mirror of
https://github.com/primedigitaltech/market-assistant.git
synced 2026-07-22 08:01:34 +08:00
1deea3af35
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
757 lines
28 KiB
Python
757 lines
28 KiB
Python
"""
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使用 ``crawler_copy/jd_pc_search`` 中的副本脚本执行流水线并生成竞品 Markdown。
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依赖环境变量 ``LOW_GI_PROJECT_ROOT``（由 Django settings 从 ``market_assistant/.env`` 注入）。
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"""
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from __future__ import annotations
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import json
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import os
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import re
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import sys
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from pathlib import Path
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from typing import Any
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from django.conf import settings
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from .models import PipelineJob
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def merge_llm_supplement_with_rules_report(llm_md: str, rules_md: str) -> str:
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"""
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**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）。
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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，
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**不再**插在篇首「## 一、」之前。
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"""
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body = (rules_md or "").strip()
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sup = (llm_md or "").strip()
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if not sup:
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return body
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if not body:
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return sup
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marker = "\n---\n\n## 九、策略与机会提示（假设清单，待验证）"
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insert = (
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"\n\n---\n\n"
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"### 8.5 大模型深度补充（与 §2～§8.4 定量内容互补）\n\n"
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"> **说明**：本段位于**第八章末**；**竞品矩阵、价盘表、统计图与 §8.2～8.4 词频等以正文各节为准**，"
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"此处为跨小节语义整合，便于衔接第九章。\n\n"
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f"{sup.strip()}\n"
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"\n---\n\n## 九、策略与机会提示（假设清单，待验证）"
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)
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if marker in body:
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return body.replace(marker, insert, 1)
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# 旧版报告标题或语言差异时的回退
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for alt in (
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"\n## 九、策略与机会提示（假设清单，待验证）",
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"\n## 九、策略与机会提示",
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):
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if alt in body and marker not in body:
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return body.replace(
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alt,
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"\n\n---\n\n### 8.5 大模型深度补充（与 §2～§8.4 定量内容互补）\n\n"
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"> **说明**：位于第八章末；**矩阵与图表以正文为准**。\n\n"
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f"{sup.strip()}\n"
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+ alt,
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1,
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)
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app = "\n## 附录 A：数据留存说明"
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if app in body:
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tail = (
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"\n\n---\n\n### 8.5 大模型深度补充（与 §2～§8.4 定量内容互补）\n\n"
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f"{sup.strip()}\n"
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)
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return body.replace(app, tail + app, 1)
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return body + "\n\n---\n\n### 8.5 大模型深度补充\n\n" + sup.strip() + "\n"
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def merge_llm_report_with_rules_charts(llm_md: str, rules_md: str) -> str:
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"""兼容旧名：等价于 ``merge_llm_supplement_with_rules_report``。"""
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return merge_llm_supplement_with_rules_report(llm_md, rules_md)
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def inject_section_bridges_into_markdown(md: str, bridges: dict[str, str]) -> str:
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"""
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在「## 一、」…「## 九、」各章标题行之后插入 ``#### 衔接分析（大模型）`` 段落。
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自第九章向前替换，避免多次插入导致偏移错位。
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"""
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out = md
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for key in "九八七六五四三二一":
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content = (bridges.get(key) or "").strip()
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if not content:
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continue
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pat = re.compile(rf"^(## {key}、[^\n]*)\n", re.MULTILINE)
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def _repl(m: re.Match[str], _c: str = content) -> str:
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return m.group(0) + "\n#### 衔接分析（大模型）\n\n" + _c + "\n\n"
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out = pat.sub(_repl, out, count=1)
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return out
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def _flat_comment_texts(comment_rows: list[dict[str, str]]) -> list[str]:
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"""全部非空评价正文（与报告统计同源）。"""
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out: list[str] = []
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for row in comment_rows:
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t = (row.get("tagCommentContent") or "").strip()
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if t:
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out.append(t)
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return out
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def _safe_dir_segment_for_job(s: str, max_len: int = 48) -> str:
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"""与 ``jd_keyword_pipeline._safe_dir_segment`` 一致，避免多线程下改模块全局。"""
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bad = '<>:"/\\|?*\n\r\t'
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t = "".join("_" if c in bad else c for c in (s or "").strip())[:max_len]
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t = t.strip(" .") or "run"
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return t
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def resolve_pipeline_run_directory_for_job(job: PipelineJob) -> Path:
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"""
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在拉起子进程前固定本次 ``run_dir``（与 ``jd_keyword_pipeline._resolve_pipeline_run_dir`` 同口径）。
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调用方负责 ``mkdir``。
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"""
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root = (settings.LOW_GI_PROJECT_ROOT or "").strip()
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if not root:
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raise RuntimeError("LOW_GI_PROJECT_ROOT 未配置")
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project_data = Path(root).resolve() / "data" / "JD"
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prd = (job.pipeline_run_dir or "").strip()
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kw = (job.keyword or "").strip()
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if prd:
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p = Path(prd).expanduser()
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if not p.is_absolute():
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p = project_data / p
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return p.resolve()
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import time
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stamp = time.strftime("%Y%m%d_%H%M%S")
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seg = _safe_dir_segment_for_job(kw)
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return (project_data / "pipeline_runs" / f"{stamp}_{seg}").resolve()
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def try_write_competitor_report_if_merged_exists(
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run_dir: Path,
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keyword: str,
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*,
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report_config: dict[str, Any] | None = None,
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) -> None:
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"""若已有合并表则补写竞品 Markdown（用于子进程被 terminate 后的部分产物）。"""
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_, kpl = _jd_crawler_modules()
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base = Path(run_dir).resolve()
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merged = base / kpl.FILE_MERGED_CSV
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if not merged.is_file():
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return
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try:
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write_competitor_analysis_for_run_dir(
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base, keyword, report_config=report_config
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)
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except Exception:
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pass
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def _jd_crawler_modules():
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root = Path(settings.CRAWLER_JD_ROOT)
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if not root.is_dir():
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raise FileNotFoundError(f"爬虫副本目录不存在: {root}")
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root_s = str(root.resolve())
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if root_s not in sys.path:
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sys.path.insert(0, root_s)
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import jd_competitor_report as jcr # noqa: WPS433
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import jd_keyword_pipeline as kpl # noqa: WPS433
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return jcr, kpl
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def get_default_report_config() -> dict[str, Any]:
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"""与 ``jd_competitor_report`` 模块常量一致的默认报告调参（供前端回填）。"""
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jcr, _ = _jd_crawler_modules()
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return {
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"llm_comment_sentiment": True,
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"llm_section_bridges": True,
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"llm_matrix_group_summaries": True,
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"llm_comment_group_summaries": True,
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"llm_price_group_summaries": True,
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"matrix_compact_section": True,
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"comment_focus_words": list(jcr.COMMENT_FOCUS_WORDS),
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"comment_scenario_groups": [
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{"label": lbl, "triggers": list(trs)}
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for lbl, trs in jcr.COMMENT_SCENARIO_GROUPS
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],
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"external_market_table_rows": [
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{"indicator": a, "value_and_scope": b, "source": c, "year": d}
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for a, b, c, d in jcr.EXTERNAL_MARKET_TABLE_ROWS
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],
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}
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def write_competitor_analysis_for_run_dir(
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run_dir: Path,
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keyword: str,
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*,
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report_config: dict[str, Any] | None = None,
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) -> Path:
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"""
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在已有流水线目录上读取 CSV / meta，写入 ``competitor_analysis.md``（不重新爬取）。
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"""
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jcr, kpl = _jd_crawler_modules()
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kw = (keyword or "").strip()
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if not kw:
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raise ValueError("keyword 不能为空")
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run_dir = Path(run_dir).resolve()
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merged_path = run_dir / kpl.FILE_MERGED_CSV
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if not merged_path.is_file():
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raise FileNotFoundError(f"缺少合并表，无法生成报告: {merged_path.name}")
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_, merged_rows = jcr._read_csv_rows(merged_path)
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_, search_export_rows = jcr._read_csv_rows(run_dir / kpl.FILE_PC_SEARCH_CSV)
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_, comment_rows = jcr._read_csv_rows(run_dir / kpl.FILE_COMMENTS_FLAT_CSV)
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meta_path = run_dir / kpl.FILE_RUN_META_JSON
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meta: dict[str, Any] | None = None
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if meta_path.is_file():
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try:
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meta = json.loads(meta_path.read_text(encoding="utf-8"))
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except json.JSONDecodeError:
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meta = None
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eff_rc: dict[str, Any] = dict(get_default_report_config())
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if isinstance(report_config, dict) and report_config:
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eff_rc.update(report_config)
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all_tx = _flat_comment_texts(comment_rows)
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suggest_path = run_dir / "keyword_suggest_llm.json"
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suggest_record: dict[str, Any] = {
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"schema_version": 3,
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"total_comment_texts": len(all_tx),
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}
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skip_kw = os.environ.get("MA_SKIP_LLM_KEYWORD_SUGGEST", "").strip().lower() in (
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"1",
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"true",
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"yes",
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)
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if not skip_kw:
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try:
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from .llm_keyword_suggest import suggest_focus_keywords_from_all_comments
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brief_pre = jcr.build_competitor_brief(
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run_dir=run_dir,
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keyword=kw,
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merged_rows=merged_rows,
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search_export_rows=search_export_rows,
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comment_rows=comment_rows,
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meta=meta,
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report_config=eff_rc,
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)
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brief_slice = {
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"keyword": brief_pre.get("keyword"),
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"comment_focus_keywords": (
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brief_pre.get("comment_focus_keywords") or []
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)[:20],
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"usage_scenarios": (brief_pre.get("usage_scenarios") or [])[:8],
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"category_mix_top": (brief_pre.get("category_mix_top") or [])[:6],
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"scope": brief_pre.get("scope"),
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}
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sug = suggest_focus_keywords_from_all_comments(
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keyword=kw,
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brief_slice=brief_slice,
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all_comment_texts=all_tx,
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)
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suggest_record.update(sug)
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base_words = list(eff_rc.get("comment_focus_words") or [])
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for w in sug.get("suggested_focus_keywords") or []:
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if isinstance(w, str):
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t = w.strip()
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if t and t not in base_words:
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base_words.append(t)
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eff_rc["comment_focus_words"] = base_words[:80]
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except Exception as e:
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suggest_record["error"] = str(e)
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suggest_record["suggested_focus_keywords"] = []
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else:
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suggest_record["skipped"] = True
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suggest_record["suggested_focus_keywords"] = []
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skip_scen = os.environ.get("MA_SKIP_LLM_SCENARIO_SUGGEST", "").strip().lower() in (
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"1",
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"true",
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"yes",
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)
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if not skip_scen:
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try:
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from .llm_keyword_suggest import suggest_scenario_groups_llm
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scen_base = [x for x in (eff_rc.get("comment_scenario_groups") or []) if isinstance(x, dict)]
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scen_out = suggest_scenario_groups_llm(
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keyword=kw,
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existing_groups=scen_base,
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all_comment_texts=all_tx,
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)
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suggest_record["suggested_scenario_groups"] = scen_out.get(
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"suggested_scenario_groups"
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) or []
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suggest_record["scenario_rationale"] = scen_out.get("scenario_rationale") or ""
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exist_labels = {
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str(x.get("label") or "").strip().lower()
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for x in scen_base
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if str(x.get("label") or "").strip()
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}
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merged_scen = list(scen_base)
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for g in suggest_record["suggested_scenario_groups"]:
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if not isinstance(g, dict):
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continue
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lab = str(g.get("label") or "").strip()
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tr_in = g.get("triggers")
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triggers: list[str] = []
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if isinstance(tr_in, list):
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for t in tr_in[:48]:
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s = str(t).strip()
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if 2 <= len(s) <= 48:
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triggers.append(s)
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if not lab or lab.lower() in exist_labels or len(triggers) < 2:
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continue
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merged_scen.append({"label": lab[:80], "triggers": triggers[:48]})
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exist_labels.add(lab.lower())
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eff_rc["comment_scenario_groups"] = merged_scen[:40]
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except Exception as e:
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suggest_record["scenario_error"] = str(e)
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suggest_record["suggested_scenario_groups"] = []
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else:
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suggest_record["scenario_skipped"] = True
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suggest_record["suggested_scenario_groups"] = []
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suggest_path.write_text(
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json.dumps(suggest_record, ensure_ascii=False, indent=2),
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encoding="utf-8",
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)
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(run_dir / "effective_report_config.json").write_text(
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json.dumps(eff_rc, ensure_ascii=False, indent=2),
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encoding="utf-8",
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)
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brief_final = jcr.build_competitor_brief(
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run_dir=run_dir,
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keyword=kw,
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merged_rows=merged_rows,
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search_export_rows=search_export_rows,
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comment_rows=comment_rows,
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meta=meta,
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report_config=eff_rc,
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)
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from .report_charts import generate_report_charts
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generate_report_charts(run_dir, brief_final)
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llm_sentiment_md = ""
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sentiment_llm_record: dict[str, Any] = {
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"schema_version": 1,
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"attempted": False,
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}
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skip_sent = os.environ.get(
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"MA_SKIP_LLM_COMMENT_SENTIMENT", ""
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).strip().lower() in ("1", "true", "yes")
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env_on = os.environ.get("MA_ENABLE_LLM_COMMENT_SENTIMENT", "").strip().lower() in (
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"1",
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"true",
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"yes",
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)
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want_sent = bool(eff_rc.get("llm_comment_sentiment")) or env_on
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if want_sent and not skip_sent:
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comment_units = jcr._comment_lines_with_product_context(
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comment_rows, merged_rows
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)
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if len(comment_units) >= 2:
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sentiment_llm_record["attempted"] = True
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try:
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from .llm_generate import generate_comment_sentiment_analysis_llm
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pl = jcr.build_comment_sentiment_llm_payload(
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comment_units,
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max_samples_positive=16,
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max_samples_negative=30,
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max_samples_mixed=10,
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max_chars_per_review=300,
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)
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pl["keyword"] = kw
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llm_sentiment_md = generate_comment_sentiment_analysis_llm(pl)
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sentiment_llm_record["ok"] = True
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sentiment_llm_record["chars"] = len(llm_sentiment_md)
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except Exception as e:
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sentiment_llm_record["ok"] = False
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sentiment_llm_record["error"] = str(e)
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else:
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sentiment_llm_record["skipped"] = "insufficient_comment_texts"
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elif skip_sent:
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sentiment_llm_record["skipped"] = "MA_SKIP_LLM_COMMENT_SENTIMENT"
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elif not want_sent:
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sentiment_llm_record["skipped"] = "not_enabled"
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(run_dir / "comment_sentiment_llm.json").write_text(
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json.dumps(sentiment_llm_record, ensure_ascii=False, indent=2),
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encoding="utf-8",
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)
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matrix_llm_record: dict[str, Any] = {
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"schema_version": 1,
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"attempted": False,
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}
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llm_matrix_groups_md = ""
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skip_mat_llm = os.environ.get(
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"MA_SKIP_LLM_MATRIX_SUMMARIES", ""
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).strip().lower() in ("1", "true", "yes")
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want_mat_llm = bool(eff_rc.get("llm_matrix_group_summaries"))
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matrix_compact = bool(eff_rc.get("matrix_compact_section", True))
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if want_mat_llm and matrix_compact and not skip_mat_llm:
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pl_groups = jcr.build_matrix_groups_llm_payload(merged_rows)
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if pl_groups:
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matrix_llm_record["attempted"] = True
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try:
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from .llm_generate import generate_matrix_group_summaries_llm
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llm_matrix_groups_md = generate_matrix_group_summaries_llm(
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pl_groups, keyword=kw
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)
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matrix_llm_record["ok"] = True
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matrix_llm_record["chars"] = len(llm_matrix_groups_md)
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except Exception as e:
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matrix_llm_record["ok"] = False
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matrix_llm_record["error"] = str(e)
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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,
|
||
merged_rows=merged_rows,
|
||
search_export_rows=search_export_rows,
|
||
comment_rows=comment_rows,
|
||
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] = {
|
||
"schema_version": 1,
|
||
"attempted": False,
|
||
}
|
||
skip_bridge = os.environ.get(
|
||
"MA_SKIP_LLM_SECTION_BRIDGES", ""
|
||
).strip().lower() in ("1", "true", "yes")
|
||
env_bridge = os.environ.get(
|
||
"MA_ENABLE_LLM_SECTION_BRIDGES", ""
|
||
).strip().lower() in ("1", "true", "yes")
|
||
want_bridge = bool(eff_rc.get("llm_section_bridges")) or env_bridge
|
||
if want_bridge and not skip_bridge:
|
||
from .llm_generate import (
|
||
generate_section_bridges_llm,
|
||
split_competitor_report_for_bridges,
|
||
)
|
||
|
||
parts = split_competitor_report_for_bridges(md)
|
||
if parts:
|
||
bridge_record["attempted"] = True
|
||
try:
|
||
bridges = generate_section_bridges_llm(
|
||
keyword=kw, brief=brief_final, sections=parts
|
||
)
|
||
bridge_record["keys_received"] = sorted(bridges.keys())
|
||
md = inject_section_bridges_into_markdown(md, bridges)
|
||
bridge_record["ok"] = True
|
||
except Exception as e:
|
||
bridge_record["ok"] = False
|
||
bridge_record["error"] = str(e)
|
||
else:
|
||
bridge_record["skipped"] = "no_h2_sections_matched"
|
||
elif skip_bridge:
|
||
bridge_record["skipped"] = "MA_SKIP_LLM_SECTION_BRIDGES"
|
||
elif not want_bridge:
|
||
bridge_record["skipped"] = "not_enabled"
|
||
|
||
(run_dir / "section_bridge_llm.json").write_text(
|
||
json.dumps(bridge_record, ensure_ascii=False, indent=2),
|
||
encoding="utf-8",
|
||
)
|
||
|
||
out_md = run_dir / "competitor_analysis.md"
|
||
out_md.write_text(md, encoding="utf-8")
|
||
return run_dir
|
||
|
||
|
||
def regenerate_competitor_report(
|
||
run_dir_str: str,
|
||
keyword: str,
|
||
*,
|
||
report_config: dict[str, Any] | None = None,
|
||
) -> Path:
|
||
"""校验 ``run_dir`` 位于 ``LOW_GI_PROJECT_ROOT/data/JD`` 下后，重写竞品 Markdown。"""
|
||
low_root = (settings.LOW_GI_PROJECT_ROOT or "").strip()
|
||
if not low_root:
|
||
raise RuntimeError("LOW_GI_PROJECT_ROOT 未配置")
|
||
base = Path(run_dir_str).expanduser().resolve()
|
||
jd_root = (Path(low_root) / "data" / "JD").resolve()
|
||
try:
|
||
base.relative_to(jd_root)
|
||
except ValueError as e:
|
||
raise ValueError("run_dir 不在京东数据目录下") from e
|
||
return write_competitor_analysis_for_run_dir(
|
||
base, keyword, report_config=report_config
|
||
)
|
||
|
||
|
||
def write_competitor_analysis_markdown(run_dir_str: str, markdown: str) -> Path:
|
||
"""将已生成的 Markdown 正文写入 ``run_dir/competitor_analysis.md``（与规则重生成同路径）。"""
|
||
low_root = (settings.LOW_GI_PROJECT_ROOT or "").strip()
|
||
if not low_root:
|
||
raise RuntimeError("LOW_GI_PROJECT_ROOT 未配置")
|
||
base = Path(run_dir_str).expanduser().resolve()
|
||
jd_root = (Path(low_root) / "data" / "JD").resolve()
|
||
try:
|
||
base.relative_to(jd_root)
|
||
except ValueError as e:
|
||
raise ValueError("run_dir 不在京东数据目录下") from e
|
||
out = base / "competitor_analysis.md"
|
||
out.write_text(markdown or "", encoding="utf-8")
|
||
return out
|
||
|
||
|
||
def build_competitor_brief_for_job(
|
||
run_dir_str: str,
|
||
keyword: str,
|
||
*,
|
||
report_config: dict[str, Any] | None = None,
|
||
) -> dict[str, Any]:
|
||
"""
|
||
读取 ``run_dir`` 下合并表 / 搜索导出 / 评价 / meta，返回与 Markdown 报告同口径的 **JSON 结构化摘要**（规则驱动）。
|
||
``run_dir`` 须位于 ``LOW_GI_PROJECT_ROOT/data/JD`` 下。
|
||
"""
|
||
low_root = (settings.LOW_GI_PROJECT_ROOT or "").strip()
|
||
if not low_root:
|
||
raise RuntimeError("LOW_GI_PROJECT_ROOT 未配置")
|
||
base = Path(run_dir_str).expanduser().resolve()
|
||
jd_root = (Path(low_root) / "data" / "JD").resolve()
|
||
try:
|
||
base.relative_to(jd_root)
|
||
except ValueError as e:
|
||
raise ValueError("run_dir 不在京东数据目录下") from e
|
||
|
||
jcr, kpl = _jd_crawler_modules()
|
||
kw = (keyword or "").strip()
|
||
if not kw:
|
||
raise ValueError("keyword 不能为空")
|
||
|
||
merged_path = base / kpl.FILE_MERGED_CSV
|
||
if not merged_path.is_file():
|
||
raise FileNotFoundError(f"缺少合并表，无法生成摘要: {merged_path.name}")
|
||
|
||
_, merged_rows = jcr._read_csv_rows(merged_path)
|
||
_, search_export_rows = jcr._read_csv_rows(base / kpl.FILE_PC_SEARCH_CSV)
|
||
_, comment_rows = jcr._read_csv_rows(base / kpl.FILE_COMMENTS_FLAT_CSV)
|
||
|
||
meta_path = base / kpl.FILE_RUN_META_JSON
|
||
meta: dict[str, Any] | None = None
|
||
if meta_path.is_file():
|
||
try:
|
||
meta = json.loads(meta_path.read_text(encoding="utf-8"))
|
||
except json.JSONDecodeError:
|
||
meta = None
|
||
|
||
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.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,
|
||
keyword=kw,
|
||
merged_rows=merged_rows,
|
||
search_export_rows=search_export_rows,
|
||
comment_rows=comment_rows,
|
||
meta=meta,
|
||
report_config=eff,
|
||
)
|
||
|
||
|
||
def run_jd_keyword_and_report(
|
||
keyword: str,
|
||
*,
|
||
max_skus: int | None = None,
|
||
page_start: int | None = None,
|
||
page_to: int | None = None,
|
||
pipeline_run_dir: str | None = None,
|
||
cookie_file_path: str | None = None,
|
||
pvid: str | None = None,
|
||
request_delay: str | None = None,
|
||
list_pages: str | None = None,
|
||
scenario_filter_enabled: bool | None = None,
|
||
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()
|
||
if not kw:
|
||
raise ValueError("keyword 不能为空")
|
||
|
||
backup: dict[str, Any] = {}
|
||
if cancel_check is not None:
|
||
backup["PIPELINE_CANCEL_CHECK"] = getattr(kpl, "PIPELINE_CANCEL_CHECK", None)
|
||
kpl.PIPELINE_CANCEL_CHECK = cancel_check
|
||
try:
|
||
if max_skus is not None:
|
||
backup["MAX_SKUS"] = kpl.MAX_SKUS
|
||
kpl.MAX_SKUS = max(1, int(max_skus))
|
||
if page_start is not None:
|
||
backup["PAGE_START"] = kpl.PAGE_START
|
||
kpl.PAGE_START = max(1, int(page_start))
|
||
if page_to is not None:
|
||
backup["PAGE_TO"] = kpl.PAGE_TO
|
||
kpl.PAGE_TO = max(1, int(page_to))
|
||
|
||
prd = (pipeline_run_dir or "").strip()
|
||
if prd:
|
||
backup["PIPELINE_RUN_DIR"] = kpl.PIPELINE_RUN_DIR
|
||
kpl.PIPELINE_RUN_DIR = prd
|
||
|
||
cf = (cookie_file_path or "").strip()
|
||
if cf:
|
||
backup["PIPELINE_COOKIE_FILE"] = kpl.PIPELINE_COOKIE_FILE
|
||
kpl.PIPELINE_COOKIE_FILE = cf
|
||
|
||
pv = (pvid or "").strip()
|
||
if pv:
|
||
backup["PVID"] = kpl.PVID
|
||
kpl.PVID = pv
|
||
|
||
rd = (request_delay or "").strip()
|
||
if rd:
|
||
backup["REQUEST_DELAY"] = kpl.REQUEST_DELAY
|
||
kpl.REQUEST_DELAY = rd
|
||
|
||
lp = (list_pages or "").strip()
|
||
if lp:
|
||
backup["LIST_PAGES"] = kpl.LIST_PAGES
|
||
kpl.LIST_PAGES = lp
|
||
|
||
if scenario_filter_enabled is not None:
|
||
backup["SCENARIO_FILTER_ENABLED"] = kpl.SCENARIO_FILTER_ENABLED
|
||
kpl.SCENARIO_FILTER_ENABLED = bool(scenario_filter_enabled)
|
||
|
||
run_dir = kpl.main(keyword=kw)
|
||
except kpl.PipelinePausedForCookie:
|
||
raise
|
||
except kpl.PipelineCancelled:
|
||
raise
|
||
finally:
|
||
for name, val in backup.items():
|
||
setattr(kpl, name, val)
|
||
|
||
# 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()
|