6a2632da70
将 Clover 从上层产品包旧仓库中独立出来,建立专属版本控制。 当前状态=纵切片端到端已打通(登录→选品→出文出图→审核→下载包), M1文案质量去套路化已验收。此提交作为后续按核销清单逐条修复的基线。 Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
110 lines
4.1 KiB
Python
110 lines
4.1 KiB
Python
"""
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prompt_composer.py — 统一 prompt 组装入口（≤100行）
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扒自：banana prompts/service.py + worker/src/copy.js prompt 逻辑
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Lead 指名接口：compose_variants / compose_preference_context
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组装逻辑委托：
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_text_prompt.py → build_prompt (文案 prompt 主体)
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preference_aggregator.py → aggregate_preference_context (飞轮上下文)
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原则：prompt 组装从这里进，不散落在 text_variants / generate_text_variants 里。
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"""
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from __future__ import annotations
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import logging
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from typing import Any
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from ._text_prompt import build_prompt, COPY_SYSTEM
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from .preference_aggregator import aggregate_preference_context
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logger = logging.getLogger(__name__)
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# ── 主接口 ────────────────────────────────────────────────────────────────────
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def compose_variants(
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product: dict,
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count: int,
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flywheel_context: str = "",
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extra_rules: str = "",
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) -> tuple[str, str]:
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"""
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一次出 count 角度文案的完整 prompt。
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返回 (system_prompt, user_prompt)。
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飞轮片段追加到 user_prompt 末尾（不改 system，避免覆盖质量红线）。
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参数：
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product — 产品档案 dict（name/selling_points/text_angles/custom_prompt 等）
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count — 需要几条
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flywheel_context— 由 compose_preference_context 返回的 prompt_fragment
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extra_rules — 额外规则（优化循环重生成时传 hint）
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"""
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combined_extra = "\n".join(filter(None, [flywheel_context, extra_rules]))
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user_prompt = build_prompt(product, count, extra_rules=combined_extra)
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logger.debug(
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"compose_variants: product=%s count=%d flywheel_len=%d",
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product.get("name", "?"), count, len(flywheel_context),
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)
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return COPY_SYSTEM, user_prompt
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def compose_preference_context(
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events: list[dict],
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product: dict,
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workspace_id: int,
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product_id: int,
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) -> dict:
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"""
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聚合偏好事件 → 可注入 prompt 的飞轮上下文。
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返回结构（对齐 API契约 GET /tasks/{id}/preference/context）：
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{
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recent_preference: str, # 人类可读摘要（前端"本次已注入"显示）
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reject_reasons: list, # 最近打回原因
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injected_count: int, # 有效信号数
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prompt_fragment: str, # 注入 compose_variants flywheel_context 的字符串
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}
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信号不足 FLYWHEEL_COLD_START 条时用产品档案冷启动。
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按 workspace_id + product_id 双维过滤（素颜霜偏好不串精华）。
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"""
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return aggregate_preference_context(events, product, workspace_id, product_id)
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# ── 辅助：解析模型返回的 JSON（给 text_variants 调用，集中不散） ──────────────
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def parse_model_output(raw: str) -> list[dict]:
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"""从 LLM 原始输出提取 JSON 数组（容错 markdown 包裹）"""
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from ._text_prompt import parse_json_array
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return parse_json_array(raw)
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# ── 辅助：图片 prompt 组装入口（预留，联调时填充）─────────────────────────────
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def compose_image_prompt(
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role_name: str,
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visual_system: dict,
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product: dict,
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extra: str = "",
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) -> str:
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"""
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单张分镜 prompt 组装（供 image_gen.generate_one_image 调用）。
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TODO: 联调后从 storyboard.plan_image_set 取 base_prompt 注入。
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role_name — 分镜角色（hook / pain_scene / closer 等）
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visual_system— build_visual_system 返回的视觉系统 dict
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extra — 追加约束（飞轮图片偏好片段，二期接入）
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"""
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name = product.get("name", "产品")
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style = visual_system.get("style", "")
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palette = visual_system.get("color_palette", "")
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base = visual_system.get("base_prompt", "")
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lines = [
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f"[{role_name}] 为产品「{name}」生成种草图。",
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base and f"视觉基调：{base}",
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style and f"摄影风格：{style}",
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palette and f"色调：{palette}",
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extra,
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]
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return "\n".join(l for l in lines if l)
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