53e0065e3e
配置 SSOT（TOML + .env） 统一错误契约 Auth 与事务边界 Redis / Celery 可靠性:业务 Redis（DB/0）与 Celery broker/backend（DB/1）显式拆分；连接池、sync client 可观测性（OpenTelemetry + LGTM）
88 lines
2.7 KiB
Python
88 lines
2.7 KiB
Python
"""DeepSeek 评测台评审：模型别名解析 + ChatOpenAI 装配。"""
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from __future__ import annotations
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from langchain_openai import ChatOpenAI
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from app.adapters.llm.openai_base_url import normalize_openai_compatible_base_url
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from app.core.config import settings
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from app.core.eval_judge_spec import EvalJudgeLlmSpec
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from app.features.evaluation.constants import eval_cfg
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from app.core.runtime_constants import llm_defaults
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def resolve_deepseek_eval_judge_model(
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requested: str,
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) -> tuple[str, dict | None, str | None]:
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"""将模型名（含旧别名）规范为 V4 的 model id、extra_body 与 reasoning_effort。
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官方：deepseek-chat / deepseek-reasoner 将弃用，分别对应 v4-flash 非思考 / 思考。
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"""
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m = (requested or "").strip()
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if m == "deepseek-chat":
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return (
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"deepseek-v4-flash",
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{"thinking": {"type": "disabled"}},
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None,
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)
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if m in (
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"deepseek-reasoner",
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"deepseek-r1",
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):
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return (
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"deepseek-v4-flash",
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{"thinking": {"type": "enabled"}},
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"high",
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)
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if m == "deepseek-v4-pro":
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return ("deepseek-v4-pro", None, "high")
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if m in ("", "deepseek-v4-flash"):
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if eval_cfg.judge_deepseek_thinking_enabled:
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return (
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"deepseek-v4-flash",
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{"thinking": {"type": "enabled"}},
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"high",
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)
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return (
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"deepseek-v4-flash",
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{"thinking": {"type": "disabled"}},
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None,
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)
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if "flash" in m.lower() or m.startswith("deepseek-v4"):
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return (m, None, None)
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return (m, None, None)
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def build_deepseek_eval_judge_spec(
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judge_model: str | None,
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) -> EvalJudgeLlmSpec | None:
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"""密钥缺失时返回 None。"""
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api_key = (settings.deepseek_api_key or "").strip()
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if not api_key:
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return None
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want = (judge_model or "").strip()
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base = normalize_openai_compatible_base_url(
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llm_defaults.deepseek_base_url,
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fallback="https://api.deepseek.com",
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)
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default_m = (eval_cfg.judge_deepseek_model or "deepseek-v4-flash").strip()
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combined = want or default_m
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model, extra, effort = resolve_deepseek_eval_judge_model(combined)
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ctx = int(eval_cfg.judge_deepseek_context_window_tokens)
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llm_kw: dict = {
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"api_key": api_key,
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"base_url": base,
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"model": model,
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"temperature": eval_cfg.judge_temperature,
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}
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if extra is not None:
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llm_kw["extra_body"] = extra
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if effort is not None:
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llm_kw["reasoning_effort"] = effort
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return EvalJudgeLlmSpec(
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llm=ChatOpenAI(**llm_kw),
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provider="deepseek",
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resolved_model=model,
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context_window_tokens=ctx,
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)
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