feat: 画像→图投影打通(按类型写标签,读写对齐;补删除客户端)

第 2 步(Neo4j)主体,三件事:

1. 新增 app/service/graph_model.py:图模型的**单一来源**。
   节点标签与主属性名会被拼进 Cypher(Neo4j 的标签不能用参数占位),因此必须来自受控常量,
   绝不能是调用方传入的字符串——否则就是 Cypher 注入;同时读服务与投影侧必须就"客户节点
   长什么样"达成一致。此前两边各写各的(投影写 :Entity{entity_id}、读服务查
   :Customer{customer_id}),结果写进去的关系永远读不出来。
   另用 RELATION_SEMANTICS 补上"谁指向谁"的方向校验:底座已有关系白名单(8 种),
   但白名单只约束关系名,不约束方向,这里补上,避免把 TRADED 写成 Customer→Tag。

2. 改 app/worker/graph_projection_worker.py:按 payload 的 source_type/target_type 写具体标签,
   投影前校验关系方向。标签与属性名全部取自 graph_model,调用方传不进任意字符串。

3. 新增 app/service/profile_graph_projection_service.py:把画像事实投影成节点与关系,
   并补上此前缺失的**投影删除客户端**(销户或记忆失效时 DETACH DELETE,同时清悬挂边)。
   只投影 user_facts(与画像同源),不直接读原始记忆——否则同一件事在画像与图里会有两种说法。
   图库故障一律返回 degraded 而不抛异常:投顾推荐可以暂时没有图,但不该因为图挂了
   导致画像更新失败。

背景(核查发现):MemorySyncOutbox 与 GraphProjectionWorker 此前是**孤儿代码**——表建了、
worker 也实现了(含幂等、重试、死信、MERGE),但没有任何代码往 outbox 写、也没有任何地方
实例化这个 worker,整条图投影链路从未接上过。这正是图中只有 Neo4j 自带 Person/Movie
示例数据的根因。本次把"画像 → 图"这条链路接通;领域事件驱动的投影仍待接入(worker 已修好可用)。

实测验证:
· 投影客户 9001 → relations=1,随后 neighbors(PREFERS) 直接读到
  {'tag_key': 'preference:risk_level=稳健型'} —— 写后读通,标签已对齐;
· delete_customer → 关系归零(degraded=False),重新投影恢复为 1,再次投影仍为 1(幂等);
· 图中标签为 Customer/Tag、关系为 PREFERS;早先探针残留的 Entity 节点已清理;
· ruff 通过、mypy 112 文件无错。
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"""图模型定义:节点类型、主属性与关系语义(**单一来源**)。
为什么必须集中定义:
1. **标签与属性名要拼进 Cypher**(Neo4j 的标签不能用查询参数占位),因此它们**必须**来自
受控常量,绝不能接受调用方传入的任意字符串,否则就是 Cypher 注入。
2. 读服务(`RelationshipService`)与投影侧需要就"客户节点长什么样"达成一致。此前投影写
`:Entity {entity_id}`、读服务查 `:Customer {customer_id}`,两边各写各的,结果是**写进去的
关系永远读不出来**。把节点规格放在一处,两边都从这里取,才不会再次漂移。
3. `RELATION_SEMANTICS` 记录每种关系连接哪两类节点,用于在投影时拒绝无意义的边
(例如把 `TRADED` 写成 Customer→Tag)。底座已有关系白名单(8 种),这里补的是
"谁指向谁"的语义约束。
"""
from dataclasses import dataclass
from typing import Any
@dataclass(frozen=True)
class NodeSpec:
"""一个节点类型的规格。`cast` 决定主键类型:客户 id 是整数(与读服务查询一致)。"""
type_key: str
label: str
property: str
cast: type
# payload 里使用的类型名 → 节点规格。未登记的类型一律拒绝。
NODE_SPECS: dict[str, NodeSpec] = {
"customer": NodeSpec("customer", "Customer", "customer_id", int),
"product": NodeSpec("product", "Product", "product_code", str),
"tag": NodeSpec("tag", "Tag", "tag_key", str),
"industry": NodeSpec("industry", "Industry", "category", str),
"event": NodeSpec("event", "Event", "event_id", str),
}
# 关系 → (允许的源节点类型, 允许的目标节点类型)
RELATION_SEMANTICS: dict[str, tuple[tuple[str, ...], tuple[str, ...]]] = {
"PREFERS": (("customer",), ("tag",)),
"HAS_GOAL": (("customer",), ("tag",)),
"INTERESTED_IN": (("customer",), ("product", "industry")),
"TRADED": (("customer",), ("product",)),
"HOLDS": (("customer",), ("product",)),
"TRIGGERED_RISK": (("customer",), ("event",)),
"BELONGS_TO_CATEGORY": (("product",), ("tag",)),
"EXPOSED_TO_INDUSTRY": (("product", "customer"), ("industry",)),
}
def node_spec(type_key: object) -> NodeSpec:
"""按类型名取节点规格;未登记的类型抛 `ValueError`(宁可失败也不拼出任意标签)。"""
key = str(type_key or "").strip().lower()
spec = NODE_SPECS.get(key)
if spec is None:
raise ValueError(f"node type is not allowed: {type_key!r}")
return spec
def node_id(spec: NodeSpec, value: Any) -> Any:
"""把主键值转成该节点应有的类型。"""
return value if spec.cast is str else spec.cast(value)
def relation_allowed(relation: str, source_type: str, target_type: str) -> bool:
"""该关系是否允许连接这两类节点。"""
expected = RELATION_SEMANTICS.get(relation)
if expected is None:
return False
sources, targets = expected
return source_type in sources and target_type in targets
def merge_node_clause(variable: str, spec: NodeSpec, id_parameter: str) -> str:
"""生成 `MERGE (a:Label {prop: $param})` 子句。
只有 `spec` 里的标签与属性名会进入查询文本(它们来自本模块常量),`$param` 走参数绑定,
因此调用方无法通过数据影响 Cypher 结构。
"""
return f"MERGE ({variable}:{spec.label} {{{spec.property}: ${id_parameter}}})"
@@ -0,0 +1,155 @@
"""画像 → 图投影:把 MySQL 里权威的画像与事实投影成 Neo4j 的节点与关系。
**为什么要有图**(这是它存在的理由,不是技术偏好):投顾要靠"买过 A 的客户还买过什么"
这类多跳关系做组合推荐,风控要靠关系网络发现关联账户与资金链路;这些查询用关系库写
既别扭又慢,用图是顺手的事。
设计约束:
1. **单向投影**:MySQL 是唯一真相,图库只是派生视图。图数据损坏或模型变更时可以直接重建
(拿同一份画像重投影即可),因此这里**不需要与 MySQL 保持事务一致**。
2. **幂等**:全部用 MERGE,重复投影不会产生重复节点或关系。
3. **降级**:图库不可用时返回 `degraded`,**绝不把异常抛给画像重建这类主链路**
—— 投顾推荐可以暂时没有图,但不能因为图挂了就让画像更新失败。
4. **只投影"已确认"的事实**:与画像同源(`user_facts`),不直接读原始记忆,
保证图里的偏好标签与画像口径一致;否则同一件事在画像和图里会有两种说法。
"""
from dataclasses import dataclass
from typing import Any
from sqlalchemy import select, text
from sqlalchemy.ext.asyncio import AsyncSession
from app.model.profile import UserFact
from app.service.graph_model import merge_node_clause, node_spec, relation_allowed
from app.service.relationship_service import RelationshipService
# 事实键 → (关系类型, 目标节点类型)
FACT_PROJECTION: dict[str, tuple[str, str]] = {
"preference:risk_level": ("PREFERS", "tag"),
"preference:asset_class": ("PREFERS", "tag"),
"preference:horizon": ("HAS_GOAL", "tag"),
"profile:family": ("PREFERS", "tag"),
}
MAX_EDGES_PER_CUSTOMER = 200
@dataclass(frozen=True)
class ProjectionOutcome:
relations: int = 0
degraded: bool = False
reason: str = ""
class ProfileGraphProjectionService:
def __init__(
self, session: AsyncSession, relationships: RelationshipService | None
) -> None:
self.session = session
self.relationships = relationships
async def project_customer(self, customer_id: int) -> ProjectionOutcome:
"""把该客户的画像事实投影到图中。"""
if self.relationships is None:
return ProjectionOutcome(degraded=True, reason="graph_client_unavailable")
edges = await self._edges(customer_id)
written = 0
try:
for relation, target_type, target_id in edges[:MAX_EDGES_PER_CUSTOMER]:
await self._merge_edge(customer_id, relation, target_type, target_id)
written += 1
except Exception as exc:
# 图库故障:如实标记降级,不伪装成功(与 Milvus/Neo4j 投影清理同一取向)
return ProjectionOutcome(relations=written, degraded=True,
reason=f"graph_write_failed:{type(exc).__name__}")
return ProjectionOutcome(relations=written)
async def delete_customer(self, customer_id: int) -> ProjectionOutcome:
"""删除该客户在图中的节点及其全部关系。
这是此前缺失的**投影删除客户端**:记忆失效或客户销户后,若图里仍留着旧关系,
投顾会拿过期偏好做推荐、风控会看到已不存在的关系网络。
`DETACH DELETE` 同时清理关系,避免留下悬挂边。
"""
if self.relationships is None:
return ProjectionOutcome(degraded=True, reason="graph_client_unavailable")
try:
await self.relationships.driver.execute_query(
"MATCH (c:Customer {customer_id: $customer_id}) DETACH DELETE c",
customer_id=int(customer_id),
)
except Exception as exc:
return ProjectionOutcome(degraded=True,
reason=f"graph_delete_failed:{type(exc).__name__}")
return ProjectionOutcome()
# ---------- 内部 ----------
async def _edges(self, customer_id: int) -> list[tuple[str, str, str]]:
"""收集要投影的边:(关系, 目标节点类型, 目标节点标识)。"""
edges: list[tuple[str, str, str]] = []
facts = list(await self.session.scalars(
select(UserFact).where(UserFact.customer_id == customer_id)
))
for fact in facts:
key = str(fact.fact_key)
projection = FACT_PROJECTION.get(key)
if projection is None:
continue
relation, target_type = projection
edges.append((relation, target_type, f"{key}={self._text(fact.fact_value)}"))
for product_code in await self._holdings(customer_id):
edges.append(("HOLDS", "product", product_code))
return edges
async def _holdings(self, customer_id: int) -> list[str]:
"""客户当前持仓的产品代码。
交易模块尚未产生数据,或表结构与预期不符时返回空列表——持仓投影自动跳过,
而不是让整次投影失败。等交易侧就绪后无需改这里的代码。
"""
try:
rows = await self.session.execute(text(
"SELECT p.product_code FROM fin_holding h "
"JOIN fin_product p ON p.id = h.product_id "
"WHERE h.customer_id = :customer_id LIMIT 50"
), {"customer_id": customer_id})
except Exception:
return []
return [str(row[0]) for row in rows if row[0]]
async def _merge_edge(
self, customer_id: int, relation: str, target_type: str, target_id: str
) -> None:
if not relation_allowed(relation, "customer", target_type):
raise ValueError(f"relationship {relation} cannot connect customer -> {target_type}")
source = node_spec("customer")
target = node_spec(target_type)
query = (
f"{merge_node_clause('a', source, 'source_id')} "
f"{merge_node_clause('b', target, 'target_id')} "
f"MERGE (a)-[r:{relation}]->(b) "
"SET r.source = $source, r.updated_at = $updated_at"
)
await self.relationships.driver.execute_query( # type: ignore[union-attr]
query,
source_id=node_spec("customer").cast(customer_id),
target_id=target_id,
source="user_facts",
updated_at=_now_iso(),
)
@staticmethod
def _text(value: Any) -> str:
if isinstance(value, str):
return value.strip().strip('"')
return str(value)
def _now_iso() -> str:
from datetime import UTC, datetime
return datetime.now(UTC).replace(tzinfo=None).isoformat()
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@@ -5,6 +5,7 @@ from sqlalchemy import select
from sqlalchemy.ext.asyncio import AsyncSession
from app.model.memory import MemorySyncOutbox
from app.service.graph_model import merge_node_clause, node_id, node_spec, relation_allowed
from app.service.relationship_service import RelationshipService
@@ -30,19 +31,31 @@ class GraphProjectionWorker:
relation = payload.get("relationship")
if relation not in RelationshipService.ALLOWED_RELATIONSHIPS:
raise ValueError("relationship is not allowed")
# 节点按**类型**写标签与主属性:原先统一写 `:Entity {entity_id}`,而读服务查的是
# `:Customer {customer_id}`,两侧从未对齐,写进去的关系永远读不出来。
# 标签与属性名来自 `graph_model` 的受控常量,调用方传不进任意字符串。
source_type = str(payload.get("source_type", "")).strip().lower()
target_type = str(payload.get("target_type", "")).strip().lower()
source = node_spec(source_type)
target = node_spec(target_type)
if not relation_allowed(relation, source_type, target_type):
raise ValueError(
f"relationship {relation} cannot connect {source_type} -> {target_type}"
)
query = (
"MERGE (a:Entity {entity_id: $source_id}) "
"MERGE (b:Entity {entity_id: $target_id}) "
f"{merge_node_clause('a', source, 'source_id')} "
f"{merge_node_clause('b', target, 'target_id')} "
f"MERGE (a)-[r:{relation}]->(b) "
"SET r.trace_id = $trace_id, r.confidence = $confidence"
"SET r.trace_id = $trace_id, r.confidence = $confidence, r.updated_at = $updated_at"
)
try:
await self.relationships.driver.execute_query(
query,
source_id=str(payload["source_id"]),
target_id=str(payload["target_id"]),
source_id=node_id(source, payload["source_id"]),
target_id=node_id(target, payload["target_id"]),
trace_id=str(payload.get("trace_id", "")),
confidence=float(payload.get("confidence", 0.0)),
updated_at=datetime.now(UTC).replace(tzinfo=None).isoformat(),
)
except Exception as exc:
if self.session is not None: