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/semantic-cache-best-practices.md

@@ -0,0 +1,72 @@
+---
+title: Configure Semantic Cache Properly
+impact: MEDIUM
+impactDescription: Correct threshold tuning balances hit rate vs accuracy
+tags: langcache, cache, threshold, ttl, semantic
+description: Configure Semantic Cache Properly
+alwaysApply: true
+---
+
+## Configure Semantic Cache Properly
+
+> **Note:** LangCache is currently in preview on Redis Cloud. Features and behavior may change.
+
+Tune similarity threshold and cache separation for optimal LangCache results.
+
+**Correct:** Tune similarity threshold for your use case.
+
+```python
+from langcache import LangCache
+
+lang_cache = LangCache(
+    server_url=f"https://{os.getenv('HOST')}",
+    cache_id=os.getenv("CACHE_ID"),
+    api_key=os.getenv("API_KEY")
+)
+
+# Stricter matching - fewer false positives (0.95 = very similar)
+result = lang_cache.search(
+    prompt="What is Redis?",
+    similarity_threshold=0.95
+)
+
+# Looser matching - higher hit rate (0.8 = somewhat similar)
+result = lang_cache.search(
+    prompt="What is Redis?",
+    similarity_threshold=0.8
+)
+```
+
+**Correct:** Use separate caches for different use cases.
+
+```python
+# Create different cache IDs in Redis Cloud for different LLM tasks
+support_cache = LangCache(
+    server_url=server_url,
+    cache_id="support-cache-id",
+    api_key=api_key
+)
+
+code_cache = LangCache(
+    server_url=server_url,
+    cache_id="code-cache-id",
+    api_key=api_key
+)
+```
+
+**Incorrect:** Using a single cache for all LLM tasks.
+
+```python
+# All tasks share one cache - responses may not be relevant
+result = lang_cache.search(prompt="How do I reset my password?")
+# Could return a code snippet if someone asked a similar coding question
+```
+
+**Best practices:**
+- Start with threshold 0.9, adjust based on your use case
+- Use custom attributes to filter results within a single cache
+- Monitor cache hit rates to evaluate effectiveness
+- Use separate cache IDs for fundamentally different LLM tasks
+
+Reference: [LangCache Best Practices](https://redis.io/docs/latest/develop/ai/langcache/)
+