初始化

llm/models.py

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+"""LLM 翻译 + 投资事件抽取数据模型 (M4)。
+
+EnTranslatedArticle 是 M4 最终落盘格式，包含中英文双语内容和抽取的事件。
+EventExtraction 是 LLM JSON 输出直接映射，经 Pydantic 强校验。
+"""
+
+import re
+from datetime import datetime
+from enum import StrEnum
+
+from pydantic import BaseModel, Field, field_validator
+
+
+class Sentiment(StrEnum):
+    """事件情绪倾向。"""
+
+    POSITIVE = "positive"  # 利好
+    NEUTRAL = "neutral"    # 中性
+    NEGATIVE = "negative"  # 利空
+
+
+# 国际财经事件类型（LLM Prompt 中展示）
+INTERNATIONAL_EVENT_TYPES: tuple[str, ...] = (
+    "财报披露",
+    "并购收购",
+    "产品发布",
+    "监管政策",
+    "宏观经济",
+    "央行决议",
+    "行业动态",
+    "技术突破",
+    "高管变动",
+    "诉讼法律",
+    "市场异动",
+    "地缘政治",
+    "大宗商品",
+    "外汇波动",
+    "其他",
+)
+
+# 美股代码正则：1-5 个大写字母
+_US_STOCK_RE = re.compile(r"^[A-Z]{1,5}$")
+
+MIN_IMPORTANCE = 1
+MAX_IMPORTANCE = 5
+
+
+class EventExtraction(BaseModel):
+    """LLM 输出的单个事件，直接映射 JSON。"""
+
+    event_type: str = Field(..., description="事件类型，见 INTERNATIONAL_EVENT_TYPES")
+    stock_codes: list[str] = Field(
+        default_factory=list,
+        description="涉及美股代码，如 AAPL、TSLA；无相关股票时为空",
+    )
+    sentiment: Sentiment = Field(..., description="positive/neutral/negative")
+    importance: int = Field(
+        ..., ge=MIN_IMPORTANCE, le=MAX_IMPORTANCE, description="1-5 重要程度"
+    )
+    summary_zh: str = Field(
+        default="",
+        max_length=200,
+        description="一句话中文事件摘要",
+    )
+
+    @field_validator("stock_codes")
+    @classmethod
+    def _validate_stock_codes(cls, v: list[str]) -> list[str]:
+        """剔除非美股代码格式、去重、统一大写。"""
+        cleaned: list[str] = []
+        seen: set[str] = set()
+        for code in v:
+            s = (code or "").strip().upper()
+            if not s or not _US_STOCK_RE.match(s):
+                continue
+            if s not in seen:
+                seen.add(s)
+                cleaned.append(s)
+        return cleaned
+
+    @field_validator("event_type")
+    @classmethod
+    def _normalize_event_type(cls, v: str) -> str:
+        s = (v or "").strip()
+        return s if s else "其他"
+
+
+class LLMTranslationOutput(BaseModel):
+    """LLM 单次调用的完整输出 JSON 映射。"""
+
+    title_zh: str = Field(..., description="中文翻译标题")
+    content_zh: str = Field(..., description="中文翻译正文")
+    events: list[EventExtraction] = Field(
+        default_factory=list, description="提取的投资事件列表"
+    )
+
+
+class EnTranslatedArticle(BaseModel):
+    """M4 最终落盘格式：双语文章 + 抽取事件 + 调用元信息。"""
+
+    # ── 来源标识 ──
+    source_id: str
+    source_name: str
+    url: str
+    url_hash: str
+
+    # ── 双语内容 ──
+    title: str = ""              # 英文原标题
+    title_zh: str = ""           # 中文翻译标题
+    content_en: str = ""         # 英文原文
+    content_zh: str = ""         # 中文翻译
+    publish_time: str = ""       # ISO 8601
+    word_count: int = 0          # 英文词数
+    word_count_zh: int = 0       # 中文译文字数
+
+    # ── 抽取事件 ──
+    events: list[EventExtraction] = Field(default_factory=list)
+
+    # ── 调用元信息 ──
+    provider: str = ""           # deepseek / qwen
+    model: str = ""
+    translated_at: datetime = Field(default_factory=datetime.now)
+    attempts: int = 1            # 实际调用次数（含重试）
+    prompt_tokens: int | None = None
+    completion_tokens: int | None = None
+
+    def short_summary(self) -> str:
+        codes = set()
+        for ev in self.events:
+            codes.update(ev.stock_codes)
+        codes_str = ",".join(sorted(codes)[:5]) or "-"
+        return (
+            f"[{self.source_id}] {self.title[:30]}... "
+            f"→ {len(self.events)}events, stocks: {codes_str}"
+        )
+
+
+class LLMCallError(Exception):
+    """LLM 调用失败（网络 / 解析 / 校验）。"""
+
+    def __init__(self, reason: str, *, attempts: int = 0) -> None:
+        super().__init__(reason)
+        self.reason = reason
+        self.attempts = attempts