6bd769ef40
- Add migration 013_city_map_coordinates.sql with map coordinates, zones, and agents table - Add /city/map API endpoint in city-service - Add /city/agents and /city/agents/online endpoints - Extend presence aggregator to include agents[] in snapshot - Add AgentsSource for fetching agent data from DB - Create CityMap component with interactive room tiles - Add useCityMap hook for fetching map data - Update useGlobalPresence to include agents - Add map/list view toggle on /city page - Add agent badges to room cards and map tiles
63 lines
1.6 KiB
Python
63 lines
1.6 KiB
Python
from pydantic import BaseModel, Field
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from typing import Literal, Optional, Dict, Any
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from datetime import datetime
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class ChatMessage(BaseModel):
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role: Literal["system", "user", "assistant"]
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content: str
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class LLMRequest(BaseModel):
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model: str = Field(..., description="Logical model name (e.g., gpt-4.1-mini)")
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messages: list[ChatMessage]
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max_tokens: Optional[int] = Field(None, ge=1, le=32000)
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temperature: Optional[float] = Field(0.7, ge=0.0, le=2.0)
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top_p: Optional[float] = Field(1.0, ge=0.0, le=1.0)
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metadata: Optional[Dict[str, Any]] = Field(default_factory=dict, description="agent_id, microdao_id, channel_id")
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class Usage(BaseModel):
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prompt_tokens: int
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completion_tokens: int
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total_tokens: int
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class LLMResponse(BaseModel):
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content: str
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usage: Usage
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provider: str
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model_resolved: str
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latency_ms: Optional[float] = None
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cached: bool = False
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class ProviderConfig(BaseModel):
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name: str
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base_url: str
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api_key: Optional[str] = None
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timeout: int = 30
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max_retries: int = 2
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class ModelConfig(BaseModel):
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logical_name: str
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provider: str
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physical_name: str
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max_tokens: Optional[int] = None
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cost_per_1k_prompt: Optional[float] = None
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cost_per_1k_completion: Optional[float] = None
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class UsageLog(BaseModel):
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timestamp: datetime
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agent_id: Optional[str]
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microdao_id: Optional[str]
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channel_id: Optional[str]
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model: str
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provider: str
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prompt_tokens: int
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completion_tokens: int
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total_tokens: int
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latency_ms: float
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success: bool
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error: Optional[str] = None
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