83 lines
1.9 KiB
Python
83 lines
1.9 KiB
Python
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from pydantic import BaseModel
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from typing import Optional
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# ===== System Health =====
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class SystemHealth(BaseModel):
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uptime_seconds: int
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cpu_percent: float
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memory_used_mb: float
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memory_total_mb: float
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memory_percent: float
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disk_used_gb: float
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disk_total_gb: float
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disk_percent: float
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active_users_24h: int
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# ===== Daily Stats Base =====
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class DailyStatItem(BaseModel):
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date: str
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count: int
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class DailyTokenStatItem(BaseModel):
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date: str
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input_tokens: int
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output_tokens: int
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# ===== Conversation Stats =====
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class ConversationStats(BaseModel):
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daily_conversations: list[DailyStatItem]
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daily_messages: list[DailyStatItem]
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daily_input_tokens: list[DailyTokenStatItem]
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daily_output_tokens: list[DailyTokenStatItem]
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totals: dict
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# ===== Knowledge Stats =====
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class KnowledgeStats(BaseModel):
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daily_new_tags: list[DailyStatItem]
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daily_documents: list[DailyStatItem]
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daily_knowledge_queries: list[DailyStatItem]
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daily_tag_relations: list[DailyStatItem]
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totals: dict
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# ===== Kanban Stats =====
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class KanbanStats(BaseModel):
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daily_new_tasks: list[DailyStatItem]
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daily_completed_tasks: list[DailyStatItem]
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daily_completion_rate: list[DailyStatItem]
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current_pending_tasks: int
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totals: dict
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# ===== Community Stats =====
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class CommunityStats(BaseModel):
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daily_posts: list[DailyStatItem]
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daily_replies: list[DailyStatItem]
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daily_ai_executions: list[DailyStatItem]
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daily_agent_calls: list[DailyStatItem]
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totals: dict
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# ===== Personal Insights =====
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class HourlyActivity(BaseModel):
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hour: int
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count: int
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class TagUsage(BaseModel):
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tag_path: str
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usage_count: int
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class PersonalInsights(BaseModel):
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hourly_activity: list[HourlyActivity]
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top_tags: list[TagUsage]
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token_trend_percent: float
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this_month_tokens: int
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last_month_tokens: int
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