From 1deea3af35d5794ea4922668d6881d02de1460cb Mon Sep 17 00:00:00 2001
From: hub-gif <2487812171@qq.com>
Date: Tue, 14 Apr 2026 10:36:28 +0800
Subject: [PATCH] feat(pipeline): LLM-extend usage scenario groups from
comments
Add suggest_scenario_groups_llm: single call with excerpt corpus + existing
label/triggers, parse scenarios JSON, merge into comment_scenario_groups
(up to 40) before build_competitor_brief. Record in keyword_suggest_llm.json
(schema_version 3). Skip with MA_SKIP_LLM_SCENARIO_SUGGEST. Update tests
and analysis view hint.
Made-with: Cursor
---
backend/pipeline/jd_runner.py | 225 ++++++++++++++++--
backend/pipeline/llm_keyword_suggest.py | 127 +++++++++-
.../tests/test_llm_keyword_suggest.py | 44 ++++
frontend/src/views/jd/JdAnalysisView.vue | 88 +++++--
4 files changed, 438 insertions(+), 46 deletions(-)
diff --git a/backend/pipeline/jd_runner.py b/backend/pipeline/jd_runner.py
index e5cfb94..940a944 100644
--- a/backend/pipeline/jd_runner.py
+++ b/backend/pipeline/jd_runner.py
@@ -18,7 +18,7 @@ from .models import PipelineJob
def merge_llm_supplement_with_rules_report(llm_md: str, rules_md: str) -> str:
"""
- **以规则引擎全文为正文**（含 §5 完整竞品矩阵、各章内嵌统计图与表格）。
+ **以规则引擎全文为正文**（含 §5 竞品矩阵——默认仅按细类价/声量条形图、无 Markdown 明细表，见 ``matrix_compact_section``；各章内嵌统计图与表格）。
大模型稿作为 **§8.5** 嵌入在 **第八章末、第九章策略** 之前，与 §8.2～8.4 等具体分析同卷连贯，
**不再**插在篇首「## 一、」之前。
@@ -166,8 +166,12 @@ def get_default_report_config() -> dict[str, Any]:
"""与 ``jd_competitor_report`` 模块常量一致的默认报告调参（供前端回填）。"""
jcr, _ = _jd_crawler_modules()
return {
- "llm_comment_sentiment": False,
- "llm_section_bridges": False,
+ "llm_comment_sentiment": True,
+ "llm_section_bridges": True,
+ "llm_matrix_group_summaries": True,
+ "llm_comment_group_summaries": True,
+ "llm_price_group_summaries": True,
+ "matrix_compact_section": True,
"comment_focus_words": list(jcr.COMMENT_FOCUS_WORDS),
"comment_scenario_groups": [
{"label": lbl, "triggers": list(trs)}
@@ -211,13 +215,13 @@ def write_competitor_analysis_for_run_dir(
except json.JSONDecodeError:
meta = None
- eff_rc: dict[str, Any] = (
- dict(report_config) if isinstance(report_config, dict) else {}
- )
+ eff_rc: dict[str, Any] = dict(get_default_report_config())
+ if isinstance(report_config, dict) and report_config:
+ eff_rc.update(report_config)
all_tx = _flat_comment_texts(comment_rows)
suggest_path = run_dir / "keyword_suggest_llm.json"
suggest_record: dict[str, Any] = {
- "schema_version": 2,
+ "schema_version": 3,
"total_comment_texts": len(all_tx),
}
skip_kw = os.environ.get("MA_SKIP_LLM_KEYWORD_SUGGEST", "").strip().lower() in (
@@ -267,6 +271,54 @@ def write_competitor_analysis_for_run_dir(
suggest_record["skipped"] = True
suggest_record["suggested_focus_keywords"] = []
+ skip_scen = os.environ.get("MA_SKIP_LLM_SCENARIO_SUGGEST", "").strip().lower() in (
+ "1",
+ "true",
+ "yes",
+ )
+ if not skip_scen:
+ try:
+ from .llm_keyword_suggest import suggest_scenario_groups_llm
+
+ scen_base = [x for x in (eff_rc.get("comment_scenario_groups") or []) if isinstance(x, dict)]
+ scen_out = suggest_scenario_groups_llm(
+ keyword=kw,
+ existing_groups=scen_base,
+ all_comment_texts=all_tx,
+ )
+ suggest_record["suggested_scenario_groups"] = scen_out.get(
+ "suggested_scenario_groups"
+ ) or []
+ suggest_record["scenario_rationale"] = scen_out.get("scenario_rationale") or ""
+ exist_labels = {
+ str(x.get("label") or "").strip().lower()
+ for x in scen_base
+ if str(x.get("label") or "").strip()
+ }
+ merged_scen = list(scen_base)
+ for g in suggest_record["suggested_scenario_groups"]:
+ if not isinstance(g, dict):
+ continue
+ lab = str(g.get("label") or "").strip()
+ tr_in = g.get("triggers")
+ triggers: list[str] = []
+ if isinstance(tr_in, list):
+ for t in tr_in[:48]:
+ s = str(t).strip()
+ if 2 <= len(s) <= 48:
+ triggers.append(s)
+ if not lab or lab.lower() in exist_labels or len(triggers) < 2:
+ continue
+ merged_scen.append({"label": lab[:80], "triggers": triggers[:48]})
+ exist_labels.add(lab.lower())
+ eff_rc["comment_scenario_groups"] = merged_scen[:40]
+ except Exception as e:
+ suggest_record["scenario_error"] = str(e)
+ suggest_record["suggested_scenario_groups"] = []
+ else:
+ suggest_record["scenario_skipped"] = True
+ suggest_record["suggested_scenario_groups"] = []
+
suggest_path.write_text(
json.dumps(suggest_record, ensure_ascii=False, indent=2),
encoding="utf-8",
@@ -304,7 +356,9 @@ def write_competitor_analysis_for_run_dir(
)
want_sent = bool(eff_rc.get("llm_comment_sentiment")) or env_on
if want_sent and not skip_sent:
- comment_units = jcr._iter_comment_text_units(comment_rows, merged_rows)
+ comment_units = jcr._comment_lines_with_product_context(
+ comment_rows, merged_rows
+ )
if len(comment_units) >= 2:
sentiment_llm_record["attempted"] = True
try:
@@ -336,6 +390,127 @@ def write_competitor_analysis_for_run_dir(
encoding="utf-8",
)
+ matrix_llm_record: dict[str, Any] = {
+ "schema_version": 1,
+ "attempted": False,
+ }
+ llm_matrix_groups_md = ""
+ skip_mat_llm = os.environ.get(
+ "MA_SKIP_LLM_MATRIX_SUMMARIES", ""
+ ).strip().lower() in ("1", "true", "yes")
+ want_mat_llm = bool(eff_rc.get("llm_matrix_group_summaries"))
+ matrix_compact = bool(eff_rc.get("matrix_compact_section", True))
+ if want_mat_llm and matrix_compact and not skip_mat_llm:
+ pl_groups = jcr.build_matrix_groups_llm_payload(merged_rows)
+ if pl_groups:
+ matrix_llm_record["attempted"] = True
+ try:
+ from .llm_generate import generate_matrix_group_summaries_llm
+
+ llm_matrix_groups_md = generate_matrix_group_summaries_llm(
+ pl_groups, keyword=kw
+ )
+ matrix_llm_record["ok"] = True
+ matrix_llm_record["chars"] = len(llm_matrix_groups_md)
+ except Exception as e:
+ matrix_llm_record["ok"] = False
+ matrix_llm_record["error"] = str(e)
+ else:
+ matrix_llm_record["skipped"] = "no_matrix_groups"
+ elif skip_mat_llm:
+ matrix_llm_record["skipped"] = "MA_SKIP_LLM_MATRIX_SUMMARIES"
+ elif not want_mat_llm:
+ matrix_llm_record["skipped"] = "not_enabled"
+ elif not matrix_compact:
+ matrix_llm_record["skipped"] = "matrix_not_compact"
+
+ (run_dir / "matrix_section_llm.json").write_text(
+ json.dumps(matrix_llm_record, ensure_ascii=False, indent=2),
+ encoding="utf-8",
+ )
+
+ comment_groups_llm_record: dict[str, Any] = {
+ "schema_version": 1,
+ "attempted": False,
+ }
+ llm_comment_groups_md = ""
+ skip_cg_llm = os.environ.get(
+ "MA_SKIP_LLM_COMMENT_GROUP_SUMMARIES", ""
+ ).strip().lower() in ("1", "true", "yes")
+ want_cg_llm = bool(eff_rc.get("llm_comment_group_summaries", True))
+ if want_cg_llm and not skip_cg_llm:
+ fw_cg, _, _ = jcr.resolve_report_tuning(eff_rc)
+ fb_cg = jcr._consumer_feedback_by_matrix_group(
+ merged_rows=merged_rows,
+ comment_rows=comment_rows,
+ sku_header="SKU(skuId)",
+ )
+ pl_cg = jcr.build_comment_groups_llm_payload(
+ feedback_groups=fb_cg,
+ focus_words=fw_cg,
+ merged_rows=merged_rows,
+ )
+ if pl_cg:
+ comment_groups_llm_record["attempted"] = True
+ try:
+ from .llm_generate import generate_comment_group_summaries_llm
+
+ llm_comment_groups_md = generate_comment_group_summaries_llm(
+ pl_cg, keyword=kw
+ )
+ comment_groups_llm_record["ok"] = True
+ comment_groups_llm_record["chars"] = len(llm_comment_groups_md)
+ except Exception as e:
+ comment_groups_llm_record["ok"] = False
+ comment_groups_llm_record["error"] = str(e)
+ else:
+ comment_groups_llm_record["skipped"] = "no_feedback_groups"
+ elif skip_cg_llm:
+ comment_groups_llm_record["skipped"] = "MA_SKIP_LLM_COMMENT_GROUP_SUMMARIES"
+ elif not want_cg_llm:
+ comment_groups_llm_record["skipped"] = "not_enabled"
+
+ (run_dir / "comment_groups_llm.json").write_text(
+ json.dumps(comment_groups_llm_record, ensure_ascii=False, indent=2),
+ encoding="utf-8",
+ )
+
+ price_section_llm_record: dict[str, Any] = {
+ "schema_version": 1,
+ "attempted": False,
+ }
+ llm_price_groups_md = ""
+ skip_pg_llm = os.environ.get(
+ "MA_SKIP_LLM_PRICE_GROUP_SUMMARIES", ""
+ ).strip().lower() in ("1", "true", "yes")
+ want_pg_llm = bool(eff_rc.get("llm_price_group_summaries", True))
+ if want_pg_llm and not skip_pg_llm:
+ pl_pg = jcr.build_price_groups_llm_payload(merged_rows)
+ if pl_pg:
+ price_section_llm_record["attempted"] = True
+ try:
+ from .llm_generate import generate_price_group_summaries_llm
+
+ llm_price_groups_md = generate_price_group_summaries_llm(
+ pl_pg, keyword=kw
+ )
+ price_section_llm_record["ok"] = True
+ price_section_llm_record["chars"] = len(llm_price_groups_md)
+ except Exception as e:
+ price_section_llm_record["ok"] = False
+ price_section_llm_record["error"] = str(e)
+ else:
+ price_section_llm_record["skipped"] = "no_price_groups"
+ elif skip_pg_llm:
+ price_section_llm_record["skipped"] = "MA_SKIP_LLM_PRICE_GROUP_SUMMARIES"
+ elif not want_pg_llm:
+ price_section_llm_record["skipped"] = "not_enabled"
+
+ (run_dir / "price_section_llm.json").write_text(
+ json.dumps(price_section_llm_record, ensure_ascii=False, indent=2),
+ encoding="utf-8",
+ )
+
md = jcr.build_competitor_markdown(
run_dir=run_dir,
keyword=kw,
@@ -345,6 +520,9 @@ def write_competitor_analysis_for_run_dir(
meta=meta,
report_config=eff_rc,
llm_sentiment_section_md=llm_sentiment_md or None,
+ llm_matrix_groups_md=llm_matrix_groups_md or None,
+ llm_comment_groups_md=llm_comment_groups_md or None,
+ llm_price_groups_md=llm_price_groups_md or None,
)
bridge_record: dict[str, Any] = {
@@ -472,17 +650,18 @@ def build_competitor_brief_for_job(
except json.JSONDecodeError:
meta = None
- eff: dict[str, Any] | None = None
- if isinstance(report_config, dict):
- eff = dict(report_config)
+ eff: dict[str, Any] = dict(get_default_report_config())
eff_path = base / "effective_report_config.json"
if eff_path.is_file():
try:
loaded = json.loads(eff_path.read_text(encoding="utf-8"))
if isinstance(loaded, dict) and loaded:
- eff = loaded
+ eff.update(loaded)
except json.JSONDecodeError:
pass
+ # 任务上显式保存的 report_config 优先于目录内快照（避免旧 effective 覆盖用户 PATCH）。
+ if isinstance(report_config, dict) and report_config:
+ eff.update(report_config)
return jcr.build_competitor_brief(
run_dir=base,
@@ -510,6 +689,10 @@ def run_jd_keyword_and_report(
report_config: dict[str, Any] | None = None,
cancel_check: Any | None = None,
) -> Path:
+ """
+ 执行京东关键词流水线至 **CSV / run_meta 落盘** 为止；**不写** ``competitor_analysis.md``。
+ ``report_config`` 保留与调用方兼容，采集阶段不使用；报告请用 ``regenerate_competitor_report`` 或 API 生成。
+ """
_, kpl = _jd_crawler_modules()
kw = (keyword or "").strip()
@@ -561,21 +744,13 @@ def run_jd_keyword_and_report(
kpl.SCENARIO_FILTER_ENABLED = bool(scenario_filter_enabled)
run_dir = kpl.main(keyword=kw)
- except kpl.PipelineCancelled as e:
- run_dir_path = Path(e.run_dir).resolve()
- merged = run_dir_path / kpl.FILE_MERGED_CSV
- if merged.is_file():
- try:
- write_competitor_analysis_for_run_dir(
- run_dir_path, kw, report_config=report_config
- )
- except Exception:
- pass
+ except kpl.PipelinePausedForCookie:
+ raise
+ except kpl.PipelineCancelled:
raise
finally:
for name, val in backup.items():
setattr(kpl, name, val)
- return write_competitor_analysis_for_run_dir(
- Path(run_dir).resolve(), kw, report_config=report_config
- )
+ # 竞品 Markdown 不在采集任务内生成；由前端「重新生成报告」或 ``regenerate_competitor_report`` 触发。
+ return Path(run_dir).resolve()
diff --git a/backend/pipeline/llm_keyword_suggest.py b/backend/pipeline/llm_keyword_suggest.py
index c26574c..ad5f58d 100644
--- a/backend/pipeline/llm_keyword_suggest.py
+++ b/backend/pipeline/llm_keyword_suggest.py
@@ -1,4 +1,4 @@
-"""在报告生成前：基于**全量**评价文本分块调用大模型，联想补充关注词（参与后续统计与报告）。"""
+"""在报告生成前：基于评价正文调用大模型，联想补充**关注词**与**使用场景触发组**（写入 effective_report_config）。"""
from __future__ import annotations
import json
@@ -11,6 +11,8 @@ from django.conf import settings
MAX_CHUNK_CHARS = 24_000
MAX_CHUNKS = 12
+# 场景联想单次送入模型的评价摘录上限（字符）；过大易顶上下文
+SCENARIO_CORPUS_MAX_CHARS = 18_000
_CHUNK_SYSTEM = """你是电商评价挖掘助手。输入 JSON 含 keyword、excerpt_index、excerpts（一段用户评价正文合集）。
任务：从 excerpts 中抽取值得纳入「关注词/卖点监测」的**中文短语**（2～12 字为主，可为词组）。
@@ -90,6 +92,127 @@ def _parse_phrases_object(raw: str) -> list[str]:
return []
+_SCENARIO_SYSTEM = """你是电商用户研究助手。输入 JSON 含：
+- ``keyword``：监测词；
+- ``existing_scenarios``：数组，每项为 ``{"label": "展示名", "triggers": ["子串1", ...]}``。统计时若评价正文**包含任一 trigger 子串**，则计入该 label（与宿主系统规则一致）。
+- ``excerpts``：多条用户评价正文摘录（已截断拼接）。
+
+任务：在**不重复** ``existing_scenarios`` 中已有 ``label``（逐字比较，勿改写字）的前提下，从 excerpts 归纳 **4～12 条**新的「用途/场景」监测组，覆盖评论里**明显出现但未被现有组覆盖**的消费情境（如「下午茶」「露营」「宿舍」等，须确有文本依据）。
+
+硬性规则：
+- **仅输出**一段 JSON：``{"scenarios": [{"label": "展示名", "triggers": ["子串1", "子串2", ...]}, ...]}``；
+- 每条 ``label`` 2～16 字；每组 ``triggers`` 3～10 条，每条 trigger 为 **2～12 字中文**子串，用于**子串命中**计数；
+- 不要医疗功效、治愈、降血糖承诺；不要与 existing 的 label 同名或仅差空格；
+- 不要输出 JSON 以外的文字。"""
+
+
+def _sample_corpus_for_scenarios(texts: list[str], *, max_chars: int) -> str:
+ """取评价正文前部拼接至 max_chars，供单次场景联想。"""
+ parts: list[str] = []
+ n = 0
+ for t in texts:
+ s = (t or "").strip()
+ if not s:
+ continue
+ extra = len(s) + 1
+ if n + extra > max_chars:
+ remain = max_chars - n - 1
+ if remain > 40:
+ parts.append(s[:remain])
+ break
+ parts.append(s)
+ n += extra
+ return "\n".join(parts)
+
+
+def _parse_scenarios_object(raw: str) -> list[dict[str, Any]]:
+ t = (raw or "").strip()
+ t = re.sub(r"^```(?:json)?\s*", "", t, flags=re.IGNORECASE)
+ t = re.sub(r"\s*```$", "", t)
+ try:
+ obj = json.loads(t)
+ except json.JSONDecodeError:
+ obj = None
+ if not isinstance(obj, dict):
+ m = re.search(r"\{[\s\S]*\}", t)
+ if not m:
+ return []
+ try:
+ obj = json.loads(m.group(0))
+ except json.JSONDecodeError:
+ return []
+ arr = obj.get("scenarios")
+ if not isinstance(arr, list):
+ return []
+ out: list[dict[str, Any]] = []
+ for item in arr:
+ if not isinstance(item, dict):
+ continue
+ label = str(item.get("label") or "").strip()[:80]
+ tr_raw = item.get("triggers")
+ triggers: list[str] = []
+ if isinstance(tr_raw, list):
+ seen_t: set[str] = set()
+ for x in tr_raw[:24]:
+ s = str(x).strip()
+ if len(s) < 2 or len(s) > 24:
+ continue
+ if s in seen_t:
+ continue
+ seen_t.add(s)
+ triggers.append(s)
+ if label and len(triggers) >= 1:
+ out.append({"label": label, "triggers": triggers[:12]})
+ return out
+
+
+def suggest_scenario_groups_llm(
+ *,
+ keyword: str,
+ existing_groups: list[dict[str, Any]],
+ all_comment_texts: list[str],
+) -> dict[str, Any]:
+ """
+ 单次调用模型，基于评价摘录扩展 ``comment_scenario_groups`` 形态的新组（label + triggers）。
+ """
+ if not all_comment_texts:
+ return {
+ "suggested_scenario_groups": [],
+ "scenario_rationale": "无评价正文可分析。",
+ }
+ existing_compact: list[dict[str, Any]] = []
+ for g in (existing_groups or [])[:36]:
+ if not isinstance(g, dict):
+ continue
+ lab = str(g.get("label") or "").strip()
+ tr = g.get("triggers")
+ ts: list[str] = []
+ if isinstance(tr, list):
+ for x in tr[:16]:
+ s = str(x).strip()
+ if s:
+ ts.append(s[:48])
+ if lab and ts:
+ existing_compact.append({"label": lab[:80], "triggers": ts})
+ excerpts = _sample_corpus_for_scenarios(
+ all_comment_texts, max_chars=SCENARIO_CORPUS_MAX_CHARS
+ )
+ payload = {
+ "keyword": keyword,
+ "existing_scenarios": existing_compact,
+ "excerpts": excerpts,
+ }
+ raw = _call_llm(_SCENARIO_SYSTEM, json.dumps(payload, ensure_ascii=False))
+ scenarios = _parse_scenarios_object(raw)
+ return {
+ "suggested_scenario_groups": scenarios[:14],
+ "scenario_rationale": (
+ f"基于约 {len(excerpts)} 字评价摘录单次调用模型；"
+ f"在 {len(existing_compact)} 组既有场景之外补充 {len(scenarios[:14])} 组候选。"
+ ),
+ }
+
+
def suggest_focus_keywords_from_all_comments(
*,
keyword: str,
@@ -102,7 +225,6 @@ def suggest_focus_keywords_from_all_comments(
if not all_comment_texts:
return {
"suggested_focus_keywords": [],
- "suggested_scenario_hints": [],
"rationale": "无评价正文可分析。",
"chunks_processed": 0,
"total_comment_texts": 0,
@@ -140,7 +262,6 @@ def suggest_focus_keywords_from_all_comments(
out_kw = merged[:22]
return {
"suggested_focus_keywords": out_kw,
- "suggested_scenario_hints": [],
"rationale": (
f"基于全量 {len(all_comment_texts)} 条评价文本，分 {len(chunks)} 段调用模型抽取短语并去重；"
f"已排除与当前关注词统计表完全相同的词。"
diff --git a/backend/pipeline/tests/test_llm_keyword_suggest.py b/backend/pipeline/tests/test_llm_keyword_suggest.py
index 172049c..5236664 100644
--- a/backend/pipeline/tests/test_llm_keyword_suggest.py
+++ b/backend/pipeline/tests/test_llm_keyword_suggest.py
@@ -21,7 +21,9 @@ from pipeline.llm_keyword_suggest import (
MAX_CHUNKS,
_chunk_comment_texts,
_parse_phrases_object,
+ _parse_scenarios_object,
suggest_focus_keywords_from_all_comments,
+ suggest_scenario_groups_llm,
)
@@ -67,6 +69,20 @@ class ParsePhrasesObjectTests(SimpleTestCase):
self.assertEqual(_parse_phrases_object("not json"), [])
+class ParseScenariosObjectTests(SimpleTestCase):
+ def test_plain_json(self) -> None:
+ raw = '{"scenarios": [{"label": "下午茶", "triggers": ["下午茶", "配咖啡"]}]}'
+ out = _parse_scenarios_object(raw)
+ self.assertEqual(len(out), 1)
+ self.assertEqual(out[0]["label"], "下午茶")
+ self.assertEqual(out[0]["triggers"], ["下午茶", "配咖啡"])
+
+ def test_fenced(self) -> None:
+ raw = '```\n{"scenarios": [{"label": "A", "triggers": ["x", "y"]}]}\n```'
+ out = _parse_scenarios_object(raw)
+ self.assertEqual(out[0]["label"], "A")
+
+
class SuggestFocusKeywordsTests(SimpleTestCase):
def test_no_comments_returns_empty(self) -> None:
out = suggest_focus_keywords_from_all_comments(
@@ -108,3 +124,31 @@ class SuggestFocusKeywordsLiveLLMTests(SimpleTestCase):
self.assertGreaterEqual(len(p), 2)
self.assertLessEqual(len(p), 24)
self.assertNotIn("甜度", kws)
+
+
+@unittest.skipUnless(
+ _llm_configured(),
+ "需要 OPENAI_* 或 LLM_* 密钥与网关地址。",
+)
+class SuggestScenarioGroupsLiveLLMTests(SimpleTestCase):
+ def test_live_suggests_new_scenario_groups(self) -> None:
+ existing = [
+ {"label": "早餐/代餐", "triggers": ["早餐", "代餐"]},
+ ]
+ comments = [
+ "下午配咖啡当下午茶还不错，办公室同事分着吃。",
+ "周末露营带了一盒，孩子当零食。",
+ ]
+ out = suggest_scenario_groups_llm(
+ keyword="饼干",
+ existing_groups=existing,
+ all_comment_texts=comments,
+ )
+ groups = out.get("suggested_scenario_groups") or []
+ self.assertIsInstance(groups, list)
+ self.assertGreater(len(groups), 0, "应至少返回 1 组新场景")
+ labels = {str(g.get("label", "")).strip().lower() for g in groups if isinstance(g, dict)}
+ self.assertNotIn("早餐/代餐", labels)
+ for g in groups:
+ tr = g.get("triggers") or []
+ self.assertGreaterEqual(len(tr), 1)
diff --git a/frontend/src/views/jd/JdAnalysisView.vue b/frontend/src/views/jd/JdAnalysisView.vue
index 1069c7d..3d022c8 100644
--- a/frontend/src/views/jd/JdAnalysisView.vue
+++ b/frontend/src/views/jd/JdAnalysisView.vue
@@ -13,7 +13,7 @@ import {
jobExportReportDocumentUrl,
} from '../../composables/useJobs'
import {
- generationInFlightKey,
+ generationInFlightKeys,
withGenerationInFlight,
} from '../../composables/useGenerationInFlight'
@@ -40,26 +40,49 @@ const reportMdForPreview = computed(() =>
reportMdWithAssetUrls(reportMd.value, selectedId.value),
)
-const genInFlight = generationInFlightKey()
+const inflight = generationInFlightKeys()
const K_PREVIEW = 'preview-report:'
const K_BRIEF = 'competitor-brief:'
const K_PACK = 'brief-pack:'
+const K_REGEN = 'regenerate-report:'
function genKeyMatches(prefix) {
const id = selectedId.value
if (!id) return false
- return genInFlight.value === `${prefix}${id}`
+ return inflight.value.includes(`${prefix}${id}`)
}
+/** 与当前选中任务一致（用于文案） */
const loading = computed(() => genKeyMatches(K_PREVIEW))
const briefLoading = computed(() => genKeyMatches(K_BRIEF))
const packLoading = computed(() => genKeyMatches(K_PACK))
+/** 仍有预览/摘要/打包请求在进行（不因换页签后选中 id 被重置而误判为空闲） */
+const previewBusyAny = computed(() => inflight.value.some((x) => x.startsWith(K_PREVIEW)))
+const briefBusyAny = computed(() => inflight.value.some((x) => x.startsWith(K_BRIEF)))
+const packBusyAny = computed(() => inflight.value.some((x) => x.startsWith(K_PACK)))
+const previewBusyJobId = computed(() => {
+ const k = inflight.value.find((x) => x.startsWith(K_PREVIEW))
+ return k ? k.slice(K_PREVIEW.length) : ''
+})
+const briefBusyJobId = computed(() => {
+ const k = inflight.value.find((x) => x.startsWith(K_BRIEF))
+ return k ? k.slice(K_BRIEF.length) : ''
+})
+const packBusyJobId = computed(() => {
+ const k = inflight.value.find((x) => x.startsWith(K_PACK))
+ return k ? k.slice(K_PACK.length) : ''
+})
const viewInFlightOtherJobId = computed(() => {
- const k = genInFlight.value
- if (!k) return null
- const i = k.lastIndexOf(':')
- if (i < 0) return null
- const jid = k.slice(i + 1)
- if (jid === selectedId.value) return null
- return jid
+ for (const k of inflight.value) {
+ const i = k.lastIndexOf(':')
+ if (i < 0) continue
+ const jid = k.slice(i + 1)
+ if (jid !== selectedId.value) return jid
+ }
+ return null
+})
+/** 从「报告生成」页发起的重新生成尚未结束（与预览/打包并行跟踪） */
+const reportRegenBusyJobId = computed(() => {
+ const k = inflight.value.find((x) => x.startsWith(K_REGEN))
+ return k ? k.slice(K_REGEN.length) : null
})
const successJobs = computed(() =>
@@ -206,11 +229,14 @@ watch(
分析报告查看
选择已成功的任务，在线阅读报告或下载。
- 流水线生成报告时会自动基于全部评价正文分块调用大模型扩展关注词，并写入统计图（PNG，见「二点五」章与简报包 report_assets）。
+ 流水线生成报告时会自动基于全部评价正文分块调用大模型扩展关注词，并单次调用归纳使用场景（在预设场景组之外追加 label+触发子串），再写入统计图（PNG，见「二点五」章与简报包 report_assets）。
一键下载简报包含报告稿、统计图、结构化 JSON、要点摘录。
需要改规则或重算，请至
报告生成。
+
+ 状态说明：「任务列表」里的待执行 / 执行中是流水线采集；本页按钮若显示请求处理中，表示当前浏览器正在等待预览/摘要/打包等读接口，二者不要混为一谈。
+
+
+ 任务 #{{ reportRegenBusyJobId }} 的重新生成报告仍在进行中（可能从「报告生成」页发起）；预览与下载可能在写入完成后才反映最新稿。
+
- 任务 #{{ viewInFlightOtherJobId }} 仍有请求进行中；当前页切换任务后若按钮已恢复，请等待该任务完成或返回对应任务查看。
+ 本浏览器对任务 #{{ viewInFlightOtherJobId }} 的预览 / 摘要 / 打包请求尚未结束（仅本页读接口等待，不是「任务列表」里的流水线执行中）。请稍候再操作或切回该任务。