feat(memory): 重构记忆系统并移除插件热重载

重构记忆系统核心模块，引入全局记忆作用域、记忆指纹去重机制和查询规划器，优化多阶段检索性能。移除插件热重载系统及其相关依赖。

主要变更：
- 引入全局记忆作用域，简化记忆管理
- 实现记忆指纹去重，避免重复记忆存储
- 新增查询规划器，支持语义查询规划和记忆类型过滤
- 优化多阶段检索，增加语义重排和权重配置
- 改进向量存储，支持嵌入维度自动解析和查询向量生成
- 增强元数据索引，支持主体索引和更新操作
- 记忆构建器支持多主体和自然语言展示
- 移除watchdog依赖和插件热重载模块
- 更新配置模板，简化记忆配置项

BREAKING CHANGE: 移除插件热重载系统，相关API和命令不再可用。记忆系统接口有较大调整，使用该系统的模块需要适配新接口。

src/common/cache_manager.py

@@ -8,6 +8,7 @@ from typing import Any, Dict, Optional, Union
 from src.common.logger import get_logger
 from src.llm_models.utils_model import LLMRequest
 from src.config.config import global_config, model_config
+from src.common.config_helpers import resolve_embedding_dimension
 from src.common.database.sqlalchemy_models import CacheEntries
 from src.common.database.sqlalchemy_database_api import db_query, db_save
 from src.common.vector_db import vector_db_service
@@ -40,7 +41,11 @@ class CacheManager:
 
             # L1 缓存 (内存)
             self.l1_kv_cache: Dict[str, Dict[str, Any]] = {}
-            embedding_dim = global_config.lpmm_knowledge.embedding_dimension
+            embedding_dim = resolve_embedding_dimension(global_config.lpmm_knowledge.embedding_dimension)
+            if not embedding_dim:
+                embedding_dim = global_config.lpmm_knowledge.embedding_dimension
+
+            self.embedding_dimension = embedding_dim
             self.l1_vector_index = faiss.IndexFlatIP(embedding_dim)
             self.l1_vector_id_to_key: Dict[int, str] = {}
 
@@ -72,7 +77,7 @@ class CacheManager:
                     embedding_array = embedding_array.flatten()
 
                 # 检查维度是否符合预期
-                expected_dim = global_config.lpmm_knowledge.embedding_dimension
+                expected_dim = getattr(CacheManager, "embedding_dimension", None) or global_config.lpmm_knowledge.embedding_dimension
                 if embedding_array.shape[0] != expected_dim:
                     logger.warning(f"嵌入向量维度不匹配: 期望 {expected_dim}, 实际 {embedding_array.shape[0]}")
                     return None

src/common/config_helpers.py

@@ -0,0 +1,42 @@
+from __future__ import annotations
+
+from typing import Optional
+
+from src.config.config import global_config, model_config
+
+
+def resolve_embedding_dimension(fallback: Optional[int] = None, *, sync_global: bool = True) -> Optional[int]:
+    """获取当前配置的嵌入向量维度。
+
+    优先顺序：
+    1. 模型配置中 `model_task_config.embedding.embedding_dimension`
+    2. 机器人配置中 `lpmm_knowledge.embedding_dimension`
+    3. 调用方提供的 fallback
+    """
+
+    candidates: list[Optional[int]] = []
+
+    try:
+        embedding_task = getattr(model_config.model_task_config, "embedding", None)
+        if embedding_task is not None:
+            candidates.append(getattr(embedding_task, "embedding_dimension", None))
+    except Exception:
+        candidates.append(None)
+
+    try:
+        candidates.append(getattr(global_config.lpmm_knowledge, "embedding_dimension", None))
+    except Exception:
+        candidates.append(None)
+
+    candidates.append(fallback)
+
+    resolved: Optional[int] = next((int(dim) for dim in candidates if dim and int(dim) > 0), None)
+
+    if resolved and sync_global:
+        try:
+            if getattr(global_config.lpmm_knowledge, "embedding_dimension", None) != resolved:
+                global_config.lpmm_knowledge.embedding_dimension = resolved  # type: ignore[attr-defined]
+        except Exception:
+            pass
+
+    return resolved