""" 使用 ``crawler_copy/jd_pc_search`` 中的副本脚本执行流水线并生成竞品 Markdown。 依赖环境变量 ``LOW_GI_PROJECT_ROOT``（由 Django settings 从 ``market_assistant/.env`` 注入）。 """ from __future__ import annotations import json import os import re import sys from pathlib import Path from typing import Any from django.conf import settings 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）。 大模型稿仅作为开篇「速读/策略补充」插入在「## 一、」之前，**不得**再用纯 LLM 稿 覆盖规则正文（否则会丢失矩阵与章节结构）。 """ body = (rules_md or "").strip() sup = (llm_md or "").strip() if not sup: return body if not body: return sup block = ( "---\n\n" "## 大模型速读与策略要点（补充）\n\n" "> **说明**：以下由大模型依据结构化摘要生成，便于速览；**完整竞品对比矩阵、全部表格、" "统计图与定量口径以正文各章（尤其 §5）为准**，请勿仅依据本段理解 SKU 明细。\n\n" f"{sup}\n" ) marker = "\n---\n\n## 一、研究范围、数据来源与局限" if marker in body: return body.replace(marker, "\n" + block + marker, 1) return block + "\n---\n\n" + body def merge_llm_report_with_rules_charts(llm_md: str, rules_md: str) -> str: """兼容旧名：等价于 ``merge_llm_supplement_with_rules_report``。""" return merge_llm_supplement_with_rules_report(llm_md, rules_md) def _flat_comment_texts(comment_rows: list[dict[str, str]]) -> list[str]: """全部非空评价正文（与报告统计同源）。""" out: list[str] = [] for row in comment_rows: t = (row.get("tagCommentContent") or "").strip() if t: out.append(t) return out def _safe_dir_segment_for_job(s: str, max_len: int = 48) -> str: """与 ``jd_keyword_pipeline._safe_dir_segment`` 一致，避免多线程下改模块全局。""" bad = '<>:"/\\|?*\n\r\t' t = "".join("_" if c in bad else c for c in (s or "").strip())[:max_len] t = t.strip(" .") or "run" return t def resolve_pipeline_run_directory_for_job(job: PipelineJob) -> Path: """ 在拉起子进程前固定本次 ``run_dir``（与 ``jd_keyword_pipeline._resolve_pipeline_run_dir`` 同口径）。 调用方负责 ``mkdir``。 """ root = (settings.LOW_GI_PROJECT_ROOT or "").strip() if not root: raise RuntimeError("LOW_GI_PROJECT_ROOT 未配置") project_data = Path(root).resolve() / "data" / "JD" prd = (job.pipeline_run_dir or "").strip() kw = (job.keyword or "").strip() if prd: p = Path(prd).expanduser() if not p.is_absolute(): p = project_data / p return p.resolve() import time stamp = time.strftime("%Y%m%d_%H%M%S") seg = _safe_dir_segment_for_job(kw) return (project_data / "pipeline_runs" / f"{stamp}_{seg}").resolve() def try_write_competitor_report_if_merged_exists( run_dir: Path, keyword: str, *, report_config: dict[str, Any] | None = None, ) -> None: """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）。""" _, kpl = _jd_crawler_modules() base = Path(run_dir).resolve() merged = base / kpl.FILE_MERGED_CSV if not merged.is_file(): return try: write_competitor_analysis_for_run_dir( base, keyword, report_config=report_config ) except Exception: pass def _jd_crawler_modules(): root = Path(settings.CRAWLER_JD_ROOT) if not root.is_dir(): raise FileNotFoundError(f"爬虫副本目录不存在: {root}") root_s = str(root.resolve()) if root_s not in sys.path: sys.path.insert(0, root_s) import jd_competitor_report as jcr # noqa: WPS433 import jd_keyword_pipeline as kpl # noqa: WPS433 return jcr, kpl 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, "comment_focus_words": list(jcr.COMMENT_FOCUS_WORDS), "comment_scenario_groups": [ {"label": lbl, "triggers": list(trs)} for lbl, trs in jcr.COMMENT_SCENARIO_GROUPS ], "external_market_table_rows": [ {"indicator": a, "value_and_scope": b, "source": c, "year": d} for a, b, c, d in jcr.EXTERNAL_MARKET_TABLE_ROWS ], } def write_competitor_analysis_for_run_dir( run_dir: Path, keyword: str, *, report_config: dict[str, Any] | None = None, ) -> Path: """ 在已有流水线目录上读取 CSV / meta，写入 ``competitor_analysis.md``（不重新爬取）。 """ jcr, kpl = _jd_crawler_modules() kw = (keyword or "").strip() if not kw: raise ValueError("keyword 不能为空") run_dir = Path(run_dir).resolve() merged_path = run_dir / kpl.FILE_MERGED_CSV if not merged_path.is_file(): raise FileNotFoundError(f"缺少合并表，无法生成报告: {merged_path.name}") _, merged_rows = jcr._read_csv_rows(merged_path) _, search_export_rows = jcr._read_csv_rows(run_dir / kpl.FILE_PC_SEARCH_CSV) _, comment_rows = jcr._read_csv_rows(run_dir / kpl.FILE_COMMENTS_FLAT_CSV) meta_path = run_dir / 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_rc: dict[str, Any] = ( dict(report_config) if isinstance(report_config, dict) else {} ) all_tx = _flat_comment_texts(comment_rows) suggest_path = run_dir / "keyword_suggest_llm.json" suggest_record: dict[str, Any] = { "schema_version": 2, "total_comment_texts": len(all_tx), } skip_kw = os.environ.get("MA_SKIP_LLM_KEYWORD_SUGGEST", "").strip().lower() in ( "1", "true", "yes", ) if not skip_kw: try: from .llm_keyword_suggest import suggest_focus_keywords_from_all_comments brief_pre = jcr.build_competitor_brief( 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, ) brief_slice = { "keyword": brief_pre.get("keyword"), "comment_focus_keywords": ( brief_pre.get("comment_focus_keywords") or [] )[:20], "usage_scenarios": (brief_pre.get("usage_scenarios") or [])[:8], "category_mix_top": (brief_pre.get("category_mix_top") or [])[:6], "scope": brief_pre.get("scope"), } sug = suggest_focus_keywords_from_all_comments( keyword=kw, brief_slice=brief_slice, all_comment_texts=all_tx, ) suggest_record.update(sug) base_words = list(eff_rc.get("comment_focus_words") or []) for w in sug.get("suggested_focus_keywords") or []: if isinstance(w, str): t = w.strip() if t and t not in base_words: base_words.append(t) eff_rc["comment_focus_words"] = base_words[:80] except Exception as e: suggest_record["error"] = str(e) suggest_record["suggested_focus_keywords"] = [] else: suggest_record["skipped"] = True suggest_record["suggested_focus_keywords"] = [] suggest_path.write_text( json.dumps(suggest_record, ensure_ascii=False, indent=2), encoding="utf-8", ) (run_dir / "effective_report_config.json").write_text( json.dumps(eff_rc, ensure_ascii=False, indent=2), encoding="utf-8", ) brief_final = jcr.build_competitor_brief( 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, ) from .report_charts import generate_report_charts generate_report_charts(run_dir, brief_final) llm_sentiment_md = "" sentiment_llm_record: dict[str, Any] = { "schema_version": 1, "attempted": False, } skip_sent = os.environ.get( "MA_SKIP_LLM_COMMENT_SENTIMENT", "" ).strip().lower() in ("1", "true", "yes") env_on = os.environ.get("MA_ENABLE_LLM_COMMENT_SENTIMENT", "").strip().lower() in ( "1", "true", "yes", ) 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) if len(comment_units) >= 2: sentiment_llm_record["attempted"] = True try: from .llm_generate import generate_comment_sentiment_analysis_llm pl = jcr.build_comment_sentiment_llm_payload(comment_units) pl["keyword"] = kw llm_sentiment_md = generate_comment_sentiment_analysis_llm(pl) sentiment_llm_record["ok"] = True sentiment_llm_record["chars"] = len(llm_sentiment_md) except Exception as e: sentiment_llm_record["ok"] = False sentiment_llm_record["error"] = str(e) else: sentiment_llm_record["skipped"] = "insufficient_comment_texts" elif skip_sent: sentiment_llm_record["skipped"] = "MA_SKIP_LLM_COMMENT_SENTIMENT" elif not want_sent: sentiment_llm_record["skipped"] = "not_enabled" (run_dir / "comment_sentiment_llm.json").write_text( json.dumps(sentiment_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, ) 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] | None = None if isinstance(report_config, dict): eff = dict(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 except json.JSONDecodeError: pass 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: _, 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.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 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 )