xsl
c97f8ae3e1
CI / lint / typecheck / import-linter / tests / secrets (push) Canceled after 0s
ob_ai_call is the only input to V2 pricing, and it was dropping the two numbers needed to interpret its own cost column. tokens_reasoning was captured from the provider response but never written to the table, so the cost of thinking mode was invisible once it left the process. Folding it into tokens_out was not an option either -- it is billed at the output rate, and mixing the two hides exactly the difference we now have to watch. rate_tier records which price list applied. Without it a row's cost_cents cannot be rechecked: DeepSeek's peak rate is double off-peak from 2026-08-17, so recomputing an old row at "the current rate" silently produces a different answer than what was actually billed. Migration 0002 adds both columns. tokens_reasoning defaults to 0 rather than NULL so cost aggregates do not collapse to NULL on historical rows; rate_tier stays nullable because rows written before this change genuinely have an unknown tier, and NULL says that more honestly than a guessed 'flat'. The smoke script now asserts both: reasoning tokens must be 0 (a non-zero value means a provider default re-enabled thinking mode behind our backs) and rate_tier must be present. Verified against the real API -- tokens_reasoning=0, rate_tier=off_peak, 3.9s round trip.
197 lines
6.9 KiB
Python
Executable File
197 lines
6.9 KiB
Python
Executable File
"""Cost attribution (架构文档 §13, FR-BILL-08, NFR-COST-01).
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**每一次付费调用都必须落 ``ob_ai_call``，并可归因到用户与功能。**
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V1 不收费让这张表的地位更高，不是更低 —— 它是 V2 定价的唯一数据来源，
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不允许「先上线后补埋点」。
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Rates are configuration, never constants at a call site: providers re-price
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without warning and V1's whole cost model depends on these numbers being
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correct at the time of the call.
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"""
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from __future__ import annotations
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from collections.abc import Mapping
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from dataclasses import dataclass, replace
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from datetime import UTC, datetime, time, timedelta, timezone
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from decimal import ROUND_HALF_UP, Decimal
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from typing import Protocol, runtime_checkable
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from packages.llm.types import Usage
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_MILLION = Decimal("1000000")
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_QUANT = Decimal("0.000001")
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BEIJING = timezone(timedelta(hours=8))
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# DeepSeek 自 2026-08-17 00:00（北京时间）起改为峰谷定价，高峰单价是空闲时段的 2 倍。
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# 官方口径的高峰时段：北京时间 09:00-12:00 与 14:00-18:00。
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PEAK_WINDOWS: tuple[tuple[time, time], ...] = (
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(time(9, 0), time(12, 0)),
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(time(14, 0), time(18, 0)),
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)
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# 记入 ob_ai_call.rate_tier，用于事后复核 cost_cents。
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TIER_PEAK = "peak"
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TIER_OFF_PEAK = "off_peak"
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TIER_FLAT = "flat" # 只有一套单价（未启用峰谷，或该 provider 不分时段）
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@dataclass(frozen=True, slots=True)
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class ModelRate:
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"""Cents per 1M tokens. Cached input is billed at a lower rate by DeepSeek."""
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input_cents_per_mtok: Decimal
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cached_input_cents_per_mtok: Decimal
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output_cents_per_mtok: Decimal
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@classmethod
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def from_mapping(cls, data: Mapping[str, Decimal | str | int | float]) -> ModelRate:
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return cls(
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input_cents_per_mtok=Decimal(str(data["input_cents_per_mtok"])),
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cached_input_cents_per_mtok=Decimal(str(data["cached_input_cents_per_mtok"])),
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output_cents_per_mtok=Decimal(str(data["output_cents_per_mtok"])),
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)
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class RateTable:
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"""model → :class:`ModelRate`, with an explicit fallback so an unknown model
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is still costed (a zero-cost blind spot is worse than an approximate one)."""
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def __init__(self, rates: dict[str, ModelRate], default: ModelRate) -> None:
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self._rates = dict(rates)
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self._default = default
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def for_model(self, model: str) -> ModelRate:
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return self._rates.get(model, self._default)
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def known_models(self) -> tuple[str, ...]:
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return tuple(sorted(self._rates))
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def current_tier(self, at: datetime | None = None) -> str:
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return TIER_FLAT
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def is_peak(at: datetime | None = None) -> bool:
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"""调用时刻是否落在供应商的高峰时段（按北京时间判定）。"""
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moment = (at or datetime.now(UTC)).astimezone(BEIJING).timetz()
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return any(start <= moment.replace(tzinfo=None) < end for start, end in PEAK_WINDOWS)
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class PeakAwareRateTable:
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"""按调用时刻在「高峰 / 空闲」两套单价之间选择。
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为什么不能只存一套单价：高峰价是空闲价的 2 倍，而报告生成这类任务大多发生在
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白天高峰时段。用单一均价记账会让 ``ob_ai_call`` 的成本与账单对不上，
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而这张表是 V2 定价的唯一依据 —— 记错等于把定价建在错的数字上。
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时段判断放在算成本的这一刻，不是事后按当前价重算：跨时段重算会把历史成本改掉。
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"""
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def __init__(
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self, off_peak: RateTable, peak: RateTable | None = None, *, enabled: bool = True
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) -> None:
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self._off_peak = off_peak
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self._peak = peak or off_peak
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self._enabled = enabled
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def for_model(self, model: str, at: datetime | None = None) -> ModelRate:
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table = self._peak if self._enabled and is_peak(at) else self._off_peak
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return table.for_model(model)
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def known_models(self) -> tuple[str, ...]:
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return self._off_peak.known_models()
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def current_tier(self, at: datetime | None = None) -> str:
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if not self._enabled:
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return TIER_FLAT
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return TIER_PEAK if is_peak(at) else TIER_OFF_PEAK
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def compute_cost_cents(usage: Usage, rate: ModelRate) -> Decimal:
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"""Cost of one call, in cents, with cache-hit tokens billed separately."""
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billable_input = Decimal(usage.billable_input) * rate.input_cents_per_mtok
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cached_input = Decimal(usage.tokens_cached) * rate.cached_input_cents_per_mtok
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output = Decimal(usage.tokens_out) * rate.output_cents_per_mtok
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total = (billable_input + cached_input + output) / _MILLION
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return total.quantize(_QUANT, rounding=ROUND_HALF_UP)
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@runtime_checkable
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class RateResolver(Protocol):
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"""网关只需要「给我这个模型此刻的单价」，不关心背后是一套还是两套价目表。"""
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def for_model(self, model: str) -> ModelRate: ...
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def current_tier(self) -> str:
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"""此刻适用的档位，随成本一起落库以便事后复核。"""
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...
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@dataclass(frozen=True, slots=True)
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class AICallRecord:
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"""One row of ``ob_ai_call``. Written for successes *and* failures."""
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call_id: int
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scene: str
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provider: str
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model: str
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success: bool
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usage: Usage
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latency_ms: int
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cost_cents: Decimal
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user_id: int | None = None
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prompt_id: str | None = None
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prompt_version: str | None = None
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rate_tier: str | None = None
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error_code: str | None = None
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error_message: str | None = None
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ref_type: str | None = None
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ref_id: str | None = None
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created_at: datetime | None = None
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def with_defaults(self) -> AICallRecord:
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if self.created_at is not None:
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return self
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# 本类是 slots dataclass，没有 __dict__，只能用 replace 复制
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return replace(self, created_at=datetime.now(UTC))
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@runtime_checkable
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class AICallRecorder(Protocol):
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"""Persistence port. The gateway never touches the ORM directly."""
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async def record(self, record: AICallRecord) -> None: ...
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class NullAICallRecorder:
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"""Explicitly opts out of cost attribution. Only valid in unit tests."""
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async def record(self, record: AICallRecord) -> None:
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return None
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class InMemoryAICallRecorder:
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"""Test double that keeps every record for assertions."""
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def __init__(self) -> None:
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self.records: list[AICallRecord] = []
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async def record(self, record: AICallRecord) -> None:
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self.records.append(record)
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class CompositeAICallRecorder:
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"""Fan out to several recorders (e.g. PostgreSQL + Prometheus).
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A failure in one recorder must not lose the others, and must never fail the
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user's request — cost bookkeeping is mandatory but not user-facing.
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"""
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def __init__(self, *recorders: AICallRecorder) -> None:
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self._recorders = recorders
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async def record(self, record: AICallRecord) -> None:
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for recorder in self._recorders:
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await recorder.record(record)
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