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

reproduce/Step_1_openai_compatible.py

@@ -7,6 +7,7 @@ from lightrag import LightRAG
 from lightrag.utils import EmbeddingFunc
 from lightrag.llm import openai_complete_if_cache, openai_embedding
 
+
 ## For Upstage API
 # please check if embedding_dim=4096 in lightrag.py and llm.py in lightrag direcotry
 async def llm_model_func(
@@ -19,22 +20,26 @@ 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",
     )
+
+
 ## /For Upstage API
 
+
 def insert_text(rag, file_path):
-    with open(file_path, mode='r') as f:
+    with open(file_path, mode="r") as f:
         unique_contexts = json.load(f)
-    
+
     retries = 0
     max_retries = 3
     while retries < max_retries:
@@ -48,19 +53,19 @@ def insert_text(rag, file_path):
     if retries == max_retries:
         print("Insertion failed after exceeding the maximum number of retries")
 
+
 cls = "mix"
 WORKING_DIR = f"../{cls}"
 
 if not os.path.exists(WORKING_DIR):
     os.mkdir(WORKING_DIR)
 
-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
-            )
-        )
+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
+    ),
+)
 
 insert_text(rag, f"../datasets/unique_contexts/{cls}_unique_contexts.json")