mirror of
https://github.com/primedigitaltech/market-assistant.git
synced 2026-07-21 23:41:39 +08:00
491 lines
17 KiB
Python
491 lines
17 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 完整竞品矩阵、各章内嵌统计图与表格）。
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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
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覆盖规则正文（否则会丢失矩阵与章节结构）。
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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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block = (
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"---\n\n"
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"## 大模型速读与策略要点（补充）\n\n"
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"> **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、"
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"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"
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f"{sup}\n"
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)
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marker = "\n---\n\n## 一、研究范围、数据来源与局限"
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if marker in body:
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return body.replace(marker, "\n" + block + marker, 1)
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return block + "\n---\n\n" + body
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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 _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": False,
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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] = (
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dict(report_config) if isinstance(report_config, dict) else {}
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)
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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": 2,
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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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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._iter_comment_text_units(comment_rows, merged_rows)
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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(comment_units)
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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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md = jcr.build_competitor_markdown(
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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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llm_sentiment_section_md=llm_sentiment_md or None,
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)
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out_md = run_dir / "competitor_analysis.md"
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out_md.write_text(md, encoding="utf-8")
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return run_dir
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def regenerate_competitor_report(
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run_dir_str: str,
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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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"""校验 ``run_dir`` 位于 ``LOW_GI_PROJECT_ROOT/data/JD`` 下后，重写竞品 Markdown。"""
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low_root = (settings.LOW_GI_PROJECT_ROOT or "").strip()
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if not low_root:
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raise RuntimeError("LOW_GI_PROJECT_ROOT 未配置")
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base = Path(run_dir_str).expanduser().resolve()
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jd_root = (Path(low_root) / "data" / "JD").resolve()
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try:
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base.relative_to(jd_root)
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except ValueError as e:
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raise ValueError("run_dir 不在京东数据目录下") from e
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return write_competitor_analysis_for_run_dir(
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base, keyword, report_config=report_config
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)
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def write_competitor_analysis_markdown(run_dir_str: str, markdown: str) -> Path:
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"""将已生成的 Markdown 正文写入 ``run_dir/competitor_analysis.md``（与规则重生成同路径）。"""
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low_root = (settings.LOW_GI_PROJECT_ROOT or "").strip()
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if not low_root:
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raise RuntimeError("LOW_GI_PROJECT_ROOT 未配置")
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base = Path(run_dir_str).expanduser().resolve()
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jd_root = (Path(low_root) / "data" / "JD").resolve()
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try:
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base.relative_to(jd_root)
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except ValueError as e:
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raise ValueError("run_dir 不在京东数据目录下") from e
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out = base / "competitor_analysis.md"
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out.write_text(markdown or "", encoding="utf-8")
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return out
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def build_competitor_brief_for_job(
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run_dir_str: str,
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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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) -> dict[str, Any]:
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"""
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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）。
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``run_dir`` 须位于 ``LOW_GI_PROJECT_ROOT/data/JD`` 下。
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"""
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low_root = (settings.LOW_GI_PROJECT_ROOT or "").strip()
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if not low_root:
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raise RuntimeError("LOW_GI_PROJECT_ROOT 未配置")
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base = Path(run_dir_str).expanduser().resolve()
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jd_root = (Path(low_root) / "data" / "JD").resolve()
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try:
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base.relative_to(jd_root)
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except ValueError as e:
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raise ValueError("run_dir 不在京东数据目录下") from e
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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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merged_path = base / 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，无法生成摘要: {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(base / kpl.FILE_PC_SEARCH_CSV)
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_, comment_rows = jcr._read_csv_rows(base / kpl.FILE_COMMENTS_FLAT_CSV)
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meta_path = base / 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: dict[str, Any] | None = None
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if isinstance(report_config, dict):
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eff = dict(report_config)
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eff_path = base / "effective_report_config.json"
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if eff_path.is_file():
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try:
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loaded = json.loads(eff_path.read_text(encoding="utf-8"))
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if isinstance(loaded, dict) and loaded:
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eff = loaded
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except json.JSONDecodeError:
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pass
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return jcr.build_competitor_brief(
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run_dir=base,
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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,
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)
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def run_jd_keyword_and_report(
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keyword: str,
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*,
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max_skus: int | None = None,
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page_start: int | None = None,
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page_to: int | None = None,
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pipeline_run_dir: str | None = None,
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cookie_file_path: str | None = None,
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pvid: str | None = None,
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request_delay: str | None = None,
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list_pages: str | None = None,
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scenario_filter_enabled: bool | None = None,
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report_config: dict[str, Any] | None = None,
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cancel_check: Any | None = None,
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) -> Path:
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_, 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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backup: dict[str, Any] = {}
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if cancel_check is not None:
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backup["PIPELINE_CANCEL_CHECK"] = getattr(kpl, "PIPELINE_CANCEL_CHECK", None)
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kpl.PIPELINE_CANCEL_CHECK = cancel_check
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try:
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if max_skus is not None:
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backup["MAX_SKUS"] = kpl.MAX_SKUS
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kpl.MAX_SKUS = max(1, int(max_skus))
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if page_start is not None:
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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
|
||
)
|