036f533881
- agent_runtime: orchestrator、core/memory/schemas 调整 - agent_monitoring API、service、agent_llm_log 模型与 database 注册 - 前端 AgentDashboard、AgentConfig、Agents/MainLayout/路由与 AgentChat - 文档：(红头)项目核心文档汇总、自主AI Agent改造完成情况、AI agent改造计划 Made-with: Cursor
235 lines
8.2 KiB
Python
235 lines
8.2 KiB
Python
"""
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Agent 监控服务 — 提供 Agent 专属统计数据
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"""
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from sqlalchemy.orm import Session
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from sqlalchemy import func, and_
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from datetime import datetime, timedelta
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from typing import Any, Dict, List, Optional
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from app.models.agent_llm_log import AgentLLMLog
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from app.models.agent import Agent
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from app.models.execution import Execution
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import logging
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logger = logging.getLogger(__name__)
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class AgentMonitoringService:
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"""Agent 监控服务"""
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@staticmethod
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def get_overview(db: Session, user_id: Optional[str] = None) -> Dict[str, Any]:
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"""
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获取 Agent 概览统计。
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- 总对话次数（Execution 中 agent_id 不为空的记录数）
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- 总 LLM 调用次数
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- 总 tokens 数（近似）
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- 总工具调用次数
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- 活跃 Agent 数
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"""
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user_filter = Agent.user_id == user_id if user_id else True
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agent_ids_query = db.query(Agent.id).filter(user_filter)
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agent_ids = {row[0] for row in agent_ids_query.all()}
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# Agent 数量
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agent_count = len(agent_ids)
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# 对话次数（Execution 表中 agent 执行的记录）
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exec_filter = Execution.agent_id.in_(agent_ids) if agent_ids else False
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chat_count = 0
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if agent_ids:
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chat_count = db.query(func.count(Execution.id)).filter(exec_filter).scalar() or 0
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# LLM 调用统计
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llm_filter = AgentLLMLog.agent_id.in_(agent_ids) if agent_ids else False
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llm_count = 0
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total_prompt = 0
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total_completion = 0
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if agent_ids:
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stats = db.query(
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func.count(AgentLLMLog.id),
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func.coalesce(func.sum(AgentLLMLog.prompt_tokens), 0),
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func.coalesce(func.sum(AgentLLMLog.completion_tokens), 0),
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).filter(llm_filter).first()
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llm_count = stats[0] or 0
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total_prompt = stats[1] or 0
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total_completion = stats[2] or 0
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# 工具调用次数（从 ExecutionLog 统计 agent 相关）
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tool_call_count = 0
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if agent_ids:
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tool_call_count = db.query(func.count(AgentLLMLog.id)).filter(
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and_(
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AgentLLMLog.agent_id.in_(agent_ids),
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AgentLLMLog.tool_name.isnot(None),
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)
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).scalar() or 0
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return {
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"agent_count": agent_count,
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"chat_count": chat_count,
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"llm_call_count": llm_count,
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"total_prompt_tokens": total_prompt,
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"total_completion_tokens": total_completion,
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"total_tokens": total_prompt + total_completion,
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"tool_call_count": tool_call_count,
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}
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@staticmethod
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def get_llm_calls(
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db: Session,
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user_id: Optional[str] = None,
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days: int = 7,
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limit: int = 50,
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) -> List[Dict[str, Any]]:
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"""获取最近 LLM 调用记录。"""
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end_time = datetime.utcnow()
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start_time = end_time - timedelta(days=days)
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filters = [AgentLLMLog.created_at >= start_time]
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if user_id:
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filters.append(AgentLLMLog.user_id == user_id)
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records = db.query(AgentLLMLog).filter(
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and_(*filters)
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).order_by(AgentLLMLog.created_at.desc()).limit(limit).all()
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return [
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{
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"id": r.id,
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"agent_id": r.agent_id,
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"session_id": r.session_id,
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"model": r.model,
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"provider": r.provider,
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"prompt_tokens": r.prompt_tokens,
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"completion_tokens": r.completion_tokens,
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"total_tokens": r.total_tokens,
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"latency_ms": r.latency_ms,
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"iteration_number": r.iteration_number,
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"step_type": r.step_type,
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"tool_name": r.tool_name,
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"status": r.status,
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"error_message": r.error_message,
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"created_at": r.created_at.isoformat() if r.created_at else None,
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}
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for r in records
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]
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@staticmethod
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def get_agent_stats(
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db: Session,
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user_id: Optional[str] = None,
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days: int = 7,
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) -> List[Dict[str, Any]]:
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"""获取各 Agent 的用量统计（按 Agent 分组）。"""
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end_time = datetime.utcnow()
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start_time = end_time - timedelta(days=days)
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filters = [AgentLLMLog.created_at >= start_time]
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if user_id:
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filters.append(AgentLLMLog.user_id == user_id)
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rows = db.query(
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AgentLLMLog.agent_id,
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Agent.name.label("agent_name"),
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func.count(AgentLLMLog.id).label("call_count"),
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func.coalesce(func.sum(AgentLLMLog.prompt_tokens), 0).label("total_prompt"),
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func.coalesce(func.sum(AgentLLMLog.completion_tokens), 0).label("total_completion"),
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func.coalesce(func.sum(AgentLLMLog.total_tokens), 0).label("total_tokens"),
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func.coalesce(func.avg(AgentLLMLog.latency_ms), 0).label("avg_latency"),
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func.count(AgentLLMLog.tool_name).label("tool_calls"),
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).outerjoin(
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Agent, AgentLLMLog.agent_id == Agent.id
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).filter(
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and_(*filters)
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).group_by(AgentLLMLog.agent_id).order_by(
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func.count(AgentLLMLog.id).desc()
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).all()
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return [
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{
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"agent_id": r.agent_id or "未知",
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"agent_name": r.agent_name or "未知 Agent",
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"call_count": r.call_count,
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"total_prompt_tokens": r.total_prompt,
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"total_completion_tokens": r.total_completion,
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"total_tokens": r.total_tokens,
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"avg_latency_ms": round(r.avg_latency, 2) if r.avg_latency else 0,
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"tool_call_count": r.tool_calls or 0,
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}
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for r in rows
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]
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@staticmethod
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def get_tool_usage(
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db: Session,
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user_id: Optional[str] = None,
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days: int = 7,
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) -> List[Dict[str, Any]]:
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"""获取工具调用频次统计。"""
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end_time = datetime.utcnow()
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start_time = end_time - timedelta(days=days)
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filters = [
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AgentLLMLog.created_at >= start_time,
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AgentLLMLog.tool_name.isnot(None),
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]
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if user_id:
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filters.append(AgentLLMLog.user_id == user_id)
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rows = db.query(
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AgentLLMLog.tool_name,
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func.count(AgentLLMLog.id).label("call_count"),
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func.coalesce(func.sum(AgentLLMLog.total_tokens), 0).label("total_tokens"),
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func.coalesce(func.avg(AgentLLMLog.latency_ms), 0).label("avg_latency"),
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).filter(
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and_(*filters)
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).group_by(AgentLLMLog.tool_name).order_by(
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func.count(AgentLLMLog.id).desc()
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).all()
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return [
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{
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"tool_name": r.tool_name or "未知",
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"call_count": r.call_count,
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"total_tokens": r.total_tokens,
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"avg_latency_ms": round(r.avg_latency, 2) if r.avg_latency else 0,
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}
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for r in rows
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]
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@staticmethod
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def get_daily_trend(
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db: Session,
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user_id: Optional[str] = None,
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days: int = 7,
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) -> List[Dict[str, Any]]:
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"""获取每日 LLM 调用趋势。"""
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end_time = datetime.utcnow()
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start_time = end_time - timedelta(days=days)
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filters = [AgentLLMLog.created_at >= start_time]
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if user_id:
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filters.append(AgentLLMLog.user_id == user_id)
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results = []
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for i in range(days):
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day_start = end_time - timedelta(days=days - i)
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day_end = day_start + timedelta(days=1)
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day_filter = and_(
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*filters,
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AgentLLMLog.created_at >= day_start,
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AgentLLMLog.created_at < day_end,
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)
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day_count = db.query(func.count(AgentLLMLog.id)).filter(day_filter).scalar() or 0
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day_tokens = db.query(
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func.coalesce(func.sum(AgentLLMLog.total_tokens), 0)
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).filter(day_filter).scalar() or 0
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results.append({
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"date": day_start.strftime("%m-%d"),
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"call_count": day_count,
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"total_tokens": day_tokens,
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})
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return results
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