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）
2026-05-22 13:44:50 +08:00
parent f09ae248f9
commit 53e0065e3e
298 changed files with 15247 additions and 4344 deletions
--- a/api/.agents/skills/redis-development/rules/vector-algorithm-choice.md
+++ b/api/.agents/skills/redis-development/rules/vector-algorithm-choice.md
@@ -0,0 +1,61 @@
+---
+title: Choose HNSW vs FLAT Based on Requirements
+impact: HIGH
+impactDescription: HNSW trades accuracy for speed, FLAT provides exact results
+tags: vector, hnsw, flat, algorithm, performance
+description: Choose HNSW vs FLAT Based on Requirements
+alwaysApply: true
+---
+
+## Choose HNSW vs FLAT Based on Requirements
+
+Select the right algorithm based on your accuracy requirements and dataset size.
+
+| Algorithm | Speed | Accuracy | Memory | Best For |
+|-----------|-------|----------|--------|----------|
+| HNSW | Fast (approximate) | ~95%+ recall tunable | Higher | Large datasets (>10k vectors) |
+| FLAT | Slower (exact) | 100% (exact) | Lower | Small datasets, accuracy-critical |
+
+**Correct:** Use HNSW for large-scale production workloads.
+
+```python
+from redisvl.schema import IndexSchema
+
+# HNSW - fast approximate search, tunable accuracy
+schema = IndexSchema.from_dict({
+    "index": {"name": "idx:docs", "prefix": "doc:"},
+    "fields": [
+        {"name": "embedding", "type": "vector", "attrs": {
+            "dims": 1536,
+            "algorithm": "HNSW",
+            "distance_metric": "COSINE",
+            "datatype": "FLOAT32",
+            "m": 16,                      # Higher = more accurate, more memory
+            "ef_construction": 200        # Higher = better index quality, slower build
+        }}
+    ]
+})
+```
+
+**Correct:** Use FLAT when exact results are required.
+
+```python
+# FLAT - exact brute-force search, guaranteed accuracy
+schema = IndexSchema.from_dict({
+    "index": {"name": "idx:small", "prefix": "small:"},
+    "fields": [
+        {"name": "embedding", "type": "vector", "attrs": {
+            "dims": 1536,
+            "algorithm": "FLAT",
+            "distance_metric": "COSINE"
+        }}
+    ]
+})
+```
+
+**Tuning HNSW accuracy vs speed:**
+- `M`: Connections per node (16-64). Higher = better recall, more memory
+- `EF_CONSTRUCTION`: Build-time parameter (100-500). Higher = better graph quality
+- `EF_RUNTIME`: Query-time parameter. Higher = better recall, slower queries
+
+Reference: [Redis Vector Search](https://redis.io/docs/latest/develop/ai/search-and-query/vectors/)