feat(memory): add WritePipeline and MemoryService facade

Introduce a layered pipeline architecture for the memory write flow:
- WritePipeline: orchestrates preprocess → extract → store → cluster → summarize
  with deadlock retry, resource cleanup, and pilot-run support
- MemoryService: facade that delegates to WritePipeline, placeholder methods
  for read/forget/reflect
- BearLogger: structured step-level logging with perf threshold alerts
- Shadow pipeline integration in MemoryAgentService (env-gated pilot run)

Also includes:
- Fix deprecated SQLAlchemy declarative_base import
- Extend Neo4j Entity fulltext index to cover description and aliases
- Migrate Pydantic schemas to v2 (ConfigDict, field_validator)

api/app/schemas/user_schema.py

@@ -1,5 +1,5 @@
 from dataclasses import field
-from pydantic import BaseModel, EmailStr, Field, field_validator, validator, ConfigDict
+from pydantic import BaseModel, EmailStr, Field, field_validator, ConfigDict
 from typing import Optional, List
 import datetime
 import uuid
@@ -90,7 +90,8 @@ class User(UserBase):
     permissions: Optional[List[str]] = None  # 用户权限列表，由 external_source 的 permissions 控制
 
     # 将 datetime 转换为毫秒时间戳
-    @validator("created_at", pre=True)
+    @field_validator("created_at", mode="before")
+    @classmethod
     def _created_at_to_ms(cls, v):
         if isinstance(v, datetime.datetime):
             return int(v.timestamp() * 1000)