Update import and model names in README

This update fixes several critical issues in the Gemini integration example:

1. Corrected import: Changed from gemini_complete to gemini_model_complete 
   (the correct function name per lightrag/llm/gemini.py)

2. Fixed parameter name: Changed 'model' to 'model_name' in gemini_model_complete()
   call to match the function signature

3. Added llm_model_name to LightRAG initialization: This is required for 
   gemini_model_complete to retrieve the model name from hashing_kv.global_config

4. Updated to latest model: gemini-1.5-flash → gemini-2.0-flash

Without these changes, users get "404 NOT_FOUND" errors as the code defaults 
to gpt-4o-mini when model_name is not properly configured.

Tested and verified working with Gemini 2.0 Flash API.

README.md

@@ -718,18 +718,18 @@ If you want to use Google Gemini models, you only need to set up LightRAG as fol
 import os
 import numpy as np
 from lightrag.utils import wrap_embedding_func_with_attrs
-from lightrag.llm.gemini import gemini_complete, gemini_embed
+from lightrag.llm.gemini import gemini_model_complete, gemini_embed
 
 # Configure the generation model
 async def llm_model_func(
     prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs
 ) -> str:
-    return await gemini_complete(
+    return await gemini_model_complete(
         prompt,
         system_prompt=system_prompt,
         history_messages=history_messages,
         api_key=os.getenv("GEMINI_API_KEY"),
-        model="gemini-1.5-flash",
+        model_name="gemini-2.0-flash",
         **kwargs
     )
 
@@ -749,6 +749,7 @@ async def embedding_func(texts: list[str]) -> np.ndarray:
 rag = LightRAG(
     working_dir=WORKING_DIR,
     llm_model_func=llm_model_func,
+    llm_model_name="gemini-2.0-flash",
     embedding_func=embedding_func
 )
 ```