'commit'

dsLightRag/Test/G2_TeachingStudent.py

@@ -1,18 +1,13 @@
 from Config.GoApiConst import MODEL_GPT35, MODEL_GPT4
 from Util.GoApiUtil import ModelInteractor
+import sys
 
-# 示例使用
+def initialize_chat_history():
-if __name__ == "__main__":
+    """初始化对话历史，包含系统提示"""
-    # 创建模型交互器实例
+    system_prompt = """
-    interactor = ModelInteractor()
-
-    # 使用不同的模型和提示词
-    model_name = MODEL_GPT4
-
-    firstPrompt = """
     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
@@ -35,8 +30,62 @@ 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 [{
+        "role": "system",
+        "content": system_prompt
+    }]
 
-    prompt_text = firstPrompt + "下面是问题：讲解一下勾股定理的证明。"
+# 示例使用
+if __name__ == "__main__":
+    # 创建模型交互器实例
+    interactor = ModelInteractor()
 
-    # 发送流式请求
+    # 使用不同的模型
-    interactor.stream_request(model_name, prompt_text)
+    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）
+    first_question = "讲解一下勾股定理的证明。"
+    print(f"\n初始问题: {first_question}")
+    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: ")
+
+        # 检查是否退出
+        if user_input.lower() in ['exit', '退出']:
+            print("对话已结束。")
+            sys.exit(0)
+
+        # 添加用户输入到对话历史
+        chat_history.append({
+            "role": "user",
+            "content": user_input
+        })
+
+        # 发送请求
+        print("\n教师助手:")
+        response_content = interactor.stream_request(model_name, chat_history)
+
+        # 添加助手回复到对话历史
+        chat_history.append({
+            "role": "assistant",
+            "content": response_content
+        })