Update mind_processor.py

src/chat/focus_chat/info_processors/mind_processor.py

@@ -1,3 +1,27 @@
+from src.chat.heart_flow.observation.chatting_observation import ChattingObservation
+from src.chat.heart_flow.observation.observation import Observation
+from src.llm_models.utils_model import LLMRequest
+from src.config.config import global_config
+import time
+import traceback
+from src.common.logger_manager import get_logger
+from src.individuality.individuality import individuality
+from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
+from src.chat.utils.json_utils import safe_json_dumps
+from src.chat.message_receive.chat_stream import chat_manager
+from src.person_info.relationship_manager import relationship_manager
+from .base_processor import BaseProcessor
+from src.chat.focus_chat.info.mind_info import MindInfo
+from typing import List, Optional
+from src.chat.heart_flow.observation.hfcloop_observation import HFCloopObservation
+from typing import Dict
+from src.chat.focus_chat.info.info_base import InfoBase
+
+logger = get_logger("processor")
+
+
+def init_prompt():
+    group_prompt = """
 你的名字是{bot_name}
 {memory_str}
 
@@ -38,3 +62,182 @@
 4. 注意群聊的时间线索，话题由谁发起，进展状况如何，思考聊天的时间线。
 6. 语言简洁自然，不要分点，不要浮夸，不要修辞，仅输出思考内容就好"""
     Prompt(private_prompt, "sub_heartflow_prompt_private_before")
+
+
+class MindProcessor(BaseProcessor):
+    log_prefix = "聊天思考"
+
+    def __init__(self, subheartflow_id: str):
+        super().__init__()
+
+        self.subheartflow_id = subheartflow_id
+
+        self.llm_model = LLMRequest(
+            model=global_config.model.focus_chat_mind,
+            temperature=global_config.model.focus_chat_mind["temp"],
+            max_tokens=800,
+            request_type="focus_chat_mind",
+        )
+
+        self.current_mind = ""
+        self.past_mind = []
+        self.structured_info = []
+        self.structured_info_str = ""
+
+        name = chat_manager.get_stream_name(self.subheartflow_id)
+        self.log_prefix = f"[{name}] "
+        self._update_structured_info_str()
+
+    def _update_structured_info_str(self):
+        """根据 structured_info 更新 structured_info_str"""
+        if not self.structured_info:
+            self.structured_info_str = ""
+            return
+
+        lines = ["【信息】"]
+        for item in self.structured_info:
+            # 简化展示，突出内容和类型，包含TTL供调试
+            type_str = item.get("type", "未知类型")
+            content_str = item.get("content", "")
+
+            if type_str == "info":
+                lines.append(f"刚刚: {content_str}")
+            elif type_str == "memory":
+                lines.append(f"{content_str}")
+            elif type_str == "comparison_result":
+                lines.append(f"数字大小比较结果: {content_str}")
+            elif type_str == "time_info":
+                lines.append(f"{content_str}")
+            elif type_str == "lpmm_knowledge":
+                lines.append(f"你知道：{content_str}")
+            else:
+                lines.append(f"{type_str}的信息: {content_str}")
+
+        self.structured_info_str = "\n".join(lines)
+        logger.debug(f"{self.log_prefix} 更新 structured_info_str: \n{self.structured_info_str}")
+
+    async def process_info(
+        self, observations: Optional[List[Observation]] = None, running_memorys: Optional[List[Dict]] = None, *infos
+    ) -> List[InfoBase]:
+        """处理信息对象
+
+        Args:
+            *infos: 可变数量的InfoBase类型的信息对象
+
+        Returns:
+            List[InfoBase]: 处理后的结构化信息列表
+        """
+        current_mind = await self.do_thinking_before_reply(observations, running_memorys)
+
+        mind_info = MindInfo()
+        mind_info.set_current_mind(current_mind)
+
+        return [mind_info]
+
+    async def do_thinking_before_reply(
+        self, observations: Optional[List[Observation]] = None, running_memorys: Optional[List[Dict]] = None
+    ):
+        """
+        在回复前进行思考，生成内心想法并收集工具调用结果
+
+        参数:
+            observations: 观察信息
+
+        返回:
+            如果return_prompt为False:
+                tuple: (current_mind, past_mind) 当前想法和过去的想法列表
+            如果return_prompt为True:
+                tuple: (current_mind, past_mind, prompt) 当前想法、过去的想法列表和使用的prompt
+        """
+
+        # ---------- 0. 更新和清理 structured_info ----------
+        if self.structured_info:
+            updated_info = []
+            for item in self.structured_info:
+                item["ttl"] -= 1
+                if item["ttl"] > 0:
+                    updated_info.append(item)
+                else:
+                    logger.debug(f"{self.log_prefix} 移除过期的 structured_info 项: {item['id']}")
+            self.structured_info = updated_info
+            self._update_structured_info_str()
+        logger.debug(
+            f"{self.log_prefix} 当前完整的 structured_info: {safe_json_dumps(self.structured_info, ensure_ascii=False)}"
+        )
+
+        memory_str = ""
+        if running_memorys:
+            memory_str = "以下是当前在聊天中，你回忆起的记忆：\n"
+            for running_memory in running_memorys:
+                memory_str += f"{running_memory['topic']}: {running_memory['content']}\n"
+
+        # ---------- 1. 准备基础数据 ----------
+        # 获取现有想法和情绪状态
+        previous_mind = self.current_mind if self.current_mind else ""
+
+        if observations is None:
+            observations = []
+        for observation in observations:
+            if isinstance(observation, ChattingObservation):
+                # 获取聊天元信息
+                is_group_chat = observation.is_group_chat
+                chat_target_info = observation.chat_target_info
+                chat_target_name = "对方"  # 私聊默认名称
+                if not is_group_chat and chat_target_info:
+                    # 优先使用person_name，其次user_nickname，最后回退到默认值
+                    chat_target_name = (
+                        chat_target_info.get("person_name") or chat_target_info.get("user_nickname") or chat_target_name
+                    )
+                # 获取聊天内容
+                chat_observe_info = observation.get_observe_info()
+                person_list = observation.person_list
+            if isinstance(observation, HFCloopObservation):
+                hfcloop_observe_info = observation.get_observe_info()
+
+        # ---------- 3. 准备个性化数据 ----------
+        # 获取个性化信息
+
+        relation_prompt = ""
+        for person in person_list:
+            relation_prompt += await relationship_manager.build_relationship_info(person, is_id=True)
+
+        template_name = "sub_heartflow_prompt_before" if is_group_chat else "sub_heartflow_prompt_private_before"
+        logger.debug(f"{self.log_prefix} 使用{'群聊' if is_group_chat else '私聊'}思考模板")
+
+        prompt = (await global_prompt_manager.get_prompt_async(template_name)).format(
+            bot_name=individuality.name,
+            memory_str=memory_str,
+            extra_info=self.structured_info_str,
+            relation_prompt=relation_prompt,
+            time_now=time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()),
+            chat_observe_info=chat_observe_info,
+            last_mind=previous_mind,
+            cycle_info_block=hfcloop_observe_info,
+            chat_target_name=chat_target_name,
+        )
+
+        content = "(不知道该想些什么...)"
+        try:
+            content, _ = await self.llm_model.generate_response_async(prompt=prompt)
+            if not content:
+                logger.warning(f"{self.log_prefix} LLM返回空结果，思考失败。")
+        except Exception as e:
+            # 处理总体异常
+            logger.error(f"{self.log_prefix} 执行LLM请求或处理响应时出错: {e}")
+            logger.error(traceback.format_exc())
+            content = "思考过程中出现错误"
+
+        # 记录初步思考结果
+        logger.debug(f"{self.log_prefix} 思考prompt: \n{prompt}\n")
+        logger.info(f"{self.log_prefix} 聊天规划: {content}")
+        self.update_current_mind(content)
+
+        return content
+
+    def update_current_mind(self, response):
+        if self.current_mind:  # 只有当 current_mind 非空时才添加到 past_mind
+            self.past_mind.append(self.current_mind)
+        self.current_mind = response
+
+
+init_prompt()