diff --git a/main/xiaozhi-server/core/connection.py b/main/xiaozhi-server/core/connection.py index b58ede82..c989b374 100644 --- a/main/xiaozhi-server/core/connection.py +++ b/main/xiaozhi-server/core/connection.py @@ -46,38 +46,6 @@ from core.utils import textUtils TAG = __name__ -# 工具调用规则 - 用于动态注入提醒 -TOOL_CALLING_RULES = """ - -【核心原则】你是拥有工具能力的智能助手。当用户请求需要实时信息或执行操作时，调用相应工具获取数据，禁止凭空编造答案。 - -- **何时必须调用工具：** - 1. 实时信息查询（新闻、非本地天气、股价、汇率等） - 2. 执行操作（播放音乐、控制设备、拍照、设置闹钟等） - 3. 知识库检索（当工具列表包含 search_from_ragflow 时，结合用户意图判断是否需要调用） - 4. 查询非今天的农历信息（明天农历、某日宜忌、节气等） - 5. 用户说"拍照"时调用 self_camera_take_photo，默认 question 参数为"描述一下看到的物品" - -- **何时无需调用工具：** - 1. `` 中已提供的信息（当前时间、今天日期、今天农历、本地天气等） - 2. 普通对话、问候、闲聊、情感交流、讲故事 - 3. 通用知识问答（非实时信息） - -- **调用规范：** - 1. 每次请求独立判断，不复用历史工具结果，需重新获取最新数据 - 2. 多任务时依次调用所有需要的工具，并依次总结每个工具的结果，不得遗漏 - 3. 严格遵循工具的参数要求，提供所有必要参数 - 4. 不确定时引导用户澄清或告知能力限制，切勿猜测或编造 - 5. 不调用未提供的工具，对话中提及的旧工具若不可用则忽略或说明 - -- **反偷懒机制（最高优先级）：** - 1. **每次独立判断：** 无论对话历史中是否调用过工具，当前请求必须根据当前需求独立判断是否需要调用 - 2. **禁止模式模仿：** 即使之前的回复没有调用工具，也不代表本次可以不调用 - 3. **自我检查：** 回复前必须自问："这个请求是否涉及实时信息或执行操作？如果是，我调用工具了吗？" - 4. **历史不等于现在：** 对话历史中的行为模式不影响当前判断，每个用户请求都是全新的开始 - -""" - auto_import_modules("plugins_func.functions") @@ -173,12 +141,6 @@ class ConnectionHandler: # llm相关变量 self.dialogue = Dialogue() - # 工具调用统计（用于监控和自动恢复） - self.tool_call_stats = { - 'last_call_turn': -1, # 上次调用工具的轮数 - 'consecutive_no_call': 0, # 连续未调用次数 - } - # tts相关变量 self.sentence_id = None # 处理TTS响应没有文本返回 @@ -898,32 +860,6 @@ class ConnectionHandler: ) ) - # 长对话工具调用提醒：当对话轮数较多时，提醒模型正确使用工具 - force_reminder = False # 是否强制提醒 - - if depth == 0 and query is not None: - dialogue_length = len(self.dialogue.dialogue) - current_turn = dialogue_length // 2 - - # 检测距离上一次连续未调用工具的情况 - if self.tool_call_stats['last_call_turn'] >= 0: - turns_since_last = current_turn - self.tool_call_stats['last_call_turn'] - if turns_since_last > 3: # 超过3轮未调用 - self.logger.bind(tag=TAG).warning( - f"检测到{turns_since_last}轮未调用工具，可能进入偷懒模式，将强制注入提醒" - ) - force_reminder = True - - # 对话历史截断：防止历史过长导致模型"偷懒模式"扩散 - # 当对话历史超过阈值时，保留最近的 10 轮对话 - # max_dialogue_turns = 10 - # if dialogue_length > max_dialogue_turns * 2: - # removed = self.dialogue.trim_history(max_turns=max_dialogue_turns) - # if removed > 0: - # self.logger.bind(tag=TAG).info( - # f"对话历史过长({dialogue_length}条)，已智能截断保留最近{max_dialogue_turns}轮，移除{removed}条消息" - # ) - # Define intent functions functions = None # 达到最大深度时，禁用工具调用，强制 LLM 直接回答 @@ -934,41 +870,8 @@ class ConnectionHandler: ): functions = self.func_handler.get_functions() - # 长对话工具调用规则强化：动态生成基于当前可用工具的提醒 - tool_call_reminder = None - if depth == 0 and query is not None and functions is not None: - dialogue_length = len(self.dialogue.dialogue) - # 当对话历史超过4条消息时，注入规则强化 - if dialogue_length > 4: - tool_summary = self._get_tool_summary(functions) - if tool_summary: - # 根据对话长度和偷懒检测，使用不同强度的提醒 - if force_reminder: - # 强提醒 - 包含完整规则前缀 - tool_call_reminder = ( - TOOL_CALLING_RULES + - f"[重要提醒] 多轮未使用工具，检查回复是否遗漏了必要的工具调用！上一轮未使用工具，本轮必须重新判断是否需要工具。" - f"当前可用工具: {tool_summary}。" - ) - reminder_level = "强" - else: - # 中等提醒 - 包含规则前缀 - tool_call_reminder = ( - TOOL_CALLING_RULES + - f"当前可用工具: {tool_summary}。" - f"仅当用户请求涉及实时信息查询或执行操作时调用，日常对话无需调用。" - ) - reminder_level = "中" - self.logger.bind(tag=TAG).debug( - f"对话历史较长({dialogue_length}条)，已注入{reminder_level}等级工具调用规则强化，当前可用工具：{tool_summary}" - ) - response_message = [] - # 如果有工具调用提醒，临时添加到对话中（标记为临时消息） - if tool_call_reminder: - self.dialogue.put(Message(role="user", content=tool_call_reminder, is_temporary=True)) - try: # 使用带记忆的对话 memory_str = None @@ -1100,15 +1003,6 @@ class ConnectionHandler: f"检测到 {len(tool_calls_list)} 个工具调用" ) - # 更新工具调用统计 - if depth == 0: - current_turn = len(self.dialogue.dialogue) // 2 - self.tool_call_stats['last_call_turn'] = current_turn - self.tool_call_stats['consecutive_no_call'] = 0 - self.logger.bind(tag=TAG).debug( - f"工具调用统计更新: 当前轮次={current_turn}" - ) - # LLM 流式阶段已播报过的文本 streamed_text = "" if len(response_message) > 0: @@ -1170,10 +1064,6 @@ class ConnectionHandler: self.tts.store_tts_text(current_sentence_id, text_buff) self.dialogue.put(Message(role="assistant", content=text_buff)) - # 更新工具调用统计：如果没有调用工具，增加计数 - if depth == 0 and not tool_call_flag: - self.tool_call_stats['consecutive_no_call'] += 1 - if depth == 0: self.tts.tts_text_queue.put( TTSMessageDTO( @@ -1189,41 +1079,11 @@ class ConnectionHandler: ) ) - # 清理临时插入的工具调用提醒消息（使用标记清理） - if tool_call_reminder and len(self.dialogue.dialogue) > 0: - original_length = len(self.dialogue.dialogue) - self.dialogue.dialogue = [ - msg for msg in self.dialogue.dialogue - if not getattr(msg, 'is_temporary', False) - ] - if len(self.dialogue.dialogue) < original_length: - self.logger.bind(tag=TAG).debug("已清理临时的工具调用提醒消息") - return True - def _get_tool_summary(self, functions: list) -> str: - """ - 从工具定义中提取摘要，用于规则强化注入 - - Args: - functions: 工具列表 - - Returns: - str: 工具名称字符串 - """ - if not functions: - return "" - - datas = [] - for func in functions: - func_info = func.get("function", {}) - name = func_info.get("name", "") - datas.append(name) - result = "、".join(datas) - return result - def _handle_function_result(self, tool_results, depth, streamed_text=""): need_llm_tools = [] + record_tools = [] for result, tool_call_data in tool_results: if result.action in [ @@ -1241,11 +1101,58 @@ class ConnectionHandler: self.tts.store_tts_text(self.sentence_id, text) self.dialogue.put(Message(role="assistant", content=text)) elif result.action == Action.REQLLM: - # 收集需要 LLM 处理的工具 need_llm_tools.append((result, tool_call_data)) + elif result.action == Action.RECORD: + record_tools.append((result, tool_call_data)) else: pass + # Action.RECORD：写入完整工具调用链（assistant(tool_calls) → tool(result) → assistant(response)） + # 模型从历史中学到工具调用模式，不额外调用LLM + if record_tools: + # 构造 assistant 消息（含 tool_calls），记录"模型调用了哪些工具" + all_tool_calls = [ + { + "id": tool_call_data["id"], + "function": { + "arguments": ( + "{}" + if tool_call_data["arguments"] == "" + else tool_call_data["arguments"] + ), + "name": tool_call_data["name"], + }, + "type": "function", + "index": idx, + } + for idx, (_, tool_call_data) in enumerate(record_tools) + ] + self.dialogue.put(Message(role="assistant", tool_calls=all_tool_calls)) + + # 写入每条工具的执行结果，记录"工具返回了什么" + for result, tool_call_data in record_tools: + text = result.result or "" + self.dialogue.put( + Message( + role="tool", + tool_call_id=( + str(uuid.uuid4()) + if tool_call_data["id"] is None + else tool_call_data["id"] + ), + content=text, + ) + ) + + # 用固定文本作为最终回复，补全标准三段式，保证下一条消息是 user 而非接 tool + response_parts = [] + for result, _ in record_tools: + resp = result.response or result.result + if resp: + response_parts.append(resp) + if response_parts: + self.dialogue.put(Message(role="assistant", content="，".join(response_parts))) + if need_llm_tools: all_tool_calls = [ { diff --git a/main/xiaozhi-server/core/utils/dialogue.py b/main/xiaozhi-server/core/utils/dialogue.py index 630453ba..eab34ba9 100644 --- a/main/xiaozhi-server/core/utils/dialogue.py +++ b/main/xiaozhi-server/core/utils/dialogue.py @@ -123,6 +123,36 @@ class Dialogue: return removed_count + def _ensure_tool_calls_complete(self, messages: List[Message]) -> List[Message]: + """ + 确保所有 tool_calls 都有对应的 tool 响应 + 修复被打断导致的悬空 tool_calls，防止大模型 API 报 400 错误 + """ + pending_tool_calls = set() + result = [] + + for msg in messages: + result.append(msg) + + if msg.role == "assistant" and msg.tool_calls: + for tc in msg.tool_calls: + tc_id = tc.get("id") if isinstance(tc, dict) else getattr(tc, "id", None) + if tc_id: + pending_tool_calls.add(tc_id) + + elif msg.role == "tool" and msg.tool_call_id: + pending_tool_calls.discard(msg.tool_call_id) + + for missing_id in pending_tool_calls: + dummy_tool_msg = Message( + role="tool", + content='{"status": "interrupted", "message": "动作已取消/被打断"}', + tool_call_id=missing_id + ) + result.append(dummy_tool_msg) + + return result + def get_llm_dialogue_with_memory( self, memory_str: str = None, voiceprint_config: dict = None ) -> List[Dict[str, str]]: @@ -175,8 +205,9 @@ class Dialogue: dialogue.append({"role": "system", "content": enhanced_system_prompt}) # 添加用户和助手的对话 - for m in self.dialogue: - if m.role != "system": # 跳过原始的系统消息 - self.getMessages(m, dialogue) + non_system_messages = [m for m in self.dialogue if m.role != "system"] + complete_messages = self._ensure_tool_calls_complete(non_system_messages) + for m in complete_messages: + self.getMessages(m, dialogue) return dialogue diff --git a/main/xiaozhi-server/plugins_func/functions/play_music.py b/main/xiaozhi-server/plugins_func/functions/play_music.py index 1195ae54..461775f6 100644 --- a/main/xiaozhi-server/plugins_func/functions/play_music.py +++ b/main/xiaozhi-server/plugins_func/functions/play_music.py @@ -67,7 +67,7 @@ def play_music(conn: "ConnectionHandler", song_name: str): task.add_done_callback(handle_done) return ActionResponse( - action=Action.NONE, result="指令已接收", response="正在为您播放音乐" + action=Action.RECORD, result="指令已接收", response="正在为您播放音乐" ) except Exception as e: conn.logger.bind(tag=TAG).error(f"处理音乐意图错误: {e}") @@ -217,7 +217,7 @@ async def play_local_music(conn: "ConnectionHandler", specific_file=None): conn.logger.bind(tag=TAG).error(f"选定的音乐文件不存在: {music_path}") return text = _get_random_play_prompt(selected_music) - conn.dialogue.put(Message(role="assistant", content=text)) + # conn.dialogue.put(Message(role="assistant", content=text)) if conn.intent_type == "intent_llm": conn.tts.tts_text_queue.put( diff --git a/main/xiaozhi-server/plugins_func/register.py b/main/xiaozhi-server/plugins_func/register.py index 5c2b0781..33a88161 100644 --- a/main/xiaozhi-server/plugins_func/register.py +++ b/main/xiaozhi-server/plugins_func/register.py @@ -28,6 +28,7 @@ class Action(Enum): NONE = (1, "啥也不干") RESPONSE = (2, "直接回复") REQLLM = (3, "调用函数后再请求llm生成回复") + RECORD = (4, "记录工具调用到对话历史，不调用LLM") def __init__(self, code, message): self.code = code