diff --git a/a_share_cli/main.py b/a_share_cli/main.py
index c00c98b..c54d171 100644
--- a/a_share_cli/main.py
+++ b/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"):
diff --git a/continuation.md b/continuation.md
index 266cb05..d87cb9e 100644
--- a/continuation.md
+++ b/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。
diff --git a/embedding/models.py b/embedding/models.py
index cb60b3b..6de9e11 100644
--- a/embedding/models.py
+++ b/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]} "
diff --git a/mcp_server/tools.py b/mcp_server/tools.py
index b61579e..cc8b7a7 100644
--- a/mcp_server/tools.py
+++ b/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])}")
diff --git a/scheduler/reporter.py b/scheduler/reporter.py
index e9f9498..abe9cd7 100644
--- a/scheduler/reporter.py
+++ b/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'{src} 📰'
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" ({','.join(list(codes_in_event)[:3])})" if codes_in_event else ""
diff --git a/scripts/backfill_qdrant_sources.py b/scripts/backfill_qdrant_sources.py
new file mode 100644
index 0000000..0dcd3ae
--- /dev/null
+++ b/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())
+
diff --git a/scripts/run_embedding.py b/scripts/run_embedding.py
index 5e5e088..496b6e9 100644
--- a/scripts/run_embedding.py
+++ b/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:
- """从 ExtractedEvent JSON 构造嵌入文本与文章元数据。"""
+) -> tuple[str, Article, str | None, list[str] | None] | None:
+ """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
- prepared: list[tuple[str, Article, str | None]] = []
+ # 准备每篇文本: (文本, 文章, 摘要, 多源列表)
+ 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,
diff --git a/scripts/run_qdrant_ingest.py b/scripts/run_qdrant_ingest.py
index fabe77e..8752c82 100644
--- a/scripts/run_qdrant_ingest.py
+++ b/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"
diff --git a/tests/test_multisource.py b/tests/test_multisource.py
new file mode 100644
index 0000000..a3ffed8
--- /dev/null
+++ b/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"]
diff --git a/vectorstore/client.py b/vectorstore/client.py
index 596afbe..1cf69a4 100644
--- a/vectorstore/client.py
+++ b/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"),
diff --git a/vectorstore/models.py b/vectorstore/models.py
index 66cbd27..227360e 100644
--- a/vectorstore/models.py
+++ b/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