Commit Graph
4 Commits
Author SHA1 Message Date
QihuAI Design a2504bce34 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).
2026-07-26 00:35:35 +08:00
QihuAI Design 256d9f33e6 chore(align): sync Git with running container as authoritative source
Phase 0 SSE 流式聊天 landed in the running container and captured in Git so
the three sources (container := image := Git) agree.

Phase 0 — SSE 流式聊天(Web 端,IM bot 路径保持不变、共用引擎):
- app/api/chat.py: 新增独立 SSE 端点
  POST /app/api/chat/conversations/{conv_id}/messages/stream
  基于 asyncio.Queue + create_task 将 _send_message_core 桥接为
  text/event-stream 生成器,吐出 delta / reasoning / done / error 事件
- app/api/chat_service.py: _send_message_core 增加 stream + on_event 参数;
  普通(无工具)聊天 round-0 主调用走 achat_complete(stream=True) 流式;
  done 事件携带 message_id / content / process_steps / used_skills /
  used_mcp / generating
- app/core/llm_client.py: 修复 _create_kwargs 硬编码 stream=False 的根因 bug
  (流式调用传 stream=True 导致 TypeError: multiple values for keyword),
  现 _create_kwargs(request_body, stream=True) 字段随参数变化
- frontend/src/pages/front/ChatPage.tsx + ChatPage.css + types/index.ts:
  发消息改为 fetch SSE 消费,实时增量渲染 AI 回复文本与思考链(reasoning),
  done 事件用 message_id 定稿并渲染过程步骤;网络/流式失败自动回退非流式

Alignment performed 2026-07-24 18:25:11. No deletions; secrets/venv/node_modules/
caches were not touched (tracked-source-only copy).
2026-07-24 18:25:15 +08:00
QihuAI Design 3788a93425 chore(align): sync Git with running container as authoritative source
Phase 0 SSE 流式聊天 landed in the running container and captured in Git so
the three sources (container := image := Git) agree.

Phase 0 — SSE 流式聊天(Web 端,IM bot 路径保持不变、共用引擎):
- app/api/chat.py: 新增独立 SSE 端点
  POST /app/api/chat/conversations/{conv_id}/messages/stream
  基于 asyncio.Queue + create_task 将 _send_message_core 桥接为
  text/event-stream 生成器,吐出 delta / reasoning / done / error 事件
- app/api/chat_service.py: _send_message_core 增加 stream + on_event 参数;
  普通(无工具)聊天 round-0 主调用走 achat_complete(stream=True) 流式;
  done 事件携带 message_id / content / process_steps / used_skills /
  used_mcp / generating
- app/core/llm_client.py: 修复 _create_kwargs 硬编码 stream=False 的根因 bug
  (流式调用传 stream=True 导致 TypeError: multiple values for keyword),
  现 _create_kwargs(request_body, stream=True) 字段随参数变化
- frontend/src/pages/front/ChatPage.tsx + ChatPage.css + types/index.ts:
  发消息改为 fetch SSE 消费,实时增量渲染 AI 回复文本与思考链(reasoning),
  done 事件用 message_id 定稿并渲染过程步骤;网络/流式失败自动回退非流式

Alignment performed 2026-07-24 16:28:19. No deletions; secrets/venv/node_modules/
caches were not touched (tracked-source-only copy).
2026-07-24 16:28:19 +08:00
QihuAI Design e8a1791c9f chore(align): sync Git with running container as authoritative source
Standards alignment (STANDARDS_AUDIT_2026-07-24): Phase 1 标准化核心 +
Phase 2 质量层 landed in the running container and captured in Git so the
three sources (container := image := Git) agree.

Backend (Phase 1 标准化核心):
- mcp_client.py: replaced hand-rolled JSON-RPC with official `mcp` SDK
  (ClientSession, streamable_http/sse transports, protocol auto-negotiation)
- core/otel.py + core/observability.py: standard OpenTelemetry SDK bridge
  (OTLP gRPC), wired to Grafana Alloy (qihu-aiflow-alloy:4317) which forwards
  metrics to Prometheus; emits aiflow_span_duration_ms / aiflow_span_count
- deployments: Grafana Alloy container + Prometheus remote-write receiver

Backend (Phase 2 质量层):
- core/rag_pipeline.py: standardized RAG (recursive overlap chunking +
  rerank hook) reusing pgvector_search
- core/llm_client.py: SDK-ified LLM + embeddings (aembed/embed) collapsing
  hand-rolled httpx calls; trust_env=False to bypass container proxy

Migrations:
- 018_normalize_marketapp_scene.sql: fixed capability allowlist
  (prevents capability wipe on restart)

Alignment performed 2026-07-24 12:18:51. No deletions; secrets/venv/node_modules/
caches were not touched (tracked-source-only copy).
2026-07-24 12:18:52 +08:00