mirror of
https://github.com/primedigitaltech/market-assistant.git
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1deea3af35
Add suggest_scenario_groups_llm: single call with excerpt corpus + existing label/triggers, parse scenarios JSON, merge into comment_scenario_groups (up to 40) before build_competitor_brief. Record in keyword_suggest_llm.json (schema_version 3). Skip with MA_SKIP_LLM_SCENARIO_SUGGEST. Update tests and analysis view hint. Made-with: Cursor
286 lines
9.7 KiB
Python
286 lines
9.7 KiB
Python
"""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）。"""
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from __future__ import annotations
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import json
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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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MAX_CHUNK_CHARS = 24_000
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MAX_CHUNKS = 12
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# 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
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SCENARIO_CORPUS_MAX_CHARS = 18_000
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_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）。
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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）。
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硬性规则：
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- 仅输出一段 JSON：{"phrases": ["短语1", ...]}，短语共 6～20 条。
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- 不要医疗功效、治愈、降血糖承诺；不要完整复制整句评价。
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- 不要输出与常见停用词无信息量的单字。
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- 不要输出 JSON 以外的文字。"""
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def _ensure_ai_crawler_path() -> None:
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root = Path(settings.CRAWLER_JD_ROOT).resolve()
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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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rs = str(root)
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if rs not in sys.path:
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sys.path.insert(0, rs)
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def _call_llm(system_prompt: str, user_prompt: str) -> str:
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_ensure_ai_crawler_path()
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import AI_crawler as ac # noqa: WPS433
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return ac.chat_completion_text(
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system_prompt=system_prompt,
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user_prompt=user_prompt,
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)
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def _chunk_comment_texts(texts: list[str]) -> list[str]:
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"""将全量评价划为若干段，控制单段字符量与最大段数。"""
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parts: list[str] = []
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cur: list[str] = []
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cur_len = 0
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for t in texts:
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s = (t or "").strip()
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if not s:
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continue
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extra = len(s) + 1
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if cur and cur_len + extra > MAX_CHUNK_CHARS:
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parts.append("\n".join(cur))
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cur = []
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cur_len = 0
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cur.append(s)
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cur_len += extra
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if cur:
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parts.append("\n".join(cur))
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if not parts:
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return []
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if len(parts) <= MAX_CHUNKS:
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return parts
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idxs = sorted(
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{min(int(i * len(parts) / MAX_CHUNKS), len(parts) - 1) for i in range(MAX_CHUNKS)}
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)
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return [parts[i] for i in idxs]
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def _parse_phrases_object(raw: str) -> list[str]:
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t = raw.strip()
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t = re.sub(r"^```(?:json)?\s*", "", t)
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t = re.sub(r"\s*```$", "", t)
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try:
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obj = json.loads(t)
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if isinstance(obj, dict) and isinstance(obj.get("phrases"), list):
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return [str(x).strip() for x in obj["phrases"] if str(x).strip()]
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except json.JSONDecodeError:
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pass
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m = re.search(r"\{[\s\S]*\}", t)
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if m:
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try:
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obj = json.loads(m.group(0))
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if isinstance(obj, dict) and isinstance(obj.get("phrases"), list):
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return [str(x).strip() for x in obj["phrases"] if str(x).strip()]
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except json.JSONDecodeError:
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pass
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return []
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_SCENARIO_SYSTEM = """你是电商用户研究助手。输入 JSON 含：
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- ``keyword``：监测词；
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- ``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）。
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- ``excerpts``：多条用户评价正文摘录（已截断拼接）。
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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）。
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硬性规则：
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- **仅输出**一段 JSON：``{"scenarios": [{"label": "展示名", "triggers": ["子串1", "子串2", ...]}, ...]}``；
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- 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；
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- 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；
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- 不要输出 JSON 以外的文字。"""
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def _sample_corpus_for_scenarios(texts: list[str], *, max_chars: int) -> str:
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"""取评价正文前部拼接至 max_chars，供单次场景联想。"""
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parts: list[str] = []
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n = 0
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for t in texts:
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s = (t or "").strip()
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if not s:
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continue
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extra = len(s) + 1
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if n + extra > max_chars:
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remain = max_chars - n - 1
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if remain > 40:
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parts.append(s[:remain])
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break
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parts.append(s)
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n += extra
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return "\n".join(parts)
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def _parse_scenarios_object(raw: str) -> list[dict[str, Any]]:
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t = (raw or "").strip()
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t = re.sub(r"^```(?:json)?\s*", "", t, flags=re.IGNORECASE)
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t = re.sub(r"\s*```$", "", t)
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try:
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obj = json.loads(t)
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except json.JSONDecodeError:
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obj = None
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if not isinstance(obj, dict):
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m = re.search(r"\{[\s\S]*\}", t)
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if not m:
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return []
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try:
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obj = json.loads(m.group(0))
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except json.JSONDecodeError:
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return []
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arr = obj.get("scenarios")
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if not isinstance(arr, list):
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return []
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out: list[dict[str, Any]] = []
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for item in arr:
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if not isinstance(item, dict):
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continue
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label = str(item.get("label") or "").strip()[:80]
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tr_raw = item.get("triggers")
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triggers: list[str] = []
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if isinstance(tr_raw, list):
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seen_t: set[str] = set()
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for x in tr_raw[:24]:
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s = str(x).strip()
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if len(s) < 2 or len(s) > 24:
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continue
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if s in seen_t:
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continue
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seen_t.add(s)
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triggers.append(s)
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if label and len(triggers) >= 1:
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out.append({"label": label, "triggers": triggers[:12]})
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return out
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def suggest_scenario_groups_llm(
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*,
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keyword: str,
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existing_groups: list[dict[str, Any]],
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all_comment_texts: list[str],
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) -> dict[str, Any]:
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"""
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单次调用模型，基于评价摘录扩展 ``comment_scenario_groups`` 形态的新组（label + triggers）。
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"""
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if not all_comment_texts:
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return {
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"suggested_scenario_groups": [],
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"scenario_rationale": "无评价正文可分析。",
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}
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existing_compact: list[dict[str, Any]] = []
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for g in (existing_groups or [])[:36]:
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if not isinstance(g, dict):
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continue
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lab = str(g.get("label") or "").strip()
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tr = g.get("triggers")
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ts: list[str] = []
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if isinstance(tr, list):
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for x in tr[:16]:
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s = str(x).strip()
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if s:
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ts.append(s[:48])
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if lab and ts:
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existing_compact.append({"label": lab[:80], "triggers": ts})
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excerpts = _sample_corpus_for_scenarios(
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all_comment_texts, max_chars=SCENARIO_CORPUS_MAX_CHARS
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)
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payload = {
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"keyword": keyword,
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"existing_scenarios": existing_compact,
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"excerpts": excerpts,
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}
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raw = _call_llm(_SCENARIO_SYSTEM, json.dumps(payload, ensure_ascii=False))
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scenarios = _parse_scenarios_object(raw)
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return {
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"suggested_scenario_groups": scenarios[:14],
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"scenario_rationale": (
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f"基于约 {len(excerpts)} 字评价摘录单次调用模型；"
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f"在 {len(existing_compact)} 组既有场景之外补充 {len(scenarios[:14])} 组候选。"
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),
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}
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def suggest_focus_keywords_from_all_comments(
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*,
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keyword: str,
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brief_slice: dict[str, Any],
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all_comment_texts: list[str],
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) -> dict[str, Any]:
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"""
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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``。
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"""
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if not all_comment_texts:
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return {
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"suggested_focus_keywords": [],
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"rationale": "无评价正文可分析。",
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"chunks_processed": 0,
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"total_comment_texts": 0,
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}
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existing: set[str] = set()
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for x in brief_slice.get("comment_focus_keywords") or []:
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if isinstance(x, dict):
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w = str(x.get("word") or "").strip()
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if w:
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existing.add(w)
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chunks = _chunk_comment_texts(all_comment_texts)
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collected: list[str] = []
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for i, ch in enumerate(chunks):
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payload = {
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"keyword": keyword,
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"excerpt_index": i + 1,
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"excerpts": ch,
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}
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raw = _call_llm(_CHUNK_SYSTEM, json.dumps(payload, ensure_ascii=False))
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collected.extend(_parse_phrases_object(raw))
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seen: set[str] = set()
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merged: list[str] = []
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for p in collected:
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t = p.strip()
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if len(t) < 2 or len(t) > 24:
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continue
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if t in seen or t in existing:
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continue
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seen.add(t)
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merged.append(t)
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out_kw = merged[:22]
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return {
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"suggested_focus_keywords": out_kw,
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"rationale": (
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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；"
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f"已排除与当前关注词统计表完全相同的词。"
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),
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"chunks_processed": len(chunks),
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"total_comment_texts": len(all_comment_texts),
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}
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# 兼容旧接口名（若仍有调用）
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def suggest_comment_keywords_llm(
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*,
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keyword: str,
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brief_slice: dict[str, Any],
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comment_samples: list[str],
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) -> dict[str, Any]:
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return suggest_focus_keywords_from_all_comments(
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keyword=keyword,
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brief_slice=brief_slice,
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all_comment_texts=list(comment_samples),
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)
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