feat: 打通多源新闻记录链路,检索/日报/知识库可见多源 (方案A+B)

- 模型层: EmbeddingResult/SearchResult 新增 sources 字段(主源居首,旧产物兜底)
- M5 run_embedding: events/deduped 产物透传 sources 进 EmbeddingResult
- M6 run_qdrant_ingest: payload 写入 sources(M5 → 回查 M4 → 兜底 [主源])
- vectorstore: 检索读取 payload.sources
- 日报 HTML / CLI search / MCP: 多源显示「财联社 / 新浪 [多源]」
- 新增 scripts/backfill_qdrant_sources.py: 指纹库为权威源,scroll+upsert 回填存量
  (本地模式 set_payload 逐点 0.65s 不可行,改走 ingest 同款快速路径)
- 新增 tests/test_multisource.py 10 个;全量 276 passed

a_share_cli/main.py

@@ -255,7 +255,9 @@ def cmd_search(args: argparse.Namespace) -> int:
             ev.get("sentiment"), ""
         )
         print(f"{i}. {sentiment_icon} {h.title}")
-        print(f"   来源: {h.source_id} | 相似度: {h.score:.4f} | 时间: {h.publish_time}")
+        src_display = " / ".join(h.sources) if len(h.sources) > 1 else (h.sources[0] if h.sources else h.source_id)
+        multi_tag = " [多源]" if len(h.sources) > 1 else ""
+        print(f"   来源: {src_display}{multi_tag} | 相似度: {h.score:.4f} | 时间: {h.publish_time}")
         if ev.get("stock_codes"):
             print(f"   代码: {','.join(ev['stock_codes'])}")
         if ev.get("event_type"):

continuation.md

@@ -4,6 +4,36 @@
 
 ---
 
+## 本次完成 (2026-08-22) — 多源新闻记录链路修复(方案 A + B)
+
+**用户需求**:一条新闻有多个来源时,全部来源都要记录并可见;此前"找不到多源"。
+
+**诊断结论**:
+- M3→M4 多源记录逻辑**本身正常**:指纹库 227 条多源、当日 sources.json 11 条、events 313 条全含 sources、MySQL news_event.sources 填充率 100% 且有 7 条真实多源(如许家印案 `["cls","sina"]`)
+- 用户"看不到"的原因:① 展示层(日报 HTML/CLI/MCP)只渲染单源 `source_id`;② 知识库链路 M5→M6 **真断点**——`EmbeddingResult` 无 `sources` 字段、Qdrant payload 只写 `source_id`、`SearchResult` 无 `sources`
+
+**修复内容**:
+- B1 模型层: `embedding/models.py` `EmbeddingResult.sources` + validator(主源居首/去重/旧产物兜底);`vectorstore/models.py` `SearchResult.sources`
+- B2 `scripts/run_embedding.py`: `_build_text_from_event/_build_text_from_article` 透传 sources 进 EmbeddingResult
+- B3 `scripts/run_qdrant_ingest.py`: payload 写 `sources`(M5 产物 → 回查 M4 → 兜底 [主源])
+- B4 `vectorstore/client.py`: 检索读取 payload.sources
+- A1 `scheduler/reporter.py`: 日报 HTML 多源时显示「财联社 / 新浪 📰」
+- A2 `a_share_cli/main.py` + `mcp_server/tools.py`: 检索展示「cecn / cscn [多源]」,MCP 返回 sources 数组
+- 存量回填 `scripts/backfill_qdrant_sources.py`(新):以指纹库 source_ids 为权威源,scroll+批量 upsert 回写 3.3 万条 payload
+  - ⚠️ 坑:本地文件模式 Qdrant 的 `set_payload` 逐点极慢(0.65s/点,33k 条约 6h),改为与 ingest 相同的 scroll+upsert 快速路径;且本地模式单进程锁,必须避开调度窗口
+
+**验证**:
+- 新增 `tests/test_multisource.py` 10 个(validator/`_result_to_point` 三优先级/文本构造透传/回填加载)
+- 全量 **276 passed**(3 个 crawler 基线失败与本次无关);ruff 干净(reporter 5 个 N806/SIM115 为既有问题未动)
+- 今日 M5/M6 重跑:313 条全含 sources(11 条多源);CLI 检索显示「来源: cecn / cscn [多源]」✓,MCP 返回 `sources: ['cecn','cscn']` ✓
+- 存量回填:待执行(等 22:00 pipeline 结束后运行,避开 Qdrant 单进程锁)
+
+**待办/遗留**:
+- 回填脚本执行 + 回填后检索验证
+- P1-2(补跑 steps 含 cninfo)、P1-3(时区一致性),待用户决策
+
+---
+
 ## 本次完成 (2026-08-22) — P1-1 修复 dedup 高重复率返回码语义
 
 **问题**:`run_dedup` 重复率 > 5% 时返回 1;`scheduler/pipeline.py` 曾把 `dedup` 的 rc=1 无条件视为成功并打日志"无新数据场景"。结果:高重复率(可能是正常无新数据,也可能是抓取源/指纹库异常)被一刀切掩盖,真实异常无法上报。

embedding/models.py

@@ -4,8 +4,9 @@ from __future__ import annotations
 
 from datetime import datetime
 from enum import StrEnum
+from typing import Self
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, model_validator
 
 
 class EmbeddingProviderType(StrEnum):
@@ -19,7 +20,11 @@ class EmbeddingResult(BaseModel):
     """单篇文章的嵌入结果(落盘格式)。"""
 
     url_hash: str = Field(..., description="主键,与 Article.url_hash 一致")
-    source_id: str = Field(..., description="来源源 id")
+    source_id: str = Field(..., description="主来源源 id")
+    sources: list[str] = Field(
+        default_factory=list,
+        description="该唯一新闻的全部来源(主源 source_id 居首),来自 M3 去重多源记录",
+    )
     title: str = Field(..., description="原文标题(便于人工检索)")
     text: str = Field(
         ..., description="实际送入 embedder 的文本(已截断/拼接)"
@@ -33,6 +38,16 @@ class EmbeddingResult(BaseModel):
     char_count: int = Field(default=0, ge=0, description="text 字符数,便于排查")
     publish_time: datetime | None = None
 
+    @model_validator(mode="after")
+    def _ensure_sources(self) -> Self:
+        """保证 sources 非空、去重且以主源 source_id 开头(旧产物无字段时兜底)。"""
+        seen: list[str] = []
+        for s in [self.source_id, *self.sources]:
+            if s and s not in seen:
+                seen.append(s)
+        self.sources = seen
+        return self
+
     def short_summary(self) -> str:
         return (
             f"[{self.source_id}] {self.title[:30]} "

mcp_server/tools.py

@@ -63,6 +63,7 @@ def _search(
             "title": r.title,
             "url": r.url,
             "source": r.source_id,
+            "sources": r.sources if r.sources else ([r.source_id] if r.source_id else []),
             "score": round(r.score, 4),
             "publish_time": r.publish_time.isoformat() if r.publish_time else None,
             "event": {
@@ -100,7 +101,10 @@ def _fmt_results(hits: list[dict[str, Any]], query: str) -> str:
             ev.get("sentiment"), ""
         )
         lines.append(f"### {i}. {h['title']}")
-        lines.append(f"- 来源: {h['source']} | 相似度: {h['score']} | {sentiment}")
+        src_list = h.get("sources") or ([h["source"]] if h.get("source") else [])
+        src_display = " / ".join(src_list)
+        multi_tag = " [多源]" if len(src_list) > 1 else ""
+        lines.append(f"- 来源: {src_display}{multi_tag} | 相似度: {h['score']} | {sentiment}")
         lines.append(f"- 时间: {h['publish_time'] or '未知'}")
         if ev.get("company_names"):
             lines.append(f"- 公司: {', '.join(ev['company_names'][:5])}")

scheduler/reporter.py

@@ -807,7 +807,10 @@ def _render_event_table(events: list[dict], show_source: bool = True,
         imp = ev.get("importance", 0)
         imp_cls = f"imp-{imp}" if imp >= 4 else ""
         title = e["title"][:70]
-        src = _source_name(e.get("source_id", ""))
+        src_ids = e.get("sources") or [e.get("source_id", "")]
+        src = " / ".join(_source_name(s) for s in src_ids if s)
+        if len(src_ids) > 1:
+            src = f'<span title="多源新闻">{src}</span> 📰'
         codes_in_event = {c.strip().split(".")[0] for c in (ev.get("stock_codes") or [])}
         star = "⭐ " if codes_in_event & wl_codes else ""
         code_str = f" <small>({','.join(list(codes_in_event)[:3])})</small>" if codes_in_event else ""

scripts/backfill_qdrant_sources.py

@@ -0,0 +1,165 @@
+"""一次性回填脚本:为 Qdrant 存量 point 的 payload 补充 sources 多源字段。
+
+背景:
+    M5/M6 旧链路未把 M3 多源记录写入 EmbeddingResult / Qdrant payload,
+    导致知识库检索只能看到单源。新链路修复后,此脚本为存量数据补齐。
+
+数据来源(按优先级):
+    1. 指纹库 data/dedup/fingerprints.sqlite3 的 source_ids 列(权威多源记录);
+    2. source_ids 为 NULL 的旧行回退 [source_id];
+    3. 指纹库查不到的 point 回退 payload.source_id。
+
+实现:
+    本地文件模式 Qdrant 的 set_payload 逐点极慢(≈0.65s/点,33k 条约 6 小时),
+    故采用与正常 ingest 相同的快速路径:
+        scroll 分页取全部 point(含向量)→ 合并 sources → 批量 upsert 回写。
+
+注意:
+    Qdrant 为本地文件模式,同一时刻仅允许一个进程打开;
+    执行前请确认无 pipeline 子进程正在运行(避开调度窗口)。
+
+用法:
+    uv run python -m scripts.backfill_qdrant_sources          # 实际执行
+    uv run python -m scripts.backfill_qdrant_sources --dry-run  # 只统计不写入
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import sqlite3
+import sys
+from pathlib import Path
+
+from loguru import logger
+from qdrant_client.http.models import PointStruct
+
+from vectorstore import DEFAULT_COLLECTION, make_qdrant_client
+
+_SCROLL_LIMIT = 1000
+_UPSERT_BATCH = 100
+
+
+def _setup_logger(level: str) -> None:
+    logger.remove()
+    logger.add(
+        sys.stderr,
+        level=level,
+        format="{time:YYYY-MM-DD HH:mm:ss} | {level} | {name} | {message}",
+    )
+
+
+def _load_fingerprint_sources(db_path: Path) -> dict[str, list[str]]:
+    """从指纹库读 url_hash -> sources(source_ids 为 NULL 时回退 [source_id])。"""
+    conn = sqlite3.connect(db_path)
+    conn.row_factory = sqlite3.Row
+    rows = conn.execute(
+        "SELECT url_hash, source_id, source_ids FROM fingerprints"
+    ).fetchall()
+    conn.close()
+
+    result: dict[str, list[str]] = {}
+    for r in rows:
+        sources: list[str] = []
+        if r["source_ids"]:
+            try:
+                val = json.loads(r["source_ids"])
+                if isinstance(val, list):
+                    sources = [s for s in val if s]
+            except (TypeError, ValueError):
+                sources = []
+        if not sources:
+            sources = [r["source_id"]] if r["source_id"] else []
+        # 保证主源居首
+        if r["source_id"] and r["source_id"] in sources:
+            sources = [r["source_id"], *[s for s in sources if s != r["source_id"]]]
+        if sources:
+            result[r["url_hash"]] = sources
+    return result
+
+
+def _build_points(
+    records: list,
+    fp_sources: dict[str, list[str]],
+) -> list[PointStruct]:
+    """把 scroll 记录转回 PointStruct,payload 补入 sources 字段。"""
+    points: list[PointStruct] = []
+    for rec in records:
+        payload = dict(rec.payload or {})
+        url_hash = payload.get("url_hash") or ""
+        source_id = payload.get("source_id") or ""
+        sources = fp_sources.get(url_hash) or ([source_id] if source_id else [])
+        if sources:
+            payload["sources"] = sources
+        points.append(PointStruct(
+            id=rec.id,
+            vector=rec.vector,
+            payload=payload,
+        ))
+    return points
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description="Qdrant 存量 payload 回填 sources 多源字段")
+    parser.add_argument("--db", default="data/dedup/fingerprints.sqlite3",
+                        help="指纹库路径")
+    parser.add_argument("--collection", default=DEFAULT_COLLECTION)
+    parser.add_argument("--dry-run", action="store_true", help="只统计不写入")
+    parser.add_argument("--log-level", default="INFO")
+    args = parser.parse_args()
+
+    _setup_logger(args.log_level)
+
+    fp_sources = _load_fingerprint_sources(Path(args.db))
+    logger.info("指纹库加载完成: {} 条,其中多源 {} 条",
+                len(fp_sources), sum(1 for v in fp_sources.values() if len(v) > 1))
+
+    client = make_qdrant_client()
+
+    # scroll 分页 + (非 dry-run) 批量 upsert 回写
+    total = 0
+    multi_cnt = 0
+    updated = 0
+    offset = None
+    while True:
+        records, offset = client.scroll(
+            collection_name=args.collection,
+            limit=_SCROLL_LIMIT,
+            offset=offset,
+            with_payload=True,
+            with_vectors=True,
+        )
+        if not records:
+            if offset is None:
+                break
+            continue
+        total += len(records)
+        multi_cnt += sum(
+            1 for r in records
+            if len((r.payload or {}).get("sources") or []) > 1
+        )
+        if not args.dry_run:
+            points = _build_points(records, fp_sources)
+            for i in range(0, len(points), _UPSERT_BATCH):
+                client.upsert(
+                    collection_name=args.collection,
+                    points=points[i : i + _UPSERT_BATCH],
+                )
+            updated += len(points)
+        if offset is None:
+            break
+
+    logger.info("Qdrant 现存 point: {} 条(其中 payload 已含多源 {} 条)", total, multi_cnt)
+    if args.dry_run:
+        logger.info("--dry-run: 不执行写入")
+        client.close()
+        return 0
+
+    logger.info("回填完成: 重新 upsert {} 条 point(payload 已补 sources)", updated)
+    client.close()
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
+

scripts/run_embedding.py

@@ -89,8 +89,11 @@ def _build_text_from_event(
     event_path: Path,
     processed_root: Path,
     day: str,
-) -> tuple[str, Article, str | None] | None:
+) -> tuple[str, Article, str | None, list[str] | None] | None:
-    """从 ExtractedEvent JSON 构造嵌入文本与文章元数据。"""
+    """从 ExtractedEvent JSON 构造嵌入文本与文章元数据。
+
+    返回 (text, article, summary, sources);sources 为 M3 多源记录。
+    """
     try:
         obj: dict[str, Any] = json.loads(event_path.read_text(encoding="utf-8"))
     except json.JSONDecodeError as e:
@@ -98,6 +101,7 @@ def _build_text_from_event(
         return None
 
     article, head, summary = _from_event_dict(obj)
+    sources = obj.get("sources") or None
     # 真正的正文要去 processed/ 找
     real_article = _load_article_by_hash(processed_root, day, article.url_hash)
     if real_article is None:
@@ -110,17 +114,21 @@ def _build_text_from_event(
             update={"publish_time": article.publish_time or real_article.publish_time}
         )
     text = compose_text(real_article, head=head, summary=summary)
-    return text, real_article, summary
+    return text, real_article, summary, sources
 
 
-def _build_text_from_article(article_path: Path) -> tuple[str, Article, str | None] | None:
+def _build_text_from_article(
+    article_path: Path,
+) -> tuple[str, Article, str | None, list[str] | None] | None:
     try:
         obj = json.loads(article_path.read_text(encoding="utf-8"))
         article = Article.model_validate(obj)
     except (json.JSONDecodeError, ValidationError) as e:
         logger.warning("跳过损坏 article 文件 {}: {}", article_path, e)
         return None
-    return compose_text(article), article, None
+    # deduped uniques JSON 含 sources 多源字段;processed 产物无此字段 → None
+    sources = obj.get("sources") or None
+    return compose_text(article), article, None, sources
 
 
 def _collect_inputs(args: argparse.Namespace) -> list[tuple[Path, str]]:
@@ -192,8 +200,8 @@ async def _run(args: argparse.Namespace) -> int:
         return 0
     logger.info("待嵌入文章数: {} (跳过已处理 {}; input={})", len(files), skipped, args.input)
 
-    # 准备每篇文本
+    # 准备每篇文本: (文本, 文章, 摘要, 多源列表)
-    prepared: list[tuple[str, Article, str | None]] = []
+    prepared: list[tuple[str, Article, str | None, list[str] | None]] = []
     for fp, kind in files:
         if kind == "event":
             built = _build_text_from_event(fp, Path(args.processed_root), args.date)
@@ -234,12 +242,12 @@ async def _run(args: argparse.Namespace) -> int:
         batch_size = args.batch_size
         for i in range(0, len(prepared), batch_size):
             batch = prepared[i : i + batch_size]
-            texts = [t for t, _, _ in batch]
+            texts = [t for t, _, _, _ in batch]
             try:
                 vectors = await provider.embed_batch(texts)
             except EmbeddingError as e:
                 logger.warning("批 {} 嵌入失败: {}", i // batch_size, e)
-                for _, art, _ in batch:
+                for _, art, _, _ in batch:
                     fail_cnt += 1
                     with failed_path.open("a", encoding="utf-8") as f:
                         f.write(
@@ -252,7 +260,7 @@ async def _run(args: argparse.Namespace) -> int:
                         )
                 continue
 
-            for (text, article, _summary), vec in zip(batch, vectors, strict=True):
+            for (text, article, _summary, sources), vec in zip(batch, vectors, strict=True):
                 if len(vec) != provider.dim:
                     logger.warning(
                         "维度不一致 url_hash={} 实际={} 预期={}",
@@ -261,6 +269,7 @@ async def _run(args: argparse.Namespace) -> int:
                 result = EmbeddingResult(
                     url_hash=article.url_hash,
                     source_id=article.source_id,
+                    sources=sources or [],
                     title=article.title,
                     text=text,
                     vector=vec,

scripts/run_qdrant_ingest.py

@@ -52,18 +52,26 @@ def _result_to_point(
 ) -> dict[str, Any] | None:
     """把 EmbeddingResult JSON dict 转换为 Qdrant Point 格式。
 
-    Payload 包含 title/url/source_id/publish_time/event/计数 等。
+    Payload 包含 title/url/source_id/sources/publish_time/event/计数 等。
     事件字段优先从 M4 ExtractedEvent 补充(EmbeddingResult 本身不含 event)。
+    sources 多源字段优先取 M5 产物,缺失时回查 M4,再兜底 [主源]。
     """
     vector = obj.get("vector")
     if not vector:
         return None
     url_hash = obj["url_hash"]
+    source_id = obj.get("source_id") or ""
+    sources = obj.get("sources") or None
+    if not sources and events_dir is not None:
+        sources = _load_sources_from_m4(events_dir, url_hash)
+    if not sources:
+        sources = [source_id] if source_id else []
     payload = {
         "url_hash": url_hash,
         "title": obj.get("title") or "",
         "url": obj.get("url") or "",
-        "source_id": obj.get("source_id") or "",
+        "source_id": source_id,
+        "sources": sources,
         "publish_time": obj.get("publish_time"),
         "char_count": obj.get("char_count"),
         "word_count": obj.get("word_count"),
@@ -93,6 +101,21 @@ def _result_to_point(
     return {"id": url_hash, "vector": vector, "payload": payload}
 
 
+def _load_sources_from_m4(events_dir: Path, url_hash: str) -> list[str] | None:
+    """从 M4 ExtractedEvent JSON 读取 sources 多源字段(旧 M5 产物兜底用)。"""
+    event_file = events_dir / f"{url_hash}.json"
+    if not event_file.is_file():
+        return None
+    try:
+        obj = json.loads(event_file.read_text(encoding="utf-8"))
+    except (json.JSONDecodeError, OSError):
+        return None
+    sources = obj.get("sources")
+    if isinstance(sources, list) and sources:
+        return [s for s in sources if s]
+    return None
+
+
 def _load_event_from_m4(events_dir: Path, url_hash: str) -> dict[str, Any] | None:
     """从 M4 ExtractedEvent JSON 中提取事件 payload 子集。"""
     event_file = events_dir / f"{url_hash}.json"

tests/test_multisource.py

@@ -0,0 +1,217 @@
+"""多源记录端到端链路测试 (方案 A + B)。
+
+覆盖:
+    - EmbeddingResult.sources validator:主源居首 / 去重 / 旧产物兜底
+    - run_qdrant_ingest._result_to_point:payload 写入 sources(
+      M5 产物优先 → 回查 M4 → 兜底 [主源])
+    - run_embedding 文本构造透传 sources
+    - 回填脚本 _load_fingerprint_sources:source_ids 解析与兜底
+"""
+
+from __future__ import annotations
+
+import json
+import sqlite3
+from pathlib import Path
+
+from embedding import EmbeddingResult
+
+# --------------------------------------------------------------------------- #
+# EmbeddingResult.sources validator
+# --------------------------------------------------------------------------- #
+
+def _emb(**overrides) -> EmbeddingResult:
+    base = {
+        "url_hash": "a" * 16,
+        "source_id": "cls",
+        "title": "t",
+        "text": "x",
+        "vector": [0.1] * 4,
+        "dim": 4,
+        "provider": "dashscope",
+        "model": "m",
+    }
+    base.update(overrides)
+    return EmbeddingResult(**base)
+
+
+def test_embedding_sources_default_fallback_to_main() -> None:
+    """旧产物无 sources → 兜底为 [主源]。"""
+    r = _emb()
+    assert r.sources == ["cls"]
+
+
+def test_embedding_sources_main_source_first_and_dedup() -> None:
+    """主源居首且去重保序。"""
+    r = _emb(sources=["sina", "cls", "eastmoney"])
+    assert r.sources == ["cls", "sina", "eastmoney"]
+
+
+def test_embedding_sources_keeps_multi() -> None:
+    r = _emb(sources=["cls", "eastmoney"])
+    assert r.sources == ["cls", "eastmoney"]
+
+
+# --------------------------------------------------------------------------- #
+# run_qdrant_ingest._result_to_point
+# --------------------------------------------------------------------------- #
+
+def test_result_to_point_sources_from_m5() -> None:
+    """M5 产物自带 sources → 直接写入 payload。"""
+    from scripts.run_qdrant_ingest import _result_to_point
+
+    obj = {
+        "url_hash": "b" * 16,
+        "vector": [0.1] * 4,
+        "title": "t",
+        "url": "https://x",
+        "source_id": "cls",
+        "sources": ["cls", "eastmoney"],
+    }
+    pt = _result_to_point(obj)
+    assert pt is not None
+    assert pt["payload"]["sources"] == ["cls", "eastmoney"]
+
+
+def test_result_to_point_sources_fallback_m4(tmp_path: Path) -> None:
+    """M5 产物无 sources → 回查 M4 events 的 sources。"""
+    from scripts.run_qdrant_ingest import _result_to_point
+
+    h = "c" * 16
+    ev_dir = tmp_path / "events"
+    ev_dir.mkdir()
+    (ev_dir / f"{h}.json").write_text(
+        json.dumps({"sources": ["sina", "yicai"], "event": {}}),
+        encoding="utf-8",
+    )
+    obj = {
+        "url_hash": h,
+        "vector": [0.1] * 4,
+        "title": "t",
+        "url": "https://x",
+        "source_id": "sina",
+    }
+    pt = _result_to_point(obj, events_dir=ev_dir)
+    assert pt is not None
+    assert pt["payload"]["sources"] == ["sina", "yicai"]
+
+
+def test_result_to_point_sources_fallback_main_only() -> None:
+    """M5/M4 均无 sources → 兜底 [主源]。"""
+    from scripts.run_qdrant_ingest import _result_to_point
+
+    obj = {
+        "url_hash": "d" * 16,
+        "vector": [0.1] * 4,
+        "title": "t",
+        "url": "https://x",
+        "source_id": "zqrb",
+    }
+    pt = _result_to_point(obj)
+    assert pt is not None
+    assert pt["payload"]["sources"] == ["zqrb"]
+
+
+# --------------------------------------------------------------------------- #
+# run_embedding 文本构造透传 sources
+# --------------------------------------------------------------------------- #
+
+def test_build_text_from_event_returns_sources(tmp_path: Path) -> None:
+    from scripts.run_embedding import _build_text_from_event
+
+    h = "e" * 16
+    ev_dir = tmp_path / "events"
+    ev_dir.mkdir()
+    proc_root = tmp_path / "processed"
+    proc_root.mkdir()
+    ev = {
+        "url_hash": h,
+        "url": "https://x",
+        "source_id": "cls",
+        "title": "标题",
+        "publish_time": None,
+        "sources": ["cls", "sina"],
+        "event": {"summary": "摘要", "sentiment": "neutral", "importance": 2,
+                   "event_type": "其他"},
+    }
+    fp = ev_dir / f"{h}.json"
+    fp.write_text(json.dumps(ev, ensure_ascii=False), encoding="utf-8")
+
+    built = _build_text_from_event(fp, proc_root, "20260616")
+    assert built is not None
+    _text, _article, _summary, sources = built
+    assert sources == ["cls", "sina"]
+
+
+def test_build_text_from_article_reads_sources(tmp_path: Path) -> None:
+    from scripts.run_embedding import _build_text_from_article
+
+    h = "f" * 16
+    art = {
+        "source_id": "cls",
+        "url": "https://x",
+        "url_hash": h,
+        "title": "标题",
+        "content": "正文内容",
+        "word_count": 4,
+        "sources": ["cls", "eastmoney"],
+    }
+    fp = tmp_path / f"{h}.json"
+    fp.write_text(json.dumps(art, ensure_ascii=False), encoding="utf-8")
+
+    built = _build_text_from_article(fp)
+    assert built is not None
+    _text, _article, _summary, sources = built
+    assert sources == ["cls", "eastmoney"]
+
+
+# --------------------------------------------------------------------------- #
+# 回填脚本 _load_fingerprint_sources
+# --------------------------------------------------------------------------- #
+
+def _make_fp_db(tmp_path: Path) -> Path:
+    db = tmp_path / "fp.sqlite3"
+    conn = sqlite3.connect(db)
+    conn.execute(
+        "CREATE TABLE fingerprints (url_hash TEXT PRIMARY KEY, source_id TEXT, "
+        "source_ids TEXT)"
+    )
+    conn.execute(
+        "INSERT INTO fingerprints VALUES (?, ?, ?)",
+        ("h1", "cls", json.dumps(["cls", "sina"])),
+    )
+    conn.execute(
+        "INSERT INTO fingerprints VALUES (?, ?, ?)", ("h2", "zqrb", None)
+    )
+    conn.commit()
+    conn.close()
+    return db
+
+
+def test_load_fingerprint_sources_parses_and_falls_back(tmp_path: Path) -> None:
+    from scripts.backfill_qdrant_sources import _load_fingerprint_sources
+
+    db = _make_fp_db(tmp_path)
+    res = _load_fingerprint_sources(db)
+    assert res["h1"] == ["cls", "sina"]
+    assert res["h2"] == ["zqrb"]  # source_ids NULL → 回退 [主源]
+
+
+def test_load_fingerprint_sources_main_first(tmp_path: Path) -> None:
+    """source_ids 顺序异常时保证主源居首。"""
+    from scripts.backfill_qdrant_sources import _load_fingerprint_sources
+
+    db = tmp_path / "fp.sqlite3"
+    conn = sqlite3.connect(db)
+    conn.execute(
+        "CREATE TABLE fingerprints (url_hash TEXT PRIMARY KEY, source_id TEXT, "
+        "source_ids TEXT)"
+    )
+    conn.execute(
+        "INSERT INTO fingerprints VALUES (?, ?, ?)",
+        ("h1", "cls", json.dumps(["sina", "cls"])),
+    )
+    conn.commit()
+    conn.close()
+    res = _load_fingerprint_sources(db)
+    assert res["h1"] == ["cls", "sina"]

vectorstore/client.py

@@ -257,6 +257,7 @@ class VectorStore:
                 title=payload.get("title") or "",
                 url=payload.get("url") or "",
                 source_id=payload.get("source_id") or "",
+                sources=payload.get("sources") or [],
                 publish_time=publish_time,
                 event=payload.get("event"),
                 char_count=payload.get("char_count"),

vectorstore/models.py

@@ -31,6 +31,10 @@ class SearchResult(BaseModel):
     title: str
     url: str
     source_id: str
+    sources: list[str] = Field(
+        default_factory=list,
+        description="全部来源(主源居首),来自 M3 去重多源记录;旧数据可能为空",
+    )
     publish_time: datetime | None = None
     event: dict[str, Any] | None = None   # EventExtraction 展开的 dict
     char_count: int | None = None