Create orchestrator.py

orchestrator.py

@@ -0,0 +1,208 @@
+import json
+import logging
+from enum import Enum, auto
+from llm_client import LLMClient
+from prompts import Prompts
+
+# ==============================================================================
+# --- 日志系统配置 (双日志系统) ---
+# ==============================================================================
+
+# 1. 调试日志 (orchestrator.log) - 记录所有技术细节
+#    用于开发者调试，包含LLM返回的原始信息、错误堆栈等。
+debug_logger = logging.getLogger('orchestrator_logger')
+debug_logger.setLevel(logging.INFO)
+if not debug_logger.handlers:
+    # 使用 mode='a' 来追加日志，而不是覆盖
+    file_handler = logging.FileHandler('orchestrator.log', mode='a', encoding='utf-8')
+    formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+    file_handler.setFormatter(formatter)
+    debug_logger.addHandler(file_handler)
+    # 阻止日志向上传播，避免在控制台输出
+    debug_logger.propagate = False
+    # 在每次程序启动时写入一个分隔符，方便区分不同的运行会话
+    debug_logger.info("\n" + "="*20 + " APPLICATION STARTED " + "="*20)
+
+
+# 2. 演示日志 (demo_show.log) - 只记录用户输入和状态变化
+#    用于展示和快速回顾对话流程。
+demo_logger = logging.getLogger('demo_logger')
+demo_logger.setLevel(logging.INFO)
+if not demo_logger.handlers:
+    # 使用 mode='a' 来追加日志
+    demo_file_handler = logging.FileHandler('demo_show.log', mode='a', encoding='utf-8')
+    # 使用更简洁的格式，只包含时间戳和消息
+    demo_formatter = logging.Formatter('%(asctime)s - %(message)s', datefmt='%Y-%m-%d %H:%M:%S')
+    demo_file_handler.setFormatter(demo_formatter)
+    demo_logger.addHandler(demo_file_handler)
+    # 同样阻止日志向上传播
+    demo_logger.propagate = False
+    # 在演示日志中也添加会话启动分隔符
+    demo_logger.info("\n" + "="*20 + " NEW DEMO SESSION STARTED " + "="*20)
+
+
+# ==============================================================================
+# --- 核心代码 ---
+# ==============================================================================
+
+class DialogueState(Enum):
+    """对话状态枚举"""
+    REQUIREMENT_ELICITATION = auto()  # 需求梳理
+    AI_MODELING = auto()              # AI建模
+
+class Orchestrator:
+    """
+    对话编排器，负责管理对话流程、状态和调用判别器。
+    """
+    def __init__(self, model="gpt-4-turbo"):
+        self.llm_client = LLMClient(model=model)
+        self.conversation_history = []
+        self.state = DialogueState.REQUIREMENT_ELICITATION
+        self.prompts = Prompts()
+        
+        # 记录初始化信息到各自的日志
+        debug_logger.info(f"Orchestrator initialized. Initial state: {self.state.name}")
+        demo_logger.info(f"Initial State: {self.state.name}")
+
+    def _format_history_for_prompt(self) -> str:
+        """将对话历史格式化为字符串"""
+        if not self.conversation_history:
+            return "对话尚未开始。"
+        return "\n".join([f"{msg['role']}: {msg['content']}" for msg in self.conversation_history])
+
+    def _check_information_sufficiency(self) -> bool:
+        """
+        【判别器部分-隐式触发】
+        使用大模型判断对话历史中信息是否足够建模。
+        """
+        if len(self.conversation_history) < 2:
+            return False
+
+        history_str = self._format_history_for_prompt()
+        prompt = self.prompts.INFORMATION_SUFFICIENCY_CHECK.format(conversation_history=history_str)
+
+        try:
+            response_str = self.llm_client.chat(messages=[{"role": "user", "content": prompt}], temperature=0.1)
+            debug_logger.info(f"Sufficiency check response: {response_str}")
+            
+            json_response = json.loads(response_str.strip())
+            
+            if json_response.get("sufficient") is True:
+                debug_logger.info(f"Information deemed sufficient. Reason: {json_response.get('reason')}")
+                return True
+            else:
+                debug_logger.info(f"Information insufficient. Missing: {json_response.get('missing_elements')}")
+                return False
+        except (json.JSONDecodeError, KeyError, Exception) as e:
+            debug_logger.error(f"Error during sufficiency check: {e}")
+            return False
+
+    def _check_explicit_trigger(self, user_input: str) -> bool:
+        """
+        【判别器部分-显式触发】
+        使用大模型判断用户是否明确要求建模。
+        """
+        prompt = self.prompts.EXPLICIT_TRIGGER_CHECK.format(user_input=user_input)
+        try:
+            intent = self.llm_client.identify_intent(prompt, temperature=0.1)
+            debug_logger.info(f"Explicit trigger check intent: '{intent}'")
+            if "StartModeling" in intent:
+                debug_logger.info("Explicit trigger to model detected.")
+                return True
+            return False
+        except Exception as e:
+            debug_logger.error(f"Error during explicit trigger check: {e}")
+            return False
+
+    def _discriminator(self, user_input: str) -> bool:
+        """
+        判别器主函数。
+        判断是否应该从“需求梳理”切换到“AI建模”。
+        """
+        # 规则1：最高优先级，检查用户是否明确要求建模
+        if self._check_explicit_trigger(user_input):
+            return True
+        
+        # 定义一个阈值来判断是否为“长文本”
+        LONG_TEXT_THRESHOLD = 200 # 字符数，你可以根据需要调整
+        # 规则2：如果用户输入的是长文本，使用专门的单文本分析器
+        if len(user_input) > LONG_TEXT_THRESHOLD:
+            debug_logger.info(f"Long input detected (length: {len(user_input)}), using single text sufficiency check.")
+            if self._check_single_text_sufficiency(user_input):
+                return True
+        # 规则3：对于常规的、渐进式对话，使用基于历史的分析器
+        # 注意：只有在不是长文本的情况下，或者长文本分析不充分时，才会走到这一步
+        if self._check_information_sufficiency():
+            return True
+            
+        return False
+
+    def _check_single_text_sufficiency(self, text_block: str) -> bool:
+        """
+        【判别器部分-单文本分析】
+        使用大模型判断单一大段文本中的信息是否足够建模。
+        """
+        prompt = self.prompts.SINGLE_TEXT_SUFFICIENCY_CHECK.format(text_block=text_block)
+        try:
+            response_str = self.llm_client.chat(messages=[{"role": "user", "content": prompt}], temperature=0.1)
+            debug_logger.info(f"Single text sufficiency check response: {response_str}")
+            
+            json_response = json.loads(response_str.strip())
+            
+            if json_response.get("sufficient") is True:
+                debug_logger.info(f"Information in single text deemed sufficient. Reason: {json_response.get('reason')}")
+                return True
+            else:
+                debug_logger.info(f"Information in single text insufficient. Missing: {json_response.get('missing_elements')}")
+                return False
+        except (json.JSONDecodeError, KeyError, Exception) as e:
+            debug_logger.error(f"Error during single text sufficiency check: {e}")
+            return False
+        
+    def process_user_message(self, user_input: str) -> str:
+        """
+        处理单轮用户输入，并返回助手的回复。
+        """
+        # 在演示日志中记录用户输入
+        demo_logger.info(f"User Input: {user_input}")
+        
+        self.conversation_history.append({"role": "user", "content": user_input})
+        
+        # 如果已在建模阶段，直接返回提示信息
+        if self.state == DialogueState.AI_MODELING:
+            response_content = "当前已处于建模阶段，请等待模型生成结果。"
+            # 记录当前状态到演示日志
+            demo_logger.info(f"Current State: {self.state.name} (No change)")
+            return response_content
+
+        # 调用判别器判断是否切换状态
+        should_switch = self._discriminator(user_input)
+
+        if should_switch:
+            self.state = DialogueState.AI_MODELING
+            # 同时记录到两个日志文件
+            debug_logger.info(f"State changed to: {self.state.name}")
+            demo_logger.info(f"State Changed To: {self.state.name}")
+            response_content = self.prompts.AI_MODELING_NOTICE
+        else:
+            # 同样，同时记录状态信息
+            debug_logger.info(f"State remains: {self.state.name}")
+            demo_logger.info(f"State Remains: {self.state.name}")
+            system_prompt = self.prompts.REQUIREMENT_ELICITATION_SYSTEM_PROMPT
+            messages = [{"role": "system", "content": system_prompt}] + self.conversation_history
+            response_content = self.llm_client.chat(messages=messages)
+
+        self.conversation_history.append({"role": "assistant", "content": response_content})
+        #demo_logger.info(f"Assistant Response: {response_content}")
+        return response_content
+
+    def reset(self):
+        """重置对话"""
+        self.conversation_history = []
+        self.state = DialogueState.REQUIREMENT_ELICITATION
+        
+        # 在两个日志中都记录重置事件
+        debug_logger.info("Orchestrator has been reset.")
+        demo_logger.info("="*20 + " SESSION RESET " + "="*20)
+        demo_logger.info(f"Initial State: {self.state.name}")
+