update insert custom kg

lightrag/llm.py

@@ -502,11 +502,12 @@ async def gpt_4o_mini_complete(
         **kwargs,
     )
 
+
 async def nvidia_openai_complete(
     prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs
 ) -> str:
     result = await openai_complete_if_cache(
-        "nvidia/llama-3.1-nemotron-70b-instruct", #context length 128k
+        "nvidia/llama-3.1-nemotron-70b-instruct",  # context length 128k
         prompt,
         system_prompt=system_prompt,
         history_messages=history_messages,
@@ -517,6 +518,7 @@ async def nvidia_openai_complete(
         return locate_json_string_body_from_string(result)
     return result
 
+
 async def azure_openai_complete(
     prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs
 ) -> str:
@@ -610,12 +612,12 @@ async def openai_embedding(
 )
 async def nvidia_openai_embedding(
     texts: list[str],
-    model: str = "nvidia/llama-3.2-nv-embedqa-1b-v1", #refer to https://build.nvidia.com/nim?filters=usecase%3Ausecase_text_to_embedding
+    model: str = "nvidia/llama-3.2-nv-embedqa-1b-v1",  # refer to https://build.nvidia.com/nim?filters=usecase%3Ausecase_text_to_embedding
     base_url: str = "https://integrate.api.nvidia.com/v1",
     api_key: str = None,
-    input_type: str = "passage", #query for retrieval, passage for embedding
-    trunc: str = "NONE", #NONE or START or END
-    encode: str = "float" #float or base64
+    input_type: str = "passage",  # query for retrieval, passage for embedding
+    trunc: str = "NONE",  # NONE or START or END
+    encode: str = "float",  # float or base64
 ) -> np.ndarray:
     if api_key:
         os.environ["OPENAI_API_KEY"] = api_key
@@ -624,10 +626,14 @@ async def nvidia_openai_embedding(
         AsyncOpenAI() if base_url is None else AsyncOpenAI(base_url=base_url)
     )
     response = await openai_async_client.embeddings.create(
-        model=model, input=texts, encoding_format=encode, extra_body={"input_type": input_type, "truncate": trunc}
+        model=model,
+        input=texts,
+        encoding_format=encode,
+        extra_body={"input_type": input_type, "truncate": trunc},
     )
     return np.array([dp.embedding for dp in response.data])
 
+
 @wrap_embedding_func_with_attrs(embedding_dim=1536, max_token_size=8191)
 @retry(
     stop=stop_after_attempt(3),