fix: delete agent 500 error + dynamic personality + deployment guide

- Fix delete agent 500: clean up FK records (agent_llm_logs, permissions,
  schedules, executions, team_members) and unbind goals/tasks before delete
- Remove hardcoded personality templates in Android, replace with dynamic
  system prompt generation from name + description
- Set promptSectionsEnabled=false to bypass PromptComposer for personality
- Add Tencent Cloud Linux deployment guide (Docker Compose)
- Accumulated backend service updates, frontend UI fixes, Android app changes

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

backend/app/api/knowledge_dashboard.py

@@ -0,0 +1,109 @@
+"""
+知识仪表盘 API — 瓶颈分析、知识条目查询、趋势统计
+"""
+from __future__ import annotations
+
+import logging
+from datetime import datetime, timedelta
+from typing import Optional
+
+from fastapi import APIRouter, Depends, Query
+from sqlalchemy import func
+from sqlalchemy.orm import Session
+
+from app.core.database import get_db
+from app.api.auth import get_current_user
+from app.models.user import User
+from app.models.knowledge_entry import KnowledgeEntry
+from app.services.bottleneck_detector import bottleneck_detector
+from app.services.optimization_engine import optimization_engine
+
+logger = logging.getLogger(__name__)
+
+router = APIRouter(prefix="/api/v1/knowledge-dashboard", tags=["knowledge-dashboard"])
+
+
+@router.get("/bottlenecks")
+def get_bottlenecks(
+    hours: int = Query(168, ge=1, le=2160, description="分析时长（小时）"),
+    current_user: User = Depends(get_current_user),
+):
+    """瓶颈分析 — 检测工作流性能瓶颈并生成优化建议。"""
+    analysis = bottleneck_detector.run_full_analysis(hours=hours)
+    optimizations = optimization_engine.generate_optimizations(analysis.get("bottlenecks", []))
+    # Build recommendations dict keyed by node_type for frontend lookup
+    recommendations_map = {}
+    for opt in optimizations:
+        recommendations_map[opt["node_type"]] = {
+            "node_type": opt["node_type"],
+            "severity": opt["severity"],
+            "current_state": opt.get("current_metrics", {}),
+            "changes": opt.get("changes", []),
+        }
+    return {
+        **analysis,
+        "recommendations": list(optimizations),
+        "optimizations": recommendations_map,
+    }
+
+
+@router.get("/entries")
+def get_knowledge_entries(
+    days: int = Query(7, ge=1, le=365, description="统计天数"),
+    limit: int = Query(50, ge=1, le=200, description="返回条数"),
+    db: Session = Depends(get_db),
+    current_user: User = Depends(get_current_user),
+):
+    """获取知识条目列表，按创建时间倒序。"""
+    since = datetime.now() - timedelta(days=days)
+    entries = (
+        db.query(KnowledgeEntry)
+        .filter(
+            KnowledgeEntry.created_at >= since,
+            KnowledgeEntry.is_active == True,
+        )
+        .order_by(KnowledgeEntry.created_at.desc())
+        .limit(limit)
+        .all()
+    )
+    return [e.to_dict() for e in entries]
+
+
+@router.get("/trend")
+def get_knowledge_trend(
+    days: int = Query(7, ge=1, le=365, description="统计天数"),
+    db: Session = Depends(get_db),
+    current_user: User = Depends(get_current_user),
+):
+    """知识条目增长趋势 — 按天统计新增数量。"""
+    since = datetime.now() - timedelta(days=days)
+
+    # GROUP BY date(created_at)
+    rows = (
+        db.query(
+            func.date(KnowledgeEntry.created_at).label("date"),
+            func.count(KnowledgeEntry.id).label("count"),
+        )
+        .filter(
+            KnowledgeEntry.created_at >= since,
+            KnowledgeEntry.is_active == True,
+        )
+        .group_by(func.date(KnowledgeEntry.created_at))
+        .order_by(func.date(KnowledgeEntry.created_at).asc())
+        .all()
+    )
+
+    # Fill in missing dates with 0 count
+    trend = []
+    current_date = since.date()
+    end_date = datetime.now().date()
+    date_counts = {row.date: row.count for row in rows}
+
+    while current_date <= end_date:
+        trend.append({
+            "date": current_date.isoformat(),
+            "count": date_counts.get(current_date, 0),
+        })
+        current_date += timedelta(days=1)
+
+    return trend