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Two defects that only surface against the real DeepSeek API, both of which break the cost model rather than the code: deepseek-v4-flash enables thinking mode by default. The same single-turn question took 13.5s with it on and 1.8s with it off, and the thinking tokens are billed at the output rate. That misses NFR-PERF-02b (15s per turn) and inflates every conversational call. SceneConfig now carries an explicit `thinking` flag, all four V1 scenes set it to false, and the provider always sends the parameter rather than relying on the upstream default. Reasoning tokens are recorded separately in Usage.tokens_reasoning so the cost of ever turning it back on is visible. DeepSeek moves to peak/off-peak pricing on 2026-08-17, with peak (Beijing 09:00-12:00 and 14:00-18:00) at twice the off-peak rate. Reports are generated mostly during peak hours, so a single blended rate would leave ob_ai_call -- the only input to V2 pricing -- off by nearly a factor of two. PeakAwareRateTable resolves the rate at call time; cost is computed and stored then, never recomputed later at whatever the current rate happens to be. Also here: - Replace the live API key in the redaction test with a synthetic one. The test that proves we redact secrets was itself a secret leak waiting for its first commit. - Correct LLM_COST_RATES_JSON, which held placeholder rates. - Make ids.SnowflakeGenerator take an injectable clock so rollover and clock-rollback are testable without patching a slots class. - Rename exceptions to the -Error suffix (N818) and clear the remaining ruff, mypy and import-linter findings, including two dead lines: a duplicate pytestmark assignment and a no-op assert on a None return. Gates: ruff, mypy (96 files incl. tests), 4/4 import contracts, detect-secrets, 166 unit + 3 integration tests, migration up/down round-trip.
2.9 KiB
Executable File
2.9 KiB
Executable File
meida_backend · 美搭服务端
「美搭」智能穿搭美学助手的服务端。当前状态是工程地基 + 一条打通的纵向切片
（登录取 token → 配额预扣 → 真实调用 DeepSeek → 记成本 → 提交配额），
业务域大多只有目录与 README，见各 packages/*/README.md。
技术选型与分层依据见仓库外的《技术架构文档》《技术选型与调研报告》，本文只讲怎么跑起来。
快速开始
cp .env.example .env # 填入 DEEPSEEK_API_KEY
make install # uv sync + 装 pre-commit
make up # PostgreSQL(pgvector) + Redis，端口 55432 / 56379
make migrate # 建表并创建 vector 扩展
make run # http://localhost:8000/api/v1/docs
验证真实链路（会产生真实 API 费用，是唯一花钱的入口）：
make smoke
它会起依赖、迁移、拉起 API、发一次真实对话，然后把 ob_ai_call
的成本行与 bl_ledger 的配额流水打出来。跑通即证明「计量 + 配额 + 大模型」三者串通。
目录结构
| 目录 | 职责 |
|---|---|
apps/api |
HTTP 入口。只做鉴权、校验、幂等、配额、统一响应，不放业务规则 |
apps/worker |
Celery 长耗时任务入口 |
packages/domain |
纯领域逻辑，不许 import 任何框架与 I/O |
packages/llm |
大模型网关：场景路由、Prompt 版本、结构化输出、成本计量、预算熔断 |
packages/shared |
配置、日志脱敏、DB、Redis、错误码、中间件 |
packages/<业务域> |
identity / billing / task / agent / kb / retrieval / report … |
services/ |
需要独立伸缩的重型服务（换装、抠图、视觉），V1 为占位 |
prompts/ |
按 <prompt_id>/<version>.txt 归档，与 ob_ai_call.prompt_version 对应 |
migrations/ |
Alembic；CI 强制 upgrade → downgrade → upgrade 往返可用 |
依赖方向由 lint-imports 强制（pyproject.toml 的 [tool.importlinter]）。
apps → packages → domain 是单向的，业务包也不得绕过网关直连 provider。
提交前
make check # lint + typecheck + imports + test + secrets，与 CI 同一套
单测不连库、不发网络请求。需要真实中间件的用例在 tests/integration，
默认跳过，用 make test-integration 显式开启。
两件容易踩的事
- Prompt 固定前缀不许含时间戳、随机值等易变内容。 命中缓存的输入单价只有未命中的
约 1/30，前缀被污染会让输入成本翻数十倍。
LLM_CACHE_PREFIX_MODE=strict下启动即失败。 - DeepSeek 自 2026-08-17 起峰谷定价，高峰（北京时间 09:00-12:00、14:00-18:00）
单价是空闲时段的 2 倍。单价配两套，由
packages/llm/cost.py按调用时刻选择； 成本在调用当时算好落库，事后不按当前价重算。