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>
94 lines
3.6 KiB
Python
94 lines
3.6 KiB
Python
"""
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app/workers/pipeline_steps.py — 生产链 Step1-4
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Step1: 查 DB（task/product）
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Step2: 查 key → Fernet 解密（局部变量，不传出，基石B）
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Step3: 构建 AI clients
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Step4: 推 task_started SSE + 飞轮上下文
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"""
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import json
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import logging
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logger = logging.getLogger(__name__)
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def load_task_and_product(db, task_id: int):
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"""Step1: 查任务 + 产品，失败返 None 或抛异常。"""
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from app.models.task import GenerationTask
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from app.models.product import Product
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task = db.query(GenerationTask).filter(GenerationTask.id == task_id).first()
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if not task:
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logger.error("task_id=%s not found", task_id)
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return None, None
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product = db.query(Product).filter(Product.id == task.product_id).first()
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if not product:
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raise ValueError(f"product_id={task.product_id} not found")
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return task, product
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def decrypt_user_key(db, operator_id: int, workspace_id: int) -> str:
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"""
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Step2: 查 key → Fernet 解密，返回 plain_key（只活在调用方局部变量）。
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绝不打印、不持久化 plain_key（基石B）。
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"""
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from app.models.workspace import UserApiKey
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from app.utils.fernet_utils import decrypt_key
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api_key_row = db.query(UserApiKey).filter(
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UserApiKey.user_id == operator_id,
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UserApiKey.workspace_id == workspace_id,
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UserApiKey.provider.in_(["openai", "apiports"]), # G6坑修复：接受主备通道名
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).first()
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if not api_key_row:
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raise ValueError("用户未配置 API Key，请先录入")
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return decrypt_key(api_key_row.encrypted_key)
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def build_clients_and_clear_key(plain_key: str):
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"""
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Step3: 构建 AIClients，plain_key 传入后立即由调用方置 None。
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返回 clients 对象。
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"""
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from app.services.ai_engine.gemini_factory import build_ai_clients
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return build_ai_clients(plain_key)
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def build_product_dict(product) -> dict:
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"""把 ORM product 转成 AI 引擎所需的 dict（不含任何 key）。"""
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return {
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"name": product.name,
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"category": product.category or "通用好物",
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"selling_points": json.loads(product.selling_points or "[]"),
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"style_tone": product.style_tone or "素人分享风",
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"text_angles": json.loads(product.text_angles or "[]"),
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"custom_prompt": product.custom_prompt or "",
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"brand_keyword": product.brand_keyword or "", # S3: 品牌词透传进生成prompt(每条植入)
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"target_audience": product.target_audience or "", # 012: 人群透传进storyboard/文案prompt
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"image_path": product.image_path or "", # 产品参考图路径（前端上传后填入）
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}
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def load_flywheel_context(db, workspace_id: int, product_id: int, product_dict: dict) -> tuple[str, dict]:
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"""
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查最近50条飞轮事件，聚合偏好上下文。
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返回 (prompt_fragment, full_ctx)。
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"""
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from app.models.flywheel import PreferenceEvent
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from app.services.ai_engine.preference_aggregator import aggregate_preference_context
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recent = db.query(PreferenceEvent).filter(
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PreferenceEvent.workspace_id == workspace_id,
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PreferenceEvent.product_id == product_id,
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).order_by(PreferenceEvent.id.desc()).limit(50).all()
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events_dicts = [
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{"signal_type": e.signal_type, "workspace_id": e.workspace_id,
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"product_id": e.product_id, "angle_label": e.angle_label or "",
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"signal_weight": e.signal_weight, "reason": e.reason or ""}
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for e in recent
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]
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ctx = aggregate_preference_context(events_dicts, product_dict, workspace_id, product_id)
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return ctx.get("prompt_fragment", ""), ctx
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