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94 lines
2.3 KiB
Python
94 lines
2.3 KiB
Python
from __future__ import annotations
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from datetime import datetime
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from typing import Literal
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from uuid import UUID
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from pydantic import BaseModel, Field
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from app.schemas.operations import VectorSelectionBBox
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class AssistantChatMessage(BaseModel):
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role: Literal["user", "assistant"]
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content: str = Field(min_length=1, max_length=4_000)
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class AssistantQueryRequest(BaseModel):
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question: str = Field(min_length=2, max_length=2_000)
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model: str | None = Field(default=None, max_length=255)
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bbox: VectorSelectionBBox | None = None
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area_id: UUID | None = None
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history: list[AssistantChatMessage] = Field(default_factory=list, max_length=8)
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class AssistantModelRead(BaseModel):
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name: str
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size_bytes: int | None = None
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parameter_size: str | None = None
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quantization_level: str | None = None
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capabilities: list[str] = Field(default_factory=list)
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class AssistantModelList(BaseModel):
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items: list[AssistantModelRead]
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total: int
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default_model: str | None = None
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class AssistantStatus(BaseModel):
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enabled: bool
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reachable: bool
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status: str
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base_url: str
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default_model: str | None = None
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model_count: int = 0
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limitation_message: str
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class AssistantContextMetric(BaseModel):
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theme: str
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label: str
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value: float
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unit: str
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source: str
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dataset_id: UUID
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observed_at: datetime | None = None
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is_estimate: bool = False
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class AssistantTemporalSeries(BaseModel):
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temporal_series_key: str
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label: str
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source: str
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first_year: int
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last_year: int
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observation_count: int
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class AssistantEstimateDisclosure(BaseModel):
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"""A value in the answer that the source itself calls an estimate.
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Derived from metric metadata rather than from the generated sentences, so
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the disclosure is present whatever wording the model chose.
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"""
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theme: str
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label: str
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unit: str
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source: str
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dataset_id: UUID
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reason: str
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class AssistantQueryResponse(BaseModel):
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answer: str
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model: str
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scope_label: str
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context_metrics: list[AssistantContextMetric]
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temporal_series: list[AssistantTemporalSeries]
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estimate_disclosures: list[AssistantEstimateDisclosure] = Field(default_factory=list)
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source_dataset_ids: list[UUID]
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warnings: list[str]
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generated_at: datetime
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