refactor(memory): 完全移除旧记忆系统，全面切换到新记忆图系统
重大变更: - 移除 default_generator.py 中对旧增强记忆系统的所有调用 - 移除 prompt.py 中的记忆构建函数 - 记忆检索全面使用新记忆图系统 - 禁用旧记忆系统的自动存储逻辑 记忆构建流程: - 记忆创建: LLM 通过 create_memory 工具主动创建 - 记忆检索: default_generator.py 自动检索 - 记忆传递: 通过 pre_built_params 传入 prompt 工具可用性: - CreateMemoryTool: available_for_llm = True - LinkMemoriesTool: available_for_llm = False - SearchMemoriesTool: available_for_llm = False 技术细节: - 提高记忆检索数量 top_k=10 - 降低重要性阈值 min_importance=0.3 - 修复类型错误和警告
This commit is contained in:
Windpicker-owo
@@ -555,164 +555,8 @@ class DefaultReplyer:
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instant_memory = None
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# 使用新的增强记忆系统检索记忆
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running_memories = []
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instant_memory = None
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if global_config.memory.enable_memory:
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try:
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# 使用新的统一记忆系统
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from src.chat.memory_system import get_memory_system
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stream = self.chat_stream
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user_info_obj = getattr(stream, "user_info", None)
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group_info_obj = getattr(stream, "group_info", None)
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memory_user_id = str(stream.stream_id)
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memory_user_display = None
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memory_aliases = []
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user_info_dict = {}
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if user_info_obj is not None:
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raw_user_id = getattr(user_info_obj, "user_id", None)
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if raw_user_id:
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memory_user_id = str(raw_user_id)
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if hasattr(user_info_obj, "to_dict"):
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try:
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user_info_dict = user_info_obj.to_dict() # type: ignore[attr-defined]
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except Exception:
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user_info_dict = {}
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candidate_keys = [
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"user_cardname",
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"user_nickname",
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"nickname",
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"remark",
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"display_name",
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"user_name",
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]
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for key in candidate_keys:
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value = user_info_dict.get(key)
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if isinstance(value, str) and value.strip():
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stripped = value.strip()
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if memory_user_display is None:
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memory_user_display = stripped
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elif stripped not in memory_aliases:
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memory_aliases.append(stripped)
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attr_keys = [
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"user_cardname",
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"user_nickname",
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"nickname",
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"remark",
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"display_name",
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"name",
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]
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for attr in attr_keys:
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value = getattr(user_info_obj, attr, None)
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if isinstance(value, str) and value.strip():
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stripped = value.strip()
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if memory_user_display is None:
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memory_user_display = stripped
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elif stripped not in memory_aliases:
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memory_aliases.append(stripped)
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alias_values = (
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user_info_dict.get("aliases")
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or user_info_dict.get("alias_names")
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or user_info_dict.get("alias")
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)
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if isinstance(alias_values, list | tuple | set):
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for alias in alias_values:
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if isinstance(alias, str) and alias.strip():
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stripped = alias.strip()
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if stripped not in memory_aliases and stripped != memory_user_display:
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memory_aliases.append(stripped)
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memory_context = {
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"user_id": memory_user_id,
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"user_display_name": memory_user_display or "",
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"user_name": memory_user_display or "",
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"nickname": memory_user_display or "",
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"sender_name": memory_user_display or "",
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"platform": getattr(stream, "platform", None),
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"chat_id": stream.stream_id,
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"stream_id": stream.stream_id,
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}
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if memory_aliases:
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memory_context["user_aliases"] = memory_aliases
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if group_info_obj is not None:
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group_name = getattr(group_info_obj, "group_name", None) or getattr(
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group_info_obj, "group_nickname", None
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)
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if group_name:
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memory_context["group_name"] = str(group_name)
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group_id = getattr(group_info_obj, "group_id", None)
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if group_id:
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memory_context["group_id"] = str(group_id)
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memory_context = {key: value for key, value in memory_context.items() if value}
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# 获取记忆系统实例
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memory_system = get_memory_system()
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# 使用统一记忆系统检索相关记忆
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enhanced_memories = await memory_system.retrieve_relevant_memories(
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query=target, user_id=memory_user_id, scope_id=stream.stream_id, context=memory_context, limit=10
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)
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# 注意：记忆存储已迁移到回复生成完成后进行，不在查询阶段执行
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# 转换格式以兼容现有代码
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running_memories = []
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if enhanced_memories:
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logger.debug(f"[记忆转换] 收到 {len(enhanced_memories)} 条原始记忆")
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for idx, memory_chunk in enumerate(enhanced_memories, 1):
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# 获取结构化内容的字符串表示
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structure_display = str(memory_chunk.content) if hasattr(memory_chunk, "content") else "unknown"
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# 获取记忆内容，优先使用display
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content = memory_chunk.display or memory_chunk.text_content or ""
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# 调试：记录每条记忆的内容获取情况
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logger.debug(
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f"[记忆转换] 第{idx}条: display={repr(memory_chunk.display)[:80]}, text_content={repr(memory_chunk.text_content)[:80]}, final_content={repr(content)[:80]}"
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)
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running_memories.append(
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{
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"content": content,
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"memory_type": memory_chunk.memory_type.value,
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"confidence": memory_chunk.metadata.confidence.value,
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"importance": memory_chunk.metadata.importance.value,
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"relevance": getattr(memory_chunk.metadata, "relevance_score", 0.5),
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"source": memory_chunk.metadata.source,
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"structure": structure_display,
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}
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)
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# 构建瞬时记忆字符串
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if running_memories:
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top_memory = running_memories[:1]
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if top_memory:
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instant_memory = top_memory[0].get("content", "")
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logger.info(
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f"增强记忆系统检索到 {len(enhanced_memories)} 条原始记忆，转换为 {len(running_memories)} 条可用记忆"
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)
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except Exception as e:
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logger.warning(f"增强记忆系统检索失败: {e}")
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running_memories = []
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instant_memory = ""
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# 尝试从记忆图系统检索记忆
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graph_memories = []
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# 使用新的记忆图系统检索记忆
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all_memories = []
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try:
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from src.memory_graph.manager_singleton import get_memory_manager, is_initialized
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@@ -722,8 +566,8 @@ class DefaultReplyer:
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# 搜索相关记忆
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memories = await manager.search_memories(
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query=target,
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top_k=5,
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min_importance=0.5,
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top_k=10, # 增加检索数量
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min_importance=0.3, # 降低最低重要性阈值，获取更多记忆
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include_forgotten=False
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)
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@@ -733,27 +577,29 @@ class DefaultReplyer:
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topic = memory.metadata.get("topic", "")
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mem_type = memory.metadata.get("memory_type", "未知")
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if topic:
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graph_memories.append({
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all_memories.append({
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"content": topic,
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"memory_type": mem_type,
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"importance": memory.importance,
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"relevance": 0.7, # 默认相关度
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"source": "memory_graph",
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})
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# 提取最重要的记忆作为瞬时记忆
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if all_memories:
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top_memory = max(all_memories, key=lambda m: m.get("importance", 0))
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instant_memory = top_memory.get("content", "")
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else:
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logger.debug("[记忆图] 未找到相关记忆")
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except Exception as e:
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logger.debug(f"[记忆图] 检索失败: {e}")
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graph_memories = []
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# 合并记忆（增强记忆系统 + 记忆图系统）
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all_memories = running_memories + graph_memories
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all_memories = []
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# 构建记忆字符串，使用方括号格式
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memory_str = ""
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has_any_memory = False
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# 添加长期记忆（来自增强记忆系统 + 记忆图系统）
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# 添加长期记忆（来自记忆图系统）
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if all_memories:
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# 使用方括号格式
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memory_parts = ["### 🧠 相关记忆 (Relevant Memories)", ""]
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@@ -2159,23 +2005,9 @@ class DefaultReplyer:
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show_actions=True,
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)
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# 异步存储聊天历史（完全非阻塞）
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memory_system = get_memory_system()
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task = asyncio.create_task(
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memory_system.process_conversation_memory(
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context={
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"conversation_text": chat_history,
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"user_id": memory_user_id,
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"scope_id": stream.stream_id,
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**memory_context,
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}
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)
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)
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# 将任务添加到全局集合以防止被垃圾回收
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_background_tasks.add(task)
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task.add_done_callback(_background_tasks.discard)
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logger.debug(f"已启动记忆存储任务，用户: {memory_user_display or memory_user_id}")
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# 旧记忆系统的自动存储已禁用
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# 新记忆系统通过 LLM 工具调用（create_memory）来创建记忆
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logger.debug(f"记忆创建通过 LLM 工具调用进行，用户: {memory_user_display or memory_user_id}")
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except asyncio.CancelledError:
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logger.debug("记忆存储任务被取消")
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@@ -398,6 +398,9 @@ class Prompt:
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"""
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start_time = time.time()
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# 初始化预构建参数字典
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pre_built_params = {}
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try:
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# --- 步骤 1: 准备构建任务 ---
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tasks = []
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@@ -406,7 +409,6 @@ class Prompt:
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# --- 步骤 1.1: 优先使用预构建的参数 ---
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# 如果参数对象中已经包含了某些block，说明它们是外部预构建的，
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# 我们将它们存起来，并跳过对应的实时构建任务。
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pre_built_params = {}
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if self.parameters.expression_habits_block:
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pre_built_params["expression_habits_block"] = self.parameters.expression_habits_block
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if self.parameters.relation_info_block:
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@@ -428,11 +430,9 @@ class Prompt:
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tasks.append(self._build_expression_habits())
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task_names.append("expression_habits")
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# 记忆块构建非常耗时，强烈建议预构建。如果没有预构建，这里会运行一个快速的后备版本。
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if self.parameters.enable_memory and not pre_built_params.get("memory_block"):
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logger.debug("memory_block未预构建，执行快速构建作为后备方案")
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tasks.append(self._build_memory_block_fast())
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task_names.append("memory_block")
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# 记忆块构建已移至 default_generator.py 的 build_memory_block 方法
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# 使用新的记忆图系统，不再在 prompt.py 中构建记忆
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# 如果需要记忆，必须通过 pre_built_params 传入
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if self.parameters.enable_relation and not pre_built_params.get("relation_info_block"):
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tasks.append(self._build_relation_info())
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@@ -637,8 +637,12 @@ class Prompt:
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logger.error(f"构建表达习惯失败: {e}")
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return {"expression_habits_block": ""}
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async def _build_memory_block(self) -> dict[str, Any]:
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"""构建与当前对话相关的记忆上下文块（完整版）."""
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# _build_memory_block 和 _build_memory_block_fast 已移除
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# 记忆构建现在完全由 default_generator.py 的 build_memory_block 方法处理
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# 使用新的记忆图系统，通过 pre_built_params 传入
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async def _REMOVED_build_memory_block(self) -> dict[str, Any]:
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"""已废弃：构建与当前对话相关的记忆上下文块（完整版）."""
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if not global_config.memory.enable_memory:
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return {"memory_block": ""}
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@@ -753,8 +757,8 @@ class Prompt:
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logger.error(f"构建记忆块失败: {e}")
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return {"memory_block": ""}
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async def _build_memory_block_fast(self) -> dict[str, Any]:
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"""快速构建记忆块（简化版），作为未预构建时的后备方案."""
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async def _REMOVED_build_memory_block_fast(self) -> dict[str, Any]:
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"""已废弃：快速构建记忆块（简化版），作为未预构建时的后备方案."""
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if not global_config.memory.enable_memory:
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return {"memory_block": ""}
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@@ -107,7 +107,7 @@ class LinkMemoriesTool(BaseTool):
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("strength", ToolParamType.FLOAT, "关系强度（0.0-1.0），默认0.7", False, None),
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]
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available_for_llm = True
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available_for_llm = False # 暂不对 LLM 开放
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async def execute(self, function_args: dict[str, Any]) -> dict[str, Any]:
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"""执行关联记忆"""
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@@ -166,7 +166,7 @@ class SearchMemoriesTool(BaseTool):
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("min_importance", ToolParamType.FLOAT, "最低重要性阈值（0.0-1.0），只返回重要性不低于此值的记忆", False, None),
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]
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available_for_llm = True
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available_for_llm = False # 暂不对 LLM 开放，记忆检索在提示词构建时自动执行
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async def execute(self, function_args: dict[str, Any]) -> dict[str, Any]:
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"""执行搜索记忆"""
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@@ -183,7 +183,7 @@ class SearchMemoriesTool(BaseTool):
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query = function_args.get("query", "")
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top_k = function_args.get("top_k", 5)
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min_importance_raw = function_args.get("min_importance")
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min_importance = float(min_importance_raw) if min_importance_raw is not None else None
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min_importance = float(min_importance_raw) if min_importance_raw is not None else 0.0
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# 搜索记忆
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memories = await manager.search_memories(
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