Separated llms from the main llm.py file and fixed some deprication bugs

lightrag/lightrag.py

@@ -6,10 +6,6 @@ from datetime import datetime
 from functools import partial
 from typing import Type, cast, Dict
 
-from .llm import (
-    gpt_4o_mini_complete,
-    openai_embedding,
-)
 from .operate import (
     chunking_by_token_size,
     extract_entities,
@@ -154,12 +150,12 @@ class LightRAG:
     )
 
     # embedding_func: EmbeddingFunc = field(default_factory=lambda:hf_embedding)
-    embedding_func: EmbeddingFunc = field(default_factory=lambda: openai_embedding)
+    embedding_func: EmbeddingFunc = None  # This must be set (we do want to separate llm from the corte, so no more default initialization)
     embedding_batch_num: int = 32
     embedding_func_max_async: int = 16
 
     # LLM
-    llm_model_func: callable = gpt_4o_mini_complete  # hf_model_complete#
+    llm_model_func: callable = None  # This must be set (we do want to separate llm from the corte, so no more default initialization)
     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