'commit'

dsRagAnything/Start.py

@@ -62,7 +62,7 @@ async def rag(request: fastapi.Request):
 
         try:
             # 初始化RAG组件
-            llm_model_func = create_llm_model_func(v_history_messages=[])
+            llm_model_func = create_llm_model_func(history_messages=[])
             vision_model_func = create_vision_model_func(llm_model_func)
             embedding_func = create_embedding_func()
 

dsRagAnything/T1_Train.py

@@ -45,7 +45,7 @@ async def main():
         enable_equation_processing=True,  # 处理公式
     )
     # 自定义的大模型函数
-    llm_model_func = create_llm_model_func(v_history_messages=[])
+    llm_model_func = create_llm_model_func(history_messages=[])
     # 自定义的可视模型函数
     vision_model_func = create_vision_model_func(llm_model_func)
     # 自定义的嵌入函数

dsRagAnything/T2_Query.py

@@ -12,7 +12,7 @@ async def load_existing_lightrag():
     WORKING_DIR = "./Topic/DongHua"
 
     # 创建 LLM 模型自定义函数
-    llm_model_func = create_llm_model_func(v_history_messages=[])
+    llm_model_func = create_llm_model_func(history_messages=[])
     # 创建可视模型自定义函数
     vision_model_func = create_vision_model_func(llm_model_func)
     # 创建嵌入模型自定义函数

dsRagAnything/Util/RagUtil.py

@@ -4,13 +4,13 @@ from lightrag.utils import EmbeddingFunc
 from Config.Config import *
 
 
-def create_llm_model_func(v_history_messages):
-    def llm_model_func(prompt, system_prompt=None, history_messages=[], **kwargs):
+def create_llm_model_func(history_messages):
+    def llm_model_func(prompt, system_prompt=None, **kwargs):
         return openai_complete_if_cache(
             LLM_MODEL_NAME,
             prompt,
             system_prompt=system_prompt,
-            history_messages=v_history_messages,
+            history_messages=history_messages,
             api_key=LLM_API_KEY,
             base_url=LLM_BASE_URL,
             **kwargs,