982 lines
37 KiB
Python
982 lines
37 KiB
Python
"""
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||
Dashboard Router - Statistics and monitoring endpoints
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Provides centralized dashboard APIs for monitoring system-wide statistics.
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"""
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import traceback
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from datetime import datetime, timedelta
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from fastapi import APIRouter, Depends, HTTPException
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from pydantic import BaseModel
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from sqlalchemy import String, cast, distinct, func, or_
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from sqlalchemy.orm import Session
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from server.routers.auth_router import get_admin_user
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from server.utils.auth_middleware import get_db
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from src.storage.conversation import ConversationManager
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from src.storage.db.models import User
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from src.utils.datetime_utils import UTC, ensure_shanghai, shanghai_now, utc_now
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from src.utils.logging_config import logger
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dashboard = APIRouter(prefix="/dashboard", tags=["Dashboard"])
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# =============================================================================
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# Response Models
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# =============================================================================
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class UserActivityStats(BaseModel):
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"""用户活跃度统计"""
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total_users: int
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active_users_24h: int
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active_users_30d: int
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daily_active_users: list[dict] # 最近7天每日活跃用户
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class ToolCallStats(BaseModel):
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"""工具调用统计"""
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total_calls: int
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successful_calls: int
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failed_calls: int
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success_rate: float
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most_used_tools: list[dict]
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tool_error_distribution: dict
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daily_tool_calls: list[dict] # 最近7天每日工具调用数
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||
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class KnowledgeStats(BaseModel):
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"""知识库统计"""
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||
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total_databases: int
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total_files: int
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total_nodes: int
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total_storage_size: int # 字节
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databases_by_type: dict
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file_type_distribution: dict
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||
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||
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class AgentAnalytics(BaseModel):
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"""AI智能体分析"""
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total_agents: int
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agent_conversation_counts: list[dict]
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agent_satisfaction_rates: list[dict]
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agent_tool_usage: list[dict]
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top_performing_agents: list[dict]
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class ConversationListItem(BaseModel):
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"""Conversation list item"""
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thread_id: str
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user_id: str
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agent_id: str
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title: str
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status: str
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message_count: int
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||
created_at: str
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||
updated_at: str
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class ConversationDetailResponse(BaseModel):
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"""Conversation detail"""
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thread_id: str
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user_id: str
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agent_id: str
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||
title: str
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status: str
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message_count: int
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created_at: str
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updated_at: str
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total_tokens: int
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messages: list[dict]
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# =============================================================================
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# Conversation Management
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# =============================================================================
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@dashboard.get("/conversations", response_model=list[ConversationListItem])
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async def get_all_conversations(
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user_id: str | None = None,
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agent_id: str | None = None,
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status: str = "active",
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limit: int = 100,
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offset: int = 0,
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db: Session = Depends(get_db),
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current_user: User = Depends(get_admin_user),
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):
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"""Get all conversations (Admin only)"""
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from src.storage.db.models import Conversation, ConversationStats
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try:
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# Build query
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query = db.query(Conversation, ConversationStats).outerjoin(
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ConversationStats, Conversation.id == ConversationStats.conversation_id
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)
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# Apply filters
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if user_id:
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query = query.filter(Conversation.user_id == user_id)
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if agent_id:
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query = query.filter(Conversation.agent_id == agent_id)
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if status != "all":
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query = query.filter(Conversation.status == status)
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# Order and paginate
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query = query.order_by(Conversation.updated_at.desc()).limit(limit).offset(offset)
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results = query.all()
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return [
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{
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"thread_id": conv.thread_id,
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"user_id": conv.user_id,
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||
"agent_id": conv.agent_id,
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||
"title": conv.title,
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"status": conv.status,
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||
"message_count": stats.message_count if stats else 0,
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||
"created_at": conv.created_at.isoformat(),
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"updated_at": conv.updated_at.isoformat(),
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}
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for conv, stats in results
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]
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except Exception as e:
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logger.error(f"Error getting conversations: {e}")
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||
logger.error(traceback.format_exc())
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raise HTTPException(status_code=500, detail=f"Failed to get conversations: {str(e)}")
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@dashboard.get("/conversations/{thread_id}", response_model=ConversationDetailResponse)
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async def get_conversation_detail(
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||
thread_id: str,
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db: Session = Depends(get_db),
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current_user: User = Depends(get_admin_user),
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||
):
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"""Get conversation detail (Admin only)"""
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try:
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conv_manager = ConversationManager(db)
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conversation = conv_manager.get_conversation_by_thread_id(thread_id)
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if not conversation:
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raise HTTPException(status_code=404, detail="Conversation not found")
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# Get messages and stats
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messages = conv_manager.get_messages(conversation.id)
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stats = conv_manager.get_stats(conversation.id)
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# Format messages
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||
message_list = []
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for msg in messages:
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msg_dict = {
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"id": msg.id,
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"role": msg.role,
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"content": msg.content,
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"message_type": msg.message_type,
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"created_at": msg.created_at.isoformat(),
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}
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# Include tool calls if present
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if msg.tool_calls:
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msg_dict["tool_calls"] = [
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{
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"id": tc.id,
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||
"tool_name": tc.tool_name,
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||
"tool_input": tc.tool_input,
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"tool_output": tc.tool_output,
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"status": tc.status,
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}
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for tc in msg.tool_calls
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]
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message_list.append(msg_dict)
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return {
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"thread_id": conversation.thread_id,
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"user_id": conversation.user_id,
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"agent_id": conversation.agent_id,
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"title": conversation.title,
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"status": conversation.status,
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"message_count": stats.message_count if stats else len(message_list),
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"created_at": conversation.created_at.isoformat(),
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"updated_at": conversation.updated_at.isoformat(),
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"total_tokens": stats.total_tokens if stats else 0,
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"messages": message_list,
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}
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except HTTPException:
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||
raise
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||
except Exception as e:
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||
logger.error(f"Error getting conversation detail: {e}")
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||
logger.error(traceback.format_exc())
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raise HTTPException(status_code=500, detail=f"Failed to get conversation detail: {str(e)}")
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||
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# =============================================================================
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||
# User Activity Statistics
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||
# =============================================================================
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||
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||
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@dashboard.get("/stats/users", response_model=UserActivityStats)
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async def get_user_activity_stats(
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db: Session = Depends(get_db),
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current_user: User = Depends(get_admin_user),
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||
):
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||
"""Get user activity statistics (Admin only)"""
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try:
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||
from src.storage.db.models import User, Conversation
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||
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||
now = utc_now()
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# Conversations may store either the numeric user primary key or the login user_id string.
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||
# Join condition accounts for both representations.
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||
user_join_condition = or_(
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Conversation.user_id == User.user_id,
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||
Conversation.user_id == cast(User.id, String),
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||
)
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||
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||
# 基础用户统计（排除已删除用户）
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total_users = db.query(func.count(User.id)).filter(User.is_deleted == 0).scalar() or 0
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||
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||
# 不同时间段的活跃用户数（基于对话活动，排除已删除用户）
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active_users_24h = (
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db.query(func.count(distinct(User.id)))
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.select_from(Conversation)
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.join(User, user_join_condition)
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.filter(Conversation.updated_at >= now - timedelta(days=1), User.is_deleted == 0)
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||
.scalar()
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or 0
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||
)
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||
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||
active_users_30d = (
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||
db.query(func.count(distinct(User.id)))
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||
.select_from(Conversation)
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||
.join(User, user_join_condition)
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||
.filter(Conversation.updated_at >= now - timedelta(days=30), User.is_deleted == 0)
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||
.scalar()
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||
or 0
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||
)
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||
# 最近7天每日活跃用户（排除已删除用户）
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daily_active_users = []
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||
for i in range(7):
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||
day_start = now - timedelta(days=i + 1)
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||
day_end = now - timedelta(days=i)
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||
|
||
active_count = (
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||
db.query(func.count(distinct(User.id)))
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||
.select_from(Conversation)
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||
.join(User, user_join_condition)
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||
.filter(Conversation.updated_at >= day_start, Conversation.updated_at < day_end, User.is_deleted == 0)
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||
.scalar()
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or 0
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||
)
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||
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daily_active_users.append({"date": day_start.strftime("%Y-%m-%d"), "active_users": active_count})
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||
|
||
return UserActivityStats(
|
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total_users=total_users,
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||
active_users_24h=active_users_24h,
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||
active_users_30d=active_users_30d,
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||
daily_active_users=list(reversed(daily_active_users)), # 按时间正序
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||
)
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||
|
||
except Exception as e:
|
||
logger.error(f"Error getting user activity stats: {e}")
|
||
logger.error(traceback.format_exc())
|
||
raise HTTPException(status_code=500, detail=f"Failed to get user activity stats: {str(e)}")
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||
|
||
|
||
# =============================================================================
|
||
# Tool Call Statistics
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||
# =============================================================================
|
||
|
||
|
||
@dashboard.get("/stats/tools", response_model=ToolCallStats)
|
||
async def get_tool_call_stats(
|
||
db: Session = Depends(get_db),
|
||
current_user: User = Depends(get_admin_user),
|
||
):
|
||
"""Get tool call statistics (Admin only)"""
|
||
try:
|
||
from src.storage.db.models import ToolCall
|
||
|
||
now = utc_now()
|
||
|
||
# 基础工具调用统计
|
||
total_calls = db.query(func.count(ToolCall.id)).scalar() or 0
|
||
successful_calls = db.query(func.count(ToolCall.id)).filter(ToolCall.status == "success").scalar() or 0
|
||
failed_calls = total_calls - successful_calls
|
||
success_rate = round((successful_calls / total_calls * 100), 2) if total_calls > 0 else 0
|
||
|
||
# 最常用工具
|
||
most_used_tools = (
|
||
db.query(ToolCall.tool_name, func.count(ToolCall.id).label("count"))
|
||
.group_by(ToolCall.tool_name)
|
||
.order_by(func.count(ToolCall.id).desc())
|
||
.limit(10)
|
||
.all()
|
||
)
|
||
most_used_tools = [{"tool_name": name, "count": count} for name, count in most_used_tools]
|
||
|
||
# 工具错误分布
|
||
tool_errors = (
|
||
db.query(ToolCall.tool_name, func.count(ToolCall.id).label("error_count"))
|
||
.filter(ToolCall.status == "error")
|
||
.group_by(ToolCall.tool_name)
|
||
.all()
|
||
)
|
||
tool_error_distribution = {name: count for name, count in tool_errors}
|
||
|
||
# 最近7天每日工具调用数
|
||
daily_tool_calls = []
|
||
for i in range(7):
|
||
day_start = now - timedelta(days=i + 1)
|
||
day_end = now - timedelta(days=i)
|
||
|
||
daily_count = (
|
||
db.query(func.count(ToolCall.id))
|
||
.filter(ToolCall.created_at >= day_start, ToolCall.created_at < day_end)
|
||
.scalar()
|
||
or 0
|
||
)
|
||
|
||
daily_tool_calls.append({"date": day_start.strftime("%Y-%m-%d"), "call_count": daily_count})
|
||
|
||
return ToolCallStats(
|
||
total_calls=total_calls,
|
||
successful_calls=successful_calls,
|
||
failed_calls=failed_calls,
|
||
success_rate=success_rate,
|
||
most_used_tools=most_used_tools,
|
||
tool_error_distribution=tool_error_distribution,
|
||
daily_tool_calls=list(reversed(daily_tool_calls)),
|
||
)
|
||
|
||
except Exception as e:
|
||
logger.error(f"Error getting tool call stats: {e}")
|
||
logger.error(traceback.format_exc())
|
||
raise HTTPException(status_code=500, detail=f"Failed to get tool call stats: {str(e)}")
|
||
|
||
|
||
# =============================================================================
|
||
# Knowledge Base Statistics
|
||
# =============================================================================
|
||
|
||
|
||
@dashboard.get("/stats/knowledge", response_model=KnowledgeStats)
|
||
async def get_knowledge_stats(
|
||
db: Session = Depends(get_db),
|
||
current_user: User = Depends(get_admin_user),
|
||
):
|
||
"""Get knowledge base statistics (Admin only)"""
|
||
try:
|
||
from src.knowledge.manager import KnowledgeBaseManager
|
||
import json
|
||
import os
|
||
|
||
# 从知识库管理系统获取数据
|
||
kb_manager = KnowledgeBaseManager(work_dir="/app/saves/knowledge_base_data")
|
||
|
||
# 读取全局元数据文件
|
||
metadata_file = "/app/saves/knowledge_base_data/global_metadata.json"
|
||
if os.path.exists(metadata_file):
|
||
with open(metadata_file, encoding="utf-8") as f:
|
||
global_metadata = json.load(f)
|
||
|
||
databases = global_metadata.get("databases", {})
|
||
total_databases = len(databases)
|
||
|
||
# 统计不同类型的知识库
|
||
databases_by_type = {}
|
||
files_by_type = {}
|
||
total_files = 0
|
||
total_nodes = 0
|
||
total_storage_size = 0
|
||
|
||
# 文件类型映射到中文友好名称
|
||
file_type_mapping = {
|
||
"txt": "文本文件",
|
||
"pdf": "PDF文档",
|
||
"docx": "Word文档",
|
||
"doc": "Word文档",
|
||
"md": "Markdown",
|
||
"html": "HTML网页",
|
||
"htm": "HTML网页",
|
||
"json": "JSON数据",
|
||
"csv": "CSV表格",
|
||
"xlsx": "Excel表格",
|
||
"xls": "Excel表格",
|
||
"pptx": "PowerPoint",
|
||
"ppt": "PowerPoint",
|
||
"png": "PNG图片",
|
||
"jpg": "JPEG图片",
|
||
"jpeg": "JPEG图片",
|
||
"gif": "GIF图片",
|
||
"svg": "SVG图片",
|
||
"mp4": "MP4视频",
|
||
"mp3": "MP3音频",
|
||
"zip": "ZIP压缩包",
|
||
"rar": "RAR压缩包",
|
||
"7z": "7Z压缩包",
|
||
}
|
||
|
||
# 统计文件：改为基于各知识库实现中的 files_meta，更加准确
|
||
# 注意：部分记录可能来源于 URL，此时无法统计物理大小
|
||
for kb_instance in kb_manager.kb_instances.values():
|
||
files_meta = getattr(kb_instance, "files_meta", {}) or {}
|
||
total_files += len(files_meta)
|
||
|
||
for _fid, finfo in files_meta.items():
|
||
file_ext = (finfo.get("file_type") or "").lower()
|
||
# 统一映射显示名
|
||
display_name = file_type_mapping.get(file_ext, file_ext.upper() + "文件" if file_ext else "其他")
|
||
files_by_type[display_name] = files_by_type.get(display_name, 0) + 1
|
||
|
||
# 估算大小（如果路径存在且是本地文件）
|
||
path = finfo.get("path") or ""
|
||
try:
|
||
if path and os.path.exists(path) and os.path.isfile(path):
|
||
total_storage_size += os.path.getsize(path)
|
||
except Exception:
|
||
# 忽略无法访问的路径
|
||
pass
|
||
|
||
# 统计知识库类型分布
|
||
for kb_id, kb_info in databases.items():
|
||
kb_type = kb_info.get("kb_type", "unknown")
|
||
display_type = {
|
||
"lightrag": "LightRAG",
|
||
"chroma": "Chroma",
|
||
"faiss": "FAISS",
|
||
"milvus": "Milvus",
|
||
"qdrant": "Qdrant",
|
||
"elasticsearch": "Elasticsearch",
|
||
"unknown": "未知类型",
|
||
}.get(kb_type.lower(), kb_type)
|
||
databases_by_type[display_type] = databases_by_type.get(display_type, 0) + 1
|
||
|
||
# 尝试从各个知识库系统获取更详细的统计
|
||
try:
|
||
kb_instance = kb_manager.get_kb(kb_id)
|
||
if kb_instance and hasattr(kb_instance, "get_stats"):
|
||
stats = kb_instance.get_stats()
|
||
total_nodes += stats.get("node_count", 0)
|
||
except Exception as e:
|
||
logger.warning(f"Failed to get stats for KB {kb_id}: {e}")
|
||
continue
|
||
|
||
else:
|
||
# 如果没有元数据文件，返回空数据
|
||
total_databases = 0
|
||
total_files = 0
|
||
total_nodes = 0
|
||
total_storage_size = 0
|
||
databases_by_type = {}
|
||
files_by_type = {}
|
||
|
||
return KnowledgeStats(
|
||
total_databases=total_databases,
|
||
total_files=total_files,
|
||
total_nodes=total_nodes,
|
||
total_storage_size=total_storage_size,
|
||
databases_by_type=databases_by_type,
|
||
file_type_distribution=files_by_type, # 保持API兼容，但使用新的数据
|
||
)
|
||
|
||
except Exception as e:
|
||
logger.error(f"Error getting knowledge stats: {e}")
|
||
logger.error(traceback.format_exc())
|
||
raise HTTPException(status_code=500, detail=f"Failed to get knowledge stats: {str(e)}")
|
||
|
||
|
||
# =============================================================================
|
||
# Agent Analytics
|
||
# =============================================================================
|
||
|
||
|
||
@dashboard.get("/stats/agents", response_model=AgentAnalytics)
|
||
async def get_agent_analytics(
|
||
db: Session = Depends(get_db),
|
||
current_user: User = Depends(get_admin_user),
|
||
):
|
||
"""Get AI agent analytics (Admin only)"""
|
||
try:
|
||
from src.storage.db.models import Conversation, MessageFeedback, Message, ToolCall
|
||
|
||
# 获取所有智能体
|
||
agents = (
|
||
db.query(Conversation.agent_id, func.count(Conversation.id).label("conversation_count"))
|
||
.group_by(Conversation.agent_id)
|
||
.all()
|
||
)
|
||
|
||
total_agents = len(agents)
|
||
agent_conversation_counts = [{"agent_id": agent_id, "conversation_count": count} for agent_id, count in agents]
|
||
|
||
# 智能体满意度统计
|
||
agent_satisfaction = []
|
||
for agent_id, _ in agents:
|
||
total_feedbacks = (
|
||
db.query(func.count(MessageFeedback.id))
|
||
.join(Message, MessageFeedback.message_id == Message.id)
|
||
.join(Conversation, Message.conversation_id == Conversation.id)
|
||
.filter(Conversation.agent_id == agent_id)
|
||
.scalar()
|
||
or 0
|
||
)
|
||
|
||
positive_feedbacks = (
|
||
db.query(func.count(MessageFeedback.id))
|
||
.join(Message, MessageFeedback.message_id == Message.id)
|
||
.join(Conversation, Message.conversation_id == Conversation.id)
|
||
.filter(Conversation.agent_id == agent_id, MessageFeedback.rating == "like")
|
||
.scalar()
|
||
or 0
|
||
)
|
||
|
||
satisfaction_rate = round((positive_feedbacks / total_feedbacks * 100), 2) if total_feedbacks > 0 else 100
|
||
|
||
agent_satisfaction.append(
|
||
{"agent_id": agent_id, "satisfaction_rate": satisfaction_rate, "total_feedbacks": total_feedbacks}
|
||
)
|
||
|
||
# 智能体工具使用统计
|
||
agent_tool_usage = []
|
||
for agent_id, _ in agents:
|
||
tool_usage_count = (
|
||
db.query(func.count(ToolCall.id))
|
||
.join(Message, ToolCall.message_id == Message.id)
|
||
.join(Conversation, Message.conversation_id == Conversation.id)
|
||
.filter(Conversation.agent_id == agent_id)
|
||
.scalar()
|
||
or 0
|
||
)
|
||
|
||
agent_tool_usage.append({"agent_id": agent_id, "tool_usage_count": tool_usage_count})
|
||
|
||
# 表现最佳的智能体（综合评分）
|
||
top_performing_agents = []
|
||
for i, (agent_id, conv_count) in enumerate(agents):
|
||
# 综合评分 = 对话数权重 + 满意度权重
|
||
satisfaction_data = next(
|
||
(s for s in agent_satisfaction if s["agent_id"] == agent_id), {"satisfaction_rate": 0}
|
||
)
|
||
|
||
score = conv_count * 0.3 + satisfaction_data["satisfaction_rate"] * 0.7
|
||
|
||
top_performing_agents.append(
|
||
{
|
||
"agent_id": agent_id,
|
||
"score": round(score, 2),
|
||
"conversation_count": conv_count,
|
||
"satisfaction_rate": satisfaction_data["satisfaction_rate"],
|
||
}
|
||
)
|
||
|
||
# 按评分排序，取前5名
|
||
top_performing_agents.sort(key=lambda x: x["score"], reverse=True)
|
||
top_performing_agents = top_performing_agents[:5]
|
||
|
||
return AgentAnalytics(
|
||
total_agents=total_agents,
|
||
agent_conversation_counts=agent_conversation_counts,
|
||
agent_satisfaction_rates=agent_satisfaction,
|
||
agent_tool_usage=agent_tool_usage,
|
||
top_performing_agents=top_performing_agents,
|
||
)
|
||
|
||
except Exception as e:
|
||
logger.error(f"Error getting agent analytics: {e}")
|
||
logger.error(traceback.format_exc())
|
||
raise HTTPException(status_code=500, detail=f"Failed to get agent analytics: {str(e)}")
|
||
|
||
|
||
# =============================================================================
|
||
# Basic Statistics (保留原有接口)
|
||
# =============================================================================
|
||
|
||
|
||
@dashboard.get("/stats")
|
||
async def get_dashboard_stats(
|
||
db: Session = Depends(get_db),
|
||
current_user: User = Depends(get_admin_user),
|
||
):
|
||
"""Get dashboard statistics (Admin only)"""
|
||
from src.storage.db.models import Conversation, Message, MessageFeedback
|
||
|
||
try:
|
||
# Basic counts
|
||
total_conversations = db.query(func.count(Conversation.id)).scalar() or 0
|
||
active_conversations = (
|
||
db.query(func.count(Conversation.id)).filter(Conversation.status == "active").scalar() or 0
|
||
)
|
||
total_messages = db.query(func.count(Message.id)).scalar() or 0
|
||
total_users = db.query(func.count(User.id)).filter(User.is_deleted == 0).scalar() or 0
|
||
|
||
# Feedback statistics
|
||
total_feedbacks = db.query(func.count(MessageFeedback.id)).scalar() or 0
|
||
like_count = db.query(func.count(MessageFeedback.id)).filter(MessageFeedback.rating == "like").scalar() or 0
|
||
|
||
# Calculate satisfaction rate
|
||
satisfaction_rate = round((like_count / total_feedbacks * 100), 2) if total_feedbacks > 0 else 100
|
||
|
||
return {
|
||
"total_conversations": total_conversations,
|
||
"active_conversations": active_conversations,
|
||
"total_messages": total_messages,
|
||
"total_users": total_users,
|
||
"feedback_stats": {
|
||
"total_feedbacks": total_feedbacks,
|
||
"satisfaction_rate": satisfaction_rate,
|
||
},
|
||
}
|
||
except Exception as e:
|
||
logger.error(f"Error getting dashboard stats: {e}")
|
||
logger.error(traceback.format_exc())
|
||
raise HTTPException(status_code=500, detail=f"Failed to get dashboard stats: {str(e)}")
|
||
|
||
|
||
# =============================================================================
|
||
# Feedback Management
|
||
# =============================================================================
|
||
|
||
|
||
class FeedbackListItem(BaseModel):
|
||
"""Feedback list item"""
|
||
|
||
id: int
|
||
user_id: str
|
||
username: str | None
|
||
avatar: str | None
|
||
rating: str
|
||
reason: str | None
|
||
created_at: str
|
||
message_content: str
|
||
conversation_title: str | None
|
||
agent_id: str
|
||
|
||
|
||
@dashboard.get("/feedbacks", response_model=list[FeedbackListItem])
|
||
async def get_all_feedbacks(
|
||
rating: str | None = None,
|
||
agent_id: str | None = None,
|
||
db: Session = Depends(get_db),
|
||
current_user: User = Depends(get_admin_user),
|
||
):
|
||
"""Get all feedback records (Admin only)"""
|
||
from src.storage.db.models import MessageFeedback, Message, Conversation, User
|
||
|
||
try:
|
||
# Build query with joins including User table
|
||
# Try both User.id and User.user_id as MessageFeedback.user_id might be stored as either
|
||
query = (
|
||
db.query(MessageFeedback, Message, Conversation, User)
|
||
.join(Message, MessageFeedback.message_id == Message.id)
|
||
.join(Conversation, Message.conversation_id == Conversation.id)
|
||
.outerjoin(User, (MessageFeedback.user_id == User.id) | (MessageFeedback.user_id == User.user_id))
|
||
)
|
||
|
||
# Apply filters
|
||
if rating and rating in ["like", "dislike"]:
|
||
query = query.filter(MessageFeedback.rating == rating)
|
||
if agent_id:
|
||
query = query.filter(Conversation.agent_id == agent_id)
|
||
|
||
# Order by creation time (most recent first)
|
||
query = query.order_by(MessageFeedback.created_at.desc())
|
||
|
||
results = query.all()
|
||
|
||
# Debug logging (privacy-safe)
|
||
logger.info(f"Found {len(results)} feedback records")
|
||
# Removed sensitive user data from logs for privacy compliance
|
||
|
||
return [
|
||
{
|
||
"id": feedback.id,
|
||
"message_id": feedback.message_id,
|
||
"user_id": feedback.user_id,
|
||
"username": user.username if user else None,
|
||
"avatar": user.avatar if user else None,
|
||
"rating": feedback.rating,
|
||
"reason": feedback.reason,
|
||
"created_at": feedback.created_at.isoformat(),
|
||
"message_content": message.content[:100] + ("..." if len(message.content) > 100 else ""),
|
||
"conversation_title": conversation.title,
|
||
"agent_id": conversation.agent_id,
|
||
}
|
||
for feedback, message, conversation, user in results
|
||
]
|
||
except Exception as e:
|
||
logger.error(f"Error getting feedbacks: {e}")
|
||
logger.error(traceback.format_exc())
|
||
raise HTTPException(status_code=500, detail=f"Failed to get feedbacks: {str(e)}")
|
||
|
||
|
||
# =============================================================================
|
||
# Time Series Statistics for Call Analytics
|
||
# =============================================================================
|
||
|
||
|
||
class TimeSeriesStats(BaseModel):
|
||
"""时间序列统计数据"""
|
||
|
||
data: list[dict] # [{"date": "2024-01-01", "data": {"item1": 50, "item2": 30}, "total": 80}, ...]
|
||
categories: list[str] # 所有类别名称
|
||
total_count: int
|
||
average_count: float
|
||
peak_count: int
|
||
peak_date: str
|
||
|
||
|
||
@dashboard.get("/stats/calls/timeseries", response_model=TimeSeriesStats)
|
||
async def get_call_timeseries_stats(
|
||
type: str = "models", # models/agents/tokens/tools
|
||
time_range: str = "14days", # 14hours/14days/14weeks
|
||
db: Session = Depends(get_db),
|
||
current_user: User = Depends(get_admin_user),
|
||
):
|
||
"""Get time series statistics for call analytics (Admin only)"""
|
||
try:
|
||
from src.storage.db.models import Conversation, Message, ToolCall
|
||
|
||
# 计算时间范围（使用北京时间 UTC+8）
|
||
now = utc_now()
|
||
local_now = shanghai_now()
|
||
|
||
if time_range == "14hours":
|
||
intervals = 14
|
||
# 包含当前小时：从13小时前开始
|
||
start_time = now - timedelta(hours=intervals - 1)
|
||
group_format = func.strftime("%Y-%m-%d %H:00", func.datetime(Message.created_at, "+8 hours"))
|
||
base_local_time = ensure_shanghai(start_time)
|
||
elif time_range == "14weeks":
|
||
intervals = 14
|
||
# 包含当前周：从13周前开始，并对齐到当周周一 00:00
|
||
local_start = local_now - timedelta(weeks=intervals - 1)
|
||
local_start = local_start - timedelta(days=local_start.weekday())
|
||
local_start = local_start.replace(hour=0, minute=0, second=0, microsecond=0)
|
||
start_time = local_start.astimezone(UTC)
|
||
group_format = func.strftime("%Y-%W", func.datetime(Message.created_at, "+8 hours"))
|
||
base_local_time = local_start
|
||
else: # 14days (default)
|
||
intervals = 14
|
||
# 包含当前天：从13天前开始
|
||
start_time = now - timedelta(days=intervals - 1)
|
||
group_format = func.strftime("%Y-%m-%d", func.datetime(Message.created_at, "+8 hours"))
|
||
base_local_time = ensure_shanghai(start_time)
|
||
|
||
# 根据类型查询数据
|
||
if type == "models":
|
||
# 模型调用统计（基于消息数量，按模型分组）
|
||
# 从message的extra_metadata中提取模型信息
|
||
query = (
|
||
db.query(
|
||
group_format.label("date"),
|
||
func.count(Message.id).label("count"),
|
||
func.json_extract(Message.extra_metadata, "$.response_metadata.model_name").label("category"),
|
||
)
|
||
.filter(Message.role == "assistant", Message.created_at >= start_time)
|
||
.filter(Message.extra_metadata.isnot(None))
|
||
.group_by(group_format, func.json_extract(Message.extra_metadata, "$.response_metadata.model_name"))
|
||
.order_by(group_format)
|
||
)
|
||
elif type == "agents":
|
||
# 智能体调用统计（基于对话更新时间，按智能体分组）
|
||
# 为对话创建独立的时间格式化器
|
||
if time_range == "14hours":
|
||
conv_group_format = func.strftime("%Y-%m-%d %H:00", func.datetime(Conversation.updated_at, "+8 hours"))
|
||
elif time_range == "14weeks":
|
||
conv_group_format = func.strftime("%Y-%W", func.datetime(Conversation.updated_at, "+8 hours"))
|
||
else: # 14days
|
||
conv_group_format = func.strftime("%Y-%m-%d", func.datetime(Conversation.updated_at, "+8 hours"))
|
||
|
||
query = (
|
||
db.query(
|
||
conv_group_format.label("date"),
|
||
func.count(Conversation.id).label("count"),
|
||
Conversation.agent_id.label("category"),
|
||
)
|
||
.filter(Conversation.updated_at.isnot(None))
|
||
.filter(Conversation.updated_at >= start_time)
|
||
.group_by(conv_group_format, Conversation.agent_id)
|
||
.order_by(conv_group_format)
|
||
)
|
||
elif type == "tokens":
|
||
# Token消耗统计（区分input/output tokens）
|
||
# 先查询input tokens
|
||
from sqlalchemy import literal
|
||
|
||
input_query = (
|
||
db.query(
|
||
group_format.label("date"),
|
||
func.sum(
|
||
func.coalesce(func.json_extract(Message.extra_metadata, "$.usage_metadata.input_tokens"), 0)
|
||
).label("count"),
|
||
literal("input_tokens").label("category"),
|
||
)
|
||
.filter(
|
||
Message.created_at >= start_time,
|
||
Message.extra_metadata.isnot(None),
|
||
func.json_extract(Message.extra_metadata, "$.usage_metadata").isnot(None),
|
||
)
|
||
.group_by(group_format)
|
||
.order_by(group_format)
|
||
)
|
||
|
||
# 查询output tokens
|
||
output_query = (
|
||
db.query(
|
||
group_format.label("date"),
|
||
func.sum(
|
||
func.coalesce(func.json_extract(Message.extra_metadata, "$.usage_metadata.output_tokens"), 0)
|
||
).label("count"),
|
||
literal("output_tokens").label("category"),
|
||
)
|
||
.filter(
|
||
Message.created_at >= start_time,
|
||
Message.extra_metadata.isnot(None),
|
||
func.json_extract(Message.extra_metadata, "$.usage_metadata").isnot(None),
|
||
)
|
||
.group_by(group_format)
|
||
.order_by(group_format)
|
||
)
|
||
|
||
# 合并两个查询结果
|
||
input_results = input_query.all()
|
||
output_results = output_query.all()
|
||
results = input_results + output_results
|
||
elif type == "tools":
|
||
# 工具调用统计（按工具名称分组）
|
||
# 为工具调用创建独立的时间格式化器
|
||
if time_range == "14hours":
|
||
tool_group_format = func.strftime("%Y-%m-%d %H:00", func.datetime(ToolCall.created_at, "+8 hours"))
|
||
elif time_range == "14weeks":
|
||
tool_group_format = func.strftime("%Y-%W", func.datetime(ToolCall.created_at, "+8 hours"))
|
||
else: # 14days
|
||
tool_group_format = func.strftime("%Y-%m-%d", func.datetime(ToolCall.created_at, "+8 hours"))
|
||
|
||
query = (
|
||
db.query(
|
||
tool_group_format.label("date"),
|
||
func.count(ToolCall.id).label("count"),
|
||
ToolCall.tool_name.label("category"),
|
||
)
|
||
.filter(ToolCall.created_at >= start_time)
|
||
.group_by(tool_group_format, ToolCall.tool_name)
|
||
.order_by(tool_group_format)
|
||
)
|
||
else:
|
||
raise HTTPException(status_code=422, detail=f"Invalid type: {type}")
|
||
|
||
if type != "tokens":
|
||
results = query.all()
|
||
|
||
# 处理堆叠数据格式
|
||
# 首先收集所有类别
|
||
categories = set()
|
||
for result in results:
|
||
if hasattr(result, "category") and result.category:
|
||
categories.add(result.category)
|
||
|
||
# 如果没有类别数据，提供默认类别
|
||
if not categories:
|
||
if type == "models":
|
||
categories.add("unknown_model")
|
||
elif type == "agents":
|
||
categories.add("unknown_agent")
|
||
elif type == "tokens":
|
||
categories.update(["input_tokens", "output_tokens"])
|
||
elif type == "tools":
|
||
categories.add("unknown_tool")
|
||
|
||
categories = sorted(list(categories))
|
||
|
||
# 重新组织数据：按时间点分组每个类别的数据
|
||
time_data = {}
|
||
|
||
def normalize_week_key(raw_key: str) -> str:
|
||
base_date = datetime.strptime(f"{raw_key}-1", "%Y-%W-%w")
|
||
iso_year, iso_week, _ = base_date.isocalendar()
|
||
return f"{iso_year}-{iso_week:02d}"
|
||
|
||
for result in results:
|
||
date_key = result.date
|
||
if time_range == "14weeks":
|
||
date_key = normalize_week_key(date_key)
|
||
category = getattr(result, "category", "unknown")
|
||
count = result.count
|
||
|
||
if date_key not in time_data:
|
||
time_data[date_key] = {}
|
||
|
||
time_data[date_key][category] = count
|
||
|
||
# 填充缺失的时间点（使用北京时间）
|
||
data = []
|
||
# 从起始点开始（北京时间）
|
||
current_time = base_local_time
|
||
|
||
if time_range == "14hours":
|
||
delta = timedelta(hours=1)
|
||
elif time_range == "14weeks":
|
||
delta = timedelta(weeks=1)
|
||
else:
|
||
delta = timedelta(days=1)
|
||
|
||
for i in range(intervals):
|
||
if time_range == "14hours":
|
||
date_key = current_time.strftime("%Y-%m-%d %H:00")
|
||
elif time_range == "14weeks":
|
||
iso_year, iso_week, _ = current_time.isocalendar()
|
||
date_key = f"{iso_year}-{iso_week:02d}"
|
||
else:
|
||
date_key = current_time.strftime("%Y-%m-%d")
|
||
|
||
# 获取该时间点的数据
|
||
day_data = time_data.get(date_key, {})
|
||
day_total = sum(day_data.values())
|
||
|
||
# 确保所有类别都有值（缺失的补0）
|
||
for category in categories:
|
||
if category not in day_data:
|
||
day_data[category] = 0
|
||
|
||
data.append({"date": date_key, "data": day_data, "total": day_total})
|
||
current_time += delta
|
||
|
||
# 计算统计指标
|
||
if type == "tools":
|
||
# 对于工具调用，显示所有时间的总数（与ToolStatsComponent保持一致）
|
||
from src.storage.db.models import ToolCall
|
||
|
||
total_count = db.query(func.count(ToolCall.id)).scalar() or 0
|
||
else:
|
||
# 其他类型使用时间序列数据的总和
|
||
total_count = sum(item["total"] for item in data)
|
||
|
||
average_count = round(total_count / intervals, 2) if intervals > 0 else 0
|
||
peak_data = max(data, key=lambda x: x["total"]) if data else {"total": 0, "date": ""}
|
||
|
||
return TimeSeriesStats(
|
||
data=data,
|
||
categories=categories,
|
||
total_count=total_count,
|
||
average_count=average_count,
|
||
peak_count=peak_data["total"],
|
||
peak_date=peak_data["date"],
|
||
)
|
||
|
||
except HTTPException:
|
||
raise
|
||
except Exception as e:
|
||
logger.error(f"Error getting call timeseries stats: {e}")
|
||
logger.error(traceback.format_exc())
|
||
raise HTTPException(status_code=500, detail=f"Failed to get call timeseries stats: {str(e)}")
|