xsl
f46ecc5eb0
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.
82 lines
4.8 KiB
Bash
Executable File
82 lines
4.8 KiB
Bash
Executable File
# 复制为 .env 后填入真实值。.env 已在 .gitignore 中，不要提交。
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# dev/staging/prod 由部署平台注入环境变量，不使用 .env（架构文档 §11.2）。
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APP_ENV=local
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APP_NAME=meida-backend
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API_PREFIX=/api/v1
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DEBUG=true
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LOG_LEVEL=INFO
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LOG_JSON=true
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# 需要额外脱敏的字段名，逗号分隔；默认清单见 packages/shared/logging.py
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LOG_EXTRA_REDACT_KEYS=
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# ── 数据库 / Redis ─────────────────────────────────────────────────────────
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# 端口刻意用 55432 / 56379，避免与本机已有的 PostgreSQL、Redis 抢端口
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DATABASE_URL=postgresql+asyncpg://meida:meida@localhost:55432/meida
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DB_POOL_SIZE=10
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DB_MAX_OVERFLOW=20
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DB_ECHO=false
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REDIS_URL=redis://localhost:56379/0
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CELERY_BROKER_URL=redis://localhost:56379/1
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CELERY_RESULT_BACKEND=redis://localhost:56379/2
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# ── 鉴权 ──────────────────────────────────────────────────────────────────
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# 生成：openssl rand -hex 32 。staging/prod 若沿用占位值会启动失败
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JWT_SECRET=change-me-local-only-do-not-use-outside-local
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JWT_ALGORITHM=HS256
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JWT_ACCESS_TTL_SECONDS=900
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JWT_ISSUER=meida
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# 免登录换 token 的调试入口，仅本地/联调可开
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DEV_AUTH_ENABLED=true
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# ── LLM 路由 ──────────────────────────────────────────────────────────────
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LLM_PRIMARY_PROVIDER=deepseek
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LLM_BACKUP_PROVIDER=zhipu
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LLM_STRUCTURED_MAX_RETRIES=2
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LLM_CIRCUIT_FAILURE_THRESHOLD=3
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LLM_CIRCUIT_COOLDOWN_SECONDS=30
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# 按场景覆盖模型与采样参数，如 {"agent_chat":{"temperature":0.5}}
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LLM_SCENE_OVERRIDES_JSON={}
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DEEPSEEK_API_KEY=sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
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DEEPSEEK_BASE_URL=https://api.deepseek.com/v1
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DEEPSEEK_MODEL=deepseek-v4-flash
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DEEPSEEK_TIMEOUT_SECONDS=30
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ZHIPU_API_KEY=
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ZHIPU_BASE_URL=https://open.bigmodel.cn/api/paas/v4
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ZHIPU_MODEL=glm-4-flash
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ZHIPU_TIMEOUT_SECONDS=30
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# ── Prompt 缓存纪律 ───────────────────────────────────────────────────────
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# strict：固定前缀里出现时间戳/随机值等易变 token 直接启动失败。
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# 命中缓存的输入价格只有未命中的 1/30，前缀被污染会让输入成本翻数十倍。
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LLM_CACHE_PREFIX_MODE=strict
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PROMPTS_DIR=prompts
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# ── 成本单价（单位：分 / 百万 token）──────────────────────────────────────
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# 取自 DeepSeek 官方价目表。2026-08-17 00:00 起改为峰谷定价：
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# 高峰（北京时间 09:00-12:00、14:00-18:00）单价是空闲时段的 2 倍。
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# 因此这里要配两套，由 packages/llm/cost.py 按调用时刻选择。
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# flash 空闲：缓存命中 ¥0.05 / 输入 ¥1.5 / 输出 ¥4.5 每百万 token
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LLM_COST_RATES_JSON={"deepseek-v4-flash":{"input_cents_per_mtok":"150","cached_input_cents_per_mtok":"5","output_cents_per_mtok":"450"},"deepseek-v4-pro":{"input_cents_per_mtok":"450","cached_input_cents_per_mtok":"15","output_cents_per_mtok":"1350"},"glm-4-flash":{"input_cents_per_mtok":"10","cached_input_cents_per_mtok":"10","output_cents_per_mtok":"10"}}
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# flash 高峰：缓存命中 ¥0.10 / 输入 ¥3 / 输出 ¥9 每百万 token
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LLM_COST_RATES_PEAK_JSON={"deepseek-v4-flash":{"input_cents_per_mtok":"300","cached_input_cents_per_mtok":"10","output_cents_per_mtok":"900"},"deepseek-v4-pro":{"input_cents_per_mtok":"900","cached_input_cents_per_mtok":"30","output_cents_per_mtok":"2700"},"glm-4-flash":{"input_cents_per_mtok":"10","cached_input_cents_per_mtok":"10","output_cents_per_mtok":"10"}}
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# 未登记的模型按这个价算。刻意取高档：宁可高估触发熔断，也不要低估成本形成盲区
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LLM_DEFAULT_COST_RATE_JSON={"input_cents_per_mtok":"300","cached_input_cents_per_mtok":"10","output_cents_per_mtok":"900"}
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LLM_PEAK_PRICING_ENABLED=true
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# ── 全局日成本熔断（NFR-COST-02）─────────────────────────────────────────
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# 按用户配额封不住总成本（总成本 = 用户数 × 人均，用户数无上限），
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# 真正封顶的只有这个全局开关。1000_00 分 = ¥1000/天
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LLM_DAILY_BUDGET_ENABLED=true
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LLM_DAILY_BUDGET_CENTS=100000
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LLM_BUDGET_TIMEZONE_OFFSET_HOURS=8
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# ── 可观测性 ──────────────────────────────────────────────────────────────
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OTEL_ENABLED=false
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OTEL_SERVICE_NAME=meida-api
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OTEL_EXPORTER_OTLP_ENDPOINT=
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PROMETHEUS_ENABLED=true
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