diff --git a/scripts/check_memory_transfer.py b/scripts/check_memory_transfer.py index 01082f242..620df08b1 100644 --- a/scripts/check_memory_transfer.py +++ b/scripts/check_memory_transfer.py @@ -6,8 +6,8 @@ from pathlib import Path project_root = Path(__file__).parent.parent sys.path.insert(0, str(project_root)) -from src.memory_graph.manager_singleton import get_unified_memory_manager from src.common.logger import get_logger +from src.memory_graph.manager_singleton import get_unified_memory_manager logger = get_logger("memory_transfer_check") @@ -22,20 +22,20 @@ def print_section(title: str): async def check_short_term_status(): """检查短期记忆状态""" print_section("1. 短期记忆状态检查") - + manager = get_unified_memory_manager() short_term = manager.short_term_manager - + # 获取统计信息 stats = short_term.get_statistics() - + print(f"📊 当前记忆数量: {stats['total_memories']}/{stats['max_memories']}") - + # 计算占用率 - if stats['max_memories'] > 0: - occupancy = stats['total_memories'] / stats['max_memories'] + if stats["max_memories"] > 0: + occupancy = stats["total_memories"] / stats["max_memories"] print(f"📈 容量占用率: {occupancy:.1%}") - + # 根据占用率给出建议 if occupancy >= 1.0: print("⚠️ 警告：已达到容量上限！应该触发紧急转移") @@ -43,25 +43,25 @@ async def check_short_term_status(): print("✅ 占用率超过50%，符合自动转移条件") else: print(f"ℹ️ 占用率未达到50%阈值，当前 {occupancy:.1%}") - + print(f"🎯 可转移记忆数: {stats['transferable_count']}") print(f"📏 转移重要性阈值: {stats['transfer_threshold']}") - + return stats async def check_transfer_candidates(): """检查当前可转移的候选记忆""" print_section("2. 转移候选记忆分析") - + manager = get_unified_memory_manager() short_term = manager.short_term_manager - + # 获取转移候选 candidates = short_term.get_memories_for_transfer() - + print(f"🎫 当前转移候选: {len(candidates)} 条\n") - + if not candidates: print("❌ 没有记忆符合转移条件！") print("\n可能原因：") @@ -69,7 +69,7 @@ async def check_transfer_candidates(): print(" 2. 短期记忆数量未超过容量限制") print(" 3. 短期记忆列表为空") return [] - + # 显示前5条候选的详细信息 print("前 5 条候选记忆：\n") for i, mem in enumerate(candidates[:5], 1): @@ -78,38 +78,38 @@ async def check_transfer_candidates(): print(f" 内容: {mem.content[:50]}...") print(f" 创建时间: {mem.created_at}") print() - + if len(candidates) > 5: print(f"... 还有 {len(candidates) - 5} 条候选记忆\n") - + # 分析重要性分布 importance_levels = { "高 (>=0.8)": sum(1 for m in candidates if m.importance >= 0.8), "中 (0.6-0.8)": sum(1 for m in candidates if 0.6 <= m.importance < 0.8), "低 (<0.6)": sum(1 for m in candidates if m.importance < 0.6), } - + print("📊 重要性分布：") for level, count in importance_levels.items(): print(f" {level}: {count} 条") - + return candidates async def check_auto_transfer_task(): """检查自动转移任务状态""" print_section("3. 自动转移任务状态") - + manager = get_unified_memory_manager() - + # 检查任务是否存在 - if not hasattr(manager, '_auto_transfer_task') or manager._auto_transfer_task is None: + if not hasattr(manager, "_auto_transfer_task") or manager._auto_transfer_task is None: print("❌ 自动转移任务未创建！") print("\n建议：调用 manager.initialize() 初始化系统") return False - + task = manager._auto_transfer_task - + # 检查任务状态 if task.done(): print("❌ 自动转移任务已结束！") @@ -121,78 +121,78 @@ async def check_auto_transfer_task(): pass print("\n建议：重启系统或手动重启任务") return False - + print("✅ 自动转移任务正在运行") - + # 检查转移缓存 - if hasattr(manager, '_transfer_cache'): + if hasattr(manager, "_transfer_cache"): cache_size = len(manager._transfer_cache) if manager._transfer_cache else 0 print(f"📦 转移缓存: {cache_size} 条记忆") - + # 检查上次转移时间 - if hasattr(manager, '_last_transfer_time'): + if hasattr(manager, "_last_transfer_time"): from datetime import datetime last_time = manager._last_transfer_time if last_time: time_diff = (datetime.now() - last_time).total_seconds() print(f"⏱️ 距上次转移: {time_diff:.1f} 秒前") - + return True async def check_long_term_status(): """检查长期记忆状态""" print_section("4. 长期记忆图谱状态") - + manager = get_unified_memory_manager() long_term = manager.long_term_manager - + # 获取图谱统计 stats = long_term.get_statistics() - + print(f"👥 人物节点数: {stats.get('person_count', 0)}") print(f"📅 事件节点数: {stats.get('event_count', 0)}") print(f"🔗 关系边数: {stats.get('edge_count', 0)}") print(f"💾 向量存储数: {stats.get('vector_count', 0)}") - + return stats async def manual_transfer_test(): """手动触发转移测试""" print_section("5. 手动转移测试") - + manager = get_unified_memory_manager() - + # 询问用户是否执行 print("⚠️ 即将手动触发一次记忆转移") print("这将把当前符合条件的短期记忆转移到长期记忆") response = input("\n是否继续? (y/n): ").strip().lower() - - if response != 'y': + + if response != "y": print("❌ 已取消手动转移") return None - + print("\n🚀 开始手动转移...") - + try: # 执行手动转移 result = await manager.manual_transfer() - + print("\n✅ 转移完成！") - print(f"\n转移结果：") + print("\n转移结果：") print(f" 已处理: {result.get('processed_count', 0)} 条") print(f" 成功转移: {len(result.get('transferred_memory_ids', []))} 条") print(f" 失败: {result.get('failed_count', 0)} 条") print(f" 跳过: {result.get('skipped_count', 0)} 条") - - if result.get('errors'): - print(f"\n错误信息：") - for error in result['errors'][:3]: # 只显示前3个错误 + + if result.get("errors"): + print("\n错误信息：") + for error in result["errors"][:3]: # 只显示前3个错误 print(f" - {error}") - + return result - + except Exception as e: print(f"\n❌ 转移失败: {e}") logger.exception("手动转移失败") @@ -202,29 +202,29 @@ async def manual_transfer_test(): async def check_configuration(): """检查相关配置""" print_section("6. 配置参数检查") - + from src.config.config import global_config - + config = global_config.memory - + print("📋 当前配置：") print(f" 短期记忆容量: {config.short_term_max_memories}") print(f" 转移重要性阈值: {config.short_term_transfer_threshold}") print(f" 批量转移大小: {config.long_term_batch_size}") print(f" 自动转移间隔: {config.long_term_auto_transfer_interval} 秒") print(f" 启用泄压清理: {config.short_term_enable_force_cleanup}") - + # 给出配置建议 print("\n💡 配置建议：") - + if config.short_term_transfer_threshold > 0.6: print(" ⚠️ 转移阈值较高(>0.6)，可能导致记忆难以转移") print(" 建议：降低到 0.4-0.5") - + if config.long_term_batch_size > 10: print(" ⚠️ 批量大小较大(>10)，可能延迟转移触发") print(" 建议：设置为 5-10") - + if config.long_term_auto_transfer_interval > 300: print(" ⚠️ 转移间隔较长(>5分钟)，可能导致转移不及时") print(" 建议：设置为 60-180 秒") @@ -235,37 +235,37 @@ async def main(): print("\n" + "=" * 60) print(" MoFox-Bot 记忆转移诊断工具") print("=" * 60) - + try: # 初始化管理器 print("\n⚙️ 正在初始化记忆管理器...") manager = get_unified_memory_manager() await manager.initialize() print("✅ 初始化完成\n") - + # 执行各项检查 await check_short_term_status() candidates = await check_transfer_candidates() task_running = await check_auto_transfer_task() await check_long_term_status() await check_configuration() - + # 综合诊断 print_section("7. 综合诊断结果") - + issues = [] - + if not candidates: issues.append("❌ 没有符合条件的转移候选") - + if not task_running: issues.append("❌ 自动转移任务未运行") - + if issues: print("🚨 发现以下问题：\n") for issue in issues: print(f" {issue}") - + print("\n建议操作：") print(" 1. 检查短期记忆的重要性评分是否合理") print(" 2. 降低配置中的转移阈值") @@ -273,7 +273,7 @@ async def main(): print(" 4. 尝试手动触发转移测试") else: print("✅ 系统运行正常，转移机制已就绪") - + if candidates: print(f"\n当前有 {len(candidates)} 条记忆等待转移") print("转移将在满足以下任一条件时自动触发：") @@ -281,20 +281,20 @@ async def main(): print(" • 短期记忆占用率超过 50%") print(" • 距上次转移超过最大延迟") print(" • 短期记忆达到容量上限") - + # 询问是否手动触发转移 if candidates: print() await manual_transfer_test() - + print_section("检查完成") print("详细诊断报告: docs/memory_transfer_diagnostic_report.md") - + except Exception as e: print(f"\n❌ 检查过程出错: {e}") logger.exception("检查脚本执行失败") return 1 - + return 0 diff --git a/scripts/clear_short_term_memory.py b/scripts/clear_short_term_memory.py index 5ef95608c..30769810c 100644 --- a/scripts/clear_short_term_memory.py +++ b/scripts/clear_short_term_memory.py @@ -17,8 +17,8 @@ from pathlib import Path PROJECT_ROOT = Path(__file__).parent.parent sys.path.insert(0, str(PROJECT_ROOT)) -from src.config.config import global_config # noqa: E402 -from src.memory_graph.short_term_manager import ShortTermMemoryManager # noqa: E402 +from src.config.config import global_config +from src.memory_graph.short_term_manager import ShortTermMemoryManager def resolve_data_dir() -> Path: diff --git a/src/chat/emoji_system/emoji_manager.py b/src/chat/emoji_system/emoji_manager.py index 873c954c2..a169950e3 100644 --- a/src/chat/emoji_system/emoji_manager.py +++ b/src/chat/emoji_system/emoji_manager.py @@ -12,17 +12,16 @@ from typing import Any, Optional, cast import json_repair from PIL import Image -from rich.traceback import install from sqlalchemy import select from src.chat.emoji_system.emoji_constants import EMOJI_DIR, EMOJI_REGISTERED_DIR, MAX_EMOJI_FOR_PROMPT from src.chat.emoji_system.emoji_entities import MaiEmoji from src.chat.emoji_system.emoji_utils import ( _emoji_objects_to_readable_list, - _to_emoji_objects, _ensure_emoji_dir, - clear_temp_emoji, + _to_emoji_objects, clean_unused_emojis, + clear_temp_emoji, list_image_files, ) from src.chat.utils.utils_image import get_image_manager, image_path_to_base64 diff --git a/src/chat/express/expression_learner.py b/src/chat/express/expression_learner.py index 5870a8bdb..df5db228e 100644 --- a/src/chat/express/expression_learner.py +++ b/src/chat/express/expression_learner.py @@ -415,20 +415,20 @@ class ExpressionLearner: .offset(offset) ) batch_expressions = list(batch_result.scalars()) - + if not batch_expressions: break # 没有更多数据 - + # 批量处理当前批次 to_delete = [] for expr in batch_expressions: # 计算时间差 time_diff_days = (current_time - expr.last_active_time) / (24 * 3600) - + # 计算衰减值 decay_value = self.calculate_decay_factor(time_diff_days) new_count = max(0.01, expr.count - decay_value) - + if new_count <= 0.01: # 标记删除 to_delete.append(expr) @@ -436,22 +436,22 @@ class ExpressionLearner: # 更新count expr.count = new_count updated_count += 1 - + # 批量删除 if to_delete: for expr in to_delete: await session.delete(expr) deleted_count += len(to_delete) - + # 提交当前批次 await session.commit() - + # 如果批次不满，说明已经处理完所有数据 if len(batch_expressions) < BATCH_SIZE: break - + offset += BATCH_SIZE - + if updated_count > 0 or deleted_count > 0: logger.info(f"全局衰减完成：更新了 {updated_count} 个表达方式，删除了 {deleted_count} 个表达方式") @@ -544,12 +544,12 @@ class ExpressionLearner: ) ) existing_exprs = list(existing_exprs_result.scalars()) - + # 构建快速查找索引 exact_match_map = {} # (situation, style) -> Expression situation_map = {} # situation -> Expression style_map = {} # style -> Expression - + for expr in existing_exprs: key = (expr.situation, expr.style) exact_match_map[key] = expr @@ -558,13 +558,13 @@ class ExpressionLearner: situation_map[expr.situation] = expr if expr.style not in style_map: style_map[expr.style] = expr - + # 批量处理所有新表达方式 for new_expr in expr_list: situation = new_expr["situation"] style_val = new_expr["style"] exact_key = (situation, style_val) - + # 优先处理完全匹配的情况 if exact_key in exact_match_map: # 完全相同：增加count，更新时间 @@ -578,8 +578,7 @@ class ExpressionLearner: logger.info(f"相同情景覆盖：'{same_situation_expr.situation}' 的表达从 '{same_situation_expr.style}' 更新为 '{style_val}'") # 更新映射 old_key = (same_situation_expr.situation, same_situation_expr.style) - if old_key in exact_match_map: - del exact_match_map[old_key] + exact_match_map.pop(old_key, None) same_situation_expr.style = style_val same_situation_expr.count = same_situation_expr.count + 1 same_situation_expr.last_active_time = current_time @@ -591,8 +590,7 @@ class ExpressionLearner: logger.info(f"相同表达覆盖：'{same_style_expr.style}' 的情景从 '{same_style_expr.situation}' 更新为 '{situation}'") # 更新映射 old_key = (same_style_expr.situation, same_style_expr.style) - if old_key in exact_match_map: - del exact_match_map[old_key] + exact_match_map.pop(old_key, None) same_style_expr.situation = situation same_style_expr.count = same_style_expr.count + 1 same_style_expr.last_active_time = current_time @@ -627,8 +625,7 @@ class ExpressionLearner: await session.delete(expr) # 从映射中移除 key = (expr.situation, expr.style) - if key in exact_match_map: - del exact_match_map[key] + exact_match_map.pop(key, None) logger.debug(f"已删除 {len(all_current_exprs) - MAX_EXPRESSION_COUNT} 个低频表达方式") # 提交数据库更改 @@ -658,31 +655,31 @@ class ExpressionLearner: # 为每个共享组内的 chat_id 训练其 StyleLearner for target_chat_id in related_chat_ids: learner = style_learner_manager.get_learner(target_chat_id) - + # 收集该 target_chat_id 对应的所有表达方式 # 如果是源 chat_id，使用 chat_dict 中的数据；否则也要训练（共享组特性） total_success = 0 total_samples = 0 - + for source_chat_id, expr_list in chat_dict.items(): # 为每个学习到的表达方式训练模型 # 使用 situation 作为输入，style 作为目标 for expr in expr_list: situation = expr["situation"] style = expr["style"] - + # 训练映射关系: situation -> style if learner.learn_mapping(situation, style): total_success += 1 total_samples += 1 - + # 保存模型 if total_samples > 0: if learner.save(style_learner_manager.model_save_path): logger.debug(f"StyleLearner 模型保存成功: {target_chat_id}") else: logger.error(f"StyleLearner 模型保存失败: {target_chat_id}") - + if target_chat_id == self.chat_id: # 只为当前 chat_id 记录详细日志 logger.info( diff --git a/src/chat/express/expression_selector.py b/src/chat/express/expression_selector.py index cfe335cd4..ceeeb4e2e 100644 --- a/src/chat/express/expression_selector.py +++ b/src/chat/express/expression_selector.py @@ -218,7 +218,7 @@ class ExpressionSelector: "type": expr_type, "create_date": expr.create_date if expr.create_date is not None else expr.last_active_time, } - + style_exprs = [expr_to_dict(expr, "style") for expr in style_query.scalars()] grammar_exprs = [expr_to_dict(expr, "grammar") for expr in grammar_query.scalars()] @@ -246,7 +246,7 @@ class ExpressionSelector: """ if not expressions_to_update: return - + # 去重处理 updates_by_key = {} affected_chat_ids = set() @@ -524,7 +524,7 @@ class ExpressionSelector: # 预处理：提前计算所有预测 style 的小写版本，避免重复计算 predicted_styles_lower = [(s.lower(), score) for s, score in predicted_styles[:20]] - + matched_expressions = [] for expr in all_expressions: db_style = expr.style or "" @@ -539,7 +539,7 @@ class ExpressionSelector: max_similarity = 1.0 best_predicted = predicted_style_lower break - + # 快速检查：子串匹配 if len(predicted_style_lower) >= 2 and len(db_style_lower) >= 2: if predicted_style_lower in db_style_lower or db_style_lower in predicted_style_lower: @@ -548,7 +548,7 @@ class ExpressionSelector: max_similarity = similarity best_predicted = predicted_style_lower continue - + # 计算字符串相似度（较慢，只在必要时使用） similarity = SequenceMatcher(None, predicted_style_lower, db_style_lower).ratio() if similarity > max_similarity: diff --git a/src/chat/interest_system/interest_manager.py b/src/chat/interest_system/interest_manager.py index ddcec1465..f25f8a39b 100644 --- a/src/chat/interest_system/interest_manager.py +++ b/src/chat/interest_system/interest_manager.py @@ -38,7 +38,7 @@ class InterestManager: self._calculation_queue = asyncio.Queue() self._worker_task = None self._shutdown_event = asyncio.Event() - + # 性能优化相关字段 self._result_cache: OrderedDict[str, InterestCalculationResult] = OrderedDict() # LRU缓存 self._cache_max_size = 1000 # 最大缓存数量 @@ -48,13 +48,13 @@ class InterestManager: self._batch_timeout = 0.1 # 批处理超时（秒） self._batch_task = None self._is_warmed_up = False # 预热状态标记 - + # 性能统计 self._cache_hits = 0 self._cache_misses = 0 self._batch_calculations = 0 self._total_calculation_time = 0.0 - + self._initialized = True async def initialize(self): @@ -67,7 +67,7 @@ class InterestManager: async def shutdown(self): """关闭管理器""" self._shutdown_event.set() - + # 取消批处理任务 if self._batch_task and not self._batch_task.done(): self._batch_task.cancel() @@ -79,7 +79,7 @@ class InterestManager: if self._current_calculator: await self._current_calculator.cleanup() self._current_calculator = None - + # 清理缓存 self._result_cache.clear() @@ -142,9 +142,9 @@ class InterestManager: interest_value=0.3, error_message="没有可用的兴趣值计算组件", ) - + message_id = getattr(message, "message_id", "") - + # 缓存查询 if use_cache and message_id: cached_result = self._get_from_cache(message_id) @@ -183,11 +183,11 @@ class InterestManager: interest_value=0.3, error_message=f"计算异常: {e!s}", ) - + # 缓存结果 if use_cache and result.success and message_id: self._put_to_cache(message_id, result) - + return result async def _async_calculate(self, message: "DatabaseMessages") -> InterestCalculationResult: @@ -249,36 +249,36 @@ class InterestManager: break except Exception as e: logger.error(f"计算工作线程异常: {e}") - + def _get_from_cache(self, message_id: str) -> InterestCalculationResult | None: """从缓存中获取结果（LRU策略）""" if message_id not in self._result_cache: return None - + # 检查TTL result = self._result_cache[message_id] if time.time() - result.timestamp > self._cache_ttl: # 过期，删除 del self._result_cache[message_id] return None - + # 更新访问顺序（LRU） self._result_cache.move_to_end(message_id) return result - + def _put_to_cache(self, message_id: str, result: InterestCalculationResult): """将结果放入缓存（LRU策略）""" # 如果已存在，更新 if message_id in self._result_cache: self._result_cache.move_to_end(message_id) - + self._result_cache[message_id] = result - + # 限制缓存大小 while len(self._result_cache) > self._cache_max_size: # 删除最旧的项 self._result_cache.popitem(last=False) - + async def calculate_interest_batch(self, messages: list["DatabaseMessages"], timeout: float | None = None) -> list[InterestCalculationResult]: """批量计算消息兴趣值（并发优化） @@ -291,11 +291,11 @@ class InterestManager: """ if not messages: return [] - + # 并发计算所有消息 tasks = [self.calculate_interest(msg, timeout=timeout) for msg in messages] results = await asyncio.gather(*tasks, return_exceptions=True) - + # 处理异常 final_results = [] for i, result in enumerate(results): @@ -309,44 +309,44 @@ class InterestManager: )) else: final_results.append(result) - + self._batch_calculations += 1 return final_results - + async def _batch_processing_worker(self): """批处理工作线程""" while not self._shutdown_event.is_set(): batch = [] deadline = time.time() + self._batch_timeout - + try: # 收集批次 while len(batch) < self._batch_size and time.time() < deadline: remaining_time = deadline - time.time() if remaining_time <= 0: break - + try: item = await asyncio.wait_for(self._batch_queue.get(), timeout=remaining_time) batch.append(item) except asyncio.TimeoutError: break - + # 处理批次 if batch: await self._process_batch(batch) - + except asyncio.CancelledError: break except Exception as e: logger.error(f"批处理工作线程异常: {e}") - + async def _process_batch(self, batch: list): """处理批次消息""" # 这里可以实现具体的批处理逻辑 # 当前版本只是占位，实际的批处理逻辑可以根据具体需求实现 pass - + async def warmup(self, sample_messages: list["DatabaseMessages"] | None = None): """预热兴趣计算器 @@ -356,10 +356,10 @@ class InterestManager: if not self._current_calculator: logger.warning("无法预热：没有可用的兴趣值计算组件") return - + logger.info("开始预热兴趣值计算器...") start_time = time.time() - + # 如果提供了样本消息，进行预热计算 if sample_messages: try: @@ -370,15 +370,15 @@ class InterestManager: logger.error(f"预热过程中出现异常: {e}") else: logger.info(f"预热完成：计算器已就绪，耗时 {time.time() - start_time:.2f}s") - + self._is_warmed_up = True - + def clear_cache(self): """清空缓存""" cleared_count = len(self._result_cache) self._result_cache.clear() logger.info(f"已清空 {cleared_count} 条缓存记录") - + def set_cache_config(self, max_size: int | None = None, ttl: int | None = None): """设置缓存配置 @@ -389,11 +389,11 @@ class InterestManager: if max_size is not None: self._cache_max_size = max_size logger.info(f"缓存最大容量设置为: {max_size}") - + if ttl is not None: self._cache_ttl = ttl logger.info(f"缓存TTL设置为: {ttl}秒") - + # 如果当前缓存超过新的最大值，清理旧数据 if max_size is not None: while len(self._result_cache) > self._cache_max_size: @@ -446,14 +446,14 @@ class InterestManager: def has_calculator(self) -> bool: """检查是否有可用的计算组件""" return self._current_calculator is not None and self._current_calculator.is_enabled - + async def adaptive_optimize(self): """自适应优化：根据性能统计自动调整参数""" if not self._current_calculator: return - + stats = self.get_statistics()["manager_statistics"] - + # 根据缓存命中率调整缓存大小 cache_hit_rate = stats["cache_hit_rate"] if cache_hit_rate < 0.5 and self._cache_max_size < 5000: @@ -469,7 +469,7 @@ class InterestManager: # 清理多余缓存 while len(self._result_cache) > self._cache_max_size: self._result_cache.popitem(last=False) - + # 根据平均计算时间调整批处理参数 avg_calc_time = stats["average_calculation_time"] if avg_calc_time > 0.5 and self._batch_size < 50: @@ -482,11 +482,11 @@ class InterestManager: new_batch_size = max(self._batch_size // 2, 5) logger.info(f"自适应优化：平均计算时间较短 ({avg_calc_time:.3f}s)，减小批次大小 {self._batch_size} -> {new_batch_size}") self._batch_size = new_batch_size - + def get_performance_report(self) -> str: """生成性能报告""" stats = self.get_statistics()["manager_statistics"] - + report = [ "=" * 60, "兴趣值管理器性能报告", @@ -504,7 +504,7 @@ class InterestManager: f"当前计算器: {stats['current_calculator'] or '无'}", "=" * 60, ] - + # 添加计算器统计 if self._current_calculator: calc_stats = self.get_statistics()["calculator_statistics"] @@ -520,7 +520,7 @@ class InterestManager: f" 平均耗时: {calc_stats['average_calculation_time']:.4f}s", "=" * 60, ]) - + return "\n".join(report) diff --git a/src/chat/message_manager/message_manager.py b/src/chat/message_manager/message_manager.py index 094ef9f3d..252fed382 100644 --- a/src/chat/message_manager/message_manager.py +++ b/src/chat/message_manager/message_manager.py @@ -30,7 +30,7 @@ logger = get_logger("message_manager") class MessageManager: """消息管理器""" - def __init__(self, check_interval: float = 5.0): + def __init__(self, check_interval: float = 5.0): self.check_interval = check_interval # 检查间隔（秒） self.is_running = False self.manager_task: asyncio.Task | None = None diff --git a/src/chat/utils/statistic.py b/src/chat/utils/statistic.py index b67378439..dd0031351 100644 --- a/src/chat/utils/statistic.py +++ b/src/chat/utils/statistic.py @@ -348,12 +348,12 @@ class StatisticOutputTask(AsyncTask): prompt_tokens = int(record.get("prompt_tokens") or 0) except (ValueError, TypeError): prompt_tokens = 0 - + try: completion_tokens = int(record.get("completion_tokens") or 0) except (ValueError, TypeError): completion_tokens = 0 - + total_tokens = prompt_tokens + completion_tokens stats[period_key][IN_TOK_BY_TYPE][request_type] += prompt_tokens @@ -378,7 +378,7 @@ class StatisticOutputTask(AsyncTask): cost = float(cost) if cost else 0.0 except (ValueError, TypeError): cost = 0.0 - + stats[period_key][TOTAL_COST] += cost stats[period_key][COST_BY_TYPE][request_type] += cost stats[period_key][COST_BY_USER][user_id] += cost diff --git a/src/memory_graph/long_term_manager.py b/src/memory_graph/long_term_manager.py index 15d58502e..f7bb99c87 100644 --- a/src/memory_graph/long_term_manager.py +++ b/src/memory_graph/long_term_manager.py @@ -969,7 +969,7 @@ class LongTermMemoryManager: content=f"临时节点 - {source_id}", metadata={"placeholder": True, "created_by": "long_term_manager_edge_creation"} ) - + if not self.memory_manager.graph_store.graph.has_node(target_id): logger.debug(f"目标节点不存在，创建占位符节点: {target_id}") self.memory_manager.graph_store.add_node( diff --git a/src/memory_graph/manager.py b/src/memory_graph/manager.py index b46d10575..9c3d180a8 100644 --- a/src/memory_graph/manager.py +++ b/src/memory_graph/manager.py @@ -1,4 +1,3 @@ -# ruff: noqa: G004, BLE001 # pylint: disable=logging-fstring-interpolation,broad-except,unused-argument # pyright: reportOptionalMemberAccess=false """ diff --git a/src/memory_graph/short_term_manager.py b/src/memory_graph/short_term_manager.py index 3a5711430..7dc4834f9 100644 --- a/src/memory_graph/short_term_manager.py +++ b/src/memory_graph/short_term_manager.py @@ -658,7 +658,7 @@ class ShortTermMemoryManager: return self._get_transfer_all_strategy() else: # "selective_cleanup" 或其他值默认使用选择性清理 return self._get_selective_cleanup_strategy() - + def _get_transfer_all_strategy(self) -> list[ShortTermMemory]: """ "一次性转移所有"策略：当短期记忆满了以后，将所有记忆转移到长期记忆 @@ -673,24 +673,24 @@ class ShortTermMemoryManager: f"将转移所有 {len(self.memories)} 条记忆到长期记忆" ) return self.memories.copy() - + # 如果还没满，检查是否有高重要性记忆需要转移 high_importance_memories = [ - mem for mem in self.memories + mem for mem in self.memories if mem.importance >= self.transfer_importance_threshold ] - + if high_importance_memories: logger.debug( f"转移策略(transfer_all): 发现 {len(high_importance_memories)} 条高重要性记忆待转移" ) return high_importance_memories - + logger.debug( f"转移策略(transfer_all): 无需转移 (当前容量 {len(self.memories)}/{self.max_memories})" ) return [] - + def _get_selective_cleanup_strategy(self) -> list[ShortTermMemory]: """ "选择性清理"策略（原有策略）：优先转移重要记忆，低重要性记忆考虑直接删除 @@ -720,11 +720,11 @@ class ShortTermMemoryManager: if len(self.memories) > self.max_memories: # 计算需要转移的数量（目标：降到上限） num_to_transfer = len(self.memories) - self.max_memories - + # 按创建时间排序低重要性记忆，优先转移最早的（可能包含过时信息） low_importance_memories.sort(key=lambda x: x.created_at) to_transfer = low_importance_memories[:num_to_transfer] - + if to_transfer: logger.debug( f"转移策略(selective): 发现 {len(to_transfer)} 条低重要性记忆待转移 " @@ -757,7 +757,7 @@ class ShortTermMemoryManager: # 使用实例配置或传入参数 if keep_ratio is None: keep_ratio = self.cleanup_keep_ratio - + current = len(self.memories) limit = int(self.max_memories * keep_ratio) if current <= self.max_memories: @@ -804,28 +804,28 @@ class ShortTermMemoryManager: self._similarity_cache.pop(mem_id, None) logger.info(f"清除 {len(memory_ids)} 条已转移的短期记忆") - + # 在 "transfer_all" 策略下，进一步删除不重要的短期记忆 if self.overflow_strategy == "transfer_all": # 计算需要删除的低重要性记忆数量 low_importance_memories = [ - mem for mem in self.memories + mem for mem in self.memories if mem.importance < self.transfer_importance_threshold ] - + if low_importance_memories: # 按重要性和创建时间排序，删除最不重要的 low_importance_memories.sort(key=lambda m: (m.importance, m.created_at)) - + # 删除所有低重要性记忆 to_delete = {mem.id for mem in low_importance_memories} self.memories = [mem for mem in self.memories if mem.id not in to_delete] - + # 更新索引 for mem_id in to_delete: self._memory_id_index.pop(mem_id, None) self._similarity_cache.pop(mem_id, None) - + logger.info( f"transfer_all 策略: 额外删除了 {len(to_delete)} 条低重要性记忆 " f"(重要性 < {self.transfer_importance_threshold:.2f})" diff --git a/src/memory_graph/storage/graph_store.py b/src/memory_graph/storage/graph_store.py index 3b4fbcae6..0fb83a522 100644 --- a/src/memory_graph/storage/graph_store.py +++ b/src/memory_graph/storage/graph_store.py @@ -936,7 +936,7 @@ class GraphStore: edge_type_enum = EdgeType.RELATION else: edge_type_enum = edge_type_value - + mem_edge = MemoryEdge( id=edge_dict["id"] or "", source_id=edge_dict["source_id"], diff --git a/src/plugin_system/base/base_interest_calculator.py b/src/plugin_system/base/base_interest_calculator.py index c8192a74f..53feb5008 100644 --- a/src/plugin_system/base/base_interest_calculator.py +++ b/src/plugin_system/base/base_interest_calculator.py @@ -124,7 +124,7 @@ class BaseInterestCalculator(ABC): logger.error(f"初始化兴趣计算器失败: {e}") self._enabled = False return False - + async def on_initialize(self): """子类可重写的初始化钩子""" pass @@ -143,7 +143,7 @@ class BaseInterestCalculator(ABC): except Exception as e: logger.error(f"清理兴趣计算器失败: {e}") return False - + async def on_cleanup(self): """子类可重写的清理钩子""" pass diff --git a/src/plugins/built_in/maizone_refactored/plugin.py b/src/plugins/built_in/maizone_refactored/plugin.py index 91d752464..ba2c8f65c 100644 --- a/src/plugins/built_in/maizone_refactored/plugin.py +++ b/src/plugins/built_in/maizone_refactored/plugin.py @@ -3,7 +3,6 @@ MaiZone（麦麦空间）- 重构版 """ import asyncio -from pathlib import Path from src.common.logger import get_logger from src.plugin_system import BasePlugin, ComponentInfo, register_plugin diff --git a/src/plugins/built_in/maizone_refactored/services/content_service.py b/src/plugins/built_in/maizone_refactored/services/content_service.py index dd29cd4ae..557cc77b3 100644 --- a/src/plugins/built_in/maizone_refactored/services/content_service.py +++ b/src/plugins/built_in/maizone_refactored/services/content_service.py @@ -151,7 +151,7 @@ class ContentService: bot_personality_side = config_api.get_global_config("personality.personality_side", "") bot_reply_style = config_api.get_global_config("personality.reply_style", "内容积极向上") qq_account = config_api.get_global_config("bot.qq_account", "") - + # 获取角色外貌描述（用于告知LLM） character_prompt = self.get_config("novelai.character_prompt", "") @@ -163,21 +163,21 @@ class ContentService: # 构建提示词 prompt_topic = f"主题是'{topic}'" if topic else "主题不限" - + # 构建人设描述 personality_desc = f"你的核心人格：{bot_personality_core}" if bot_personality_side: personality_desc += f"\n你的人格侧面：{bot_personality_side}" personality_desc += f"\n\n你的表达方式：{bot_reply_style}" - + # 检查是否启用AI配图（统一开关） ai_image_enabled = self.get_config("ai_image.enable_ai_image", False) provider = self.get_config("ai_image.provider", "siliconflow") - + # NovelAI配图指引（内置） novelai_guide = "" output_format = '{"text": "说说正文内容"}' - + if ai_image_enabled and provider == "novelai": # 构建角色信息提示 character_info = "" @@ -195,7 +195,7 @@ class ContentService: - 例如：可以搭配各种表情（smile, laugh, serious, thinking, surprised等） - **鼓励创意**：根据说说内容自由发挥，让画面更丰富生动！ """ - + novelai_guide = f""" **配图说明：** 这条说说会使用NovelAI Diffusion模型（二次元风格）生成配图。 @@ -258,7 +258,7 @@ class ContentService: - 运动风："masterpiece, best quality, 1girl, sportswear, running in park, energetic, morning light, trees background, dynamic pose, healthy lifestyle" - 咖啡馆："masterpiece, best quality, 1girl, sitting in cozy cafe, holding coffee cup, warm lighting, wooden table, books beside, peaceful atmosphere" """ - output_format = '''{"text": "说说正文内容", "image": {"prompt": "详细的英文提示词（包含画质+主体+场景+氛围+光线+色彩）", "negative_prompt": "负面词", "include_character": true/false, "aspect_ratio": "方图/横图/竖图"}}''' + output_format = """{"text": "说说正文内容", "image": {"prompt": "详细的英文提示词（包含画质+主体+场景+氛围+光线+色彩）", "negative_prompt": "负面词", "include_character": true/false, "aspect_ratio": "方图/横图/竖图"}}""" elif ai_image_enabled and provider == "siliconflow": novelai_guide = """ **配图说明：** @@ -277,8 +277,8 @@ class ContentService: - "sunset over the calm ocean, golden hour, orange and purple sky, gentle waves, peaceful and serene mood, wide angle view" - "cherry blossoms in spring, soft pink petals falling, blue sky, sunlight filtering through branches, peaceful park scene, gentle breeze" """ - output_format = '''{"text": "说说正文内容", "image": {"prompt": "详细的英文描述（主体+场景+氛围+光线+细节）"}}''' - + output_format = """{"text": "说说正文内容", "image": {"prompt": "详细的英文描述（主体+场景+氛围+光线+细节）"}}""" + prompt = f""" {personality_desc} @@ -333,20 +333,20 @@ class ContentService: if json_text.endswith("```"): json_text = json_text[:-3] json_text = json_text.strip() - + data = json5.loads(json_text) story_text = data.get("text", "") image_info = data.get("image", {}) - + # 确保图片信息完整 if not isinstance(image_info, dict): image_info = {} - + logger.info(f"成功生成说说：'{story_text}'") logger.info(f"配图信息: {image_info}") - + return story_text, image_info - + except Exception as e: logger.error(f"解析JSON失败: {e}, 原始响应: {response[:200]}") # 降级处理：只返回文本，空配图信息 diff --git a/src/plugins/built_in/maizone_refactored/services/image_service.py b/src/plugins/built_in/maizone_refactored/services/image_service.py index ba6eeb9f5..dce886e02 100644 --- a/src/plugins/built_in/maizone_refactored/services/image_service.py +++ b/src/plugins/built_in/maizone_refactored/services/image_service.py @@ -42,7 +42,7 @@ class ImageService: try: api_key = str(self.get_config("siliconflow.api_key", "")) image_num = self.get_config("ai_image.image_number", 1) - + if not api_key: logger.warning("硅基流动API未配置，跳过图片生成") return False, None @@ -237,7 +237,7 @@ class ImageService: image.save(save_path, format="PNG") logger.info(f"图片已保存至: {save_path}") success_count += 1 - + # 记录第一张图片路径 if first_img_path is None: first_img_path = save_path diff --git a/src/plugins/built_in/maizone_refactored/services/novelai_service.py b/src/plugins/built_in/maizone_refactored/services/novelai_service.py index 58c3168cf..4a56f94c6 100644 --- a/src/plugins/built_in/maizone_refactored/services/novelai_service.py +++ b/src/plugins/built_in/maizone_refactored/services/novelai_service.py @@ -2,14 +2,11 @@ NovelAI图片生成服务 - 空间插件专用 独立实现，不依赖其他插件 """ -import asyncio -import base64 +import io import random import uuid import zipfile -import io from pathlib import Path -from typing import Optional import aiohttp from PIL import Image @@ -21,50 +18,50 @@ logger = get_logger("MaiZone.NovelAIService") class MaiZoneNovelAIService: """空间插件的NovelAI图片生成服务（独立实现）""" - + def __init__(self, get_config): self.get_config = get_config - + # NovelAI配置 self.api_key = self.get_config("novelai.api_key", "") self.base_url = "https://image.novelai.net/ai/generate-image" self.model = "nai-diffusion-4-5-full" - + # 代理配置 proxy_host = self.get_config("novelai.proxy_host", "") proxy_port = self.get_config("novelai.proxy_port", 0) self.proxy = f"http://{proxy_host}:{proxy_port}" if proxy_host and proxy_port else "" - + # 生成参数 self.steps = 28 self.scale = 5.0 self.sampler = "k_euler" self.noise_schedule = "karras" - + # 角色提示词（当LLM决定包含角色时使用） self.character_prompt = self.get_config("novelai.character_prompt", "") self.base_negative_prompt = self.get_config("novelai.base_negative_prompt", "nsfw, nude, explicit, sexual content, lowres, bad anatomy, bad hands") - + # 图片保存目录（使用统一配置） plugin_dir = Path(__file__).parent.parent self.image_dir = plugin_dir / "images" self.image_dir.mkdir(parents=True, exist_ok=True) - + if self.api_key: logger.info(f"NovelAI图片生成已配置，模型: {self.model}") - + def is_available(self) -> bool: """检查NovelAI服务是否可用""" return bool(self.api_key) - + async def generate_image_from_prompt_data( self, prompt: str, - negative_prompt: Optional[str] = None, + negative_prompt: str | None = None, include_character: bool = False, width: int = 1024, height: int = 1024 - ) -> tuple[bool, Optional[Path], str]: + ) -> tuple[bool, Path | None, str]: """根据提示词生成图片 Args: @@ -79,14 +76,14 @@ class MaiZoneNovelAIService: """ if not self.api_key: return False, None, "NovelAI API Key未配置" - + try: # 处理角色提示词 final_prompt = prompt if include_character and self.character_prompt: final_prompt = f"{self.character_prompt}, {prompt}" - logger.info(f"包含角色形象，添加角色提示词") - + logger.info("包含角色形象，添加角色提示词") + # 合并负面提示词 final_negative = self.base_negative_prompt if negative_prompt: @@ -94,37 +91,37 @@ class MaiZoneNovelAIService: final_negative = f"{final_negative}, {negative_prompt}" else: final_negative = negative_prompt - - logger.info(f"🎨 开始生成图片...") + + logger.info("🎨 开始生成图片...") logger.info(f" 尺寸: {width}x{height}") logger.info(f" 正面提示词: {final_prompt[:100]}...") logger.info(f" 负面提示词: {final_negative[:100]}...") - + # 构建请求payload payload = self._build_payload(final_prompt, final_negative, width, height) - + # 发送请求 image_data = await self._call_novelai_api(payload) if not image_data: return False, None, "API请求失败" - + # 保存图片 image_path = await self._save_image(image_data) if not image_path: return False, None, "图片保存失败" - + logger.info(f"✅ 图片生成成功: {image_path}") return True, image_path, "生成成功" - + except Exception as e: logger.error(f"生成图片时出错: {e}", exc_info=True) - return False, None, f"生成失败: {str(e)}" - + return False, None, f"生成失败: {e!s}" + def _build_payload(self, prompt: str, negative_prompt: str, width: int, height: int) -> dict: """构建NovelAI API请求payload""" is_v4_model = "diffusion-4" in self.model is_v3_model = "diffusion-3" in self.model - + parameters = { "width": width, "height": height, @@ -139,7 +136,7 @@ class MaiZoneNovelAIService: "sm_dyn": False, "noise_schedule": self.noise_schedule if is_v4_model else "native", } - + # V4.5模型使用新格式 if is_v4_model: parameters.update({ @@ -183,39 +180,39 @@ class MaiZoneNovelAIService: # V3使用negative_prompt字段 elif is_v3_model: parameters["negative_prompt"] = negative_prompt - + payload = { "input": prompt, "model": self.model, "action": "generate", "parameters": parameters } - + # V4.5需要额外字段 if is_v4_model: payload["use_new_shared_trial"] = True - + return payload - - async def _call_novelai_api(self, payload: dict) -> Optional[bytes]: + + async def _call_novelai_api(self, payload: dict) -> bytes | None: """调用NovelAI API""" headers = { "Authorization": f"Bearer {self.api_key}", "Content-Type": "application/json", } - + connector = None request_kwargs = { "json": payload, "headers": headers, "timeout": aiohttp.ClientTimeout(total=120) } - + if self.proxy: request_kwargs["proxy"] = self.proxy connector = aiohttp.TCPConnector() logger.info(f"使用代理: {self.proxy}") - + try: async with aiohttp.ClientSession(connector=connector) as session: async with session.post(self.base_url, **request_kwargs) as resp: @@ -223,31 +220,31 @@ class MaiZoneNovelAIService: error_text = await resp.text() logger.error(f"API请求失败 ({resp.status}): {error_text[:200]}") return None - + img_data = await resp.read() logger.info(f"收到响应数据: {len(img_data)} bytes") - + # 检查是否是ZIP文件 - if img_data[:4] == b'PK\x03\x04': + if img_data[:4] == b"PK\x03\x04": logger.info("检测到ZIP格式，解压中...") return self._extract_from_zip(img_data) - elif img_data[:4] == b'\x89PNG': + elif img_data[:4] == b"\x89PNG": logger.info("检测到PNG格式") return img_data else: logger.warning(f"未知文件格式，前4字节: {img_data[:4].hex()}") return img_data - + except Exception as e: logger.error(f"API调用失败: {e}", exc_info=True) return None - - def _extract_from_zip(self, zip_data: bytes) -> Optional[bytes]: + + def _extract_from_zip(self, zip_data: bytes) -> bytes | None: """从ZIP中提取PNG""" try: with zipfile.ZipFile(io.BytesIO(zip_data)) as zf: for filename in zf.namelist(): - if filename.lower().endswith('.png'): + if filename.lower().endswith(".png"): img_data = zf.read(filename) logger.info(f"从ZIP提取: {filename} ({len(img_data)} bytes)") return img_data @@ -256,20 +253,20 @@ class MaiZoneNovelAIService: except Exception as e: logger.error(f"解压ZIP失败: {e}") return None - - async def _save_image(self, image_data: bytes) -> Optional[Path]: + + async def _save_image(self, image_data: bytes) -> Path | None: """保存图片到本地""" try: filename = f"novelai_{uuid.uuid4().hex[:12]}.png" filepath = self.image_dir / filename - + # 写入文件 with open(filepath, "wb") as f: f.write(image_data) f.flush() import os os.fsync(f.fileno()) - + # 验证图片 try: with Image.open(filepath) as img: @@ -278,9 +275,9 @@ class MaiZoneNovelAIService: logger.info(f"图片验证成功: {img.format} {img.size}") except Exception as e: logger.warning(f"图片验证失败: {e}") - + return filepath - + except Exception as e: logger.error(f"保存图片失败: {e}") return None diff --git a/src/plugins/built_in/maizone_refactored/services/qzone_service.py b/src/plugins/built_in/maizone_refactored/services/qzone_service.py index 4bb3c75af..74f1692bd 100644 --- a/src/plugins/built_in/maizone_refactored/services/qzone_service.py +++ b/src/plugins/built_in/maizone_refactored/services/qzone_service.py @@ -5,7 +5,6 @@ QQ空间服务模块 import asyncio import base64 -import os import random import time from collections.abc import Callable @@ -85,25 +84,25 @@ class QZoneService: async def send_feed(self, topic: str, stream_id: str | None) -> dict[str, Any]: """发送一条说说（支持AI配图）""" cross_context = await self._get_cross_context() - + # 检查是否启用AI配图 ai_image_enabled = self.get_config("ai_image.enable_ai_image", False) provider = self.get_config("ai_image.provider", "siliconflow") - + image_path = None - + if ai_image_enabled: # 启用AI配图：文本模型生成说说+图片提示词 story, image_info = await self.content_service.generate_story_with_image_info(topic, context=cross_context) if not story: return {"success": False, "message": "生成说说内容失败"} - + # 根据provider调用对应的生图服务 if provider == "novelai": try: from .novelai_service import MaiZoneNovelAIService novelai_service = MaiZoneNovelAIService(self.get_config) - + if novelai_service.is_available(): # 解析画幅 aspect_ratio = image_info.get("aspect_ratio", "方图") @@ -113,8 +112,8 @@ class QZoneService: "竖图": (832, 1216), } width, height = size_map.get(aspect_ratio, (1024, 1024)) - - logger.info(f"🎨 开始生成NovelAI配图...") + + logger.info("🎨 开始生成NovelAI配图...") success, img_path, msg = await novelai_service.generate_image_from_prompt_data( prompt=image_info.get("prompt", ""), negative_prompt=image_info.get("negative_prompt"), @@ -122,18 +121,18 @@ class QZoneService: width=width, height=height ) - + if success and img_path: image_path = img_path - logger.info(f"✅ NovelAI配图生成成功") + logger.info("✅ NovelAI配图生成成功") else: logger.warning(f"⚠️ NovelAI配图生成失败: {msg}") else: logger.warning("NovelAI服务不可用（未配置API Key）") - + except Exception as e: logger.error(f"NovelAI配图生成出错: {e}", exc_info=True) - + elif provider == "siliconflow": try: # 调用硅基流动生成图片 @@ -143,9 +142,9 @@ class QZoneService: ) if success and img_path: image_path = img_path - logger.info(f"✅ 硅基流动配图生成成功") + logger.info("✅ 硅基流动配图生成成功") else: - logger.warning(f"⚠️ 硅基流动配图生成失败") + logger.warning("⚠️ 硅基流动配图生成失败") except Exception as e: logger.error(f"硅基流动配图生成出错: {e}", exc_info=True) else: @@ -161,13 +160,13 @@ class QZoneService: # 加载图片 images_bytes = [] - + # 使用AI生成的图片 if image_path and image_path.exists(): try: with open(image_path, "rb") as f: images_bytes.append(f.read()) - logger.info(f"添加AI配图到说说") + logger.info("添加AI配图到说说") except Exception as e: logger.error(f"读取AI配图失败: {e}") diff --git a/src/plugins/built_in/napcat_adapter/plugin.py b/src/plugins/built_in/napcat_adapter/plugin.py index ee8cdfc4a..baac2f069 100644 --- a/src/plugins/built_in/napcat_adapter/plugin.py +++ b/src/plugins/built_in/napcat_adapter/plugin.py @@ -416,18 +416,18 @@ class NapcatAdapterPlugin(BasePlugin): "reply_at_rate": ConfigField(type=float, default=0.5, description="回复时@的概率（0.0-1.0）"), # ========== 视频消息处理配置 ========== "enable_video_processing": ConfigField( - type=bool, - default=True, + type=bool, + default=True, description="是否启用视频消息处理（下载和解析）。关闭后视频消息将显示为 [视频消息] 占位符，不会进行下载" ), "video_max_size_mb": ConfigField( - type=int, - default=100, + type=int, + default=100, description="允许下载的视频文件最大大小（MB），超过此大小的视频将被跳过" ), "video_download_timeout": ConfigField( - type=int, - default=60, + type=int, + default=60, description="视频下载超时时间（秒），若超时将中止下载" ), }, diff --git a/src/plugins/built_in/napcat_adapter/src/handlers/to_core/message_handler.py b/src/plugins/built_in/napcat_adapter/src/handlers/to_core/message_handler.py index 864960f6e..c820dfb57 100644 --- a/src/plugins/built_in/napcat_adapter/src/handlers/to_core/message_handler.py +++ b/src/plugins/built_in/napcat_adapter/src/handlers/to_core/message_handler.py @@ -42,14 +42,14 @@ class MessageHandler: def set_plugin_config(self, config: dict[str, Any]) -> None: """设置插件配置，并根据配置初始化视频下载器""" self.plugin_config = config - + # 如果启用了视频处理，根据配置初始化视频下载器 if config_api.get_plugin_config(config, "features.enable_video_processing", True): from ..video_handler import VideoDownloader - + max_size = config_api.get_plugin_config(config, "features.video_max_size_mb", 100) timeout = config_api.get_plugin_config(config, "features.video_download_timeout", 60) - + self._video_downloader = VideoDownloader(max_size_mb=max_size, download_timeout=timeout) logger.debug(f"视频下载器已初始化: max_size={max_size}MB, timeout={timeout}s") @@ -341,7 +341,7 @@ class MessageHandler: if not downloader: from ..video_handler import get_video_downloader downloader = get_video_downloader() - + download_result = await downloader.download_video(video_url) if not download_result["success"]: