(initialize_rag())