simon
3c65701449
- 新增 configs/llm_models.yaml: 4 个场景(event_extraction/daily_report/stock_report/embedding)
可独立配置 provider/model/api_key_env/base_url_env/temperature 等,含用途与模型要求说明
- 新增 configs/loader.py: YAML 场景加载器(优先级: CLI 参数 > YAML > .env > 内置默认)
- llm/client.py: load_llm_config 支持 scene 参数,LLMConfig 增加 max_attempts
- embedding/factory+remote+local: provider/model/batch_limit 支持场景覆盖
- scheduler/reporter+stock_reporter: 日报/个股摘要接入场景配置
- dedup: Fingerprint.source_ids 多源记录 + 旧库自动迁移 + DedupResult 多源字段
- scripts/run_dedup: uniques JSON 的 sources 字段 + data/deduped/{day}/sources.json 汇总
- scripts/run_event_extraction: 接入 event_extraction 场景
- 补充测试: 场景优先级/零值、多源合并、旧库迁移、embedding 场景覆盖
91 lines
3.1 KiB
Python
91 lines
3.1 KiB
Python
"""三层去重模块的数据模型。"""
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from __future__ import annotations
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from datetime import datetime
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from enum import StrEnum
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from typing import Literal, Self
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from pydantic import BaseModel, Field, model_validator
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class DedupLayer(StrEnum):
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"""命中去重的层。"""
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URL = "url" # L1: 完全相同 URL
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CONTENT = "content" # L2: 标准化后 content 完全一致
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SIMHASH = "simhash" # L3: SimHash 汉明距离 <= 阈值
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class Fingerprint(BaseModel):
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"""单篇文章的指纹记录,持久化到 SQLite。
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source_ids: 同一内容组(去重后视为同一篇新闻)的全部来源列表,
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第一位是主源(即本指纹的 source_id);重复文章命中时由
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Deduper.ingest 自动合并,实现「一条唯一新闻记录多个源」。
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"""
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url_hash: str = Field(..., description="主键,与 Article.url_hash 一致")
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content_hash: str = Field(..., description="标准化 content 的 SHA1[:16]")
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simhash: int = Field(..., description="64 位 SimHash 整数(无符号)")
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source_id: str
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url: str
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title: str
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publish_date: str | None = Field(default=None, description="YYYY-MM-DD,用于时间窗口")
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ingested_at: datetime = Field(default_factory=datetime.now)
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source_ids: list[str] = Field(
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default_factory=list,
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description="同内容组全部来源(去重合并),始终包含 source_id 且其居首",
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)
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@model_validator(mode="after")
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def _ensure_source_ids(self) -> Self:
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"""保证 source_ids 非空、去重且以主源 source_id 开头。"""
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seen: list[str] = []
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for s in [self.source_id, *self.source_ids]:
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if s and s not in seen:
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seen.append(s)
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self.source_ids = seen
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return self
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class DedupResult(BaseModel):
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"""对单篇文章的判重结果。"""
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url_hash: str
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is_duplicate: bool
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matched_layer: DedupLayer | None = None
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matched_url_hash: str | None = None
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matched_url: str | None = None
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matched_title: str | None = None
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matched_source_id: str | None = Field(
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default=None, description="匹配指纹的主源 source_id"
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)
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all_source_ids: list[str] = Field(
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default_factory=list,
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description="该内容组(唯一新闻)的全部来源;含匹配指纹自身的来源",
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)
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hamming_distance: int | None = Field(
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default=None, description="仅 SimHash 层有值"
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)
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def short_summary(self) -> str:
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if not self.is_duplicate:
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return f"[UNIQUE] {self.url_hash}"
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layer = self.matched_layer.value if self.matched_layer else "?"
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extra = f" hd={self.hamming_distance}" if self.hamming_distance is not None else ""
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return f"[DUP/{layer}] {self.url_hash} ~ {self.matched_url_hash}{extra}"
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class DedupStats(BaseModel):
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"""指纹库统计。"""
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total: int = 0
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by_source: dict[str, int] = Field(default_factory=dict)
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earliest: str | None = None
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latest: str | None = None
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# 类型别名,便于在批处理日志中归类
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DedupVerdict = Literal["unique", "duplicate"]
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