ff21116ff8
- constants: 新增 TEXT_NARRATIVE_BY_STRATEGY(A痛点/B场景/C成分),与图片侧同轴 - build_prompt: 加 strategy_narrative 参数并注入 prompt - text_variants: 全链路透传(含优化轮) - run_text_generation: 改循环三套,text_count均摊(divmod余前补),跨套去重,打_strategy标记 - TextCandidate: 加 strategy String(4) 字段 + 迁移021(已upgrade head) - packaging: 打包按strategy精准配对文图(texts_by_strategy映射+三层兜底) - SSE text_candidate 事件携带 strategy 独立agent交叉验证7改造点全过,边界(text_count<3/无别名/不截断)无must-fix Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
196 lines
9.6 KiB
Python
196 lines
9.6 KiB
Python
"""
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text_variants.py — 文案双轨主入口（≤100行）
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轨A: generate_text_variants — 调 LLM 出 N 角度 JSON
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轨B: text_import_handler — 导入外部文案进候选池
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prompt 组装/解析见 _text_prompt.py；评分/去重见 text_scoring.py
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"""
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from __future__ import annotations
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import asyncio
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import logging
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import os
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from typing import Any
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from .constants import MAX_OPTIMIZE_ROUNDS
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from ._text_prompt import COPY_SYSTEM, build_prompt, parse_json_array, build_local_drafts
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from .text_scoring import score_copy, dedupe_copies
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from .llm_scorer import llm_score_copy
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from .banned_word_checker import check_and_fix, build_entries_from_db, CheckResult
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logger = logging.getLogger(__name__)
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async def _call_llm(client: Any, prompt: str, max_tokens: int = 8192) -> str:
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"""统一 LLM 调用，client 由 worker 注入，隔离 key。
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G1坑修复：AIClients 没有 .chat.completions，正确方法是 .chat_complete()
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S8: 503/429 指数退避重试（最多3次，2^attempt 秒），其他异常直接降级返 ''。
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max_tokens 由调用方按批量缩放：opus 会尽量填满输出空间，8192 token 的生成
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单批 >60s 必撞 apiports 网关上限返 503(task46 实测每请求恰挂 ~61s)。实测单条
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max_tokens=1500~2500 仅 16~18s。故按条数动态收，墙钟压进 60s 网关窗口内。
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"""
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import httpx
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# 倩倩姐2026-06-13拍板"加大重试+拉长退避"：apiports负载波动时单条opus也会被
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# 拖过60s返503，短退避(1/2/4s)赶不开高负载窗口。故重试5次、退避拉长到最长30s，
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# 给中转站负载回落留时间。墙钟换稳定(MVP免费阶段可接受)。
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max_attempts = 5
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backoff = [5, 10, 20, 30] # 第1~4次重试前等待秒数，拉长跨过apiports高负载窗口
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for attempt in range(max_attempts):
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try:
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return await client.chat_complete(
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messages=[
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{"role": "system", "content": COPY_SYSTEM},
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{"role": "user", "content": prompt},
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],
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model=client._model,
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max_tokens=max_tokens,
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temperature=0.75,
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)
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except httpx.HTTPStatusError as exc:
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status = exc.response.status_code if exc.response is not None else 0
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if status in (503, 429) and attempt < max_attempts - 1:
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wait = backoff[min(attempt, len(backoff) - 1)]
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logger.warning(
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"LLM 返回 %s，第%d/%d次重试，等待 %ds: %s",
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status, attempt + 1, max_attempts - 1, wait, exc,
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)
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await asyncio.sleep(wait)
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continue
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logger.error("LLM HTTP错误(不可重试或已达上限): %s: %s", type(exc).__name__, exc)
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return ""
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except Exception as exc:
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# 其他异常（超时/网络断开等）不重试，直接降级
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logger.error("LLM 调用失败: %s: %s", type(exc).__name__, exc)
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return ""
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return ""
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# apiports 网关单次响应有 ~60s 上限，claude 一次生成 >4 条长文案会超时返 503。
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# 故分批：每批最多 4 条，串行调用合并。批大小可经 TEXT_BATCH_SIZE 调。
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TEXT_BATCH_SIZE = int(os.environ.get("TEXT_BATCH_SIZE", "4"))
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async def _generate_one_batch(llm_client: Any, product: dict, batch_n: int, extra: str,
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strategy_narrative: str = "") -> list[dict]:
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"""生成一批 batch_n 条，含解析重试（最多2次）。失败返回空列表。
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max_tokens 按条数缩放(每条约 1800 token，封顶 8192)，压进 apiports 60s 网关窗口。"""
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batch_max_tokens = min(8192, max(1800, batch_n * 1800))
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for attempt in range(2):
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raw = await _call_llm(llm_client, build_prompt(
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product, batch_n, extra_rules=extra, strategy_narrative=strategy_narrative,
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), batch_max_tokens)
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parsed = parse_json_array(raw)
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if parsed:
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return parsed
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logger.warning("文案批(%d条)第%d次解析失败%s", batch_n, attempt + 1,
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"，重试" if attempt == 0 else "，放弃本批")
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return []
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async def _generate_in_batches(llm_client: Any, product: dict, count: int, extra: str,
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strategy_narrative: str = "") -> list[dict]:
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"""把 count 条按 TEXT_BATCH_SIZE 分批，串行调用合并。
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串行而非并发：opus 单批就慢(~300s)且 apiports 限并发，多批 gather 会触发
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大面积 503 雪崩(task45 实测)。故改串行，墙钟换稳定。"""
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sizes: list[int] = []
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remaining = count
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while remaining > 0:
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n = min(TEXT_BATCH_SIZE, remaining)
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sizes.append(n)
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remaining -= n
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collected: list[dict] = []
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for n in sizes:
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r = await _generate_one_batch(llm_client, product, n, extra, strategy_narrative)
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collected.extend(r)
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return collected
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async def generate_text_variants(
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llm_client: Any,
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product: dict,
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count: int,
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previous_copies: list[dict] | None = None,
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banned_word_rows: list[dict] | None = None,
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flywheel_context: str = "",
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strategy_narrative: str = "",
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) -> list[dict]:
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"""轨A：一次出 count 条不同角度文案，三层兜底，自动优化循环。
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strategy_narrative：本套正交叙事主线(A痛点/B场景/C成分)，由调用方按套传入，
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贯穿首批生成与优化轮，确保同套内文案同一叙事不串味。"""
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banned_entries = build_entries_from_db(banned_word_rows or [])
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extra = flywheel_context
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copies: list[dict] = await _generate_in_batches(
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llm_client, product, count, extra, strategy_narrative)
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if not copies:
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copies = list(build_local_drafts(product, count)) # generator → list
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candidates: list[dict] = []
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for c in copies:
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ban: CheckResult = check_and_fix(
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f"{c.get('title','')} {c.get('content','')}",
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banned_entries or None,
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)
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scored = await llm_score_copy(llm_client, c, product, [e.word for e in banned_entries])
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c.update({"source": "ai", "score": scored["score"], "score_detail": scored["score_detail"],
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"passed": scored["passed"], "banned_word_status": ban.status,
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"verdict": scored.get("verdict", ""), "summary": scored.get("summary", "")})
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if ban.status == "auto_fixed" and ban.fixed_text:
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c["content"] = ban.fixed_text
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candidates.append(c)
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failed = [c for c in candidates if not c["passed"] and c["banned_word_status"] != "hard_block"]
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# 优化轮默认关闭：apiports 60s 网关限制下优化轮的 _call_llm 常需白等 60s 才 503，
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# 严重拖慢出文案（实测 +100s+）。质量优化等北哥 prompt 方案到位再开（架构已留位）。
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optimize_enabled = os.environ.get("TEXT_OPTIMIZE_ENABLED", "false").lower() == "true"
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rounds = MAX_OPTIMIZE_ROUNDS if optimize_enabled else 0
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for _ in range(rounds):
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if not failed:
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break
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# 优化轮也受 60s 网关上限约束：一次最多重生成 TEXT_BATCH_SIZE 条
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batch_failed = failed[:TEXT_BATCH_SIZE]
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hint = "\n".join(
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f"标题「{c['title']}」{c['score']}分，需改进：" +
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"；".join(d["note"] for d in c.get("score_detail", []) if d["score"] < d["max"] * 0.72)
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for c in batch_failed
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)
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raw2 = await _call_llm(llm_client, build_prompt(
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product, len(batch_failed),
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extra_rules=f"以下文案未达标，请重新生成并改进：\n{hint}\n不要重复已有标题和角度。",
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strategy_narrative=strategy_narrative,
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), min(8192, max(1800, len(batch_failed) * 1800)))
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if not raw2:
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# LLM 失败（如 503/超时）：优化是锦上添花，原始候选已够用，不再耗时重试
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logger.warning("文案优化轮 LLM 失败，沿用原始候选不再重试")
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break
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for nc in parse_json_array(raw2):
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sc2 = await llm_score_copy(llm_client, nc, product, [e.word for e in banned_entries])
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nc.update({"source": "ai", "score": sc2["score"], "score_detail": sc2["score_detail"],
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"passed": sc2["passed"], "banned_word_status": "pass",
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"verdict": sc2.get("verdict", ""), "summary": sc2.get("summary", "")})
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candidates.append(nc)
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failed = [c for c in candidates if not c["passed"]]
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return dedupe_copies(candidates, previous_copies or [])[:count]
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def text_import_handler(
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raw_text: str,
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product: dict,
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banned_word_rows: list[dict] | None = None,
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) -> dict:
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"""轨B：导入外部文案（豆包等）直接进候选池，source=import"""
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banned_entries = build_entries_from_db(banned_word_rows or [])
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lines = raw_text.strip().splitlines()
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title = lines[0].strip() if lines else ""
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content = "\n".join(lines[1:]).strip() if len(lines) > 1 else raw_text.strip()
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candidate: dict = {"title": title, "content": content, "tags": [], "angle": "import",
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"buyingPoint": "", "coverTitle": title, "imageBrief": "", "source": "import"}
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ban = check_and_fix(f"{title} {content}", banned_entries or None)
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# 轨B(导入外部文案)走机械 score_copy 而非 AI 评委：导入的是用户自带成品，评分仅作
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# 参考展示不卡发布；且本函数同步、改 await 会扩大到调用方。AI 评委只用于轨A生成链路。
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scored = score_copy(candidate, product, [e.word for e in banned_entries])
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candidate.update({"score": scored["score"], "score_detail": scored["score_detail"],
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"passed": scored["passed"], "banned_word_status": ban.status})
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return candidate
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