'commit'

dsLightRag/Test/G2_TeachingStudent.py

@@ -1,13 +1,14 @@
-from Config.GoApiConst import MODEL_GPT35, MODEL_GPT4
-from Util.GoApiUtil import ModelInteractor
 import sys
 
+from Util import LlmUtil
+
+
 def initialize_chat_history():
     """初始化对话历史，包含系统提示"""
     system_prompt = """
     STRICT RULES
 Be an approachable-yet-dynamic teacher,who helps the user learn by guiding
- them through their studies.
+them through their studies.
 
 1.Get to know the user.lf you don't know their goals or grade level,ask the
 user before diving in.(Keep this lightweight!)If they don't answer,aim for
@@ -30,41 +31,18 @@ not alecture.
 Above all:DO NOT DO THE USER'S WORK FOR THEM. Don't answer homework questions - Help the user find the answer,by working 
 with them collaboratively and building from what they already know.
     """
-    return [{
+    return [{"role": "system", "content": system_prompt}]
-        "role": "system",
+
-        "content": system_prompt
-    }]
 
-# 示例使用
 if __name__ == "__main__":
-    # 创建模型交互器实例
-    interactor = ModelInteractor()
-
-    # 使用不同的模型
-    model_name = MODEL_GPT4
-    print(f"使用模型: {model_name}")
-
     # 初始化对话历史
     chat_history = initialize_chat_history()
     
-    # 第一轮问题（可选）
+    # 欢迎消息
-    first_question = "讲解一下勾股定理的证明。"
+    print("教师助手已启动。输入 'exit' 或 '退出' 结束对话。")
-    print(f"\n初始问题: {first_question}")
+    print("你可以开始提问了，例如: '讲解一下勾股定理的证明'")
-    chat_history.append({
-        "role": "user",
-        "content": first_question
-    })
-
-    # 发送第一轮请求
-    print("\n教师助手:")
-    response_content = interactor.stream_request(model_name, chat_history)
-    chat_history.append({
-        "role": "assistant",
-        "content": response_content
-    })
     
     # 多轮对话循环
-    print("\n多轮对话已启动。输入 'exit' 或 '退出' 结束对话。")
     while True:
         # 获取用户输入
         user_input = input("\n你: ")
@@ -75,17 +53,20 @@ if __name__ == "__main__":
             sys.exit(0)
 
         # 添加用户输入到对话历史
-        chat_history.append({
+        chat_history.append({"role": "user", "content": user_input})
-            "role": "user",
-            "content": user_input
-        })
 
-        # 发送请求
+        # 发送请求（传递完整对话历史）
         print("\n教师助手:")
-        response_content = interactor.stream_request(model_name, chat_history)
+        try:
+            # 调用LlmUtil获取响应，传递对话历史
+            response_content = LlmUtil.get_llm_response(user_input, chat_history)
             
-        # 添加助手回复到对话历史
+            # 打印响应
-        chat_history.append({
+            print(response_content)
-            "role": "assistant",
+            
-            "content": response_content
+            # 添加助手回复到对话历史
-        })
+            chat_history.append({"role": "assistant", "content": response_content})
+        except Exception as e:
+            print(f"发生错误: {str(e)}")
+            # 从对话历史中移除最后添加的用户输入，以便用户可以重试
+            chat_history.pop()

dsLightRag/Util/GoApiUtil.py

@@ -3,7 +3,7 @@ import requests
 from Config.Config import GPTNB_API_KEY
 
 class ModelInteractor:
-    def __init__(self, api_key=GPTNB_API_KEY, api_url="https://goapi.gptnb.ai/v1/chat/completions"):
+    def __init__(self, api_key=GPTNB_API_KEY, api_url="https://goapi.gptnb.ai/v1/chat/completions"):  # 修复URL
         self.api_key = api_key
         self.api_url = api_url
         self.headers = {
@@ -45,7 +45,7 @@ class ModelInteractor:
             response = requests.post(
                 self.api_url,
                 headers=self.headers,
-                data=json.dumps(payload),
+                data=json.dumps(payload),  # 确保变量名一致
                 stream=True,
                 timeout=30
             )