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https://github.com/primedigitaltech/market-assistant.git
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fix(strategy): 探针模式下从 structured_brief 移除按细类关注词/场景子串
补充 _omit_ch8_probe_wordchart_fields：删除 consumer_feedback 内 focus_keyword_hits 与 scenarios_top；与文本挖掘主口径一致。导出脚本与 omission 说明同步；更新全量输入快照。 Made-with: Cursor
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269
backend/pipeline/demos/dump_strategy_llm_input_md.py
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269
backend/pipeline/demos/dump_strategy_llm_input_md.py
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@ -0,0 +1,269 @@
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"""
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导出与「策略大模型润色」一次调用一致的完整输入，生成 Markdown（供对照）。
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用法（在项目 backend 目录）::
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python -m pipeline.demos.dump_strategy_llm_input_md [--job-id 12] [--matrix-index 0]
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若未指定 --matrix-index，则默认收窄到第一个矩阵分组（与「选第一个细类」等效）。
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"""
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from __future__ import annotations
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import argparse
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import json
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import os
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import sys
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from pathlib import Path
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# Django
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os.environ.setdefault("DJANGO_SETTINGS_MODULE", "market_assistant.settings")
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def _strategy_decisions_empty() -> dict:
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return {
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"product_role": "",
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"time_horizon": "",
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"success_criteria": "",
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"non_goals": "",
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"battlefield_one_line": "",
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"positioning_choice": "",
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"competitive_stance": "",
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"pillar_product": "",
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"pillar_price": "",
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"pillar_channel": "",
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"pillar_comm": "",
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"audience_segment": "",
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"competitor_reference": "",
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"resource_notes": "",
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"marketing_strategy": "",
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"general_strategy": "",
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"ack_risk_keywords": False,
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"ack_risk_price": False,
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"ack_risk_concentration": False,
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}
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def main() -> int:
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import django
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django.setup()
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from django.utils import timezone
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from pipeline.jd.runner import build_competitor_brief_for_job
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from pipeline.llm.generate_strategy import (
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STRATEGY_SYSTEM,
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STRATEGY_USER_PREFIX,
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_omit_ch8_probe_wordchart_fields,
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_truncate_strategy_narrative,
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)
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from pipeline.models import JobStatus, PipelineJob
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from pipeline.reporting.brief_compact import compact_brief_for_llm
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from pipeline.reporting.brief_strategy_scope import (
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filter_brief_for_strategy_matrix_group,
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list_matrix_groups_for_api,
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)
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from pipeline.reporting.report_matrix_group_evidence import (
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load_report_matrix_group_evidence_markdown,
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)
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from pipeline.reporting.report_strategy_excerpt import load_report_strategy_excerpt
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from pipeline.reporting.strategy_draft import (
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build_strategy_draft_markdown,
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filter_strategy_hints_for_ch8_probe,
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report_uses_chapter8_text_mining_probe,
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)
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p = argparse.ArgumentParser()
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p.add_argument("--job-id", type=int, default=None)
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p.add_argument(
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"--matrix-index",
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type=int,
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default=0,
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help="矩阵分组下标；设为 -1 表示不收窄（全部分类）",
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)
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p.add_argument(
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"--no-scope",
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action="store_true",
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help="与 --matrix-index -1 相同：不收窄 brief、不抽细类报告节选",
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)
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p.add_argument(
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"-o",
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"--output",
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type=str,
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default=None,
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help="输出 .md 路径（默认：docs/planning/策略生成-LLM全量输入快照.md）",
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)
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args = p.parse_args()
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backend_dir = Path(__file__).resolve().parents[2]
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repo_root = backend_dir.parent
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default_out = (
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repo_root / "docs" / "planning" / "策略生成-LLM全量输入快照.md"
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)
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out_path = Path(args.output) if args.output else default_out
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job_id = args.job_id
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if job_id:
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job = PipelineJob.objects.filter(pk=job_id).first()
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else:
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job = (
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PipelineJob.objects.filter(status=JobStatus.SUCCESS)
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.exclude(run_dir="")
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.order_by("-id")
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.first()
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)
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if not job:
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print("无可用成功任务（需 run_dir 非空）", file=sys.stderr)
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return 1
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rc = job.report_config if isinstance(job.report_config, dict) else None
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brief = build_competitor_brief_for_job(
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job.run_dir,
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job.keyword,
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report_config=rc,
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)
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matrix_groups = list_matrix_groups_for_api(brief)
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group_names = [g.get("group") for g in matrix_groups if isinstance(g, dict)]
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scoped_label = ""
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matrix_index: int | None = args.matrix_index
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if args.no_scope:
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matrix_index = -1
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if matrix_index is not None and matrix_index >= 0:
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mg = brief.get("matrix_by_group")
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if isinstance(mg, list) and matrix_index < len(mg):
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scoped_label = (mg[matrix_index].get("group") or "").strip()
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brief = filter_brief_for_strategy_matrix_group(
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brief, matrix_group_index=matrix_index
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)
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else:
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print(f"matrix_index {matrix_index} 超出范围", file=sys.stderr)
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return 1
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gen_at = timezone.now().isoformat()
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sd = _strategy_decisions_empty()
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rules_md = build_strategy_draft_markdown(
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job_id=job.id,
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keyword=job.keyword,
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brief=brief,
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business_notes="",
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generated_at_iso=gen_at,
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strategy_decisions=sd,
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report_config=rc,
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)
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excerpt_raw, excerpt_src = load_report_strategy_excerpt(job.run_dir)
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excerpt_raw = (excerpt_raw or "").strip()
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evidence_md = ""
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evidence_src = "none"
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if scoped_label:
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evidence_md, evidence_src = load_report_matrix_group_evidence_markdown(
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job.run_dir,
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scoped_label,
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)
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compact_max = 80_000
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excerpt_max = 24_000
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compact = compact_brief_for_llm(brief, max_chars=compact_max)
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if report_uses_chapter8_text_mining_probe(rc):
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compact = dict(compact)
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_omit_ch8_probe_wordchart_fields(compact)
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if isinstance(compact.get("strategy_hints"), list):
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compact["strategy_hints"] = filter_strategy_hints_for_ch8_probe(
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compact["strategy_hints"]
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)
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ex = (
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_truncate_strategy_narrative(excerpt_raw, excerpt_max)
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if excerpt_raw
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else ""
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)
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ev_max = min(24_000, max(3_000, excerpt_max + excerpt_max // 2))
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gm = (
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_truncate_strategy_narrative(evidence_md.strip(), ev_max)
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if evidence_md
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else ""
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)
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payload: dict = {
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"job_id": job.id,
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"keyword": job.keyword,
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"generated_at_iso": gen_at,
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"strategy_decisions": sd,
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"business_notes": "",
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"structured_brief": compact,
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"rules_draft_markdown": rules_md,
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"report_strategy_excerpt": ex,
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"report_matrix_group_evidence_md": gm,
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"chapter8_text_mining_probe": bool(
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report_uses_chapter8_text_mining_probe(rc)
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),
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}
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if report_uses_chapter8_text_mining_probe(rc):
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payload["structured_brief_omission_note"] = (
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"已启用第八章文本挖掘（探针为主）：structured_brief 已省略顶层「关注词/场景子串计数」、按细类 feedback 中的 focus_keyword_hits/scenarios_top，"
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"以及「与条形图同源的 strategy_hints 句子」，避免与报告 §8 主口径冲突。**不得**再以这类子串计数或预设场景占比作为论据。"
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"用户与评论侧须依报告 §8 文本挖掘归纳及 `report_matrix_group_evidence_md`；**促销、满减、券价差**须与报告第六章、`price_promotion_signals` 及下方 `report_strategy_excerpt`（第九章）对齐，不得省略报告已写明的活动建议。"
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)
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user_body = STRATEGY_USER_PREFIX + json.dumps(payload, ensure_ascii=False)
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full_chars = len(STRATEGY_SYSTEM) + len(user_body)
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out_path.parent.mkdir(parents=True, exist_ok=True)
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lines: list[str] = [
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"# 策略生成 · 大模型一次调用的「全量输入」快照",
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"",
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"> **生成方式**：本机 `pipeline.demos.dump_strategy_llm_input_md` 按与 "
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"`generate_strategy_draft_markdown_llm` 相同的 payload 组装逻辑导出。",
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"> **与线上一致性**：与真实接口相比，表单字段此处均为空默认；"
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"你只要把当时提交的 `strategy_decisions` / `business_notes` 代入即与线上等价。",
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"",
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"## 快照元数据",
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"",
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f"- **任务 ID**：{job.id}",
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f"- **关键词**：{job.keyword}",
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f"- **run_dir**：`{job.run_dir}`",
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f"- **矩阵分组**：{matrix_index if matrix_index is not None and matrix_index >= 0 else '未收窄（全部分类）'}{f' → 「{scoped_label}」' if scoped_label else ''}",
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f"- **本任务可选细类（节选）**：{group_names[:20]}{'…' if len(group_names) > 20 else ''}",
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f"- **第九章节选来源**：{excerpt_src}，约 {len(ex)} 字符",
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f"- **细类报告节选来源**：{evidence_src}，约 {len(gm)} 字符",
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f"- **System 字符数**：{len(STRATEGY_SYSTEM)}",
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f"- **User 消息字符数**：{len(user_body)}",
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f"- **合计约**：{full_chars} 字符",
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"",
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"---",
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"",
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"## 1. System 提示词（完整 `STRATEGY_SYSTEM`）",
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"",
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"```text",
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STRATEGY_SYSTEM,
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"```",
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"",
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"---",
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"",
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"## 2. User 消息（完整：`STRATEGY_USER_PREFIX` + JSON）",
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"",
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"以下为网关 **user** 角色一次发送的完整字符串（前缀 + 单行 JSON）。",
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"",
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"```text",
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user_body,
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"```",
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"",
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"---",
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"",
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"## 3. 同上 JSON 的排版版（便于人眼查看 `structured_brief` 结构）",
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"",
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"说明：若与第 2 节有任何不一致，以第 2 节（真实入参）为准。",
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"",
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"```json",
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json.dumps(payload, ensure_ascii=False, indent=2),
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"```",
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"",
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]
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out_path.write_text("\n".join(lines), encoding="utf-8")
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print(f"Wrote {out_path} ({out_path.stat().st_size // 1024} KB)")
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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@ -13,6 +13,39 @@ from ..reporting.strategy_draft import (
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)
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from .llm_client import call_llm, estimate_chat_input_tokens, llm_context_window_size
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def _omit_ch8_probe_wordchart_fields(compact: dict[str, Any]) -> None:
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"""
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第八章文本挖掘（探针）为主时，去掉与**预设关注词/场景条形图**同源的统计字段，
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避免与报告 §8 文本挖掘主口径「两用数据」。
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仅影响传入大模型的 ``structured_brief``；``brief`` 全量仍可由规则稿使用。
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"""
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for k in (
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"comment_focus_keywords",
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"usage_scenarios",
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"usage_scenarios_denominator",
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"usage_scenarios_by_matrix_group",
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):
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compact.pop(k, None)
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cfb = compact.get("consumer_feedback_by_matrix_group")
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if not isinstance(cfb, list):
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return
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slim: list[Any] = []
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for g in cfb:
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if not isinstance(g, dict):
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slim.append(g)
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continue
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slim.append(
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{
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k: v
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for k, v in g.items()
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if k not in ("focus_keyword_hits", "scenarios_top")
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}
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)
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compact["consumer_feedback_by_matrix_group"] = slim
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STRATEGY_DATA_RULES = """**全局禁止编造（适用于输出全文各节、各表、各段；独立策略稿与报告第九章策略归纳**共用**本段，硬性）**：
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- **事实与数字**：销量、GMV、占比、价带、条数、份额、券面额、满减/满折门槛、到手价、店铺/品牌计数与排名、SKU 数、接口返回量等，**仅可**来自**本次调用输入 JSON** 中已给出的字段（策略稿为 `structured_brief`、`rules_draft_markdown` 内摘录、`report_strategy_excerpt`、可选 **`report_matrix_group_evidence_md`**（与同任务报告第五～第八章细类大模型小节同源）、`strategy_decisions`、`business_notes`；第九章嵌入为 `competitor_brief`、可选 `prior_chapter_llm_narratives`）；**禁止**凭空新增、改口径或写成「已监测证实」而无字段支撑。
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- **主体与名称**：**禁止**引入上述输入中**未出现**的**具体**品牌名、店铺名、SKU 名、商品标题作为**事实陈述**；若 `strategy_decisions`/备注/brief/节选已含则可写；否则用「头部/同类竞品」等泛称或「待业务指定对标」。
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@ -153,13 +186,7 @@ def generate_strategy_draft_markdown_llm(
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compact = compact_brief_for_llm(brief, max_chars=compact_max)
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if report_uses_chapter8_text_mining_probe(report_config):
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compact = dict(compact)
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for k in (
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"comment_focus_keywords",
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"usage_scenarios",
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"usage_scenarios_denominator",
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"usage_scenarios_by_matrix_group",
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):
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compact.pop(k, None)
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_omit_ch8_probe_wordchart_fields(compact)
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if isinstance(compact.get("strategy_hints"), list):
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compact["strategy_hints"] = filter_strategy_hints_for_ch8_probe(
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compact["strategy_hints"]
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@ -195,9 +222,9 @@ def generate_strategy_draft_markdown_llm(
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}
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if report_uses_chapter8_text_mining_probe(report_config):
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payload["structured_brief_omission_note"] = (
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"已启用第八章文本挖掘（探针为主）：structured_brief 已省略「关注词/场景子串计数」及「与条形图同源的 strategy_hints 句子」，"
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"避免与报告 §8 主口径冲突。**不得**再以词频或预设场景占比作为论据。"
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"用户与评论侧须依报告 §8 文本挖掘归纳；**促销、满减、券价差**须与报告第六章、`price_promotion_signals` 及下方 `report_strategy_excerpt`（第九章）对齐，不得省略报告已写明的活动建议。"
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"已启用第八章文本挖掘（探针为主）：structured_brief 已省略顶层「关注词/场景子串计数」、按细类 feedback 中的 focus_keyword_hits/scenarios_top，"
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"以及「与条形图同源的 strategy_hints 句子」，避免与报告 §8 主口径冲突。**不得**再以这类子串计数或预设场景占比作为论据。"
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"用户与评论侧须依报告 §8 文本挖掘归纳及 `report_matrix_group_evidence_md`；**促销、满减、券价差**须与报告第六章、`price_promotion_signals` 及下方 `report_strategy_excerpt`（第九章）对齐，不得省略报告已写明的活动建议。"
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)
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raw = json.dumps(payload, ensure_ascii=False)
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if len(raw) > 500_000:
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@ -3,6 +3,7 @@ from __future__ import annotations
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from django.test import SimpleTestCase
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from pipeline.llm.generate_strategy import _omit_ch8_probe_wordchart_fields
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from pipeline.reporting.strategy_draft import build_strategy_draft_markdown
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@ -163,3 +164,24 @@ class StrategyDraftTests(SimpleTestCase):
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self.assertIn("样本内品牌较分散", md)
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self.assertIn("price_promotion_signals", md)
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self.assertIn("price_promotion_signals", md)
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def test_ch8_probe_omit_wordchart_nested_in_consumer_feedback(self) -> None:
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compact = {
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"comment_focus_keywords": [{"word": "x", "count": 1}],
|
||||
"usage_scenarios": [],
|
||||
"consumer_feedback_by_matrix_group": [
|
||||
{
|
||||
"group": "饼干",
|
||||
"comment_rows": 10,
|
||||
"focus_keyword_hits": [{"word": "口感", "count": 5}],
|
||||
"scenarios_top": [{"scenario": "早餐", "count": 2}],
|
||||
}
|
||||
],
|
||||
}
|
||||
_omit_ch8_probe_wordchart_fields(compact)
|
||||
self.assertNotIn("comment_focus_keywords", compact)
|
||||
self.assertNotIn("focus_keyword_hits", compact["consumer_feedback_by_matrix_group"][0])
|
||||
self.assertNotIn("scenarios_top", compact["consumer_feedback_by_matrix_group"][0])
|
||||
self.assertEqual(
|
||||
compact["consumer_feedback_by_matrix_group"][0].get("comment_rows"), 10
|
||||
)
|
||||
|
||||
763
docs/planning/策略生成-LLM全量输入快照.md
Normal file
763
docs/planning/策略生成-LLM全量输入快照.md
Normal file
File diff suppressed because one or more lines are too long
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Reference in New Issue
Block a user