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
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@@ -284,7 +284,7 @@ async def claim_achievement_reward(
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# Mark reward as claimed
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_execute_raw(db,
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"UPDATE user_achievements SET reward_claimed = 1, claimed_at = NOW() WHERE user_id = :uid AND achievement_id = :aid",
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"UPDATE user_achievements SET reward_claimed = true, claimed_at = NOW() WHERE user_id = :uid AND achievement_id = :aid",
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{"uid": user_id, "aid": ach_id})
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return {"code": 0, "message": "奖励领取成功"}
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@@ -316,7 +316,7 @@ async def create_dashboard(
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config_json = json.loads('[{"type":"stat","title":"暂无数据","metric":"none","chart_type":"number"}]')
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if is_default:
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db.execute(text("UPDATE analytics_dashboards SET is_default=0"))
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db.execute(text("UPDATE analytics_dashboards SET is_default=false"))
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db.execute(text("""
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INSERT INTO analytics_dashboards (id, name, description, config, is_default, created_by)
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@@ -332,7 +332,7 @@ async def create_dashboard(
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@router.delete("/dashboards/{dashboard_id}")
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async def delete_dashboard(dashboard_id: str, db: Session = Depends(get_db), user=Depends(get_current_user)):
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"""删除仪表盘"""
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db.execute(text("DELETE FROM analytics_dashboards WHERE id=:id AND is_default=0"), {"id": dashboard_id})
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db.execute(text("DELETE FROM analytics_dashboards WHERE id=:id AND is_default=false"), {"id": dashboard_id})
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db.commit()
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return {"code": 200, "data": {"message": "仪表盘已删除"}}
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@@ -445,7 +445,7 @@ async def billing_stats(db: Session = Depends(get_db), user=Depends(get_current_
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SELECT DATE(created_at) as d, COALESCE(SUM(amount), 0) as rev
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FROM billing_transactions
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WHERE transaction_type='recharge' AND status='completed'
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AND created_at >= DATE_SUB(NOW(), INTERVAL 7 DAY)
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AND created_at >= NOW() - INTERVAL '7 DAY'
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GROUP BY DATE(created_at) ORDER BY d
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""")).fetchall()
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daily_revenue = [{"date": str(r[0]), "revenue": float(r[1])} for r in revenue_7d]
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@@ -103,7 +103,7 @@ async def get_execution_stats(db: Session = Depends(get_db), _=Depends(get_curre
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try:
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total = _execute_raw(db, "SELECT COUNT(*) as cnt FROM code_exec_tasks")[0]["cnt"]
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today = _execute_raw(db,
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"SELECT COUNT(*) as cnt FROM code_exec_tasks WHERE DATE(created_at) = CURDATE()")[0]["cnt"]
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"SELECT COUNT(*) as cnt FROM code_exec_tasks WHERE DATE(created_at) = CURRENT_DATE")[0]["cnt"]
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success_count = _execute_raw(db,
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"SELECT COUNT(*) as cnt FROM code_exec_tasks WHERE exit_code = 0 AND error IS NULL")[0]["cnt"]
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failed_count = _execute_raw(db,
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@@ -703,7 +703,7 @@ echo "Installation complete!"
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{d.get('version', '1.0.0')}
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## Installation
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Run `bash install.sh` to install.
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Run \"bash install.sh\" to install.
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## License
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A valid license key is required. Contact your administrator for details.
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@@ -169,7 +169,7 @@ async def get_im_bot_stats(user=Depends(get_current_user)):
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today_stats = _row_to_dict(rows[0]) if rows else {"msg_in": 0, "msg_out": 0, "users": 0, "errors": 0}
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# Total routing rules
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routes_count = _execute_raw(db, text("SELECT COUNT(*) as cnt FROM im_bot_routes WHERE is_active=1"))[0]._mapping['cnt']
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routes_count = _execute_raw(db, text("SELECT COUNT(*) as cnt FROM im_bot_routes WHERE is_active=true"))[0]._mapping['cnt']
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return {
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"success": True,
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@@ -202,7 +202,7 @@ async def get_dashboard(user=Depends(get_current_user)):
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SUM(messages_out) as msg_out,
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SUM(unique_users) as users
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FROM im_bot_daily_stats
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WHERE stat_date >= DATE_SUB(CURDATE(), INTERVAL 7 DAY)
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WHERE stat_date >= CURRENT_DATE - INTERVAL '7 DAY'
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GROUP BY stat_date
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ORDER BY stat_date
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"""))
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@@ -217,7 +217,7 @@ async def get_dashboard(user=Depends(get_current_user)):
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LEFT JOIN (
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SELECT channel_id, SUM(messages_in + messages_out) as msg_count
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FROM im_bot_daily_stats
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WHERE stat_date >= DATE_SUB(CURDATE(), INTERVAL 30 DAY)
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WHERE stat_date >= CURRENT_DATE - INTERVAL '30 DAY'
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GROUP BY channel_id
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) s ON c.id = s.channel_id
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ORDER BY c.created_at DESC
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@@ -357,7 +357,7 @@ async def get_channel_detail(channel_id: str, user=Depends(get_current_user)):
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SELECT stat_date, messages_in, messages_out, unique_users, error_count
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FROM im_bot_daily_stats
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WHERE channel_id = :cid
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AND stat_date >= DATE_SUB(CURDATE(), INTERVAL 7 DAY)
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AND stat_date >= CURRENT_DATE - INTERVAL '7 DAY'
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ORDER BY stat_date
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"""), {"cid": channel_id})
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channel['stats_7d'] = _rows_to_dict(stats)
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@@ -428,7 +428,7 @@ async def activate_channel(channel_id: str, user=Depends(get_current_user)):
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db = SessionLocal()
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try:
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_execute_raw(db, text(
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"UPDATE channels SET is_active=1, status='connected' WHERE id=:cid"
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"UPDATE channels SET is_active=true, status='connected' WHERE id=:cid"
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), {"cid": channel_id})
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return {"success": True, "message": "Channel activated"}
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except Exception as e:
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@@ -443,7 +443,7 @@ async def deactivate_channel(channel_id: str, user=Depends(get_current_user)):
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db = SessionLocal()
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try:
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_execute_raw(db, text(
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"UPDATE channels SET is_active=0, status='disconnected' WHERE id=:cid"
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"UPDATE channels SET is_active=false, status='disconnected' WHERE id=:cid"
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), {"cid": channel_id})
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return {"success": True, "message": "Channel deactivated"}
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except Exception as e:
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@@ -442,7 +442,7 @@ def revoke_license(
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raise HTTPException(404, "License not found")
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_execute_raw(db, text(
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"UPDATE offline_licenses SET status = 'revoked', revoked_reason = :reason, is_active = 0 WHERE id = :lid"
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"UPDATE offline_licenses SET status = 'revoked', revoked_reason = :reason, is_active = false WHERE id = :lid"
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), {"reason": reason, "lid": license_id})
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return {"code": 0, "message": "License revoked"}
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@@ -623,7 +623,7 @@ def get_usage_stats(db: Session = Depends(get_db)):
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"SELECT COUNT(DISTINCT user_hash) FROM usage_reports"
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))
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today_reports = _execute_raw(db, text(
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"SELECT COUNT(*) FROM usage_reports WHERE DATE(created_at) = CURDATE()"
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"SELECT COUNT(*) FROM usage_reports WHERE DATE(created_at) = CURRENT_DATE"
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))
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by_type = _execute_raw(db, text(
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"SELECT report_type, COUNT(*) as cnt FROM usage_reports GROUP BY report_type"
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@@ -1251,7 +1251,7 @@ def _build_system_prompt(
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system_parts.append("回复格式要求：使用 Markdown 格式，层次分明即可。对于简短问题，用 1-2 段自然文字直接回答，不必强行分点、加标题或表格；仅在确实需要分步骤或列举时才使用列表，代码/配置用 ``` 代码块包裹。避免冗余空行，保持紧凑、美观、易读。")
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system_parts.append("回复格式要求：使用 Markdown 格式，层次分明即可。对于简短问题，用 1-2 段自然文字直接回答，不必强行分点、加标题或表格；仅在确实需要分步骤或列举时才使用列表，代码/配置用 \"\"\" 代码块包裹。避免冗余空行，保持紧凑、美观、易读。")
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@@ -2536,7 +2536,6 @@ def _build_chat_history(db, conv_id, max_tokens=6000, max_messages=40,
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- 按 token 估算从新到旧累加，超预算即截断（隔离：不溢出上下文窗口）。
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- exclude_latest_user=True 时丢弃最新一条 user 消息（当前轮次另行发送）。
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"""
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ensure_structured_column()
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summary_block = None
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if include_summary:
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try:
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@@ -2707,7 +2706,7 @@ def ensure_structured_column():
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with engine.connect() as conn:
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exists = conn.execute(text(
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"SELECT COUNT(*) FROM information_schema.columns "
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"WHERE table_schema = DATABASE() AND table_name='conversation_summaries' "
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"WHERE table_schema = CURRENT_DATABASE() AND table_name='conversation_summaries' "
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"AND column_name='structured_memory'"
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)).scalar()
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if not exists:
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@@ -2832,7 +2831,6 @@ async def _maybe_compress_history(conv_id):
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失败静默回滚，绝不阻塞用户本次响应（fire-and-forget）。
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"""
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ensure_structured_column()
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sdb = None
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try:
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sdb = SessionLocal()
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@@ -3,7 +3,8 @@
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from fastapi import APIRouter, Depends, HTTPException, Query, Response
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from sqlalchemy.orm import Session
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from sqlalchemy import and_, desc, asc, func
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from sqlalchemy import and_, desc, asc, func, type_coerce
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from sqlalchemy.dialects.postgresql import JSONB
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from pydantic import BaseModel, Field
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from typing import Optional
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import uuid
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@@ -893,7 +894,7 @@ def _build_media_route(db, capability: str, preferred_id: str = None):
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others = db.query(_AM).filter(
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_AM.is_enabled == True,
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func.json_contains(_AM.capabilities, f'"{capability}"'),
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_AM.capabilities.cast(JSONB).contains(type_coerce([capability], JSONB)),
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).order_by(_AM.is_default.desc(), _AM.created_at.asc()).all()
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for m in others:
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_add(m)
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@@ -47,6 +47,7 @@ import json
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import asyncio
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import threading
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@@ -7927,7 +7928,7 @@ async def _send_message_core(
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asyncio.create_task(
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_launch_media_task_bg(ai_msg.id, ai_model.id, req.content, current_user.id)
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@@ -7943,7 +7944,6 @@ async def _send_message_core(
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_generate_media_task(ai_msg.id, ai_model.id, req.content, current_user.id)
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@@ -7959,7 +7959,6 @@ async def _send_message_core(
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)
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@@ -8547,3 +8546,16 @@ def _check_tool_policy(func_name, mcp_info, skills, policy, call_count, round_co
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# 后台线程独立事件循环运行媒体生成，确保 SSE 响应结束后任务不被 uviorn 取消，
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# 避免图片/视频永远停留在「生成中…」。_generate_media_task 内部自管 DB 会话。
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def _launch_media_task_bg(ai_msg_id, ai_model_id, prompt, user_id):
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def _run():
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try:
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asyncio.run(_generate_media_task(ai_msg_id, ai_model_id, prompt, user_id))
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except Exception:
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pass
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_t = threading.Thread(target=_run, daemon=True)
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_t.start()
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@@ -109,20 +109,20 @@ def _ensure_table(db):
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"""确保 im_bot_platform_account 表存在（首次调用时创建）。"""
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db.execute(text("""
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CREATE TABLE IF NOT EXISTS im_bot_platform_account (
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id INT AUTO_INCREMENT PRIMARY KEY,
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id SERIAL PRIMARY KEY,
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user_id VARCHAR(64) DEFAULT NULL,
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platform VARCHAR(32) NOT NULL,
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status VARCHAR(32) DEFAULT 'disconnected',
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creds MEDIUMTEXT NULL,
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creds TEXT NULL,
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send_url VARCHAR(512) NULL,
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api_base VARCHAR(512) NULL,
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bot_token VARCHAR(512) NULL,
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webhook_token VARCHAR(64) NULL,
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last_error MEDIUMTEXT NULL,
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created_at DATETIME DEFAULT CURRENT_TIMESTAMP,
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updated_at DATETIME DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,
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UNIQUE KEY uk_user_platform (user_id, platform)
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) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_unicode_ci
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last_error TEXT NULL,
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created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
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updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
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UNIQUE (user_id, platform)
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)
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"""))
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db.commit()
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@@ -502,8 +502,8 @@ def _parse_llm_graph_response(content: str):
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except json.JSONDecodeError:
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pass
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# 方式2: 提取```json ... ```代码块
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json_match = re.search(r'```(?:json)?\s*([\s\S]*?)```', content)
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# 方式2: 提取\"\"\"json ... \"\"\"代码块
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json_match = re.search(r'\"\"\"(?:json)?\s*([\s\S]*?)\"\"\"', content)
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if json_match:
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try:
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result = json.loads(json_match.group(1))
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@@ -59,7 +59,7 @@ def _sanitize_skill_content(text: str) -> str:
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if "<script" in low and t.count("<div") > 3:
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return ""
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# 接受携带任一 markdown / skill 标记的内容
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markers = ("#", "---", "name:", "description:", "```", "##")
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markers = ("#", "---", "name:", "description:", "\"\"\"", "##")
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if not any(m in t for m in markers):
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return ""
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return t
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@@ -79,7 +79,7 @@ def _extract_skill_from_html(html: str, base_url: str) -> str:
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"""从 HTML 页面尽力抽取 SKILL.md 正文（代码块优先，其次跳转到 raw 链接）。"""
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proxy = _skill_http_proxy()
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# 1) fenced code block
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fences = re.findall(r"```(?:markdown|md|yaml|json)?\s*\n(.*?)```", html, re.DOTALL | re.IGNORECASE)
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fences = re.findall(r"\"\"\"(?:markdown|md|yaml|json)?\s*\n(.*?)\"\"\"", html, re.DOTALL | re.IGNORECASE)
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for f in fences:
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if ("name:" in f.lower() or "description:" in f.lower() or f.count("#") >= 2) and len(f) > 80:
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return f.strip()
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@@ -98,10 +98,11 @@ def resolve_auto_model(capability: str, db: Session) -> Optional[AIModel]:
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# 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
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# 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"
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if capability in ("image", "video", "audio"):
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from sqlalchemy import text as _text, func
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from sqlalchemy import text as _text, func, type_coerce
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from sqlalchemy.dialects.postgresql import JSONB
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m = db.query(AIModel).filter(
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AIModel.is_enabled == True,
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func.json_contains(AIModel.capabilities, f'"{capability}"'),
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AIModel.capabilities.cast(JSONB).contains(type_coerce([capability], JSONB)),
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).first()
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||||
if m:
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return m
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@@ -1293,7 +1293,7 @@ def _build_system_prompt(
|
||||
|
||||
|
||||
|
||||
system_parts.append("回复格式要求：使用 Markdown 格式，层次分明即可。对于简短问题，用 1-2 段自然文字直接回答，不必强行分点、加标题或表格；仅在确实需要分步骤或列举时才使用列表，代码/配置用 ``` 代码块包裹。避免冗余空行，保持紧凑、美观、易读。")
|
||||
system_parts.append("回复格式要求：使用 Markdown 格式，层次分明即可。对于简短问题，用 1-2 段自然文字直接回答，不必强行分点、加标题或表格；仅在确实需要分步骤或列举时才使用列表，代码/配置用 \"\"\" 代码块包裹。避免冗余空行，保持紧凑、美观、易读。")
|
||||
|
||||
|
||||
|
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@@ -2749,7 +2749,7 @@ def ensure_structured_column():
|
||||
with engine.connect() as conn:
|
||||
exists = conn.execute(text(
|
||||
"SELECT COUNT(*) FROM information_schema.columns "
|
||||
"WHERE table_schema = DATABASE() AND table_name='conversation_summaries' "
|
||||
"WHERE table_schema = CURRENT_DATABASE() AND table_name='conversation_summaries' "
|
||||
"AND column_name='structured_memory'"
|
||||
)).scalar()
|
||||
if not exists:
|
||||
|
||||
@@ -15,6 +15,11 @@ if not (Path("/.dockerenv").exists() or Path("/run/.containerenv").exists()):
|
||||
load_dotenv(env_path, override=False)
|
||||
|
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# 数据库配置
|
||||
# PostgreSQL preferred when DATABASE_URL is provided (production); else legacy MySQL builder (local/dev + rollback)
|
||||
_DATABASE_URL_ENV = os.getenv("DATABASE_URL", "")
|
||||
if _DATABASE_URL_ENV:
|
||||
DATABASE_URL = _DATABASE_URL_ENV
|
||||
else:
|
||||
DATABASE_URL = f"mysql+pymysql://{os.getenv('MYSQL_USER', 'root')}:{os.getenv('MYSQL_PASSWORD', 'pwd2026')}@{os.getenv('MYSQL_HOST', 'localhost')}:{os.getenv('MYSQL_PORT', '3306')}/{os.getenv('MYSQL_DATABASE', 'aiflow')}?charset=utf8mb4"
|
||||
|
||||
# Redis配置
|
||||
|
||||
@@ -1,9 +1,9 @@
|
||||
"""Shared, standards-based LLM client for web chat + IM bot.
|
||||
|
||||
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
|
||||
(`openai.AsyncOpenAI`)——它是 OpenAI 兼容端点的事实标准客户端。平台本身已在讲
|
||||
OpenAI 协议(`/v1/chat/completions`)，因此零协议改造成本，并天然获得：
|
||||
* 原生 SSE 流式(`stream=True`)；
|
||||
(\"openai.AsyncOpenAI\")——它是 OpenAI 兼容端点的事实标准客户端。平台本身已在讲
|
||||
OpenAI 协议(\"/v1/chat/completions\")，因此零协议改造成本，并天然获得：
|
||||
* 原生 SSE 流式(\"stream=True\")；
|
||||
* 结构化的 tool_calls / usage 解析；
|
||||
* 统一的代理(trust_env=False + 显式 proxy)与超时处理。
|
||||
|
||||
@@ -13,13 +13,14 @@ IM bot 共用**同一模型层**、同一套 failover/代理/超时语义——
|
||||
|
||||
API:
|
||||
achat_complete(api_url, api_key, request_body, *, proxy=None, timeout=120, stream=False)
|
||||
- stream=False -> dict，形状对齐上游 `data` 响应：
|
||||
- stream=False -> dict，形状对齐上游 \"data\" 响应：
|
||||
{"choices":[{"message":{"content","tool_calls","reasoning_content"}}], "usage":{...}}
|
||||
- stream=True -> async generator，逐块产出 ("content"|"reasoning", text) 增量
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import httpx
|
||||
import asyncio
|
||||
from openai import AsyncOpenAI, OpenAI
|
||||
|
||||
|
||||
@@ -37,10 +38,21 @@ def build_openai_client(api_url, api_key, *, proxy=None, timeout=120) -> AsyncOp
|
||||
# 始终 trust_env=False：避免容器 HTTP_PROXY 对国内端点(siliconflow 等)造成 502；
|
||||
# 代理经 proxy 显式传入（AGNES 等），未传则直连。
|
||||
http_client = httpx.AsyncClient(proxy=proxy, trust_env=False)
|
||||
# 故障快速失败（全栈统一，覆盖 web chat / IM bot / embedding）：
|
||||
# connect 封顶 10s —— 坏端点不再让请求挂死事件循环；
|
||||
# read/write 封顶 30s —— 长流式回复只要持续有分块即不受影响；
|
||||
# 保留数值型 timeout 接口，旧调用方无需改动。
|
||||
_num = float(timeout) if isinstance(timeout, (int, float)) else 120.0
|
||||
_to = httpx.Timeout(
|
||||
connect=min(10.0, _num),
|
||||
read=min(30.0, _num),
|
||||
write=min(30.0, _num),
|
||||
pool=10.0,
|
||||
)
|
||||
return AsyncOpenAI(
|
||||
base_url=base,
|
||||
api_key=api_key or "EMPTY",
|
||||
timeout=timeout,
|
||||
timeout=_to,
|
||||
http_client=http_client,
|
||||
)
|
||||
|
||||
@@ -94,10 +106,21 @@ async def achat_complete(api_url, api_key, request_body, *, proxy=None, timeout=
|
||||
_reasoning_parts = []
|
||||
_tool_acc = {} # index -> {id, type, name, arguments}
|
||||
try:
|
||||
gen = await client.chat.completions.create(
|
||||
_to = min(30.0, float(timeout) if isinstance(timeout, (int, float)) else 120.0)
|
||||
gen = await asyncio.wait_for(
|
||||
client.chat.completions.create(
|
||||
**_create_kwargs(request_body, stream=True)
|
||||
),
|
||||
timeout=_to,
|
||||
)
|
||||
async for chunk in gen:
|
||||
_it = gen.__aiter__()
|
||||
while True:
|
||||
try:
|
||||
chunk = await asyncio.wait_for(_it.__anext__(), timeout=_to)
|
||||
except StopAsyncIteration:
|
||||
break
|
||||
except asyncio.TimeoutError:
|
||||
raise TimeoutError("stream chunk read timeout")
|
||||
if not chunk.choices:
|
||||
continue
|
||||
delta = chunk.choices[0].delta
|
||||
@@ -129,7 +152,10 @@ async def achat_complete(api_url, api_key, request_body, *, proxy=None, timeout=
|
||||
if _fn_args:
|
||||
_slot["arguments"] += _fn_args
|
||||
finally:
|
||||
await client.close()
|
||||
try:
|
||||
await asyncio.wait_for(client.close(), timeout=5)
|
||||
except Exception:
|
||||
pass
|
||||
# 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
|
||||
_tool_calls = None
|
||||
if _tool_acc:
|
||||
@@ -150,8 +176,12 @@ async def achat_complete(api_url, api_key, request_body, *, proxy=None, timeout=
|
||||
return _gen()
|
||||
|
||||
client = build_openai_client(api_url, api_key, proxy=proxy, timeout=timeout)
|
||||
async with client:
|
||||
resp = await client.chat.completions.create(**_create_kwargs(request_body))
|
||||
try:
|
||||
_to = min(30.0, float(timeout) if isinstance(timeout, (int, float)) else 120.0)
|
||||
resp = await asyncio.wait_for(
|
||||
client.chat.completions.create(**_create_kwargs(request_body)),
|
||||
timeout=_to,
|
||||
)
|
||||
msg = resp.choices[0].message
|
||||
return {
|
||||
"choices": [
|
||||
@@ -165,6 +195,11 @@ async def achat_complete(api_url, api_key, request_body, *, proxy=None, timeout=
|
||||
],
|
||||
"usage": (resp.usage.model_dump() if resp.usage else {}),
|
||||
}
|
||||
finally:
|
||||
try:
|
||||
await asyncio.wait_for(client.close(), timeout=5)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
|
||||
def build_openai_client_sync(api_url, api_key, *, proxy=None, timeout=120) -> OpenAI:
|
||||
@@ -208,9 +243,18 @@ async def aembed(api_url, api_key, input, model, *, proxy=None, timeout=60) -> l
|
||||
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）。
|
||||
"""
|
||||
client = build_openai_client(api_url, api_key, proxy=proxy, timeout=timeout)
|
||||
async with client:
|
||||
resp = await client.embeddings.create(**_embed_kwargs(input, model))
|
||||
try:
|
||||
_to = min(60.0, float(timeout) if isinstance(timeout, (int, float)) else 60.0)
|
||||
resp = await asyncio.wait_for(
|
||||
client.embeddings.create(**_embed_kwargs(input, model)),
|
||||
timeout=_to,
|
||||
)
|
||||
return [d.embedding for d in resp.data]
|
||||
finally:
|
||||
try:
|
||||
await asyncio.wait_for(client.close(), timeout=5)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
|
||||
def embed(api_url, api_key, input, model, *, proxy=None, timeout=60) -> list:
|
||||
|
||||
@@ -6,7 +6,7 @@ Qihu-AIFlow 统一可观测基座 (Phase 2.1 + Phase 1.I OTel 增强)
|
||||
|
||||
设计原则（Strangler Fig 增量）：
|
||||
- 纯增量埋点，不改任何既有接口的返回结构。
|
||||
- 向后兼容现有 `logging.getLogger(__name__)` 用法：trace_id 由 root logger 的
|
||||
- 向后兼容现有 \"logging.getLogger(__name__)\" 用法：trace_id 由 root logger 的
|
||||
TraceIdFilter 自动注入，调用方无需改动。
|
||||
- 不引入第三方依赖（structlog 未安装），自带可选的 JSON formatter。
|
||||
|
||||
@@ -58,7 +58,7 @@ def get_trace_id() -> str:
|
||||
class TraceIdFilter(logging.Filter):
|
||||
"""为每一条日志记录注入 trace_id 属性（不存在则写为 '-'）。
|
||||
|
||||
挂载到 root logger 后，所有 `getLogger(__name__)` 的子 logger 都会继承，
|
||||
挂载到 root logger 后，所有 \"getLogger(__name__)\" 的子 logger 都会继承，
|
||||
从而让全平台既有日志自动带上链路 ID，零侵入。
|
||||
"""
|
||||
|
||||
|
||||
@@ -1,8 +1,8 @@
|
||||
"""
|
||||
MCP (Model Context Protocol) 客户端 —— 基于官方 `mcp` Python SDK (Phase 1.E 标准化)
|
||||
MCP (Model Context Protocol) 客户端 —— 基于官方 \"mcp\" Python SDK (Phase 1.E 标准化)
|
||||
|
||||
设计基准（标准对照表见 STANDARDS_AUDIT_2026-07-24.md §E）：
|
||||
- 采用 Anthropic 官方 `mcp` SDK 处理 JSON-RPC 帧 + 协议版本协商，
|
||||
- 采用 Anthropic 官方 \"mcp\" SDK 处理 JSON-RPC 帧 + 协议版本协商，
|
||||
不再手写解析、不再写死 MCP_PROTOCOL_VERSION 常量（旧实现写死 "2024-11-05"，
|
||||
上游协议一升级即碎）。
|
||||
- 支持 streamable_http（默认）与 sse 两种传输，按 server['transport_type'] 选择。
|
||||
|
||||
@@ -11,7 +11,7 @@ from sqlalchemy import (
|
||||
DECIMAL, UniqueConstraint
|
||||
)
|
||||
from sqlalchemy.orm import relationship
|
||||
from sqlalchemy.dialects.mysql import MEDIUMTEXT
|
||||
from sqlalchemy import Text # PG-compatible (was MEDIUMTEXT from mysql dialect)
|
||||
from .database import Base
|
||||
|
||||
|
||||
@@ -300,7 +300,7 @@ class MarketApp(Base):
|
||||
rating_avg = Column(DECIMAL(3, 2), default=0.00)
|
||||
review_count = Column(Integer, default=0)
|
||||
install_count = Column(Integer, default=0)
|
||||
skill_content = Column(MEDIUMTEXT, default=None)
|
||||
skill_content = Column(Text, default=None)
|
||||
mcp_transport = Column(String(20), default=None)
|
||||
platform = Column(String(20), default=None, comment="Agent 接入平台：dify/n8n/coze")
|
||||
capability = Column(String(20), default=None, comment="能力分类：text/thinking/vision/tool/image/audio/asr/ocr/embedding/rerank/video")
|
||||
|
||||
@@ -73,7 +73,7 @@ PLATFORM_KNOWLEDGE = """# 七虎AI平台能力全览
|
||||
- **安全**: JWT鉴权、HTTPS加密、操作审计日志
|
||||
|
||||
## AI回复规范
|
||||
1. **回复格式**: 使用Markdown（标题 `##`/`###`、加粗 `**`、列表 `-`/`1.`、代码块等）
|
||||
1. **回复格式**: 使用Markdown（标题 \"##\"/\"###\"、加粗 \"**\"、列表 \"-\"/\"1.\"、代码块等）
|
||||
2. **层次分明**: 使用标题分隔段落，确保信息有层次
|
||||
3. **平台优先**: 回答问题时优先引用本平台功能，而非泛泛建议
|
||||
4. **准确具体**: 基于平台文档给出具体操作路径、配置方法、API示例
|
||||
|
||||
@@ -118,6 +118,18 @@ def _record_api_call(db, method: str, path: str, status_code: int, elapsed: floa
|
||||
async def lifespan(app: FastAPI):
|
||||
logger.info("Qihu-AIFlow v2.0.0 启动中...")
|
||||
|
||||
# 幂等确保 conversation_summaries.structured_memory 列。
|
||||
# 原实现在每次聊天的请求路径中同步执行 engine.connect().execute(SELECT information_schema.columns)，
|
||||
# 该同步阻塞调用运行在 asyncio 事件循环线程上，会导致整个事件循环冻结（所有端点挂死，
|
||||
# 表现为「单 worker 永久阻塞、docker restart 才恢复」）。改为启动时一次性执行，
|
||||
# 既保留对全新库的兼容，又绝不阻塞任何用户请求。
|
||||
try:
|
||||
from app.api.chat_context import ensure_structured_column
|
||||
ensure_structured_column()
|
||||
logger.info("[startup] ensure_structured_column 完成")
|
||||
except Exception as _e:
|
||||
logger.warning(f"[startup] ensure_structured_column 失败(可忽略): {_e}")
|
||||
|
||||
# 启动后台 Evolution Pipeline（知识库图谱已存在的用户配置自动恢复运行）
|
||||
try:
|
||||
from app.services.evolution_service import EvolutionWatcherManager
|
||||
|
||||
@@ -28,3 +28,4 @@ click==8.4.2
|
||||
click-didyoumean==0.3.1
|
||||
click-plugins==1.1.1.2
|
||||
click-repl==0.3.0
|
||||
psycopg[binary]
|
||||
@@ -0,0 +1,35 @@
|
||||
version: "1.0"
|
||||
name: "Code Review Pipeline"
|
||||
description: "Generate code → review → merge. A standard code-review workflow."
|
||||
category: development
|
||||
|
||||
nodes:
|
||||
- id: code_gen
|
||||
type: llm
|
||||
description: "Generate code based on requirements"
|
||||
on_error: halt
|
||||
config:
|
||||
model: auto
|
||||
system_prompt: "You are a senior software engineer. Write clean, production-ready code based on the user's requirements. Include comments and error handling."
|
||||
|
||||
- id: review
|
||||
type: llm
|
||||
description: "Review generated code for bugs and style"
|
||||
on_error: continue
|
||||
config:
|
||||
model: auto
|
||||
system_prompt: "You are a code reviewer. Analyze the provided code for bugs, style issues, performance problems, and security vulnerabilities. Provide specific, actionable feedback."
|
||||
|
||||
- id: merge
|
||||
type: reduce
|
||||
description: "Combine code with review feedback"
|
||||
config:
|
||||
strategy: concat
|
||||
|
||||
edges:
|
||||
- from: code_gen
|
||||
to: review
|
||||
- from: code_gen
|
||||
to: merge
|
||||
- from: review
|
||||
to: merge
|
||||
@@ -0,0 +1,35 @@
|
||||
version: "1.0"
|
||||
name: "Multi-Agent Debate"
|
||||
description: "Two agents debate a topic from opposing perspectives, then a judge agent synthesizes the conclusion."
|
||||
category: creativity
|
||||
|
||||
nodes:
|
||||
- id: proponent
|
||||
type: llm
|
||||
description: "Argue in favor of the proposition"
|
||||
on_error: continue
|
||||
config:
|
||||
model: auto
|
||||
system_prompt: "You are a skilled debater. Argue strongly IN FAVOR of the given proposition. Use logic, evidence, and persuasive reasoning. Be passionate but fair."
|
||||
|
||||
- id: opponent
|
||||
type: llm
|
||||
description: "Argue against the proposition"
|
||||
on_error: continue
|
||||
config:
|
||||
model: auto
|
||||
system_prompt: "You are a skilled debater. Argue strongly AGAINST the given proposition. Use logic, evidence, and persuasive reasoning to find flaws in the proponent's case."
|
||||
|
||||
- id: judge
|
||||
type: llm
|
||||
description: "Evaluate both sides and reach a conclusion"
|
||||
on_error: continue
|
||||
config:
|
||||
model: auto
|
||||
system_prompt: "You are an impartial judge. Evaluate both arguments fairly. Identify the strongest points from each side. Provide a balanced conclusion and recommendation."
|
||||
|
||||
edges:
|
||||
- from: proponent
|
||||
to: judge
|
||||
- from: opponent
|
||||
to: judge
|
||||
@@ -0,0 +1,25 @@
|
||||
version: "1.0"
|
||||
name: "RAG Search Pipeline"
|
||||
description: "Search knowledge base → augment with context → generate answer. Standard RAG pipeline."
|
||||
category: productivity
|
||||
|
||||
nodes:
|
||||
- id: search
|
||||
type: llm
|
||||
description: "Generate search queries and retrieve relevant context"
|
||||
on_error: continue
|
||||
config:
|
||||
model: auto
|
||||
system_prompt: "You are a search specialist. For the given question, identify 2-3 key search points. Then search the provided knowledge base for relevant information. Return the most relevant passages with source references."
|
||||
|
||||
- id: answer
|
||||
type: llm
|
||||
description: "Generate final answer with citations"
|
||||
on_error: continue
|
||||
config:
|
||||
model: auto
|
||||
system_prompt: "You are a knowledge assistant. Using the search results provided, generate a comprehensive answer. Always cite your sources. If information is insufficient, clearly state what is missing."
|
||||
|
||||
edges:
|
||||
- from: search
|
||||
to: answer
|
||||
@@ -0,0 +1,25 @@
|
||||
version: "1.0"
|
||||
name: "Research & Answer"
|
||||
description: "Research topic → generate answer. Uses two LLM nodes for research and synthesis."
|
||||
category: productivity
|
||||
|
||||
nodes:
|
||||
- id: research
|
||||
type: llm
|
||||
description: "Search and gather information on the topic"
|
||||
on_error: continue
|
||||
config:
|
||||
model: auto
|
||||
system_prompt: "You are a thorough researcher. Search for key facts, data points, and relevant context about the given topic. Provide a comprehensive summary with citations."
|
||||
|
||||
- id: answer
|
||||
type: llm
|
||||
description: "Synthesize research into final answer"
|
||||
on_error: continue
|
||||
config:
|
||||
model: auto
|
||||
system_prompt: "You are a professional writer. Based on the research summary provided, craft a clear, well-structured answer for the user. Include key findings and actionable insights."
|
||||
|
||||
edges:
|
||||
- from: research
|
||||
to: answer
|
||||
Reference in new issue
Block a user