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21 Commits
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v1.0.5
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6dc4e2b943 |
@@ -2,3 +2,4 @@
|
||||
客户端请按照标准的RN架构目录写代码
|
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后端请按照标准的python FastAPI 架构目录写代码
|
||||
现在多语言仅支持 EN / TC
|
||||
整个task.md执行完毕后需要在对应的overview.md标记,并且说明变更的文件名
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|
||||
@@ -2,3 +2,4 @@
|
||||
根据对应的plan.md 生成task.md
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||||
任务清单详细可执行
|
||||
执行完要标记
|
||||
整个task.md执行完毕后需要在对应的overview.md标记
|
||||
|
||||
1
.cursor/commands/myspec.test.md
Normal file
@@ -0,0 +1 @@
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||||
使用测试工具完成集成测试,并给我一份简单的测试报告
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@@ -28,4 +28,5 @@ modules/ 可嵌套 modules/,每层都独立规范。
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||||
输出时根据这个结构生成内容时,请保持文件职责清晰。
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||||
简短记录项目的该层每个spec的内容 ,每次编码完成后更新overview.md
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可以通过nvm 切换node版本
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在对数据库操作中,禁止执行破坏性操作,如果必须请让我同意,并回复:允许操作数据库
|
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|
||||
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||||
5
.gitea/workflows/README.md
Normal file
@@ -0,0 +1,5 @@
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docker exec -it gitea-runner bash
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# 然后在容器里安装 Node.js
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curl -fsSL https://deb.nodesource.com/setup_20.x | bash -
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apt-get install -y nodejs
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node -v
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116
.gitea/workflows/server-build.yml
Normal file
@@ -0,0 +1,116 @@
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name: Build and Push Server Docker Image
|
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|
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# 手动触发 workflow:从哪个分支运行,就打包哪个分支的代码
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on:
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workflow_dispatch:
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|
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jobs:
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build-and-push:
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runs-on: ubuntu-latest
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|
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steps:
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# 1️⃣ Checkout 仓库代码
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- name: Checkout repository
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uses: actions/checkout@v4
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with:
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fetch-depth: 0
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# 需要能 push tag(请在仓库 Secrets 配置 RUNNER_TOKEN)
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token: ${{ secrets.RUNNER_TOKEN }}
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persist-credentials: true
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|
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# 2️⃣ 自动递增 tag 并推送回 Gitea 仓库(默认按 vX.Y.Z 的 patch +1)
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- name: Auto bump tag and push to repository
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shell: bash
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run: |
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set -euo pipefail
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|
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# 配置提交信息(用于创建注释 tag)
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git config user.name "gitea-actions"
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git config user.email "actions@local"
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|
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# 确保本地有最新 tags
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git fetch --tags --force
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|
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# 取最新的 semver tag(vX.Y.Z),按版本号排序
|
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LATEST_TAG="$(git tag --list 'v*' --sort=-v:refname | head -n 1 || true)"
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echo "LATEST_TAG=${LATEST_TAG}"
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|
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if [[ -z "${LATEST_TAG}" ]]; then
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NEXT_TAG="v1.0.0"
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else
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if [[ "${LATEST_TAG}" =~ ^v([0-9]+)\.([0-9]+)\.([0-9]+)$ ]]; then
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MAJOR="${BASH_REMATCH[1]}"
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MINOR="${BASH_REMATCH[2]}"
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PATCH="${BASH_REMATCH[3]}"
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NEXT_TAG="v${MAJOR}.${MINOR}.$((PATCH + 1))"
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else
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# 如果最新 tag 不符合 vX.Y.Z,回退到 v1.0.0,避免误解析
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NEXT_TAG="v1.0.0"
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fi
|
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fi
|
||||
|
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echo "NEXT_TAG=${NEXT_TAG}"
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|
||||
# 如果 tag 已存在则直接复用(避免重复运行失败)
|
||||
if git rev-parse -q --verify "refs/tags/${NEXT_TAG}" >/dev/null; then
|
||||
echo "Tag ${NEXT_TAG} 已存在,跳过创建。"
|
||||
else
|
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git tag -a "${NEXT_TAG}" -m "Release ${NEXT_TAG}"
|
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git push origin "${NEXT_TAG}"
|
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fi
|
||||
|
||||
# 输出给后续步骤使用
|
||||
if [[ -n "${GITHUB_ENV:-}" ]]; then
|
||||
echo "IMAGE_TAG=${NEXT_TAG}" >> "$GITHUB_ENV"
|
||||
fi
|
||||
# 兼容部分 Gitea Runner 环境变量命名
|
||||
if [[ -n "${GITEA_ENV:-}" ]]; then
|
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echo "IMAGE_TAG=${NEXT_TAG}" >> "$GITEA_ENV"
|
||||
fi
|
||||
|
||||
# 3️⃣ 设置镜像仓库与镜像名称(自建 Registry / Docker Hub 都可)
|
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- name: Set image variables
|
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shell: bash
|
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run: |
|
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# 自建镜像仓库地址(例如:docker.damer.fun 或 registry.example.com)
|
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# 如要推送 Docker Hub:可把 REGISTRY 留空并把 IMAGE_REPO 设置为 yourname/xxx
|
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REGISTRY="${DOCKER_REGISTRY:-docker.damer.fun}"
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# 镜像仓库路径(不含 registry)
|
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IMAGE_REPO="${DOCKER_IMAGE_REPO:-damer/mindfulness-server}"
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|
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if [[ -n "$REGISTRY" ]]; then
|
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IMAGE_NAME="$REGISTRY/$IMAGE_REPO"
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else
|
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IMAGE_NAME="$IMAGE_REPO"
|
||||
fi
|
||||
|
||||
if [[ -n "${GITHUB_ENV:-}" ]]; then
|
||||
echo "REGISTRY=$REGISTRY" >> "$GITHUB_ENV"
|
||||
echo "IMAGE_REPO=$IMAGE_REPO" >> "$GITHUB_ENV"
|
||||
echo "IMAGE_NAME=$IMAGE_NAME" >> "$GITHUB_ENV"
|
||||
fi
|
||||
if [[ -n "${GITEA_ENV:-}" ]]; then
|
||||
echo "REGISTRY=$REGISTRY" >> "$GITEA_ENV"
|
||||
echo "IMAGE_REPO=$IMAGE_REPO" >> "$GITEA_ENV"
|
||||
echo "IMAGE_NAME=$IMAGE_NAME" >> "$GITEA_ENV"
|
||||
fi
|
||||
|
||||
# 4️⃣ 登录镜像仓库(自建 Registry)
|
||||
- name: Login to Docker Registry
|
||||
uses: docker/login-action@v3
|
||||
with:
|
||||
registry: docker.damer.fun
|
||||
username: ${{ secrets.DOCKER_USERNAME }}
|
||||
password: ${{ secrets.DOCKER_TOKEN }}
|
||||
|
||||
# 5️⃣ 构建 Docker 镜像(使用 server/ 作为构建上下文)
|
||||
- name: Build Docker Image
|
||||
shell: bash
|
||||
run: |
|
||||
docker build -f server/Dockerfile -t "$IMAGE_NAME:$IMAGE_TAG" server
|
||||
|
||||
# 6️⃣ 推送 Docker 镜像到镜像仓库
|
||||
- name: Push Docker Image
|
||||
shell: bash
|
||||
run: |
|
||||
docker push "$IMAGE_NAME:$IMAGE_TAG"
|
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4
.gitignore
vendored
@@ -4,6 +4,10 @@
|
||||
.DS_Store
|
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*.pem
|
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# Python(运行产物)
|
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__pycache__/
|
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*.py[cod]
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||||
|
||||
# Node / JS
|
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node_modules/
|
||||
npm-debug.*
|
||||
|
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@@ -1,11 +1,11 @@
|
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{
|
||||
"expo": {
|
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"name": "Hey Mama",
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"slug": "hey-mama",
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"name": "client",
|
||||
"slug": "client",
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||||
"version": "1.0.0",
|
||||
"orientation": "portrait",
|
||||
"icon": "./assets/images/icon.png",
|
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"scheme": "heymama",
|
||||
"scheme": "client",
|
||||
"userInterfaceStyle": "automatic",
|
||||
"newArchEnabled": true,
|
||||
"splash": {
|
||||
@@ -15,7 +15,7 @@
|
||||
},
|
||||
"ios": {
|
||||
"supportsTablet": true,
|
||||
"bundleIdentifier": "com.heymama.app"
|
||||
"bundleIdentifier": "com.damer.mindfulness"
|
||||
},
|
||||
"android": {
|
||||
"adaptiveIcon": {
|
||||
|
||||
@@ -1,5 +1,5 @@
|
||||
import { useEffect, useLayoutEffect, useMemo, useState, useCallback, useRef } from 'react';
|
||||
import { StyleSheet, View, Dimensions, Text, Pressable, PanResponder, Animated as RNAnimated } from 'react-native';
|
||||
import { StyleSheet, View, Dimensions, Text, Pressable, PanResponder, Animated as RNAnimated, ImageBackground } from 'react-native';
|
||||
import { useTranslation } from 'react-i18next';
|
||||
import { useNavigation, useFocusEffect } from 'expo-router';
|
||||
import Animated, {
|
||||
@@ -14,12 +14,20 @@ import Animated, {
|
||||
import { MOCK_CONTENT } from '@/src/constants/mockContent';
|
||||
import {
|
||||
addFavorite,
|
||||
getRecoFeedCache,
|
||||
getRecoFeedHistory,
|
||||
getThemeMode,
|
||||
getUserProfile,
|
||||
getUserProfileScoring,
|
||||
recordRecoFeedServed,
|
||||
recordRecoFeedTouched,
|
||||
setRecoFeedCache,
|
||||
setReaction,
|
||||
setThemeMode,
|
||||
type RecoFeedCacheItem,
|
||||
type ThemeMode,
|
||||
} from '@/src/storage/appStorage';
|
||||
import { fetchRecoFeed } from '@/src/services/recoApi';
|
||||
|
||||
import ProfileModal from '@/components/home/ProfileModal';
|
||||
import ThemeModal from '@/components/home/ThemeModal';
|
||||
@@ -31,6 +39,40 @@ import LikeIcon from '@/assets/images/icon/like_icon.svg';
|
||||
|
||||
const { height: SCREEN_HEIGHT } = Dimensions.get('window');
|
||||
|
||||
// 预定义风景图列表
|
||||
const NATURE_IMAGES = [
|
||||
require('@/assets/theme/nature/1.png'),
|
||||
require('@/assets/theme/nature/2.png'),
|
||||
require('@/assets/theme/nature/3.png'),
|
||||
require('@/assets/theme/nature/4.png'),
|
||||
require('@/assets/theme/nature/5.png'),
|
||||
require('@/assets/theme/nature/6.png'),
|
||||
require('@/assets/theme/nature/7.png'),
|
||||
require('@/assets/theme/nature/8.png'),
|
||||
require('@/assets/theme/nature/9.png'),
|
||||
require('@/assets/theme/nature/10.png'),
|
||||
require('@/assets/theme/nature/11.png'),
|
||||
require('@/assets/theme/nature/12.png'),
|
||||
require('@/assets/theme/nature/13.png'),
|
||||
require('@/assets/theme/nature/14.png'),
|
||||
require('@/assets/theme/nature/15.png'),
|
||||
require('@/assets/theme/nature/17.png'),
|
||||
require('@/assets/theme/nature/18.png'),
|
||||
require('@/assets/theme/nature/19.png'),
|
||||
require('@/assets/theme/nature/20.png'),
|
||||
require('@/assets/theme/nature/22.png'),
|
||||
];
|
||||
|
||||
// 预定义颜色列表
|
||||
const THEME_COLORS = [
|
||||
'#F7D9BF',
|
||||
'#CBF2D8',
|
||||
'#F5CDDE',
|
||||
'#F2ECCB',
|
||||
'#E2CBF2',
|
||||
'#CBD9F2',
|
||||
];
|
||||
|
||||
export default function HomeScreen() {
|
||||
const { t } = useTranslation();
|
||||
const navigation = useNavigation();
|
||||
@@ -41,8 +83,11 @@ export default function HomeScreen() {
|
||||
const [profileName, setProfileName] = useState<string | undefined>(undefined);
|
||||
const [busy, setBusy] = useState(false);
|
||||
const [likeFilled, setLikeFilled] = useState(false);
|
||||
const [feedItems, setFeedItems] = useState<Array<{ content_id: number; text: string }>>([]);
|
||||
|
||||
const item = useMemo(() => MOCK_CONTENT[index % MOCK_CONTENT.length], [index]);
|
||||
const currentList = feedItems.length > 0 ? feedItems : MOCK_CONTENT;
|
||||
const item = useMemo(() => currentList[index % currentList.length], [currentList, index]);
|
||||
const currentContentId = typeof (item as any)?.content_id === 'number' ? Number((item as any).content_id) : null;
|
||||
|
||||
// 动画相关 Shared Values
|
||||
const translateY = useSharedValue(0);
|
||||
@@ -66,12 +111,75 @@ export default function HomeScreen() {
|
||||
}, [])
|
||||
);
|
||||
|
||||
const backgroundColor = themeMode === 'color' ? '#F3D0E1' : '#F4D6C2';
|
||||
// 首次进入:先读缓存,再拉后端 feed(失败则保持 mock/缓存)
|
||||
useEffect(() => {
|
||||
let cancelled = false;
|
||||
(async () => {
|
||||
const cache = await getRecoFeedCache();
|
||||
if (!cancelled && cache?.items?.length) {
|
||||
setFeedItems(cache.items.map((x: RecoFeedCacheItem) => ({ content_id: x.content_id, text: x.text })));
|
||||
}
|
||||
|
||||
const scoring = await getUserProfileScoring();
|
||||
if (!scoring) return;
|
||||
|
||||
try {
|
||||
const hist = await getRecoFeedHistory();
|
||||
const out = await fetchRecoFeed({
|
||||
k: 30,
|
||||
user_profile: {
|
||||
profile_version: scoring.profile_version,
|
||||
profile_source: scoring.profile_source,
|
||||
profile_generated_at: scoring.profile_generated_at,
|
||||
profile_confidence: scoring.profile_confidence,
|
||||
profile_answered: scoring.profile_answered,
|
||||
stage: scoring.stage,
|
||||
emotion_score: scoring.emotion_score,
|
||||
context: scoring.context,
|
||||
need: scoring.need,
|
||||
},
|
||||
already_recommended_ids: hist.already_recommended_ids,
|
||||
touched_or_viewed_ids: hist.touched_or_viewed_ids,
|
||||
});
|
||||
|
||||
if (!cancelled && out.items?.length) {
|
||||
setFeedItems(out.items.map((x) => ({ content_id: x.content_id, text: x.text })));
|
||||
await setRecoFeedCache({
|
||||
saved_at: new Date().toISOString(),
|
||||
items: out.items.map((x) => ({ content_id: x.content_id, text: x.text })),
|
||||
meta: out.meta as Record<string, unknown>,
|
||||
});
|
||||
await recordRecoFeedServed(out.items.map((x) => x.content_id));
|
||||
}
|
||||
} catch {
|
||||
// 忽略:保持缓存/本地 mock
|
||||
}
|
||||
})();
|
||||
return () => {
|
||||
cancelled = true;
|
||||
};
|
||||
}, []);
|
||||
|
||||
const backgroundColor = useMemo(() => {
|
||||
if (themeMode === 'color') {
|
||||
const colorIndex = Math.floor(index / 10) % THEME_COLORS.length;
|
||||
return THEME_COLORS[colorIndex];
|
||||
}
|
||||
return '#F4D6C2'; // 风景模式下的默认底色(图片加载前显示)
|
||||
}, [themeMode, index]);
|
||||
|
||||
// 计算当前应该显示的风景图索引(滑动 10 次切换一张)
|
||||
const natureImageIndex = useMemo(() => {
|
||||
return Math.floor(index / 10) % NATURE_IMAGES.length;
|
||||
}, [index]);
|
||||
|
||||
const currentNatureImage = NATURE_IMAGES[natureImageIndex];
|
||||
|
||||
useLayoutEffect(() => {
|
||||
navigation.setOptions({
|
||||
headerShadowVisible: false,
|
||||
headerStyle: { backgroundColor },
|
||||
headerStyle: { backgroundColor: themeMode === 'scenery' ? 'transparent' : backgroundColor },
|
||||
headerTransparent: themeMode === 'scenery',
|
||||
headerRight: () => (
|
||||
<View style={styles.headerRight}>
|
||||
<CircleIconButton
|
||||
@@ -89,7 +197,7 @@ export default function HomeScreen() {
|
||||
</View>
|
||||
),
|
||||
});
|
||||
}, [backgroundColor, navigation, t]);
|
||||
}, [backgroundColor, themeMode, navigation, t]);
|
||||
|
||||
const textAnimatedStyle = useAnimatedStyle(() => ({
|
||||
transform: [{ translateY: translateY.value }],
|
||||
@@ -105,6 +213,11 @@ export default function HomeScreen() {
|
||||
if (busy) return;
|
||||
setBusy(true);
|
||||
|
||||
// 记录“看过/划过”的内容 id(用于下一次向后端请求时去重/频控)
|
||||
if (typeof currentContentId === 'number') {
|
||||
void recordRecoFeedTouched(currentContentId);
|
||||
}
|
||||
|
||||
// 1. 当前文案向上移动并消失
|
||||
translateY.value = withTiming(-40, { duration: 300, easing: Easing.out(Easing.quad) });
|
||||
opacity.value = withTiming(0, { duration: 300 }, (finished) => {
|
||||
@@ -125,7 +238,7 @@ export default function HomeScreen() {
|
||||
});
|
||||
}
|
||||
});
|
||||
}, [busy, index, translateY, opacity]);
|
||||
}, [busy, currentContentId, index, translateY, opacity]);
|
||||
|
||||
const lastTapRef = useRef<number>(0);
|
||||
|
||||
@@ -176,10 +289,11 @@ export default function HomeScreen() {
|
||||
|
||||
// 2. 保存到收藏夹,包含当前背景信息
|
||||
await addFavorite({
|
||||
favId: String(Date.now()), // 生成唯一 ID
|
||||
id: item.id,
|
||||
date: dateStr,
|
||||
themeMode: themeMode,
|
||||
background: backgroundColor, // 目前存储的是颜色值
|
||||
background: themeMode === 'scenery' ? String(natureImageIndex) : backgroundColor,
|
||||
});
|
||||
|
||||
// 3. 爱心缩放动画
|
||||
@@ -202,8 +316,15 @@ export default function HomeScreen() {
|
||||
|
||||
return (
|
||||
<View style={[styles.container, { backgroundColor }]} {...panResponder.panHandlers}>
|
||||
<Animated.View style={[styles.card, textAnimatedStyle]}>
|
||||
<Text style={styles.text}>{item.text}</Text>
|
||||
{themeMode === 'scenery' && (
|
||||
<ImageBackground
|
||||
source={currentNatureImage}
|
||||
style={StyleSheet.absoluteFill}
|
||||
resizeMode="cover"
|
||||
/>
|
||||
)}
|
||||
<Animated.View style={[styles.card, textAnimatedStyle, themeMode === 'scenery' && styles.sceneryCard]}>
|
||||
<Text style={[styles.text, themeMode === 'scenery' && styles.sceneryText]}>{item.text}</Text>
|
||||
</Animated.View>
|
||||
|
||||
<View style={styles.actions}>
|
||||
@@ -216,9 +337,13 @@ export default function HomeScreen() {
|
||||
style={styles.reactionInner}
|
||||
>
|
||||
{likeFilled ? (
|
||||
<LikeFilledIcon width={35} height={36} />
|
||||
<LikeFilledIcon width={35} height={36} style={{ color: '#EA6969' }} />
|
||||
) : (
|
||||
<LikeIcon width={35} height={36} />
|
||||
<LikeIcon
|
||||
width={35}
|
||||
height={36}
|
||||
style={{ color: themeMode === 'scenery' ? '#FFFFFF' : '#5E2A28' }}
|
||||
/>
|
||||
)}
|
||||
</Pressable>
|
||||
</Animated.View>
|
||||
@@ -277,9 +402,15 @@ const styles = StyleSheet.create({
|
||||
justifyContent: 'center',
|
||||
},
|
||||
card: {
|
||||
paddingHorizontal: 30,
|
||||
alignItems: 'center',
|
||||
position: 'absolute',
|
||||
top: 0,
|
||||
left: 0,
|
||||
right: 0,
|
||||
bottom: 0,
|
||||
justifyContent: 'center',
|
||||
alignItems: 'center',
|
||||
paddingHorizontal: 30,
|
||||
zIndex: 5, // 降低层级,防止遮挡底部按钮
|
||||
},
|
||||
text: {
|
||||
fontSize: 22,
|
||||
@@ -288,6 +419,16 @@ const styles = StyleSheet.create({
|
||||
fontWeight: '700',
|
||||
textAlign: 'center',
|
||||
},
|
||||
sceneryCard: {
|
||||
// 风景模式下稍微收窄文案宽度,增加呼吸感
|
||||
paddingHorizontal: 50,
|
||||
},
|
||||
sceneryText: {
|
||||
color: '#FFFFFF',
|
||||
textShadowColor: 'rgba(0, 0, 0, 0.5)',
|
||||
textShadowOffset: { width: 0, height: 1 },
|
||||
textShadowRadius: 4,
|
||||
},
|
||||
actions: {
|
||||
position: 'absolute',
|
||||
bottom: SCREEN_HEIGHT * 0.16,
|
||||
@@ -295,6 +436,7 @@ const styles = StyleSheet.create({
|
||||
right: 0,
|
||||
flexDirection: 'row',
|
||||
justifyContent: 'center',
|
||||
zIndex: 20, // 提升层级,确保在最顶层可点击
|
||||
},
|
||||
reactionButton: {
|
||||
alignItems: 'center',
|
||||
|
||||
@@ -5,7 +5,16 @@ import { OnboardingLayout } from '@/components/onboarding/OnboardingLayout';
|
||||
import { NameInputStep } from '@/components/onboarding/NameInputStep';
|
||||
import { SelectionStep } from '@/components/onboarding/SelectionStep';
|
||||
import { ReminderStep } from '@/components/onboarding/ReminderStep';
|
||||
import { setOnboardingCompleted, setUserProfile, setDailyReminderSettings } from '@/src/storage/appStorage';
|
||||
import { buildUserProfileFromQuestionnaire, mapOnboardingSelectionsToQuestionnaireAnswers } from '@/src/features/userProfileScoring';
|
||||
import { fetchRecoFeed } from '@/src/services/recoApi';
|
||||
import {
|
||||
recordRecoFeedServed,
|
||||
setOnboardingCompleted,
|
||||
setUserProfile,
|
||||
setDailyReminderSettings,
|
||||
setUserProfileScoring,
|
||||
setRecoFeedCache,
|
||||
} from '@/src/storage/appStorage';
|
||||
|
||||
const STEPS = [
|
||||
{ id: 'name', type: 'name', title: '我可以怎么称呼你?' },
|
||||
@@ -71,6 +80,40 @@ export default function OnboardingScreen() {
|
||||
const { status } = await Notifications.requestPermissionsAsync();
|
||||
const pushEnabled = status === 'granted';
|
||||
|
||||
// 将 Onboarding 选择映射为标准问卷枚举(允许跳过)
|
||||
const answers = mapOnboardingSelectionsToQuestionnaireAnswers(selections);
|
||||
|
||||
// 生成用户画像(供推荐/Push/Widget 复用)
|
||||
const scoringProfile = buildUserProfileFromQuestionnaire(answers);
|
||||
await setUserProfileScoring(scoringProfile);
|
||||
|
||||
// Onboarding 结束后预拉取一次 Feed 文案(失败不阻塞进入首页)
|
||||
try {
|
||||
const { items, meta } = await fetchRecoFeed({
|
||||
k: 30,
|
||||
user_profile: {
|
||||
profile_version: scoringProfile.profile_version,
|
||||
profile_source: scoringProfile.profile_source,
|
||||
profile_generated_at: scoringProfile.profile_generated_at,
|
||||
profile_confidence: scoringProfile.profile_confidence,
|
||||
profile_answered: scoringProfile.profile_answered,
|
||||
stage: scoringProfile.stage,
|
||||
emotion_score: scoringProfile.emotion_score,
|
||||
context: scoringProfile.context,
|
||||
need: scoringProfile.need,
|
||||
},
|
||||
});
|
||||
|
||||
await setRecoFeedCache({
|
||||
saved_at: new Date().toISOString(),
|
||||
items: items.map((x) => ({ content_id: x.content_id, text: x.text })),
|
||||
meta: meta as Record<string, unknown>,
|
||||
});
|
||||
await recordRecoFeedServed(items.map((x) => x.content_id));
|
||||
} catch {
|
||||
// 网络失败时使用首页本地 mock 兜底
|
||||
}
|
||||
|
||||
await setUserProfile({
|
||||
name,
|
||||
intents: Object.values(selections).flat()
|
||||
@@ -97,20 +140,30 @@ export default function OnboardingScreen() {
|
||||
}
|
||||
};
|
||||
|
||||
const onSkip = () => {
|
||||
const onSkip = async () => {
|
||||
// 跳过整个 Onboarding:仍生成一个“全跳过”的最小画像,保证下游可用
|
||||
const scoringProfile = buildUserProfileFromQuestionnaire({});
|
||||
await setUserProfileScoring(scoringProfile);
|
||||
|
||||
// 标记已完成,避免下次启动再次进入 Onboarding
|
||||
await setOnboardingCompleted(true);
|
||||
router.replace('/(app)/home');
|
||||
};
|
||||
|
||||
// 题目为单选:再次点击可取消;选择其他选项会替换为唯一选项
|
||||
const handleToggleSelection = (id: string) => {
|
||||
setSelections(prev => {
|
||||
const currentIds = prev[currentStep.id] || [];
|
||||
const nextIds = currentIds.includes(id)
|
||||
? currentIds.filter(i => i !== id)
|
||||
: [...currentIds, id];
|
||||
const nextIds = currentIds.includes(id) ? [] : [id];
|
||||
return { ...prev, [currentStep.id]: nextIds };
|
||||
});
|
||||
};
|
||||
|
||||
const handleSkipStep = () => {
|
||||
setSelections((prev) => ({ ...prev, [currentStep.id]: [] }));
|
||||
onNext();
|
||||
};
|
||||
|
||||
return (
|
||||
<OnboardingLayout
|
||||
title={currentStep.title}
|
||||
@@ -134,6 +187,7 @@ export default function OnboardingScreen() {
|
||||
selectedIds={selections[currentStep.id] || []}
|
||||
onToggle={handleToggleSelection}
|
||||
onNext={onNext}
|
||||
onSkip={handleSkipStep}
|
||||
/>
|
||||
)}
|
||||
|
||||
|
||||
@@ -1,9 +1,8 @@
|
||||
import { useEffect } from 'react';
|
||||
import { ActivityIndicator, StyleSheet, View } from 'react-native';
|
||||
import { useRouter } from 'expo-router';
|
||||
import AsyncStorage from '@react-native-async-storage/async-storage';
|
||||
|
||||
import { getOnboardingCompleted, getConsentAccepted, setOnboardingCompleted, setConsentAccepted } from '@/src/storage/appStorage';
|
||||
import { getOnboardingCompleted, getConsentAccepted } from '@/src/storage/appStorage';
|
||||
|
||||
/**
|
||||
* 启动分发:根据 consent 和 onboarding 状态跳转
|
||||
@@ -14,10 +13,6 @@ export default function Index() {
|
||||
useEffect(() => {
|
||||
let cancelled = false;
|
||||
(async () => {
|
||||
// 【完全重置】:清除本地存储的所有数据(收藏、设置、引导状态等)
|
||||
await AsyncStorage.clear();
|
||||
console.log('AsyncStorage has been cleared.');
|
||||
|
||||
// 1. 检查是否同意协议
|
||||
const consentAccepted = await getConsentAccepted();
|
||||
if (cancelled) return;
|
||||
@@ -27,9 +22,17 @@ export default function Index() {
|
||||
return;
|
||||
}
|
||||
|
||||
// 2. 检查 Onboarding
|
||||
// 2. 检查 Onboarding 是否已完成
|
||||
const completed = await getOnboardingCompleted();
|
||||
router.replace(completed ? '/(app)/home' : '/(onboarding)/onboarding');
|
||||
if (cancelled) return;
|
||||
|
||||
if (completed) {
|
||||
// 如果已经完成过流程,直接进 Home
|
||||
router.replace('/(app)/home');
|
||||
} else {
|
||||
// 如果是首次进入(或未完成流程),进入 Onboarding
|
||||
router.replace('/(onboarding)/onboarding');
|
||||
}
|
||||
})();
|
||||
return () => {
|
||||
cancelled = true;
|
||||
@@ -46,4 +49,3 @@ export default function Index() {
|
||||
const styles = StyleSheet.create({
|
||||
container: { flex: 1, alignItems: 'center', justifyContent: 'center' },
|
||||
});
|
||||
|
||||
|
||||
BIN
client/assets/theme/nature/1.png
Normal file
|
After Width: | Height: | Size: 358 KiB |
BIN
client/assets/theme/nature/10.png
Normal file
|
After Width: | Height: | Size: 629 KiB |
BIN
client/assets/theme/nature/11.png
Normal file
|
After Width: | Height: | Size: 532 KiB |
BIN
client/assets/theme/nature/12.png
Normal file
|
After Width: | Height: | Size: 127 KiB |
BIN
client/assets/theme/nature/13.png
Normal file
|
After Width: | Height: | Size: 449 KiB |
BIN
client/assets/theme/nature/14.png
Normal file
|
After Width: | Height: | Size: 525 KiB |
BIN
client/assets/theme/nature/15.png
Normal file
|
After Width: | Height: | Size: 693 KiB |
BIN
client/assets/theme/nature/17.png
Normal file
|
After Width: | Height: | Size: 458 KiB |
BIN
client/assets/theme/nature/18.png
Normal file
|
After Width: | Height: | Size: 593 KiB |
BIN
client/assets/theme/nature/19.png
Normal file
|
After Width: | Height: | Size: 414 KiB |
BIN
client/assets/theme/nature/2.png
Normal file
|
After Width: | Height: | Size: 381 KiB |
BIN
client/assets/theme/nature/20.png
Normal file
|
After Width: | Height: | Size: 461 KiB |
BIN
client/assets/theme/nature/22.png
Normal file
|
After Width: | Height: | Size: 606 KiB |
BIN
client/assets/theme/nature/3.png
Normal file
|
After Width: | Height: | Size: 384 KiB |
BIN
client/assets/theme/nature/4.png
Normal file
|
After Width: | Height: | Size: 388 KiB |
BIN
client/assets/theme/nature/5.png
Normal file
|
After Width: | Height: | Size: 236 KiB |
BIN
client/assets/theme/nature/6.png
Normal file
|
After Width: | Height: | Size: 721 KiB |
BIN
client/assets/theme/nature/7.png
Normal file
|
After Width: | Height: | Size: 358 KiB |
BIN
client/assets/theme/nature/8.png
Normal file
|
After Width: | Height: | Size: 415 KiB |
BIN
client/assets/theme/nature/9.png
Normal file
|
After Width: | Height: | Size: 214 KiB |
@@ -50,6 +50,29 @@ type Props = {
|
||||
type Page = 'root' | 'favorites' | 'dailyReminder' | 'widget' | 'language' | 'widgetHowTo';
|
||||
type NavDirection = 'forward' | 'back';
|
||||
|
||||
const NATURE_IMAGES = [
|
||||
require('@/assets/theme/nature/1.png'),
|
||||
require('@/assets/theme/nature/2.png'),
|
||||
require('@/assets/theme/nature/3.png'),
|
||||
require('@/assets/theme/nature/4.png'),
|
||||
require('@/assets/theme/nature/5.png'),
|
||||
require('@/assets/theme/nature/6.png'),
|
||||
require('@/assets/theme/nature/7.png'),
|
||||
require('@/assets/theme/nature/8.png'),
|
||||
require('@/assets/theme/nature/9.png'),
|
||||
require('@/assets/theme/nature/10.png'),
|
||||
require('@/assets/theme/nature/11.png'),
|
||||
require('@/assets/theme/nature/12.png'),
|
||||
require('@/assets/theme/nature/13.png'),
|
||||
require('@/assets/theme/nature/14.png'),
|
||||
require('@/assets/theme/nature/15.png'),
|
||||
require('@/assets/theme/nature/17.png'),
|
||||
require('@/assets/theme/nature/18.png'),
|
||||
require('@/assets/theme/nature/19.png'),
|
||||
require('@/assets/theme/nature/20.png'),
|
||||
require('@/assets/theme/nature/22.png'),
|
||||
];
|
||||
|
||||
export default function ProfileModal({ visible, name: propName, onClose }: Props) {
|
||||
const { t } = useTranslation();
|
||||
|
||||
@@ -260,11 +283,11 @@ function FavoritesPage({ visible, page }: { visible: boolean; page: Page }) {
|
||||
setFavorites(list);
|
||||
}
|
||||
|
||||
async function handleRemove(id: string) {
|
||||
async function handleRemove(favId: string) {
|
||||
// 1. 调用存储层移除收藏
|
||||
await removeFavorite(id);
|
||||
await removeFavorite(favId);
|
||||
// 2. 更新本地状态
|
||||
setFavorites(prev => prev.filter(item => item.id !== id));
|
||||
setFavorites(prev => prev.filter(item => item.favId !== favId));
|
||||
}
|
||||
|
||||
return (
|
||||
@@ -274,7 +297,7 @@ function FavoritesPage({ visible, page }: { visible: boolean; page: Page }) {
|
||||
) : (
|
||||
<FlatList
|
||||
data={favorites}
|
||||
keyExtractor={(it) => it.id}
|
||||
keyExtractor={(it) => it.favId}
|
||||
contentContainerStyle={styles.favList}
|
||||
showsVerticalScrollIndicator={false}
|
||||
renderItem={({ item }) => (
|
||||
@@ -290,11 +313,30 @@ function FavoritesPage({ visible, page }: { visible: boolean; page: Page }) {
|
||||
<View style={styles.favRight}>
|
||||
<View style={[
|
||||
styles.favThumb,
|
||||
{ backgroundColor: item.background } // 动态同步 Home 页的背景
|
||||
item.themeMode === 'scenery' ? {} : { backgroundColor: item.background }
|
||||
]}>
|
||||
<Text style={styles.favThumbText} numberOfLines={4}>{item.text}</Text>
|
||||
{item.themeMode === 'scenery' ? (
|
||||
<View style={StyleSheet.absoluteFill}>
|
||||
<Image
|
||||
source={NATURE_IMAGES[parseInt(item.background)]}
|
||||
style={{
|
||||
width: width * 0.6,
|
||||
height: 800, // 假设原图较高,设置一个较大的高度
|
||||
position: 'absolute',
|
||||
bottom: 0, // 关键:将图片底部对齐容器底部
|
||||
}}
|
||||
resizeMode="cover"
|
||||
/>
|
||||
</View>
|
||||
) : null}
|
||||
<Text style={[
|
||||
styles.favThumbText,
|
||||
item.themeMode === 'scenery' && { color: '#FFFFFF', textShadowColor: 'rgba(0,0,0,0.5)', textShadowOffset: {width:0, height:1}, textShadowRadius: 3 }
|
||||
]} numberOfLines={4}>
|
||||
{item.text}
|
||||
</Text>
|
||||
<Pressable
|
||||
onPress={() => handleRemove(item.id)}
|
||||
onPress={() => handleRemove(item.favId)}
|
||||
style={styles.favRemoveBtn}
|
||||
hitSlop={10}
|
||||
>
|
||||
@@ -765,6 +807,7 @@ const styles = StyleSheet.create({
|
||||
position: 'relative',
|
||||
borderWidth: 1,
|
||||
borderColor: 'rgba(119, 47, 0, 0.05)',
|
||||
overflow: 'hidden',
|
||||
},
|
||||
favThumbText: {
|
||||
fontSize: 15,
|
||||
|
||||
@@ -18,9 +18,10 @@ interface SelectionStepProps {
|
||||
selectedIds: string[];
|
||||
onToggle: (id: string) => void;
|
||||
onNext: () => void;
|
||||
onSkip?: () => void;
|
||||
}
|
||||
|
||||
export function SelectionStep({ options, selectedIds, onToggle, onNext }: SelectionStepProps) {
|
||||
export function SelectionStep({ options, selectedIds, onToggle, onNext, onSkip }: SelectionStepProps) {
|
||||
const hasSelection = selectedIds.length > 0;
|
||||
|
||||
return (
|
||||
@@ -48,15 +49,19 @@ export function SelectionStep({ options, selectedIds, onToggle, onNext }: Select
|
||||
|
||||
{/* 底部按钮:距离底部 12% 高度 */}
|
||||
<View style={styles.footer}>
|
||||
<TouchableOpacity
|
||||
onPress={onNext}
|
||||
disabled={!hasSelection}
|
||||
activeOpacity={0.8}
|
||||
>
|
||||
<View style={styles.footerRow}>
|
||||
{onSkip && (
|
||||
<TouchableOpacity onPress={onSkip} activeOpacity={0.8} style={styles.skipBtn}>
|
||||
<SerifText style={styles.skipText}>跳过</SerifText>
|
||||
</TouchableOpacity>
|
||||
)}
|
||||
|
||||
<TouchableOpacity onPress={onNext} disabled={!hasSelection} activeOpacity={0.8}>
|
||||
{hasSelection ? <BtnClicked width={87} height={57} /> : <BtnNotClicked width={87} height={57} />}
|
||||
</TouchableOpacity>
|
||||
</View>
|
||||
</View>
|
||||
</View>
|
||||
);
|
||||
}
|
||||
|
||||
@@ -99,5 +104,20 @@ const styles = StyleSheet.create({
|
||||
left: 0,
|
||||
right: 0,
|
||||
alignItems: 'center',
|
||||
}
|
||||
},
|
||||
footerRow: {
|
||||
flexDirection: 'row',
|
||||
alignItems: 'center',
|
||||
gap: 16,
|
||||
},
|
||||
skipBtn: {
|
||||
paddingVertical: 10,
|
||||
paddingHorizontal: 14,
|
||||
borderRadius: 12,
|
||||
backgroundColor: 'rgba(0,0,0,0.04)',
|
||||
},
|
||||
skipText: {
|
||||
fontSize: 16,
|
||||
color: OnboardingColors.textMuted,
|
||||
},
|
||||
});
|
||||
|
||||
@@ -59,5 +59,13 @@ target 'client' do
|
||||
:mac_catalyst_enabled => false,
|
||||
:ccache_enabled => ccache_enabled?(podfile_properties),
|
||||
)
|
||||
|
||||
# 生成并随归档产物携带 dSYM(用于崩溃符号化与 Upload Symbols Failed 修复)
|
||||
installer.pods_project.targets.each do |target|
|
||||
target.build_configurations.each do |build_config|
|
||||
build_config.build_settings['DEBUG_INFORMATION_FORMAT'] = 'dwarf-with-dsym'
|
||||
build_config.build_settings['DWARF_DSYM_FILE_SHOULD_ACCOMPANY_PRODUCT'] = 'YES'
|
||||
end
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
@@ -3,9 +3,6 @@ PODS:
|
||||
- ExpoModulesCore
|
||||
- EXConstants (18.0.13):
|
||||
- ExpoModulesCore
|
||||
- EXJSONUtils (0.15.0)
|
||||
- EXManifests (1.0.10):
|
||||
- ExpoModulesCore
|
||||
- EXNotifications (0.32.16):
|
||||
- ExpoModulesCore
|
||||
- Expo (54.0.32):
|
||||
@@ -33,177 +30,6 @@ PODS:
|
||||
- ReactCommon/turbomodule/core
|
||||
- ReactNativeDependencies
|
||||
- Yoga
|
||||
- expo-dev-client (6.0.20):
|
||||
- EXManifests
|
||||
- expo-dev-launcher
|
||||
- expo-dev-menu
|
||||
- expo-dev-menu-interface
|
||||
- EXUpdatesInterface
|
||||
- expo-dev-launcher (6.0.20):
|
||||
- EXManifests
|
||||
- expo-dev-launcher/Main (= 6.0.20)
|
||||
- expo-dev-menu
|
||||
- expo-dev-menu-interface
|
||||
- ExpoModulesCore
|
||||
- EXUpdatesInterface
|
||||
- hermes-engine
|
||||
- RCTRequired
|
||||
- RCTTypeSafety
|
||||
- React-Core
|
||||
- React-Core-prebuilt
|
||||
- React-debug
|
||||
- React-Fabric
|
||||
- React-featureflags
|
||||
- React-graphics
|
||||
- React-ImageManager
|
||||
- React-jsi
|
||||
- React-jsinspector
|
||||
- React-NativeModulesApple
|
||||
- React-RCTAppDelegate
|
||||
- React-RCTFabric
|
||||
- React-renderercss
|
||||
- React-rendererdebug
|
||||
- React-utils
|
||||
- ReactAppDependencyProvider
|
||||
- ReactCodegen
|
||||
- ReactCommon/turbomodule/bridging
|
||||
- ReactCommon/turbomodule/core
|
||||
- ReactNativeDependencies
|
||||
- Yoga
|
||||
- expo-dev-launcher/Main (6.0.20):
|
||||
- EXManifests
|
||||
- expo-dev-launcher/Unsafe
|
||||
- expo-dev-menu
|
||||
- expo-dev-menu-interface
|
||||
- ExpoModulesCore
|
||||
- EXUpdatesInterface
|
||||
- hermes-engine
|
||||
- RCTRequired
|
||||
- RCTTypeSafety
|
||||
- React-Core
|
||||
- React-Core-prebuilt
|
||||
- React-debug
|
||||
- React-Fabric
|
||||
- React-featureflags
|
||||
- React-graphics
|
||||
- React-ImageManager
|
||||
- React-jsi
|
||||
- React-jsinspector
|
||||
- React-NativeModulesApple
|
||||
- React-RCTAppDelegate
|
||||
- React-RCTFabric
|
||||
- React-renderercss
|
||||
- React-rendererdebug
|
||||
- React-utils
|
||||
- ReactAppDependencyProvider
|
||||
- ReactCodegen
|
||||
- ReactCommon/turbomodule/bridging
|
||||
- ReactCommon/turbomodule/core
|
||||
- ReactNativeDependencies
|
||||
- Yoga
|
||||
- expo-dev-launcher/Unsafe (6.0.20):
|
||||
- EXManifests
|
||||
- expo-dev-menu
|
||||
- expo-dev-menu-interface
|
||||
- ExpoModulesCore
|
||||
- EXUpdatesInterface
|
||||
- hermes-engine
|
||||
- RCTRequired
|
||||
- RCTTypeSafety
|
||||
- React-Core
|
||||
- React-Core-prebuilt
|
||||
- React-debug
|
||||
- React-Fabric
|
||||
- React-featureflags
|
||||
- React-graphics
|
||||
- React-ImageManager
|
||||
- React-jsi
|
||||
- React-jsinspector
|
||||
- React-NativeModulesApple
|
||||
- React-RCTAppDelegate
|
||||
- React-RCTFabric
|
||||
- React-renderercss
|
||||
- React-rendererdebug
|
||||
- React-utils
|
||||
- ReactAppDependencyProvider
|
||||
- ReactCodegen
|
||||
- ReactCommon/turbomodule/bridging
|
||||
- ReactCommon/turbomodule/core
|
||||
- ReactNativeDependencies
|
||||
- Yoga
|
||||
- expo-dev-menu (7.0.18):
|
||||
- expo-dev-menu/Main (= 7.0.18)
|
||||
- expo-dev-menu/ReactNativeCompatibles (= 7.0.18)
|
||||
- hermes-engine
|
||||
- RCTRequired
|
||||
- RCTTypeSafety
|
||||
- React-Core
|
||||
- React-Core-prebuilt
|
||||
- React-debug
|
||||
- React-Fabric
|
||||
- React-featureflags
|
||||
- React-graphics
|
||||
- React-ImageManager
|
||||
- React-jsi
|
||||
- React-NativeModulesApple
|
||||
- React-RCTFabric
|
||||
- React-renderercss
|
||||
- React-rendererdebug
|
||||
- React-utils
|
||||
- ReactCodegen
|
||||
- ReactCommon/turbomodule/bridging
|
||||
- ReactCommon/turbomodule/core
|
||||
- ReactNativeDependencies
|
||||
- Yoga
|
||||
- expo-dev-menu-interface (2.0.0)
|
||||
- expo-dev-menu/Main (7.0.18):
|
||||
- EXManifests
|
||||
- expo-dev-menu-interface
|
||||
- ExpoModulesCore
|
||||
- hermes-engine
|
||||
- RCTRequired
|
||||
- RCTTypeSafety
|
||||
- React-Core
|
||||
- React-Core-prebuilt
|
||||
- React-debug
|
||||
- React-Fabric
|
||||
- React-featureflags
|
||||
- React-graphics
|
||||
- React-ImageManager
|
||||
- React-jsi
|
||||
- React-jsinspector
|
||||
- React-NativeModulesApple
|
||||
- React-RCTFabric
|
||||
- React-renderercss
|
||||
- React-rendererdebug
|
||||
- React-utils
|
||||
- ReactCodegen
|
||||
- ReactCommon/turbomodule/bridging
|
||||
- ReactCommon/turbomodule/core
|
||||
- ReactNativeDependencies
|
||||
- Yoga
|
||||
- expo-dev-menu/ReactNativeCompatibles (7.0.18):
|
||||
- hermes-engine
|
||||
- RCTRequired
|
||||
- RCTTypeSafety
|
||||
- React-Core
|
||||
- React-Core-prebuilt
|
||||
- React-debug
|
||||
- React-Fabric
|
||||
- React-featureflags
|
||||
- React-graphics
|
||||
- React-ImageManager
|
||||
- React-jsi
|
||||
- React-NativeModulesApple
|
||||
- React-RCTFabric
|
||||
- React-renderercss
|
||||
- React-rendererdebug
|
||||
- React-utils
|
||||
- ReactCodegen
|
||||
- ReactCommon/turbomodule/bridging
|
||||
- ReactCommon/turbomodule/core
|
||||
- ReactNativeDependencies
|
||||
- Yoga
|
||||
- ExpoAsset (12.0.12):
|
||||
- ExpoModulesCore
|
||||
- ExpoFileSystem (19.0.21):
|
||||
@@ -248,8 +74,6 @@ PODS:
|
||||
- ExpoModulesCore
|
||||
- ExpoWebBrowser (15.0.10):
|
||||
- ExpoModulesCore
|
||||
- EXUpdatesInterface (2.0.0):
|
||||
- ExpoModulesCore
|
||||
- FBLazyVector (0.81.5)
|
||||
- hermes-engine (0.81.5):
|
||||
- hermes-engine/Pre-built (= 0.81.5)
|
||||
@@ -1974,28 +1798,6 @@ PODS:
|
||||
- ReactCommon/turbomodule/core
|
||||
- ReactNativeDependencies
|
||||
- Yoga
|
||||
- RNGestureHandler (2.30.0):
|
||||
- hermes-engine
|
||||
- RCTRequired
|
||||
- RCTTypeSafety
|
||||
- React-Core
|
||||
- React-Core-prebuilt
|
||||
- React-debug
|
||||
- React-Fabric
|
||||
- React-featureflags
|
||||
- React-graphics
|
||||
- React-ImageManager
|
||||
- React-jsi
|
||||
- React-NativeModulesApple
|
||||
- React-RCTFabric
|
||||
- React-renderercss
|
||||
- React-rendererdebug
|
||||
- React-utils
|
||||
- ReactCodegen
|
||||
- ReactCommon/turbomodule/bridging
|
||||
- ReactCommon/turbomodule/core
|
||||
- ReactNativeDependencies
|
||||
- Yoga
|
||||
- RNReanimated (4.1.6):
|
||||
- hermes-engine
|
||||
- RCTRequired
|
||||
@@ -2238,26 +2040,19 @@ PODS:
|
||||
DEPENDENCIES:
|
||||
- "EXApplication (from `../node_modules/.pnpm/expo-application@7.0.8_expo@54.0.32/node_modules/expo-application/ios`)"
|
||||
- "EXConstants (from `../node_modules/.pnpm/expo-constants@18.0.13_expo@54.0.32_react-native@0.81.5_@babel+core@7.28.6_@types+react@19.1.17_react@19.1.0_/node_modules/expo-constants/ios`)"
|
||||
- "EXJSONUtils (from `../node_modules/.pnpm/expo-json-utils@0.15.0/node_modules/expo-json-utils/ios`)"
|
||||
- "EXManifests (from `../node_modules/.pnpm/expo-manifests@1.0.10_expo@54.0.32/node_modules/expo-manifests/ios`)"
|
||||
- "EXNotifications (from `../node_modules/.pnpm/expo-notifications@0.32.16_expo@54.0.32_react-native@0.81.5_@babel+core@7.28.6_@types+r_758952db70529f49bda448def1c13c49/node_modules/expo-notifications/ios`)"
|
||||
- "Expo (from `../node_modules/.pnpm/expo@54.0.32_@babel+core@7.28.6_@expo+metro-runtime@6.1.2_expo-router@6.0.22_react-nati_18ad48ba284ee86e6eb1cb0f939697b0/node_modules/expo`)"
|
||||
- "expo-dev-client (from `../node_modules/.pnpm/expo-dev-client@6.0.20_expo@54.0.32/node_modules/expo-dev-client/ios`)"
|
||||
- "expo-dev-launcher (from `../node_modules/.pnpm/expo-dev-launcher@6.0.20_expo@54.0.32/node_modules/expo-dev-launcher`)"
|
||||
- "expo-dev-menu (from `../node_modules/.pnpm/expo-dev-menu@7.0.18_expo@54.0.32/node_modules/expo-dev-menu`)"
|
||||
- "expo-dev-menu-interface (from `../node_modules/.pnpm/expo-dev-menu-interface@2.0.0_expo@54.0.32/node_modules/expo-dev-menu-interface/ios`)"
|
||||
- "EXNotifications (from `../node_modules/.pnpm/expo-notifications@0.32.16_expo@54.0.32_react-native@0.81.5_@babel+core@7.28.6_@types+react@1_nvlvke5tn7wk5pigfsu7j4ieeq/node_modules/expo-notifications/ios`)"
|
||||
- "Expo (from `../node_modules/.pnpm/expo@54.0.32_@babel+core@7.28.6_@expo+metro-runtime@6.1.2_expo-router@6.0.22_react-native@0.8_7rhpxisdkrzvrgzbu7ct455kta/node_modules/expo`)"
|
||||
- "ExpoAsset (from `../node_modules/.pnpm/expo-asset@12.0.12_expo@54.0.32_react-native@0.81.5_@babel+core@7.28.6_@types+react@19.1.17_react@19.1.0__react@19.1.0/node_modules/expo-asset/ios`)"
|
||||
- "ExpoFileSystem (from `../node_modules/.pnpm/expo-file-system@19.0.21_expo@54.0.32_react-native@0.81.5_@babel+core@7.28.6_@types+react@19.1.17_react@19.1.0_/node_modules/expo-file-system/ios`)"
|
||||
- "ExpoFont (from `../node_modules/.pnpm/expo-font@14.0.11_expo@54.0.32_react-native@0.81.5_@babel+core@7.28.6_@types+react@19.1.17_react@19.1.0__react@19.1.0/node_modules/expo-font/ios`)"
|
||||
- "ExpoHead (from `../node_modules/.pnpm/expo-router@6.0.22_@expo+metro-runtime@6.1.2_@types+react@19.1.17_expo-constants@18.0.1_bd9aa16746ed7110429f931eb008e6d2/node_modules/expo-router/ios`)"
|
||||
- "ExpoHead (from `../node_modules/.pnpm/expo-router@6.0.22_@expo+metro-runtime@6.1.2_@types+react@19.1.17_expo-constants@18.0.13_expo_rjurfbyy5kjn57nkkfxix5iqea/node_modules/expo-router/ios`)"
|
||||
- "ExpoKeepAwake (from `../node_modules/.pnpm/expo-keep-awake@15.0.8_expo@54.0.32_react@19.1.0/node_modules/expo-keep-awake/ios`)"
|
||||
- "ExpoLinearGradient (from `../node_modules/.pnpm/expo-linear-gradient@15.0.8_expo@54.0.32_react-native@0.81.5_@babel+core@7.28.6_@types+_53aef72480df9baa4504f4743d9c64bb/node_modules/expo-linear-gradient/ios`)"
|
||||
- "ExpoLinearGradient (from `../node_modules/.pnpm/expo-linear-gradient@15.0.8_expo@54.0.32_react-native@0.81.5_@babel+core@7.28.6_@types+react@_e6k2hjkd5k4lph2ersbp3gfshy/node_modules/expo-linear-gradient/ios`)"
|
||||
- "ExpoLinking (from `../node_modules/.pnpm/expo-linking@8.0.11_expo@54.0.32_react-native@0.81.5_@babel+core@7.28.6_@types+react@19.1.17_react@19.1.0__react@19.1.0/node_modules/expo-linking/ios`)"
|
||||
- "ExpoLocalization (from `../node_modules/.pnpm/expo-localization@17.0.8_expo@54.0.32_react@19.1.0/node_modules/expo-localization/ios`)"
|
||||
- "ExpoModulesCore (from `../node_modules/.pnpm/expo-modules-core@3.0.29_react-native@0.81.5_@babel+core@7.28.6_@types+react@19.1.17_react@19.1.0__react@19.1.0/node_modules/expo-modules-core`)"
|
||||
- "ExpoSplashScreen (from `../node_modules/.pnpm/expo-splash-screen@31.0.13_expo@54.0.32/node_modules/expo-splash-screen/ios`)"
|
||||
- "ExpoWebBrowser (from `../node_modules/.pnpm/expo-web-browser@15.0.10_expo@54.0.32_react-native@0.81.5_@babel+core@7.28.6_@types+react@19.1.17_react@19.1.0_/node_modules/expo-web-browser/ios`)"
|
||||
- "EXUpdatesInterface (from `../node_modules/.pnpm/expo-updates-interface@2.0.0_expo@54.0.32/node_modules/expo-updates-interface/ios`)"
|
||||
- "FBLazyVector (from `../node_modules/.pnpm/react-native@0.81.5_@babel+core@7.28.6_@types+react@19.1.17_react@19.1.0/node_modules/react-native/Libraries/FBLazyVector`)"
|
||||
- "hermes-engine (from `../node_modules/.pnpm/react-native@0.81.5_@babel+core@7.28.6_@types+react@19.1.17_react@19.1.0/node_modules/react-native/sdks/hermes-engine/hermes-engine.podspec`)"
|
||||
- "RCTDeprecation (from `../node_modules/.pnpm/react-native@0.81.5_@babel+core@7.28.6_@types+react@19.1.17_react@19.1.0/node_modules/react-native/ReactApple/Libraries/RCTFoundation/RCTDeprecation`)"
|
||||
@@ -2294,7 +2089,7 @@ DEPENDENCIES:
|
||||
- "React-logger (from `../node_modules/.pnpm/react-native@0.81.5_@babel+core@7.28.6_@types+react@19.1.17_react@19.1.0/node_modules/react-native/ReactCommon/logger`)"
|
||||
- "React-Mapbuffer (from `../node_modules/.pnpm/react-native@0.81.5_@babel+core@7.28.6_@types+react@19.1.17_react@19.1.0/node_modules/react-native/ReactCommon`)"
|
||||
- "React-microtasksnativemodule (from `../node_modules/.pnpm/react-native@0.81.5_@babel+core@7.28.6_@types+react@19.1.17_react@19.1.0/node_modules/react-native/ReactCommon/react/nativemodule/microtasks`)"
|
||||
- "react-native-safe-area-context (from `../node_modules/.pnpm/react-native-safe-area-context@5.6.2_react-native@0.81.5_@babel+core@7.28.6_@types+reac_14122a3aa345cfabcc022a0f638ef16d/node_modules/react-native-safe-area-context`)"
|
||||
- "react-native-safe-area-context (from `../node_modules/.pnpm/react-native-safe-area-context@5.6.2_react-native@0.81.5_@babel+core@7.28.6_@types+react@19.1_azuxgonsvxb2yngtegtuvyxcpi/node_modules/react-native-safe-area-context`)"
|
||||
- "React-NativeModulesApple (from `../node_modules/.pnpm/react-native@0.81.5_@babel+core@7.28.6_@types+react@19.1.17_react@19.1.0/node_modules/react-native/ReactCommon/react/nativemodule/core/platform/ios`)"
|
||||
- "React-oscompat (from `../node_modules/.pnpm/react-native@0.81.5_@babel+core@7.28.6_@types+react@19.1.17_react@19.1.0/node_modules/react-native/ReactCommon/oscompat`)"
|
||||
- "React-perflogger (from `../node_modules/.pnpm/react-native@0.81.5_@babel+core@7.28.6_@types+react@19.1.17_react@19.1.0/node_modules/react-native/ReactCommon/reactperflogger`)"
|
||||
@@ -2326,12 +2121,11 @@ DEPENDENCIES:
|
||||
- ReactCodegen (from `build/generated/ios`)
|
||||
- "ReactCommon/turbomodule/core (from `../node_modules/.pnpm/react-native@0.81.5_@babel+core@7.28.6_@types+react@19.1.17_react@19.1.0/node_modules/react-native/ReactCommon`)"
|
||||
- "ReactNativeDependencies (from `../node_modules/.pnpm/react-native@0.81.5_@babel+core@7.28.6_@types+react@19.1.17_react@19.1.0/node_modules/react-native/third-party-podspecs/ReactNativeDependencies.podspec`)"
|
||||
- "RNCAsyncStorage (from `../node_modules/.pnpm/@react-native-async-storage+async-storage@2.2.0_react-native@0.81.5_@babel+core@7.28.6__ce3c4972004f3d6791573ec5b64bee38/node_modules/@react-native-async-storage/async-storage`)"
|
||||
- "RNGestureHandler (from `../node_modules/.pnpm/react-native-gesture-handler@2.30.0_react-native@0.81.5_@babel+core@7.28.6_@types+react_39cf7da47c9c8531caaa923ee740e293/node_modules/react-native-gesture-handler`)"
|
||||
- "RNReanimated (from `../node_modules/.pnpm/react-native-reanimated@4.1.6_@babel+core@7.28.6_react-native-worklets@0.5.1_@babel+cor_c7c888bd389fb93c9cfe2d3c1c8b0777/node_modules/react-native-reanimated`)"
|
||||
- "RNCAsyncStorage (from `../node_modules/.pnpm/@react-native-async-storage+async-storage@2.2.0_react-native@0.81.5_@babel+core@7.28.6_@types_fp4qq3a7mejmut52v6jrlvxlzi/node_modules/@react-native-async-storage/async-storage`)"
|
||||
- "RNReanimated (from `../node_modules/.pnpm/react-native-reanimated@4.1.6_@babel+core@7.28.6_react-native-worklets@0.5.1_@babel+core@7.28_ky3sbxf6i7nkyacc2hzg3xcz4q/node_modules/react-native-reanimated`)"
|
||||
- "RNScreens (from `../node_modules/.pnpm/react-native-screens@4.16.0_react-native@0.81.5_@babel+core@7.28.6_@types+react@19.1.17_react@19.1.0__react@19.1.0/node_modules/react-native-screens`)"
|
||||
- "RNSVG (from `../node_modules/.pnpm/react-native-svg@15.12.1_react-native@0.81.5_@babel+core@7.28.6_@types+react@19.1.17_react@19.1.0__react@19.1.0/node_modules/react-native-svg`)"
|
||||
- "RNWorklets (from `../node_modules/.pnpm/react-native-worklets@0.5.1_@babel+core@7.28.6_react-native@0.81.5_@babel+core@7.28.6_@_40f69326ce21d3f9f6d74d3965fd9adf/node_modules/react-native-worklets`)"
|
||||
- "RNWorklets (from `../node_modules/.pnpm/react-native-worklets@0.5.1_@babel+core@7.28.6_react-native@0.81.5_@babel+core@7.28.6_@types+_5atwepuw3zy3crkgvetf35tkve/node_modules/react-native-worklets`)"
|
||||
- "Yoga (from `../node_modules/.pnpm/react-native@0.81.5_@babel+core@7.28.6_@types+react@19.1.17_react@19.1.0/node_modules/react-native/ReactCommon/yoga`)"
|
||||
|
||||
EXTERNAL SOURCES:
|
||||
@@ -2339,22 +2133,10 @@ EXTERNAL SOURCES:
|
||||
:path: "../node_modules/.pnpm/expo-application@7.0.8_expo@54.0.32/node_modules/expo-application/ios"
|
||||
EXConstants:
|
||||
:path: "../node_modules/.pnpm/expo-constants@18.0.13_expo@54.0.32_react-native@0.81.5_@babel+core@7.28.6_@types+react@19.1.17_react@19.1.0_/node_modules/expo-constants/ios"
|
||||
EXJSONUtils:
|
||||
:path: "../node_modules/.pnpm/expo-json-utils@0.15.0/node_modules/expo-json-utils/ios"
|
||||
EXManifests:
|
||||
:path: "../node_modules/.pnpm/expo-manifests@1.0.10_expo@54.0.32/node_modules/expo-manifests/ios"
|
||||
EXNotifications:
|
||||
:path: "../node_modules/.pnpm/expo-notifications@0.32.16_expo@54.0.32_react-native@0.81.5_@babel+core@7.28.6_@types+r_758952db70529f49bda448def1c13c49/node_modules/expo-notifications/ios"
|
||||
:path: "../node_modules/.pnpm/expo-notifications@0.32.16_expo@54.0.32_react-native@0.81.5_@babel+core@7.28.6_@types+react@1_nvlvke5tn7wk5pigfsu7j4ieeq/node_modules/expo-notifications/ios"
|
||||
Expo:
|
||||
:path: "../node_modules/.pnpm/expo@54.0.32_@babel+core@7.28.6_@expo+metro-runtime@6.1.2_expo-router@6.0.22_react-nati_18ad48ba284ee86e6eb1cb0f939697b0/node_modules/expo"
|
||||
expo-dev-client:
|
||||
:path: "../node_modules/.pnpm/expo-dev-client@6.0.20_expo@54.0.32/node_modules/expo-dev-client/ios"
|
||||
expo-dev-launcher:
|
||||
:path: "../node_modules/.pnpm/expo-dev-launcher@6.0.20_expo@54.0.32/node_modules/expo-dev-launcher"
|
||||
expo-dev-menu:
|
||||
:path: "../node_modules/.pnpm/expo-dev-menu@7.0.18_expo@54.0.32/node_modules/expo-dev-menu"
|
||||
expo-dev-menu-interface:
|
||||
:path: "../node_modules/.pnpm/expo-dev-menu-interface@2.0.0_expo@54.0.32/node_modules/expo-dev-menu-interface/ios"
|
||||
:path: "../node_modules/.pnpm/expo@54.0.32_@babel+core@7.28.6_@expo+metro-runtime@6.1.2_expo-router@6.0.22_react-native@0.8_7rhpxisdkrzvrgzbu7ct455kta/node_modules/expo"
|
||||
ExpoAsset:
|
||||
:path: "../node_modules/.pnpm/expo-asset@12.0.12_expo@54.0.32_react-native@0.81.5_@babel+core@7.28.6_@types+react@19.1.17_react@19.1.0__react@19.1.0/node_modules/expo-asset/ios"
|
||||
ExpoFileSystem:
|
||||
@@ -2362,11 +2144,11 @@ EXTERNAL SOURCES:
|
||||
ExpoFont:
|
||||
:path: "../node_modules/.pnpm/expo-font@14.0.11_expo@54.0.32_react-native@0.81.5_@babel+core@7.28.6_@types+react@19.1.17_react@19.1.0__react@19.1.0/node_modules/expo-font/ios"
|
||||
ExpoHead:
|
||||
:path: "../node_modules/.pnpm/expo-router@6.0.22_@expo+metro-runtime@6.1.2_@types+react@19.1.17_expo-constants@18.0.1_bd9aa16746ed7110429f931eb008e6d2/node_modules/expo-router/ios"
|
||||
:path: "../node_modules/.pnpm/expo-router@6.0.22_@expo+metro-runtime@6.1.2_@types+react@19.1.17_expo-constants@18.0.13_expo_rjurfbyy5kjn57nkkfxix5iqea/node_modules/expo-router/ios"
|
||||
ExpoKeepAwake:
|
||||
:path: "../node_modules/.pnpm/expo-keep-awake@15.0.8_expo@54.0.32_react@19.1.0/node_modules/expo-keep-awake/ios"
|
||||
ExpoLinearGradient:
|
||||
:path: "../node_modules/.pnpm/expo-linear-gradient@15.0.8_expo@54.0.32_react-native@0.81.5_@babel+core@7.28.6_@types+_53aef72480df9baa4504f4743d9c64bb/node_modules/expo-linear-gradient/ios"
|
||||
:path: "../node_modules/.pnpm/expo-linear-gradient@15.0.8_expo@54.0.32_react-native@0.81.5_@babel+core@7.28.6_@types+react@_e6k2hjkd5k4lph2ersbp3gfshy/node_modules/expo-linear-gradient/ios"
|
||||
ExpoLinking:
|
||||
:path: "../node_modules/.pnpm/expo-linking@8.0.11_expo@54.0.32_react-native@0.81.5_@babel+core@7.28.6_@types+react@19.1.17_react@19.1.0__react@19.1.0/node_modules/expo-linking/ios"
|
||||
ExpoLocalization:
|
||||
@@ -2377,8 +2159,6 @@ EXTERNAL SOURCES:
|
||||
:path: "../node_modules/.pnpm/expo-splash-screen@31.0.13_expo@54.0.32/node_modules/expo-splash-screen/ios"
|
||||
ExpoWebBrowser:
|
||||
:path: "../node_modules/.pnpm/expo-web-browser@15.0.10_expo@54.0.32_react-native@0.81.5_@babel+core@7.28.6_@types+react@19.1.17_react@19.1.0_/node_modules/expo-web-browser/ios"
|
||||
EXUpdatesInterface:
|
||||
:path: "../node_modules/.pnpm/expo-updates-interface@2.0.0_expo@54.0.32/node_modules/expo-updates-interface/ios"
|
||||
FBLazyVector:
|
||||
:path: "../node_modules/.pnpm/react-native@0.81.5_@babel+core@7.28.6_@types+react@19.1.17_react@19.1.0/node_modules/react-native/Libraries/FBLazyVector"
|
||||
hermes-engine:
|
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@@ -2451,7 +2231,7 @@ EXTERNAL SOURCES:
|
||||
React-microtasksnativemodule:
|
||||
:path: "../node_modules/.pnpm/react-native@0.81.5_@babel+core@7.28.6_@types+react@19.1.17_react@19.1.0/node_modules/react-native/ReactCommon/react/nativemodule/microtasks"
|
||||
react-native-safe-area-context:
|
||||
:path: "../node_modules/.pnpm/react-native-safe-area-context@5.6.2_react-native@0.81.5_@babel+core@7.28.6_@types+reac_14122a3aa345cfabcc022a0f638ef16d/node_modules/react-native-safe-area-context"
|
||||
:path: "../node_modules/.pnpm/react-native-safe-area-context@5.6.2_react-native@0.81.5_@babel+core@7.28.6_@types+react@19.1_azuxgonsvxb2yngtegtuvyxcpi/node_modules/react-native-safe-area-context"
|
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React-NativeModulesApple:
|
||||
:path: "../node_modules/.pnpm/react-native@0.81.5_@babel+core@7.28.6_@types+react@19.1.17_react@19.1.0/node_modules/react-native/ReactCommon/react/nativemodule/core/platform/ios"
|
||||
React-oscompat:
|
||||
@@ -2515,43 +2295,34 @@ EXTERNAL SOURCES:
|
||||
ReactNativeDependencies:
|
||||
:podspec: "../node_modules/.pnpm/react-native@0.81.5_@babel+core@7.28.6_@types+react@19.1.17_react@19.1.0/node_modules/react-native/third-party-podspecs/ReactNativeDependencies.podspec"
|
||||
RNCAsyncStorage:
|
||||
:path: "../node_modules/.pnpm/@react-native-async-storage+async-storage@2.2.0_react-native@0.81.5_@babel+core@7.28.6__ce3c4972004f3d6791573ec5b64bee38/node_modules/@react-native-async-storage/async-storage"
|
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RNGestureHandler:
|
||||
:path: "../node_modules/.pnpm/react-native-gesture-handler@2.30.0_react-native@0.81.5_@babel+core@7.28.6_@types+react_39cf7da47c9c8531caaa923ee740e293/node_modules/react-native-gesture-handler"
|
||||
:path: "../node_modules/.pnpm/@react-native-async-storage+async-storage@2.2.0_react-native@0.81.5_@babel+core@7.28.6_@types_fp4qq3a7mejmut52v6jrlvxlzi/node_modules/@react-native-async-storage/async-storage"
|
||||
RNReanimated:
|
||||
:path: "../node_modules/.pnpm/react-native-reanimated@4.1.6_@babel+core@7.28.6_react-native-worklets@0.5.1_@babel+cor_c7c888bd389fb93c9cfe2d3c1c8b0777/node_modules/react-native-reanimated"
|
||||
:path: "../node_modules/.pnpm/react-native-reanimated@4.1.6_@babel+core@7.28.6_react-native-worklets@0.5.1_@babel+core@7.28_ky3sbxf6i7nkyacc2hzg3xcz4q/node_modules/react-native-reanimated"
|
||||
RNScreens:
|
||||
:path: "../node_modules/.pnpm/react-native-screens@4.16.0_react-native@0.81.5_@babel+core@7.28.6_@types+react@19.1.17_react@19.1.0__react@19.1.0/node_modules/react-native-screens"
|
||||
RNSVG:
|
||||
:path: "../node_modules/.pnpm/react-native-svg@15.12.1_react-native@0.81.5_@babel+core@7.28.6_@types+react@19.1.17_react@19.1.0__react@19.1.0/node_modules/react-native-svg"
|
||||
RNWorklets:
|
||||
:path: "../node_modules/.pnpm/react-native-worklets@0.5.1_@babel+core@7.28.6_react-native@0.81.5_@babel+core@7.28.6_@_40f69326ce21d3f9f6d74d3965fd9adf/node_modules/react-native-worklets"
|
||||
:path: "../node_modules/.pnpm/react-native-worklets@0.5.1_@babel+core@7.28.6_react-native@0.81.5_@babel+core@7.28.6_@types+_5atwepuw3zy3crkgvetf35tkve/node_modules/react-native-worklets"
|
||||
Yoga:
|
||||
:path: "../node_modules/.pnpm/react-native@0.81.5_@babel+core@7.28.6_@types+react@19.1.17_react@19.1.0/node_modules/react-native/ReactCommon/yoga"
|
||||
|
||||
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||||
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||||
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||||
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PRODUCT_BUNDLE_IDENTIFIER = com.damer.mindfulness;
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SUPPORTS_MACCATALYST = NO;
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SWIFT_OBJC_BRIDGING_HEADER = "client/client-Bridging-Header.h";
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IPHONEOS_DEPLOYMENT_TARGET = 15.1;
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PRODUCT_BUNDLE_IDENTIFIER = com.anonymous.client;
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PRODUCT_BUNDLE_IDENTIFIER = com.damer.mindfulness;
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PRODUCT_NAME = client;
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SUPPORTED_PLATFORMS = "iphoneos iphonesimulator";
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SUPPORTS_MACCATALYST = NO;
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@@ -543,7 +551,7 @@
|
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SUPPORTS_XR_DESIGNED_FOR_IPHONE_IPAD = NO;
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SWIFT_OBJC_BRIDGING_HEADER = "client/client-Bridging-Header.h";
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SWIFT_VERSION = 5.0;
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TARGETED_DEVICE_FAMILY = "1,2";
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TARGETED_DEVICE_FAMILY = 1;
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VERSIONING_SYSTEM = "apple-generic";
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};
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name = Release;
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@@ -680,8 +688,9 @@
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CURRENT_PROJECT_VERSION = 2;
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DEVELOPMENT_TEAM = WS92GPX9H2;
|
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|
||||
GCC_C_LANGUAGE_STANDARD = gnu17;
|
||||
GENERATE_INFOPLIST_FILE = YES;
|
||||
@@ -699,7 +708,7 @@
|
||||
MTL_ENABLE_DEBUG_INFO = INCLUDE_SOURCE;
|
||||
MTL_FAST_MATH = YES;
|
||||
OTHER_SWIFT_FLAGS = "$(inherited) -D EXPO_CONFIGURATION_DEBUG";
|
||||
PRODUCT_BUNDLE_IDENTIFIER = com.anonymous.client.emotionwidget;
|
||||
PRODUCT_BUNDLE_IDENTIFIER = com.damer.mindfulness.emotionwidget;
|
||||
PRODUCT_NAME = "$(TARGET_NAME)";
|
||||
SKIP_INSTALL = YES;
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||||
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||||
@@ -713,7 +722,7 @@
|
||||
SWIFT_OPTIMIZATION_LEVEL = "-Onone";
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||||
SWIFT_UPCOMING_FEATURE_MEMBER_IMPORT_VISIBILITY = YES;
|
||||
SWIFT_VERSION = 5.0;
|
||||
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|
||||
TARGETED_DEVICE_FAMILY = 1;
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||||
};
|
||||
name = Debug;
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||||
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||||
@@ -732,8 +741,9 @@
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
DEVELOPMENT_TEAM = WS92GPX9H2;
|
||||
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|
||||
GCC_C_LANGUAGE_STANDARD = gnu17;
|
||||
GENERATE_INFOPLIST_FILE = YES;
|
||||
@@ -750,7 +760,7 @@
|
||||
MARKETING_VERSION = 1.0.0;
|
||||
MTL_FAST_MATH = YES;
|
||||
OTHER_SWIFT_FLAGS = "$(inherited) -D EXPO_CONFIGURATION_RELEASE";
|
||||
PRODUCT_BUNDLE_IDENTIFIER = com.anonymous.client.emotionwidget;
|
||||
PRODUCT_BUNDLE_IDENTIFIER = com.damer.mindfulness.emotionwidget;
|
||||
PRODUCT_NAME = "$(TARGET_NAME)";
|
||||
SKIP_INSTALL = YES;
|
||||
STRING_CATALOG_GENERATE_SYMBOLS = YES;
|
||||
@@ -763,7 +773,7 @@
|
||||
SWIFT_EMIT_LOC_STRINGS = YES;
|
||||
SWIFT_UPCOMING_FEATURE_MEMBER_IMPORT_VISIBILITY = YES;
|
||||
SWIFT_VERSION = 5.0;
|
||||
TARGETED_DEVICE_FAMILY = "1,2";
|
||||
TARGETED_DEVICE_FAMILY = 1;
|
||||
};
|
||||
name = Release;
|
||||
};
|
||||
|
||||
|
Before Width: | Height: | Size: 58 KiB After Width: | Height: | Size: 142 KiB |
@@ -19,7 +19,7 @@
|
||||
<key>CFBundlePackageType</key>
|
||||
<string>$(PRODUCT_BUNDLE_PACKAGE_TYPE)</string>
|
||||
<key>CFBundleShortVersionString</key>
|
||||
<string>1.0.0</string>
|
||||
<string>$(MARKETING_VERSION)</string>
|
||||
<key>CFBundleSignature</key>
|
||||
<string>????</string>
|
||||
<key>CFBundleURLTypes</key>
|
||||
@@ -28,12 +28,12 @@
|
||||
<key>CFBundleURLSchemes</key>
|
||||
<array>
|
||||
<string>client</string>
|
||||
<string>com.anonymous.client</string>
|
||||
<string>$(PRODUCT_BUNDLE_IDENTIFIER)</string>
|
||||
</array>
|
||||
</dict>
|
||||
</array>
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||||
<key>CFBundleVersion</key>
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||||
<string>1</string>
|
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<string>$(CURRENT_PROJECT_VERSION)</string>
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<key>LSMinimumSystemVersion</key>
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||||
<string>12.0</string>
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||||
<key>LSRequiresIPhoneOS</key>
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@@ -52,7 +52,7 @@
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||||
<key>RCTNewArchEnabled</key>
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||||
<true/>
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<key>UILaunchStoryboardName</key>
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<string>SplashScreen</string>
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<string></string>
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||||
<key>UIRequiredDeviceCapabilities</key>
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||||
<array>
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<string>arm64</string>
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||||
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||||
<plist version="1.0">
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<dict>
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<key>aps-environment</key>
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<string>development</string>
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<string>production</string>
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</dict>
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||||
</plist>
|
||||
@@ -1,54 +1,28 @@
|
||||
import WidgetKit
|
||||
import SwiftUI
|
||||
|
||||
// V2:纯色背景 + 随机文案小组件(Small/Medium/Large + 点击跳转 Home)
|
||||
// V1:写死文案的小组件(Small/Medium/Large + 点击跳转 Home)
|
||||
|
||||
struct EmotionProvider: TimelineProvider {
|
||||
private let quotes = [
|
||||
"你已经很努力了,今天也值得被温柔对待。",
|
||||
"轻轻呼吸,感受当下的每一刻。",
|
||||
"所有的压力,都会在深呼吸中慢慢消散。",
|
||||
"给生活一点留白,给自己一点温柔。",
|
||||
"不要走得太快,等一等落下的灵魂。",
|
||||
"世界虽嘈杂,但你可以拥有一颗宁静的心。",
|
||||
"每一个瞬间,都是生命最好的安排。",
|
||||
"抱抱自己,辛苦了,亲爱的。",
|
||||
"慢一点也没关系,只要你在前行。",
|
||||
"今天,你对自己微笑了吗?",
|
||||
"愿你历经山河,仍觉得人间值得。",
|
||||
"心简单,世界就简单;心平顺,生活就平顺。",
|
||||
"即使生活偶尔晦暗,你也要成为自己的光。",
|
||||
"别让琐事挤走生活的快乐,别让压力消磨奋斗的激情。"
|
||||
]
|
||||
|
||||
func placeholder(in context: Context) -> EmotionEntry {
|
||||
EmotionEntry(date: Date(), text: quotes[0])
|
||||
EmotionEntry(date: Date())
|
||||
}
|
||||
|
||||
func getSnapshot(in context: Context, completion: @escaping (EmotionEntry) -> ()) {
|
||||
let entry = EmotionEntry(date: Date(), text: quotes.randomElement() ?? quotes[0])
|
||||
completion(entry)
|
||||
completion(EmotionEntry(date: Date()))
|
||||
}
|
||||
|
||||
func getTimeline(in context: Context, completion: @escaping (Timeline<EmotionEntry>) -> ()) {
|
||||
var entries: [EmotionEntry] = []
|
||||
let currentDate = Date()
|
||||
|
||||
// 生成未来 24 小时的 6 个条目,每 4 小时更换一次随机文案
|
||||
for hourOffset in 0..<6 {
|
||||
let entryDate = Calendar.current.date(byAdding: .hour, value: hourOffset * 4, to: currentDate)!
|
||||
let entry = EmotionEntry(date: entryDate, text: quotes.randomElement() ?? quotes[0])
|
||||
entries.append(entry)
|
||||
}
|
||||
|
||||
let timeline = Timeline(entries: entries, policy: .atEnd)
|
||||
completion(timeline)
|
||||
// V1:内容写死,不做数据更新;给一个较长的刷新间隔(系统仍可能自行调度)
|
||||
let entry = EmotionEntry(date: Date())
|
||||
let nextUpdate = Calendar.current.date(byAdding: .day, value: 7, to: Date())
|
||||
?? Date().addingTimeInterval(60 * 60 * 24 * 7)
|
||||
completion(Timeline(entries: [entry], policy: .after(nextUpdate)))
|
||||
}
|
||||
}
|
||||
|
||||
struct EmotionEntry: TimelineEntry {
|
||||
let date: Date
|
||||
let text: String
|
||||
}
|
||||
|
||||
struct EmotionWidgetView: View {
|
||||
@@ -56,37 +30,160 @@ struct EmotionWidgetView: View {
|
||||
@Environment(\.widgetFamily) var family
|
||||
|
||||
private let title = "正念"
|
||||
private let text = "你已经很努力了,今天也值得被温柔对待。"
|
||||
private let deepLink = URL(string: "client:///(app)/home")
|
||||
|
||||
// 背景色 #F7D9BF
|
||||
private let backgroundColor = Color(red: 247/255, green: 217/255, blue: 191/255)
|
||||
// 文本颜色(深咖色,适合搭配浅橘色背景)
|
||||
private let textColor = Color(red: 74/255, green: 52/255, blue: 40/255)
|
||||
|
||||
var body: some View {
|
||||
VStack(alignment: .center, spacing: 0) {
|
||||
Spacer(minLength: 0)
|
||||
switch family {
|
||||
case .systemSmall:
|
||||
smallView()
|
||||
case .systemMedium:
|
||||
mediumView()
|
||||
case .systemLarge:
|
||||
largeView()
|
||||
default:
|
||||
smallView()
|
||||
}
|
||||
}
|
||||
|
||||
Text(entry.text)
|
||||
.font(.system(size: family == .systemSmall ? 17 : 20, weight: .medium))
|
||||
.foregroundColor(textColor)
|
||||
.lineSpacing(6)
|
||||
.multilineTextAlignment(.center)
|
||||
.minimumScaleFactor(0.7)
|
||||
.fixedSize(horizontal: false, vertical: true)
|
||||
// 统一的“卡片背景”风格(iOS 15 兼容)
|
||||
private func cardBackground(colors: [Color]) -> some View {
|
||||
ZStack {
|
||||
LinearGradient(
|
||||
colors: colors,
|
||||
startPoint: .topLeading,
|
||||
endPoint: .bottomTrailing
|
||||
)
|
||||
// 轻微光斑,增加层次
|
||||
RadialGradient(
|
||||
gradient: Gradient(colors: [Color.white.opacity(0.16), Color.white.opacity(0.0)]),
|
||||
center: .topTrailing,
|
||||
startRadius: 10,
|
||||
endRadius: 180
|
||||
)
|
||||
}
|
||||
.overlay(
|
||||
RoundedRectangle(cornerRadius: 18, style: .continuous)
|
||||
.stroke(Color.white.opacity(0.14), lineWidth: 1)
|
||||
)
|
||||
.cornerRadius(18)
|
||||
}
|
||||
|
||||
private func chip(_ text: String) -> some View {
|
||||
Text(text)
|
||||
.font(.system(size: 12, weight: .semibold))
|
||||
.foregroundColor(Color.white.opacity(0.9))
|
||||
.padding(.horizontal, 10)
|
||||
.padding(.vertical, 6)
|
||||
.background(Color.white.opacity(0.14))
|
||||
.cornerRadius(999)
|
||||
}
|
||||
|
||||
private func smallView() -> some View {
|
||||
ZStack {
|
||||
cardBackground(colors: [
|
||||
Color(red: 0.06, green: 0.08, blue: 0.12),
|
||||
Color(red: 0.13, green: 0.16, blue: 0.22),
|
||||
])
|
||||
|
||||
VStack(alignment: .leading, spacing: 10) {
|
||||
HStack {
|
||||
chip(title)
|
||||
Spacer(minLength: 0)
|
||||
}
|
||||
|
||||
Text(text)
|
||||
.font(.system(size: 15, weight: .semibold))
|
||||
.foregroundColor(Color.white.opacity(0.92))
|
||||
.lineSpacing(2)
|
||||
.lineLimit(4)
|
||||
|
||||
Spacer(minLength: 0)
|
||||
|
||||
if family != .systemSmall {
|
||||
Text("Hey Mama")
|
||||
.font(.system(size: 10, weight: .semibold))
|
||||
.foregroundColor(textColor.opacity(0.3))
|
||||
.padding(.bottom, 4)
|
||||
Text("点我回到 App")
|
||||
.font(.system(size: 11, weight: .medium))
|
||||
.foregroundColor(Color.white.opacity(0.65))
|
||||
}
|
||||
.padding(14)
|
||||
}
|
||||
.widgetURL(deepLink)
|
||||
}
|
||||
|
||||
private func mediumView() -> some View {
|
||||
ZStack {
|
||||
cardBackground(colors: [
|
||||
Color(red: 0.06, green: 0.08, blue: 0.12),
|
||||
Color(red: 0.09, green: 0.11, blue: 0.17),
|
||||
])
|
||||
|
||||
HStack(alignment: .top, spacing: 14) {
|
||||
VStack(alignment: .leading, spacing: 10) {
|
||||
chip(title)
|
||||
Text(text)
|
||||
.font(.system(size: 17, weight: .semibold))
|
||||
.foregroundColor(Color.white.opacity(0.92))
|
||||
.lineSpacing(3)
|
||||
.lineLimit(5)
|
||||
|
||||
Spacer(minLength: 0)
|
||||
|
||||
Text("轻轻呼吸,回到当下")
|
||||
.font(.system(size: 12, weight: .medium))
|
||||
.foregroundColor(Color.white.opacity(0.7))
|
||||
}
|
||||
|
||||
// 右侧装饰区:让版面更饱满
|
||||
VStack(alignment: .trailing, spacing: 8) {
|
||||
Text(entry.date, style: .time)
|
||||
.font(.system(size: 12, weight: .semibold))
|
||||
.foregroundColor(Color.white.opacity(0.8))
|
||||
Spacer(minLength: 0)
|
||||
Text("今日")
|
||||
.font(.system(size: 28, weight: .bold))
|
||||
.foregroundColor(Color.white.opacity(0.12))
|
||||
}
|
||||
}
|
||||
.padding(family == .systemSmall ? 16 : 24)
|
||||
.frame(maxWidth: .infinity, maxHeight: .infinity) // 强制撑开容器
|
||||
.background(backgroundColor) // 将背景色直接应用到容器上
|
||||
.padding(16)
|
||||
}
|
||||
.widgetURL(deepLink)
|
||||
}
|
||||
|
||||
private func largeView() -> some View {
|
||||
ZStack {
|
||||
cardBackground(colors: [
|
||||
Color(red: 0.06, green: 0.08, blue: 0.12),
|
||||
Color(red: 0.14, green: 0.18, blue: 0.28),
|
||||
])
|
||||
|
||||
VStack(alignment: .leading, spacing: 14) {
|
||||
HStack {
|
||||
chip(title)
|
||||
Spacer(minLength: 0)
|
||||
Text(entry.date, style: .time)
|
||||
.font(.system(size: 12, weight: .semibold))
|
||||
.foregroundColor(Color.white.opacity(0.78))
|
||||
}
|
||||
|
||||
Text(text)
|
||||
.font(.system(size: 20, weight: .semibold))
|
||||
.foregroundColor(Color.white.opacity(0.92))
|
||||
.lineSpacing(4)
|
||||
.lineLimit(8)
|
||||
|
||||
Spacer(minLength: 0)
|
||||
|
||||
HStack {
|
||||
Text("点我回到 Home")
|
||||
.font(.system(size: 12, weight: .medium))
|
||||
.foregroundColor(Color.white.opacity(0.7))
|
||||
Spacer(minLength: 0)
|
||||
Text("🌿")
|
||||
.font(.system(size: 18))
|
||||
.opacity(0.9)
|
||||
}
|
||||
}
|
||||
.padding(18)
|
||||
}
|
||||
.widgetURL(deepLink)
|
||||
}
|
||||
}
|
||||
@@ -97,13 +194,7 @@ struct EmotionWidget: Widget {
|
||||
|
||||
var body: some WidgetConfiguration {
|
||||
StaticConfiguration(kind: kind, provider: EmotionProvider()) { entry in
|
||||
if #available(iOS 17.0, *) {
|
||||
EmotionWidgetView(entry: entry)
|
||||
.containerBackground(Color(red: 247/255, green: 217/255, blue: 191/255), for: .widget)
|
||||
} else {
|
||||
EmotionWidgetView(entry: entry)
|
||||
.background(Color(red: 247/255, green: 217/255, blue: 191/255))
|
||||
}
|
||||
}
|
||||
.configurationDisplayName("情绪小组件")
|
||||
.description("一段温柔提醒,陪你回到当下。")
|
||||
|
||||
@@ -20,9 +20,26 @@ function getOptionalEnv(name: string, fallback: string): string {
|
||||
return process.env[name] ?? fallback;
|
||||
}
|
||||
|
||||
export const APP_ENV = (getOptionalEnv('EXPO_PUBLIC_ENV', 'dev') as AppEnv) ?? 'dev';
|
||||
export type AppRuntimeEnv = 'local' | 'dev' | 'prod';
|
||||
|
||||
export const API_BASE_URL = getRequiredEnv('EXPO_PUBLIC_API_BASE_URL');
|
||||
export const APP_ENV = (getOptionalEnv('EXPO_PUBLIC_ENV', 'local') as AppRuntimeEnv) ?? 'local';
|
||||
|
||||
function getApiBaseUrl(env: AppRuntimeEnv): string {
|
||||
// 向后兼容:若直接提供了 EXPO_PUBLIC_API_BASE_URL,则优先使用(不再强制要求 *_DEV/_PROD)
|
||||
const direct = process.env.EXPO_PUBLIC_API_BASE_URL;
|
||||
if (direct && String(direct).trim()) return String(direct).trim();
|
||||
|
||||
// 约定:local/dev/prod 三套域名分别配置, 便于后续直接切环境而不改代码
|
||||
if (env === 'local') {
|
||||
return getOptionalEnv('EXPO_PUBLIC_API_BASE_URL_LOCAL', 'http://localhost:8000');
|
||||
}
|
||||
if (env === 'dev') {
|
||||
return getOptionalEnv('EXPO_PUBLIC_API_BASE_URL_DEV', getOptionalEnv('EXPO_PUBLIC_API_BASE_URL_LOCAL', 'http://localhost:8000'));
|
||||
}
|
||||
return getOptionalEnv('EXPO_PUBLIC_API_BASE_URL_PROD', getOptionalEnv('EXPO_PUBLIC_API_BASE_URL_LOCAL', 'http://localhost:8000'));
|
||||
}
|
||||
|
||||
export const API_BASE_URL = getApiBaseUrl(APP_ENV);
|
||||
|
||||
/**
|
||||
* 默认语言策略:
|
||||
|
||||
@@ -0,0 +1,51 @@
|
||||
import { describe, expect, it } from 'vitest';
|
||||
|
||||
import { buildUserProfileFromQuestionnaire } from '../index';
|
||||
import { mapOnboardingSelectionsToQuestionnaireAnswers } from '../onboardingMapping';
|
||||
|
||||
describe('Onboarding → UserProfileScoring 集成', () => {
|
||||
it('完整作答:Onboarding 选择能正确映射并生成画像', () => {
|
||||
const selections = {
|
||||
status: ['pregnant'],
|
||||
emotion: ['calm'],
|
||||
influence: ['work'],
|
||||
support: ['balance'],
|
||||
};
|
||||
|
||||
const answers = mapOnboardingSelectionsToQuestionnaireAnswers(selections);
|
||||
expect(answers).toEqual({
|
||||
mom_stage: 'expecting',
|
||||
emotion: 'calm',
|
||||
context: 'work',
|
||||
need: 'rest_balance',
|
||||
});
|
||||
|
||||
const p = buildUserProfileFromQuestionnaire(answers, {
|
||||
generatedAt: '2026-01-30T00:00:00Z',
|
||||
now: '2026-01-30T00:00:00Z',
|
||||
});
|
||||
|
||||
expect(p.stage).toEqual({ expecting: 1, parenting: 0, unknown: 0 });
|
||||
expect(p.emotion_score).toBe(0.8);
|
||||
expect(p.context).toEqual({ work: 1 });
|
||||
expect(p.need).toEqual({ rest_balance: 1 });
|
||||
expect(p.profile_answered).toEqual({ stage: true, emotion: true, context: true, need: true });
|
||||
});
|
||||
|
||||
it('全部跳过:仍能生成最小可计算画像(unknown=1)', () => {
|
||||
const answers = mapOnboardingSelectionsToQuestionnaireAnswers({});
|
||||
expect(answers).toEqual({ mom_stage: null, emotion: null, context: null, need: null });
|
||||
|
||||
const p = buildUserProfileFromQuestionnaire(answers, {
|
||||
generatedAt: '2026-01-30T00:00:00Z',
|
||||
now: '2026-01-30T00:00:00Z',
|
||||
});
|
||||
|
||||
expect(p.stage).toEqual({ unknown: 1 });
|
||||
expect(p.emotion_score).toBeNull();
|
||||
expect(p.context).toEqual({});
|
||||
expect(p.need).toEqual({});
|
||||
expect(p.profile_answered).toEqual({ stage: false, emotion: false, context: false, need: false });
|
||||
});
|
||||
});
|
||||
|
||||
@@ -5,6 +5,8 @@ export type {
|
||||
UserProfileV1_2_Extended,
|
||||
} from './types';
|
||||
|
||||
export type { OnboardingSelections } from './onboardingMapping';
|
||||
|
||||
export {
|
||||
buildUserProfileFromQuestionnaire,
|
||||
computeProfileAnswered,
|
||||
@@ -13,3 +15,5 @@ export {
|
||||
normalizeAnswers,
|
||||
} from './scoring';
|
||||
|
||||
export { mapOnboardingSelectionsToQuestionnaireAnswers } from './onboardingMapping';
|
||||
|
||||
|
||||
61
client/src/features/userProfileScoring/onboardingMapping.ts
Normal file
@@ -0,0 +1,61 @@
|
||||
import type { QuestionnaireAnswersV1_2 } from './types';
|
||||
|
||||
/**
|
||||
* Onboarding UI 的选项 ID → 标准问卷枚举(可跳过)
|
||||
*
|
||||
* 说明:
|
||||
* - UI 侧每题目前是单选,但数据结构是 string[];这里取第 1 个作为答案
|
||||
* - 不存在错误处理:未知/非法值统一按“跳过”处理(返回 null)
|
||||
*/
|
||||
export type OnboardingSelections = Record<string, string[] | undefined>;
|
||||
|
||||
export function mapOnboardingSelectionsToQuestionnaireAnswers(
|
||||
selections: OnboardingSelections
|
||||
): QuestionnaireAnswersV1_2 {
|
||||
return {
|
||||
mom_stage: mapMomStage(selections.status?.[0]),
|
||||
emotion: mapEmotion(selections.emotion?.[0]),
|
||||
context: mapContext(selections.influence?.[0]),
|
||||
need: mapNeed(selections.support?.[0]),
|
||||
};
|
||||
}
|
||||
|
||||
function mapMomStage(raw: string | undefined): QuestionnaireAnswersV1_2['mom_stage'] {
|
||||
// 跳过:null(显式跳过)
|
||||
if (!raw) return null;
|
||||
// UI id → 标准枚举
|
||||
if (raw === 'pregnant') return 'expecting';
|
||||
if (raw === 'has_kids') return 'parenting';
|
||||
if (raw === 'no_fill') return 'unknown';
|
||||
// 其他非法值:按跳过处理
|
||||
return null;
|
||||
}
|
||||
|
||||
function mapEmotion(raw: string | undefined): QuestionnaireAnswersV1_2['emotion'] {
|
||||
if (!raw) return null;
|
||||
// UI 当前选项:happy/calm/stressed/low
|
||||
if (raw === 'happy') return 'joyful';
|
||||
if (raw === 'calm') return 'calm';
|
||||
if (raw === 'stressed') return 'overwhelmed';
|
||||
if (raw === 'low') return 'low';
|
||||
return null;
|
||||
}
|
||||
|
||||
function mapContext(raw: string | undefined): QuestionnaireAnswersV1_2['context'] {
|
||||
if (!raw) return null;
|
||||
// UI id 已与标准枚举一致:family/work/relationship/friends/health
|
||||
if (raw === 'family' || raw === 'work' || raw === 'relationship' || raw === 'friends' || raw === 'health') return raw;
|
||||
return null;
|
||||
}
|
||||
|
||||
function mapNeed(raw: string | undefined): QuestionnaireAnswersV1_2['need'] {
|
||||
if (!raw) return null;
|
||||
// UI id → 标准枚举
|
||||
if (raw === 'emotional') return 'emotional_support';
|
||||
if (raw === 'parenting') return 'parenting_pressure';
|
||||
if (raw === 'self_worth') return 'self_worth';
|
||||
if (raw === 'anxiety') return 'anxiety_relief';
|
||||
if (raw === 'balance') return 'rest_balance';
|
||||
return null;
|
||||
}
|
||||
|
||||
63
client/src/services/recoApi.ts
Normal file
@@ -0,0 +1,63 @@
|
||||
import i18n from 'i18next';
|
||||
|
||||
import { API_BASE_URL } from '@/src/constants/env';
|
||||
import type { UserProfileV1_2 } from '@/src/features/userProfileScoring';
|
||||
|
||||
export type RecommendedItem = {
|
||||
content_id: number;
|
||||
text: string;
|
||||
final_score: number;
|
||||
fallback_level_final: number;
|
||||
explanations?: Record<string, unknown> | null;
|
||||
};
|
||||
|
||||
export type RecoMeta = Record<string, unknown>;
|
||||
|
||||
export type RecoEngineResult = {
|
||||
items: RecommendedItem[];
|
||||
meta: RecoMeta;
|
||||
};
|
||||
|
||||
export type RecoRequest = {
|
||||
k?: number;
|
||||
user_profile: UserProfileV1_2;
|
||||
already_recommended_ids?: Array<string | number>;
|
||||
touched_or_viewed_ids?: Array<string | number>;
|
||||
now?: string; // ISO8601(可选)
|
||||
};
|
||||
|
||||
function withTimeout(ms: number): AbortController {
|
||||
const controller = new AbortController();
|
||||
setTimeout(() => controller.abort(), ms);
|
||||
return controller;
|
||||
}
|
||||
|
||||
export async function fetchRecoFeed(req: RecoRequest): Promise<RecoEngineResult> {
|
||||
const controller = withTimeout(12_000);
|
||||
const url = `${API_BASE_URL}/v1/reco/feed`;
|
||||
|
||||
const res = await fetch(url, {
|
||||
method: 'POST',
|
||||
headers: {
|
||||
'Content-Type': 'application/json',
|
||||
// 让后端做 locale 选择(目前后端只区分 en/tc)
|
||||
'Accept-Language': i18n.language || 'en',
|
||||
},
|
||||
body: JSON.stringify({
|
||||
k: req.k,
|
||||
user_profile: req.user_profile,
|
||||
already_recommended_ids: req.already_recommended_ids ?? [],
|
||||
touched_or_viewed_ids: req.touched_or_viewed_ids ?? [],
|
||||
now: req.now,
|
||||
}),
|
||||
signal: controller.signal,
|
||||
});
|
||||
|
||||
if (!res.ok) {
|
||||
const text = await res.text().catch(() => '');
|
||||
throw new Error(`推荐接口请求失败:${res.status} ${res.statusText} ${text}`.trim());
|
||||
}
|
||||
|
||||
return (await res.json()) as RecoEngineResult;
|
||||
}
|
||||
|
||||
@@ -1,4 +1,5 @@
|
||||
import AsyncStorage from '@react-native-async-storage/async-storage';
|
||||
import type { UserProfileV1_2_Extended } from '@/src/features/userProfileScoring';
|
||||
|
||||
/**
|
||||
* 本地存储 key 统一管理,避免 UI 里散落硬编码
|
||||
@@ -9,6 +10,9 @@ const KEY_CONTENT_REACTIONS = 'content.reactions';
|
||||
const KEY_FAVORITES_ITEMS = 'favorites.items';
|
||||
const KEY_CONSENT_ACCEPTED = 'consent.accepted';
|
||||
const KEY_USER_PROFILE = 'user.profile';
|
||||
const KEY_USER_PROFILE_SCORING = 'user.profileScoring';
|
||||
const KEY_RECO_FEED_CACHE = 'reco.feedCache';
|
||||
const KEY_RECO_FEED_HISTORY = 'reco.feedHistory';
|
||||
const KEY_UI_THEME_MODE = 'ui.theme.mode';
|
||||
const KEY_DAILY_REMINDER_SETTINGS = 'dailyReminder.settings';
|
||||
|
||||
@@ -20,11 +24,42 @@ export type UserProfile = {
|
||||
name?: string;
|
||||
intents?: string[];
|
||||
};
|
||||
|
||||
/**
|
||||
* 用户画像(问卷打分输出)
|
||||
* 说明:用于推荐/Push/Widget 统一复用;结构以 `src/features/userProfileScoring` 输出为准。
|
||||
*/
|
||||
export type UserProfileScoring = UserProfileV1_2_Extended;
|
||||
export type DailyReminderSettings = {
|
||||
timesPerDay: number;
|
||||
pushEnabled: boolean;
|
||||
};
|
||||
|
||||
export type RecoFeedCacheItem = {
|
||||
content_id: number;
|
||||
text: string;
|
||||
};
|
||||
|
||||
export type RecoFeedCache = {
|
||||
saved_at: string; // ISO8601
|
||||
items: RecoFeedCacheItem[];
|
||||
meta?: Record<string, unknown>;
|
||||
};
|
||||
|
||||
/**
|
||||
* Feed 链路可观测输入(用于下一次请求携带给后端)
|
||||
*
|
||||
* - already_recommended_ids:本设备已下发过的内容(避免重复下发)
|
||||
* - touched_or_viewed_ids:本设备用户已看过/划过的内容(用于频控/去重/降重复)
|
||||
*
|
||||
* 说明:后端不需要“实时知道”,只要在下一次拉取时带上即可。
|
||||
*/
|
||||
export type RecoFeedHistory = {
|
||||
updated_at: string; // ISO8601
|
||||
already_recommended_ids: number[];
|
||||
touched_or_viewed_ids: number[];
|
||||
};
|
||||
|
||||
|
||||
async function getJson<T>(key: string, fallback: T): Promise<T> {
|
||||
const raw = await AsyncStorage.getItem(key);
|
||||
@@ -70,6 +105,7 @@ export async function setReaction(contentId: string, reaction: Reaction): Promis
|
||||
}
|
||||
|
||||
export type FavoriteItem = {
|
||||
favId: string; // 唯一标识,支持重复点赞同一文案
|
||||
id: string;
|
||||
date: string;
|
||||
themeMode: ThemeMode;
|
||||
@@ -82,15 +118,15 @@ export async function getFavorites(): Promise<FavoriteItem[]> {
|
||||
|
||||
export async function addFavorite(item: FavoriteItem): Promise<void> {
|
||||
const list = await getFavorites();
|
||||
if (list.some(i => i.id === item.id)) return;
|
||||
// 允许重复点赞,不再根据 id 去重
|
||||
const newList = [item, ...list];
|
||||
console.log('Adding to favorites, new list size:', newList.length);
|
||||
await setJson(KEY_FAVORITES_ITEMS, newList);
|
||||
}
|
||||
|
||||
export async function removeFavorite(contentId: string): Promise<void> {
|
||||
export async function removeFavorite(favId: string): Promise<void> {
|
||||
const list = await getFavorites();
|
||||
const next = list.filter(item => item.id !== contentId);
|
||||
const next = list.filter(item => item.favId !== favId);
|
||||
await setJson(KEY_FAVORITES_ITEMS, next);
|
||||
}
|
||||
|
||||
@@ -122,6 +158,103 @@ export async function setUserProfile(profile: UserProfile): Promise<void> {
|
||||
await setJson(KEY_USER_PROFILE, { ...current, ...profile });
|
||||
}
|
||||
|
||||
export async function getUserProfileScoring(): Promise<UserProfileScoring | null> {
|
||||
const raw = await AsyncStorage.getItem(KEY_USER_PROFILE_SCORING);
|
||||
if (!raw) return null;
|
||||
try {
|
||||
return JSON.parse(raw) as UserProfileScoring;
|
||||
} catch {
|
||||
return null;
|
||||
}
|
||||
}
|
||||
|
||||
export async function setUserProfileScoring(profile: UserProfileScoring): Promise<void> {
|
||||
await setJson(KEY_USER_PROFILE_SCORING, profile);
|
||||
}
|
||||
|
||||
export async function getRecoFeedCache(): Promise<RecoFeedCache | null> {
|
||||
const raw = await AsyncStorage.getItem(KEY_RECO_FEED_CACHE);
|
||||
if (!raw) return null;
|
||||
try {
|
||||
return JSON.parse(raw) as RecoFeedCache;
|
||||
} catch {
|
||||
return null;
|
||||
}
|
||||
}
|
||||
|
||||
export async function setRecoFeedCache(cache: RecoFeedCache): Promise<void> {
|
||||
await setJson(KEY_RECO_FEED_CACHE, cache);
|
||||
}
|
||||
|
||||
export async function getRecoFeedHistory(): Promise<RecoFeedHistory> {
|
||||
const raw = await AsyncStorage.getItem(KEY_RECO_FEED_HISTORY);
|
||||
if (!raw) {
|
||||
return {
|
||||
updated_at: new Date().toISOString(),
|
||||
already_recommended_ids: [],
|
||||
touched_or_viewed_ids: [],
|
||||
};
|
||||
}
|
||||
try {
|
||||
const parsed = JSON.parse(raw) as Partial<RecoFeedHistory>;
|
||||
return {
|
||||
updated_at: typeof parsed.updated_at === 'string' ? parsed.updated_at : new Date().toISOString(),
|
||||
already_recommended_ids: Array.isArray(parsed.already_recommended_ids)
|
||||
? parsed.already_recommended_ids.filter((x) => Number.isFinite(x)).map((x) => Number(x))
|
||||
: [],
|
||||
touched_or_viewed_ids: Array.isArray(parsed.touched_or_viewed_ids)
|
||||
? parsed.touched_or_viewed_ids.filter((x) => Number.isFinite(x)).map((x) => Number(x))
|
||||
: [],
|
||||
};
|
||||
} catch {
|
||||
return {
|
||||
updated_at: new Date().toISOString(),
|
||||
already_recommended_ids: [],
|
||||
touched_or_viewed_ids: [],
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
export async function setRecoFeedHistory(history: RecoFeedHistory): Promise<void> {
|
||||
await setJson(KEY_RECO_FEED_HISTORY, history);
|
||||
}
|
||||
|
||||
function uniqKeepLatest(list: number[], max: number): number[] {
|
||||
const seen = new Set<number>();
|
||||
const out: number[] = [];
|
||||
for (let i = list.length - 1; i >= 0; i -= 1) {
|
||||
const v = list[i];
|
||||
if (!Number.isFinite(v)) continue;
|
||||
if (seen.has(v)) continue;
|
||||
seen.add(v);
|
||||
out.push(v);
|
||||
if (out.length >= max) break;
|
||||
}
|
||||
return out.reverse();
|
||||
}
|
||||
|
||||
export async function recordRecoFeedServed(contentIds: number[]): Promise<void> {
|
||||
if (!contentIds?.length) return;
|
||||
const h = await getRecoFeedHistory();
|
||||
const next = {
|
||||
...h,
|
||||
updated_at: new Date().toISOString(),
|
||||
already_recommended_ids: uniqKeepLatest([...h.already_recommended_ids, ...contentIds], 500),
|
||||
};
|
||||
await setRecoFeedHistory(next);
|
||||
}
|
||||
|
||||
export async function recordRecoFeedTouched(contentId: number): Promise<void> {
|
||||
if (!Number.isFinite(contentId)) return;
|
||||
const h = await getRecoFeedHistory();
|
||||
const next = {
|
||||
...h,
|
||||
updated_at: new Date().toISOString(),
|
||||
touched_or_viewed_ids: uniqKeepLatest([...h.touched_or_viewed_ids, contentId], 500),
|
||||
};
|
||||
await setRecoFeedHistory(next);
|
||||
}
|
||||
|
||||
export async function getDailyReminderSettings(): Promise<DailyReminderSettings> {
|
||||
const s = await getJson<DailyReminderSettings>(KEY_DAILY_REMINDER_SETTINGS, {
|
||||
timesPerDay: 3,
|
||||
|
||||
20
server/.dockerignore
Normal file
@@ -0,0 +1,20 @@
|
||||
.venv
|
||||
__pycache__/
|
||||
*.pyc
|
||||
*.pyo
|
||||
*.pyd
|
||||
.pytest_cache/
|
||||
.mypy_cache/
|
||||
.ruff_cache/
|
||||
|
||||
# 本地/测试数据
|
||||
.test.db
|
||||
*.db
|
||||
|
||||
# 测试与开发脚本(按需移除)
|
||||
tests/
|
||||
.env*
|
||||
|
||||
# Git 元数据
|
||||
.git/
|
||||
.gitignore
|
||||
BIN
server/.test.db
Normal file
27
server/Dockerfile
Normal file
@@ -0,0 +1,27 @@
|
||||
FROM python:3.11-slim
|
||||
|
||||
# 运行时基础环境
|
||||
ENV PYTHONDONTWRITEBYTECODE=1 \
|
||||
PYTHONUNBUFFERED=1
|
||||
|
||||
WORKDIR /app
|
||||
|
||||
# 系统依赖(按需扩展;多数依赖为纯 Python)
|
||||
RUN apt-get update \
|
||||
&& apt-get install -y --no-install-recommends build-essential curl \
|
||||
&& rm -rf /var/lib/apt/lists/*
|
||||
|
||||
# 先复制依赖清单以利用 Docker layer cache
|
||||
COPY requirements.txt /app/requirements.txt
|
||||
RUN python -m pip install -U pip \
|
||||
&& pip install --no-cache-dir -r /app/requirements.txt
|
||||
|
||||
# 复制后端代码与迁移配置
|
||||
COPY app /app/app
|
||||
COPY alembic /app/alembic
|
||||
COPY alembic.ini /app/alembic.ini
|
||||
|
||||
EXPOSE 8000
|
||||
|
||||
# 生产镜像默认不开启 reload
|
||||
CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"]
|
||||
62
server/app/api/limits.py
Normal file
@@ -0,0 +1,62 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import time
|
||||
from dataclasses import dataclass, field
|
||||
from typing import Dict, Tuple
|
||||
|
||||
from fastapi import HTTPException, Request
|
||||
|
||||
|
||||
@dataclass
|
||||
class FixedWindowRateLimiter:
|
||||
"""
|
||||
固定窗口限流(内存版)。
|
||||
|
||||
约束:
|
||||
- 适用于单进程/单实例;多进程/多实例下不共享计数(V1 可接受)
|
||||
- 窗口粒度:按分钟 bucket(window_seconds 建议为 60)
|
||||
"""
|
||||
|
||||
limit: int
|
||||
window_seconds: int
|
||||
_counters: Dict[Tuple[str, int], int] = field(default_factory=dict)
|
||||
_last_gc_bucket: int = 0
|
||||
|
||||
def _bucket(self, now_ts: float) -> int:
|
||||
return int(now_ts // float(self.window_seconds))
|
||||
|
||||
def _gc(self, current_bucket: int) -> None:
|
||||
# 每隔一段时间清理一次,避免 dict 无限增长(保留最近 3 个 bucket)
|
||||
if self._last_gc_bucket == current_bucket:
|
||||
return
|
||||
self._last_gc_bucket = current_bucket
|
||||
keep_from = current_bucket - 2
|
||||
to_delete = [k for k in self._counters.keys() if k[1] < keep_from]
|
||||
for k in to_delete:
|
||||
self._counters.pop(k, None)
|
||||
|
||||
def allow(self, *, key: str, now_ts: float) -> None:
|
||||
bucket = self._bucket(now_ts)
|
||||
self._gc(bucket)
|
||||
|
||||
k = (str(key), int(bucket))
|
||||
n = int(self._counters.get(k, 0)) + 1
|
||||
self._counters[k] = n
|
||||
if n > int(self.limit):
|
||||
raise HTTPException(status_code=429, detail="rate_limited")
|
||||
|
||||
|
||||
_reco_rate_limiter = FixedWindowRateLimiter(limit=10, window_seconds=60)
|
||||
|
||||
|
||||
async def rate_limit_reco_by_ip(request: Request) -> None:
|
||||
"""
|
||||
推荐接口限流:按 IP,1 分钟 10 次。
|
||||
"""
|
||||
|
||||
ip = "unknown"
|
||||
if request.client and request.client.host:
|
||||
ip = str(request.client.host)
|
||||
|
||||
_reco_rate_limiter.allow(key=ip, now_ts=time.time())
|
||||
|
||||
156
server/app/api/v1/reco.py
Normal file
@@ -0,0 +1,156 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from datetime import datetime, timezone
|
||||
from typing import Any, Optional
|
||||
|
||||
from fastapi import APIRouter, Depends, Header
|
||||
from pydantic import BaseModel, Field
|
||||
from sqlalchemy.ext.asyncio import AsyncSession
|
||||
|
||||
from app.api.limits import rate_limit_reco_by_ip
|
||||
from app.db.session import get_db
|
||||
from app.features.personalized_reco.content_repository.interface import ContentRepository
|
||||
from app.features.personalized_reco.content_repository.sqlalchemy_repo import SqlAlchemyContentRepository
|
||||
from app.features.personalized_reco.reco_engine import recommend
|
||||
from app.features.personalized_reco.reco_engine.types import RecoConstraints, RecoEngineResult
|
||||
from app.features.user_profile_scoring.types import UserProfileV1_2
|
||||
|
||||
router = APIRouter(
|
||||
prefix="/v1/reco",
|
||||
tags=["reco"],
|
||||
dependencies=[Depends(rate_limit_reco_by_ip)],
|
||||
)
|
||||
|
||||
|
||||
class RecoRequest(BaseModel):
|
||||
k: Optional[int] = None
|
||||
user_profile: UserProfileV1_2
|
||||
already_recommended_ids: list[Any] = Field(default_factory=list)
|
||||
touched_or_viewed_ids: list[Any] = Field(default_factory=list)
|
||||
now: Optional[datetime] = None
|
||||
|
||||
|
||||
def _parse_now_from_header(x_now: Optional[str]) -> Optional[datetime]:
|
||||
if not x_now:
|
||||
return None
|
||||
raw = str(x_now).strip()
|
||||
if not raw:
|
||||
return None
|
||||
# 支持 Z
|
||||
if raw.endswith("Z"):
|
||||
raw = raw[:-1] + "+00:00"
|
||||
try:
|
||||
dt = datetime.fromisoformat(raw)
|
||||
except Exception:
|
||||
return None
|
||||
if dt.tzinfo is None:
|
||||
dt = dt.replace(tzinfo=timezone.utc)
|
||||
return dt
|
||||
|
||||
|
||||
def _pick_now(*, header_now: Optional[str], body_now: Optional[datetime]) -> datetime:
|
||||
dt = _parse_now_from_header(header_now)
|
||||
if dt is not None:
|
||||
return dt
|
||||
if body_now is not None:
|
||||
if body_now.tzinfo is None:
|
||||
return body_now.replace(tzinfo=timezone.utc)
|
||||
return body_now
|
||||
return datetime.now(timezone.utc)
|
||||
|
||||
|
||||
def _pick_locale_from_accept_language(accept_language: Optional[str]) -> str:
|
||||
"""
|
||||
从 Accept-Language 映射 locale:
|
||||
- 缺失/空 -> en
|
||||
- 含 zh-TW/zh-HK/tc -> tc
|
||||
- 其他 -> en
|
||||
"""
|
||||
|
||||
raw = (accept_language or "").strip().lower()
|
||||
if not raw:
|
||||
return "en"
|
||||
if "zh-tw" in raw or "zh-hk" in raw or "tc" in raw:
|
||||
return "tc"
|
||||
return "en"
|
||||
|
||||
|
||||
async def get_reco_repo(db: AsyncSession = Depends(get_db)) -> ContentRepository:
|
||||
"""
|
||||
构造推荐 repo(可在测试中 override,避免依赖真实 DB)。
|
||||
"""
|
||||
|
||||
return SqlAlchemyContentRepository(db)
|
||||
|
||||
|
||||
@router.post("/feed", response_model=RecoEngineResult)
|
||||
async def reco_feed(
|
||||
req: RecoRequest,
|
||||
repo: ContentRepository = Depends(get_reco_repo),
|
||||
x_now: Optional[str] = Header(default=None, alias="X-Now"),
|
||||
accept_language: Optional[str] = Header(default=None, alias="Accept-Language"),
|
||||
) -> RecoEngineResult:
|
||||
k_i = 30 if req.k is None else int(req.k)
|
||||
now = _pick_now(header_now=x_now, body_now=req.now)
|
||||
locale = _pick_locale_from_accept_language(accept_language)
|
||||
|
||||
return await recommend(
|
||||
repo=repo,
|
||||
scene="feed",
|
||||
user_profile=req.user_profile,
|
||||
already_recommended_ids=list(req.already_recommended_ids or []),
|
||||
touched_or_viewed_ids=list(req.touched_or_viewed_ids or []),
|
||||
k=k_i,
|
||||
now=now,
|
||||
locale=locale,
|
||||
constraints=RecoConstraints(),
|
||||
)
|
||||
|
||||
|
||||
@router.post("/push", response_model=RecoEngineResult)
|
||||
async def reco_push(
|
||||
req: RecoRequest,
|
||||
repo: ContentRepository = Depends(get_reco_repo),
|
||||
x_now: Optional[str] = Header(default=None, alias="X-Now"),
|
||||
accept_language: Optional[str] = Header(default=None, alias="Accept-Language"),
|
||||
) -> RecoEngineResult:
|
||||
k_i = 1 if req.k is None else int(req.k)
|
||||
now = _pick_now(header_now=x_now, body_now=req.now)
|
||||
locale = _pick_locale_from_accept_language(accept_language)
|
||||
|
||||
return await recommend(
|
||||
repo=repo,
|
||||
scene="push",
|
||||
user_profile=req.user_profile,
|
||||
already_recommended_ids=list(req.already_recommended_ids or []),
|
||||
touched_or_viewed_ids=list(req.touched_or_viewed_ids or []),
|
||||
k=k_i,
|
||||
now=now,
|
||||
locale=locale,
|
||||
constraints=RecoConstraints(),
|
||||
)
|
||||
|
||||
|
||||
@router.post("/widget", response_model=RecoEngineResult)
|
||||
async def reco_widget(
|
||||
req: RecoRequest,
|
||||
repo: ContentRepository = Depends(get_reco_repo),
|
||||
x_now: Optional[str] = Header(default=None, alias="X-Now"),
|
||||
accept_language: Optional[str] = Header(default=None, alias="Accept-Language"),
|
||||
) -> RecoEngineResult:
|
||||
k_i = 1 if req.k is None else int(req.k)
|
||||
now = _pick_now(header_now=x_now, body_now=req.now)
|
||||
locale = _pick_locale_from_accept_language(accept_language)
|
||||
|
||||
return await recommend(
|
||||
repo=repo,
|
||||
scene="widget",
|
||||
user_profile=req.user_profile,
|
||||
already_recommended_ids=list(req.already_recommended_ids or []),
|
||||
touched_or_viewed_ids=list(req.touched_or_viewed_ids or []),
|
||||
k=k_i,
|
||||
now=now,
|
||||
locale=locale,
|
||||
constraints=RecoConstraints(),
|
||||
)
|
||||
|
||||
6
server/app/features/personalized_reco/__init__.py
Normal file
@@ -0,0 +1,6 @@
|
||||
"""
|
||||
Personalized Reco(个性化推荐)功能模块集合。
|
||||
|
||||
该目录用于承载推荐引擎与其子模块(数据访问、打分、重排、可观测等)。
|
||||
"""
|
||||
|
||||
@@ -0,0 +1,8 @@
|
||||
"""
|
||||
Content Repository(候选查询与数据访问层)。
|
||||
|
||||
说明:
|
||||
- 本模块为推荐引擎提供可注入的数据访问接口(与 ORM/SQL 解耦)。
|
||||
- 负责将 DB 存储形态规范化为上层稳定的 ContentProfile 结构。
|
||||
"""
|
||||
|
||||
@@ -0,0 +1,36 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Protocol
|
||||
|
||||
from app.features.personalized_reco.content_repository.types import ContentProfileDTO
|
||||
|
||||
|
||||
class ContentRepository(Protocol):
|
||||
"""
|
||||
推荐引擎依赖的内容数据访问抽象接口(用于解耦 ORM/SQL)。
|
||||
"""
|
||||
|
||||
async def fetch_candidates(
|
||||
self,
|
||||
*,
|
||||
scene: str,
|
||||
user_profile: object,
|
||||
fallback_level: int,
|
||||
limit: int,
|
||||
locale: str,
|
||||
exclude_content_ids: list[int] | None = None,
|
||||
) -> list[ContentProfileDTO]:
|
||||
"""
|
||||
按场景与用户画像拉取候选内容画像(用于候选池)。
|
||||
"""
|
||||
|
||||
async def fetch_contents_by_ids(
|
||||
self,
|
||||
*,
|
||||
content_ids: list[int],
|
||||
locale: str,
|
||||
) -> list[ContentProfileDTO]:
|
||||
"""
|
||||
按 content_id 批量获取内容画像(去重、按输入顺序返回;缺语言/缺记录的 id 跳过)。
|
||||
"""
|
||||
|
||||
@@ -0,0 +1,130 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Any
|
||||
|
||||
from app.features.personalized_reco.content_repository.types import Locale, normalize_locale
|
||||
|
||||
|
||||
CONTEXT_KEYS: tuple[str, ...] = ("family", "work", "relationship", "friends", "health")
|
||||
NEED_KEYS: tuple[str, ...] = (
|
||||
"emotional_support",
|
||||
"parenting_pressure",
|
||||
"self_worth",
|
||||
"anxiety_relief",
|
||||
"rest_balance",
|
||||
)
|
||||
|
||||
|
||||
def _normalize_discrete_score(v: Any, *, default: float = 0.5) -> float:
|
||||
"""
|
||||
将 suitability 的离散值规范化为 0/0.5/1。
|
||||
|
||||
非法值一律兜底 default(默认 0.5)。
|
||||
"""
|
||||
|
||||
try:
|
||||
if v in (0, 0.0):
|
||||
return 0.0
|
||||
if v in (0.5,):
|
||||
return 0.5
|
||||
if v in (1, 1.0):
|
||||
return 1.0
|
||||
# 允许字符串形式的 "0"/"0.5"/"1"
|
||||
if isinstance(v, str):
|
||||
s = v.strip()
|
||||
if s == "0":
|
||||
return 0.0
|
||||
if s == "0.5":
|
||||
return 0.5
|
||||
if s == "1":
|
||||
return 1.0
|
||||
except Exception:
|
||||
return default
|
||||
return default
|
||||
|
||||
|
||||
def normalize_suitability(raw: Any, *, keys: tuple[str, ...]) -> dict[str, float]:
|
||||
"""
|
||||
解析 suitability JSON,缺失时补齐全 0.5。
|
||||
|
||||
raw 期望为 dict;否则视为缺失。
|
||||
"""
|
||||
|
||||
data: dict[str, Any] = raw if isinstance(raw, dict) else {}
|
||||
return {k: _normalize_discrete_score(data.get(k), default=0.5) for k in keys}
|
||||
|
||||
|
||||
def normalize_review_confidence(raw: Any) -> float:
|
||||
"""
|
||||
review_confidence 缺失/NULL 时兜底 0.7。
|
||||
"""
|
||||
|
||||
try:
|
||||
if raw is None:
|
||||
return 0.7
|
||||
v = float(raw)
|
||||
if 0.0 <= v <= 1.0:
|
||||
return v
|
||||
except Exception:
|
||||
pass
|
||||
return 0.7
|
||||
|
||||
|
||||
def normalize_personalization_power(raw: Any) -> float:
|
||||
"""
|
||||
DB 约定存 0/5/10,读取层输出 0/0.5/1。
|
||||
"""
|
||||
|
||||
try:
|
||||
if raw is None:
|
||||
return 0.0
|
||||
v = int(raw)
|
||||
if v == 0:
|
||||
return 0.0
|
||||
if v == 5:
|
||||
return 0.5
|
||||
if v == 10:
|
||||
return 1.0
|
||||
except Exception:
|
||||
pass
|
||||
return 0.0
|
||||
|
||||
|
||||
_RISK_FLAG_MAP: dict[str, str] = {
|
||||
"block_stage_unknown": "unsafe_for_stage_unknown",
|
||||
"block_stage_parenting": "unsafe_for_stage_parenting",
|
||||
"block_emotion_low": "unsafe_for_emotion_low",
|
||||
"block_health_sensitive": "block_health_medical",
|
||||
}
|
||||
|
||||
|
||||
def normalize_risk_flags(raw_flags: list[str] | None) -> list[str]:
|
||||
"""
|
||||
risk_flags 旧→新映射、去重、稳定排序(字典序)。
|
||||
"""
|
||||
|
||||
flags = raw_flags or []
|
||||
mapped: set[str] = set()
|
||||
for f in flags:
|
||||
if not f:
|
||||
continue
|
||||
name = _RISK_FLAG_MAP.get(f, f)
|
||||
mapped.add(name)
|
||||
return sorted(mapped)
|
||||
|
||||
|
||||
def pick_text(*, text_en: str | None, text_tc: str | None, locale: str) -> str | None:
|
||||
"""
|
||||
按 locale 选择输出文案文本。
|
||||
|
||||
当前仅支持 EN/TC,且不允许语言回退:
|
||||
- locale=en*:必须使用 text_en
|
||||
- locale=tc/zh-TW/zh-HK:必须使用 text_tc
|
||||
"""
|
||||
|
||||
loc: Locale = normalize_locale(locale)
|
||||
if loc == "en":
|
||||
return text_en if text_en else None
|
||||
# loc == "tc"
|
||||
return text_tc if text_tc else None
|
||||
|
||||
@@ -0,0 +1,265 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from collections import defaultdict
|
||||
from dataclasses import dataclass
|
||||
from typing import Any, Iterable
|
||||
|
||||
from sqlalchemy import Select, and_, desc, not_, select
|
||||
from sqlalchemy.ext.asyncio import AsyncSession
|
||||
|
||||
from app.db.models.content import Content
|
||||
from app.db.models.content_profile import ContentProfile
|
||||
from app.db.models.content_risk_flag import ContentRiskFlag
|
||||
from app.features.personalized_reco.content_repository.interface import ContentRepository
|
||||
from app.features.personalized_reco.content_repository.normalization import (
|
||||
CONTEXT_KEYS,
|
||||
NEED_KEYS,
|
||||
normalize_personalization_power,
|
||||
normalize_review_confidence,
|
||||
normalize_risk_flags,
|
||||
normalize_suitability,
|
||||
pick_text,
|
||||
)
|
||||
from app.features.personalized_reco.content_repository.types import ContentProfileDTO
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class _UserSignals:
|
||||
"""
|
||||
从 user_profile 中提取 repository 级别需要的最小信号。
|
||||
|
||||
注意:更复杂的规则(Hard Filter/Scoring/Rerank)不在本层处理。
|
||||
"""
|
||||
|
||||
missing_need: bool
|
||||
missing_context: bool
|
||||
missing_emotion: bool
|
||||
stage: str | None # expecting/parenting/unknown/general/None
|
||||
|
||||
|
||||
def _bool(v: Any) -> bool:
|
||||
return bool(v)
|
||||
|
||||
|
||||
def _extract_user_signals(user_profile: object) -> _UserSignals:
|
||||
"""
|
||||
兼容 pydantic model / dict / 其他对象的最小字段读取。
|
||||
"""
|
||||
|
||||
def _get(obj: Any, key: str, default: Any = None) -> Any:
|
||||
if obj is None:
|
||||
return default
|
||||
if isinstance(obj, dict):
|
||||
return obj.get(key, default)
|
||||
return getattr(obj, key, default)
|
||||
|
||||
need = _get(user_profile, "need", {}) or {}
|
||||
context = _get(user_profile, "context", {}) or {}
|
||||
emotion_score = _get(user_profile, "emotion_score", None)
|
||||
|
||||
missing_need = len(need) == 0
|
||||
missing_context = len(context) == 0
|
||||
missing_emotion = emotion_score is None
|
||||
|
||||
# stage: from user_profile.stage (one-hot)
|
||||
stage_obj = _get(user_profile, "stage", None)
|
||||
stage: str | None = None
|
||||
if stage_obj is not None:
|
||||
expecting = _get(stage_obj, "expecting", None)
|
||||
parenting = _get(stage_obj, "parenting", None)
|
||||
unknown = _get(stage_obj, "unknown", None)
|
||||
if _bool(expecting):
|
||||
stage = "expecting"
|
||||
elif _bool(parenting):
|
||||
stage = "parenting"
|
||||
elif _bool(unknown):
|
||||
stage = "unknown"
|
||||
|
||||
return _UserSignals(
|
||||
missing_need=missing_need,
|
||||
missing_context=missing_context,
|
||||
missing_emotion=missing_emotion,
|
||||
stage=stage,
|
||||
)
|
||||
|
||||
|
||||
def _dedupe_preserve_order(ids: Iterable[int]) -> list[int]:
|
||||
seen: set[int] = set()
|
||||
out: list[int] = []
|
||||
for i in ids:
|
||||
if i in seen:
|
||||
continue
|
||||
seen.add(i)
|
||||
out.append(i)
|
||||
return out
|
||||
|
||||
|
||||
class SqlAlchemyContentRepository(ContentRepository):
|
||||
"""
|
||||
基于 SQLAlchemy AsyncSession 的 ContentRepository 实现。
|
||||
"""
|
||||
|
||||
def __init__(self, session: AsyncSession):
|
||||
self._session = session
|
||||
|
||||
async def fetch_contents_by_ids(self, *, content_ids: list[int], locale: str) -> list[ContentProfileDTO]:
|
||||
"""
|
||||
- 输入去重
|
||||
- 输出顺序与输入一致(按首次出现顺序)
|
||||
- 缺记录或缺目标语言文本:跳过
|
||||
- 不产生 N+1(主体+画像一次,flags 一次)
|
||||
"""
|
||||
|
||||
unique_ids = _dedupe_preserve_order(content_ids)
|
||||
if not unique_ids:
|
||||
return []
|
||||
|
||||
# locale 文本存在性过滤(不允许语言回退)
|
||||
# en -> 必须 text_en;tc -> 必须 text_tc
|
||||
# 过滤在 DB 层做,避免后续组装无意义
|
||||
from app.features.personalized_reco.content_repository.types import normalize_locale
|
||||
|
||||
loc = normalize_locale(locale)
|
||||
text_filter = Content.text_en.is_not(None) if loc == "en" else Content.text_tc.is_not(None)
|
||||
|
||||
stmt: Select = (
|
||||
select(Content, ContentProfile)
|
||||
.join(ContentProfile, Content.content_id == ContentProfile.content_id)
|
||||
.where(and_(Content.content_id.in_(unique_ids), text_filter))
|
||||
)
|
||||
|
||||
rows = (await self._session.execute(stmt)).all()
|
||||
if not rows:
|
||||
return []
|
||||
|
||||
# 先组装主体+画像,后续再补 risk_flags
|
||||
by_id: dict[int, dict[str, Any]] = {}
|
||||
valid_ids: list[int] = []
|
||||
for content, profile in rows:
|
||||
cid = int(content.content_id)
|
||||
text = pick_text(text_en=content.text_en, text_tc=content.text_tc, locale=locale)
|
||||
if not text:
|
||||
continue
|
||||
by_id[cid] = {
|
||||
"content": content,
|
||||
"profile": profile,
|
||||
"text": text,
|
||||
}
|
||||
valid_ids.append(cid)
|
||||
|
||||
if not by_id:
|
||||
return []
|
||||
|
||||
# 批量取 flags(避免 join 行膨胀)
|
||||
flags_stmt = select(ContentRiskFlag.content_id, ContentRiskFlag.flag).where(
|
||||
ContentRiskFlag.content_id.in_(list(by_id.keys()))
|
||||
)
|
||||
flags_rows = (await self._session.execute(flags_stmt)).all()
|
||||
flags_map: dict[int, list[str]] = defaultdict(list)
|
||||
for cid, flag in flags_rows:
|
||||
flags_map[int(cid)].append(str(flag))
|
||||
|
||||
result_by_id: dict[int, ContentProfileDTO] = {}
|
||||
for cid, payload in by_id.items():
|
||||
content: Content = payload["content"]
|
||||
profile: ContentProfile = payload["profile"]
|
||||
text: str = payload["text"]
|
||||
|
||||
dto = ContentProfileDTO(
|
||||
content_id=cid,
|
||||
text=text,
|
||||
stage=profile.stage, # type: ignore[arg-type]
|
||||
emotion_score=float(profile.emotion_score) if profile.emotion_score is not None else None,
|
||||
context_suitability=normalize_suitability(profile.context_suitability_json, keys=CONTEXT_KEYS),
|
||||
need_suitability=normalize_suitability(profile.need_suitability_json, keys=NEED_KEYS),
|
||||
personalization_power=normalize_personalization_power(profile.personalization_power),
|
||||
risk_flags=normalize_risk_flags(flags_map.get(cid)),
|
||||
author_id=content.author_id,
|
||||
template_id=content.template_id,
|
||||
review_confidence=normalize_review_confidence(profile.review_confidence),
|
||||
)
|
||||
result_by_id[cid] = dto
|
||||
|
||||
# 按输入顺序返回(跳过缺失/被过滤的)
|
||||
out: list[ContentProfileDTO] = []
|
||||
for cid in unique_ids:
|
||||
dto = result_by_id.get(cid)
|
||||
if dto is not None:
|
||||
out.append(dto)
|
||||
return out
|
||||
|
||||
async def fetch_candidates(
|
||||
self,
|
||||
*,
|
||||
scene: str,
|
||||
user_profile: object,
|
||||
fallback_level: int,
|
||||
limit: int,
|
||||
locale: str,
|
||||
exclude_content_ids: list[int] | None = None,
|
||||
) -> list[ContentProfileDTO]:
|
||||
"""
|
||||
两段式候选召回:
|
||||
1) 先查候选 content_id 列表(含粗过滤、locale 过滤、limit*multiplier)
|
||||
2) 再批量补全字段(复用 fetch_contents_by_ids)
|
||||
"""
|
||||
|
||||
if limit <= 0:
|
||||
return []
|
||||
|
||||
signals = _extract_user_signals(user_profile)
|
||||
effective_fallback = int(fallback_level)
|
||||
if signals.missing_need or signals.missing_context or signals.missing_emotion:
|
||||
effective_fallback = max(effective_fallback, 1)
|
||||
|
||||
# locale 文本存在性过滤(不允许语言回退)
|
||||
from app.features.personalized_reco.content_repository.types import normalize_locale
|
||||
|
||||
loc = normalize_locale(locale)
|
||||
text_filter = Content.text_en.is_not(None) if loc == "en" else Content.text_tc.is_not(None)
|
||||
|
||||
filters: list[Any] = [text_filter]
|
||||
if exclude_content_ids:
|
||||
filters.append(not_(Content.content_id.in_(exclude_content_ids)))
|
||||
|
||||
# fallback 约束(repository 只做“降级约束”,不做 hard filter)
|
||||
if effective_fallback >= 1:
|
||||
# personalization_power <= 5 代表 <= 0.5
|
||||
filters.append(ContentProfile.personalization_power <= 5)
|
||||
if effective_fallback >= 2:
|
||||
filters.append(ContentProfile.personalization_power == 0)
|
||||
filters.append(ContentProfile.stage == "general")
|
||||
if effective_fallback >= 3:
|
||||
filters.append(ContentProfile.is_safe_pool.is_(True))
|
||||
filters.append(ContentProfile.personalization_power == 0)
|
||||
filters.append(ContentProfile.stage == "general")
|
||||
|
||||
# stage 粗过滤(仅 L0/L1 才做“用户阶段 + general”;L2/L3 已强制 general)
|
||||
if effective_fallback < 2:
|
||||
user_stage = signals.stage
|
||||
if user_stage in {"expecting", "parenting"}:
|
||||
filters.append(ContentProfile.stage.in_([user_stage, "general"]))
|
||||
else:
|
||||
# unknown 或无法判定:仅取 general,避免误推
|
||||
filters.append(ContentProfile.stage == "general")
|
||||
|
||||
multiplier = 5
|
||||
raw_limit = max(limit * multiplier, limit)
|
||||
|
||||
stmt_ids = (
|
||||
select(Content.content_id)
|
||||
.join(ContentProfile, Content.content_id == ContentProfile.content_id)
|
||||
.where(and_(*filters))
|
||||
.order_by(desc(ContentProfile.updated_at))
|
||||
.limit(raw_limit)
|
||||
)
|
||||
|
||||
candidate_ids_rows = (await self._session.execute(stmt_ids)).scalars().all()
|
||||
candidate_ids = [int(x) for x in candidate_ids_rows]
|
||||
if not candidate_ids:
|
||||
return []
|
||||
|
||||
# 复用按 ID 批量补全(会再次做 locale 过滤,但成本可接受,且可保证一致行为)
|
||||
items = await self.fetch_contents_by_ids(content_ids=candidate_ids, locale=locale)
|
||||
return items[:limit]
|
||||
|
||||
@@ -0,0 +1,64 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Literal, Optional
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
|
||||
# 当前阶段仅支持 EN / TC(繁体中文)
|
||||
Locale = Literal["en", "tc"]
|
||||
|
||||
|
||||
def normalize_locale(locale: str) -> Locale:
|
||||
"""
|
||||
将客户端传入的 locale 归一化为内部枚举(仅 EN / TC)。
|
||||
|
||||
约定:
|
||||
- 任何以 "en" 开头的 locale 归一化为 "en"(例如 en、en-US)
|
||||
- "tc"/"zh-TW"/"zh-HK" 归一化为 "tc"
|
||||
- 其他 locale 视为不支持
|
||||
"""
|
||||
|
||||
raw = (locale or "").strip()
|
||||
if not raw:
|
||||
raise ValueError("locale 不能为空(当前仅支持 en/tc)")
|
||||
|
||||
low = raw.lower()
|
||||
if low.startswith("en"):
|
||||
return "en"
|
||||
if low in {"tc", "zh-tw", "zh-hk", "zh_tw", "zh_hk"}:
|
||||
return "tc"
|
||||
|
||||
raise ValueError(f"不支持的 locale:{locale!r}(当前仅支持 en/tc)")
|
||||
|
||||
|
||||
ContentStage = Literal["general", "expecting", "parenting", "unknown"]
|
||||
|
||||
|
||||
class ContentProfileDTO(BaseModel):
|
||||
"""
|
||||
推荐模块消费的内容画像(稳定字段契约)。
|
||||
|
||||
注意:
|
||||
- text 已按 locale 选择,不允许语言回退(缺语言文本的内容不返回)
|
||||
- emotion_score 为 None 表示 general
|
||||
- personalization_power 对上统一为 0/0.5/1
|
||||
- review_confidence 缺失时兜底 0.7
|
||||
"""
|
||||
|
||||
content_id: int
|
||||
text: str
|
||||
stage: ContentStage
|
||||
emotion_score: Optional[float] = None
|
||||
|
||||
context_suitability: dict[str, float] = Field(default_factory=dict)
|
||||
need_suitability: dict[str, float] = Field(default_factory=dict)
|
||||
|
||||
personalization_power: float
|
||||
risk_flags: list[str] = Field(default_factory=list)
|
||||
|
||||
# 可选字段
|
||||
author_id: Optional[str] = None
|
||||
template_id: Optional[str] = None
|
||||
review_confidence: float = 0.7
|
||||
|
||||
@@ -0,0 +1,20 @@
|
||||
"""
|
||||
个性化推荐|Observability 子模块(可观测性与打点载荷)
|
||||
|
||||
说明:
|
||||
- 只负责统一 `RecoMeta` 结构与构建(builder),不负责埋点 SDK/落库/上报实现。
|
||||
- `RecoMeta` 需要同时被 `reco-engine` 与 `integration-api-worker` 使用。
|
||||
"""
|
||||
|
||||
from .builder import RecoMetaBuilder
|
||||
from .types import MissingFields, RecoMeta
|
||||
from .utils import compute_empty_reason, compute_missing_fields
|
||||
|
||||
__all__ = [
|
||||
"MissingFields",
|
||||
"RecoMeta",
|
||||
"RecoMetaBuilder",
|
||||
"compute_empty_reason",
|
||||
"compute_missing_fields",
|
||||
]
|
||||
|
||||
136
server/app/features/personalized_reco/observability/builder.py
Normal file
@@ -0,0 +1,136 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
from datetime import datetime
|
||||
from typing import Any, Optional
|
||||
|
||||
from app.features.personalized_reco.observability.types import MissingFields, RecoMeta, Scene
|
||||
from app.features.personalized_reco.observability.utils import compute_empty_reason, compute_missing_fields
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def _non_negative_int(value: Any, *, default: int = 0) -> int:
|
||||
try:
|
||||
n = int(value)
|
||||
except Exception:
|
||||
return int(default)
|
||||
return max(0, int(n))
|
||||
|
||||
|
||||
class RecoMetaBuilder:
|
||||
"""
|
||||
在推荐 pipeline 中逐阶段填充 RecoMeta,避免“散落字段/散落日志”。
|
||||
|
||||
说明(V1):
|
||||
- set 调用允许任意顺序;build 时会做防御式兜底与单调性修正
|
||||
- 单调性约束:raw >= after_hard_filter >= after_dedup >= after_freqcap >= served_k
|
||||
"""
|
||||
|
||||
def __init__(self, *, scene: Scene, user_profile: object, k: int, now: Optional[datetime] = None) -> None:
|
||||
self.scene: Scene = scene
|
||||
self.user_profile = user_profile
|
||||
self.k = _non_negative_int(k, default=0)
|
||||
self.now = now
|
||||
|
||||
self._raw: Optional[int] = None
|
||||
self._after_hard: Optional[int] = None
|
||||
self._after_dedup: Optional[int] = None
|
||||
self._after_freqcap: Optional[int] = None
|
||||
self._served_k: Optional[int] = None
|
||||
self._fallback_level_final: Optional[int] = None
|
||||
|
||||
self._risk_filtered_count_by_flag: dict[str, int] = {}
|
||||
self._freqcap_filtered_counts: dict[str, int] = {}
|
||||
self._config_snapshot: dict[str, Any] = {}
|
||||
|
||||
def set_candidate_pool_size_raw(self, n: Any) -> "RecoMetaBuilder":
|
||||
self._raw = _non_negative_int(n)
|
||||
return self
|
||||
|
||||
def set_after_hard_filter(self, n: Any, *, risk_filtered_count_by_flag: Optional[dict[str, Any]] = None) -> "RecoMetaBuilder":
|
||||
self._after_hard = _non_negative_int(n)
|
||||
if risk_filtered_count_by_flag:
|
||||
self._risk_filtered_count_by_flag = {str(k): _non_negative_int(v) for k, v in risk_filtered_count_by_flag.items()}
|
||||
return self
|
||||
|
||||
def set_after_dedup(self, n: Any) -> "RecoMetaBuilder":
|
||||
self._after_dedup = _non_negative_int(n)
|
||||
return self
|
||||
|
||||
def set_after_freqcap(self, n: Any, *, freqcap_filtered_counts: Optional[dict[str, Any]] = None) -> "RecoMetaBuilder":
|
||||
self._after_freqcap = _non_negative_int(n)
|
||||
if freqcap_filtered_counts:
|
||||
self._freqcap_filtered_counts = {str(k): _non_negative_int(v) for k, v in freqcap_filtered_counts.items()}
|
||||
return self
|
||||
|
||||
def set_fallback_level_final(self, level: Any, *, reason: Optional[str] = None) -> "RecoMetaBuilder":
|
||||
# reason 预留,V1 先不入 meta(可放入 config_snapshot 或后续字段)
|
||||
self._fallback_level_final = _non_negative_int(level, default=0)
|
||||
if reason:
|
||||
self._config_snapshot.setdefault("fallback_trigger_reason", str(reason))
|
||||
return self
|
||||
|
||||
def set_served_k(self, n: Any) -> "RecoMetaBuilder":
|
||||
self._served_k = _non_negative_int(n)
|
||||
return self
|
||||
|
||||
def set_config_snapshot(self, snapshot: dict[str, Any]) -> "RecoMetaBuilder":
|
||||
self._config_snapshot = dict(snapshot or {})
|
||||
return self
|
||||
|
||||
def build(self) -> RecoMeta:
|
||||
missing: MissingFields = compute_missing_fields(self.user_profile)
|
||||
conf_u = getattr(self.user_profile, "profile_confidence", 1.0)
|
||||
try:
|
||||
conf_u_f = float(conf_u)
|
||||
except Exception:
|
||||
conf_u_f = 1.0
|
||||
if conf_u_f != conf_u_f:
|
||||
conf_u_f = 1.0
|
||||
|
||||
raw = self._raw if self._raw is not None else 0
|
||||
after_hard = self._after_hard if self._after_hard is not None else raw
|
||||
after_dedup = self._after_dedup if self._after_dedup is not None else after_hard
|
||||
after_freqcap = self._after_freqcap if self._after_freqcap is not None else after_dedup
|
||||
served_k = self._served_k if self._served_k is not None else 0
|
||||
|
||||
# 防御式单调性修正(以最保守值输出)
|
||||
if after_hard > raw:
|
||||
logger.debug("after_hard_filter(%s) > raw(%s),已修正为 raw", after_hard, raw)
|
||||
after_hard = raw
|
||||
if after_dedup > after_hard:
|
||||
logger.debug("after_dedup(%s) > after_hard_filter(%s),已修正为 after_hard_filter", after_dedup, after_hard)
|
||||
after_dedup = after_hard
|
||||
if after_freqcap > after_dedup:
|
||||
logger.debug("after_freqcap(%s) > after_dedup(%s),已修正为 after_dedup", after_freqcap, after_dedup)
|
||||
after_freqcap = after_dedup
|
||||
if served_k > after_freqcap:
|
||||
logger.debug("served_k(%s) > after_freqcap(%s),已修正为 after_freqcap", served_k, after_freqcap)
|
||||
served_k = after_freqcap
|
||||
|
||||
fallback_level_final = self._fallback_level_final if self._fallback_level_final is not None else 0
|
||||
|
||||
empty_reason = compute_empty_reason(
|
||||
served_k=served_k,
|
||||
candidate_pool_size_raw=raw,
|
||||
candidate_pool_size_after_hard_filter=after_hard,
|
||||
candidate_pool_size_after_freqcap=after_freqcap,
|
||||
)
|
||||
|
||||
return RecoMeta(
|
||||
scene=self.scene,
|
||||
candidate_pool_size_raw=int(raw),
|
||||
candidate_pool_size_after_hard_filter=int(after_hard),
|
||||
candidate_pool_size_after_dedup=int(after_dedup),
|
||||
candidate_pool_size_after_freqcap=int(after_freqcap),
|
||||
fallback_level_final=int(fallback_level_final),
|
||||
served_k=int(served_k),
|
||||
empty_reason=empty_reason,
|
||||
conf_U=float(conf_u_f),
|
||||
missing_fields=missing,
|
||||
risk_filtered_count_by_flag=dict(self._risk_filtered_count_by_flag),
|
||||
freqcap_filtered_counts=dict(self._freqcap_filtered_counts),
|
||||
config_snapshot=dict(self._config_snapshot),
|
||||
)
|
||||
|
||||
51
server/app/features/personalized_reco/observability/types.py
Normal file
@@ -0,0 +1,51 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Any, Literal, Optional
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
Scene = Literal["feed", "push", "widget"]
|
||||
|
||||
EmptyReason = Literal["hard_filter_all", "freqcap_all", "pool_empty", "unknown"]
|
||||
|
||||
|
||||
class MissingFields(BaseModel):
|
||||
"""
|
||||
画像字段缺失情况(布尔结构)。
|
||||
"""
|
||||
|
||||
need: bool = False
|
||||
context: bool = False
|
||||
emotion: bool = False
|
||||
|
||||
|
||||
class RecoMeta(BaseModel):
|
||||
"""
|
||||
推荐模块统一可观测载荷(返回给调用方;调用方负责上报/落库/打点)。
|
||||
"""
|
||||
|
||||
scene: Scene
|
||||
|
||||
candidate_pool_size_raw: int = 0
|
||||
candidate_pool_size_after_hard_filter: int = 0
|
||||
candidate_pool_size_after_dedup: int = 0
|
||||
candidate_pool_size_after_freqcap: int = 0
|
||||
|
||||
fallback_level_final: int = 0
|
||||
served_k: int = 0
|
||||
|
||||
# served_k=0 时必填;served_k>0 时建议为 None
|
||||
empty_reason: Optional[EmptyReason] = None
|
||||
|
||||
conf_U: float = 1.0
|
||||
missing_fields: MissingFields = Field(default_factory=MissingFields)
|
||||
|
||||
# 可选:Hard Filter 风险命中统计(按 flag 聚合)
|
||||
risk_filtered_count_by_flag: dict[str, int] = Field(default_factory=dict)
|
||||
|
||||
# 可选:Freqcap 过滤统计(sentence/author/template)
|
||||
freqcap_filtered_counts: dict[str, int] = Field(default_factory=dict)
|
||||
|
||||
# 可选:调参快照(V1 可先只在内部事件使用)
|
||||
config_snapshot: dict[str, Any] = Field(default_factory=dict)
|
||||
|
||||
61
server/app/features/personalized_reco/observability/utils.py
Normal file
@@ -0,0 +1,61 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Optional
|
||||
|
||||
from app.features.personalized_reco.observability.types import EmptyReason, MissingFields
|
||||
|
||||
|
||||
def compute_missing_fields(user_profile: object) -> MissingFields:
|
||||
"""
|
||||
判定用户画像缺失字段(对齐算法规则 V1.2 口径)。
|
||||
|
||||
规则:
|
||||
- need:user_profile.need 为空对象 {} 或不存在
|
||||
- context:user_profile.context 为空对象 {} 或不存在
|
||||
- emotion:user_profile.emotion_score 为 None 或不存在
|
||||
"""
|
||||
|
||||
need = getattr(user_profile, "need", None)
|
||||
context = getattr(user_profile, "context", None)
|
||||
emotion_score = getattr(user_profile, "emotion_score", None)
|
||||
|
||||
need_missing = not bool(need)
|
||||
context_missing = not bool(context)
|
||||
emotion_missing = emotion_score is None
|
||||
|
||||
return MissingFields(need=need_missing, context=context_missing, emotion=emotion_missing)
|
||||
|
||||
|
||||
def compute_empty_reason(
|
||||
*,
|
||||
served_k: int,
|
||||
candidate_pool_size_raw: int,
|
||||
candidate_pool_size_after_hard_filter: int,
|
||||
candidate_pool_size_after_freqcap: int,
|
||||
) -> Optional[EmptyReason]:
|
||||
"""
|
||||
判定 empty_reason(served_k=0 必填)。
|
||||
|
||||
规则(对齐 plan):
|
||||
- served_k>0 -> None
|
||||
- raw==0 -> pool_empty
|
||||
- raw>0 且 after_hard_filter==0 -> hard_filter_all
|
||||
- after_freqcap==0 -> freqcap_all
|
||||
- 其他 -> unknown
|
||||
"""
|
||||
|
||||
if int(served_k) > 0:
|
||||
return None
|
||||
|
||||
raw = int(candidate_pool_size_raw)
|
||||
after_hard = int(candidate_pool_size_after_hard_filter)
|
||||
after_freqcap = int(candidate_pool_size_after_freqcap)
|
||||
|
||||
if raw == 0:
|
||||
return "pool_empty"
|
||||
if raw > 0 and after_hard == 0:
|
||||
return "hard_filter_all"
|
||||
if after_freqcap == 0:
|
||||
return "freqcap_all"
|
||||
return "unknown"
|
||||
|
||||
@@ -0,0 +1,11 @@
|
||||
"""
|
||||
Reco Engine(推荐引擎编排)。
|
||||
|
||||
该模块负责将候选拉取、硬过滤、软打分、重排/频控、回退梯度串成一个稳定 Pipeline,
|
||||
并输出统一结构:items + meta(可观测字段)。
|
||||
"""
|
||||
|
||||
from app.features.personalized_reco.reco_engine.orchestrator import recommend
|
||||
|
||||
__all__ = ["recommend"]
|
||||
|
||||
@@ -0,0 +1,14 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from app.features.personalized_reco.reco_engine.types import RecoEngineConfig, Scene
|
||||
|
||||
|
||||
def get_default_engine_config(scene: Scene) -> RecoEngineConfig:
|
||||
"""
|
||||
获取推荐引擎默认配置(返回副本,避免被意外修改)。
|
||||
"""
|
||||
|
||||
# V1:三种场景目前共用一套默认值;保留 scene 参数便于后续按场景拆分
|
||||
base = RecoEngineConfig()
|
||||
return RecoEngineConfig.model_validate(base.model_dump())
|
||||
|
||||
128
server/app/features/personalized_reco/reco_engine/hard_filter.py
Normal file
@@ -0,0 +1,128 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from collections import defaultdict
|
||||
from typing import Any, Iterable, Optional
|
||||
|
||||
from app.features.personalized_reco.content_repository.types import ContentProfileDTO
|
||||
from app.features.personalized_reco.reco_engine.types import HardFilterResult, RecoConstraints, Scene
|
||||
|
||||
|
||||
def _user_stage_key(user_profile: object) -> str:
|
||||
"""
|
||||
从 user_profile.stage(one-hot) 提取用户阶段。
|
||||
约定:unknown 通常必填,但这里做防御。
|
||||
"""
|
||||
|
||||
stage_obj = getattr(user_profile, "stage", None)
|
||||
if stage_obj is None:
|
||||
return "unknown"
|
||||
if getattr(stage_obj, "expecting", 0) == 1:
|
||||
return "expecting"
|
||||
if getattr(stage_obj, "parenting", 0) == 1:
|
||||
return "parenting"
|
||||
return "unknown"
|
||||
|
||||
|
||||
def _user_emotion_score(user_profile: object) -> Optional[float]:
|
||||
v = getattr(user_profile, "emotion_score", None)
|
||||
if v is None:
|
||||
return None
|
||||
try:
|
||||
f = float(v)
|
||||
except Exception:
|
||||
return None
|
||||
if f != f:
|
||||
return None
|
||||
return f
|
||||
|
||||
|
||||
def _count_hits(counter: dict[str, int], hits: Iterable[str]) -> None:
|
||||
for h in hits:
|
||||
counter[str(h)] += 1
|
||||
|
||||
|
||||
def hard_filter(
|
||||
*,
|
||||
scene: Scene,
|
||||
user_profile: object,
|
||||
candidates: list[ContentProfileDTO],
|
||||
constraints: Optional[RecoConstraints] = None,
|
||||
) -> HardFilterResult:
|
||||
"""
|
||||
Hard Filter(硬过滤)。
|
||||
|
||||
V1:仅实现硬规则集合(不做软惩罚,不做扩展 hard_rules)。
|
||||
"""
|
||||
|
||||
cons = constraints or RecoConstraints()
|
||||
|
||||
exclude_author_ids = set([a for a in (cons.exclude_author_ids or []) if a is not None and str(a).strip() != ""])
|
||||
exclude_template_ids = set([t for t in (cons.exclude_template_ids or []) if t is not None and str(t).strip() != ""])
|
||||
exclude_content_ids = set([int(x) for x in (cons.exclude_content_ids or []) if x is not None])
|
||||
|
||||
u_stage = _user_stage_key(user_profile)
|
||||
u_emotion = _user_emotion_score(user_profile)
|
||||
emotion_low = u_emotion is not None and float(u_emotion) <= 0.2
|
||||
|
||||
kept: list[ContentProfileDTO] = []
|
||||
removed_count = 0
|
||||
|
||||
# 统计:按命中 key 聚合计数(risk_flags 直接用 flag 字符串;跨维度/约束用 rule:* / constraint:* 前缀)
|
||||
hit_counts: dict[str, int] = defaultdict(int)
|
||||
hits_by_content_id: dict[int, list[str]] = {}
|
||||
|
||||
for c in candidates or []:
|
||||
cid = int(c.content_id)
|
||||
hits: list[str] = []
|
||||
|
||||
# 约束:按 content_id/author_id/template_id 排除(视为硬过滤)
|
||||
if cid in exclude_content_ids:
|
||||
hits.append("constraint:exclude_content_id")
|
||||
if c.author_id and c.author_id in exclude_author_ids:
|
||||
hits.append("constraint:exclude_author_id")
|
||||
if c.template_id and c.template_id in exclude_template_ids:
|
||||
hits.append("constraint:exclude_template_id")
|
||||
|
||||
flags = set([str(x) for x in (c.risk_flags or []) if x is not None and str(x).strip() != ""])
|
||||
|
||||
# 全场景必挡
|
||||
if "block_health_medical" in flags:
|
||||
hits.append("block_health_medical")
|
||||
|
||||
# 与用户阶段相关
|
||||
if u_stage == "unknown" and "unsafe_for_stage_unknown" in flags:
|
||||
hits.append("unsafe_for_stage_unknown")
|
||||
if u_stage == "parenting" and "unsafe_for_stage_parenting" in flags:
|
||||
hits.append("unsafe_for_stage_parenting")
|
||||
|
||||
# 与用户情绪相关
|
||||
if emotion_low and "unsafe_for_emotion_low" in flags:
|
||||
hits.append("unsafe_for_emotion_low")
|
||||
|
||||
# 跨维度规则:unknown stage + parenting_pressure 强命中 + 高个性化
|
||||
if u_stage == "unknown":
|
||||
try:
|
||||
need_val = float(c.need_suitability.get("parenting_pressure", 0.0))
|
||||
except Exception:
|
||||
need_val = 0.0
|
||||
if need_val >= 1.0 and float(getattr(c, "personalization_power", 0.0)) >= 1.0:
|
||||
hits.append("rule:unknown_stage_parenting_pressure_power1")
|
||||
|
||||
if hits:
|
||||
removed_count += 1
|
||||
# 单条去重后再计数,避免同 key 重复
|
||||
uniq_hits = sorted(set(hits))
|
||||
hits_by_content_id[cid] = uniq_hits
|
||||
_count_hits(hit_counts, uniq_hits)
|
||||
continue
|
||||
|
||||
hits_by_content_id[cid] = []
|
||||
kept.append(c)
|
||||
|
||||
return HardFilterResult(
|
||||
kept_items=kept,
|
||||
removed_count=int(removed_count),
|
||||
risk_filtered_count_by_flag=dict(hit_counts),
|
||||
hits_by_content_id=hits_by_content_id,
|
||||
)
|
||||
|
||||
@@ -0,0 +1,396 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
from collections import defaultdict
|
||||
from datetime import datetime
|
||||
from typing import Any, Optional
|
||||
|
||||
from app.features.personalized_reco.content_repository.interface import ContentRepository
|
||||
from app.features.personalized_reco.content_repository.types import ContentProfileDTO, normalize_locale
|
||||
from app.features.personalized_reco.observability.builder import RecoMetaBuilder
|
||||
from app.features.personalized_reco.reco_engine.defaults import get_default_engine_config
|
||||
from app.features.personalized_reco.reco_engine.hard_filter import hard_filter
|
||||
from app.features.personalized_reco.reco_engine.types import RecoConstraints, RecoEngineConfig, RecoEngineResult, RecommendedItem, Scene
|
||||
from app.features.personalized_reco.reco_engine.utils import (
|
||||
clamp_personalization_power,
|
||||
merge_exclude_ids,
|
||||
normalize_or_default_locale,
|
||||
)
|
||||
from app.features.personalized_reco.rerank_freqcap.rerank import rerank_and_freqcap
|
||||
from app.features.personalized_reco.rerank_freqcap.types import ScoredCandidate
|
||||
from app.features.personalized_reco.scoring.defaults import get_default_config as get_default_score_config
|
||||
from app.features.personalized_reco.scoring.score import score_content
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def _safe_int(value: Any, *, default: int = 0) -> int:
|
||||
try:
|
||||
n = int(value)
|
||||
except Exception:
|
||||
return int(default)
|
||||
return int(n)
|
||||
|
||||
|
||||
def _light_score_summary(score_result: Any) -> dict[str, Any]:
|
||||
"""
|
||||
轻量 explanations:只保留少量关键字段,避免 payload 过大。
|
||||
"""
|
||||
|
||||
bd = getattr(score_result, "breakdown", None)
|
||||
if bd is None:
|
||||
return {}
|
||||
|
||||
def _get(name: str) -> Optional[float]:
|
||||
v = getattr(bd, name, None)
|
||||
if v is None:
|
||||
return None
|
||||
try:
|
||||
f = float(v)
|
||||
except Exception:
|
||||
return None
|
||||
if f != f:
|
||||
return None
|
||||
return f
|
||||
|
||||
out: dict[str, Any] = {
|
||||
"missing_fields": list(getattr(bd, "missing_fields", []) or []),
|
||||
"S_core": _get("S_core"),
|
||||
"S_personal": _get("S_personal"),
|
||||
"P_uncertainty": _get("P_uncertainty"),
|
||||
"P_risk": _get("P_risk"),
|
||||
"P_widget_emotion_out_of_range": _get("P_widget_emotion_out_of_range"),
|
||||
}
|
||||
# 删除 None,减少噪音
|
||||
return {k: v for k, v in out.items() if v is not None and v != []}
|
||||
|
||||
|
||||
def _apply_fallback_level_to_content(content: ContentProfileDTO, *, fallback_level: int) -> ContentProfileDTO:
|
||||
"""
|
||||
对内容做防御式一致性处理(与回退梯度一致)。
|
||||
"""
|
||||
|
||||
p2 = clamp_personalization_power(content.personalization_power, fallback_level=fallback_level)
|
||||
if p2 == content.personalization_power:
|
||||
return content
|
||||
return content.model_copy(update={"personalization_power": float(p2)})
|
||||
|
||||
|
||||
def _merge_counter(dst: dict[str, int], src: dict[str, Any]) -> None:
|
||||
for k, v in (src or {}).items():
|
||||
try:
|
||||
n = int(v)
|
||||
except Exception:
|
||||
n = 0
|
||||
dst[str(k)] = int(dst.get(str(k), 0)) + max(0, int(n))
|
||||
|
||||
|
||||
async def recommend(
|
||||
*,
|
||||
repo: ContentRepository,
|
||||
scene: Scene,
|
||||
user_profile: object,
|
||||
already_recommended_ids: list[Any],
|
||||
touched_or_viewed_ids: list[Any],
|
||||
k: int,
|
||||
now: datetime,
|
||||
locale: Optional[str] = None,
|
||||
constraints: Optional[RecoConstraints] = None,
|
||||
config: Optional[RecoEngineConfig] = None,
|
||||
) -> RecoEngineResult:
|
||||
"""
|
||||
Reco Engine 主入口:编排候选→过滤→打分→重排→回退,并输出 items + meta。
|
||||
"""
|
||||
|
||||
cfg = config or get_default_engine_config(scene)
|
||||
cons = constraints or RecoConstraints()
|
||||
|
||||
k_i = max(0, _safe_int(k, default=0))
|
||||
meta_builder = RecoMetaBuilder(scene=scene, user_profile=user_profile, k=k_i, now=now)
|
||||
|
||||
if k_i <= 0:
|
||||
meta_builder.set_candidate_pool_size_raw(0).set_after_hard_filter(0).set_after_dedup(0).set_after_freqcap(0).set_served_k(0).set_fallback_level_final(0)
|
||||
meta_builder.set_config_snapshot({"engine_note": "k<=0,直接返回空结果"})
|
||||
return RecoEngineResult(items=[], meta=meta_builder.build())
|
||||
|
||||
# locale:默认 en;严格校验仅支持 en/tc
|
||||
raw_locale = normalize_or_default_locale(locale)
|
||||
try:
|
||||
effective_locale = normalize_locale(raw_locale)
|
||||
except Exception as e:
|
||||
meta_builder.set_config_snapshot({"error": str(e), "stage": "normalize_locale", "locale": raw_locale})
|
||||
meta_builder.set_candidate_pool_size_raw(0).set_after_hard_filter(0).set_after_dedup(0).set_after_freqcap(0).set_served_k(0).set_fallback_level_final(0)
|
||||
return RecoEngineResult(items=[], meta=meta_builder.build())
|
||||
|
||||
# 聚合统计(跨回退层级累加,确保 meta 单调性成立)
|
||||
raw_total = 0
|
||||
after_hard_total = 0
|
||||
after_dedup_total = 0
|
||||
after_freqcap_total = 0
|
||||
|
||||
risk_counts_total: dict[str, int] = defaultdict(int)
|
||||
freqcap_counts_total: dict[str, int] = defaultdict(int)
|
||||
|
||||
fallback_trace: list[dict[str, Any]] = []
|
||||
selected: list[ScoredCandidate] = []
|
||||
selected_level_by_id: dict[int, int] = {}
|
||||
|
||||
last_fallback_level = 0
|
||||
last_reason = None
|
||||
|
||||
for level in [0, 1, 2, 3]:
|
||||
last_fallback_level = int(level)
|
||||
k_remaining = max(0, k_i - len(selected))
|
||||
if k_remaining <= 0:
|
||||
break
|
||||
|
||||
# Feed:允许不足且不补齐时,拿到任何结果就停止
|
||||
if scene == "feed" and cfg.feed_allow_partial and (not cfg.feed_fill_with_fallback) and len(selected) > 0:
|
||||
break
|
||||
|
||||
# exclude_ids:already/touched + constraints.exclude + 已选内容(避免跨层重复)
|
||||
exclude_ids = merge_exclude_ids(
|
||||
already_recommended_ids=list(already_recommended_ids or []) + [int(x.content_id) for x in selected],
|
||||
touched_or_viewed_ids=list(touched_or_viewed_ids or []),
|
||||
extra_exclude_content_ids=list(cons.exclude_content_ids or []),
|
||||
)
|
||||
|
||||
multiplier = int(cfg.candidate_multiplier_feed if scene == "feed" else cfg.candidate_multiplier_push_widget)
|
||||
base_limit = max(int(cfg.min_candidates_per_level), int(k_remaining) * max(1, int(multiplier)))
|
||||
if cons.max_candidates_limit is not None and int(cons.max_candidates_limit) > 0:
|
||||
limit = min(base_limit, int(cons.max_candidates_limit))
|
||||
else:
|
||||
limit = base_limit
|
||||
|
||||
# 1) Candidate
|
||||
try:
|
||||
cands = await repo.fetch_candidates(
|
||||
scene=scene,
|
||||
user_profile=user_profile,
|
||||
fallback_level=int(level),
|
||||
limit=int(limit),
|
||||
locale=str(effective_locale),
|
||||
exclude_content_ids=exclude_ids,
|
||||
)
|
||||
except Exception as e:
|
||||
logger.exception("fetch_candidates 失败:%s", e)
|
||||
last_reason = "error:fetch_candidates"
|
||||
fallback_trace.append(
|
||||
{
|
||||
"level": int(level),
|
||||
"raw": 0,
|
||||
"after_hard": 0,
|
||||
"after_dedup": 0,
|
||||
"after_freqcap": 0,
|
||||
"served_total": len(selected),
|
||||
"error": str(e),
|
||||
}
|
||||
)
|
||||
continue
|
||||
|
||||
raw_total += len(cands)
|
||||
|
||||
if not cands:
|
||||
last_reason = "pool_empty"
|
||||
fallback_trace.append(
|
||||
{
|
||||
"level": int(level),
|
||||
"raw": 0,
|
||||
"after_hard": 0,
|
||||
"after_dedup": 0,
|
||||
"after_freqcap": 0,
|
||||
"served_total": len(selected),
|
||||
"reason": "pool_empty",
|
||||
}
|
||||
)
|
||||
continue
|
||||
|
||||
# 2) Hard Filter
|
||||
hf = hard_filter(scene=scene, user_profile=user_profile, candidates=cands, constraints=cons)
|
||||
kept = [x for x in hf.kept_items if isinstance(x, ContentProfileDTO)]
|
||||
after_hard_total += len(kept)
|
||||
_merge_counter(risk_counts_total, hf.risk_filtered_count_by_flag)
|
||||
|
||||
if not kept:
|
||||
last_reason = "hard_filter_all"
|
||||
fallback_trace.append(
|
||||
{
|
||||
"level": int(level),
|
||||
"raw": len(cands),
|
||||
"after_hard": 0,
|
||||
"after_dedup": 0,
|
||||
"after_freqcap": 0,
|
||||
"served_total": len(selected),
|
||||
"reason": "hard_filter_all",
|
||||
}
|
||||
)
|
||||
continue
|
||||
|
||||
# 3) Soft Scoring
|
||||
score_cfg = get_default_score_config(scene)
|
||||
if scene == "push":
|
||||
# Push:强制启用不确定性惩罚(与 spec 对齐)
|
||||
score_cfg = score_cfg.model_copy(update={"enable_uncertainty_penalty": True})
|
||||
|
||||
scored: list[ScoredCandidate] = []
|
||||
for c in kept:
|
||||
c2 = _apply_fallback_level_to_content(c, fallback_level=int(level))
|
||||
try:
|
||||
s = score_content(scene=scene, user_profile=user_profile, content_profile=c2, config=score_cfg, pass_filters=True, now=now)
|
||||
except Exception as e:
|
||||
# 单条异常不影响整体
|
||||
logger.exception("score_content 失败 content_id=%s:%s", getattr(c2, "content_id", None), e)
|
||||
continue
|
||||
|
||||
cid = int(c2.content_id)
|
||||
hits = hf.hits_by_content_id.get(cid, [])
|
||||
extra: dict[str, Any] = {
|
||||
"text": c2.text,
|
||||
"fallback_level_used": int(level),
|
||||
}
|
||||
if cfg.enable_explanations:
|
||||
extra["hard_filter_hits"] = hits
|
||||
extra["score_summary"] = _light_score_summary(s)
|
||||
|
||||
scored.append(
|
||||
ScoredCandidate(
|
||||
content_id=cid,
|
||||
final_score=float(getattr(s, "final_score", 0.0)),
|
||||
author_id=c2.author_id,
|
||||
template_id=c2.template_id,
|
||||
content_profile=c2,
|
||||
extra=extra,
|
||||
)
|
||||
)
|
||||
|
||||
if not scored:
|
||||
last_reason = "empty_after_scoring"
|
||||
fallback_trace.append(
|
||||
{
|
||||
"level": int(level),
|
||||
"raw": len(cands),
|
||||
"after_hard": len(kept),
|
||||
"after_dedup": 0,
|
||||
"after_freqcap": 0,
|
||||
"served_total": len(selected),
|
||||
"reason": "empty_after_scoring",
|
||||
}
|
||||
)
|
||||
continue
|
||||
|
||||
# 4) Rerank/Freqcap
|
||||
try:
|
||||
rer = rerank_and_freqcap(
|
||||
scene=scene,
|
||||
scored_candidates=scored,
|
||||
already_recommended_ids=list(already_recommended_ids or []) + [int(x.content_id) for x in selected],
|
||||
touched_or_viewed_ids=list(touched_or_viewed_ids or []),
|
||||
k=int(k_remaining),
|
||||
recent_author_ids=cons.recent_author_ids,
|
||||
recent_template_ids=cons.recent_template_ids,
|
||||
)
|
||||
except Exception as e:
|
||||
logger.exception("rerank_and_freqcap 失败:%s", e)
|
||||
last_reason = "error:rerank_and_freqcap"
|
||||
fallback_trace.append(
|
||||
{
|
||||
"level": int(level),
|
||||
"raw": len(cands),
|
||||
"after_hard": len(kept),
|
||||
"after_dedup": 0,
|
||||
"after_freqcap": 0,
|
||||
"served_total": len(selected),
|
||||
"error": str(e),
|
||||
}
|
||||
)
|
||||
continue
|
||||
|
||||
after_dedup_total += int(rer.meta.candidate_pool_size_after_dedup)
|
||||
after_freqcap_total += int(rer.meta.candidate_pool_size_after_freqcap)
|
||||
_merge_counter(freqcap_counts_total, rer.meta.freqcap_filtered_counts)
|
||||
|
||||
served_level = list(rer.ranked_items or [])[:k_remaining]
|
||||
if not served_level:
|
||||
last_reason = "freqcap_all"
|
||||
fallback_trace.append(
|
||||
{
|
||||
"level": int(level),
|
||||
"raw": len(cands),
|
||||
"after_hard": len(kept),
|
||||
"after_dedup": int(rer.meta.candidate_pool_size_after_dedup),
|
||||
"after_freqcap": int(rer.meta.candidate_pool_size_after_freqcap),
|
||||
"served_total": len(selected),
|
||||
"reason": "freqcap_all",
|
||||
}
|
||||
)
|
||||
continue
|
||||
|
||||
for it in served_level:
|
||||
cid = int(it.content_id)
|
||||
selected.append(it)
|
||||
selected_level_by_id[cid] = int(level)
|
||||
|
||||
last_reason = None
|
||||
fallback_trace.append(
|
||||
{
|
||||
"level": int(level),
|
||||
"raw": len(cands),
|
||||
"after_hard": len(kept),
|
||||
"after_dedup": int(rer.meta.candidate_pool_size_after_dedup),
|
||||
"after_freqcap": int(rer.meta.candidate_pool_size_after_freqcap),
|
||||
"served_total": len(selected),
|
||||
"served_added": len(served_level),
|
||||
}
|
||||
)
|
||||
|
||||
if len(selected) >= k_i:
|
||||
break
|
||||
|
||||
# 组装输出 items(按 selected 顺序)
|
||||
items: list[RecommendedItem] = []
|
||||
for c in selected[:k_i]:
|
||||
cid = int(c.content_id)
|
||||
text = ""
|
||||
if isinstance(c.extra, dict):
|
||||
text = str(c.extra.get("text") or "")
|
||||
|
||||
explanations = None
|
||||
if cfg.enable_explanations and isinstance(c.extra, dict):
|
||||
explanations = {
|
||||
"fallback_level_used": c.extra.get("fallback_level_used"),
|
||||
"hard_filter_hits": c.extra.get("hard_filter_hits"),
|
||||
"score_summary": c.extra.get("score_summary"),
|
||||
}
|
||||
|
||||
items.append(
|
||||
RecommendedItem(
|
||||
content_id=cid,
|
||||
text=text,
|
||||
final_score=float(c.final_score),
|
||||
fallback_level_final=int(selected_level_by_id.get(cid, last_fallback_level)),
|
||||
explanations=explanations,
|
||||
)
|
||||
)
|
||||
|
||||
served_k = len(items)
|
||||
|
||||
# meta:使用聚合统计,确保单调性约束成立(raw>=after_hard>=after_dedup>=after_freqcap>=served_k)
|
||||
# 注意:聚合统计理论上可能出现 after_* > raw_total(例如 repo 返回重复/异常),此处交由 builder 防御修正
|
||||
meta_builder.set_candidate_pool_size_raw(int(raw_total))
|
||||
meta_builder.set_after_hard_filter(int(after_hard_total), risk_filtered_count_by_flag=dict(risk_counts_total))
|
||||
meta_builder.set_after_dedup(int(after_dedup_total))
|
||||
meta_builder.set_after_freqcap(int(after_freqcap_total), freqcap_filtered_counts=dict(freqcap_counts_total))
|
||||
meta_builder.set_served_k(int(served_k))
|
||||
meta_builder.set_fallback_level_final(int(last_fallback_level), reason=last_reason)
|
||||
|
||||
meta_builder.set_config_snapshot(
|
||||
{
|
||||
"fallback_trace": fallback_trace,
|
||||
"engine_config": cfg.model_dump(),
|
||||
"constraints": cons.model_dump(),
|
||||
"locale": effective_locale,
|
||||
}
|
||||
)
|
||||
|
||||
return RecoEngineResult(items=items, meta=meta_builder.build())
|
||||
|
||||
101
server/app/features/personalized_reco/reco_engine/types.py
Normal file
@@ -0,0 +1,101 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from datetime import datetime
|
||||
from typing import Any, Literal, Optional
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from app.features.personalized_reco.observability.types import RecoMeta
|
||||
|
||||
Scene = Literal["feed", "push", "widget"]
|
||||
|
||||
|
||||
class RecoConstraints(BaseModel):
|
||||
"""
|
||||
推荐请求的可选约束(调用方可按需传入)。
|
||||
"""
|
||||
|
||||
exclude_content_ids: list[int] = Field(default_factory=list)
|
||||
exclude_author_ids: list[str] = Field(default_factory=list)
|
||||
exclude_template_ids: list[str] = Field(default_factory=list)
|
||||
|
||||
# 候选池上限(用于资源保护)
|
||||
max_candidates_limit: Optional[int] = None
|
||||
|
||||
# Push/Widget 作者/模板冷却窗口内的历史集合(增强频控输入)
|
||||
# 说明:若不提供(None),rerank_freqcap 会记录缺失并跳过该维度过滤
|
||||
recent_author_ids: Optional[list[str]] = None
|
||||
recent_template_ids: Optional[list[str]] = None
|
||||
|
||||
|
||||
class RecoEngineConfig(BaseModel):
|
||||
"""
|
||||
引擎级配置(V1 可调参项)。
|
||||
"""
|
||||
|
||||
# Feed 是否允许 served_k < k(允许不足)
|
||||
feed_allow_partial: bool = True
|
||||
# Feed 是否在不足时继续回退补齐
|
||||
feed_fill_with_fallback: bool = True
|
||||
|
||||
# 候选拉取倍率(limit = min(max_candidates_limit, k * multiplier))
|
||||
candidate_multiplier_feed: int = 10
|
||||
candidate_multiplier_push_widget: int = 30
|
||||
|
||||
# 每层回退的最大候选数量下限(避免 k=1 但候选过少)
|
||||
min_candidates_per_level: int = 30
|
||||
|
||||
# explanations 默认开启(但应保持轻量)
|
||||
enable_explanations: bool = True
|
||||
|
||||
|
||||
class RecommendedItem(BaseModel):
|
||||
"""
|
||||
引擎最终下发的推荐项。
|
||||
"""
|
||||
|
||||
content_id: int
|
||||
text: str
|
||||
final_score: float
|
||||
fallback_level_final: int
|
||||
|
||||
# 解释信息:默认开启,但建议保持轻量(避免 payload 过大)
|
||||
explanations: Optional[dict[str, Any]] = None
|
||||
|
||||
|
||||
class RecoEngineResult(BaseModel):
|
||||
"""
|
||||
引擎输出容器:items + meta。
|
||||
"""
|
||||
|
||||
items: list[RecommendedItem] = Field(default_factory=list)
|
||||
meta: RecoMeta
|
||||
|
||||
|
||||
class HardFilterResult(BaseModel):
|
||||
"""
|
||||
Hard Filter 输出。
|
||||
"""
|
||||
|
||||
kept_items: list[Any] = Field(default_factory=list)
|
||||
removed_count: int = 0
|
||||
risk_filtered_count_by_flag: dict[str, int] = Field(default_factory=dict)
|
||||
# 每条内容的命中信息(仅用于 explanations;默认可为空)
|
||||
hits_by_content_id: dict[int, list[str]] = Field(default_factory=dict)
|
||||
|
||||
|
||||
class RecommendRequest(BaseModel):
|
||||
"""
|
||||
内部便捷结构(单测/集成时可用)。
|
||||
"""
|
||||
|
||||
scene: Scene
|
||||
user_profile: Any
|
||||
already_recommended_ids: list[Any] = Field(default_factory=list)
|
||||
touched_or_viewed_ids: list[Any] = Field(default_factory=list)
|
||||
k: int = 1
|
||||
now: datetime
|
||||
locale: Optional[str] = None
|
||||
constraints: Optional[RecoConstraints] = None
|
||||
config: Optional[RecoEngineConfig] = None
|
||||
|
||||
90
server/app/features/personalized_reco/reco_engine/utils.py
Normal file
@@ -0,0 +1,90 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Any, Iterable, Optional
|
||||
|
||||
|
||||
def normalize_int_id_list(mixed_ids: Iterable[Any]) -> list[int]:
|
||||
"""
|
||||
将混合类型的 id 列表归一化为 int 列表。
|
||||
|
||||
规则:
|
||||
- int/可转 int 的 str -> int
|
||||
- 其他(None/空字符串/不可解析)忽略
|
||||
"""
|
||||
|
||||
out: list[int] = []
|
||||
for x in mixed_ids or []:
|
||||
if x is None:
|
||||
continue
|
||||
if isinstance(x, bool):
|
||||
# 避免 True/False 被当作 1/0
|
||||
continue
|
||||
try:
|
||||
s = str(x).strip()
|
||||
if s == "":
|
||||
continue
|
||||
out.append(int(s))
|
||||
except Exception:
|
||||
continue
|
||||
return out
|
||||
|
||||
|
||||
def merge_exclude_ids(
|
||||
*,
|
||||
already_recommended_ids: Iterable[Any],
|
||||
touched_or_viewed_ids: Iterable[Any],
|
||||
extra_exclude_content_ids: Optional[Iterable[int]] = None,
|
||||
) -> list[int]:
|
||||
"""
|
||||
合并并去重排除 id(保持首次出现顺序)。
|
||||
"""
|
||||
|
||||
merged = list(normalize_int_id_list(list(already_recommended_ids or []) + list(touched_or_viewed_ids or [])))
|
||||
if extra_exclude_content_ids:
|
||||
merged += [int(x) for x in extra_exclude_content_ids if x is not None]
|
||||
|
||||
seen: set[int] = set()
|
||||
out: list[int] = []
|
||||
for cid in merged:
|
||||
if cid in seen:
|
||||
continue
|
||||
seen.add(cid)
|
||||
out.append(cid)
|
||||
return out
|
||||
|
||||
|
||||
def normalize_or_default_locale(locale: Optional[str]) -> str:
|
||||
"""
|
||||
locale 防御式归一化:
|
||||
- 未传/空 -> 默认 "en"
|
||||
- 其他 -> 原样返回,由下游 normalize_locale 做严格校验
|
||||
"""
|
||||
|
||||
if locale is None:
|
||||
return "en"
|
||||
raw = str(locale).strip()
|
||||
return raw or "en"
|
||||
|
||||
|
||||
def clamp_personalization_power(power: Any, *, fallback_level: int) -> float:
|
||||
"""
|
||||
按回退层级对 personalization_power 做防御式约束。
|
||||
|
||||
- L0:不改
|
||||
- L1:<= 0.5
|
||||
- L2/L3:= 0
|
||||
"""
|
||||
|
||||
try:
|
||||
p = float(power)
|
||||
except Exception:
|
||||
p = 0.0
|
||||
if p != p:
|
||||
p = 0.0
|
||||
|
||||
if int(fallback_level) >= 2:
|
||||
return 0.0
|
||||
if int(fallback_level) >= 1:
|
||||
return min(p, 0.5)
|
||||
return p
|
||||
|
||||
@@ -0,0 +1,22 @@
|
||||
"""
|
||||
个性化推荐|Rerank & Freqcap 子模块(重排 / 去重 / 频控)
|
||||
|
||||
说明(V1):
|
||||
- 本模块在 Soft Scoring 后执行,消费候选的 `final_score`,输出可下发的排序结果。
|
||||
- 仅做 Dedup / Freqcap / Feed MMR,不做 Soft Scoring 与 Hard Filter。
|
||||
"""
|
||||
|
||||
from .defaults import get_default_config
|
||||
from .rerank import rerank_and_freqcap
|
||||
from .types import RerankConfig, RerankMeta, RerankResult, ScoredCandidate, Scene
|
||||
|
||||
__all__ = [
|
||||
"RerankConfig",
|
||||
"RerankMeta",
|
||||
"RerankResult",
|
||||
"ScoredCandidate",
|
||||
"Scene",
|
||||
"get_default_config",
|
||||
"rerank_and_freqcap",
|
||||
]
|
||||
|
||||
@@ -0,0 +1,41 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from app.features.personalized_reco.rerank_freqcap.types import RerankConfig, Scene
|
||||
|
||||
|
||||
_DEFAULTS: dict[Scene, RerankConfig] = {
|
||||
# Feed:MMR λ=0.7;冷却参数不强制使用
|
||||
"feed": RerankConfig(
|
||||
mmr_lambda=0.7,
|
||||
top_n_for_mmr=200,
|
||||
cooldown_sentence_days=0,
|
||||
cooldown_author_days=0,
|
||||
cooldown_template_days=0,
|
||||
),
|
||||
# Push:工程默认(来自算法规则的建议参数)
|
||||
"push": RerankConfig(
|
||||
mmr_lambda=0.7,
|
||||
top_n_for_mmr=200,
|
||||
cooldown_sentence_days=14,
|
||||
cooldown_author_days=7,
|
||||
cooldown_template_days=7,
|
||||
),
|
||||
# Widget:工程默认
|
||||
"widget": RerankConfig(
|
||||
mmr_lambda=0.7,
|
||||
top_n_for_mmr=200,
|
||||
cooldown_sentence_days=7,
|
||||
cooldown_author_days=7,
|
||||
cooldown_template_days=7,
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
def get_default_config(scene: Scene) -> RerankConfig:
|
||||
"""
|
||||
获取指定场景的默认参数(返回副本,避免被意外修改)。
|
||||
"""
|
||||
|
||||
base = _DEFAULTS[scene]
|
||||
return RerankConfig.model_validate(base.model_dump())
|
||||
|
||||
208
server/app/features/personalized_reco/rerank_freqcap/rerank.py
Normal file
@@ -0,0 +1,208 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Any, Iterable, Optional
|
||||
|
||||
from app.features.personalized_reco.rerank_freqcap.defaults import get_default_config
|
||||
from app.features.personalized_reco.rerank_freqcap.types import RerankConfig, RerankMeta, RerankResult, ScoredCandidate, Scene
|
||||
from app.features.personalized_reco.rerank_freqcap.utils import as_finite_float, build_tags, clamp, jaccard, normalize_int_id_set
|
||||
|
||||
|
||||
def _sort_by_score_desc(cands: list[ScoredCandidate]) -> list[ScoredCandidate]:
|
||||
return sorted(cands, key=lambda x: as_finite_float(x.final_score, default=float("-inf")), reverse=True)
|
||||
|
||||
|
||||
def _dedup_by_seen_ids(
|
||||
cands: list[ScoredCandidate],
|
||||
*,
|
||||
seen_ids: set[int],
|
||||
) -> tuple[list[ScoredCandidate], int]:
|
||||
kept: list[ScoredCandidate] = []
|
||||
removed = 0
|
||||
for c in cands:
|
||||
if int(c.content_id) in seen_ids:
|
||||
removed += 1
|
||||
continue
|
||||
kept.append(c)
|
||||
return kept, removed
|
||||
|
||||
|
||||
def _apply_author_template_freqcap(
|
||||
cands: list[ScoredCandidate],
|
||||
*,
|
||||
recent_author_ids: Optional[Iterable[str]],
|
||||
recent_template_ids: Optional[Iterable[str]],
|
||||
) -> tuple[list[ScoredCandidate], dict[str, int], list[str]]:
|
||||
"""
|
||||
V1 策略:
|
||||
- 若 recent_*_ids 未提供(None),不执行该维度过滤,但在 meta 记录缺失
|
||||
- 若提供,则执行硬过滤
|
||||
"""
|
||||
|
||||
filtered_counts: dict[str, int] = {"author": 0, "template": 0}
|
||||
missing: list[str] = []
|
||||
|
||||
author_set: set[str] | None
|
||||
if recent_author_ids is None:
|
||||
author_set = None
|
||||
missing.append("author")
|
||||
else:
|
||||
author_set = set([a for a in recent_author_ids if a is not None and str(a).strip() != ""])
|
||||
|
||||
template_set: set[str] | None
|
||||
if recent_template_ids is None:
|
||||
template_set = None
|
||||
missing.append("template")
|
||||
else:
|
||||
template_set = set([t for t in recent_template_ids if t is not None and str(t).strip() != ""])
|
||||
|
||||
out: list[ScoredCandidate] = []
|
||||
for c in cands:
|
||||
if author_set is not None and c.author_id and c.author_id in author_set:
|
||||
filtered_counts["author"] += 1
|
||||
continue
|
||||
if template_set is not None and c.template_id and c.template_id in template_set:
|
||||
filtered_counts["template"] += 1
|
||||
continue
|
||||
out.append(c)
|
||||
|
||||
# 只返回真正生效的维度计数(避免 meta 噪音)
|
||||
effective_counts: dict[str, int] = {}
|
||||
if author_set is not None:
|
||||
effective_counts["author"] = int(filtered_counts["author"])
|
||||
if template_set is not None:
|
||||
effective_counts["template"] = int(filtered_counts["template"])
|
||||
|
||||
missing_sorted = sorted(set(missing))
|
||||
return out, effective_counts, missing_sorted
|
||||
|
||||
|
||||
def _sim(a: ScoredCandidate, b: ScoredCandidate, *, tags_a: set[str], tags_b: set[str]) -> float:
|
||||
# 离散特征版(V1 推荐),对齐 plan.md
|
||||
if int(a.content_id) == int(b.content_id):
|
||||
return 1.0
|
||||
|
||||
sim = 0.0
|
||||
if a.template_id and b.template_id and a.template_id == b.template_id:
|
||||
sim += 0.6
|
||||
if a.author_id and b.author_id and a.author_id == b.author_id:
|
||||
sim += 0.3
|
||||
|
||||
sim += 0.1 * jaccard(tags_a, tags_b)
|
||||
return clamp(sim, 0.0, 1.0)
|
||||
|
||||
|
||||
def _mmr_rerank(
|
||||
*,
|
||||
candidates: list[ScoredCandidate],
|
||||
k: int,
|
||||
lam: float,
|
||||
) -> list[ScoredCandidate]:
|
||||
if k <= 0:
|
||||
return []
|
||||
|
||||
if not candidates:
|
||||
return []
|
||||
|
||||
lam_f = clamp(as_finite_float(lam, default=0.7), 0.0, 1.0)
|
||||
|
||||
# 预计算 tags,避免重复构造
|
||||
tags_map: dict[int, set[str]] = {}
|
||||
for c in candidates:
|
||||
tags_map[int(c.content_id)] = build_tags(c)
|
||||
|
||||
remaining = _sort_by_score_desc(list(candidates))
|
||||
selected: list[ScoredCandidate] = []
|
||||
|
||||
# Top1:最高分
|
||||
selected.append(remaining.pop(0))
|
||||
|
||||
while remaining and len(selected) < k:
|
||||
best_idx = 0
|
||||
best_val = float("-inf")
|
||||
|
||||
for idx, c in enumerate(remaining):
|
||||
rel = as_finite_float(c.final_score, default=float("-inf"))
|
||||
|
||||
tags_c = tags_map.get(int(c.content_id), set())
|
||||
max_sim = 0.0
|
||||
for s in selected:
|
||||
tags_s = tags_map.get(int(s.content_id), set())
|
||||
max_sim = max(max_sim, _sim(c, s, tags_a=tags_c, tags_b=tags_s))
|
||||
|
||||
val = lam_f * float(rel) - (1.0 - lam_f) * float(max_sim)
|
||||
if val > best_val:
|
||||
best_val = val
|
||||
best_idx = idx
|
||||
|
||||
selected.append(remaining.pop(best_idx))
|
||||
|
||||
return selected
|
||||
|
||||
|
||||
def rerank_and_freqcap(
|
||||
*,
|
||||
scene: Scene,
|
||||
scored_candidates: list[ScoredCandidate],
|
||||
already_recommended_ids: list[Any],
|
||||
touched_or_viewed_ids: list[Any],
|
||||
k: int,
|
||||
config: Optional[RerankConfig] = None,
|
||||
recent_author_ids: Optional[list[str]] = None,
|
||||
recent_template_ids: Optional[list[str]] = None,
|
||||
) -> RerankResult:
|
||||
"""
|
||||
主入口:对 scored_candidates 做去重/频控/重排,输出最终可下发序列。
|
||||
|
||||
V1 约定:
|
||||
- 冷却窗口“按天”由调用方保证输入集合已经裁剪到窗口内,本模块以“集合代表窗口内历史”为准
|
||||
- Feed 默认只做 dedup + MMR;Push/Widget 做 dedup + freqcap + TopK
|
||||
"""
|
||||
|
||||
cfg = config or get_default_config(scene)
|
||||
|
||||
# seen_ids = already_recommended_ids ∪ touched_or_viewed_ids
|
||||
seen_ids = normalize_int_id_set(list(already_recommended_ids) + list(touched_or_viewed_ids))
|
||||
|
||||
# 先按分数降序,保证 Top1 与 TopK 一致
|
||||
base_sorted = _sort_by_score_desc(list(scored_candidates))
|
||||
|
||||
after_dedup, removed_sentence = _dedup_by_seen_ids(base_sorted, seen_ids=seen_ids)
|
||||
candidate_pool_size_after_dedup = len(after_dedup)
|
||||
|
||||
missing_history_fields: list[str] = []
|
||||
freqcap_counts: dict[str, int] = {"sentence": int(removed_sentence)}
|
||||
|
||||
after_freqcap = after_dedup
|
||||
|
||||
# Push/Widget:作者/模板冷却(增强项)
|
||||
if scene in {"push", "widget"}:
|
||||
after_freqcap, dim_counts, missing = _apply_author_template_freqcap(
|
||||
after_freqcap,
|
||||
recent_author_ids=recent_author_ids,
|
||||
recent_template_ids=recent_template_ids,
|
||||
)
|
||||
missing_history_fields = missing
|
||||
freqcap_counts.update(dim_counts)
|
||||
else:
|
||||
# Feed:不强制作者/模板冷却(V1 可选,这里默认跳过)
|
||||
missing_history_fields = []
|
||||
|
||||
candidate_pool_size_after_freqcap = len(after_freqcap)
|
||||
|
||||
ranked: list[ScoredCandidate]
|
||||
if scene == "feed":
|
||||
# MMR 前截断,避免性能问题
|
||||
top_n = int(cfg.top_n_for_mmr) if int(cfg.top_n_for_mmr) > 0 else len(after_freqcap)
|
||||
mmr_pool = after_freqcap[:top_n]
|
||||
ranked = _mmr_rerank(candidates=mmr_pool, k=int(k), lam=cfg.mmr_lambda)
|
||||
else:
|
||||
ranked = after_freqcap[: max(0, int(k))]
|
||||
|
||||
meta = RerankMeta(
|
||||
candidate_pool_size_after_dedup=int(candidate_pool_size_after_dedup),
|
||||
candidate_pool_size_after_freqcap=int(candidate_pool_size_after_freqcap),
|
||||
missing_history_fields=missing_history_fields,
|
||||
freqcap_filtered_counts=freqcap_counts,
|
||||
)
|
||||
return RerankResult(ranked_items=ranked, meta=meta)
|
||||
|
||||
@@ -0,0 +1,61 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Any, Literal, Optional
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from app.features.personalized_reco.content_repository.types import ContentProfileDTO
|
||||
|
||||
Scene = Literal["feed", "push", "widget"]
|
||||
|
||||
|
||||
class ScoredCandidate(BaseModel):
|
||||
"""
|
||||
Soft Scoring 后的候选项(本模块消费的最小字段集合)。
|
||||
|
||||
说明:
|
||||
- `content_profile` 用于 Feed 的标签/相似度计算;缺失时需降级为仅使用 author/template 等字段
|
||||
"""
|
||||
|
||||
content_id: int
|
||||
final_score: float
|
||||
|
||||
author_id: Optional[str] = None
|
||||
template_id: Optional[str] = None
|
||||
|
||||
content_profile: Optional[ContentProfileDTO] = None
|
||||
|
||||
# 允许透传额外字段(例如 text、breakdown 等),便于上层直接下发
|
||||
extra: dict[str, Any] = Field(default_factory=dict)
|
||||
|
||||
|
||||
class RerankConfig(BaseModel):
|
||||
"""
|
||||
重排/频控配置(可调参)。
|
||||
"""
|
||||
|
||||
# Feed:MMR
|
||||
mmr_lambda: float = 0.7
|
||||
top_n_for_mmr: int = 200
|
||||
|
||||
# Push/Widget:冷却窗口(V1 主要用于配置与可观测;真正按天需要带时间戳的历史)
|
||||
cooldown_sentence_days: int = 14
|
||||
cooldown_author_days: int = 7
|
||||
cooldown_template_days: int = 7
|
||||
|
||||
|
||||
class RerankMeta(BaseModel):
|
||||
candidate_pool_size_after_dedup: int
|
||||
candidate_pool_size_after_freqcap: int
|
||||
|
||||
# 例如未提供 recent_author_ids/recent_template_ids 时记录 ["author","template"]
|
||||
missing_history_fields: list[str] = Field(default_factory=list)
|
||||
|
||||
# 可选但建议:按维度统计被过滤数量
|
||||
freqcap_filtered_counts: dict[str, int] = Field(default_factory=dict)
|
||||
|
||||
|
||||
class RerankResult(BaseModel):
|
||||
ranked_items: list[ScoredCandidate] = Field(default_factory=list)
|
||||
meta: RerankMeta
|
||||
|
||||
107
server/app/features/personalized_reco/rerank_freqcap/utils.py
Normal file
@@ -0,0 +1,107 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
from typing import Any, Iterable
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def clamp(value: float, min_value: float, max_value: float) -> float:
|
||||
if value != value: # NaN
|
||||
return min_value
|
||||
return max(min_value, min(max_value, value))
|
||||
|
||||
|
||||
def as_finite_float(value: Any, *, default: float) -> float:
|
||||
try:
|
||||
f = float(value)
|
||||
except Exception:
|
||||
return float(default)
|
||||
if f != f:
|
||||
return float(default)
|
||||
if f == float("inf") or f == float("-inf"):
|
||||
return float(default)
|
||||
return f
|
||||
|
||||
|
||||
def normalize_int_id_set(values: Iterable[Any]) -> set[int]:
|
||||
"""
|
||||
将历史 ID 列表归一化为 int 集合(支持 str/int 混用)。
|
||||
|
||||
说明:
|
||||
- 无法转换的值会被忽略,并记录 debug 日志(不影响主流程)
|
||||
"""
|
||||
|
||||
out: set[int] = set()
|
||||
for v in values:
|
||||
try:
|
||||
if isinstance(v, bool):
|
||||
# 避免 True/False 被当作 1/0
|
||||
raise ValueError("bool 不是合法 id")
|
||||
out.add(int(v))
|
||||
except Exception:
|
||||
logger.debug("历史 id 无法转为 int,已忽略:%r", v)
|
||||
return out
|
||||
|
||||
|
||||
def jaccard(a: set[str], b: set[str]) -> float:
|
||||
if not a and not b:
|
||||
return 0.0
|
||||
inter = len(a & b)
|
||||
union = len(a | b)
|
||||
return float(inter) / float(union) if union > 0 else 0.0
|
||||
|
||||
|
||||
def argmax_key(d: dict[str, Any] | None) -> str | None:
|
||||
"""
|
||||
从 suitability 字典中取最大值 key(V1 用作代表标签)。
|
||||
- 空字典/None -> None
|
||||
- 值非法 -> 按 default=0 处理
|
||||
"""
|
||||
|
||||
if not d:
|
||||
return None
|
||||
best_k: str | None = None
|
||||
best_v = float("-inf")
|
||||
for k, v in d.items():
|
||||
fv = as_finite_float(v, default=0.0)
|
||||
if fv > best_v:
|
||||
best_v = fv
|
||||
best_k = k
|
||||
return best_k
|
||||
|
||||
|
||||
def build_tags(candidate: Any) -> set[str]:
|
||||
"""
|
||||
构造离散标签集合(V1 写死):
|
||||
- stage:<stage>
|
||||
- need:<argmax_key>
|
||||
- context:<argmax_key>
|
||||
|
||||
说明:
|
||||
- candidate 可能是 ScoredCandidate 或具备 content_profile 的对象
|
||||
- 字段缺失时自动降级(只返回可得标签)
|
||||
"""
|
||||
|
||||
tags: set[str] = set()
|
||||
|
||||
cp = getattr(candidate, "content_profile", None)
|
||||
if cp is None:
|
||||
return tags
|
||||
|
||||
stage = getattr(cp, "stage", None)
|
||||
if stage:
|
||||
tags.add(f"stage:{stage}")
|
||||
|
||||
need = getattr(cp, "need_suitability", None)
|
||||
need_k = argmax_key(need)
|
||||
if need_k:
|
||||
tags.add(f"need:{need_k}")
|
||||
|
||||
ctx = getattr(cp, "context_suitability", None)
|
||||
ctx_k = argmax_key(ctx)
|
||||
if ctx_k:
|
||||
tags.add(f"context:{ctx_k}")
|
||||
|
||||
return tags
|
||||
|
||||
22
server/app/features/personalized_reco/scoring/__init__.py
Normal file
@@ -0,0 +1,22 @@
|
||||
"""
|
||||
个性化推荐|Scoring 子模块(软打分与惩罚项)
|
||||
|
||||
说明:
|
||||
- 本模块只做软打分与本模块定义的惩罚项(P_uncertainty、Widget 情绪软降权)。
|
||||
- Hard Filter / 频控重排 / 新鲜度等由其他模块产出,通过入参注入(缺省按 0)。
|
||||
"""
|
||||
|
||||
from .defaults import get_default_config
|
||||
from .score import score_content
|
||||
from .types import ExternalTerms, Scene, ScoreBreakdown, ScoreConfig, ScoreResult
|
||||
|
||||
__all__ = [
|
||||
"ExternalTerms",
|
||||
"Scene",
|
||||
"ScoreBreakdown",
|
||||
"ScoreConfig",
|
||||
"ScoreResult",
|
||||
"get_default_config",
|
||||
"score_content",
|
||||
]
|
||||
|
||||
44
server/app/features/personalized_reco/scoring/defaults.py
Normal file
@@ -0,0 +1,44 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from app.features.personalized_reco.scoring.types import Scene, ScoreConfig
|
||||
|
||||
|
||||
_DEFAULTS: dict[Scene, ScoreConfig] = {
|
||||
# 来源:设计说明文档/個性化推薦算法規則.md(V1 建议权重)
|
||||
"feed": ScoreConfig(
|
||||
w_need=0.35,
|
||||
w_emotion=0.20,
|
||||
w_stage=0.15,
|
||||
w_context=0.30,
|
||||
# Feed 默认不启用不确定性惩罚(可按需开启)
|
||||
enable_uncertainty_penalty=False,
|
||||
),
|
||||
"push": ScoreConfig(
|
||||
w_need=0.45,
|
||||
w_emotion=0.35,
|
||||
w_stage=0.15,
|
||||
w_context=0.05,
|
||||
# Push 默认启用不确定性惩罚
|
||||
enable_uncertainty_penalty=True,
|
||||
),
|
||||
"widget": ScoreConfig(
|
||||
w_need=0.25,
|
||||
w_emotion=0.25,
|
||||
w_stage=0.30,
|
||||
w_context=0.20,
|
||||
# Widget 默认不启用不确定性惩罚(可按需开启)
|
||||
enable_uncertainty_penalty=False,
|
||||
widget_emotion_soft_range=(0.4, 0.8),
|
||||
widget_emotion_penalty_gamma=0.25,
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
def get_default_config(scene: Scene) -> ScoreConfig:
|
||||
"""
|
||||
获取指定场景的默认打分参数(返回副本,避免被意外修改)。
|
||||
"""
|
||||
|
||||
base = _DEFAULTS[scene]
|
||||
return ScoreConfig.model_validate(base.model_dump())
|
||||
|
||||
201
server/app/features/personalized_reco/scoring/score.py
Normal file
@@ -0,0 +1,201 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from datetime import datetime
|
||||
from typing import Optional
|
||||
|
||||
from app.features.personalized_reco.content_repository.types import ContentProfileDTO
|
||||
from app.features.personalized_reco.scoring.defaults import get_default_config
|
||||
from app.features.personalized_reco.scoring.types import ExternalTerms, Scene, ScoreBreakdown, ScoreConfig, ScoreResult
|
||||
from app.features.personalized_reco.scoring.utils import as_finite_float, clamp, pick_one_hot_key
|
||||
from app.features.user_profile_scoring.types import UserProfileV1_2
|
||||
|
||||
|
||||
def _missing_fields(user_profile: UserProfileV1_2) -> list[str]:
|
||||
missing: list[str] = []
|
||||
if not user_profile.need:
|
||||
missing.append("need")
|
||||
if not user_profile.context:
|
||||
missing.append("context")
|
||||
if user_profile.emotion_score is None:
|
||||
missing.append("emotion")
|
||||
return missing
|
||||
|
||||
|
||||
def _score_need(user_profile: UserProfileV1_2, content: ContentProfileDTO) -> float:
|
||||
key = pick_one_hot_key(user_profile.need) # type: ignore[arg-type]
|
||||
if key is None:
|
||||
return 0.5
|
||||
raw = content.need_suitability.get(key, 0.5)
|
||||
return clamp(as_finite_float(raw, default=0.5), 0.0, 1.0)
|
||||
|
||||
|
||||
def _score_context(user_profile: UserProfileV1_2, content: ContentProfileDTO) -> float:
|
||||
key = pick_one_hot_key(user_profile.context) # type: ignore[arg-type]
|
||||
if key is None:
|
||||
return 0.5
|
||||
raw = content.context_suitability.get(key, 0.5)
|
||||
return clamp(as_finite_float(raw, default=0.5), 0.0, 1.0)
|
||||
|
||||
|
||||
def _score_emotion(user_profile: UserProfileV1_2, content: ContentProfileDTO) -> float:
|
||||
# V1.2:用户情绪缺失 -> 0.8
|
||||
if user_profile.emotion_score is None:
|
||||
return 0.8
|
||||
|
||||
# 文案 general(emotion_score=None)-> 0.8
|
||||
if content.emotion_score is None:
|
||||
return 0.8
|
||||
|
||||
u = clamp(as_finite_float(user_profile.emotion_score, default=0.8), 0.0, 1.0)
|
||||
c = clamp(as_finite_float(content.emotion_score, default=0.8), 0.0, 1.0)
|
||||
return clamp(1.0 - abs(u - c), 0.0, 1.0)
|
||||
|
||||
|
||||
def _user_stage_key(user_profile: UserProfileV1_2) -> str:
|
||||
# 约定:UserStageOneHot.unknown 必填;但这里仍做防御
|
||||
stage = user_profile.stage
|
||||
if getattr(stage, "expecting", 0) == 1:
|
||||
return "expecting"
|
||||
if getattr(stage, "parenting", 0) == 1:
|
||||
return "parenting"
|
||||
if getattr(stage, "unknown", 1) == 1:
|
||||
return "unknown"
|
||||
return "unknown"
|
||||
|
||||
|
||||
def _score_stage(user_profile: UserProfileV1_2, content: ContentProfileDTO) -> float:
|
||||
# 对齐算法规则:
|
||||
# - general=1;命中=1;unknown对非unknown=0.7;其余=0
|
||||
if content.stage == "general":
|
||||
return 1.0
|
||||
|
||||
u_stage = _user_stage_key(user_profile)
|
||||
if content.stage == u_stage:
|
||||
return 1.0
|
||||
|
||||
if u_stage == "unknown" and content.stage != "unknown":
|
||||
return 0.7
|
||||
|
||||
return 0.0
|
||||
|
||||
|
||||
def _score_personal(alpha: float, personalization_power: float, s_need: float, s_context: float) -> float:
|
||||
power = clamp(as_finite_float(personalization_power, default=0.0), 0.0, 1.0)
|
||||
a = as_finite_float(alpha, default=0.0)
|
||||
return float(a) * float(power) * max(float(s_need), float(s_context))
|
||||
|
||||
|
||||
def _penalty_uncertainty(beta: float, user_profile: UserProfileV1_2, content: ContentProfileDTO) -> float:
|
||||
b = as_finite_float(beta, default=0.0)
|
||||
power = clamp(as_finite_float(content.personalization_power, default=0.0), 0.0, 1.0)
|
||||
|
||||
# V1 约定:conf_U 缺失时按 1.0(避免过惩罚)
|
||||
conf_u = clamp(as_finite_float(getattr(user_profile, "profile_confidence", 1.0), default=1.0), 0.0, 1.0)
|
||||
conf_c = clamp(as_finite_float(getattr(content, "review_confidence", 0.7), default=0.7), 0.0, 1.0)
|
||||
|
||||
return float(b) * (1.0 - float(conf_u)) * (1.0 - float(conf_c)) * float(power)
|
||||
|
||||
|
||||
def _widget_emotion_penalty(scene: Scene, content: ContentProfileDTO, config: ScoreConfig) -> float:
|
||||
if scene != "widget":
|
||||
return 0.0
|
||||
if content.emotion_score is None:
|
||||
return 0.0
|
||||
|
||||
lo, hi = config.widget_emotion_soft_range
|
||||
lo_f = as_finite_float(lo, default=0.4)
|
||||
hi_f = as_finite_float(hi, default=0.8)
|
||||
width = hi_f - lo_f
|
||||
if width <= 0:
|
||||
return 0.0
|
||||
|
||||
e = clamp(as_finite_float(content.emotion_score, default=0.6), 0.0, 1.0)
|
||||
if e < lo_f:
|
||||
d = lo_f - e
|
||||
elif e > hi_f:
|
||||
d = e - hi_f
|
||||
else:
|
||||
d = 0.0
|
||||
|
||||
gamma = as_finite_float(config.widget_emotion_penalty_gamma, default=0.25)
|
||||
raw = float(gamma) * float(d) / float(width)
|
||||
return clamp(raw, 0.0, float(gamma))
|
||||
|
||||
|
||||
def score_content(
|
||||
*,
|
||||
scene: Scene,
|
||||
user_profile: UserProfileV1_2,
|
||||
content_profile: ContentProfileDTO,
|
||||
config: Optional[ScoreConfig] = None,
|
||||
pass_filters: bool = True,
|
||||
external_terms: Optional[ExternalTerms] = None,
|
||||
now: Optional[datetime] = None, # 预留:V1 不使用
|
||||
) -> ScoreResult:
|
||||
"""
|
||||
主入口:对单条内容 Cᵢ 进行软打分,返回 final_score 与 breakdown。
|
||||
|
||||
说明(V1):
|
||||
- `pass_filters` 来自 Hard Filter(本模块不做硬过滤)
|
||||
- `external_terms` 可注入 S_fresh / P_fatigue / P_repeat / P_risk(缺省按 0)
|
||||
- `now` 预留给未来的 freshness/时间衰减(V1 不实现)
|
||||
"""
|
||||
|
||||
cfg = config or get_default_config(scene)
|
||||
ext = external_terms or ExternalTerms()
|
||||
|
||||
missing = _missing_fields(user_profile)
|
||||
|
||||
s_need = _score_need(user_profile, content_profile)
|
||||
s_context = _score_context(user_profile, content_profile)
|
||||
s_emotion = _score_emotion(user_profile, content_profile)
|
||||
s_stage = _score_stage(user_profile, content_profile)
|
||||
|
||||
w_need = as_finite_float(cfg.w_need, default=0.0)
|
||||
w_emotion = as_finite_float(cfg.w_emotion, default=0.0)
|
||||
w_stage = as_finite_float(cfg.w_stage, default=0.0)
|
||||
w_context = as_finite_float(cfg.w_context, default=0.0)
|
||||
|
||||
s_core = float(w_need) * s_need + float(w_emotion) * s_emotion + float(w_stage) * s_stage + float(w_context) * s_context
|
||||
|
||||
s_personal = _score_personal(cfg.alpha, content_profile.personalization_power, s_need, s_context)
|
||||
|
||||
p_uncertainty = 0.0
|
||||
if cfg.enable_uncertainty_penalty:
|
||||
p_uncertainty = _penalty_uncertainty(cfg.beta, user_profile, content_profile)
|
||||
|
||||
p_widget = _widget_emotion_penalty(scene, content_profile, cfg)
|
||||
|
||||
s_fresh = as_finite_float(ext.S_fresh, default=0.0)
|
||||
p_fatigue = as_finite_float(ext.P_fatigue, default=0.0)
|
||||
p_repeat = as_finite_float(ext.P_repeat, default=0.0)
|
||||
p_risk_external = as_finite_float(ext.P_risk, default=0.0)
|
||||
|
||||
# Widget 软降权并入 P_risk(但在 breakdown 中单独暴露,便于打点)
|
||||
p_risk = float(p_risk_external) + float(p_widget)
|
||||
|
||||
raw_final = s_core + s_personal + float(s_fresh) - float(p_fatigue) - float(p_repeat) - float(p_risk) - float(p_uncertainty)
|
||||
final_score = float(raw_final) if pass_filters else 0.0
|
||||
|
||||
breakdown = ScoreBreakdown(
|
||||
scene=scene,
|
||||
**{
|
||||
"pass": bool(pass_filters),
|
||||
},
|
||||
missing_fields=missing,
|
||||
S_need=float(s_need),
|
||||
S_context=float(s_context),
|
||||
S_stage=float(s_stage),
|
||||
S_emotion=float(s_emotion),
|
||||
S_core=float(s_core),
|
||||
S_personal=float(s_personal),
|
||||
S_fresh=float(s_fresh),
|
||||
P_fatigue=float(p_fatigue),
|
||||
P_repeat=float(p_repeat),
|
||||
P_risk=float(p_risk),
|
||||
P_uncertainty=float(p_uncertainty),
|
||||
P_widget_emotion_out_of_range=float(p_widget),
|
||||
)
|
||||
|
||||
return ScoreResult(final_score=float(final_score), breakdown=breakdown)
|
||||
|
||||
84
server/app/features/personalized_reco/scoring/types.py
Normal file
@@ -0,0 +1,84 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Literal
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
Scene = Literal["feed", "push", "widget"]
|
||||
|
||||
|
||||
class ScoreConfig(BaseModel):
|
||||
"""
|
||||
打分配置(可调参)。
|
||||
|
||||
说明:
|
||||
- 默认值由 `defaults.get_default_config(scene)` 提供
|
||||
- 本模块不负责回退梯度(fallback_level)策略;仅做防御式 clamp
|
||||
"""
|
||||
|
||||
w_need: float
|
||||
w_emotion: float
|
||||
w_stage: float
|
||||
w_context: float
|
||||
|
||||
alpha: float = 0.15
|
||||
beta: float = 0.30
|
||||
|
||||
enable_uncertainty_penalty: bool = False
|
||||
|
||||
# Widget 情绪软区间与软降权强度
|
||||
widget_emotion_soft_range: tuple[float, float] = (0.4, 0.8)
|
||||
widget_emotion_penalty_gamma: float = 0.25
|
||||
|
||||
|
||||
class ExternalTerms(BaseModel):
|
||||
"""
|
||||
外部注入项(V1 可选)。
|
||||
|
||||
说明:
|
||||
- 由 `rerank-freqcap` 或 `reco-engine` 产出
|
||||
- 本模块缺省按 0,保证可排序与输出结构稳定
|
||||
"""
|
||||
|
||||
S_fresh: float = 0.0
|
||||
P_fatigue: float = 0.0
|
||||
P_repeat: float = 0.0
|
||||
P_risk: float = 0.0
|
||||
|
||||
|
||||
class ScoreBreakdown(BaseModel):
|
||||
"""
|
||||
可观测分解项(用于调参与回归测试)。
|
||||
"""
|
||||
|
||||
scene: Scene
|
||||
passed: bool = Field(alias="pass")
|
||||
|
||||
missing_fields: list[str] = Field(default_factory=list)
|
||||
|
||||
S_need: float
|
||||
S_context: float
|
||||
S_stage: float
|
||||
S_emotion: float
|
||||
|
||||
S_core: float
|
||||
S_personal: float
|
||||
S_fresh: float
|
||||
|
||||
P_fatigue: float
|
||||
P_repeat: float
|
||||
P_risk: float
|
||||
P_uncertainty: float
|
||||
|
||||
# Widget 专用:区间外软降权(建议保留,便于打点)
|
||||
P_widget_emotion_out_of_range: float = 0.0
|
||||
|
||||
model_config = {
|
||||
"populate_by_name": True,
|
||||
}
|
||||
|
||||
|
||||
class ScoreResult(BaseModel):
|
||||
final_score: float
|
||||
breakdown: ScoreBreakdown
|
||||
|
||||
59
server/app/features/personalized_reco/scoring/utils.py
Normal file
@@ -0,0 +1,59 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
from typing import Any
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def clamp(value: float, min_value: float, max_value: float) -> float:
|
||||
"""
|
||||
将值裁剪到区间内,并对 NaN 做兜底。
|
||||
"""
|
||||
|
||||
if value != value: # NaN
|
||||
return min_value
|
||||
return max(min_value, min(max_value, value))
|
||||
|
||||
|
||||
def as_finite_float(value: Any, *, default: float) -> float:
|
||||
"""
|
||||
将任意值尽量转为有限 float;失败则返回 default。
|
||||
"""
|
||||
|
||||
try:
|
||||
f = float(value)
|
||||
except Exception:
|
||||
return float(default)
|
||||
|
||||
# NaN / inf 都视为不可用
|
||||
if f != f:
|
||||
return float(default)
|
||||
if f == float("inf") or f == float("-inf"):
|
||||
return float(default)
|
||||
return f
|
||||
|
||||
|
||||
def pick_one_hot_key(one_hot: dict[str, Any] | None) -> str | None:
|
||||
"""
|
||||
从稀疏 one-hot({key: 1})中取唯一 key。
|
||||
|
||||
约定:
|
||||
- None / {} → 缺失,返回 None
|
||||
- 单 key → 返回该 key
|
||||
- 多 key → 取“字典序最小”的 key,并记录 debug 日志(避免静默歧义)
|
||||
"""
|
||||
|
||||
if not one_hot:
|
||||
return None
|
||||
|
||||
keys = [k for k, v in one_hot.items() if v == 1 or v is True]
|
||||
if not keys:
|
||||
return None
|
||||
if len(keys) == 1:
|
||||
return keys[0]
|
||||
|
||||
chosen = sorted(keys)[0]
|
||||
logger.debug("one-hot 出现多个 key=1,已按字典序选择:chosen=%s keys=%s", chosen, keys)
|
||||
return chosen
|
||||
|
||||
@@ -1,6 +1,7 @@
|
||||
from fastapi import FastAPI
|
||||
|
||||
from app.core.config import get_settings
|
||||
from app.api.v1.reco import router as reco_router
|
||||
from app.api.v1.user_profile_scoring import router as user_profile_router
|
||||
|
||||
|
||||
@@ -17,6 +18,7 @@ def create_app() -> FastAPI:
|
||||
|
||||
# 业务路由
|
||||
app.include_router(user_profile_router)
|
||||
app.include_router(reco_router)
|
||||
|
||||
@app.get("/healthz")
|
||||
async def healthz() -> dict:
|
||||
|
||||
168
server/app/tasks/reco.py
Normal file
@@ -0,0 +1,168 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
from datetime import datetime, timezone
|
||||
from typing import Any, Optional
|
||||
|
||||
from celery import shared_task
|
||||
|
||||
from app.db.session import AsyncSessionLocal
|
||||
from app.features.personalized_reco.content_repository.sqlalchemy_repo import SqlAlchemyContentRepository
|
||||
from app.features.personalized_reco.content_repository.types import normalize_locale
|
||||
from app.features.personalized_reco.reco_engine import recommend
|
||||
from app.features.personalized_reco.reco_engine.types import RecoConstraints, RecoEngineResult, Scene
|
||||
from app.features.user_profile_scoring.types import UserProfileV1_2
|
||||
|
||||
|
||||
def _ensure_now(now: Optional[datetime]) -> datetime:
|
||||
if now is None:
|
||||
return datetime.now(timezone.utc)
|
||||
if now.tzinfo is None:
|
||||
return now.replace(tzinfo=timezone.utc)
|
||||
return now
|
||||
|
||||
|
||||
def _ensure_locale(locale: Optional[str]) -> str:
|
||||
raw = (locale or "").strip() or "en"
|
||||
# 严格校验只支持 en/tc(允许 en-US 等在 normalize_locale 内归一化)
|
||||
return str(normalize_locale(raw))
|
||||
|
||||
|
||||
async def _run_reco_async(
|
||||
*,
|
||||
scene: Scene,
|
||||
user_profile: UserProfileV1_2,
|
||||
already_recommended_ids: list[Any],
|
||||
touched_or_viewed_ids: list[Any],
|
||||
k: int,
|
||||
now: datetime,
|
||||
locale: str,
|
||||
) -> RecoEngineResult:
|
||||
async with AsyncSessionLocal() as session:
|
||||
repo = SqlAlchemyContentRepository(session)
|
||||
return await recommend(
|
||||
repo=repo,
|
||||
scene=scene,
|
||||
user_profile=user_profile,
|
||||
already_recommended_ids=list(already_recommended_ids or []),
|
||||
touched_or_viewed_ids=list(touched_or_viewed_ids or []),
|
||||
k=int(k),
|
||||
now=now,
|
||||
locale=locale,
|
||||
constraints=RecoConstraints(),
|
||||
)
|
||||
|
||||
|
||||
def _run_reco_sync(
|
||||
*,
|
||||
scene: Scene,
|
||||
user_profile: UserProfileV1_2,
|
||||
already_recommended_ids: list[Any],
|
||||
touched_or_viewed_ids: list[Any],
|
||||
k: int,
|
||||
now: Optional[datetime],
|
||||
locale: Optional[str],
|
||||
) -> dict[str, Any]:
|
||||
effective_now = _ensure_now(now)
|
||||
effective_locale = _ensure_locale(locale)
|
||||
result = asyncio.run(
|
||||
_run_reco_async(
|
||||
scene=scene,
|
||||
user_profile=user_profile,
|
||||
already_recommended_ids=already_recommended_ids,
|
||||
touched_or_viewed_ids=touched_or_viewed_ids,
|
||||
k=int(k),
|
||||
now=effective_now,
|
||||
locale=effective_locale,
|
||||
)
|
||||
)
|
||||
# 默认不存结果,但返回值可用于开发调试(worker 通常 ignore_result)
|
||||
return result.model_dump()
|
||||
|
||||
|
||||
@shared_task(name="tasks.reco.generate")
|
||||
def generate(
|
||||
*,
|
||||
scene: Scene,
|
||||
user_profile: dict[str, Any],
|
||||
already_recommended_ids: Optional[list[Any]] = None,
|
||||
touched_or_viewed_ids: Optional[list[Any]] = None,
|
||||
k: Optional[int] = None,
|
||||
now: Optional[str] = None,
|
||||
locale: Optional[str] = None,
|
||||
) -> dict[str, Any]:
|
||||
"""
|
||||
推荐生成任务(通用入口)。
|
||||
|
||||
说明:
|
||||
- 入参尽量保持小(避免 Redis 队列膨胀)
|
||||
- 默认 worker 配置为 ignore_result,但这里仍返回结构,便于本地调试
|
||||
"""
|
||||
|
||||
# 解析 user_profile(严格按 V1.2)
|
||||
u = UserProfileV1_2.model_validate(user_profile or {})
|
||||
|
||||
# k 默认按场景(与 API 一致)
|
||||
if k is None:
|
||||
k_i = 30 if scene == "feed" else 1
|
||||
else:
|
||||
k_i = int(k)
|
||||
|
||||
# now 支持 ISO 字符串
|
||||
dt: Optional[datetime]
|
||||
if not now:
|
||||
dt = None
|
||||
else:
|
||||
raw = str(now).strip()
|
||||
if raw.endswith("Z"):
|
||||
raw = raw[:-1] + "+00:00"
|
||||
try:
|
||||
dt = datetime.fromisoformat(raw)
|
||||
except Exception:
|
||||
dt = None
|
||||
|
||||
return _run_reco_sync(
|
||||
scene=scene,
|
||||
user_profile=u,
|
||||
already_recommended_ids=list(already_recommended_ids or []),
|
||||
touched_or_viewed_ids=list(touched_or_viewed_ids or []),
|
||||
k=k_i,
|
||||
now=dt,
|
||||
locale=locale,
|
||||
)
|
||||
|
||||
|
||||
def _deliver_push_placeholder(payload: dict[str, Any]) -> None:
|
||||
"""
|
||||
Push 下游写入占位函数(V1 不接真实推送系统)。
|
||||
"""
|
||||
|
||||
_ = payload
|
||||
return None
|
||||
|
||||
|
||||
@shared_task(name="tasks.reco.push_once")
|
||||
def push_once(
|
||||
*,
|
||||
user_profile: dict[str, Any],
|
||||
already_recommended_ids: Optional[list[Any]] = None,
|
||||
touched_or_viewed_ids: Optional[list[Any]] = None,
|
||||
now: Optional[str] = None,
|
||||
locale: Optional[str] = None,
|
||||
) -> dict[str, Any]:
|
||||
"""
|
||||
单次 Push 生成(占位任务)。
|
||||
"""
|
||||
|
||||
payload = generate(
|
||||
scene="push",
|
||||
user_profile=user_profile,
|
||||
already_recommended_ids=already_recommended_ids,
|
||||
touched_or_viewed_ids=touched_or_viewed_ids,
|
||||
k=1,
|
||||
now=now,
|
||||
locale=locale,
|
||||
)
|
||||
_deliver_push_placeholder(payload)
|
||||
return payload
|
||||
|
||||
@@ -5,6 +5,7 @@ uvicorn[standard]>=0.27
|
||||
SQLAlchemy>=2.0
|
||||
aiomysql>=0.2
|
||||
greenlet>=3.0
|
||||
aiosqlite>=0.20
|
||||
|
||||
# 配置
|
||||
pydantic>=2.6
|
||||
@@ -19,3 +20,5 @@ redis>=5.0
|
||||
|
||||
# 测试
|
||||
pytest>=8.0
|
||||
pytest-asyncio>=0.23
|
||||
httpx>=0.27
|
||||
|
||||
145
server/run.sh
Executable file
@@ -0,0 +1,145 @@
|
||||
#!/usr/bin/env bash
|
||||
set -euo pipefail
|
||||
|
||||
# 一键启动 FastAPI 后端:
|
||||
# - 自动创建/复用虚拟环境(.venv)
|
||||
# - 自动安装 requirements.txt 依赖
|
||||
# - 自动启动 uvicorn(默认开启 --reload)
|
||||
#
|
||||
# 用法示例:
|
||||
# ./run.sh # 默认 host=0.0.0.0 port=8000 env=dev reload=on
|
||||
# ./run.sh --env prod # 使用 .env.prod(若存在且可被 source)
|
||||
# ./run.sh --port 9000 # 改端口
|
||||
# ./run.sh --no-reload # 关闭热更新
|
||||
# ./run.sh --install-only # 只安装依赖,不启动
|
||||
|
||||
usage() {
|
||||
cat <<'EOF'
|
||||
用法:
|
||||
./run.sh [--env dev|prod] [--host 0.0.0.0] [--port 8000] [--no-reload] [--skip-install] [--install-only]
|
||||
|
||||
参数:
|
||||
--env dev|prod 优先尝试加载 .env.dev 或 .env.prod(如果存在)。
|
||||
--host <host> uvicorn host(默认 0.0.0.0)
|
||||
--port <port> uvicorn port(默认 8000)
|
||||
--no-reload 关闭 uvicorn --reload
|
||||
--skip-install 跳过依赖安装(默认会安装/更新 requirements.txt)
|
||||
--install-only 只安装依赖,不启动服务
|
||||
-h, --help 显示帮助
|
||||
|
||||
说明:
|
||||
- 若你的 .env.* 不是 shell 可 source 的格式(例如包含空格/特殊字符未加引号),建议改成 KEY=value 形式。
|
||||
- 启动后访问:
|
||||
/healthz 健康检查
|
||||
/docs OpenAPI 文档
|
||||
EOF
|
||||
}
|
||||
|
||||
# 始终从脚本所在目录运行(避免在别处执行导致路径错)
|
||||
SCRIPT_DIR="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" && pwd)"
|
||||
cd "$SCRIPT_DIR"
|
||||
|
||||
ENV_NAME="dev"
|
||||
HOST="0.0.0.0"
|
||||
PORT="8000"
|
||||
RELOAD="1"
|
||||
SKIP_INSTALL="0"
|
||||
INSTALL_ONLY="0"
|
||||
|
||||
while [[ $# -gt 0 ]]; do
|
||||
case "$1" in
|
||||
--env)
|
||||
ENV_NAME="${2:-}"
|
||||
shift 2
|
||||
;;
|
||||
--host)
|
||||
HOST="${2:-}"
|
||||
shift 2
|
||||
;;
|
||||
--port)
|
||||
PORT="${2:-}"
|
||||
shift 2
|
||||
;;
|
||||
--no-reload)
|
||||
RELOAD="0"
|
||||
shift 1
|
||||
;;
|
||||
--skip-install)
|
||||
SKIP_INSTALL="1"
|
||||
shift 1
|
||||
;;
|
||||
--install-only)
|
||||
INSTALL_ONLY="1"
|
||||
shift 1
|
||||
;;
|
||||
-h|--help)
|
||||
usage
|
||||
exit 0
|
||||
;;
|
||||
*)
|
||||
echo "未知参数:$1" >&2
|
||||
echo "" >&2
|
||||
usage >&2
|
||||
exit 2
|
||||
;;
|
||||
esac
|
||||
done
|
||||
|
||||
if [[ "$ENV_NAME" != "dev" && "$ENV_NAME" != "prod" ]]; then
|
||||
echo "--env 仅支持 dev 或 prod,当前:$ENV_NAME" >&2
|
||||
exit 2
|
||||
fi
|
||||
|
||||
ENV_FILE=".env.${ENV_NAME}"
|
||||
if [[ -f "$ENV_FILE" ]]; then
|
||||
# 让 source 进来的变量自动 export(供 pydantic-settings/应用读取)
|
||||
set -a
|
||||
# shellcheck disable=SC1090
|
||||
source "$ENV_FILE"
|
||||
set +a
|
||||
fi
|
||||
|
||||
# 选择 python 命令(优先 python3)
|
||||
PY_BIN=""
|
||||
if command -v python3 >/dev/null 2>&1; then
|
||||
PY_BIN="python3"
|
||||
elif command -v python >/dev/null 2>&1; then
|
||||
PY_BIN="python"
|
||||
else
|
||||
echo "未找到 python/python3,请先安装 Python 3.11+。" >&2
|
||||
exit 1
|
||||
fi
|
||||
|
||||
VENV_DIR=".venv"
|
||||
if [[ ! -d "$VENV_DIR" ]]; then
|
||||
echo "创建虚拟环境:$VENV_DIR"
|
||||
"$PY_BIN" -m venv "$VENV_DIR"
|
||||
fi
|
||||
|
||||
# 激活虚拟环境
|
||||
# shellcheck disable=SC1091
|
||||
source "$VENV_DIR/bin/activate"
|
||||
|
||||
if [[ "$SKIP_INSTALL" == "0" ]]; then
|
||||
if [[ -f "requirements.txt" ]]; then
|
||||
echo "升级 pip 并安装依赖(requirements.txt)"
|
||||
python -m pip install -U pip
|
||||
python -m pip install -r requirements.txt
|
||||
else
|
||||
echo "未找到 requirements.txt,跳过依赖安装。" >&2
|
||||
fi
|
||||
fi
|
||||
|
||||
if [[ "$INSTALL_ONLY" == "1" ]]; then
|
||||
echo "依赖安装完成(install-only),退出。"
|
||||
exit 0
|
||||
fi
|
||||
|
||||
UVICORN_ARGS=(app.main:app --host "$HOST" --port "$PORT")
|
||||
if [[ "$RELOAD" == "1" ]]; then
|
||||
UVICORN_ARGS+=(--reload)
|
||||
fi
|
||||
|
||||
echo "启动服务:uvicorn ${UVICORN_ARGS[*]}"
|
||||
exec uvicorn "${UVICORN_ARGS[@]}"
|
||||
|
||||
223
server/tests/conftest.py
Normal file
@@ -0,0 +1,223 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import os
|
||||
import sys
|
||||
from pathlib import Path
|
||||
from typing import AsyncIterator, Callable
|
||||
from urllib.parse import urlparse
|
||||
|
||||
import pytest
|
||||
import pytest_asyncio
|
||||
import sqlalchemy as sa
|
||||
from alembic import command
|
||||
from alembic.config import Config
|
||||
from sqlalchemy import event
|
||||
from sqlalchemy.ext.asyncio import AsyncEngine, AsyncSession, async_sessionmaker, create_async_engine
|
||||
|
||||
# 确保在任何 pytest rootdir 下都能 `import app.*`
|
||||
SERVER_DIR = Path(__file__).resolve().parents[1] # .../server
|
||||
if str(SERVER_DIR) not in sys.path:
|
||||
sys.path.insert(0, str(SERVER_DIR))
|
||||
|
||||
|
||||
def _read_env_kv(env_path: Path) -> dict[str, str]:
|
||||
"""
|
||||
读取 .env 文件中的 KEY=VALUE(最小实现,避免引入额外依赖)。
|
||||
"""
|
||||
|
||||
data: dict[str, str] = {}
|
||||
if not env_path.exists():
|
||||
return data
|
||||
for raw in env_path.read_text(encoding="utf-8").splitlines():
|
||||
line = raw.strip()
|
||||
if not line or line.startswith("#"):
|
||||
continue
|
||||
if "=" not in line:
|
||||
continue
|
||||
k, v = line.split("=", 1)
|
||||
k = k.strip()
|
||||
v = v.strip().strip('"').strip("'")
|
||||
if k:
|
||||
data[k] = v
|
||||
return data
|
||||
|
||||
|
||||
def _get_database_url() -> str:
|
||||
"""
|
||||
获取测试用数据库连接串。
|
||||
|
||||
约定(与 alembic/env.py 保持一致):
|
||||
- 优先读取环境变量 `DATABASE_URL`
|
||||
- 若未设置,则按 `APP_ENV`(默认 dev)读取 `server/.env.dev` 或 `server/.env.prod`
|
||||
"""
|
||||
|
||||
env_url = (os.getenv("DATABASE_URL") or "").strip()
|
||||
if env_url:
|
||||
return env_url
|
||||
|
||||
server_dir = Path(__file__).resolve().parents[1] # .../server
|
||||
app_env = (os.getenv("APP_ENV") or "dev").strip() or "dev"
|
||||
env_file = server_dir / (".env.prod" if app_env == "prod" else ".env.dev")
|
||||
kv = _read_env_kv(env_file)
|
||||
url = (kv.get("DATABASE_URL") or "").strip()
|
||||
if url:
|
||||
return url
|
||||
|
||||
raise RuntimeError(
|
||||
"缺少 DATABASE_URL:请设置环境变量 DATABASE_URL,或在 server/.env.dev(或 .env.prod)中配置 DATABASE_URL。"
|
||||
)
|
||||
|
||||
|
||||
def _assert_safe_mysql_test_db(url: str) -> None:
|
||||
"""
|
||||
为了避免对开发库造成破坏性影响,集成测试只允许连接到“测试库”。
|
||||
|
||||
规则(V1):
|
||||
- 必须是 mysql+aiomysql://...
|
||||
- 为避免误连生产库:不允许数据库名为 'mindfulness'(prod 默认库名)
|
||||
- 建议使用独立测试库(例如 mindfulness_dev_test)
|
||||
"""
|
||||
|
||||
if not url.startswith("mysql+"):
|
||||
raise RuntimeError(f"当前仅允许 MySQL 集成测试(mysql+aiomysql)。实际:{url!r}")
|
||||
|
||||
parsed = urlparse(url.replace("mysql+aiomysql://", "mysql://", 1))
|
||||
db_name = (parsed.path or "").lstrip("/")
|
||||
if db_name.lower() == "mindfulness":
|
||||
raise RuntimeError(
|
||||
"为避免误连生产库,集成测试不允许连接到数据库 'mindfulness'。"
|
||||
"请改用 dev 测试库(例如 mindfulness_dev_test 或 mindfulness_dev)。"
|
||||
)
|
||||
|
||||
|
||||
def _run_alembic_upgrade_head() -> None:
|
||||
"""
|
||||
使用 Alembic 将测试库升级到最新 schema。
|
||||
|
||||
说明:
|
||||
- 依赖 env.py 内部读取 DATABASE_URL
|
||||
- 仅在 session 级别执行一次,避免每个测试都跑迁移
|
||||
"""
|
||||
|
||||
server_dir = Path(__file__).resolve().parents[1] # .../server
|
||||
alembic_ini = server_dir / "alembic.ini"
|
||||
cfg = Config(str(alembic_ini))
|
||||
# 确保脚本路径正确(alembic.ini 里一般已配置,这里兜底)
|
||||
cfg.set_main_option("script_location", "alembic")
|
||||
command.upgrade(cfg, "head")
|
||||
|
||||
def _assert_schema_exists(url: str) -> None:
|
||||
"""
|
||||
非破坏性检查:要求目标库已经存在所需表。
|
||||
|
||||
说明:
|
||||
- 默认不在测试中运行 Alembic(避免任何 schema 变更)
|
||||
- 若要自动迁移,请设置环境变量 ALLOW_SCHEMA_MIGRATION=1
|
||||
"""
|
||||
|
||||
allow_migration = (os.getenv("ALLOW_SCHEMA_MIGRATION") or "").strip() == "1"
|
||||
if allow_migration:
|
||||
_run_alembic_upgrade_head()
|
||||
return
|
||||
|
||||
# 使用 PyMySQL 做同步检查,避免依赖 MySQLdb(不要求系统安装 mysqlclient)
|
||||
sync_url = url.replace("mysql+aiomysql://", "mysql+pymysql://", 1)
|
||||
engine = sa.create_engine(sync_url, future=True)
|
||||
try:
|
||||
insp = sa.inspect(engine)
|
||||
tables = set(insp.get_table_names())
|
||||
required = {"contents", "content_profiles", "content_risk_flags"}
|
||||
missing = sorted(required - tables)
|
||||
if missing:
|
||||
raise RuntimeError(
|
||||
"集成测试检测到 schema 不完整(缺少表:"
|
||||
+ ", ".join(missing)
|
||||
+ ")。为避免破坏性操作,测试不会自动迁移。"
|
||||
"请先手动在该库执行 `alembic upgrade head`,或设置 ALLOW_SCHEMA_MIGRATION=1 允许测试自动迁移。"
|
||||
)
|
||||
finally:
|
||||
engine.dispose()
|
||||
|
||||
|
||||
@pytest.fixture(scope="session", autouse=True)
|
||||
def _migrate_db_once() -> None:
|
||||
"""
|
||||
Session 级 schema 检查(仅在配置了数据库连接时启用)。
|
||||
|
||||
说明:
|
||||
- 纯函数单元测试不需要 MySQL;若未配置 DATABASE_URL,则跳过检查
|
||||
- 集成测试(依赖 db_session/async_engine)仍会在获取 DATABASE_URL 时失败,从而提示用户配置
|
||||
"""
|
||||
|
||||
try:
|
||||
url = _get_database_url()
|
||||
except RuntimeError:
|
||||
return
|
||||
|
||||
_assert_safe_mysql_test_db(url)
|
||||
_assert_schema_exists(url)
|
||||
|
||||
|
||||
@pytest_asyncio.fixture
|
||||
async def async_engine() -> AsyncIterator[AsyncEngine]:
|
||||
url = _get_database_url()
|
||||
_assert_safe_mysql_test_db(url)
|
||||
engine = create_async_engine(url, pool_pre_ping=True)
|
||||
try:
|
||||
yield engine
|
||||
finally:
|
||||
await engine.dispose()
|
||||
|
||||
|
||||
@pytest_asyncio.fixture
|
||||
async def db_session(async_engine: AsyncEngine) -> AsyncIterator[AsyncSession]:
|
||||
"""
|
||||
提供一个干净的 AsyncSession。
|
||||
|
||||
清理策略:每个测试都在事务中执行,并在结束时回滚(不做 DELETE/TRUNCATE)。
|
||||
|
||||
说明:
|
||||
- 用例里严禁调用 session.commit(),只允许 flush()
|
||||
- 这样不会对测试库产生持久化写入,更不会影响开发库
|
||||
"""
|
||||
|
||||
SessionLocal: async_sessionmaker[AsyncSession] = async_sessionmaker(
|
||||
bind=async_engine,
|
||||
expire_on_commit=False,
|
||||
autoflush=False,
|
||||
autocommit=False,
|
||||
)
|
||||
|
||||
async with SessionLocal() as session:
|
||||
trans = await session.begin()
|
||||
try:
|
||||
yield session
|
||||
finally:
|
||||
await trans.rollback()
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def query_counter(async_engine: AsyncEngine) -> Callable[[], int]:
|
||||
"""
|
||||
返回一个函数:调用可获得当前累计查询次数。
|
||||
"""
|
||||
|
||||
count = {"n": 0}
|
||||
|
||||
def before_cursor_execute(*args, **kwargs): # type: ignore[no-untyped-def]
|
||||
count["n"] += 1
|
||||
|
||||
event.listen(async_engine.sync_engine, "before_cursor_execute", before_cursor_execute)
|
||||
|
||||
def get_count() -> int:
|
||||
return int(count["n"])
|
||||
|
||||
def fin() -> None:
|
||||
event.remove(async_engine.sync_engine, "before_cursor_execute", before_cursor_execute)
|
||||
|
||||
# 用 yield 确保测试后移除监听,避免重复绑定导致统计偏大
|
||||
try:
|
||||
yield get_count # type: ignore[misc]
|
||||
finally:
|
||||
fin()
|
||||
|
||||
178
server/tests/test_content_repository.py
Normal file
@@ -0,0 +1,178 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from datetime import datetime
|
||||
|
||||
import pytest
|
||||
|
||||
from app.db.models.content import Content
|
||||
from app.db.models.content_profile import ContentProfile
|
||||
from app.db.models.content_risk_flag import ContentRiskFlag
|
||||
from app.features.personalized_reco.content_repository.sqlalchemy_repo import SqlAlchemyContentRepository
|
||||
|
||||
|
||||
def _ctx_json() -> dict:
|
||||
return {"family": 0.5, "work": 0.5, "relationship": 0.5, "friends": 0.5, "health": 0.5}
|
||||
|
||||
|
||||
def _need_json() -> dict:
|
||||
return {
|
||||
"emotional_support": 0.5,
|
||||
"parenting_pressure": 0.5,
|
||||
"self_worth": 0.5,
|
||||
"anxiety_relief": 0.5,
|
||||
"rest_balance": 0.5,
|
||||
}
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_fetch_contents_by_ids_locale_no_fallback(db_session, query_counter):
|
||||
# content 1: 只有英文
|
||||
c1 = Content(text_en="hello", text_tc=None, author_id="a1", template_id="t1")
|
||||
db_session.add(c1)
|
||||
await db_session.flush()
|
||||
db_session.add(
|
||||
ContentProfile(
|
||||
content_id=c1.content_id,
|
||||
stage="general",
|
||||
emotion_score=None,
|
||||
context_suitability_json=_ctx_json(),
|
||||
need_suitability_json=_need_json(),
|
||||
personalization_power=10,
|
||||
review_confidence=None,
|
||||
is_safe_pool=False,
|
||||
)
|
||||
)
|
||||
db_session.add(ContentRiskFlag(content_id=c1.content_id, flag="block_stage_unknown"))
|
||||
|
||||
# content 2: 只有繁中
|
||||
c2 = Content(text_en=None, text_tc="繁體中文", author_id="a2", template_id="t2")
|
||||
db_session.add(c2)
|
||||
await db_session.flush()
|
||||
db_session.add(
|
||||
ContentProfile(
|
||||
content_id=c2.content_id,
|
||||
stage="general",
|
||||
emotion_score=None,
|
||||
context_suitability_json=_ctx_json(),
|
||||
need_suitability_json=_need_json(),
|
||||
personalization_power=0,
|
||||
review_confidence=0.9,
|
||||
is_safe_pool=True,
|
||||
)
|
||||
)
|
||||
db_session.add(ContentRiskFlag(content_id=c2.content_id, flag="block_health_sensitive"))
|
||||
|
||||
await db_session.flush()
|
||||
|
||||
repo = SqlAlchemyContentRepository(db_session)
|
||||
start = query_counter()
|
||||
|
||||
# en:只能拿到有 text_en 的内容(不允许回退到 text_tc)
|
||||
en_items = await repo.fetch_contents_by_ids(content_ids=[c2.content_id, c1.content_id], locale="en")
|
||||
assert [x.content_id for x in en_items] == [c1.content_id]
|
||||
assert en_items[0].text == "hello"
|
||||
assert en_items[0].personalization_power == 1.0
|
||||
assert en_items[0].review_confidence == 0.7 # NULL -> 0.7
|
||||
assert "unsafe_for_stage_unknown" in en_items[0].risk_flags
|
||||
assert "block_stage_unknown" not in en_items[0].risk_flags
|
||||
|
||||
# tc:只能拿到有 text_tc 的内容(不允许回退到 text_en)
|
||||
tc_items = await repo.fetch_contents_by_ids(content_ids=[c1.content_id, c2.content_id], locale="tc")
|
||||
assert [x.content_id for x in tc_items] == [c2.content_id]
|
||||
assert tc_items[0].text == "繁體中文"
|
||||
assert tc_items[0].review_confidence == 0.9
|
||||
assert "block_health_medical" in tc_items[0].risk_flags
|
||||
assert "block_health_sensitive" not in tc_items[0].risk_flags
|
||||
|
||||
# 两次调用各自 2 次查询(主体+画像一次,flags 一次),总计应为常数级
|
||||
end = query_counter()
|
||||
assert (end - start) <= 4
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_fetch_candidates_fallback_and_locale_filter(db_session, query_counter):
|
||||
# 构造 4 条英文内容:power 0/5/10,安全池标记不同
|
||||
contents = []
|
||||
for i, (power, safe) in enumerate([(0, True), (5, False), (10, False), (0, False)], start=1):
|
||||
c = Content(text_en=f"en_{i}", text_tc=None, author_id=f"a{i}", template_id=f"t{i}")
|
||||
db_session.add(c)
|
||||
await db_session.flush()
|
||||
db_session.add(
|
||||
ContentProfile(
|
||||
content_id=c.content_id,
|
||||
stage="general",
|
||||
emotion_score=None,
|
||||
context_suitability_json=_ctx_json(),
|
||||
need_suitability_json=_need_json(),
|
||||
personalization_power=power,
|
||||
review_confidence=None,
|
||||
is_safe_pool=safe,
|
||||
# 让测试数据在候选排序中排到最前,避免依赖“库为空”
|
||||
updated_at=datetime(2099, 1, 1, 0, 0, 0),
|
||||
)
|
||||
)
|
||||
contents.append(c)
|
||||
await db_session.flush()
|
||||
inserted_ids = {int(c.content_id) for c in contents}
|
||||
|
||||
repo = SqlAlchemyContentRepository(db_session)
|
||||
|
||||
class _MinimalUser:
|
||||
# need/context/emotion_score 全缺失 -> effective_fallback 至少 L1
|
||||
need = {}
|
||||
context = {}
|
||||
emotion_score = None
|
||||
stage = {"unknown": 1}
|
||||
|
||||
start = query_counter()
|
||||
|
||||
# 入参 L0,但因为缺失字段,effective_fallback=1 -> power<=5(排除 power=10)
|
||||
items_l0 = await repo.fetch_candidates(
|
||||
scene="feed",
|
||||
user_profile=_MinimalUser(),
|
||||
fallback_level=0,
|
||||
limit=3,
|
||||
locale="en-US",
|
||||
)
|
||||
powers = [x.personalization_power for x in items_l0]
|
||||
assert 1.0 not in powers
|
||||
assert all(x.text.startswith("en_") for x in items_l0)
|
||||
|
||||
# L2:强制 power=0 且 stage=general(这里都 general),只剩 power=0 的两条
|
||||
items_l2 = await repo.fetch_candidates(
|
||||
scene="feed",
|
||||
user_profile=_MinimalUser(),
|
||||
fallback_level=2,
|
||||
limit=2,
|
||||
locale="en",
|
||||
)
|
||||
assert all(x.personalization_power == 0.0 for x in items_l2)
|
||||
assert all(x.text.startswith("en_") for x in items_l2)
|
||||
|
||||
# L3:只安全池(is_safe_pool=true)且 power=0
|
||||
items_l3 = await repo.fetch_candidates(
|
||||
scene="feed",
|
||||
user_profile=_MinimalUser(),
|
||||
fallback_level=3,
|
||||
limit=1,
|
||||
locale="en",
|
||||
)
|
||||
assert len(items_l3) == 1
|
||||
assert items_l3[0].text.startswith("en_")
|
||||
assert items_l3[0].personalization_power == 0.0
|
||||
|
||||
# locale 过滤:tc 请求下这些内容都没有 text_tc -> 返回空
|
||||
items_tc = await repo.fetch_candidates(
|
||||
scene="feed",
|
||||
user_profile=_MinimalUser(),
|
||||
fallback_level=0,
|
||||
limit=10,
|
||||
locale="tc",
|
||||
)
|
||||
# 不要求库为空:只断言“不会把本次插入的 en-only 测试数据返回出来”
|
||||
assert not any(x.content_id in inserted_ids for x in items_tc)
|
||||
|
||||
end = query_counter()
|
||||
# 期望为常数级(每次 fetch_candidates:1 次取 ids + 2 次补全),这里 4 次调用 -> <= 12
|
||||
assert (end - start) <= 12
|
||||
|
||||
165
server/tests/test_integration_api_worker.py
Normal file
@@ -0,0 +1,165 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import importlib
|
||||
from datetime import datetime, timezone
|
||||
from typing import Any
|
||||
|
||||
import pytest
|
||||
from fastapi.testclient import TestClient
|
||||
|
||||
|
||||
def _set_min_env(monkeypatch: pytest.MonkeyPatch) -> None:
|
||||
# 让 Settings 可构造(引擎不会在单测中真正连接 DB/Redis)
|
||||
monkeypatch.setenv(
|
||||
"DATABASE_URL",
|
||||
"mysql+aiomysql://u:p@127.0.0.1:3306/mindfulness_dev_test?charset=utf8mb4",
|
||||
)
|
||||
monkeypatch.setenv("REDIS_URL", "redis://127.0.0.1:6379/0")
|
||||
monkeypatch.setenv("CELERY_BROKER_URL", "redis://127.0.0.1:6379/0")
|
||||
|
||||
|
||||
def _user_profile_dict() -> dict[str, Any]:
|
||||
return {
|
||||
"profile_version": "v1.2",
|
||||
"profile_source": "questionnaire",
|
||||
"profile_generated_at": "2026-02-02T12:00:00Z",
|
||||
"profile_confidence": 1.0,
|
||||
"profile_answered": {"stage": True, "emotion": False, "context": False, "need": False},
|
||||
"stage": {"unknown": 1},
|
||||
"emotion_score": None,
|
||||
"context": {},
|
||||
"need": {},
|
||||
}
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def client(monkeypatch: pytest.MonkeyPatch):
|
||||
_set_min_env(monkeypatch)
|
||||
|
||||
# 清理 settings cache,避免被其他测试污染
|
||||
from app.core import config as config_mod
|
||||
|
||||
config_mod.get_settings.cache_clear()
|
||||
|
||||
# 重新加载 main,确保使用最新 env
|
||||
import app.main as main_mod
|
||||
|
||||
importlib.reload(main_mod)
|
||||
|
||||
app = main_mod.create_app()
|
||||
|
||||
# override repo(避免依赖真实 DB)
|
||||
from app.api.v1 import reco as reco_mod
|
||||
from app.features.personalized_reco.content_repository.types import ContentProfileDTO
|
||||
|
||||
class _FakeRepo:
|
||||
async def fetch_candidates(self, **kwargs): # type: ignore[no-untyped-def]
|
||||
# 返回一条可下发内容
|
||||
return [
|
||||
ContentProfileDTO(
|
||||
content_id=1,
|
||||
text="t1",
|
||||
stage="general",
|
||||
emotion_score=None,
|
||||
context_suitability={},
|
||||
need_suitability={},
|
||||
personalization_power=0.0,
|
||||
risk_flags=[],
|
||||
author_id=None,
|
||||
template_id=None,
|
||||
review_confidence=0.7,
|
||||
)
|
||||
]
|
||||
|
||||
async def fetch_contents_by_ids(self, **kwargs): # type: ignore[no-untyped-def]
|
||||
return []
|
||||
|
||||
async def _override_repo(): # type: ignore[no-untyped-def]
|
||||
return _FakeRepo()
|
||||
|
||||
app.dependency_overrides[reco_mod.get_reco_repo] = _override_repo
|
||||
|
||||
# 清空限流计数,避免跨测试污染
|
||||
import app.api.limits as limits_mod
|
||||
|
||||
limits_mod._reco_rate_limiter._counters.clear() # type: ignore[attr-defined]
|
||||
limits_mod._reco_rate_limiter._last_gc_bucket = 0 # type: ignore[attr-defined]
|
||||
|
||||
return TestClient(app)
|
||||
|
||||
|
||||
def test_accept_language_mapping_to_tc(client: TestClient, monkeypatch: pytest.MonkeyPatch) -> None:
|
||||
# 固定时间,避免跨分钟 flake
|
||||
import app.api.limits as limits_mod
|
||||
|
||||
monkeypatch.setattr(limits_mod.time, "time", lambda: 1738497600.0) # 2025-02-02 12:00:00Z 的某个时间戳
|
||||
|
||||
resp = client.post(
|
||||
"/v1/reco/feed",
|
||||
json={"user_profile": _user_profile_dict(), "already_recommended_ids": [], "touched_or_viewed_ids": []},
|
||||
headers={"Accept-Language": "zh-TW,zh;q=0.9"},
|
||||
)
|
||||
assert resp.status_code == 200
|
||||
data = resp.json()
|
||||
assert data["meta"]["config_snapshot"]["locale"] == "tc"
|
||||
|
||||
|
||||
def test_x_now_header_priority_over_body_now(client: TestClient, monkeypatch: pytest.MonkeyPatch) -> None:
|
||||
import app.api.limits as limits_mod
|
||||
|
||||
monkeypatch.setattr(limits_mod.time, "time", lambda: 1738497600.0)
|
||||
|
||||
resp = client.post(
|
||||
"/v1/reco/push",
|
||||
json={
|
||||
"user_profile": _user_profile_dict(),
|
||||
"now": "2026-02-01T00:00:00Z",
|
||||
"already_recommended_ids": [],
|
||||
"touched_or_viewed_ids": [],
|
||||
},
|
||||
headers={"X-Now": "2026-02-02T12:00:00Z", "Accept-Language": "en-US"},
|
||||
)
|
||||
assert resp.status_code == 200
|
||||
data = resp.json()
|
||||
# meta.config_snapshot 里没有 now,但 served_k 应该正常
|
||||
assert data["meta"]["served_k"] == 1
|
||||
|
||||
|
||||
def test_rate_limit_10_per_minute(client: TestClient, monkeypatch: pytest.MonkeyPatch) -> None:
|
||||
import app.api.limits as limits_mod
|
||||
|
||||
monkeypatch.setattr(limits_mod.time, "time", lambda: 1738497600.0)
|
||||
|
||||
body = {"user_profile": _user_profile_dict(), "already_recommended_ids": [], "touched_or_viewed_ids": []}
|
||||
for _ in range(10):
|
||||
r = client.post("/v1/reco/widget", json=body)
|
||||
assert r.status_code == 200
|
||||
|
||||
r = client.post("/v1/reco/widget", json=body)
|
||||
assert r.status_code == 429
|
||||
assert r.json()["detail"] == "rate_limited"
|
||||
|
||||
|
||||
def test_celery_tasks_can_call_generate(monkeypatch: pytest.MonkeyPatch) -> None:
|
||||
_set_min_env(monkeypatch)
|
||||
from app.core import config as config_mod
|
||||
|
||||
config_mod.get_settings.cache_clear()
|
||||
|
||||
import app.tasks.reco as reco_tasks
|
||||
|
||||
# monkeypatch async runner,避免依赖 DB
|
||||
async def _fake_run_reco_async(**kwargs): # type: ignore[no-untyped-def]
|
||||
from app.features.personalized_reco.observability.types import RecoMeta
|
||||
from app.features.personalized_reco.reco_engine.types import RecoEngineResult, RecommendedItem
|
||||
|
||||
return RecoEngineResult(
|
||||
items=[RecommendedItem(content_id=1, text="t1", final_score=1.0, fallback_level_final=0, explanations={})],
|
||||
meta=RecoMeta(scene="push", served_k=1),
|
||||
)
|
||||
|
||||
monkeypatch.setattr(reco_tasks, "_run_reco_async", _fake_run_reco_async)
|
||||
|
||||
out = reco_tasks.generate(scene="push", user_profile=_user_profile_dict(), k=1)
|
||||
assert out["meta"]["served_k"] == 1
|
||||
|
||||
129
server/tests/test_observability.py
Normal file
@@ -0,0 +1,129 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from datetime import datetime, timezone
|
||||
|
||||
import pytest
|
||||
|
||||
from app.features.personalized_reco.observability.builder import RecoMetaBuilder
|
||||
from app.features.personalized_reco.observability.utils import compute_empty_reason, compute_missing_fields
|
||||
from app.features.user_profile_scoring.types import ProfileAnswered, UserProfileV1_2, UserStageOneHot
|
||||
|
||||
|
||||
def _u(*, need: dict | None = None, context: dict | None = None, emotion_score=None, conf_u: float = 0.9) -> UserProfileV1_2:
|
||||
now = datetime.now(tz=timezone.utc)
|
||||
return UserProfileV1_2(
|
||||
profile_generated_at=now,
|
||||
profile_confidence=conf_u,
|
||||
profile_answered=ProfileAnswered(stage=True, emotion=True, context=True, need=True),
|
||||
stage=UserStageOneHot(unknown=1),
|
||||
emotion_score=emotion_score,
|
||||
context=context or {},
|
||||
need=need or {},
|
||||
)
|
||||
|
||||
|
||||
def test_compute_missing_fields() -> None:
|
||||
u1 = _u(need={}, context={}, emotion_score=None)
|
||||
m1 = compute_missing_fields(u1)
|
||||
assert m1.need is True
|
||||
assert m1.context is True
|
||||
assert m1.emotion is True
|
||||
|
||||
u2 = _u(need={"x": 1}, context={"y": 1}, emotion_score=0.6)
|
||||
m2 = compute_missing_fields(u2)
|
||||
assert m2.need is False
|
||||
assert m2.context is False
|
||||
assert m2.emotion is False
|
||||
|
||||
|
||||
def test_compute_empty_reason_branches() -> None:
|
||||
assert (
|
||||
compute_empty_reason(
|
||||
served_k=1,
|
||||
candidate_pool_size_raw=0,
|
||||
candidate_pool_size_after_hard_filter=0,
|
||||
candidate_pool_size_after_freqcap=0,
|
||||
)
|
||||
is None
|
||||
)
|
||||
|
||||
assert (
|
||||
compute_empty_reason(
|
||||
served_k=0,
|
||||
candidate_pool_size_raw=0,
|
||||
candidate_pool_size_after_hard_filter=0,
|
||||
candidate_pool_size_after_freqcap=0,
|
||||
)
|
||||
== "pool_empty"
|
||||
)
|
||||
|
||||
assert (
|
||||
compute_empty_reason(
|
||||
served_k=0,
|
||||
candidate_pool_size_raw=10,
|
||||
candidate_pool_size_after_hard_filter=0,
|
||||
candidate_pool_size_after_freqcap=0,
|
||||
)
|
||||
== "hard_filter_all"
|
||||
)
|
||||
|
||||
assert (
|
||||
compute_empty_reason(
|
||||
served_k=0,
|
||||
candidate_pool_size_raw=10,
|
||||
candidate_pool_size_after_hard_filter=5,
|
||||
candidate_pool_size_after_freqcap=0,
|
||||
)
|
||||
== "freqcap_all"
|
||||
)
|
||||
|
||||
assert (
|
||||
compute_empty_reason(
|
||||
served_k=0,
|
||||
candidate_pool_size_raw=10,
|
||||
candidate_pool_size_after_hard_filter=5,
|
||||
candidate_pool_size_after_freqcap=3,
|
||||
)
|
||||
== "unknown"
|
||||
)
|
||||
|
||||
|
||||
def test_builder_outputs_stable_fields_and_monotonic_counts() -> None:
|
||||
u = _u(need={"emotional_support": 1}, context={}, emotion_score=None, conf_u=0.2)
|
||||
|
||||
# 故意设置“非单调”的输入,验证 builder 的防御修正
|
||||
meta = (
|
||||
RecoMetaBuilder(scene="feed", user_profile=u, k=30)
|
||||
.set_candidate_pool_size_raw(10)
|
||||
.set_after_hard_filter(12) # 非法:大于 raw
|
||||
.set_after_dedup(20) # 非法:大于 after_hard
|
||||
.set_after_freqcap(15) # 非法:大于 after_dedup(修正后会与 after_dedup 对齐)
|
||||
.set_served_k(99) # 非法:大于 after_freqcap
|
||||
.set_fallback_level_final(1, reason="freqcap_all")
|
||||
.build()
|
||||
)
|
||||
|
||||
d = meta.model_dump()
|
||||
for k in [
|
||||
"scene",
|
||||
"candidate_pool_size_raw",
|
||||
"candidate_pool_size_after_hard_filter",
|
||||
"candidate_pool_size_after_dedup",
|
||||
"candidate_pool_size_after_freqcap",
|
||||
"fallback_level_final",
|
||||
"served_k",
|
||||
"empty_reason",
|
||||
"conf_U",
|
||||
"missing_fields",
|
||||
]:
|
||||
assert k in d
|
||||
|
||||
assert meta.candidate_pool_size_raw == 10
|
||||
assert meta.candidate_pool_size_after_hard_filter == 10
|
||||
assert meta.candidate_pool_size_after_dedup == 10
|
||||
assert meta.candidate_pool_size_after_freqcap == 10
|
||||
assert meta.served_k == 10
|
||||
assert meta.conf_U == pytest.approx(0.2)
|
||||
assert meta.missing_fields.context is True
|
||||
assert meta.missing_fields.emotion is True
|
||||
|
||||