chore: added pre-commit-hooks and ruff formatting for commit-hooks

examples/lightrag_openai_compatible_demo.py

@@ -6,10 +6,11 @@ from lightrag.utils import EmbeddingFunc
 import numpy as np
 
 WORKING_DIR = "./dickens"
-    
+
 if not os.path.exists(WORKING_DIR):
     os.mkdir(WORKING_DIR)
 
+
 async def llm_model_func(
     prompt, system_prompt=None, history_messages=[], **kwargs
 ) -> str:
@@ -20,17 +21,19 @@ async def llm_model_func(
         history_messages=history_messages,
         api_key=os.getenv("UPSTAGE_API_KEY"),
         base_url="https://api.upstage.ai/v1/solar",
-        **kwargs
+        **kwargs,
     )
 
+
 async def embedding_func(texts: list[str]) -> np.ndarray:
     return await openai_embedding(
         texts,
         model="solar-embedding-1-large-query",
         api_key=os.getenv("UPSTAGE_API_KEY"),
-        base_url="https://api.upstage.ai/v1/solar"
+        base_url="https://api.upstage.ai/v1/solar",
     )
 
+
 # function test
 async def test_funcs():
     result = await llm_model_func("How are you?")
@@ -39,6 +42,7 @@ async def test_funcs():
     result = await embedding_func(["How are you?"])
     print("embedding_func: ", result)
 
+
 asyncio.run(test_funcs())
 
 
@@ -46,10 +50,8 @@ rag = LightRAG(
     working_dir=WORKING_DIR,
     llm_model_func=llm_model_func,
     embedding_func=EmbeddingFunc(
-        embedding_dim=4096,
-        max_token_size=8192,
-        func=embedding_func
-    )
+        embedding_dim=4096, max_token_size=8192, func=embedding_func
+    ),
 )
 
 
@@ -57,13 +59,21 @@ with open("./book.txt") as f:
     rag.insert(f.read())
 
 # Perform naive search
-print(rag.query("What are the top themes in this story?", param=QueryParam(mode="naive")))
+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")))
+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")))
+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")))
+print(
+    rag.query("What are the top themes in this story?", param=QueryParam(mode="hybrid"))
+)