diff --git a/src/heart_flow/observation/hfcloop_observation.py b/src/heart_flow/observation/hfcloop_observation.py index c2d012667..f920761ce 100644 --- a/src/heart_flow/observation/hfcloop_observation.py +++ b/src/heart_flow/observation/hfcloop_observation.py @@ -47,12 +47,9 @@ class HFCloopObservation: if cycle.action_type == "reply": consecutive_text_replies += 1 # 获取回复内容，如果不存在则返回'[空回复]' - response_text = cycle.response_info.get("response_text", []) - # 使用简单的 join 来格式化回复内容列表 - formatted_response = "[空回复]" if not response_text else " ".join(response_text) - responses_for_prompt.append(formatted_response) + response_text = cycle.response_info.get("response_text", "[空回复]") + responses_for_prompt.append(response_text) else: - # 一旦遇到非文本回复，连续性中断 break # 根据连续文本回复的数量构建提示信息 diff --git a/src/plugins/heartFC_chat/heartflow_prompt_builder.py b/src/plugins/heartFC_chat/heartflow_prompt_builder.py index ddd1c9fd4..39ccce53e 100644 --- a/src/plugins/heartFC_chat/heartflow_prompt_builder.py +++ b/src/plugins/heartFC_chat/heartflow_prompt_builder.py @@ -85,17 +85,17 @@ def init_prompt(): 【回复原则】 1. 不回复(no_reply)适用： - - 话题无关/无聊/不感兴趣 - - 最后一条消息是你自己发的且无人回应你 - - 讨论你不懂的专业话题 - - 你发送了太多消息，且无人回复 + - 话题无关/无聊/不感兴趣 + - 最后一条消息是你自己发的且无人回应你 + - 讨论你不懂的专业话题 + - 你发送了太多消息，且无人回复 2. 回复(reply)适用： - - 有实质性内容需要表达 - - 有人提到你，但你还没有回应他 - - 在合适的时候添加表情（不要总是添加） - - 如果你要回复特定某人的某句话，或者你想回复较早的消息，请在target中指定那句话的原始文本 - + - 有实质性内容需要表达 + - 有人提到你，但你还没有回应他 + - 在合适的时候添加表情（不要总是添加） + - 如果你要回复特定某人的某句话，或者你想回复较早的消息，请在target中指定那句话的原始文本 + 3. 回复target选择： -如果选择了target，不用特别提到某个人的人名 - 除非有明确的回复目标，否则不要添加target @@ -106,9 +106,9 @@ def init_prompt(): -一次只回复一个话题 5. 自我对话处理： - - 如果是自己发的消息想继续，需自然衔接 - - 避免重复或评价自己的发言 - - 不要和自己聊天 + - 如果是自己发的消息想继续，需自然衔接 + - 避免重复或评价自己的发言 + - 不要和自己聊天 决策任务 {action_options_text} @@ -869,124 +869,6 @@ class PromptBuilder: logger.error(traceback.format_exc()) return "[构建 Planner Prompt 时出错]" - async def build_planner_prompt_parallel( - self, - is_group_chat: bool, - chat_target_info: Optional[dict], - cycle_history: Deque["CycleDetail"], - observed_messages_str: str, - structured_info: str, - current_available_actions: Dict[str, str], - ) -> str: - """ - 构建并行规划器的提示词 (不依赖SubMind的思考结果) - 这个方法与build_planner_prompt类似，但不需要current_mind参数， - 允许与submind的思考过程并行执行 - - 参数: - is_group_chat: 是否为群聊 - chat_target_info: 目标聊天信息 - cycle_history: 循环历史 - observed_messages_str: 观察到的消息 - structured_info: 结构化信息字符串 - current_available_actions: 当前可用的动作 - - 返回: - str: 规划器提示词 - """ - try: - # --- Determine chat context --- - chat_context_description = "你现在正在一个群聊中" - chat_target_name = None # Only relevant for private - if not is_group_chat and chat_target_info: - chat_target_name = ( - chat_target_info.get("person_name") or chat_target_info.get("user_nickname") or "对方" - ) - chat_context_description = f"你正在和 {chat_target_name} 私聊" - # --- End determining chat context --- - - # Structured info block - structured_info_block = "" - if structured_info: - structured_info_block = f"以下是一些额外的信息：\n{structured_info}\n" - - # Chat content block - chat_content_block = "" - if observed_messages_str: - # Use triple quotes for multi-line string literal - chat_content_block = f"""观察到的最新聊天内容如下： ---- -{observed_messages_str} ----""" - else: - chat_content_block = "当前没有观察到新的聊天内容。\\n" - - # Current mind block (并行模式专用) - current_mind_block = "" - - # Cycle info block (using passed cycle_history) - cycle_info_block = "" - recent_active_cycles = [] - for cycle in reversed(cycle_history): - if cycle.action_taken: - recent_active_cycles.append(cycle) - if len(recent_active_cycles) == 3: - break - - consecutive_text_replies = 0 - responses_for_prompt = [] - - for cycle in recent_active_cycles: - if cycle.action_type == "text_reply": - consecutive_text_replies += 1 - response_text = cycle.response_info.get("response_text", []) - formatted_response = "[空回复]" if not response_text else " ".join(response_text) - responses_for_prompt.append(formatted_response) - else: - break - - if consecutive_text_replies >= 3: - cycle_info_block = f'你已经连续回复了三条消息（最近: "{responses_for_prompt[0]}"，第二近: "{responses_for_prompt[1]}"，第三近: "{responses_for_prompt[2]}"）。你回复的有点多了，请注意' - elif consecutive_text_replies == 2: - cycle_info_block = f'你已经连续回复了两条消息（最近: "{responses_for_prompt[0]}"，第二近: "{responses_for_prompt[1]}"），请注意' - elif consecutive_text_replies == 1: - cycle_info_block = f'你刚刚已经回复一条消息（内容: "{responses_for_prompt[0]}"）' - - if cycle_info_block: - cycle_info_block = f"\n【近期回复历史】\n{cycle_info_block}\n" - else: - cycle_info_block = "\n【近期回复历史】\n(最近没有连续文本回复)\n" - - individuality = Individuality.get_instance() - prompt_personality = individuality.get_prompt(x_person=2, level=2) - - action_options_text = "当前你可以选择的行动有：\n" - action_keys = list(current_available_actions.keys()) - for name in action_keys: - desc = current_available_actions[name] - action_options_text += f"- '{name}': {desc}\n" - example_action_key = action_keys[0] if action_keys else "no_reply" - - planner_prompt_template = await global_prompt_manager.get_prompt_async("planner_prompt") - - prompt = planner_prompt_template.format( - bot_name=global_config.BOT_NICKNAME, - prompt_personality=prompt_personality, - chat_context_description=chat_context_description, - structured_info_block=structured_info_block, - chat_content_block=chat_content_block, - current_mind_block=current_mind_block, - cycle_info_block=cycle_info_block, - action_options_text=action_options_text, - example_action=example_action_key, - ) - return prompt - - except Exception as e: - logger.error(f"[PromptBuilder] 构建并行 Planner 提示词时出错: {e}") - logger.error(traceback.format_exc()) - return "[构建并行 Planner Prompt 时出错]" - init_prompt() prompt_builder = PromptBuilder() diff --git a/src/plugins/heartFC_chat/memory_activator.py b/src/plugins/heartFC_chat/memory_activator.py index e5991583e..aa60869d8 100644 --- a/src/plugins/heartFC_chat/memory_activator.py +++ b/src/plugins/heartFC_chat/memory_activator.py @@ -13,27 +13,28 @@ from typing import List, Dict logger = get_logger("memory_activator") -Prompt( - """ +def init_prompt(): + # --- Group Chat Prompt --- + memory_activator_prompt = """ 你是一个记忆分析器，你需要根据以下信息来进行会议 以下是一场聊天中的信息，请根据这些信息，总结出几个关键词作为记忆回忆的触发词 {obs_info_text} 请输出一个json格式，包含以下字段： - { + {{ "keywords": ["关键词1", "关键词2", "关键词3",......] - } + }} 不要输出其他多余内容，只输出json格式就好 - """, - "memory_activator_prompt", -) + """ + + Prompt(memory_activator_prompt, "memory_activator_prompt") class MemoryActivator: def __init__(self): self.summary_model = LLMRequest( - model=global_config.llm_observation, temperature=0.7, max_tokens=300, request_type="chat_observation" + model=global_config.llm_summary, temperature=0.7, max_tokens=300, request_type="chat_observation" ) self.running_memory = [] @@ -58,7 +59,10 @@ class MemoryActivator: elif isinstance(observation, HFCloopObservation): obs_info_text += observation.get_observe_info() - prompt = global_prompt_manager.format_prompt("memory_activator_prompt", obs_info_text=obs_info_text) + prompt = await global_prompt_manager.format_prompt( + "memory_activator_prompt", + obs_info_text=obs_info_text, + ) logger.debug(f"prompt: {prompt}") @@ -66,7 +70,9 @@ class MemoryActivator: logger.debug(f"response: {response}") - keywords = get_keywords_from_json(response) + # 只取response的第一个元素（字符串） + response_str = response[0] + keywords = list(get_keywords_from_json(response_str)) # 调用记忆系统获取相关记忆 related_memory = await HippocampusManager.get_instance().get_memory_from_topic( @@ -75,9 +81,25 @@ class MemoryActivator: logger.debug(f"获取到的记忆: {related_memory}") + # 激活时，所有已有记忆的duration+1，达到3则移除 + for m in self.running_memory[:]: + m['duration'] = m.get('duration', 1) + 1 + self.running_memory = [m for m in self.running_memory if m['duration'] < 4] + if related_memory: for topic, memory in related_memory: - self.running_memory.append({"topic": topic, "content": memory, "timestamp": datetime.now().isoformat()}) - logger.debug(f"添加新记忆: {topic} - {memory}") + # 检查是否已存在相同topic和content的记忆 + exists = any(m['topic'] == topic and m['content'] == memory for m in self.running_memory) + if not exists: + self.running_memory.append({ + "topic": topic, + "content": memory, + "timestamp": datetime.now().isoformat(), + "duration": 1 + }) + logger.debug(f"添加新记忆: {topic} - {memory}") return self.running_memory + + +init_prompt() \ No newline at end of file