refactor(api): TOML 配置 SSOT、统一错误契约、Auth/事务加固与可观测性 (#33)

配置 SSOT（TOML + .env）
统一错误契约
Auth 与事务边界
Redis / Celery 可靠性:业务 Redis（DB/0）与 Celery broker/backend（DB/1）显式拆分；连接池、sync client
可观测性（OpenTelemetry + LGTM）

api/.agents/skills/redis-development/rules/vector-hybrid-search.md

@@ -0,0 +1,52 @@
+---
+title: Use Hybrid Search for Better Results
+impact: MEDIUM
+impactDescription: Combining vector + filters improves relevance and reduces search space
+tags: vector, hybrid, filters, redisvl, search
+description: Use Hybrid Search for Better Results
+alwaysApply: true
+---
+
+## Use Hybrid Search for Better Results
+
+Combine vector similarity with attribute filtering for more relevant results. In this rule, "hybrid" means filtered vector search. Redis and RedisVL also use "hybrid search" for text + vector fusion via `FT.HYBRID` / `HybridQuery`.
+
+**Correct:** Apply filters to reduce search space.
+
+```python
+from redisvl.query import VectorQuery
+from redisvl.query.filter import Num, Tag
+
+filters = (Tag("category") == "technology") & (Num("date") >= 2024) & (Num("date") <= 2025)
+
+query = VectorQuery(
+    vector=query_embedding,
+    vector_field_name="embedding",
+    return_fields=["content", "category", "date"],
+    num_results=10,
+    filter_expression=filters
+)
+
+results = index.query(query)
+```
+
+**Incorrect:** Searching entire vector space when filters apply.
+
+```python
+# Bad: No filter - searches all vectors then filters client-side
+results = index.query(VectorQuery(
+    vector=query_embedding,
+    vector_field_name="embedding",
+    num_results=1000
+))
+# Client-side filtering - wasteful
+filtered = [r for r in results if r["category"] == "technology"]
+```
+
+**Tips:**
+- Use TAG fields for category filters
+- Use NUMERIC fields for date/price ranges
+- Redis auto-selects the filtered vector execution strategy; tune `hybrid_policy` only when needed
+- For true text + vector fusion, use `HybridQuery` on Redis >= 8.4.0 with redis-py >= 7.1.0; use `AggregateHybridQuery` on earlier Redis versions
+
+Reference: [Redis Vector Search](https://redis.io/docs/latest/develop/ai/search-and-query/vectors/)