Refactor code formatting in lightrag_api_openai_compatible_demo.py

lightrag/lightrag.py

@@ -85,9 +85,7 @@ class LightRAG:
 
     # LLM
     llm_model_func: callable = gpt_4o_mini_complete  # hf_model_complete#
-    llm_model_name: str = (
-        "meta-llama/Llama-3.2-1B-Instruct"  #'meta-llama/Llama-3.2-1B'#'google/gemma-2-2b-it'
-    )
+    llm_model_name: str = "meta-llama/Llama-3.2-1B-Instruct"  #'meta-llama/Llama-3.2-1B'#'google/gemma-2-2b-it'
     llm_model_max_token_size: int = 32768
     llm_model_max_async: int = 16
 

lightrag/llm.py

@@ -286,7 +286,9 @@ async def hf_model_if_cache(
     output = hf_model.generate(
         **input_ids, max_new_tokens=512, num_return_sequences=1, early_stopping=True
     )
-    response_text = hf_tokenizer.decode(output[0][len(inputs["input_ids"][0]):], skip_special_tokens=True)
+    response_text = hf_tokenizer.decode(
+        output[0][len(inputs["input_ids"][0]) :], skip_special_tokens=True
+    )
     if hashing_kv is not None:
         await hashing_kv.upsert({args_hash: {"return": response_text, "model": model}})
     return response_text