402 lines
13 KiB
Python
402 lines
13 KiB
Python
"""食谱系统信号源 API
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功能：
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1. 接收食谱系统的 webhook 请求
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2. 将食谱识别需求转换为 PMFlow 信号
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3. 自动关联到指定项目（食谱系统项目）
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"""
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from fastapi import APIRouter, Depends, HTTPException, Query
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from sqlalchemy.ext.asyncio import AsyncSession
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from pydantic import BaseModel, Field
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from typing import Optional, Dict, Any
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import json
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from ..database import get_db
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from ..dependencies.tenant import get_tenant_id
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from ..core.logging import get_logger
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from ..models import Signal, ProjectSignalLink, Project
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router = APIRouter(prefix="/signal-sources", tags=["Signal Sources"])
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logger = get_logger(__name__)
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RECIPE_PROJECT_KEYWORDS = ("食谱", "recipe", "sstc")
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async def _resolve_target_project_id(
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*,
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db: AsyncSession,
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tenant_id: int,
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project_id: Optional[int],
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) -> int:
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"""解析目标项目：优先显式 project_id，其次按关键字自动匹配。"""
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from sqlalchemy import select, or_
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if project_id is not None:
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result = await db.execute(
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select(Project.id).where(
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Project.tenant_id == tenant_id,
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Project.id == int(project_id),
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).limit(1)
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)
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found = result.scalar_one_or_none()
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if found is None:
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raise HTTPException(status_code=404, detail=f"project_id={project_id} 不存在或无权限")
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return int(found)
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keyword_conditions = []
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for keyword in RECIPE_PROJECT_KEYWORDS:
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keyword_conditions.extend([
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Project.title.ilike(f"%{keyword}%"),
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Project.name.ilike(f"%{keyword}%"),
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])
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match_result = await db.execute(
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select(Project.id).where(
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Project.tenant_id == tenant_id,
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or_(*keyword_conditions),
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).limit(1)
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)
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matched = match_result.scalar_one_or_none()
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if matched is not None:
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return int(matched)
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# 兜底：若租户仅存在一个活跃项目，自动绑定，避免“已调用但未入库”。
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active_result = await db.execute(
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select(Project.id).where(
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Project.tenant_id == tenant_id,
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Project.status == "active",
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).limit(2)
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)
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active_ids = list(active_result.scalars().all())
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if len(active_ids) == 1:
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return int(active_ids[0])
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raise HTTPException(
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status_code=422,
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detail="未找到目标项目，请在请求中显式传入 project_id（或确保项目名包含 食谱/recipe/sstc）",
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)
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# ============================================================================
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# 食谱系统信号源 Webhook
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# ============================================================================
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class RecipeRecognitionRequest(BaseModel):
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"""食谱识别请求"""
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# 食谱信息
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food_name: str = Field(..., description="食物名称")
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food_description: Optional[str] = Field(None, description="食物描述")
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image_url: Optional[str] = Field(None, description="图片URL")
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category: Optional[str] = Field(None, description="分类")
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cuisine: Optional[str] = Field(None, description="菜系")
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# 营养信息（如果来源系统已分析）
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calories: Optional[float] = Field(None, description="热量（千卡）")
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protein: Optional[float] = Field(None, description="蛋白质（克）")
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fat: Optional[float] = Field(None, description="脂肪（克）")
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carbs: Optional[float] = Field(None, description="碳水化合物（克）")
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fiber: Optional[float] = Field(None, description="膳食纤维（克）")
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# 意图
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intent: str = Field(
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default="analyze",
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description="意图：analyze-热量分析, save-保存食谱, recommend-推荐菜谱"
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)
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# 用户信息（可选）
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user_name: Optional[str] = Field(None, description="用户名")
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class RecipeRecognitionResponse(BaseModel):
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"""响应"""
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success: bool
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signal_id: Optional[int] = None
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project_id: Optional[int] = None
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message: str
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details: Optional[Dict[str, Any]] = None
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@router.post("/recipe/webhook", response_model=RecipeRecognitionResponse, summary="食谱系统 Webhook")
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async def recipe_webhook(
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request: RecipeRecognitionRequest,
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project_id: Optional[int] = Query(None, description="项目ID，不传则自动查找食谱系统项目"),
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db: AsyncSession = Depends(get_db),
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tenant_id: int = Depends(get_tenant_id),
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):
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"""
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接收食谱系统的识别请求
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用法：
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- 食谱系统完成拍照识别后，调用此 API 将需求推送到 PMFlow
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- PMFlow 会自动创建信号并关联到食谱系统项目
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请求示例：
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```json
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{
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"food_name": "红烧肉",
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"food_description": "一块红烧肉，约200克，肥瘦相间",
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"image_url": "https://example.com/image.jpg",
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"category": "肉类",
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"cuisine": "中式",
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"calories": 350,
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"protein": 20,
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"fat": 25,
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"carbs": 10,
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"fiber": 0,
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"intent": "analyze",
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"user_name": "张三"
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}
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```
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"""
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try:
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# 1. 查找目标项目
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target_project_id = await _resolve_target_project_id(
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db=db,
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tenant_id=tenant_id,
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project_id=project_id,
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)
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# 2. 构建信号内容
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content_parts = []
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content_parts.append(f"食物：{request.food_name}")
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if request.food_description:
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content_parts.append(f"描述：{request.food_description}")
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# 营养信息
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if request.calories or request.protein or request.fat or request.carbs or request.fiber:
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nutrition = []
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if request.calories:
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nutrition.append(f"热量 {request.calories} kcal")
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if request.protein:
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nutrition.append(f"蛋白 {request.protein}g")
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if request.fat:
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nutrition.append(f"脂肪 {request.fat}g")
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if request.carbs:
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nutrition.append(f"碳水 {request.carbs}g")
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if request.fiber:
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nutrition.append(f"纤维 {request.fiber}g")
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content_parts.append("营养：" + ", ".join(nutrition))
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if request.category:
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content_parts.append(f"分类：{request.category}")
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if request.cuisine:
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content_parts.append(f"菜系：{request.cuisine}")
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content_parts.append(f"意图：{request.intent}")
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content = "\n".join(content_parts)
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# 3. 构建标题
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intent_text = {
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"analyze": "热量分析",
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"save": "保存食谱",
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"recommend": "推荐菜谱"
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}.get(request.intent, request.intent)
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title = f"🍽️ 食谱{intent_text}：{request.food_name}"
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# 4. 构建摘要
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summary = f"食物：{request.food_name}"
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if request.calories:
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summary += f"\n热量：{request.calories} kcal"
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if request.category:
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summary += f"\n分类：{request.category}"
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summary += f"\n意图：{intent_text}"
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# 5. 构建信号元数据
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signal_metadata = {
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"source": "recipe_system",
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"intent": request.intent,
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"category": request.category,
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"cuisine": request.cuisine,
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"nutrition": {
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"calories": request.calories,
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"protein": request.protein,
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"fat": request.fat,
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"carbs": request.carbs,
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"fiber": request.fiber,
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},
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"image_url": request.image_url,
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"user_name": request.user_name,
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}
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# 6. 创建信号
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signal = Signal(
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tenant_id=tenant_id,
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source="recipe_system",
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title=title,
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content=content,
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summary=summary,
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signal_type="recipe_recognition",
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priority="medium",
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impact_score=0.5,
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status="待处理",
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processed=False,
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strategic_analysis=json.dumps(signal_metadata, ensure_ascii=False),
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)
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db.add(signal)
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await db.flush() # 获取 signal.id
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# 7. 关联到项目
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link = ProjectSignalLink(
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tenant_id=tenant_id,
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project_id=target_project_id,
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signal_id=signal.id,
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)
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db.add(link)
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await db.commit()
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await db.refresh(signal)
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return RecipeRecognitionResponse(
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success=True,
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signal_id=signal.id,
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project_id=target_project_id,
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message="信号创建成功并已关联到食谱系统项目",
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details={
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"signal_id": signal.id,
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"project_id": target_project_id,
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"title": title,
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"status": signal.status,
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}
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)
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except HTTPException:
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await db.rollback()
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raise
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except Exception as e:
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await db.rollback()
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logger.exception(
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"recipe_webhook_create_signal_failed",
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tenant_id=tenant_id,
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project_id=project_id,
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food_name=request.food_name,
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error=str(e),
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)
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raise HTTPException(status_code=500, detail=f"创建信号失败: {str(e)}")
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@router.get("/recipe/status/{signal_id}", summary="查询食谱信号状态")
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async def get_recipe_signal_status(
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signal_id: int,
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db: AsyncSession = Depends(get_db),
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tenant_id: int = Depends(get_tenant_id),
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):
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"""查询食谱识别信号的处理状态"""
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from sqlalchemy import select
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result = await db.execute(
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select(Signal).where(
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Signal.id == signal_id,
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Signal.tenant_id == tenant_id,
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Signal.source == "recipe_system",
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)
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)
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signal = result.scalar_one_or_none()
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if not signal:
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raise HTTPException(status_code=404, detail="信号不存在")
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return {
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"signal_id": signal.id,
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"title": signal.title,
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"status": signal.status,
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"processed": signal.processed,
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"summary": signal.summary,
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}
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# ============================================================================
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# 批量导入食谱记录
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# ============================================================================
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class RecipeBatchRequest(BaseModel):
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"""批量导入请求"""
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recipes: list[RecipeRecognitionRequest] = Field(..., description="食谱列表")
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@router.post("/recipe/batch", response_model=RecipeRecognitionResponse, summary="批量导入食谱")
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async def recipe_batch_import(
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request: RecipeBatchRequest,
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project_id: Optional[int] = Query(None, description="项目ID"),
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db: AsyncSession = Depends(get_db),
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tenant_id: int = Depends(get_tenant_id),
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):
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"""批量导入食谱记录，每个都会创建信号"""
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try:
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target_project_id = await _resolve_target_project_id(
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db=db,
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tenant_id=tenant_id,
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project_id=project_id,
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)
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signal_ids = []
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for recipe in request.recipes:
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# 构建内容
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content = f"食物：{recipe.food_name}"
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if recipe.calories:
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content += f"\n热量：{recipe.calories} kcal"
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title = f"🍽️ 食谱记录：{recipe.food_name}"
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metadata = {
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"source": "recipe_system",
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"intent": recipe.intent,
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"nutrition": {
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"calories": recipe.calories,
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"protein": recipe.protein,
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"fat": recipe.fat,
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"carbs": recipe.carbs,
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}
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}
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signal = Signal(
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tenant_id=tenant_id,
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source="recipe_system",
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title=title,
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content=content,
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summary=f"食物：{recipe.food_name}",
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signal_type="recipe_recognition",
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priority="low",
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impact_score=0.3,
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status="待处理",
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processed=True, # 批量导入的视为已处理
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strategic_analysis=json.dumps(metadata),
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)
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db.add(signal)
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await db.flush()
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# 关联项目
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link = ProjectSignalLink(
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tenant_id=tenant_id,
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project_id=target_project_id,
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signal_id=signal.id,
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)
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db.add(link)
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signal_ids.append(signal.id)
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await db.commit()
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return RecipeRecognitionResponse(
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success=True,
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signal_id=len(signal_ids), # 返回数量
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project_id=target_project_id,
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message=f"成功导入 {len(signal_ids)} 条食谱记录",
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details={"signal_count": len(signal_ids)}
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)
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except HTTPException:
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await db.rollback()
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raise
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except Exception as e:
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await db.rollback()
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logger.exception(
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"recipe_batch_import_failed",
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tenant_id=tenant_id,
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project_id=project_id,
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recipe_count=len(request.recipes),
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error=str(e),
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)
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raise HTTPException(status_code=500, detail=f"批量导入失败: {str(e)}")
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