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).
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@@ -1,9 +1,9 @@
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"""Shared, standards-based LLM client for web chat + IM bot.
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平台红线(07-24)：模型层以成熟/开源标准为设计基准。本模块使用官方 OpenAI SDK
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(`openai.AsyncOpenAI`)——它是 OpenAI 兼容端点的事实标准客户端。平台本身已在讲
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OpenAI 协议(`/v1/chat/completions`)，因此零协议改造成本，并天然获得：
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* 原生 SSE 流式(`stream=True`)；
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(\"openai.AsyncOpenAI\")——它是 OpenAI 兼容端点的事实标准客户端。平台本身已在讲
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OpenAI 协议(\"/v1/chat/completions\")，因此零协议改造成本，并天然获得：
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* 原生 SSE 流式(\"stream=True\")；
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* 结构化的 tool_calls / usage 解析；
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* 统一的代理(trust_env=False + 显式 proxy)与超时处理。
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@@ -13,13 +13,14 @@ IM bot 共用**同一模型层**、同一套 failover/代理/超时语义——
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API:
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achat_complete(api_url, api_key, request_body, *, proxy=None, timeout=120, stream=False)
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- stream=False -> dict，形状对齐上游 `data` 响应：
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- stream=False -> dict，形状对齐上游 \"data\" 响应：
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{"choices":[{"message":{"content","tool_calls","reasoning_content"}}], "usage":{...}}
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- stream=True -> async generator，逐块产出 ("content"|"reasoning", text) 增量
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"""
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from __future__ import annotations
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import httpx
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import asyncio
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from openai import AsyncOpenAI, OpenAI
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@@ -37,10 +38,21 @@ def build_openai_client(api_url, api_key, *, proxy=None, timeout=120) -> AsyncOp
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# 始终 trust_env=False：避免容器 HTTP_PROXY 对国内端点(siliconflow 等)造成 502；
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# 代理经 proxy 显式传入（AGNES 等），未传则直连。
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http_client = httpx.AsyncClient(proxy=proxy, trust_env=False)
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# 故障快速失败（全栈统一，覆盖 web chat / IM bot / embedding）：
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# connect 封顶 10s —— 坏端点不再让请求挂死事件循环；
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# read/write 封顶 30s —— 长流式回复只要持续有分块即不受影响；
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# 保留数值型 timeout 接口，旧调用方无需改动。
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_num = float(timeout) if isinstance(timeout, (int, float)) else 120.0
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_to = httpx.Timeout(
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connect=min(10.0, _num),
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read=min(30.0, _num),
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write=min(30.0, _num),
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pool=10.0,
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)
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return AsyncOpenAI(
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base_url=base,
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api_key=api_key or "EMPTY",
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timeout=timeout,
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timeout=_to,
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http_client=http_client,
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)
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@@ -94,10 +106,21 @@ async def achat_complete(api_url, api_key, request_body, *, proxy=None, timeout=
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_reasoning_parts = []
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_tool_acc = {} # index -> {id, type, name, arguments}
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try:
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gen = await client.chat.completions.create(
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**_create_kwargs(request_body, stream=True)
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_to = min(30.0, float(timeout) if isinstance(timeout, (int, float)) else 120.0)
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gen = await asyncio.wait_for(
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client.chat.completions.create(
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**_create_kwargs(request_body, stream=True)
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),
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timeout=_to,
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)
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async for chunk in gen:
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_it = gen.__aiter__()
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while True:
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try:
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chunk = await asyncio.wait_for(_it.__anext__(), timeout=_to)
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except StopAsyncIteration:
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break
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except asyncio.TimeoutError:
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raise TimeoutError("stream chunk read timeout")
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if not chunk.choices:
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continue
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delta = chunk.choices[0].delta
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@@ -129,7 +152,10 @@ async def achat_complete(api_url, api_key, request_body, *, proxy=None, timeout=
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if _fn_args:
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_slot["arguments"] += _fn_args
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finally:
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await client.close()
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try:
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await asyncio.wait_for(client.close(), timeout=5)
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except Exception:
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pass
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# 流式结束时吐出已组装的完整 message（含 tool_calls），供调用方无缝衔接工具循环
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_tool_calls = None
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if _tool_acc:
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@@ -150,8 +176,12 @@ async def achat_complete(api_url, api_key, request_body, *, proxy=None, timeout=
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return _gen()
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client = build_openai_client(api_url, api_key, proxy=proxy, timeout=timeout)
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async with client:
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resp = await client.chat.completions.create(**_create_kwargs(request_body))
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try:
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_to = min(30.0, float(timeout) if isinstance(timeout, (int, float)) else 120.0)
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resp = await asyncio.wait_for(
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client.chat.completions.create(**_create_kwargs(request_body)),
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timeout=_to,
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)
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msg = resp.choices[0].message
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return {
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"choices": [
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@@ -165,6 +195,11 @@ async def achat_complete(api_url, api_key, request_body, *, proxy=None, timeout=
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],
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"usage": (resp.usage.model_dump() if resp.usage else {}),
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}
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finally:
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try:
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await asyncio.wait_for(client.close(), timeout=5)
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except Exception:
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pass
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def build_openai_client_sync(api_url, api_key, *, proxy=None, timeout=120) -> OpenAI:
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@@ -208,9 +243,18 @@ async def aembed(api_url, api_key, input, model, *, proxy=None, timeout=60) -> l
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嵌入调用收敛到同一标准模型层（与聊天共用代理/超时/failover 语义）。
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"""
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client = build_openai_client(api_url, api_key, proxy=proxy, timeout=timeout)
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async with client:
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resp = await client.embeddings.create(**_embed_kwargs(input, model))
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try:
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_to = min(60.0, float(timeout) if isinstance(timeout, (int, float)) else 60.0)
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resp = await asyncio.wait_for(
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client.embeddings.create(**_embed_kwargs(input, model)),
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timeout=_to,
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)
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return [d.embedding for d in resp.data]
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finally:
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try:
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await asyncio.wait_for(client.close(), timeout=5)
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except Exception:
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pass
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def embed(api_url, api_key, input, model, *, proxy=None, timeout=60) -> list:
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