chore(demo): 单细类8.3情感LLM试跑脚本（不整报告重生成）

Made-with: Cursor

backend/pipeline/demos/run_sentiment_one_matrix_group_demo.py

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+"""
+仅针对**单个矩阵细类**试跑「8.3 评价正/负向主题」同源载荷与模型调用，**不**重写整份 competitor_analysis.md。
+
+  cd backend
+  python -m pipeline.demos.run_sentiment_one_matrix_group_demo --run-dir "../data/JD/pipeline_runs/20260413_104252_低GI" --group 饼干
+  python -m pipeline.demos.run_sentiment_one_matrix_group_demo --run-dir "..." --group 饼干 --live
+
+加 ``--live`` 才会真实请求大模型；否则只打印该细类评价条数与是否找到分组。
+产出（仅 ``--live`` 且成功）：``run_dir/sentiment_8_3_one_group__{细类}.md`` 与同目录 ``sentiment_8_3_one_group__{细类}.json`` 元数据。
+"""
+from __future__ import annotations
+
+import argparse
+import json
+import re
+import sys
+import traceback
+from pathlib import Path
+from typing import Any
+
+BACKEND_ROOT = Path(__file__).resolve().parents[2]
+if str(BACKEND_ROOT) not in sys.path:
+    sys.path.insert(0, str(BACKEND_ROOT))
+import os
+
+os.environ.setdefault("DJANGO_SETTINGS_MODULE", "config.settings")
+
+import django  # noqa: E402
+
+django.setup()
+
+JCR_ROOT = BACKEND_ROOT / "crawler_copy" / "jd_pc_search"
+if str(JCR_ROOT) not in sys.path:
+    sys.path.insert(0, str(JCR_ROOT))
+
+from pipeline.competitor_report import jd_report as jcr  # noqa: E402
+import jd_keyword_pipeline as kpl  # noqa: E402
+
+from pipeline.csv.schema import MERGED_FIELD_TO_CSV_HEADER  # noqa: E402
+
+
+def _safe_filename_part(s: str) -> str:
+    t = (s or "").strip()
+    if not t:
+        return "group"
+    return re.sub(r'[<>:"/\\|?*]', "_", t)[:80]
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description="单细类评价正/负向主题 LLM（8.3 同源），不生成整报告",
+    )
+    parser.add_argument("--run-dir", type=str, required=True, help="流水线目录")
+    parser.add_argument(
+        "--group",
+        type=str,
+        default="饼干",
+        help="矩阵细类名，须与第五章/feedback 分组名完全一致（默认：饼干）",
+    )
+    parser.add_argument("--keyword", type=str, default="", help="覆盖监测词（默认读 run_meta.keyword）")
+    parser.add_argument(
+        "--live",
+        action="store_true",
+        help="真实调用大模型并写入 run_dir 下 md/json",
+    )
+    args = parser.parse_args()
+
+    run_dir = Path(args.run_dir).expanduser().resolve()
+    merged_path = run_dir / kpl.FILE_MERGED_CSV
+    if not merged_path.is_file():
+        raise SystemExit(f"缺少合并表: {merged_path}")
+
+    _, merged_rows = jcr._read_csv_rows(merged_path)
+    _, comment_rows = jcr._read_csv_rows(run_dir / kpl.FILE_COMMENTS_FLAT_CSV)
+
+    meta_path = run_dir / kpl.FILE_RUN_META_JSON
+    meta_kw = ""
+    if meta_path.is_file():
+        try:
+            meta = json.loads(meta_path.read_text(encoding="utf-8"))
+            meta_kw = str(meta.get("keyword") or "").strip()
+        except json.JSONDecodeError:
+            pass
+    keyword = (args.keyword or meta_kw or "监测词").strip()
+
+    want = (args.group or "").strip()
+    feedback_groups = jcr._consumer_feedback_by_matrix_group(
+        merged_rows=merged_rows,
+        comment_rows=comment_rows,
+        sku_header=MERGED_FIELD_TO_CSV_HEADER["sku_id"],
+    )
+    names = [g[0] for g in feedback_groups]
+    match: tuple[str, list[dict[str, str]], list[str]] | None = None
+    for item in feedback_groups:
+        if item[0] == want:
+            match = item
+            break
+    if match is None:
+        print(f"未找到细类「{want}」。当前分组：{names}", flush=True)
+        raise SystemExit(2)
+
+    gname, cr_g, _tu = match
+    sub_merged = [
+        r
+        for r in merged_rows
+        if jcr._competitor_matrix_group_key(r) == gname
+    ]
+    units, scores = jcr._iter_comment_text_units_and_scores(cr_g, sub_merged)
+    print(
+        f"run_dir={run_dir}\nkeyword={keyword!r}\ngroup={gname!r}\n"
+        f"comment_flat_rows={len(cr_g)} effective_text_units={len(units)}",
+        flush=True,
+    )
+    if len(units) < 2:
+        print("有效评价正文不足 2 条，与生产流水线一致将跳过该细类。", flush=True)
+        raise SystemExit(3)
+
+    if not args.live:
+        print("\n(dry-run：加 --live 将调用大模型并写入 sentiment_8_3_one_group__*.md)", flush=True)
+        return
+
+    from pipeline.llm.generate import generate_comment_sentiment_analysis_llm  # noqa: WPS433
+
+    sku_h = MERGED_FIELD_TO_CSV_HEADER["sku_id"]
+    title_h = MERGED_FIELD_TO_CSV_HEADER["title"]
+    attr_units = jcr._comment_lines_with_product_context(
+        cr_g,
+        merged_rows,
+        sku_header=sku_h,
+        title_h=title_h,
+    )
+    if len(attr_units) != len(units):
+        attr_units = list(units)
+
+    try:
+        pl = jcr.build_comment_sentiment_llm_payload(
+            units,
+            scores=scores,
+            attributed_texts=attr_units,
+            max_samples_positive=16,
+            max_samples_negative=30,
+            max_samples_mixed=10,
+            max_chars_per_review=360,
+            semantic_pool_max=40,
+            shuffle_seed=f"{keyword}|{gname}",
+        )
+        pl["keyword"] = keyword
+        pl["matrix_group_focus"] = gname
+        md_one = generate_comment_sentiment_analysis_llm(pl)
+    except Exception as e:
+        print(f"FAIL: {e}", flush=True)
+        traceback.print_exc()
+        raise SystemExit(1) from e
+
+    body = f"#### {gname}\n\n{md_one.strip()}\n"
+    stem = _safe_filename_part(gname)
+    out_md = run_dir / f"sentiment_8_3_one_group__{stem}.md"
+    out_md.write_text(
+        "### 8.3 评价正/负向主题（按细类 · 大模型）· 单类试跑\n\n"
+        f"> keyword={keyword!r}，仅本节为脚本独立产出，未合并进 competitor_analysis.md。\n\n"
+        + body,
+        encoding="utf-8",
+    )
+    rec: dict[str, Any] = {
+        "schema_version": 1,
+        "demo": "run_sentiment_one_matrix_group_demo",
+        "group": gname,
+        "keyword": keyword,
+        "ok": True,
+        "chars": len(md_one),
+        "markdown_file": out_md.name,
+    }
+    (run_dir / f"sentiment_8_3_one_group__{stem}.json").write_text(
+        json.dumps(rec, ensure_ascii=False, indent=2),
+        encoding="utf-8",
+    )
+    print(f"ok，已写 {out_md.name}（{len(md_one)} 字模型正文）", flush=True)
+
+
+if __name__ == "__main__":
+    main()