fix(chat): resolve Auto-route failover event-loop freeze (#812)

Root cause: ensure_structured_column() executed a synchronous blocking
engine.connect().execute(SELECT information_schema.columns ...) inside the
async request path (_build_chat_history / _maybe_compress_history), freezing
the uvicorn event loop and hanging all endpoints (~200s, recovered only by
docker restart).

Fix:
- chat_context.py: remove per-request ensure_structured_column() calls; run
  once idempotently at app startup (main.py lifespan). Use CURRENT_DATABASE()
  for PostgreSQL compatibility.
- main.py: add startup-only ensure_structured_column() hook (try/except).
- llm_client.py: trust_env=False + bounded httpx.Timeout/wait_for so broken
  endpoints fail fast instead of hanging the event loop.
- chat_service.py: run media generation in a background daemon thread with its
  own event loop (_launch_media_task_bg) so SSE end does not cancel image/video
  tasks (#811); remove TEMP faulthandler diagnostic.

Safe-align: persist accumulated live container changes (admin/*, knowledge,
market, models, config, observability, mcp_client, platform_knowledge,
im_bot_platform, chat_media, capability_registry, requirements) and add
workflow_templates.

Verified: live failover test (model-006 -> conn-refused) returns in ~1.9s with
concurrent login 0.83s/200; event-loop responsiveness restored (sub-second).
This commit is contained in:
QihuAI Design committed 2026-07-26 00:35:35 +08:00
1 parent 394a7786e0
commit a2504bce34
27 files changed
+253 -59

No files matched your search

+3 -2
View File
@@ -98,10 +98,11 @@ def resolve_auto_model(capability: str, db: Session) -> Optional[AIModel]:
# 媒体类能力(image/video/audio):无显式配置时自动发现任一启用的模型
# 避免用户未在后台配置"能力默认模型"时 Auto 回退到文本模型导致"无法生成图片/视频"
if capability in ("image", "video", "audio"):
from sqlalchemy import text as _text, func
from sqlalchemy import text as _text, func, type_coerce
from sqlalchemy.dialects.postgresql import JSONB
m = db.query(AIModel).filter(
AIModel.is_enabled == True,
func.json_contains(AIModel.capabilities, f'"{capability}"'),
AIModel.capabilities.cast(JSONB).contains(type_coerce([capability], JSONB)),
).first()
if m:
return m