"""在报告生成前：基于评价正文调用大模型，联想补充**关注词**与**使用场景触发组**（写入 effective_report_config）。""" from __future__ import annotations import json import re import sys from pathlib import Path from typing import Any from django.conf import settings MAX_CHUNK_CHARS = 24_000 MAX_CHUNKS = 12 # 场景联想单次送入模型的评价摘录上限（字符）；过大易顶上下文 SCENARIO_CORPUS_MAX_CHARS = 18_000 _CHUNK_SYSTEM = """你是电商评价挖掘助手。输入 JSON 含 keyword、excerpt_index、excerpts（一段用户评价正文合集）。 任务：从 excerpts 中抽取值得纳入「关注词/卖点监测」的**中文短语**（2～12 字为主，可为词组）。 硬性规则： - 仅输出一段 JSON：{"phrases": ["短语1", ...]}，短语共 6～20 条。 - 不要医疗功效、治愈、降血糖承诺；不要完整复制整句评价。 - 不要输出与常见停用词无信息量的单字。 - 不要输出 JSON 以外的文字。""" def _ensure_ai_crawler_path() -> None: root = Path(settings.CRAWLER_JD_ROOT).resolve() if not root.is_dir(): raise FileNotFoundError(f"爬虫副本目录不存在: {root}") rs = str(root) if rs not in sys.path: sys.path.insert(0, rs) def _call_llm(system_prompt: str, user_prompt: str) -> str: _ensure_ai_crawler_path() import AI_crawler as ac # noqa: WPS433 return ac.chat_completion_text( system_prompt=system_prompt, user_prompt=user_prompt, ) def _chunk_comment_texts(texts: list[str]) -> list[str]: """将全量评价划为若干段，控制单段字符量与最大段数。""" parts: list[str] = [] cur: list[str] = [] cur_len = 0 for t in texts: s = (t or "").strip() if not s: continue extra = len(s) + 1 if cur and cur_len + extra > MAX_CHUNK_CHARS: parts.append("\n".join(cur)) cur = [] cur_len = 0 cur.append(s) cur_len += extra if cur: parts.append("\n".join(cur)) if not parts: return [] if len(parts) <= MAX_CHUNKS: return parts idxs = sorted( {min(int(i * len(parts) / MAX_CHUNKS), len(parts) - 1) for i in range(MAX_CHUNKS)} ) return [parts[i] for i in idxs] def _parse_phrases_object(raw: str) -> list[str]: t = raw.strip() t = re.sub(r"^```(?:json)?\s*", "", t) t = re.sub(r"\s*```$", "", t) try: obj = json.loads(t) if isinstance(obj, dict) and isinstance(obj.get("phrases"), list): return [str(x).strip() for x in obj["phrases"] if str(x).strip()] except json.JSONDecodeError: pass m = re.search(r"\{[\s\S]*\}", t) if m: try: obj = json.loads(m.group(0)) if isinstance(obj, dict) and isinstance(obj.get("phrases"), list): return [str(x).strip() for x in obj["phrases"] if str(x).strip()] except json.JSONDecodeError: pass return [] _SCENARIO_SYSTEM = """你是电商用户研究助手。输入 JSON 含： - ``keyword``：监测词； - ``existing_scenarios``：数组，每项为 ``{"label": "展示名", "triggers": ["子串1", ...]}``。统计时若评价正文**包含任一 trigger 子串**，则计入该 label（与宿主系统规则一致）。 - ``excerpts``：多条用户评价正文摘录（已截断拼接）。 任务：在**不重复** ``existing_scenarios`` 中已有 ``label``（逐字比较，勿改写字）的前提下，从 excerpts 归纳 **4～12 条**新的「用途/场景」监测组，覆盖评论里**明显出现但未被现有组覆盖**的消费情境（如「下午茶」「露营」「宿舍」等，须确有文本依据）。 硬性规则： - **仅输出**一段 JSON：``{"scenarios": [{"label": "展示名", "triggers": ["子串1", "子串2", ...]}, ...]}``； - 每条 ``label`` 2～16 字；每组 ``triggers`` 3～10 条，每条 trigger 为 **2～12 字中文**子串，用于**子串命中**计数； - 不要医疗功效、治愈、降血糖承诺；不要与 existing 的 label 同名或仅差空格； - 不要输出 JSON 以外的文字。""" def _sample_corpus_for_scenarios(texts: list[str], *, max_chars: int) -> str: """取评价正文前部拼接至 max_chars，供单次场景联想。""" parts: list[str] = [] n = 0 for t in texts: s = (t or "").strip() if not s: continue extra = len(s) + 1 if n + extra > max_chars: remain = max_chars - n - 1 if remain > 40: parts.append(s[:remain]) break parts.append(s) n += extra return "\n".join(parts) def _parse_scenarios_object(raw: str) -> list[dict[str, Any]]: t = (raw or "").strip() t = re.sub(r"^```(?:json)?\s*", "", t, flags=re.IGNORECASE) t = re.sub(r"\s*```$", "", t) try: obj = json.loads(t) except json.JSONDecodeError: obj = None if not isinstance(obj, dict): m = re.search(r"\{[\s\S]*\}", t) if not m: return [] try: obj = json.loads(m.group(0)) except json.JSONDecodeError: return [] arr = obj.get("scenarios") if not isinstance(arr, list): return [] out: list[dict[str, Any]] = [] for item in arr: if not isinstance(item, dict): continue label = str(item.get("label") or "").strip()[:80] tr_raw = item.get("triggers") triggers: list[str] = [] if isinstance(tr_raw, list): seen_t: set[str] = set() for x in tr_raw[:24]: s = str(x).strip() if len(s) < 2 or len(s) > 24: continue if s in seen_t: continue seen_t.add(s) triggers.append(s) if label and len(triggers) >= 1: out.append({"label": label, "triggers": triggers[:12]}) return out def suggest_scenario_groups_llm( *, keyword: str, existing_groups: list[dict[str, Any]], all_comment_texts: list[str], ) -> dict[str, Any]: """ 单次调用模型，基于评价摘录扩展 ``comment_scenario_groups`` 形态的新组（label + triggers）。 """ if not all_comment_texts: return { "suggested_scenario_groups": [], "scenario_rationale": "无评价正文可分析。", } existing_compact: list[dict[str, Any]] = [] for g in (existing_groups or [])[:36]: if not isinstance(g, dict): continue lab = str(g.get("label") or "").strip() tr = g.get("triggers") ts: list[str] = [] if isinstance(tr, list): for x in tr[:16]: s = str(x).strip() if s: ts.append(s[:48]) if lab and ts: existing_compact.append({"label": lab[:80], "triggers": ts}) excerpts = _sample_corpus_for_scenarios( all_comment_texts, max_chars=SCENARIO_CORPUS_MAX_CHARS ) payload = { "keyword": keyword, "existing_scenarios": existing_compact, "excerpts": excerpts, } raw = _call_llm(_SCENARIO_SYSTEM, json.dumps(payload, ensure_ascii=False)) scenarios = _parse_scenarios_object(raw) return { "suggested_scenario_groups": scenarios[:14], "scenario_rationale": ( f"基于约 {len(excerpts)} 字评价摘录单次调用模型；" f"在 {len(existing_compact)} 组既有场景之外补充 {len(scenarios[:14])} 组候选。" ), } def suggest_focus_keywords_from_all_comments( *, keyword: str, brief_slice: dict[str, Any], all_comment_texts: list[str], ) -> dict[str, Any]: """ 读取全量评价（在服务端分块），每块调用模型抽取短语，合并去重后返回 ``suggested_focus_keywords``。 """ if not all_comment_texts: return { "suggested_focus_keywords": [], "rationale": "无评价正文可分析。", "chunks_processed": 0, "total_comment_texts": 0, } existing: set[str] = set() for x in brief_slice.get("comment_focus_keywords") or []: if isinstance(x, dict): w = str(x.get("word") or "").strip() if w: existing.add(w) chunks = _chunk_comment_texts(all_comment_texts) collected: list[str] = [] for i, ch in enumerate(chunks): payload = { "keyword": keyword, "excerpt_index": i + 1, "excerpts": ch, } raw = _call_llm(_CHUNK_SYSTEM, json.dumps(payload, ensure_ascii=False)) collected.extend(_parse_phrases_object(raw)) seen: set[str] = set() merged: list[str] = [] for p in collected: t = p.strip() if len(t) < 2 or len(t) > 24: continue if t in seen or t in existing: continue seen.add(t) merged.append(t) out_kw = merged[:22] return { "suggested_focus_keywords": out_kw, "rationale": ( f"基于全量 {len(all_comment_texts)} 条评价文本，分 {len(chunks)} 段调用模型抽取短语并去重；" f"已排除与当前关注词统计表完全相同的词。" ), "chunks_processed": len(chunks), "total_comment_texts": len(all_comment_texts), } # 兼容旧接口名（若仍有调用） def suggest_comment_keywords_llm( *, keyword: str, brief_slice: dict[str, Any], comment_samples: list[str], ) -> dict[str, Any]: return suggest_focus_keywords_from_all_comments( keyword=keyword, brief_slice=brief_slice, all_comment_texts=list(comment_samples), )