c1ac2a6ab9
8维草案(banana不变量+俊达配方+3链接):标题公式/开头钩子/正文结构/
卖点呈现/情绪语气emoji/目标人群/话题标签/视觉配图节奏。
- _benchmark_prompt.py: 8维vision prompt(不打分,提炼可复用配方)
- benchmark_analyzer.py: 引擎核心(vision读截图+手填亮点→8维JSON,失败兜底)
- benchmarks.py: POST /benchmarks/{id}/analyze 端点(写features_json,流转analyze_status)
端到端实测:8维齐全0缺失,真调LLM(conf0.9非兜底),落库done,质量高(识别收藏钩子/成分背书/诚实标未体现)
67 lines
2.7 KiB
Python
67 lines
2.7 KiB
Python
"""
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app/services/ai_engine/benchmark_analyzer.py — 第2环 标杆8维分析引擎
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复用 products.analyze_product_image 的 vision 范式，解耦成 service。
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输入：爆款截图 bytes + 手填亮点；输出：8维配方 JSON（写入 benchmark_notes.features_json）。
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key 链路遵基石B：plain_key 只活在调用方局部，本模块只收已构建的 clients。
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"""
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import json
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import logging
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import re
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from app.services.ai_engine._benchmark_prompt import BENCHMARK_8DIM_PROMPT
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logger = logging.getLogger(__name__)
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# 8维字段顺序（与 prompt JSON key 一致；analyze_status 流转 done 时校验齐全）
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DIM_KEYS = [
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"title_formula", "opening_hook", "content_structure", "selling_point_style",
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"emotion_tone", "target_audience", "topic_tags", "visual_rhythm",
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]
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def parse_benchmark_json(raw: str) -> dict:
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"""从模型返回文本提取 JSON（容错 markdown ```json 包裹）。"""
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cleaned = re.sub(r"```json\s*", "", raw)
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cleaned = re.sub(r"```\s*", "", cleaned)
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m = re.search(r"\{[\s\S]*\}", cleaned)
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if not m:
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raise ValueError("标杆分析返回中未找到 JSON")
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return json.loads(m.group())
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def fallback_benchmark(highlights: str | None = None) -> dict:
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"""vision 失败兜底：保证 8 维不空手，标 source=fallback 供前端提示。"""
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note = highlights or "截图未体现"
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return {
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"title_formula": note, "opening_hook": note, "content_structure": note,
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"selling_point_style": note, "emotion_tone": note, "target_audience": note,
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"topic_tags": note, "visual_rhythm": note,
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"confidence": 0.4, "source": "fallback",
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}
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async def analyze_benchmark(clients, screenshot: bytes | None, highlights: str | None) -> dict:
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"""
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跑 8 维分析。截图走 vision；无截图但有手填亮点则把亮点拼进 prompt 兜底。
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返回结构化 8 维 dict（含 confidence/source）。失败不抛，返回 fallback。
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"""
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prompt = BENCHMARK_8DIM_PROMPT
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if highlights:
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prompt = f"{prompt}\n\n运营补充的亮点描述（作为分析参考）：{highlights}"
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images = [screenshot] if screenshot else []
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try:
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if not images:
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# 无截图：纯文本兜底（gpt_vision_analyze 支持空图走文本）
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logger.info("analyze_benchmark: 无截图，走手填亮点文本分析")
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raw = await clients.gpt_vision_analyze(prompt, images)
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result = parse_benchmark_json(raw)
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result["source"] = "vision" if images else "text"
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return result
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except Exception as e:
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logger.warning("analyze_benchmark failed, fallback: %s", e)
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result = fallback_benchmark(highlights)
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result["warning"] = f"标杆分析失败，已兜底：{e}"
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return result
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