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refactor(jd): 矩阵分组、价统与 LLM 分组载荷拆至 competitor_report
新增 matrix_group（复用 pipeline.matrix_group_label）、price_stats、ingredients、llm_group_payloads；jd_competitor_report 仅保留报告编排与 Markdown；更新 matrix_group_label 模块说明。 Made-with: Cursor
This commit is contained in:
hub-gif
parent
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commit
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"""配料列清洗：与 ``AI_crawler.normalize_ingredients_text_for_csv`` 等口径对齐。"""
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from __future__ import annotations
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import re
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def _is_ingredient_url_blob(s: str) -> bool:
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"""详情主图 URL 串（分号分隔）或单列以 http 开头。"""
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t = (s or "").strip()
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if not t:
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return False
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if t.startswith(("http://", "https://")):
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return True
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head = t[:400]
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if ("https://" in head or "http://" in head) and (
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";" in t or len(t) > 180 or t.count("http") >= 2
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):
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return True
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return False
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def _ingredients_from_product_attributes(attrs: str) -> str:
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m = re.search(r"配料(?:表)?[:：]\s*([^;；]+)", attrs or "")
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return m.group(1).strip() if m else ""
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def _ingredients_single_line(s: str) -> str:
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"""与 ``AI_crawler.normalize_ingredients_text_for_csv`` 一致：多行配料压成一行（行间 ``；``），便于表格/CSV。"""
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t = (s or "").replace("\r\n", "\n").replace("\r", "\n").strip()
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if not t:
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return ""
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lines = [ln.strip() for ln in t.split("\n") if ln.strip()]
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if len(lines) <= 1:
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return lines[0] if lines else ""
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return "；".join(lines)
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__all__ = [
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"_ingredients_from_product_attributes",
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"_ingredients_single_line",
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"_is_ingredient_url_blob",
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]
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"""按细类矩阵分组的 LLM 载荷（矩阵/价盘/促销/评价/场景）。"""
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from __future__ import annotations
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from collections import Counter
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from typing import Any
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from pipeline.csv_schema import JD_SEARCH_CSV_HEADERS, MERGED_FIELD_TO_CSV_HEADER
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from .comment_sentiment import _comment_keyword_hits
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from .constants import (
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_COMMENT_CSV_BODY,
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_COMMENT_CSV_SKU,
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_COUPON_SHOW_PRICE_KEY,
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_DETAIL_PRICE_FINAL_CSV_KEYS,
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_LEGACY_COUPON_SHOW_PRICE_KEY,
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_LEGACY_RANK_TAGLINE_KEY,
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_LEGACY_SELLING_POINT_KEY,
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_LIST_SHOW_PRICE_CELL_KEYS,
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_MERGED_SHOP_CELL_KEYS,
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_RANK_TAGLINE_KEY,
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_SELLING_POINT_KEY,
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)
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from .csv_io import _cell, _collect_prices, _md_cell
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from .ingredients import _ingredients_from_product_attributes, _ingredients_single_line
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from .matrix_group import _competitor_matrix_group_key, _merged_rows_grouped_for_matrix
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from .price_stats import _price_stats_extended
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def _comment_scenario_counts(
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texts: list[str],
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scenario_groups: tuple[tuple[str, tuple[str, ...]], ...],
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) -> tuple[Counter[str], int]:
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"""每组统计「至少命中一个触发词」的条数。返回 (各组条数, 有效文本条数)。"""
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c: Counter[str] = Counter()
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n = len(texts)
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for blob in texts:
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for label, triggers in scenario_groups:
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if any(t in blob for t in triggers):
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c[label] += 1
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return c, n
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def _text_hits_scenario_triggers(
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text: str,
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scenario_groups: tuple[tuple[str, tuple[str, ...]], ...],
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) -> bool:
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blob = text or ""
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for _lbl, triggers in scenario_groups:
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if any(t in blob for t in triggers):
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return True
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return False
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def _group_keyword_hits(
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comment_rows_in_group: list[dict[str, str]],
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texts_fallback: list[str],
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*,
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focus_words: tuple[str, ...],
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) -> Counter[str]:
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h = _comment_keyword_hits(comment_rows_in_group, focus_words)
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if h:
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return h
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if not texts_fallback:
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return Counter()
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blob = "\n".join(texts_fallback)
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c: Counter[str] = Counter()
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for w in focus_words:
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if len(w) < 2:
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continue
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n = blob.count(w)
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if n:
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c[w] += n
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return c
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def _matrix_excerpt_line_for_llm(row: dict[str, str], title_h: str) -> str:
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title = _md_cell(_cell(row, title_h), 100)
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sp = _md_cell(_cell(row, _SELLING_POINT_KEY, _LEGACY_SELLING_POINT_KEY), 120)
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ing_raw = _ingredients_from_product_attributes(
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_cell(
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row,
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MERGED_FIELD_TO_CSV_HEADER["detail_product_attributes"],
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"detail_product_attributes",
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)
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)
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ing = _md_cell(_ingredients_single_line(ing_raw), 100) if ing_raw else ""
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chunks: list[str] = []
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if title:
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chunks.append(title)
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if sp:
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chunks.append(f"卖点:{sp}")
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if ing:
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chunks.append(f"配料:{ing}")
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return "｜".join(chunks) if chunks else "（无标题摘录）"
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def _listing_price_snippet_for_llm(row: dict[str, str], title_h: str) -> str:
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title = _md_cell(_cell(row, title_h), 72)
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lp = _cell(row, *_LIST_SHOW_PRICE_CELL_KEYS)
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cp = _cell(row, _COUPON_SHOW_PRICE_KEY, _LEGACY_COUPON_SHOW_PRICE_KEY)
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dp = _cell(row, *_DETAIL_PRICE_FINAL_CSV_KEYS)
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return f"{title}｜标价:{lp}｜券后:{cp}｜详情价:{dp}"
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def build_matrix_groups_llm_payload(
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merged_rows: list[dict[str, str]],
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*,
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title_h: str,
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sku_header: str = "",
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) -> list[dict[str, Any]]:
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"""供 ``generate_matrix_group_summaries_llm``：与 §5 细类划分一致。"""
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_ = sku_header
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if not merged_rows:
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return []
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out: list[dict[str, Any]] = []
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for gname, grows in _merged_rows_grouped_for_matrix(merged_rows):
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prices = _collect_prices(grows)
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pst = _price_stats_extended(prices) if prices else {"n": 0}
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lines = [_matrix_excerpt_line_for_llm(r, title_h) for r in grows[:24]]
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out.append(
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{
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"group": gname,
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"sku_count": len(grows),
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"price_stats": pst,
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"lines": lines,
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}
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)
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return out
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def build_price_groups_llm_payload(
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merged_rows: list[dict[str, str]],
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*,
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title_h: str,
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sku_header: str = "",
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) -> list[dict[str, Any]]:
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"""供 ``generate_price_group_summaries_llm``。"""
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_ = sku_header
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if not merged_rows:
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return []
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out: list[dict[str, Any]] = []
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for gname, grows in _merged_rows_grouped_for_matrix(merged_rows):
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prices = _collect_prices(grows)
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pst = _price_stats_extended(prices) if prices else {"n": 0}
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snippets = [_listing_price_snippet_for_llm(r, title_h) for r in grows[:16]]
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out.append(
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{
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"group": gname,
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"sku_count": len(grows),
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"price_stats": pst,
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"listing_snippets": snippets,
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}
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)
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return out
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def _promo_snippet_for_llm(row: dict[str, str], title_h: str) -> str:
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"""单条 SKU：合并表「促销摘要」「榜单排名」「榜单类文案」摘录，供促销 LLM 用（不含列表卖点/腰带列，避免固定词表匹配的粗口径）。"""
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title = _md_cell(_cell(row, title_h), 56)
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promo = _cell(
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row,
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MERGED_FIELD_TO_CSV_HEADER["buyer_promo_text"],
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"buyer_promo_text",
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)
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br = _cell(
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row,
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MERGED_FIELD_TO_CSV_HEADER["buyer_ranking_line"],
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"buyer_ranking_line",
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)
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belt = _cell(row, _RANK_TAGLINE_KEY, _LEGACY_RANK_TAGLINE_KEY)
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parts: list[str] = [title]
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if promo.strip():
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parts.append(
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f"{MERGED_FIELD_TO_CSV_HEADER['buyer_promo_text']}:{_md_cell(promo, 360)}"
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)
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if br.strip():
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parts.append(
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f"{MERGED_FIELD_TO_CSV_HEADER['buyer_ranking_line']}:{_md_cell(br, 120)}"
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)
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if belt.strip():
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parts.append(
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f"{JD_SEARCH_CSV_HEADERS['hot_list_rank']}:{_md_cell(belt, 80)}"
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)
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return "｜".join(parts) if len(parts) > 1 else (parts[0] if parts else "")
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def build_promo_groups_llm_payload(
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merged_rows: list[dict[str, str]],
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*,
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title_h: str,
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sku_header: str = "",
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) -> list[dict[str, Any]]:
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"""供 ``generate_promo_group_summaries_llm``：与 §5/§6 细类划分一致。"""
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_ = sku_header
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if not merged_rows:
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return []
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out: list[dict[str, Any]] = []
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for gname, grows in _merged_rows_grouped_for_matrix(merged_rows):
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snippets = [_promo_snippet_for_llm(r, title_h) for r in grows[:16]]
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nonempty = sum(
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1
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for r in grows
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if _cell(
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r,
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MERGED_FIELD_TO_CSV_HEADER["buyer_promo_text"],
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"buyer_promo_text",
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).strip()
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)
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out.append(
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{
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"group": gname,
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"sku_count": len(grows),
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"rows_with_buyer_promo_text": nonempty,
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"promo_snippets": snippets,
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}
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)
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return out
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def build_comment_groups_llm_payload(
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*,
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feedback_groups: list[tuple[str, list[dict[str, str]], list[str]]],
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focus_words: tuple[str, ...],
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merged_rows: list[dict[str, str]],
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sku_header: str,
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title_h: str,
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) -> list[dict[str, Any]]:
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"""供 ``generate_comment_group_summaries_llm``。"""
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if not feedback_groups:
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return []
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sku_meta: dict[str, tuple[str, str, str]] = {}
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for row in merged_rows:
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sku = _cell(row, sku_header).strip()
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if not sku:
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continue
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gk = _competitor_matrix_group_key(row)
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if not gk:
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continue
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sku_meta[sku] = (
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gk,
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_cell(row, title_h),
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_cell(row, *_MERGED_SHOP_CELL_KEYS),
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)
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out: list[dict[str, Any]] = []
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for gname, cr, tu in feedback_groups:
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if not tu and not cr:
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continue
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gh = _group_keyword_hits(cr, tu, focus_words=focus_words)
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focus_hit_lines = [
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f"「{w}」{n} 次" for w, n in gh.most_common(14) if n > 0
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]
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snippets: list[str] = []
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for row in cr[:48]:
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txt = _cell(row, _COMMENT_CSV_BODY, "tagCommentContent")
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if not txt:
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continue
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sku = _cell(row, _COMMENT_CSV_SKU, "sku").strip()
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meta = sku_meta.get(sku)
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if meta:
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sg, tit, shop = meta
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prefix = (
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f"【细类：{sg}｜SKU：{sku}｜品名：{_md_cell(tit, 60)}｜"
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f"店铺：{_md_cell(shop, 28)}】"
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)
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snippets.append(prefix + txt[:300])
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else:
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snippets.append(
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f"【细类：{gname}｜SKU：{sku or '—'}】" + txt[:320]
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)
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if len(snippets) >= 16:
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break
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eff = tu[:28]
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if len(tu) > 28:
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eff = list(eff) + [f"…共 {len(tu)} 条有效文本，此处截断"]
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out.append(
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{
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"group": gname,
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"comment_flat_rows": f"评价行 {len(cr)}；有效文本单元 {len(tu)}",
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"effective_text_lines": eff,
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"focus_hit_lines": focus_hit_lines,
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"sample_text_snippets": snippets,
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}
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)
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return out
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def build_scenario_groups_llm_payload(
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*,
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feedback_groups: list[tuple[str, list[dict[str, str]], list[str]]],
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scenario_groups: tuple[tuple[str, tuple[str, ...]], ...],
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merged_rows: list[dict[str, str]],
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sku_header: str,
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title_h: str,
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) -> dict[str, Any]:
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"""供 ``generate_scenario_group_summaries_llm``；计数与 §8.3 图右栏（场景）一致。"""
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if not feedback_groups:
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return {}
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sku_meta: dict[str, tuple[str, str, str]] = {}
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for row in merged_rows:
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sku = _cell(row, sku_header).strip()
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if not sku:
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continue
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gk = _competitor_matrix_group_key(row)
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if not gk:
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continue
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sku_meta[sku] = (
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gk,
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_cell(row, title_h),
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_cell(row, *_MERGED_SHOP_CELL_KEYS),
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)
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lexicon = [
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{"label": lbl, "trigger_examples": list(trigs[:12])}
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for lbl, trigs in scenario_groups
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]
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groups_out: list[dict[str, Any]] = []
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for gname, cr, tu in feedback_groups:
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if not tu and not cr:
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continue
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scen_g, scen_ng = _comment_scenario_counts(tu, scenario_groups)
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dist: list[dict[str, Any]] = []
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for lbl, n in scen_g.most_common():
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if n <= 0:
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continue
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dist.append(
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{
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"scenario": lbl,
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"mention_rows": int(n),
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"share_of_effective_texts": round(
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float(n) / float(scen_ng), 4
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)
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if scen_ng > 0
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else 0.0,
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}
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)
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snippets: list[str] = []
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for row in cr:
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txt = _cell(row, _COMMENT_CSV_BODY, "tagCommentContent")
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if not txt:
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continue
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if not _text_hits_scenario_triggers(txt, scenario_groups):
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continue
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sku = _cell(row, _COMMENT_CSV_SKU, "sku").strip()
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meta = sku_meta.get(sku)
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if meta:
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sg, tit, shop = meta
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prefix = (
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f"【细类：{sg}\uff5cSKU：{sku}\uff5c品名：{_md_cell(tit, 60)}\uff5c"
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f"店铺：{_md_cell(shop, 28)}】"
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)
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snippets.append(prefix + txt[:300])
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else:
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snippets.append(
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f"【细类：{gname}\uff5cSKU：{sku or '—'}】" + txt[:320]
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)
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if len(snippets) >= 16:
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break
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if len(snippets) < 5:
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for row in cr:
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txt = _cell(row, _COMMENT_CSV_BODY, "tagCommentContent")
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if not txt:
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continue
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sku = _cell(row, _COMMENT_CSV_SKU, "sku").strip()
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meta = sku_meta.get(sku)
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if meta:
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sg, tit, shop = meta
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prefix = (
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f"【细类：{sg}\uff5cSKU：{sku}\uff5c品名：{_md_cell(tit, 60)}\uff5c"
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f"店铺：{_md_cell(shop, 28)}】"
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)
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snippets.append(prefix + txt[:260])
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else:
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snippets.append(
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f"【细类：{gname}\uff5cSKU：{sku or '—'}】" + txt[:280]
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)
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if len(snippets) >= 10:
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break
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groups_out.append(
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{
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"group": gname,
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"effective_text_count": int(scen_ng),
|
||||
"scenario_distribution": dist[:18],
|
||||
"sample_text_snippets": snippets,
|
||||
}
|
||||
)
|
||||
if not groups_out:
|
||||
return {}
|
||||
return {"scenario_lexicon": lexicon, "groups": groups_out}
|
||||
|
||||
|
||||
__all__ = [
|
||||
"build_comment_groups_llm_payload",
|
||||
"build_matrix_groups_llm_payload",
|
||||
"build_price_groups_llm_payload",
|
||||
"build_promo_groups_llm_payload",
|
||||
"build_scenario_groups_llm_payload",
|
||||
"_comment_scenario_counts",
|
||||
"_group_keyword_hits",
|
||||
"_listing_price_snippet_for_llm",
|
||||
"_matrix_excerpt_line_for_llm",
|
||||
"_promo_snippet_for_llm",
|
||||
"_text_hits_scenario_triggers",
|
||||
]
|
||||
@ -0,0 +1,76 @@
|
||||
"""竞品矩阵细类键：与 ``pipeline.matrix_group_label`` 及 §5 矩阵/扇图同源。"""
|
||||
from __future__ import annotations
|
||||
|
||||
from collections import Counter
|
||||
|
||||
from pipeline.matrix_group_label import (
|
||||
matrix_group_label_from_detail_path as _matrix_group_label_from_path,
|
||||
)
|
||||
|
||||
from .csv_io import _detail_category_path_cell
|
||||
|
||||
|
||||
def _matrix_group_label_from_detail_path(row: dict[str, str]) -> str:
|
||||
return _matrix_group_label_from_path(_detail_category_path_cell(row))
|
||||
|
||||
|
||||
def _competitor_matrix_group_key(row: dict[str, str]) -> str:
|
||||
"""
|
||||
竞品矩阵分组：§5 / §8 / 统计图共用。
|
||||
**仅**依据 ``detail_category_path``；列为空或路径段均为无意义编码时不参与矩阵（返回空串）。
|
||||
"""
|
||||
return _matrix_group_label_from_detail_path(row)
|
||||
|
||||
|
||||
def _merged_rows_grouped_for_matrix(
|
||||
merged_rows: list[dict[str, str]],
|
||||
) -> list[tuple[str, list[dict[str, str]]]]:
|
||||
buckets: dict[str, list[dict[str, str]]] = {}
|
||||
for row in merged_rows:
|
||||
k = _competitor_matrix_group_key(row)
|
||||
if not k:
|
||||
continue
|
||||
buckets.setdefault(k, []).append(row)
|
||||
|
||||
def sort_key(item: tuple[str, list[dict[str, str]]]) -> tuple[int, int, str]:
|
||||
name, rows = item
|
||||
miss = name.startswith("未归类")
|
||||
return (1 if miss else 0, -len(rows), name)
|
||||
|
||||
return sorted(buckets.items(), key=sort_key)
|
||||
|
||||
|
||||
def _category_mix(
|
||||
rows: list[dict[str, str]], *, top_k: int = 12
|
||||
) -> list[tuple[str, int]]:
|
||||
"""
|
||||
按「可读细类标签」统计 SKU 分布（与 §5 ``_competitor_matrix_group_key`` 同源）；
|
||||
仅含 ``detail_category_path`` 可解析为展示名的行。
|
||||
|
||||
返回 ``most_common(top_k)``，并将未列入 Top K 的款数合并为「（其余细类）」，
|
||||
使各块 SKU 数之和等于有效矩阵 SKU 总数（与扇形图、简报 ``category_mix_top`` 一致）。
|
||||
"""
|
||||
labels: list[str] = []
|
||||
for r in rows:
|
||||
k = _matrix_group_label_from_detail_path(r)
|
||||
if k:
|
||||
labels.append(k)
|
||||
if not labels:
|
||||
return []
|
||||
c = Counter(labels)
|
||||
common = c.most_common(top_k)
|
||||
accounted = sum(v for _, v in common)
|
||||
total = sum(c.values())
|
||||
rest = total - accounted
|
||||
out: list[tuple[str, int]] = list(common)
|
||||
if rest > 0:
|
||||
out.append(("（其余细类）", rest))
|
||||
return out
|
||||
|
||||
|
||||
__all__ = [
|
||||
"_category_mix",
|
||||
"_competitor_matrix_group_key",
|
||||
"_matrix_group_label_from_detail_path",
|
||||
"_merged_rows_grouped_for_matrix",
|
||||
]
|
||||
@ -0,0 +1,32 @@
|
||||
"""合并表价格列的汇总统计（价盘/LLM 载荷等共用）。"""
|
||||
from __future__ import annotations
|
||||
|
||||
import statistics
|
||||
from typing import Any
|
||||
|
||||
|
||||
def _price_stats_extended(prices: list[float]) -> dict[str, Any]:
|
||||
if not prices:
|
||||
return {}
|
||||
out: dict[str, Any] = {
|
||||
"min": min(prices),
|
||||
"max": max(prices),
|
||||
"mean": statistics.mean(prices),
|
||||
"n": len(prices),
|
||||
}
|
||||
if len(prices) >= 2:
|
||||
out["stdev"] = statistics.stdev(prices)
|
||||
if len(prices) >= 2:
|
||||
out["median"] = statistics.median(prices)
|
||||
if len(prices) >= 4:
|
||||
s = sorted(prices)
|
||||
n = len(s)
|
||||
mid = n // 2
|
||||
lower = s[:mid] if n % 2 else s[:mid]
|
||||
upper = s[mid + 1 :] if n % 2 else s[mid:]
|
||||
out["q1"] = statistics.median(lower) if lower else s[0]
|
||||
out["q3"] = statistics.median(upper) if upper else s[-1]
|
||||
return out
|
||||
|
||||
|
||||
__all__ = ["_price_stats_extended"]
|
||||
@ -28,7 +28,6 @@ import json
|
||||
import math
|
||||
import random
|
||||
import re
|
||||
import statistics
|
||||
import sys
|
||||
from collections import Counter
|
||||
from pathlib import Path
|
||||
@ -66,6 +65,27 @@ from competitor_report.comment_sentiment import ( # noqa: E402
|
||||
_merge_comment_previews,
|
||||
_parse_comment_score,
|
||||
)
|
||||
from competitor_report.ingredients import ( # noqa: E402
|
||||
_ingredients_from_product_attributes,
|
||||
_ingredients_single_line,
|
||||
_is_ingredient_url_blob,
|
||||
)
|
||||
from competitor_report.llm_group_payloads import ( # noqa: E402
|
||||
build_comment_groups_llm_payload,
|
||||
build_matrix_groups_llm_payload,
|
||||
build_price_groups_llm_payload,
|
||||
build_promo_groups_llm_payload,
|
||||
build_scenario_groups_llm_payload,
|
||||
_comment_scenario_counts,
|
||||
_group_keyword_hits,
|
||||
_text_hits_scenario_triggers,
|
||||
)
|
||||
from competitor_report.matrix_group import ( # noqa: E402
|
||||
_category_mix,
|
||||
_competitor_matrix_group_key,
|
||||
_merged_rows_grouped_for_matrix,
|
||||
)
|
||||
from competitor_report.price_stats import _price_stats_extended # noqa: E402
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# 运行配置（按需改这里；与 competitor_report.constants 中默认关注词等配合使用）
|
||||
@ -120,318 +140,6 @@ def _comment_lines_with_product_context(
|
||||
return out
|
||||
|
||||
|
||||
def _matrix_excerpt_line_for_llm(row: dict[str, str], title_h: str) -> str:
|
||||
title = _md_cell(_cell(row, title_h), 100)
|
||||
sp = _md_cell(_cell(row, _SELLING_POINT_KEY, _LEGACY_SELLING_POINT_KEY), 120)
|
||||
ing_raw = _ingredients_from_product_attributes(
|
||||
_cell(
|
||||
row,
|
||||
MERGED_FIELD_TO_CSV_HEADER["detail_product_attributes"],
|
||||
"detail_product_attributes",
|
||||
)
|
||||
)
|
||||
ing = _md_cell(_ingredients_single_line(ing_raw), 100) if ing_raw else ""
|
||||
chunks: list[str] = []
|
||||
if title:
|
||||
chunks.append(title)
|
||||
if sp:
|
||||
chunks.append(f"卖点:{sp}")
|
||||
if ing:
|
||||
chunks.append(f"配料:{ing}")
|
||||
return "｜".join(chunks) if chunks else "（无标题摘录）"
|
||||
|
||||
|
||||
def _listing_price_snippet_for_llm(row: dict[str, str], title_h: str) -> str:
|
||||
title = _md_cell(_cell(row, title_h), 72)
|
||||
lp = _cell(row, *_LIST_SHOW_PRICE_CELL_KEYS)
|
||||
cp = _cell(row, _COUPON_SHOW_PRICE_KEY, _LEGACY_COUPON_SHOW_PRICE_KEY)
|
||||
dp = _cell(row, *_DETAIL_PRICE_FINAL_CSV_KEYS)
|
||||
return f"{title}｜标价:{lp}｜券后:{cp}｜详情价:{dp}"
|
||||
|
||||
|
||||
def build_matrix_groups_llm_payload(
|
||||
merged_rows: list[dict[str, str]],
|
||||
*,
|
||||
title_h: str,
|
||||
sku_header: str = "",
|
||||
) -> list[dict[str, Any]]:
|
||||
"""供 ``generate_matrix_group_summaries_llm``：与 §5 细类划分一致。"""
|
||||
_ = sku_header
|
||||
if not merged_rows:
|
||||
return []
|
||||
out: list[dict[str, Any]] = []
|
||||
for gname, grows in _merged_rows_grouped_for_matrix(merged_rows):
|
||||
prices = _collect_prices(grows)
|
||||
pst = _price_stats_extended(prices) if prices else {"n": 0}
|
||||
lines = [_matrix_excerpt_line_for_llm(r, title_h) for r in grows[:24]]
|
||||
out.append(
|
||||
{
|
||||
"group": gname,
|
||||
"sku_count": len(grows),
|
||||
"price_stats": pst,
|
||||
"lines": lines,
|
||||
}
|
||||
)
|
||||
return out
|
||||
|
||||
|
||||
def build_price_groups_llm_payload(
|
||||
merged_rows: list[dict[str, str]],
|
||||
*,
|
||||
title_h: str,
|
||||
sku_header: str = "",
|
||||
) -> list[dict[str, Any]]:
|
||||
"""供 ``generate_price_group_summaries_llm``。"""
|
||||
_ = sku_header
|
||||
if not merged_rows:
|
||||
return []
|
||||
out: list[dict[str, Any]] = []
|
||||
for gname, grows in _merged_rows_grouped_for_matrix(merged_rows):
|
||||
prices = _collect_prices(grows)
|
||||
pst = _price_stats_extended(prices) if prices else {"n": 0}
|
||||
snippets = [_listing_price_snippet_for_llm(r, title_h) for r in grows[:16]]
|
||||
out.append(
|
||||
{
|
||||
"group": gname,
|
||||
"sku_count": len(grows),
|
||||
"price_stats": pst,
|
||||
"listing_snippets": snippets,
|
||||
}
|
||||
)
|
||||
return out
|
||||
|
||||
|
||||
def _promo_snippet_for_llm(row: dict[str, str], title_h: str) -> str:
|
||||
"""单条 SKU：合并表「促销摘要」「榜单排名」「榜单类文案」摘录，供促销 LLM 用（不含列表卖点/腰带列，避免固定词表匹配的粗口径）。"""
|
||||
title = _md_cell(_cell(row, title_h), 56)
|
||||
promo = _cell(
|
||||
row,
|
||||
MERGED_FIELD_TO_CSV_HEADER["buyer_promo_text"],
|
||||
"buyer_promo_text",
|
||||
)
|
||||
br = _cell(
|
||||
row,
|
||||
MERGED_FIELD_TO_CSV_HEADER["buyer_ranking_line"],
|
||||
"buyer_ranking_line",
|
||||
)
|
||||
belt = _cell(row, _RANK_TAGLINE_KEY, _LEGACY_RANK_TAGLINE_KEY)
|
||||
parts: list[str] = [title]
|
||||
if promo.strip():
|
||||
parts.append(
|
||||
f"{MERGED_FIELD_TO_CSV_HEADER['buyer_promo_text']}:{_md_cell(promo, 360)}"
|
||||
)
|
||||
if br.strip():
|
||||
parts.append(
|
||||
f"{MERGED_FIELD_TO_CSV_HEADER['buyer_ranking_line']}:{_md_cell(br, 120)}"
|
||||
)
|
||||
if belt.strip():
|
||||
parts.append(
|
||||
f"{JD_SEARCH_CSV_HEADERS['hot_list_rank']}:{_md_cell(belt, 80)}"
|
||||
)
|
||||
return "｜".join(parts) if len(parts) > 1 else (parts[0] if parts else "")
|
||||
|
||||
|
||||
def build_promo_groups_llm_payload(
|
||||
merged_rows: list[dict[str, str]],
|
||||
*,
|
||||
title_h: str,
|
||||
sku_header: str = "",
|
||||
) -> list[dict[str, Any]]:
|
||||
"""供 ``generate_promo_group_summaries_llm``：与 §5/§6 细类划分一致。"""
|
||||
_ = sku_header
|
||||
if not merged_rows:
|
||||
return []
|
||||
out: list[dict[str, Any]] = []
|
||||
for gname, grows in _merged_rows_grouped_for_matrix(merged_rows):
|
||||
snippets = [_promo_snippet_for_llm(r, title_h) for r in grows[:16]]
|
||||
nonempty = sum(
|
||||
1
|
||||
for r in grows
|
||||
if _cell(
|
||||
r,
|
||||
MERGED_FIELD_TO_CSV_HEADER["buyer_promo_text"],
|
||||
"buyer_promo_text",
|
||||
).strip()
|
||||
)
|
||||
out.append(
|
||||
{
|
||||
"group": gname,
|
||||
"sku_count": len(grows),
|
||||
"rows_with_buyer_promo_text": nonempty,
|
||||
"promo_snippets": snippets,
|
||||
}
|
||||
)
|
||||
return out
|
||||
|
||||
|
||||
def build_comment_groups_llm_payload(
|
||||
*,
|
||||
feedback_groups: list[tuple[str, list[dict[str, str]], list[str]]],
|
||||
focus_words: tuple[str, ...],
|
||||
merged_rows: list[dict[str, str]],
|
||||
sku_header: str,
|
||||
title_h: str,
|
||||
) -> list[dict[str, Any]]:
|
||||
"""供 ``generate_comment_group_summaries_llm``。"""
|
||||
if not feedback_groups:
|
||||
return []
|
||||
sku_meta: dict[str, tuple[str, str, str]] = {}
|
||||
for row in merged_rows:
|
||||
sku = _cell(row, sku_header).strip()
|
||||
if not sku:
|
||||
continue
|
||||
gk = _competitor_matrix_group_key(row)
|
||||
if not gk:
|
||||
continue
|
||||
sku_meta[sku] = (
|
||||
gk,
|
||||
_cell(row, title_h),
|
||||
_cell(row, *_MERGED_SHOP_CELL_KEYS),
|
||||
)
|
||||
out: list[dict[str, Any]] = []
|
||||
for gname, cr, tu in feedback_groups:
|
||||
if not tu and not cr:
|
||||
continue
|
||||
gh = _group_keyword_hits(cr, tu, focus_words=focus_words)
|
||||
focus_hit_lines = [
|
||||
f"「{w}」{n} 次" for w, n in gh.most_common(14) if n > 0
|
||||
]
|
||||
snippets: list[str] = []
|
||||
for row in cr[:48]:
|
||||
txt = _cell(row, _COMMENT_CSV_BODY, "tagCommentContent")
|
||||
if not txt:
|
||||
continue
|
||||
sku = _cell(row, _COMMENT_CSV_SKU, "sku").strip()
|
||||
meta = sku_meta.get(sku)
|
||||
if meta:
|
||||
sg, tit, shop = meta
|
||||
prefix = (
|
||||
f"【细类：{sg}｜SKU：{sku}｜品名：{_md_cell(tit, 60)}｜"
|
||||
f"店铺：{_md_cell(shop, 28)}】"
|
||||
)
|
||||
snippets.append(prefix + txt[:300])
|
||||
else:
|
||||
snippets.append(
|
||||
f"【细类：{gname}｜SKU：{sku or '—'}】" + txt[:320]
|
||||
)
|
||||
if len(snippets) >= 16:
|
||||
break
|
||||
eff = tu[:28]
|
||||
if len(tu) > 28:
|
||||
eff = list(eff) + [f"…共 {len(tu)} 条有效文本，此处截断"]
|
||||
out.append(
|
||||
{
|
||||
"group": gname,
|
||||
"comment_flat_rows": f"评价行 {len(cr)}；有效文本单元 {len(tu)}",
|
||||
"effective_text_lines": eff,
|
||||
"focus_hit_lines": focus_hit_lines,
|
||||
"sample_text_snippets": snippets,
|
||||
}
|
||||
)
|
||||
return out
|
||||
|
||||
|
||||
def build_scenario_groups_llm_payload(
|
||||
*,
|
||||
feedback_groups: list[tuple[str, list[dict[str, str]], list[str]]],
|
||||
scenario_groups: tuple[tuple[str, tuple[str, ...]], ...],
|
||||
merged_rows: list[dict[str, str]],
|
||||
sku_header: str,
|
||||
title_h: str,
|
||||
) -> dict[str, Any]:
|
||||
"""供 ``generate_scenario_group_summaries_llm``；计数与 §8.3 图右栏（场景）一致。"""
|
||||
if not feedback_groups:
|
||||
return {}
|
||||
sku_meta: dict[str, tuple[str, str, str]] = {}
|
||||
for row in merged_rows:
|
||||
sku = _cell(row, sku_header).strip()
|
||||
if not sku:
|
||||
continue
|
||||
gk = _competitor_matrix_group_key(row)
|
||||
if not gk:
|
||||
continue
|
||||
sku_meta[sku] = (
|
||||
gk,
|
||||
_cell(row, title_h),
|
||||
_cell(row, *_MERGED_SHOP_CELL_KEYS),
|
||||
)
|
||||
lexicon = [
|
||||
{"label": lbl, "trigger_examples": list(trigs[:12])}
|
||||
for lbl, trigs in scenario_groups
|
||||
]
|
||||
groups_out: list[dict[str, Any]] = []
|
||||
for gname, cr, tu in feedback_groups:
|
||||
if not tu and not cr:
|
||||
continue
|
||||
scen_g, scen_ng = _comment_scenario_counts(tu, scenario_groups)
|
||||
dist: list[dict[str, Any]] = []
|
||||
for lbl, n in scen_g.most_common():
|
||||
if n <= 0:
|
||||
continue
|
||||
dist.append(
|
||||
{
|
||||
"scenario": lbl,
|
||||
"mention_rows": int(n),
|
||||
"share_of_effective_texts": round(
|
||||
float(n) / float(scen_ng), 4
|
||||
)
|
||||
if scen_ng > 0
|
||||
else 0.0,
|
||||
}
|
||||
)
|
||||
snippets: list[str] = []
|
||||
for row in cr:
|
||||
txt = _cell(row, _COMMENT_CSV_BODY, "tagCommentContent")
|
||||
if not txt:
|
||||
continue
|
||||
if not _text_hits_scenario_triggers(txt, scenario_groups):
|
||||
continue
|
||||
sku = _cell(row, _COMMENT_CSV_SKU, "sku").strip()
|
||||
meta = sku_meta.get(sku)
|
||||
if meta:
|
||||
sg, tit, shop = meta
|
||||
prefix = (
|
||||
f"【细类：{sg}\uff5cSKU：{sku}\uff5c品名：{_md_cell(tit, 60)}\uff5c"
|
||||
f"店铺：{_md_cell(shop, 28)}】"
|
||||
)
|
||||
snippets.append(prefix + txt[:300])
|
||||
else:
|
||||
snippets.append(
|
||||
f"【细类：{gname}\uff5cSKU：{sku or '—'}】" + txt[:320]
|
||||
)
|
||||
if len(snippets) >= 16:
|
||||
break
|
||||
if len(snippets) < 5:
|
||||
for row in cr:
|
||||
txt = _cell(row, _COMMENT_CSV_BODY, "tagCommentContent")
|
||||
if not txt:
|
||||
continue
|
||||
sku = _cell(row, _COMMENT_CSV_SKU, "sku").strip()
|
||||
meta = sku_meta.get(sku)
|
||||
if meta:
|
||||
sg, tit, shop = meta
|
||||
prefix = (
|
||||
f"【细类：{sg}\uff5cSKU：{sku}\uff5c品名：{_md_cell(tit, 60)}\uff5c"
|
||||
f"店铺：{_md_cell(shop, 28)}】"
|
||||
)
|
||||
snippets.append(prefix + txt[:260])
|
||||
else:
|
||||
snippets.append(
|
||||
f"【细类：{gname}\uff5cSKU：{sku or '—'}】" + txt[:280]
|
||||
)
|
||||
if len(snippets) >= 10:
|
||||
break
|
||||
groups_out.append(
|
||||
{
|
||||
"group": gname,
|
||||
"effective_text_count": int(scen_ng),
|
||||
"scenario_distribution": dist[:18],
|
||||
"sample_text_snippets": snippets,
|
||||
}
|
||||
)
|
||||
if not groups_out:
|
||||
return {}
|
||||
return {"scenario_lexicon": lexicon, "groups": groups_out}
|
||||
|
||||
def _mermaid_pie_focus_keywords(hits: Counter[str], *, top_k: int = 8) -> str:
|
||||
"""关注词全局 Top 的 Mermaid pie（便于渲染或导出工具识别）。"""
|
||||
@ -455,29 +163,6 @@ def _mermaid_pie_focus_keywords(hits: Counter[str], *, top_k: int = 8) -> str:
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
def _comment_scenario_counts(
|
||||
texts: list[str],
|
||||
scenario_groups: tuple[tuple[str, tuple[str, ...]], ...],
|
||||
) -> tuple[Counter[str], int]:
|
||||
"""每组统计「至少命中一个触发词」的条数。返回 (各组条数, 有效文本条数)。"""
|
||||
c: Counter[str] = Counter()
|
||||
n = len(texts)
|
||||
for blob in texts:
|
||||
for label, triggers in scenario_groups:
|
||||
if any(t in blob for t in triggers):
|
||||
c[label] += 1
|
||||
return c, n
|
||||
|
||||
|
||||
def _text_hits_scenario_triggers(
|
||||
text: str,
|
||||
scenario_groups: tuple[tuple[str, tuple[str, ...]], ...],
|
||||
) -> bool:
|
||||
blob = text or ""
|
||||
for _lbl, triggers in scenario_groups:
|
||||
if any(t in blob for t in triggers):
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
def _scenario_summary_bullets(counter: Counter[str], n_texts: int, top_k: int = 5) -> list[str]:
|
||||
@ -540,26 +225,6 @@ def _comment_text_units_for_matrix_group(
|
||||
return texts
|
||||
|
||||
|
||||
def _group_keyword_hits(
|
||||
comment_rows_in_group: list[dict[str, str]],
|
||||
texts_fallback: list[str],
|
||||
*,
|
||||
focus_words: tuple[str, ...],
|
||||
) -> Counter[str]:
|
||||
h = _comment_keyword_hits(comment_rows_in_group, focus_words)
|
||||
if h:
|
||||
return h
|
||||
if not texts_fallback:
|
||||
return Counter()
|
||||
blob = "\n".join(texts_fallback)
|
||||
c: Counter[str] = Counter()
|
||||
for w in focus_words:
|
||||
if len(w) < 2:
|
||||
continue
|
||||
n = blob.count(w)
|
||||
if n:
|
||||
c[w] += n
|
||||
return c
|
||||
|
||||
|
||||
def _consumer_feedback_by_matrix_group(
|
||||
@ -741,28 +406,6 @@ def _counter_mix_top_rows_with_remainder(
|
||||
return out
|
||||
|
||||
|
||||
def _price_stats_extended(prices: list[float]) -> dict[str, Any]:
|
||||
if not prices:
|
||||
return {}
|
||||
out: dict[str, Any] = {
|
||||
"min": min(prices),
|
||||
"max": max(prices),
|
||||
"mean": statistics.mean(prices),
|
||||
"n": len(prices),
|
||||
}
|
||||
if len(prices) >= 2:
|
||||
out["stdev"] = statistics.stdev(prices)
|
||||
if len(prices) >= 2:
|
||||
out["median"] = statistics.median(prices)
|
||||
if len(prices) >= 4:
|
||||
s = sorted(prices)
|
||||
n = len(s)
|
||||
mid = n // 2
|
||||
lower = s[:mid] if n % 2 else s[:mid]
|
||||
upper = s[mid + 1 :] if n % 2 else s[mid:]
|
||||
out["q1"] = statistics.median(lower) if lower else s[0]
|
||||
out["q3"] = statistics.median(upper) if upper else s[-1]
|
||||
return out
|
||||
|
||||
|
||||
def _search_list_proxies(rows: list[dict[str, str]]) -> dict[str, Any]:
|
||||
@ -803,118 +446,6 @@ def _search_list_proxies(rows: list[dict[str, str]]) -> dict[str, Any]:
|
||||
}
|
||||
|
||||
|
||||
def _category_token_meaningless(seg: str) -> bool:
|
||||
"""纯数字类目 ID、空串或疑似内部编码的段，不宜直接作为矩阵分组展示名。"""
|
||||
t = (seg or "").strip()
|
||||
if not t:
|
||||
return True
|
||||
if t.isdigit():
|
||||
return True
|
||||
if len(t) >= 14 and re.fullmatch(r"[A-Za-z0-9_\-]+", t):
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
def _matrix_display_segment_from_parts(parts: list[str]) -> str | None:
|
||||
"""
|
||||
与历史逻辑一致的主选段；若该段无意义则自右向左找第一段可读文本
|
||||
（避免「仅类目码」或中间段为数字 ID 时，把路径误解析成无意义的细类展示名）。
|
||||
"""
|
||||
if not parts:
|
||||
return None
|
||||
if len(parts) >= 4:
|
||||
preferred = parts[-2]
|
||||
elif len(parts) >= 3:
|
||||
preferred = parts[1]
|
||||
elif len(parts) >= 2:
|
||||
preferred = parts[1]
|
||||
else:
|
||||
preferred = parts[0]
|
||||
order: list[str] = []
|
||||
if preferred:
|
||||
order.append(preferred)
|
||||
if len(parts) >= 2:
|
||||
order.append(parts[-2])
|
||||
order.append(parts[-1])
|
||||
order.extend(reversed(parts))
|
||||
seen: set[str] = set()
|
||||
for cand in order:
|
||||
if not cand or cand in seen:
|
||||
continue
|
||||
seen.add(cand)
|
||||
if not _category_token_meaningless(cand):
|
||||
return cand.strip()
|
||||
return None
|
||||
|
||||
|
||||
def _matrix_group_label_from_path(path: str) -> str:
|
||||
"""由 ``detail_category_path`` 文本解析细类展示名；空或无可读段则返回空串。"""
|
||||
t = (path or "").strip()
|
||||
if not t:
|
||||
return ""
|
||||
parts = [p.strip() for p in t.replace("＞", ">").split(">") if p.strip()]
|
||||
if not parts:
|
||||
return ""
|
||||
key = _matrix_display_segment_from_parts(parts)
|
||||
return (key[:80] if key else "")
|
||||
|
||||
|
||||
def _matrix_group_label_from_detail_path(row: dict[str, str]) -> str:
|
||||
return _matrix_group_label_from_path(_detail_category_path_cell(row))
|
||||
|
||||
|
||||
def _competitor_matrix_group_key(row: dict[str, str]) -> str:
|
||||
"""
|
||||
竞品矩阵分组：§5 / §8 / 统计图共用。
|
||||
**仅**依据 ``detail_category_path``；列为空或路径段均为无意义编码时不参与矩阵（返回空串）。
|
||||
"""
|
||||
return _matrix_group_label_from_detail_path(row)
|
||||
|
||||
|
||||
def _merged_rows_grouped_for_matrix(
|
||||
merged_rows: list[dict[str, str]],
|
||||
) -> list[tuple[str, list[dict[str, str]]]]:
|
||||
buckets: dict[str, list[dict[str, str]]] = {}
|
||||
for row in merged_rows:
|
||||
k = _competitor_matrix_group_key(row)
|
||||
if not k:
|
||||
continue
|
||||
buckets.setdefault(k, []).append(row)
|
||||
|
||||
def sort_key(item: tuple[str, list[dict[str, str]]]) -> tuple[int, int, str]:
|
||||
name, rows = item
|
||||
miss = name.startswith("未归类")
|
||||
return (1 if miss else 0, -len(rows), name)
|
||||
|
||||
return sorted(buckets.items(), key=sort_key)
|
||||
|
||||
|
||||
def _category_mix(
|
||||
rows: list[dict[str, str]], *, top_k: int = 12
|
||||
) -> list[tuple[str, int]]:
|
||||
"""
|
||||
按「可读细类标签」统计 SKU 分布（与 §5 ``_competitor_matrix_group_key`` 同源）；
|
||||
仅含 ``detail_category_path`` 可解析为展示名的行。
|
||||
|
||||
返回 ``most_common(top_k)``，并将未列入 Top K 的款数合并为「（其余细类）」，
|
||||
使各块 SKU 数之和等于有效矩阵 SKU 总数（与扇形图、简报 ``category_mix_top`` 一致）。
|
||||
"""
|
||||
labels: list[str] = []
|
||||
for r in rows:
|
||||
k = _matrix_group_label_from_detail_path(r)
|
||||
if k:
|
||||
labels.append(k)
|
||||
if not labels:
|
||||
return []
|
||||
c = Counter(labels)
|
||||
common = c.most_common(top_k)
|
||||
accounted = sum(v for _, v in common)
|
||||
total = sum(c.values())
|
||||
rest = total - accounted
|
||||
out: list[tuple[str, int]] = list(common)
|
||||
if rest > 0:
|
||||
out.append(("（其余细类）", rest))
|
||||
return out
|
||||
|
||||
|
||||
def _structure_shops(rows: list[dict[str, str]], *, list_export: bool) -> list[str]:
|
||||
@ -946,35 +477,6 @@ def _structure_brands(rows: list[dict[str, str]], *, list_export: bool) -> list[
|
||||
]
|
||||
|
||||
|
||||
def _is_ingredient_url_blob(s: str) -> bool:
|
||||
"""详情主图 URL 串（分号分隔）或单列以 http 开头。"""
|
||||
t = (s or "").strip()
|
||||
if not t:
|
||||
return False
|
||||
if t.startswith(("http://", "https://")):
|
||||
return True
|
||||
head = t[:400]
|
||||
if ("https://" in head or "http://" in head) and (
|
||||
";" in t or len(t) > 180 or t.count("http") >= 2
|
||||
):
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
def _ingredients_from_product_attributes(attrs: str) -> str:
|
||||
m = re.search(r"配料(?:表)?[:：]\s*([^;；]+)", attrs or "")
|
||||
return m.group(1).strip() if m else ""
|
||||
|
||||
|
||||
def _ingredients_single_line(s: str) -> str:
|
||||
"""与 ``AI_crawler.normalize_ingredients_text_for_csv`` 一致：多行配料压成一行（行间 ``；``），便于表格/CSV。"""
|
||||
t = (s or "").replace("\r\n", "\n").replace("\r", "\n").strip()
|
||||
if not t:
|
||||
return ""
|
||||
lines = [ln.strip() for ln in t.split("\n") if ln.strip()]
|
||||
if len(lines) <= 1:
|
||||
return lines[0] if lines else ""
|
||||
return "；".join(lines)
|
||||
|
||||
|
||||
def _matrix_ingredients_cell(row: dict[str, str], *, max_len: int = 420) -> str:
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
"""
|
||||
与 ``jd_competitor_report._matrix_group_label_from_path`` 同源：
|
||||
与 ``competitor_report.matrix_group`` / 历史 ``jd_competitor_report`` 中路径解析逻辑同源：
|
||||
从商详 ``detail_category_path`` 解析 §5 竞品矩阵用的类目展示名（如饼干、米）。
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user