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meida_backend/packages/llm/gateway.py
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feat(cost): persist reasoning tokens and the peak/off-peak tier
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.
2026-08-14 01:02:13 +08:00

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"""The LLM gateway (架构文档 §8.1).
Built-in capabilities, all exercised on every call:
=================== =========================================================
场景化路由 ``scene → (provider, model, temperature, max_tokens, timeout)``
故障转移 + 熔断 primary 超时/5xx/限流 → 自动切备用;连续失败熔断,冷却后半开
结构化输出 传入 Pydantic Schema → JSON 约束 + 解析失败重试 + 校验
Prompt 版本 模板文件按 id+version 加载,版本号随每次调用落库
缓存前缀纪律 固定前缀含时间戳/随机 ID/昵称 → 拒绝或告警 (§7.2, TD-04)
成本计量 每次 provider 调用(含失败与重试)写一行 ``ob_ai_call``
预算保护 平台级日成本硬熔断:超限即拒绝非关键场景 (§13)
=================== =========================================================
Deliberately not here yet: streaming (SSE), 审核联动 (input/output moderation),
per-user rate limiting, A/B prompt split by user hash. See packages/llm/README.md.
"""
from __future__ import annotations
import re
import time
from collections.abc import Callable, Mapping, Sequence
from decimal import Decimal
from pydantic import BaseModel, ValidationError
from packages.domain.ids import next_id
from packages.llm.budget import DailyBudgetGuard
from packages.llm.circuit import CircuitBreakerRegistry
from packages.llm.cost import AICallRecord, AICallRecorder, RateResolver, compute_cost_cents
from packages.llm.errors import (
AllProvidersFailedError,
CircuitOpenError,
ProviderError,
SceneNotRoutedError,
StructuredOutputError,
)
from packages.llm.prompts import PromptRegistry, validate_cache_prefix
from packages.llm.providers.base import LLMProvider
from packages.llm.routing import SceneRouter
from packages.llm.types import (
Completion,
Message,
ProviderRequest,
Scene,
SceneConfig,
Usage,
)
from packages.shared.logging import get_logger
logger = get_logger(__name__)
_JSON_FENCE_RE = re.compile(r"```(?:json)?\s*(.*?)```", re.DOTALL)
_STRUCTURED_INVALID = "llm_structured_output_invalid"
def extract_json(text: str) -> str:
"""Pull the JSON object out of a model reply that may be fenced or chatty."""
match = _JSON_FENCE_RE.search(text)
candidate = (match.group(1) if match else text).strip()
start, end = candidate.find("{"), candidate.rfind("}")
if start != -1 and end > start:
return candidate[start : end + 1]
return candidate
class LLMGateway:
"""Single entry point for every LLM call in the platform."""
def __init__(
self,
*,
providers: Mapping[str, LLMProvider],
router: SceneRouter,
prompts: PromptRegistry,
rates: RateResolver,
recorder: AICallRecorder,
budget: DailyBudgetGuard,
breakers: CircuitBreakerRegistry | None = None,
id_factory: Callable[[], int] = next_id,
timer: Callable[[], float] = time.perf_counter,
) -> None:
self._providers = dict(providers)
self._router = router
self._prompts = prompts
self._rates = rates
self._recorder = recorder
self._budget = budget
self._breakers = breakers or CircuitBreakerRegistry()
self._id_factory = id_factory
self._timer = timer
# -- public API ---------------------------------------------------------
async def complete(
self,
*,
scene: Scene,
messages: Sequence[Message] = (),
prompt_vars: Mapping[str, str] | None = None,
schema: type[BaseModel] | None = None,
user_id: int | None = None,
ref_type: str | None = None,
ref_id: str | None = None,
) -> Completion:
config = self._router.resolve(scene)
# Gate 1: platform-wide daily cost breaker, checked before any spend.
await self._budget.ensure_within_budget(scene=scene.value, critical=config.critical)
template = self._prompts.get(config.prompt_id, config.prompt_version)
violations = validate_cache_prefix(
template.system_prefix, mode=self._prompts.cache_prefix_mode, ref=template.ref
)
if violations:
logger.warning(
"llm_prompt_cache_prefix_violation",
prompt=template.ref,
violations=[str(v) for v in violations],
)
composed = self._compose(
system_prefix=template.system_prefix,
user_text=template.render_user(dict(prompt_vars or {})),
messages=messages,
schema=schema,
)
failures: list[Exception] = []
for provider_name in config.providers:
provider = self._providers.get(provider_name)
if provider is None:
failures.append(
SceneNotRoutedError(f"provider '{provider_name}' is not registered")
)
continue
breaker = self._breakers.get(provider_name)
if not breaker.allow():
logger.warning(
"llm_circuit_open", provider=provider_name, state=breaker.state.value
)
failures.append(CircuitOpenError(provider_name, f"circuit {breaker.state.value}"))
continue
try:
completion = await self._call_provider(
provider=provider,
config=config,
messages=composed,
schema=schema,
user_id=user_id,
prompt_id=template.prompt_id,
prompt_version=template.version,
ref_type=ref_type,
ref_id=ref_id,
)
except ProviderError as exc:
if not exc.retryable:
# A malformed request would fail on the backup too.
raise
breaker.record_failure()
failures.append(exc)
logger.warning(
"llm_provider_failed",
provider=provider_name,
scene=scene.value,
error_code=exc.code,
circuit=breaker.state.value,
)
continue
except StructuredOutputError as exc:
breaker.record_failure()
failures.append(exc)
continue
breaker.record_success()
return completion
raise AllProvidersFailedError(scene.value, failures)
def breaker_states(self) -> dict[str, str]:
return self._breakers.snapshot()
async def aclose(self) -> None:
for provider in self._providers.values():
await provider.aclose()
# -- internals ----------------------------------------------------------
@staticmethod
def _compose(
*,
system_prefix: str,
user_text: str,
messages: Sequence[Message],
schema: type[BaseModel] | None,
) -> list[Message]:
"""架构文档 §7.2 ③: 固定前缀放最前以命中缓存,变量部分一律排在后面。"""
composed: list[Message] = [Message(role="system", content=system_prefix)]
composed.extend(messages)
if user_text:
composed.append(Message(role="user", content=user_text))
if schema is not None:
composed.append(Message(role="user", content=_schema_instruction(schema)))
return composed
async def _call_provider(
self,
*,
provider: LLMProvider,
config: SceneConfig,
messages: Sequence[Message],
schema: type[BaseModel] | None,
user_id: int | None,
prompt_id: str,
prompt_version: str,
ref_type: str | None,
ref_id: str | None,
) -> Completion:
model = config.model_for(provider.name, provider.default_model)
conversation: list[Message] = list(messages)
max_attempts = config.structured_max_retries + 1 if schema is not None else 1
schema_name = schema.__name__ if schema is not None else "<none>"
last_detail = "no attempt was made"
for attempt in range(1, max_attempts + 1):
request = ProviderRequest(
model=model,
messages=tuple(conversation),
temperature=config.temperature,
max_tokens=config.max_tokens,
timeout_seconds=config.timeout_seconds,
json_mode=schema is not None,
thinking=config.thinking,
)
call_id = self._id_factory()
started = self._timer()
try:
response = await provider.complete(request)
except ProviderError as exc:
await self._write_record(
AICallRecord(
call_id=call_id,
scene=config.scene.value,
provider=provider.name,
model=model,
success=False,
usage=Usage(),
latency_ms=self._elapsed_ms(started),
cost_cents=Decimal("0"),
user_id=user_id,
prompt_id=prompt_id,
prompt_version=prompt_version,
rate_tier=self._rates.current_tier(),
error_code=exc.code,
error_message=str(exc)[:1000],
ref_type=ref_type,
ref_id=ref_id,
)
)
raise
latency_ms = self._elapsed_ms(started)
# 单价与档位在同一刻取,成本落库后不再回算 —— 跨时段重算会改掉历史成本。
rate_tier = self._rates.current_tier()
cost = compute_cost_cents(response.usage, self._rates.for_model(response.model))
parsed: BaseModel | None = None
success = True
error_code: str | None = None
if schema is not None:
try:
parsed = schema.model_validate_json(extract_json(response.text))
except (ValidationError, ValueError) as exc:
success = False
error_code = _STRUCTURED_INVALID
last_detail = str(exc)[:400]
await self._write_record(
AICallRecord(
call_id=call_id,
scene=config.scene.value,
provider=provider.name,
model=response.model,
success=success,
usage=response.usage,
latency_ms=latency_ms,
cost_cents=cost,
user_id=user_id,
prompt_id=prompt_id,
prompt_version=prompt_version,
rate_tier=rate_tier,
error_code=error_code,
error_message=last_detail if not success else None,
ref_type=ref_type,
ref_id=ref_id,
)
)
# Budget accrues on every attempt, including ones we then discard —
# a validation-retry loop costs real money and must be visible.
await self._budget.add_cost(cost)
if success:
return Completion(
text=response.text,
scene=config.scene,
provider=provider.name,
model=response.model,
prompt_id=prompt_id,
prompt_version=prompt_version,
usage=response.usage,
latency_ms=latency_ms,
cost_cents=cost,
call_id=call_id,
parsed=parsed,
attempts=attempt,
)
logger.warning(
"llm_structured_output_invalid",
provider=provider.name,
scene=config.scene.value,
schema=schema_name,
attempt=attempt,
)
conversation.append(Message(role="assistant", content=response.text))
conversation.append(Message(role="user", content=_repair_instruction(last_detail)))
raise StructuredOutputError(provider.name, schema_name, max_attempts, last_detail)
def _elapsed_ms(self, started: float) -> int:
return max(int((self._timer() - started) * 1000), 0)
async def _write_record(self, record: AICallRecord) -> None:
"""Cost attribution is mandatory but must never break the user's request."""
try:
await self._recorder.record(record.with_defaults())
except Exception:
logger.exception("ai_call_record_failed", call_id=record.call_id, scene=record.scene)
def _schema_instruction(schema: type[BaseModel]) -> str:
import json
return (
"请只输出一个 JSON 对象,不要包含解释文字或代码块标记。"
"该对象必须严格符合以下 JSON Schema:\n"
f"{json.dumps(schema.model_json_schema(), ensure_ascii=False)}"
)
def _repair_instruction(detail: str) -> str:
return (
"上一次输出不符合要求的 JSON Schema,校验错误如下:\n"
f"{detail}\n"
"请重新只输出修正后的 JSON 对象,不要包含任何其他文字。"
)