commit 6a2632da70a37ed0fe04258f126663c4ed2a2f0c Author: yangqianqian <5845211314@qq.com> Date: Tue Jun 16 11:30:22 2026 +0800 baseline: Clover 独立仓库首次基线提交 将 Clover 从上层产品包旧仓库中独立出来,建立专属版本控制。 当前状态=纵切片端到端已打通(登录→选品→出文出图→审核→下载包), M1文案质量去套路化已验收。此提交作为后续按核销清单逐条修复的基线。 Co-Authored-By: Claude Opus 4.8 diff --git a/.claude/rules/api.md b/.claude/rules/api.md new file mode 100644 index 0000000..068838d --- /dev/null +++ b/.claude/rules/api.md @@ -0,0 +1,42 @@ +--- +paths: + - "src/api/**" + - "src/routers/**" + - "src/services/**" +--- + +# API 层开发规则（Clover 后端） + +> 承接架构方案v0.3 基石 + API契约.md，冲突以架构方案为准。 + +## 路由规范 +- RESTful 命名：资源用名词复数（/tasks, /products, /api-keys） +- 版本前缀：/api/v1/ +- 控制器只做：参数校验 → 调 service → 格式化响应，**不含业务逻辑** + +## 响应格式（统一包络） +- 成功：`{ "code": 0, "data": {...} }` +- 失败：`{ "code": <错误码>, "message": "用户可读信息" }` +- HTTP 状态码与业务 code 分离（见契约§0七类错误码） + +## 安全（基石B/C） +- 所有用户输入必须校验（类型、长度、范围） +- SQL 查询使用参数化，禁止字符串拼接 +- **API Key 绝不进 Celery 参数**，只传 task_id；worker 内查库→Fernet解密→局部变量 +- FERNET_KEY 走环境变量，绝不进代码库；解密不落盘、不打日志 +- 敏感操作记审计日志（ai_call_logs） + +## 多租户隔离（红线） +- 所有查询强制带 workspace_id 条件 +- 写操作 + 切 workspace 必须查 workspace_members 校验权限 +- workspace_id / product_id 从 JWT + 上下文自动带，前端不传（防越权） +- 所有 workspace 相关 Repo 继承 base_workspace_repo + +## 错误处理（官网V1坑3） +- service 错误不许静默吞，至少打 error log +- AI/token站调用失败归因到个人 key（错误码50002） +- DB 枚举约束要有对应代码侧常量文件 + +## 业务参数不写死（基石A） +- 禁止出现品类/数量/角度任何枚举常量 +- "看起来像枚举"的业务概念 → 做成数据不做成代码 diff --git a/.claude/rules/db.md b/.claude/rules/db.md new file mode 100644 index 0000000..2bf42c2 --- /dev/null +++ b/.claude/rules/db.md @@ -0,0 +1,49 @@ +--- +paths: + - "src/models/**" + - "alembic/**" + - "migrations/**" +--- + +# 数据库规则（Clover · MySQL + Alembic） + +> 承接架构方案v0.3 第三章（16表）。一期建14张，matrix_accounts/preference_profile 二期预留。 + +## 迁移 +- 每次 schema 变更创建迁移文件，不手动改数据库 +- 迁移文件不可修改已提交的，只能新增 +- 迁移必须包含 up 和 down +- 按序：001(banana搬3张) → 002(多租户基础) → 003(业务7张) → 004(飞轮) + +## 查询 +- 禁止 SELECT *，明确列出字段 +- 大表查询必须有索引支撑，新增查询模式时同步加索引 +- N+1 问题：关联查询用 JOIN 或批量加载 +- 飞轮聚合走 MySQL JOIN（MongoDB 只存 AI debug trace，业务不依赖） + +## 命名 +- 表名：snake_case 复数（users, generation_tasks, preference_events） +- 字段名：snake_case（created_at, workspace_id） +- 索引名：idx_表名_字段名 + +## 关键约束（架构方案） +- 任务主键：自增 BIGINT + mongo_trace_id VARCHAR(24) +- user_api_keys：UNIQUE(user_id, workspace_id, provider) +- 所有业务表必须有 workspace_id（多租户逻辑隔离） +- preference_events 必须有 workspace_id + product_id（跨公司隔离+按产品分开学） + +## 状态机（generation_tasks.status） +pending → generating → pending_selection → pending_review → approved/rejected → archived +- 打回后回到 pending_selection +- 非法流转返错误码 40901 + +## 字段红线 +- API Key 字段：encrypted_key（Fernet），**不存 url 字段**（token站固定自家站） +- users 表：删除 banana 的 credits 字段 +- eval_score：留 NULL（不接 banana 假评分） +- data_ownership：client_data / platform_asset（洞7分层归属） +- banned_words.level：auto_fix / soft_warn / hard_block（三级） + +## 枚举约束 +- DB 枚举约束要有对应代码侧常量文件（消除三套命名打架） +- 品类（products.category）是纯数据字段，禁止任何品类枚举常量 diff --git a/.claude/rules/frontend.md b/.claude/rules/frontend.md new file mode 100644 index 0000000..e4fa63c --- /dev/null +++ b/.claude/rules/frontend.md @@ -0,0 +1,46 @@ +--- +paths: + - "src/components/**" + - "src/app/**" + - "src/pages/**" + - "src/stores/**" +--- + +# 前端开发规则（Clover · Next.js） + +> 承接 前端交互设计.md（5屏金标准）+ API契约.md §3 DTO。 + +## 组件规范 +- 单一职责：一个组件只做一件事 +- Props 必须定义类型（TypeScript interface） +- 避免 prop drilling 超 3 层，用 Context 或状态管理 + +## DTO 对接（堵官网V1坑4） +- 所有 DTO 字段严格按 API契约§3，**不许自己假设嵌套结构** +- 后端响应与契约不符立即报 Lead，不私下适配 +- workspace_id / product_id 不在前端传 + +## SSE（异步生图电影） +- 按 event_seq 去重，断线重连补发历史事件 +- 11类事件按契约§2渲染：0/N进度 + 谁好谁先冒 + 单批失败重试 + 能离开 +- flywheel_injected 事件 → 显"本次已注入：最近偏好/打回原因" + +## 样式 +- 用 CSS Modules 或 Tailwind，不用全局样式 +- 响应断点统一：sm(640) md(768) lg(1024) xl(1280) +- 颜色/间距用 design token，不硬编码 + +## 可访问性 +- 交互元素必须有 aria-label +- 图片必须有 alt 文本 +- 键盘导航可用 + +## 红线（CLAUDE.md） +- ❌ 不展示 Token 余额、不做积分页（key 去中转站后台看） +- ❌ 飞轮隐形：无"训练AI"按钮，只在生成时显注入提示 +- ❌ 不做"自动发布到小红书"——只到确认预览 +- ❌ 不做移动端（一期） + +## 状态管理 +- 4 个 Store：任务 / 候选 / 审核 / 全局 +- 异步态用骨架屏，错误态对接契约§0错误码 diff --git a/.claude/settings.json b/.claude/settings.json new file mode 100644 index 0000000..f2bdb56 --- /dev/null +++ b/.claude/settings.json @@ -0,0 +1,53 @@ +{ + "//说明": "Clover 仓库根 Hooks 配置。四道质量门禁，承接治理指南§9.3模板。退出码 0=放行 2=阻止 其他非零=脚本错误。母本在 启动包/.claude/。", + "env": { + "CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS": "1" + }, + "hooks": { + "PreToolUse": [ + { + "//门禁1": "危险命令拦截", + "matcher": "Bash", + "hooks": [ + { + "type": "command", + "command": "cmd=$(jq -r '.tool_input.command // empty'); if echo \"$cmd\" | grep -qE 'rm -rf|DROP TABLE|--force|--no-verify|reset --hard|git push.*main|git push.*master'; then echo \"危险命令被拦截: $cmd\" >&2; exit 2; fi; exit 0" + } + ] + }, + { + "//门禁2": "敏感文件保护 + 源目录只读保护", + "matcher": "Edit|Write", + "hooks": [ + { + "type": "command", + "command": "file=$(jq -r '.tool_input.file_path // empty'); if echo \"$file\" | grep -qE '\\.env|\\.key|credentials|secrets|FERNET'; then echo \"禁止编辑敏感文件: $file\" >&2; exit 2; fi; if echo \"$file\" | grep -qE 'O2中间件平台/banana|产品包/worker|万牛会L1准备/worker'; then echo \"源目录只读，禁止修改(铁律): $file\" >&2; exit 2; fi; exit 0" + } + ] + } + ], + "PostToolUse": [ + { + "//门禁3": "自动 lint", + "matcher": "Write|Edit", + "hooks": [ + { + "type": "command", + "command": "file=$(jq -r '.tool_input.file_path // empty'); case \"$file\" in *.ts|*.tsx|*.js|*.jsx) npx eslint --fix \"$file\" 2>&1 || true;; *.py) ruff check --fix \"$file\" 2>&1 || true;; esac; exit 0" + } + ] + } + ], + "Stop": [ + { + "//门禁4": "测试验证 + 临时文件提醒", + "hooks": [ + { + "type": "command", + "command": "bash -c 'echo \"请确认是否已运行测试并通过(软件测试过≠内容质量过,内容需北哥抽检)。\" >&2; tmp=$(find . -name \"*_tmp*\" 2>/dev/null | head -5); if [ -n \"$tmp\" ]; then echo \"发现临时文件待清理:\" >&2; echo \"$tmp\" >&2; fi; exit 0'" + } + ] + } + ] + } +} diff --git a/.claude/skills/clover-loop/SKILL.md b/.claude/skills/clover-loop/SKILL.md new file mode 100644 index 0000000..c6e6ed6 --- /dev/null +++ b/.claude/skills/clover-loop/SKILL.md @@ -0,0 +1,67 @@ +--- +name: clover-loop +version: 1.0.0 +description: | + Clover 内容生产闭环开发 SKILL（改造自 ralph-loop）。 + Memory is Poison, Tests are Cure —— 但 Clover 的 Test 不止单元测试，还有北哥的眼睛。 + 六阶段闭环：调研→约束→生成→验证→校准→闭环。质量过关=机器过滤+北哥抽检两阶段。 +triggers: + - clover-loop + - 内容闭环 + - 生产闭环 +allowed-tools: + - Bash + - Read + - Write + - Edit + - Glob + - AskUserQuestion +--- + +# Clover Loop SKILL + +> 核心：机器层(machine_passed)只是进抽检队列，北哥点头(human_passed)才是真过关。 + +## 六阶段 + +### P0 调研（ralph-loop 没有，北哥"先调研"） +读架构方案v0.3 + 对应 PRD + 扒包落盘，确认本任务边界。 +不充分就进 P2 = 在错误的层做对的事（PSR-L 教训）。 + +### P1 约束 +冻结 task_spec：输入/输出/验证命令/期望exit_code/质量阈值。 +`python scripts/loop_state.py create ` + +### P2 生成 +写代码 + 写验证脚本（必须含断言、返回exit_code）。 +`python scripts/loop_state.py advance` + +### P3 验证（机器层） +跑机器门禁：lint / test / 违禁词 / 五维≥90 / 去重。 +全过 → status=machine_passed → 进 P4；不过 → 记失败，回 P2。 + +### P4 校准（ralph-loop 没有，专治"分高但烂"） +机器过的内容样本，按维度比对北哥标准；同质化/跑偏单独标记。 +产出**抽检样本包**交北哥 → 北哥判断回写质量标准（喂飞轮）。 + +### P5 闭环 +- 北哥抽检通过 → status=human_passed → 归档 + 结晶 +- 北哥打回 / 验证不过 → 分析根因 + - 同层撞墙 **2 次** → 强制 Pivot（换层不换参），记 pivots[] + - Pivot 后仍不过 / 北哥连续否决同方向 → status=blocked，停循环请人介入 + +## 状态机（.clover-loop-state.json） +5 态：in_progress / machine_passed / human_passed / pivoted / blocked +关键：machine_passed ≠ human_passed + +## 命令 +| 命令 | 功能 | +|------|------| +| /clover-loop | 启动六阶段闭环 | +| /clover-loop:status | 查当前状态 | +| /clover-loop:pivot | 手动触发换层 | + +## 铁律 +- 撞墙 2 次必 Pivot（对齐 CLAUDE.md 失败2次） +- 北哥不耐烦/连续否决 = 路径选错层的信号，立即 Pivot 不调参 +- 软件测试过 ≠ 内容质量过 diff --git a/.claude/skills/clover-loop/scripts/loop_state.py b/.claude/skills/clover-loop/scripts/loop_state.py new file mode 100644 index 0000000..3cfbbde --- /dev/null +++ b/.claude/skills/clover-loop/scripts/loop_state.py @@ -0,0 +1,133 @@ +#!/usr/bin/env python3 +""" +Clover Loop State Manager（改造自 ralph-loop state_manager） +六阶段 + 5态 + 机器/人工双层验收 + Pivot 追踪。 +""" +import json +import sys +from datetime import datetime +from pathlib import Path + +STATE_FILE = ".clover-loop-state.json" +PHASES = {"0": "调研", "1": "约束", "2": "生成", "3": "验证", "4": "校准", "5": "闭环"} + + +def _path(work_dir=None): + base = Path(work_dir) if work_dir else Path.cwd() + return base / STATE_FILE + + +def load(work_dir=None): + p = _path(work_dir) + return json.load(open(p, encoding="utf-8")) if p.exists() else None + + +def save(state, work_dir=None): + state["updated_at"] = datetime.now().isoformat() + json.dump(state, open(_path(work_dir), "w", encoding="utf-8"), + indent=2, ensure_ascii=False) + + +def create(task_id, work_dir=None): + state = { + "task_id": task_id, + "created_at": datetime.now().isoformat(), + "updated_at": datetime.now().isoformat(), + "current_phase": 0, + "max_retries_per_layer": 2, # 单层撞墙2次→Pivot + "machine_gate": {"lint": None, "test": None, "banned_word": None, + "score": None, "dedup": None}, + "human_review": {"status": "pending", "sampled": 0, + "approved": 0, "rejected": 0}, + "status": "in_progress", # in_progress/machine_passed/human_passed/pivoted/blocked + "layer_attempts": {}, # {层名: 失败次数} + "pivots": [], + "logs": [], + } + save(state, work_dir) + return state + + +def advance(state, work_dir=None): + cur = state["current_phase"] + if cur < 5: + state["current_phase"] = cur + 1 + save(state, work_dir) + return state + + +def machine_gate(state, key, result, work_dir=None): + """记录机器门禁单项结果。全过则置 machine_passed。""" + state["machine_gate"][key] = result + g = state["machine_gate"] + passed = all(v in ("pass", True) or (isinstance(v, (int, float)) and v >= 90) + for v in g.values() if v is not None) + if passed and all(v is not None for v in g.values()): + state["status"] = "machine_passed" + save(state, work_dir) + return state + + +def record_fail(state, layer, error=None, work_dir=None): + """记录某层失败。同层2次→强制Pivot。""" + state["layer_attempts"][layer] = state["layer_attempts"].get(layer, 0) + 1 + state["logs"].append({"ts": datetime.now().isoformat(), + "layer": layer, "error": error}) + if state["layer_attempts"][layer] >= state["max_retries_per_layer"]: + state["status"] = "pivoted" + state["pivots"].append({"layer": layer, "ts": datetime.now().isoformat(), + "reason": f"{layer}层撞墙{state['layer_attempts'][layer]}次"}) + save(state, work_dir) + return state + + +def human_review(state, action, work_dir=None): + """北哥抽检：approve/reject。approve累计达标→human_passed。""" + hr = state["human_review"] + hr["sampled"] += 1 + if action == "approve": + hr["approved"] += 1 + hr["status"] = "approved" + state["status"] = "human_passed" + elif action == "reject": + hr["rejected"] += 1 + hr["status"] = "rejected" + state["status"] = "blocked" # 打回=停循环请人介入 + save(state, work_dir) + return state + + +if __name__ == "__main__": + if len(sys.argv) < 2: + print("用法: loop_state.py [args]") + print(" create / advance / gate ") + print(" fail [error] / review ") + sys.exit(1) + cmd = sys.argv[1] + if cmd == "create": + print(json.dumps(create(sys.argv[2]), ensure_ascii=False, indent=2)) + elif cmd == "load": + s = load() + print(json.dumps(s, ensure_ascii=False, indent=2) if s else "无状态") + elif cmd == "advance": + s = load() + if s: + advance(s) + print(f"进入 P{s['current_phase']} {PHASES[str(s['current_phase'])]}") + elif cmd == "gate": + s = load() + if s: + key, raw = sys.argv[2], sys.argv[3] + val = float(raw) if raw.replace(".", "").isdigit() else raw + machine_gate(s, key, val) + print(f"门禁 {key}={val}, status={s['status']}") + elif cmd == "fail": + s = load() + if s: + record_fail(s, sys.argv[2], sys.argv[3] if len(sys.argv) > 3 else None) + print(f"{sys.argv[2]}层失败{s['layer_attempts'][sys.argv[2]]}次, status={s['status']}") + elif cmd == "review": + s = load() + if s: + human_review(s, sys.argv[2]) + print(f"抽检{sys.argv[2]}, status={s['status']}") diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..b10ff7e --- /dev/null +++ b/.gitignore @@ -0,0 +1,29 @@ +# 密钥 / 环境变量（绝不入库 — CLAUDE.md 红线：key 收口环境变量） +.env +.env.* +!.env.example +*.key +*.pem + +# Python +__pycache__/ +*.py[cod] +.venv/ +venv/ +*.egg-info/ + +# Node / 前端 +node_modules/ +.next/ +dist/ +build/ + +# 本地数据 / 日志 +logs/ +*.log +.DS_Store + +# Celery / Redis 本地 +celerybeat-schedule +dump.rdb +.env.bak diff --git a/Agent团队与循环任务设计.md b/Agent团队与循环任务设计.md new file mode 100644 index 0000000..bafc82d --- /dev/null +++ b/Agent团队与循环任务设计.md @@ -0,0 +1,174 @@ +# Clover Agent 团队 + 循环任务设计（开工质量把关） + +> 用途：开工前把"agent 怎么组队、循环任务怎么保质量"定死，作为 board 4 启动包 + board 7 编排的地基。 +> 决策人：倩倩姐拍板机制，北哥定内容质量标准。 + +## 0. 三方源材料对照（逐字读完） + +| 来源 | 给了什么 | 对 Clover 的用法 | +|------|---------|-----------------| +| 官方《Agent Team 详解》17页 | 架构原理 + 4 协作模式 + Hooks 门禁 + 成本控制 | 团队拓扑 + 主协作模式 | +| 官方《项目治理指南》41页 | 四级治理 + 质量四件套 + 全套可抄模板 | Clover 定级 L3 + 模板直接套 | +| ralph-loop 源码 | 循环任务最小实现(4Phase+状态机+重试) | 循环任务骨架 | +| 逆向项目档案(真跑过的6角色team) | PSR-L + 撞墙血泪 + 角色卡三段式 | 循环升级 + 角色卡规范 | +| 北哥会议录音 | goal模式 + 闭环架构 + 判断力锚架构 | 循环任务的"魂" | +| 官网V1经验萃取(自产) | 6保留 + 6坑 | 规避坑 + 继承机制 | + +## 1. 把关结论速览 + +源材料读完，对 Clover 最关键的迭代是 3 条： + +1. **循环任务**从 ralph-loop 的"测试即真理"升级成北哥的"闭环架构"——ralph-loop 只认 exit_code（能跑就过），北哥要"调研→生成→验证→**校准**→不过返工"，且**质量过关的最终拍板权在人不在机器**。 +2. **agent team** 主模式定为"全栈并行·契约优先"——integration-lead 先冻结 OpenAPI 契约，前后端再并行开工，堵住官网 V1"前端假设字段、联调爆炸"的坑。 +3. **启动包必须提前备好**，启动日只 validate 不 generate——官网 V1 最大的坑是脚手架启动当天临时造、第一天下午才能安全开发；这次源材料给了全套可抄模板，彻底规避。 + +定级：Clover 属治理指南的 **L3 协作级**（5-15 人体量、多模块、有质量要求），继承 L3 全套（质量四件套 + Agent Team + CI）。 + +两个已拍板决策： +- 循环任务质量判断 = **机器过滤 + 北哥抽检两阶段** +- 团队规模 = **精简 5 角色** + +## 2. 循环任务设计（loop task） + +ralph-loop 骨架能用，但直接套到 Clover 有 3 个质量漏洞，补 3 道闸。 + +### 2.1 三个漏洞 → 三道闸 + +| ralph-loop 原版 | Clover 场景漏洞 | 补的闸 | +|----------------|----------------|--------| +| exit_code=0 就算过 | 文案能生成≠不同质化；图能出≠不跑偏 | 验收**两层**：机器先过滤(违禁词/五维评分≥90/去重) → **北哥人工抽检定标准** | +| 4 Phase 无"调研/校准" | 上来就生成，不先看清全流程 | 补成北哥闭环：**调研→生成→验证→校准→返工** | +| max_retries=3 硬切 | 撞墙3次还在原地换参数 | 逆向铁律：**撞墙2次必 Pivot**(换层不换参)，对齐 CLAUDE.md 失败2次规则 | + +核心一句：**"Memory is Poison, Tests are Cure"是对的，但 Clover 的 Test 不止单元测试，还有北哥的眼睛**——落在架构方案 v0.3 基石 D"人工判断标准落点"和飞轮四点验收。 + +### 2.2 Clover 闭环六阶段（升级版 ralph-loop） + +``` +P0 调研 → 读架构方案v0.3 + PRD + 扒包落盘，确认本任务边界（对应北哥"先调研"） +P1 约束 → 冻结 task_spec：输入/输出/验证命令/期望exit_code/质量阈值 +P2 生成 → 写代码 + 写验证脚本（验证脚本必须含断言、返回exit_code） +P3 验证 → 跑验证脚本：机器层（lint/test/违禁词/五维≥90/去重） +P4 校准 → ⭐新增：机器过的样本，按维度比对北哥标准；同质化/跑偏单独标记 +P5 闭环 → 通过→归档+结晶；不过→分析根因，撞墙2次换层(Pivot)，超限暂停请人介入 +``` + +P4 校准是 ralph-loop 没有的，专治"分数高但实际烂"。校准产出**抽检样本包**交北哥，北哥的判断回写成质量标准（喂飞轮）。 + +### 2.3 状态机（沿用 ralph-loop 的 .clover-loop-state.json） + +```json +{ + "task_id": "TASK-xxx", "current_phase": 2, "attempts": 1, + "max_retries_per_layer": 2, // 单层撞墙上限2次→Pivot + "machine_gate": {"lint":"pass","test":"pass","banned_word":"pass","score":92,"dedup":"pass"}, + "human_review": {"status":"pending","sampled":0,"approved":0,"rejected":0}, + "status": "in_progress", // in_progress / machine_passed / human_passed / pivoted / blocked + "pivots": [], "logs": [] +} +``` + +关键差异：状态从 ralph-loop 的 2 态（completed/max_retries_exceeded）扩成 5 态，**machine_passed ≠ human_passed**——机器过只是进抽检队列，北哥点头才是真过。这就是"质量过关不是能下载"的落地。 + +### 2.4 触发与人介入 + +- 单层（同一个错误层）连续失败 **2 次** → 不再换参数，强制 Pivot 换思路/换层，记 `pivots[]`。 +- Pivot 后仍不过 或 北哥抽检打回 → status=blocked，停循环，结构化报告请北哥/倩倩姐介入。 +- 北哥不耐烦/连续否决同方向 = **路径选错了层**的信号（逆向项目血泪），立即 Pivot 不要再调参。 + +## 3. Agent 团队编排（agent team） + +### 3.1 拓扑：Orchestrator-Workers，精简 5 角色 + +``` + ┌─────────────┐ + │ Lead/集成 │ 冻结契约+全局状态+最终验收（兼 integration-lead） + └──────┬──────┘ + ┌────────────────┼────────────────┐ + ┌────▼────┐ ┌─────▼─────┐ ┌────▼─────┐ + │ 后端 BE │ │ 前端 FE │ │ 审查 QA │ 审查兼测试运行 + └─────────┘ └───────────┘ └──────────┘ + └────────────────┬────────────────┘ + ┌──────▼──────┐ + │ AI引擎 AIE │ 文案/生图/去水印核心（产品命脉，单列） + └─────────────┘ +``` + +砍掉官网 V1 的独立"文档 agent"，文档并入各 agent 的 standup 自我披露。AI 引擎从后端拆出来单列——它是 Clover 的命脉（文案内核/分镜/去水印），值得一个专人。 + +### 3.2 主协作模式：全栈并行·契约优先（Contract First） + +1. **Lead 先冻结接口契约**（OpenAPI + TS 类型），契约里 `type:object` 不许留空（官网 V1 坑4）。 +2. 契约冻结后，BE / FE / AIE **并行开工**，互相不等。 +3. integration-lead（=Lead）**开工当天做契约偏离全量扫描**，产出结构化偏离清单（官网 V1 保留3）。 + +### 3.3 审查模式：异构对抗（仅用于关键决策点） + +PRD 审查、安全审计、架构选型用对抗三角：Builder 出方案 → Critic（魔鬼代言人）找漏洞 → Synthesizer 综合。日常开发不用，避免 token 浪费。 + +### 3.4 成本控制（混合模型） + +| 角色/任务 | 模型 | 理由 | +|----------|------|------| +| Lead / AI引擎核心 / 架构决策 | opus | 命脉，要最强推理 | +| 后端 / 前端 日常编码 | sonnet | 平衡 | +| QA 跑测试/分析日志 / 违禁词/评分 | haiku | 量大、逻辑简单、省钱 | + +完成即关闭（Aggressive Cleanup）：agent 干完任务立即 shutdown，闲置 agent 维持上下文也烧 token。 + +### 3.5 质量门禁（继承 + 叠加） + +- **Stop Hook** 强制跑测试（官网 V1 保留5）：不允许"我觉得对了"就算完。 +- **但软件测试过 ≠ 内容质量过**：循环任务的内容产出叠加 §2 的机器过滤 + 北哥抽检。 + +## 4. 角色卡规范（5 角色） + +吸收逆向项目比官网 V1 更狠的"三段式 + 反模式"，每张角色卡含 5 块： + +1. **读什么**（必读文档，精确到文件路径） +2. **写什么**（产出物边界） +3. **依赖谁**（上游契约 / 下游消费方） +4. **强制 + 禁止**（禁止项极具体，如"❌ api 层不能 import 基础设施层"而非"别写坏代码"） +5. **standup 自我披露**（每天写：文件清单/TODO/已知bug/契约不一致处） + +| 角色 | 读 | 写 | 依赖 | 模型 | +|------|----|----|------|------| +| Lead/集成 | 架构方案v0.3 + 3份PRD + CLAUDE.md | 契约冻结 + 偏离清单 + 最终验收 | 全员 standup | opus | +| 后端 BE | PRD-后端 + rules/api.md + rules/db.md | API + 队列 + 存档 + 多租户 | Lead契约 | sonnet | +| 前端 FE | PRD-前端 + 前端交互设计.md + rules/frontend.md | 5屏 + 飞轮隐形 + 异步生图电影 | Lead契约 | sonnet | +| AI引擎 AIE | 产品包扒包落盘 + banana扒包 + PRD-后端Ch4 | 文案双轨/生图edits/去水印/评分 | Lead契约 | opus | +| 审查 QA | 全员产出 + 验证脚本 | 跑测试 + 契约符合 + 清理临时文件 | 全员代码 | haiku | + +## 5. 反模式清单（agent 必须 NOT 做） + +吸收逆向项目的"反模式10条"思路，结合 Clover 红线： + +1. ❌ 不动 banana / 产品包源目录（只读复制，不 cd 进去 Edit/Write）——CLAUDE.md 铁律。 +2. ❌ 不边盖边修：Clover 是全新独立项目，不在 banana 上建。 +3. ❌ 契约 `type:object` 不许留空——冻结前所有嵌套对象一级子字段必须定义（官网V1坑4）。 +4. ❌ service 错误不许静默吞——至少打 error log（官网V1坑3）。 +5. ❌ 同一层撞墙超 2 次还在调参——必须 Pivot 换层（逆向血泪 + CLAUDE.md失败2次）。 +6. ❌ 前端不展示 Token 余额 / 不做积分页（CLAUDE.md前端红线）。 +7. ❌ 不自动发布到小红书——只到"确认预览"，发布是人的动作。 +8. ❌ 内容"评分高"不等于"质量过关"——必须过北哥抽检才算 human_passed。 +9. ❌ 临时调试文件不许留——带 `_tmp` 后缀，QA agent 收尾清理（官网V1坑5）。 +10. ❌ P0 模块不许无 plan 就开工——integration-lead 检查项，standup 报"未plan的P0"（官网V1坑6）。 +11. ❌ 新建文件不超 100 行，单次编辑不超 100 行（CLAUDE.md 约束）。 +12. ❌ key 不进 Celery、不落盘明文——Fernet 加密只传 task_id（架构方案基石B）。 + +## 6. 落到 board 的下一步 + +本文档把"怎么组队、循环怎么保质量"定死了，接下来： + +- **board 4 启动包**（启动前一天备好，启动日只 validate）： + - 5 张角色卡 .md（按 §4 规范展开） + - 契约 OpenAPI 初稿（Lead 冻结用） + - CLAUDE.md 三层（全局/项目/模块）+ rules/(api/frontend/db) + - Hooks 配置（危险命令拦截/自动lint/Stop测试/敏感文件保护，模板已从源材料抄到） + - 循环任务 SKILL（按 §2 六阶段，改造 ralph-loop）+ state_manager + - 种子数据（品类/角度槽位/违禁词三级表） +- **board 5 执行约束**：把 §5 反模式 + 北哥抽检 SOP 固化。 +- **board 6 验收清单**：三道验收线（内部1A生产链+1B飞轮四点 → 北哥质量过关 → 上线）。 +- **board 7 Agent 编排**：把 §3 拓扑 + 协作模式写成可执行的开工 prompt（goal 模式）。 + +> 全部作业包完成后，按倩倩姐的常规要求：另起一个独立 AI 审核全部开工前流程，再开工。 diff --git a/CLAUDE.md b/CLAUDE.md new file mode 100644 index 0000000..8723a83 --- /dev/null +++ b/CLAUDE.md @@ -0,0 +1,70 @@ +# Clover 项目约束（CLAUDE.md） + +> 本文件是 Clover 项目的事件级约束，agent 团队开工必读。 +> 倩倩姐口头红线当场落盘，不写当不存在。 + +## 🔴 架构铁律（最高优先级，违反即返工） + +### 1. Clover 是全新独立项目 +- 所有代码在 `北哥小红书产品/Clover/` 下从头建立。 +- **不在 banana 上盖、不在产品包上盖、绝不边盖边修。** + +### 2. banana / 产品包 = 只读素材源 +- 两个来源文件夹**只做读取和复制**，**禁止任何修改**： + - banana：`/Users/qiyu/Documents/企业培训项目/O2中间件平台/banana`（只读） + - 产品包✅上线版：`/Users/qiyu/Documents/企业培训项目/万牛会L1准备/worker`（**真正部署上线版,扒这个**） + - 🔴产品包旧版 `万牛会L1准备/产品包/worker` 是开发副本/旧版,**不要扒**(copy/image比上线版旧100~200行) +- 工作方式：需要哪部分零件 → 复制出来 → 在 Clover 里结合本项目迭代/优化/重建。 +- 不允许 `cd` 进这两个目录做 Edit/Write/git 操作。 + +### 3. 技术栈 +- 后端统一 Python（FastAPI+Celery+双库+Redis），产品包的 JS 内核**重写成 Python** 融进 Clover。 +- 重写必须仔细对照防逻辑走样（TDD + agent团队 + 另一AI审核兜底）。 + +## 🔴 已拍板的产品决策（不可推翻） +- 生图：**gpt-image-2 主**（codeproxy中转站），Gemini 备。 +- 生图接口：优先 `/images/edits`（带产品参考图），加重试退避。 +- token：统一走自家中转站 url；key 按账号隔离，各人自录自用各算各的。 +- **模型只用最强档（倩倩姐2026-06-12拍板）**：Claude 系用 **4.8**（claude-opus-4-8），GPT 系用 **5.5**；通道目前仍只用 apiports 那一套（文案暂不补 codeproxy 第二路径，等真出现频繁挂再说）。 + - **模型兜底铁律（倩倩姐2026-06-12拍板）**：claude-opus-4-8 一定可用。**若 gpt-5.5 探活不可用，回落到 claude-opus-4-8（最强档），绝不降级到 sonnet 或任何弱档；绝不偷偷降级当没事，探活不可用立即回报。** +- **品牌词由客户输入**（产品级字段，随产品固定），植入文案每条+生图特写图（第2/6张）。 + - 🔴 **品牌词植入位置=文案+图片文字排版层，绝不P瓶身（倩倩姐2026-06-15拍板）**：瓶身=产品本身，忠于参考图原样，禁止改/加/P瓶身文字（合规+真实双重要求）。品牌词"倍分子"出现在①文案每条②图片的标题/贴纸/标注等文字排版层，不是非要印到瓶身上。AC9实测task61 hook图瓶身只印"素颜霜"=正确，不是bug。曾误判要去P瓶身被驳回。 +- **每用户并发上限 5 个任务**（客户侧实际跑几个再观察调整）。 +- **生图出3套**：套间差异按 ①文案角度 ②视觉风格 ③叙事结构（套1痛点先行/套2场景先行/套3成分背书先行）三个正交维度拉开，**不按封面分**，要不重复。 +- **配图去AI化对齐大卫xhs（倩倩姐2026-06-15拍板）**：大卫 `小红书系统图片定制/xhs-tool/backend/infrastructure/imagePostProcess.js`（只读素材源）运营实测去AI化能力强，Clover 后处理对齐它：①**尺寸可选**(RATIO_MAP多比例，**±2%容差内不resize**避免无谓重采样)，常规默认 **3:4=1024×1536**(gpt原生尺寸，**不强缩1080×1440**，核销表原S4方向作废) ②SynthID破除(缩2px裁1px边+亮度/饱和微调，开关控制) ③高保真重编码(jpeg quality100/chroma4:4:4去元数据) ④重试里支持选尺寸。**先打通单套配图(含去AI化+尺寸可选)，再扩3套正交**。 +- **配图三套叙事措辞=倩倩姐先起草**（2026-06-15）：痛点先行/场景先行/成分背书先行的具体prompt措辞由倩倩姐起草给北哥过目，不用临时占位糊弄；扩3套时填入。 +- **文案待提质量项（倩倩姐2026-06-15）**：①80合格线是临时妥协，方向是提生成质量顶分数不降标准 ②生成文案每条套路化重复"买不买跟我没关系"收尾句式要去模板化。详见记忆 clover-quality-line-80-temp。 +- 🔴 **合格线=80，禁止擅改（倩倩姐2026-06-15二次拍板）**：`constants.py QUALITY_PASS_SCORE=80`。coder 曾擅自改85被驳回。提质量靠优化生成prompt顶分数，**绝不靠提高门槛/降标准凑数**。改这个值必须倩倩姐当面拍板。 +- 🔴 **卖点三套来源·系统通用化（倩倩姐2026-06-15拍板，P0）**：系统是通用的，**产品靠上传才有，绝不内置写死任何具体产品**(素颜霜只是北哥一个客户的一个产品)。卖点三套来源(产品包本有，Clover重写漏了)：①**从产品图片提取**(vision读图→卖点/人群/成分/视觉系统，**走GPT现有通道最强档，不补GEMINI_KEY**) ②**用户上传填写**(北哥文案3.txt即此套) ③**产品库内置**(fallback模板，留口即可)。成分(烟酰胺等)**并入selling_points不单列字段**。配图文字图片**先做一致**，后续留**canvas排版口子**(底图+代码叠字)。北哥禁AI是怕查重+AI化，**我们已做去AI化(对齐大卫xhs)，此担忧已解决，不再当决策点**。 +- **文案过滤=先展示后台补（倩倩姐2026-06-12拍板）**：合格文案(passed且≥90分且非hard_block)立即展示不卡用户；合格数不足用户要的条数时，**后台异步补充生成**直到够数或达上限。不是"只展示不补"，也不是"凑不够就卡着不出"。 +- **13环目标导向**：原始是13环（北哥回填解读与落点.md），banana本给了最硬4环引擎(找竞品/写文案/配图/裂变)。这轮**补全第2环竞品爆款分析(=标杆8特征)+第11环裂变**两个缺失引擎环；裂变单独做但在工作台内呈现（1爆款→N套完整笔记包，非只换文案）。 +- 软件费：MVP 免费，收费逻辑后续单独讨论（先留接口关闭）。 +- 飞轮：**开放给北哥测试使用即启动**（用即喂料，不是埋点不激活）。 +- 🔴 **飞轮是产品最大亮点·必须做扎实+前端必须呈现（倩倩姐2026-06-16拍板）**：飞轮不是后台默默跑的暗功能，要让客户**直观感到"确实进入飞轮了、确实越用越好"**。两条入口都要做： + - ①**改稿进飞轮（D1，最强信号，必做）**：运营改AI稿→diff喂回飞轮。**入飞轮时机=先激进(改了就入)跑一周，北哥用后按真实信号质量校准是否加"过审才入"闸门**（细案E18/E19）。需补 SignalType.text_edit + TextCandidate.edited字段 + 改稿端点(走task_actions不碰review.py)。 + - ②**AI评图分进飞轮（E12，做扎实版）**：AI评图分**只做"生成时筛选+前端展示"**，前端要呈现"这张图AI评了X分+已被选入飞轮"。**真正影响下次生成的飞轮权重只认真实信号（选了哪张/改了什么/过审与否），不拿AI主观分当权重**——守住eval_score留NULL铁律，AI分是展示层不是权重层。 + - ③**前端呈现=轻量化关键节点反馈（不做独立飞轮主视图大页E01）**：在生成/选择/改稿/审核节点即时提示"已学习你的偏好""本次参考了你上周改的稿""飞轮已积累N条信号"，嵌现有流程，纵切片友好。 +- 🔴 **竞品笔记数据监控=不做（倩倩姐2026-06-16拍板）**：E04竞品数据全监控/反推关键词=自动抓小红书,命中合规反爬红线,直接砍,不进任何期。 +- 发布：不自动发小红书，产出"达人素材交付包"人工发。 +- 数据归属：原始数据(输入+产出+素材包)归客户可导出；飞轮偏好归平台。 +- 违禁词：三级处理(自动改写🟢/软提示🟡/硬拦截🔴)，默认分级北哥可调。 +- 链接采集：不做原始抓取（死路），走对外截图手填 / 内部登录态插件。 + +## 🔴 验收线 +- **质量过关才算完，不是"能下载"就算完**，最终北哥认可。 + +## 🔴 前端红线 +- **不展示 Token 余额/用量**：key 和用量去中转站后台看，前端只录 key 不显示余额。 +- 不做积分中心/billing 页（MVP免费）。 + +## 🔴 安全红线 +- **SSE 认证用一次性短票 ticket，绝不把 JWT 放进 URL**（倩倩姐2026-06-08拍板）：EventSource 不支持 header，故 SSE 连接前先用 JWT 调一个签发接口换一张只活 30-60 秒、仅对该 SSE 流有效的一次性 ticket，URL 只带 ticket（`?ticket=`）。就算被日志/浏览器历史记下也立即失效、换不了别的接口。禁止 `?token=` 直传 JWT。 + +## 🔴 执行约束 +- 🔴 **建法铁律（防打补丁循环·先读 启动包/执行约束.md §0）**：①纵切片优先——先用最丑的方式打通端到端一条龙(登录→选品→出1文1图→审核→下载1个包)，这条没真跑通禁止精修任何单块功能 ②"完成"唯一定义=验收清单某项真勾上(非"代码写完")，报完成必附对应验收项 ③验收清单当每日门禁，每阶段自问"多勾上哪一项了还是只是码更多了"。看进度问的是"到一条龙走通还差几步"不是"还要补几个洞"。 +- 新建文件**目标 ≤100 行、上限 200 行**（倩倩姐2026-06-08放宽：ORM模型/枚举常量/内聚算法硬拆反而割裂可读性，故放宽到200行内可接受，**超200行必须拆**）；单次编辑 ≤100 行（超了先建框架再Edit补）。 +- 质量标准/优化prompt 等北哥方案到位再填，架构先留可调位。 +- 🔴 **本轮交付层级=全量（倩倩姐2026-06-12拍板）**：终点线=一条龙过质量关 + 补全第2环标杆分析 + 第11环裂变。KR1链路连通/KR2质量达标/KR3自主可验/KR4引擎补全 全绿才算"全勾完"。 +- 🔴 **打断节奏=攒批集中问（倩倩姐2026-06-12拍板）**：自主连续推进，把"只有倩倩姐能判断"的点（某条文案过不过关/某图品牌词够不够清晰/要不要砍需求）攒成批次，每完成一个环集中问一次，不一条条点yes。 +- 🔴 **核销机制=孙总方法论（2026-06-12录音提炼，已结晶 ~/.claude/crystals/cc-methods/ai-collab-checkpoint-gating.md）**：①双层核销表(总核销表+每模块分核销表)，做完一项物理勾一项 ②每模块收尾产"压缩存档/end-of文件"记录做到什么程度，断点可续 ③小步快跑+高频check，不攒大块 ④**验收标准写到90分清晰度**(模糊=放水=AI按40分自认通过) ⑤标准做减法砍到最少步 ⑥门禁要硬,不通过物理卡死 ⑦**绝不信AI第一遍自评**(实测谎报"都做了"实际20-30%),必独立交叉核对。 +- 🔴 **工作模式=自运行核销机器（倩倩姐2026-06-12拍板）**：①**强制用workflow的agent team执行,不许自己瞎干** ②**必做交叉验证**(另起独立agent重核,不信第一遍) ③规则/验收清单**先全部列出来**再做(你要先看到规则) ④做完一个勾一个、任务只减不增 ⑤倩倩姐**只验最后结果**,过程中要她判断的点**攒成批集中问**,不一条条点yes。 diff --git a/Clover架构方案.md b/Clover架构方案.md new file mode 100644 index 0000000..5e28f7e --- /dev/null +++ b/Clover架构方案.md @@ -0,0 +1,286 @@ +# Clover 🍀 · 统一产品技术架构方案 + +> 项目代号：**Clover（三叶草）** —— 取"种草"之意，接小红书业务 + 倩倩姐设计稿的小草视觉。 +> 对外正式品牌名待定，开发期用此代号。 +> 版本：**v0.3（7洞对齐版，2026-06-04）** —— 经9-agent团队5轮审核(R1多维独立+R2挑刺+R3交叉验证+R4一致性+R5汇总)，对齐06-04的7洞决策，修复13处脱节/漏洞 +> 受众：产品负责人（可读）+ 开发团队（可落地） +> 关联：[../banana复用评估.md] [../产品架构蓝图.md] [../前端交互设计.md] [../采集框架.md] [test/各洞验证落盘.md] +> +> **v0.3修订摘要**：M1标杆采集定性(对外截图手填) / M2 Celery不传key(修安全矛盾) / M3去水印+达人素材交付包 / M4文案双轨+可调prompt / M5 delivery_packages表 / M6 token-url收口自家站 / M7软件MVP免费 / M8违禁词三级处理+banned_words表 / M9飞轮权重填定 / M10偏好表data_ownership字段 / M11 JWT表述工程化 / M12-13可选优化 + +--- + +## 〇、一句话定性 + +一座"小红书内容工厂"：北哥是第一个车间，将来郑州客户来了直接开新车间，互不干扰。 +核心差异化 = **偏好飞轮**（越用越懂你的"老师傅"）。技术底座复用 banana 半成品，但新建干净仓库抽离。 + +--- + +## 〇.5、基石约束（不可动摇，违反即返工） + +> 经两轮外部AI审核 + 两轮自校验，沉淀出以下铁律。任何代码/设计违反这些 = 必须返工。 + +### A. "凡业务参数，一律不写死"（最高原则） +- **品类**不写死：预置素颜霜/精华/面膜 + 客户自助上传新品类(source区分)，代码和prompt禁止出现品类枚举 +- **生成数量**不写死：文案出几条、图出几张，由用户自己设定。飞轮信号强弱随用户设定走(设10选3=强信号，设3选1=弱信号)，系统不锁死 +- **文案角度**不写死：痛点/成分/场景等角度跟着产品走，或引导客户自己输入，落进产品档案当可配置项 +- 判断标准：任何"看起来像枚举/常量"的业务概念，先问"客户会不会想改？"会→做成数据不做成代码 + +### B. 安全铁律（API Key） +- 明文key**绝不进Celery参数**(Redis broker会存)。Celery只传task_id，worker内部查库→解密→调模型，明文只在函数局部变量 +- key用Fernet加密存MySQL；**解密用的FERNET_KEY走环境变量/密钥管理，绝不进代码库**(锁和钥匙不放一起) +- key解密不落盘、不打日志 +- 每账号自录自用，按key各算各的，禁止合并站级key混算 + +### C. 多租户隔离 +- workspace_id **逻辑隔离**(同一MySQL用workspace_id强制过滤)，**不是物理隔离**(暂不做每客户独立DB) +- JWT放user_id/current_workspace_id/role当加速字段，但**所有写操作+切换workspace必须查 workspace_members 校验当前权限**(读操作可信JWT加速) + +### D. 采集↔生产接缝不能断（漏洞C） +- 采集框架里北哥团队填的"好图标准/好坏样本/质检标准"等**人工判断标准，必须在Clover里有落点**：进系统→帮组长审核→最终蒸馏成AI审核员 +- 这是采集层和生产层的接缝，设计时必须打通，不能让采集成果悬空 + +### E. 飞轮验收必须可测（不靠"感觉更准"） +- events正确写入 ✓ / aggregator返回明确prompt片段 ✓ / 下次Gemini prompt trace含该片段 ✓ / UI显示"本次已注入:最近偏好/打回原因" ✓ +- 四点全过才算飞轮MVP达标 + +### F. 文档诚实 +- 不放"N个agent生成"这类AI背书；用工程化表述(JWT签名防篡改/短期有效/关键接口查membership)，不用"写死不可伪造"这种话 + +--- + +## 一、整体架构总览 + +### 分层（从上到下） +``` +浏览器(Next.js) + 熟手：选题→看生成→8选3文案→挑图→提审 + 组长：看审核队列→通过/打回(写原因) + 管理员：维护品牌库/标杆/成员 + │ HTTPS/SSE(流式推进度) +FastAPI(API层) + 认证：JWT(含workspace_id+role，HS256签名防篡改、每请求验签) + 权限：读操作凭JWT加速；写操作+切换workspace必须查workspace_members校验权限(逻辑隔离) + Key解密：Fernet解密，只在worker内存活，不落盘 + │ 同步↓ 异步↓(推Celery队列) +业务应用层 Celery Worker(后台) + 品牌库管理 ①分析标杆笔记 + 飞轮信号采集 ②批量生成文案(一次出5角度) + 审核流转 ③并发生成图片(asyncio.gather) + 偏好上下文组装 ④偏好重算(第二期激活) + │ │ +数据层(双引擎) + MySQL(主存储,一期14表) MongoDB(只写AI debug trace) + 业务全量数据/账号/权限 AI调用过程快照，出错排查用 + 品牌库/候选/飞轮信号 业务逻辑不依赖它 + API Key(Fernet加密) Redis(Celery队列+SSE推送通道) +``` + +### 一次任务的数据流转 +``` +1.管理员预置品牌库(产品档案+标杆笔记) +2.熟手发起任务：选产品+选标杆 → 后端检查有没有配key(没有→引导去配) + → **只把task_id推入Celery队列(绝不推key，遵基石B)** +3.Worker异步：①分析标杆提8特征 ②生成5角度文案(动态注入卖点+违禁词+风格+偏好) + ③并发生图A/B/C → 每步推SSE，前端实时看进度 +4.熟手8选3文案 → 飞轮信号入口1；挑图 → 入口2 +5.提交审核 → 组长看队列 +6.组长通过(+5分,最重)/打回(写原因,下次自动注入prompt提醒AI别再犯) +7.归档(永久保留) +``` + +### 偏好飞轮怎么转（产品的魂） +``` +【采信号】选文案+3 / 选图+3 / 审核通过+5(最重) / 打回写原因-3 / 重新生成-1 → 全进 preference_events 表 + (此为开发起步默认权重，北哥调教后可配置校准) +【聚合】 下次生成查最近50条 → 哪个角度被选最多(L3个人) + 打回原因最近3条原文拼进prompt + 不足5条信号 → 用产品档案冷启动 +【三层继承】L1公司品牌基线 > L2矩阵号人设 > L3个人手感(运行时叠加，有L3用L3，没有往上兜底) + ⚠️ 按产品维度分开学：素颜霜偏好不串到精华/面膜 +【越用越准】1次=全靠静态基线均匀分布；10次=知道你爱"痛点切入"；30次=措辞从"供参考"升为明确指令 +【诚实克制】banana的假评分(88分)MVP彻底不接，eval_score留NULL，等真评分(组长标准蒸馏)再装 +``` + +--- + +## 二、核心设计决策（已拍板） + +开发期不得推翻，变更需产品负责人重新确认。 + +| 决策项 | 结论 | +|---|---| +| 主存储 | MySQL存所有业务数据(飞轮聚合要JOIN)，MongoDB只存AI debug trace | +| API Key | 每账号自录自用、Fernet加密、不同workspace各自独立，禁止混算；**token站url统一收口=自家站endpoint，账号表只存encrypted_key不存url字段**(06-04洞4，旧"兼容任意中转站url"已废止) | +| 软件费 | **MVP阶段免费，不做计费墙**(06-04洞4)；token耗材费走自家站按key隔离，不在产品层计费；收费逻辑产品成熟后单独议 | +| Key解密位置 | 只在Celery worker内存局部变量，不落盘不打日志 | +| 多租户隔离 | workspace_id写进JWT，所有查询强制带此条件(安全红线) | +| 任务主键 | MySQL自增BIGINT + mongo_trace_id VARCHAR(24)，飞轮JOIN性能好 | +| 文案生成 | **双轨**(06-04洞2)：轨A=自带强模型一次返5角度JSON(不串行不并发5次)；轨B=客户导入外部文案(豆包等)直接进候选池跳过AI；**客户可在产品档案配自调prompt** | +| 图片生成 | asyncio.gather并发，一次A/B/C三策略各一张(复用banana) | +| 加密算法 | cryptography.fernet.Fernet，环境变量FERNET_KEY，全仓统一 | +| 策略命名 | 对外API用A/B/C，内部method用minimal_edit系，建中心常量文件消除三套打架 | +| 审核记录 | 不建独立notes表，审核字段平铺generation_tasks，打回原因存preference_events | +| 偏好快照 | preference_profile表预建结构，MVP不写入，实时查events，第二期开物化缓存 | +| 品类动态 | products.category是纯数据字段，代码和prompt禁止出现任何品类枚举 | +| **审核演进** | **先组长人工审为主→积累"过/不过+理由"蒸馏人工标准→生图够好+标准清晰后，AI当审核员接班(第三期)** | + +--- + +## 三、数据模型全览（16张表 = 一期14张 + 二期预留2张） + +> 14张=banana搬3 + 多租户基础3(不含matrix_accounts) + 业务主体7 + 飞轮1。matrix_accounts、preference_profile 二期预留，一期不建。 +> 06-04增补：业务主体从5张增至7张(加 delivery_packages 素材交付包 + banned_words 违禁词库)。 + +``` +Alembic 001 — 从banana搬3张(改造) + users 用户账号(删credits字段) + login_records 登录记录(无改) + user_preferences UI设置偏好(无改，API Key另建表) + +Alembic 002 — 多租户基础(全新) + workspaces 租户/公司(北哥=1个) + workspace_members 用户↔workspace+角色 + user_api_keys 个人API Key(Fernet加密) + [matrix_accounts] 矩阵号 —— 二期预留，一期不建(矩阵号一期不做) + +Alembic 003 — 业务主体7张(全新) + products 产品档案(卖点/违禁词/风格/调性/标签/文案角度/可调prompt/source/workspace_id) + benchmark_notes 标杆笔记(爆款链接/图/亮点说明，一对多挂product) + generation_tasks 生产任务(状态机+审核字段平铺) + text_candidates 文案候选(角度数量用户设定，MySQL主存储；source=ai/import区分双轨) + image_candidates 图片候选(数量用户设定) + delivery_packages 达人素材交付包(洞6)：id/workspace_id/product_id/theme/status(pending/ready/downloaded)/package_path/created_at + banned_words 违禁词库(洞5)：word/level(auto_fix自动改/soft_warn软提示/hard_block硬拦截)/replacement/updatable/workspace_id(可覆盖) + +Alembic 004 — 飞轮信号(全新) + preference_events 飞轮信号日志(所有信号全在此；含data_ownership字段=client_data/platform_asset，洞7分层归属) + [preference_profile] 偏好快照 —— 二期预留，一期不建(一期实时查events；二期同步加data_ownership标记) +``` + +### 关键字段 +**任务状态机**：pending→generating→pending_selection→pending_review→approved/rejected→archived +（打回后回到pending_selection；修订轮次靠查events统计） + +**generation_tasks 审核字段(平铺，需明确定义)**： +review_status / reviewer_id / reviewed_at / reject_reason / approved_at / archived_at + +**preference_events**： +- signal_type: text_select | image_select | approve | reject_with_reason | regenerate +- signal_weight(已定初始默认值，北哥调教后可校准)：选文案+3 / 选图+3 / 审核通过+5(最重) / 打回-3 / 重新生成-1 +- angle_label: 跟着产品的"文案角度"配置走，不写死 +- reason: 打回原因原文（不做AI归纳） +- **data_ownership: client_data(原始输入产出，客户可导出) | platform_asset(飞轮蒸馏成果，归平台)** —— 洞7分层归属技术口子 +- workspace_id + product_id: 都必须有（跨公司隔离 + 按产品分开学） + +**user_api_keys**：UNIQUE(user_id, workspace_id, provider) + +**usage记录(一期必做，否则计费/排障断档)**：每次AI调用记 usage(谁/用了多少token)，存 ai_call_logs 或 mongo debug trace 的 usage 字段；token站查询失败要有兜底；调用失败归因到个人key + +--- + +## 四、复用 / 复活 / 新造清单 + +开发第一步先看这张表：哪些直接搬、哪些改造、哪些新写。 + +### 🟢 直接复用（复制基本不改） +| banana模块 | 改动量 | +|---|---| +| gemini_service.py 核心AI方法 | 只改__init__ 4行，AI调用方法一行不动 | +| json_stream_extractor.py 流式解析 | 加variants到watched_keys | +| constants SCHEMA_METHODS | 扩展补全 | +| orchestrator analyze流程 | 数据源从Mongo改读MySQL products/benchmark_notes | +| celery_tasks 任务框架 | task_id改BIGINT，**只传task_id(绝不传key，遵基石B)**，worker内部查库取encrypted_key→FERNET_KEY解密，加第4任务壳 | +| api/stream.py SSE框架 | 补发历史事件(修漏洞)，结构不变 | +| prompt_composer.py | 新增compose_variants方法，原方法保留 | +| docker-compose | 直接复用，调环境变量名 | + +### 🟡 复活（banana写好但没接线的死代码） +| 死代码 | 怎么复活 | +|---|---| +| task_skills.py Invariants 8特征 | analyze后把8特征写入events.signal_meta，飞轮统计维度从单一angle扩到8维(第三期) | +| loader.py PromptLoader版本alias | 新建variants模板走版本机制，未来改prompt只改模板不改代码 | +| gemini_service evaluate_schema | MVP彻底不搬，eval_score留NULL，第二/三期接真评分(避免假信号污染飞轮) | + +### 🔴 完全新造（banana没有，灵魂部件） +| 新造模块 | 作用 | +|---|---| +| gemini_factory.py + Fernet解密 | 解决全局单例，每任务用自己的key构建实例 | +| preference_collector.py | 飞轮3信号入口(选文案/选图/审核)写入events | +| preference_aggregator.py | 实时聚合events→prompt片段，三层继承叠加 | +| api/products + benchmark_notes | 品牌库CRUD，含source=preset预置品类 | +| api/matrix_accounts | 矩阵号CRUD(MVP前端可后做，库和接口先建) | +| api/api_keys | 录入/删除/查询(只显后4位)，首登强制引导 | +| middleware/workspace_guard | 所有接口强制注入workspace_id，防串数据 | +| api/review | 组长待审列表/通过/打回 | +| generate_text_variants | 一次调用5角度JSON，替代banana单条循环 | +| text_import_handler | 轨B：导入外部文案(豆包等)直接进候选池，跳过AI生成(洞2文案双轨) | +| banned_word_checker | 违禁词三级扫描:🟢自动改写/🟡软提示给建议词/🔴硬拦截，词库可配置(洞5) | +| image_postprocessor | 出图后处理:重编码去C2PA元数据+转JPG/轻重采样削像素水印+压缩(洞3去水印) | +| package_exporter | 生成达人素材交付包:按笔记分文件夹+图(01/02命名)+文案.txt+发布清单+合规说明(洞6) | +| _combine_benchmarks | 1-5篇标杆合并成综合reference | +| base_workspace_repo | 所有workspace相关Repo继承，强制过滤 | +| Alembic 001-004 | 按序建14张表(一期) | +| constants/strategies.py | 统一命名中心文件，消除三套打架 | + +--- + +## 五、构建顺序（三期，一期拆1A/1B） + +> 修正：原"一期3-4周"塞太满。拆成 1A生产链闭环 + 1B飞轮闭环，先证明能产出，再证明会学习。 + +### 一期·1A：生产链闭环（先让北哥能产出内容） +手动建北哥账号(不接短信)、手动/截图录标杆、产品库、按用户设定数量出文案(双轨)、出图去水印、选择、审核、打包交付。 +1. **基础设施**：建仓库+6容器；Alembic 001-002(不含matrix_accounts)；手动初始化北哥workspace+管理员 +2. **品牌库**：Alembic 003；产品档案CRUD(含文案角度+可调prompt可配置)；标杆笔记CRUD；预置素颜霜1条；初始化banned_words(MVP起步5词:美白/祛斑/速效/医用/药妆，updatable，北哥合规标准来了批量导入) + - ⚠️ 标杆录入主通道=**截图+手填亮点**(洞1实测确定可行)；登录态采集=北哥内部插件，二期可选 +3. **API Key录入**：user_api_keys+Fernet+录入界面；FERNET_KEY走环境变量；token站url固定自家站；没配key时按钮置灰引导 +4. **核心生产链**：中心常量文件；GeminiService改造+gemini_factory(key只在worker内解密)；analyze搬入；**文案双轨(轨A生成5角度/轨B导入外部文案)**；**违禁词三级扫描**；并发生图(数量用户设定)；Celery只传task_id；SSE推送 +5. **选择+审核+交付包**：选文案/选图/提审/审核队列/通过打回(审核字段明确定义)；**出图后处理(去水印)**→**生成达人素材交付包** + - 出图后处理(洞3)：图像库重编码去C2PA元数据 + 转JPG/轻重采样削像素水印 + 压缩瘦身，对客户透明 + - 交付包结构(洞6)：按笔记分文件夹，每夹含图(01/02/03命名防传错序)+文案.txt(标题+正文+标签)，附📋发布清单.txt + ✅合规说明.txt(违禁词已过/已去水印) + - 验收：北哥1名熟手不靠开发，从选产品→生成→选择→组长审→**下载达人能直接发的素材包**，全程跑通 + +### 一期·1B：飞轮闭环（让产出会学习） +1. Alembic 004(只建preference_events，不建preference_profile) +2. 三信号入口接线(选文案/选图/审核) +3. compose_preference_context 实时聚合(最近50条，workspace_id硬过滤) +4. 偏好+打回原因注入prompt + - 验收(四点全过)：events正确写入 / aggregator返回明确片段 / 下次prompt trace含该片段 / UI显示"本次已注入" + +**一期整体验收**：北哥熟手独立跑完生产链；飞轮选过3次后下次生成能看到偏好摘要并验证注入。 + +### 二期：多团队+对外销售（再2-3周） +> 留口子已在地基，二期只"打开注册开关"，不回头改表。 +1. 手机号+验证码自助注册(选短信服务商) + workspace创建 + 邀请成员 +2. matrix_accounts建表+矩阵号管理UI；L2人设层激活 +3. preference_profile物化(events满10条触发重算，加载<50ms) +4. 管理后台：各workspace用量统计+飞轮健康度 + +### 三期：效果回流+AI审核员（看业务） +1. 发布效果回填(赞/收藏/评论→outcome_score加权) +2. L3 vs L1偏差检测(个人偏好偏离品牌基线>60%提示，防学坏) +3. Invariants 8维特征接线(飞轮统计从单一angle扩到8维) +4. **AI审核员**：用积累的"组长过不过+理由"蒸馏人工标准，训练AI审核员逐步接班(补banana假评分窟窿，呼应基石D) + +--- + +## 六、待拍板判断点 + +### 已拍板（2026-06-03） +- ✅ 项目代号：**Clover**，文件夹建在 北哥小红书产品/Clover/ +- ✅ 先做北哥单车间，但地基留口子，其他车间可快速打通 +- ✅ 审核：先组长人工审为主 → 蒸馏人工标准 → 后期AI审核员接班 +- ✅ 注册方式：**手机号+验证码**（对标小红书，已查证小红书就是手机号注册）。需接短信验证码服务商(阿里云/腾讯云SMS) +- ✅ 矩阵号：**一期不做**。L2人设层一期不激活，飞轮一期只跑"L1品牌基线+L3个人手感"两层；matrix_accounts表仍建好留口子，二期补L2 +- ✅ 排版：**不做独立排版环**，第7环并入第6环——AI生图时直接把文字/版式排在图上(出成品图) +- ✅ **标杆采集通道（06-04洞1已拍板，实测3条真链接裸抓全返回风控假页）**： + - 对外SaaS一期主通道=**截图+手填亮点**（客户自己截图，零平台风险） + - 链接自动读取=**登录态采集方案**（本地已有跑通代码：n8n-nodes-xiaohongshu + xhs-sign-server，Playwright跑小红书自家JS算签名），但"系统替客户取数据"=违规+封号风险，只作**北哥内部自用插件、二期可选模块，不进对外标配** + - 官方蒲公英/聚光接口=二期看北哥企业资质再议 +- ✅ **软件收费（06-04洞4已拍板）**：MVP阶段软件**免费**，不做计费墙；token耗材费走自家token站、按账号key隔离，不在产品层计费。收费逻辑(底层麻烦)产品成熟后单独讨论 + +### 待拍板 +- **P1 短信服务商**：用阿里云短信还是腾讯云短信？(影响成本和接入，都需企业实名)（注：仅二期自助注册才需要，一期手动建北哥账号不阻塞） +- ~~P2 标杆链接读不到时的兜底~~ → **已由06-04洞1解决**：对外=截图手填(主通道)，登录态采集作内部插件，不再是待拍板项 +- **P2 北哥账号开通**：一期北哥账号由我们手动建(已定，不接短信)；自助注册留二期 diff --git a/PRD/PRD-0-总纲.md b/PRD/PRD-0-总纲.md new file mode 100644 index 0000000..9667c6d --- /dev/null +++ b/PRD/PRD-0-总纲.md @@ -0,0 +1,55 @@ +# Clover PRD-0 总纲 + +> 3份PRD的"对齐层"：前后端共享的目标/范围/流程/验收/里程碑，避免两份重复。 +> **承接层**：决策见 `../Clover架构方案.md`(v0.3) + `../CLAUDE.md`；前端见 `PRD-前端.md`；后端见 `PRD-后端.md`。 +> 读PRD顺序：先读本总纲对齐全局 → 按角色分工读前端/后端 → 冲突以架构方案v0.3+CLAUDE.md为准。 + +## 1. 产品定位 +- 一句话：北哥团队的小红书达人素材"中央厨房"（多租户SaaS） +- 界面名：龙石小红书内容生产平台 ; 项目代号：Clover🍀 +- 核心差异化 = **偏好飞轮**(越用越懂你的"老师傅") + +## 2. 目标用户与三角色 +- 内容生产方(北哥团队)：熟手(运营·每天产内容) + 组长(审核) + 管理员(北哥/老板·配业务) +- 发布方(达人)：只收素材包,不进系统 +- 平台方(我们)：收口token,持有飞轮偏好资产 + +## 3. 范围边界（做/不做） +- ✅做：①产品配置 ②生成文案(双轨) ③生成图 ④裂变 ⑤存档打包交付 + 飞轮(开放测试即启动) +- ❌不做：软件收费/达人进系统/自动发布小红书/原始链接裸抓/移动端 + +## 4. 核心用户旅程（5屏·前后端共识） +``` +管理员配置台(偶尔) → 熟手:开任务→挑文案(出N选若干)→挑图(出N选若干,N用户设定,图≤8)→确认预览→提审 + → 组长:审核台 通过/打回 → 熟手导出打包 → 人工发/给达人 +``` +- 飞轮全程隐形记录(挑文案+3/挑图+3/通过+5/打回-3/重生成-1) + +## 5. 全流程5环节定义 +①产品配置(管理员录) ②生成文案(轨A一次5角度/轨B导入外部) ③生成图(gpt-image-2主+edits+分镜) ④裂变(1爆款→N笔记包) ⑤存档打包(达人素材交付包) ; 飞轮贯穿②③④ + +## 6. 关键产品决策（引用,不展开） +- 见架构方案§二决策表 + CLAUDE.md：gpt-image-2主/token-url收口/MVP免费/飞轮即用即启/违禁词三级/数据归属分层/质量过关才验收 + +## 7. 验收线（总·分三道） +1. **内部验收**(我们)：一期1A生产链跑通(熟手独立从配置→生成→选择→组长审→下载能发的素材包) + 1B飞轮四点验收(events写入/聚合返片段/下次prompt含片段/UI显示已注入) +2. **北哥验收**：质量过关(不是能下载就算完,北哥认可内容质量) +3. **上线**：北哥验收通过 → 上线 +- 🔴 质量标准/优化prompt 由北哥方案注入,架构留可调位 + +## 8. 名词表 +- 达人素材交付包：按笔记分文件夹+图01/02命名+文案.txt+发布清单+合规说明,人工发 +- 偏好飞轮：隐形记录用户选择,越用越懂,三层继承(L1品牌>L2人设二期>L3个人) +- storyboard分镜：一套图N张各担叙事角色(钩子/痛点/证明/质感/背书/转化) +- 爆款参考度：裂变时学爆款的强弱(低30/中/高85防抄袭) +- 双轨：轨A自家AI生成/轨B导入豆包等外部文案 + +## 9. 里程碑（MVP切片·引用架构方案§五） +| 期 | 范围 | 验收 | +|----|------|------| +| 一期1A | 生产链闭环(配置/文案双轨/生图去水印/选择/审核/打包) | 熟手独立跑通下载能发素材包 | +| 一期1B | 飞轮闭环(三信号入口/实时聚合/注入prompt) | 飞轮四点全过 | +| 二期 | 多团队对外(手机注册/矩阵号L2/偏好物化/管理后台) | 留口子已在地基 | +| 三期 | 效果回流+AI审核员(蒸馏组长标准接班) | 看业务 | +- 🔴 切法：先证明能产出(1A)→再证明会学习(1B)→再对外(二期)→再智能(三期) +- 一期不做但建表：matrix_accounts/preference_profile diff --git a/PRD/PRD-前端.md b/PRD/PRD-前端.md new file mode 100644 index 0000000..5cf3b35 --- /dev/null +++ b/PRD/PRD-前端.md @@ -0,0 +1,106 @@ +# Clover PRD-前端（界面名：龙石小红书内容生产平台） + +> 前端技术框架。**承接层**：5屏画面金标准见 `../../前端交互设计.md`(经倩倩姐确认)，视觉见 `../../前端设计参考图.png`。本文①承接5屏②补工程细节③对接后端契约——**唯一冻结源 = `../启动包/API契约.md`**(端点§1 / SSE事件§2 / 错误码§0)，不看 PRD-后端。 +> 技术栈：Next.js+React+TS+Tailwind+Zustand(范式扒banana/frontend,全新重建)。PC优先,最小宽度1280px,不做移动端。 +> 🔴 红线(CLAUDE.md)：不展示Token余额/不做积分页/key只录不显余额。 + +## 0. 一句话定位 +前端就是"挑挑挑"：挑文案(出N条选若干)→挑图(出N张选若干，N=用户设定，图最多8)→组长挑过不过。飞轮全程隐形记录。 + +## 1. 三角色 + 路由表 +| 角色 | 默认首页 | 能进的页 | +|------|---------|---------| +| 熟手(运营) | /tasks/new | 开任务/挑文案/挑图/确认预览/历史 | +| 组长 | /review | 审核台/历史 | +| 管理员(北哥/老板) | /config | 配置台(产品/标杆/成员/违禁词)+全部 | + +路由表： +- /login(登录+首登key引导) / /dashboard(工作台) / /tasks/new(开任务) +- /tasks/[id]/text(挑文案) / /images(挑图) / /confirm(确认预览·熟手提审前) +- /review(组长审核台) / /history(历史归档) / /config(配置台·管理员) +- 🔴 角色路由守卫：/review只组长+管理员;/config只管理员;越权重定向工作台 + +## 2. 全局布局（对齐设计图） +- 顶部：标题"龙石小红书内容生产平台"+右上角当前用户(运营-小李) +- 5步流程条(1开新任务/2选择文案/3选择图片/4确认文案/5成片发布)——熟手常驻;组长/管理员登录后不走此条(各进自己首页) +- 左侧8项导航(工作台/开新任务/选择文案/选择图片/建立任务/任务列表/历史归档/配置中心) +- 主区 + 右侧配置预览面板 +- 🔴 底部原Token余额位置(设计图568320)→**移除**,替换为版本号/留白小草装饰 +- 视觉：暖橙/奶油色+绿色小草 + +## 3. 五屏详述（承接前端交互设计.md） + +### 屏1 配置台（管理员·/config） +- 产品库：素颜霜/面膜/精华卡片+[加产品](品类不写死,客户自助加) +- 每产品配：卖点/品牌词/违禁词(三级可调)/固定标签/风格调性/可调prompt/参考图 +- **标杆笔记管理**(一期主通道=截图上传+手填亮点;列表展示;关联产品) +- 成员管理(workspace邀请/角色分配) ; key录入(各人自录,只录不显余额) +- → 飞轮的"出厂设置",配一次天天用 + +### 屏2 开新任务（熟手·/tasks/new·设计图主表单） +- 选产品[素颜霜▾] + **今天主题输入框**(如"黄皮提亮·早八伪素颜",驱动当次方向,关键字段勿漏) +- 单条数量/生成数量(可+/-,数量不写死) + 生成方向 +- 双轨入口：轨A[开始批量生成文案] / 轨B[导入外部文案](粘贴豆包等) +- 右侧配置预览面板：产品名称/卖点/成分/质地/标签/参考图3张 + [编辑配置](点击→侧滑面板原地编辑,改完实时刷新预览) +- **生成前显示"本次已注入：最近偏好X/打回原因Y"**(调GET /preference/context,基石E④) +- 底部"最近任务"横向卡(4个,任务名+状态+进度条) + +### 屏3 挑文案（熟手·/tasks/[id]/text·飞轮第1次学） +- 生成N条文案卡(可多选),每卡：标题+正文+标签+**五维分数**(标题/情绪/买点/关键词/合规,展示分数条+总分,数据来自后端scoreCopy) +- 轨B导入的文案标注来源区分(source=import角标) +- 选中触发飞轮+3(隐形,无"训练AI"按钮) ; [用这N条去生图→] + +### 屏4 挑图+出成品（熟手·/tasks/[id]/images·飞轮第2次学） +- N批图缩略图(可多选) ; 选中触发飞轮+3 +- 选中图+文案→自动套版预览(封面+内页拼成笔记长相) +- [满意,交组长审→] + +### 屏4.5 确认预览（熟手提审前·/tasks/[id]/confirm） +- 熟手提审前最后预览整套笔记 ; [提交审核] ; [重新生成](飞轮-1) +- ⚠️ 注意:通过/打回是组长动作,不在此页(修正骨架视角错位) + +### 屏5 组长审核台（组长·/review·飞轮最强信号） +- 待审笔记列表(N)：每条[预览]+[✓通过]+[✗打回+原因] +- 通过+5(权重最高,飞轮最强对信号) ; 打回必填原因(下次自动注入prompt提醒AI别再犯,-3) +- 打回原因输入：模态框,必填校验 +- 通过→进成品库(熟手可去屏导出打包) ; 打回→任务回pending_selection +- 导出后人工去小红书/给达人发(工具到此为止,诚实边界:小红书无接口不自动发) + +## 4. 异步生图电影 + 飞轮隐形 + 错误态 + +### 4.1 异步生图进度（承接交互设计.md §2·体验核心） +对接后端SSE事件(契约§2)： +- 点生图→不空白干等：显"正在生成N批…0/N [进度条] 预计30-60秒,可先去忙别的好了提醒" +- 并发跑,**谁好谁先冒**(好一批亮一批,对接 image_candidate 事件,每个带 event_seq) ; "3/N"实时计数 +- 单批失败标红+[⟳重试](不拖垮其他批) ; "完成9/10,这9批够挑了[继续→]"(不强求全齐) +- 🔴 铁律:老demo"点完干等18分钟转圈到崩"绝不能再有 + +### 4.2 飞轮隐形运转（交互设计.md §3） +- 前端**无任何"训练AI/打分"按钮**(会吓到运营),只如实把动作记给后端(带谁+哪账号) +- 三动作埋点:屏3勾文案+3 / 屏4勾图+3 / 屏5通过+5打回-3 / 屏4.5重生成-1 +- "变聪明"在后端,前端只管操作顺滑+显示"本次已注入" + +### 4.3 错误态UI（对接契约§0 数字错误码，前端按 code 映射语义，**不硬编码字符串码**） +| code | 语义 | 前端响应(非统一toast) | +|------|------|----------------------| +| 42201 | 业务校验失败(未配key/违禁词硬拦截) | 未配key→引导配key页/banner按钮置灰(首登强制引导)；违禁词→显示触发词+级别,提交前后立即提示 | +| 50002 | AI/token站调用失败 | "生图通道繁忙"特殊提示(区别普通错误)+[重试] | +| 50001 | 服务端错误 | "生成失败"+[重试]按钮 | +| 40901 | 状态机非法流转 | 提示当前状态不可操作,刷新看最新态 | +| 40301 | 越权访问 workspace | "无权访问"提示,跳回工作区列表 | +| 40101 | 未认证/JWT失效 | 自动触发token刷新(扒banana已实现),失败则跳登录 | +| 40001 | 参数校验失败 | 表单字段级红字提示 | + +> 前端维护一份 `errorCodeMap`(code→{文案,处理动作})常量，所有错误统一查表，违禁词/限速等细分信息从 `message` 字段读。 +- 真实状况兜底(交互设计.md §4)：关页面回来任务还在(后端有状态不靠前端内存)/多人各看各的 + +## 5. 工程细节 +- 状态管理4Store(参banana)：authStore(user/workspace_id/role/token) / taskStore(任务id/状态/已选文案图id) / generationStore(SSE进度/各图状态/已完成N) / preferenceStore(本次注入偏好摘要) +- SSE客户端(扒banana sse.ts,5次重连退避)+断线重连UI("连接断开重连中1/5…"+"重连失败任务后台仍在,可刷新") +- 组件库：流程条/文案卡(带五维分)/分镜图卡/配置预览面板/交付包预览/飞轮注入提示横幅/打回原因模态框/单图进度卡/审核操作栏。🔴 删除"Token余额组件" +- 骨架屏：文案卡/图卡/审核队列loading ; 表单校验：产品必选/主题字数限/数量范围 +- API客户端(扒banana api.ts含token刷新) ; AuthGuard+角色守卫(扒banana) +- 可扒banana前端零件：authStore范式/api.ts/sse.ts/AuthGuard/NextUI组件;表单字段全重建(Clover字段不同) + +## 6. ❌不做（CLAUDE.md红线） +- Token余额/用量展示(去中转站后台看) / 积分中心billing页 / 移动端适配 diff --git a/PRD/PRD-后端.md b/PRD/PRD-后端.md new file mode 100644 index 0000000..389e283 --- /dev/null +++ b/PRD/PRD-后端.md @@ -0,0 +1,114 @@ +# Clover PRD-后端 + +> 后端技术框架。**本文是"承接层"**：架构决策见 `../Clover架构方案.md`(v0.3,金标准)，本文只做①引用架构决策②补PRD独有工程细节③承接扒包上线版新事实。 +> 技术栈：FastAPI + Celery + MySQL + MongoDB + Redis（栈范式扒自banana，全新重建） +> 🔴 源码只读复制：扒**上线版** `万牛会L1准备/worker/src`(非旧版产品包) + banana。 + +## 0. 怎么读这份PRD +- 数据模型16表/基石ABCDEF/三期构建顺序/复用复活新造清单 → **见架构方案 §三/§〇.5/§五/§四**，本文不复制。 +- 本文重点：第3章API契约细节、第9章环境变量、第10章工程规范（这些架构方案没细化）。 +- 与架构方案冲突时：**以架构方案v0.3 + CLAUDE.md为准**。 + +## 1. 后端架构总览 +- 分层：API层 / Service层(AI引擎) / Model层(双库) / 队列层(Celery+SSE) —— 详见架构方案§一 +- AIProvider抽象层(扒banana)：**gpt-image-2主/Gemini备**，由环境变量 IMAGE_PROVIDER_PRIMARY 配置(不写死) +- 🔴 三条铁律入口(详见架构方案§〇.5)：A业务参数不写死 / B key不进Celery+Fernet加密 / C workspace逻辑隔离 + +## 2. 数据模型 +- **完整16表清单+字段+Alembic001-004分批 → 见架构方案§三**，本文不重抄。 +- 🔴 PRD骨架旧表名修正(以架构方案为准)： + - api_keys→**user_api_keys**(UNIQUE user_id+workspace_id+provider) + - tasks→**generation_tasks**(审核字段平铺:review_status/reviewer_id/reviewed_at/reject_reason/approved_at/archived_at) + - 无assets表→实为 **text_candidates + image_candidates**(text_candidates有source=ai/import区分双轨) + - 补 **login_records**(扒banana) / 补 **ai_call_logs**(usage记录,按key计费排障基础,一期必做) + - data_ownership是**preference_events的字段**(client_data/platform_asset)，不是独立表 +- 任务状态机7态：pending→generating→pending_selection→pending_review→approved/rejected→archived(打回回pending_selection) +- MongoDB**只存AI debug trace**(业务不依赖)；可调prompt在 products 表字段，**不在Mongo** +- 关键索引(架构没列,PRD补)：generation_tasks(workspace_id,status) / preference_events(workspace_id,product_id,created_at) + +## 3. API 契约 → 唯一冻结源：`启动包/API契约.md` + +> ⚠️ 契约统一：本 PRD **不再另写一套契约**。端点/响应包络/错误码/SSE事件/分页， +> 全部以 `Clover/启动包/API契约.md` 为唯一冻结源（避免前后端分叉，外部AI审核P0-1/2/8）。 +> 开工首步：Lead 据 API契约.md 生成并冻结 `Clover/contracts/openapi.yaml`（"契约优先"落地，非口号）。 + +本 PRD 第3章只补**契约文档未覆盖的后端实现约定**（不重复端点定义）： +- 任务软删用标记位不物理删；DELETE /tasks/{id} 走 archived 态。 +- 限流：同用户并发生成任务数上限（建议2，可配）；生图每张~4min 异步不占连接。 +- SSE token 走 header（Authorization），不走 query（对齐契约，纠 banana 旧法）。 +- preference 写入**不暴露独立埋点端点**：选择/审核业务接口内部写 events，前端只调业务动作（外部AI审核P1-14）。 +- preference 读：GET /preference/context 已列入 API契约端点清单（外部AI审核P2-13）。 + +## 4. AI 引擎（扒上线版 worker/src 重写Python·核心） +> 算法逻辑整体扒上线版copy.js/image.js/split.js重写为Python，逻辑照搬防走样(TDD对照)。 + +### 4.1 文案引擎（扒copy.js上线版） +- **轨A**：一次调强模型返**N角度JSON**(N=用户设定text_count，不串行不并发，新造generate_text_variants)——这是去同质化关键，勿照抄旧版单条循环。数量不写死(基石A)。 +- **轨B**：导入外部文案(豆包等)直接进候选池跳过AI(text_import_handler,text_candidates.source=import,洞2) +- 保留(扒上线版重写Python集成进轨A)：三层兜底Claude→Gemini→本地 / 五维打分90过线 / 自动优化循环 / **正文+开头去重(splitPublishableContent/contentSignature)** / **角度槽位(angleSlotsForCategory,作可迁移数据非代码常量)** / 内部提示剥离(content只放可发布正文) +- 可调prompt：products表字段，北哥可配(等北哥优质样本+判断标准注入,质量责任在北哥侧) + +### 4.2 生图引擎（扒image.js上线版+改造） +- 扒保留：storyboard 10角色分镜 / getNarrativeRoles(3/6/8张链路) / 6品类proofStrategy / 素材路由 / buildVisualSystem成组视觉 +- 🔴 生图通道(扒上线版已成熟,直接用)： + - 主备由 IMAGE_PROVIDER_PRIMARY=gpt/FALLBACK=gemini 配置(imageProviderOrder) + - GPT走 **edits带产品参考图**(requestGptImageWithReferences)，**禁纯文生图防跑偏**(无产品图报错) + - 产品图=**不可修改商品锚点**,禁改包装/换产品/混历史产品(防串货) +- 🔴 补(上线版仍缺)：**生图通道加重试退避**(gpt-image-2实测502,baseline验证必要) +- 🔴 一期执行模型(纠正混淆,外部AI审核P0-3/4)： + - **一期只做 storyboard 分镜图**：一篇笔记出 N 张叙事图(N=用户设定image_count，默认3，**合法1-8**，对齐worker getNarrativeRoles 3/6/8档链路，**不是固定10选3**) + - 每张 storyboard 图走 provider 主备(gpt edits→gemini fallback) + 重试退避 + - **A/B/C 三策略不在一期主链路**：A/B/C 是"同一目标图的三种改图策略"，留给二期的"单图重生成/改图"功能，不与 storyboard 分镜混跑(否则任务数×UI 爆) + - getNarrativeRoles 角色框架(hook/pain_scene/applied_proof/texture/scenario/tutorial/social_proof/closer)是可迁移数据，按 image_count 取前 N 个角色 + +### 4.3 裂变引擎（扒split.js上线版） +- 1爆款→N完整笔记包(标题+正文+标签+imagePlan分镜+维度+人群+场景+痛点) +- 爆款参考度调节(低30/中/高85防抄袭) ; generateImages=true串生图 +- _combine_benchmarks：1-5篇标杆合并成综合reference(新造) + +### 4.4 去水印（image_postprocessor·新造·洞3） +- 🔴 主方案=**重编码去C2PA元数据+转JPG+轻重采样削像素水印+压缩**(Pillow,对客户透明)；保留可视质量，**C2PA元数据可去除，私有像素水印只能削弱不保证100%清除**(诚实表述,外部AI审核P1-9) +- 备选=banana remove_watermark(Gemini重绘)——⚠️对海报中文大字有改字风险,仅特殊场景用,非主通道 +- eval_score：**一期留NULL**,不接banana假评分(88分污染飞轮),等组长标准蒸馏真评分(三期) + +## 5. 异步队列（扒banana骨架） +- Celery任务：生图异步(每张~4min) / **只传task_id绝不传key(基石B)** / worker内查库取encrypted_key→FERNET_KEY解密→局部变量,不落盘不打日志 +- gemini_factory：每任务用自己key构建实例(新造,解决全局单例) +- 失败处理：Celery task失败→更新任务状态→SSE推task_failed(带code) ; 生图502重试退避 +- SSE：Redis Pub/Sub推进度(扒banana event_bus) + +## 6. 多租户与安全（基石BC,扒banana JWT） +- JWT HS256每请求验签,放user_id/workspace_id/role加速字段 +- 🔴 读写分层(基石C，外部AI审核P1-7细化)： + - **写操作+切workspace**：必须查 workspace_members 校验权限 + - **列表读**：用 JWT 的 current_workspace_id 过滤即可(加速) + - **单资源读**(任务详情/候选详情/下载包)：仍须校验 resource.workspace_id == current_workspace_id，不能笼统信JWT(防越权读他人资源) +- base_workspace_repo：所有workspace相关Repo继承,强制带workspace_id过滤(新造,防串数据) +- workspace_guard中间件：FastAPI依赖注入,所有业务接口强制注入workspace_id +- token-url收口：统一自家中转站endpoint；**账号表只存encrypted_key不存url字段**(旧"兼容任意url"已废止) +- 上传安全(扒banana)：magic number校验+扩展名白名单+路径穿越防护 + +## 7. 存档打包（package_exporter·新造·洞6） +- 达人素材交付包：按笔记分文件夹+图(01/02/03命名防错序)+文案.txt(标题+正文+标签)+📋发布清单.txt+✅合规说明.txt(违禁词已过/已去水印) +- delivery_packages表:status(pending/ready/downloaded)/package_path +- 数据可导出(归客户,client_data) ; 飞轮偏好归平台(platform_asset) + +## 8. 偏好飞轮（新造·灵魂·开放测试即启动） +- 三信号入口(preference_collector写events)：选文案+3/选图+3/通过+5/打回-3/重生成-1(初始默认权重,北哥可校准) +- 实时聚合(preference_aggregator)：查最近50条→最常选角度+打回原因近3条原文拼进prompt;不足5条用产品档案冷启动 +- 三层继承：L1品牌基线>L2人设(二期)>L3个人手感;⚠️按产品维度分开学(素颜霜不串精华) +- 🔴 飞轮MVP验收四点(基石E,全过才达标)：events正确写入✓ / aggregator返回明确prompt片段✓ / 下次prompt trace含该片段✓ / UI显示"本次已注入:最近偏好/打回原因"✓ + +## 9. 配置与环境（架构没列,PRD补） +- 环境变量完整清单(必填项启动时校验,缺失则启动失败报错不静默)： + - FERNET_KEY(必填,key解密,绝不进代码库) / JWT_SECRET(必填≥32字符) / DATABASE_URL / MONGO_URI / REDIS_URL + - IMAGE_PROVIDER_PRIMARY=gpt / IMAGE_PROVIDER_FALLBACK=gemini / IMAGE_API_BASE(自家中转站url) / IMAGE_MODEL=gpt-image-2 + - CLAUDE_API_URL / GEMINI_API_URL(都收口自家站) +- 6容器Docker Compose(扒banana,调环境变量名) ; 文件存储:交付包路径规则uploads/packages/{workspace}/{task}/ + +## 10. 工程规范与可观测 +- 健康检查 GET /health(Docker健康检查用) +- Alembic迁移：001-004按序;回滚策略;FERNET_KEY缺失启动保护 +- 可观测：每次AI调用记ai_call_logs(谁/多少token,失败归因到个人key) ; MongoDB存调用trace排障 +- 错误处理：统一错误码(§3.2) ; Celery失败SSE推送 + diff --git a/PRD/前端PRD交叉审核报告.md b/PRD/前端PRD交叉审核报告.md new file mode 100644 index 0000000..9e44dd4 --- /dev/null +++ b/PRD/前端PRD交叉审核报告.md @@ -0,0 +1,34 @@ +# 前端PRD交叉审核报告（2026-06-05） + +> 审核方式：agent对照 设计图 + 前端交互设计.md(5屏金标准) + 架构方案三角色旅程 + 后端PRD契约 + banana前端范式，逐条核对前端PRD骨架。 +> 重大发现：**已有 `前端交互设计.md` 是经倩倩姐确认的5屏金标准**(配置台/开任务/挑文案/挑图/组长审核+异步生图电影+飞轮隐形+6真实状况)，前端PRD骨架没承接它→这是骨架"看不懂"的根因。铁律:好的保留,这份5屏设计原样吃进PRD。 + +## 审出问题（11遗漏+4不一致+工程空白） + +### 🔴 重大遗漏 +1. **组长审核台**整体缺失(屏5)：审核队列/预览/通过+5/打回写原因(飞轮最强信号)/审后跳转。三角色只画了熟手 +2. **"今天主题"输入框**漏了(设计图屏2关键字段)：驱动当次文案方向,缺了=无方向生成 +3. **文案双轨轨B**前端入口缺(导入豆包等外部文案/source=import的UI区分) +4. **飞轮"本次已注入偏好"提示UI**缺(基石E验收点④,后端专开GET /preference/context供它调) +5. **SSE逐图进度电影**只一行(交互设计.md §2:N/10计数+好一批亮一批+单批失败标红重试+能离开降焦虑) +6. **配置预览面板"编辑配置"入口**交互未描述(跳转/侧滑/模态?) +7. **错误态UI**只"toast"一笔带过(后端7错误码各不同:KEY_NOT_CONFIGURED=引导配key页/BANNED_WORD=显违禁词级别/502重试等) +8. **管理员配置台**缺(屏1:产品档案/标杆/成员/违禁词CRUD) +9. **标杆笔记管理界面**缺(一期主通道=截图上传+手填亮点) +10. **"最近任务"底部区**缺(设计图底部4个进度条卡) +11. **五维评分**没说哪五维/怎么展示(数字/雷达/进度条)/数据来自哪字段 + +### 🟡 不一致 +- C1:设计图有Token余额568320,PRD已正确标"不展示",但没说底部位置替换成什么 +- C2:第6章组件库还残留"Token余额组件"(与3bis矛盾,删) +- C3:3.6"确认文案"混淆熟手/组长视角(通过打回是组长在独立审核台做,不是熟手确认页) +- C4:5步流程条对组长/管理员是否隐藏未说(组长主界面是审核队列不走1→5) + +### 🟢 工程空白(建议补) +状态管理4Store(auth/task/generation/preference)/路由表/SSE断线重连UI/骨架屏/表单校验/响应式断点(建议最小1280px PC优先)/角色路由守卫(/review只组长管理员)/首登key引导流 + +## 🟦 CC处理判断 +- 铁律好的保留：**前端交互设计.md的5屏设计原样吃进PRD**,不重新发明 +- 设计图(5步流程条)与交互设计.md(5屏)合并:开新任务/选文案/选图/确认预览(熟手提审前)/组长审核台;配置台进左侧导航 +- 前端PRD同样做承接层:引用交互设计.md的5屏画面+设计图视觉,补工程细节(路由/Store/错误态/角色守卫)+对接后端契约(SSE事件/错误码) +- 优先级:组长审核台/错误态UI/飞轮注入提示 三项直接卡飞轮和生产链验收 diff --git a/PRD/后端PRD交叉审核报告.md b/PRD/后端PRD交叉审核报告.md new file mode 100644 index 0000000..5a43c83 --- /dev/null +++ b/PRD/后端PRD交叉审核报告.md @@ -0,0 +1,42 @@ +# 后端PRD交叉审核报告（2026-06-05） + +> 审核方式：agent对照架构方案v0.3 + CLAUDE.md + 7洞落盘 + 扒包(上线版) 逐条核对后端PRD骨架完整性。 +> 结论：后端PRD骨架"只提到名词，没承接细节"，架构方案v0.3里拍过板的细节几乎都没落进PRD——这是倩倩姐"看不懂"的根因。 + +## 审出的问题（18遗漏+5不一致+10工程空白） + +### 🔴 重大遗漏（架构v0.3拍了板但PRD没承接） +1. 基石A 业务参数不写死(品类/数量/角度) 无落点 +2. 基石B key机制不完整(缺FERNET_KEY环境变量/gemini_factory每任务建实例/解密不落盘) +3. 基石C 读写权限分层(写操作查workspace_members)+base_workspace_repo中间件 缺失 +4. 基石D 采集↔生产接缝(人工判断标准落点) 完全没有 +5. 基石E 飞轮四点验收 没列 +6. 16表精确清单+Alembic001-004分批 缺(且PRD表名错:api_keys应为user_api_keys/tasks应为generation_tasks/无assets表应为text+image_candidates/无login_records/无ai_call_logs) +7. 任务状态机7态(pending→generating→pending_selection→pending_review→approved/rejected→archived) 缺 +8. generation_tasks审核字段平铺(6字段,不建独立notes表) 缺 +9. preference_events字段(signal_type5枚举/weight/angle_label/reason/data_ownership/workspace_id+product_id) 缺 +10. 文案轨A"一次出5角度JSON"规格 缺(易被agent照抄产品包单条串行) +11. 文案轨B(导入外部文案text_import_handler+source字段) 完全缺 +12. storyboard分镜与A/B/C并发两套生图逻辑如何整合 没说清 +13. IMAGE_PROVIDER_PRIMARY环境变量配主备+禁纯文生图+产品锚点防串货 缺 +14. 三期构建顺序1A/1B/二期/三期+一期不做项 缺 +15. eval_score留NULL不接banana假评分 缺 +16. 标杆采集通道分层(截图手填主/登录态插件内部) 缺 +17. ai_call_logs(usage记录,按key计费排障基础) 缺 +18. MongoDB只存debug trace(业务不依赖)边界 缺(PRD误把prompt_template放Mongo,实际可调prompt在products表) + +### 🟡 不一致 +- C1: PRD把data_ownership列成独立表,实际是preference_events的字段 +- C2: 生图把产品包10角色/6品类 和 edits新接口 混在一起没说整合 +- C3: 去水印主次反了(主=re-encode/Pillow,banana重绘是可选且海报有改字风险) +- C4: 三层兜底/五维打分要标明"扒产品包重写Python集成进轨A" +- C5: token-url收口缺反面约束(账号表不存url字段/旧兼容废止) + +### 🟢 所有来源都没细化、PRD该补的工程细节 +错误码规范/SSE事件类型清单/分页规范/限流(生图4min)/文件存储路径/DB索引/Alembic回滚+FERNET_KEY缺失启动保护/健康检查/环境变量完整清单/workspace_guard注入时机 + +## 🟦 CC的处理判断（不照搬agent建议） +- agent建议把后端PRD扩成14章、把架构方案§四§五原文照搬进PRD → **不采纳全照搬** +- 理由：架构方案v0.3已是金标准,PRD原文复制=两份打架+维护噩梦 +- **正确做法：后端PRD做成"承接层"——①引用架构方案(不复制)②重点补PRD独有的工程细节(§四绿色那10项:错误码/SSE/分页/限流/索引/环境变量等)③把扒包上线版的生图新事实写进去** +- 数据模型这种基础:PRD用"引用架构方案§三 + 标注扒包修正点"方式,不重抄16表 diff --git a/backend/Dockerfile b/backend/Dockerfile new file mode 100644 index 0000000..14d6ee4 --- /dev/null +++ b/backend/Dockerfile @@ -0,0 +1,15 @@ +FROM python:3.12-slim + +WORKDIR /app + +# 系统依赖：curl 供 healthcheck；无需 libmysqlclient-dev（用 pymysql 纯 Python 驱动） +RUN apt-get update && apt-get install -y --no-install-recommends \ + curl && \ + rm -rf /var/lib/apt/lists/* + +COPY requirements.txt . +RUN pip install --no-cache-dir -r requirements.txt + +COPY . . + +EXPOSE 8000 diff --git a/backend/README.md b/backend/README.md new file mode 100644 index 0000000..5dc7cf1 --- /dev/null +++ b/backend/README.md @@ -0,0 +1,30 @@ +# Clover Backend + +FastAPI + Celery + MySQL + MongoDB + Redis + +## 目录结构 +``` +backend/ + app/ + api/v1/ # 路由层（auth/products/tasks/review/delivery） + services/ # 业务逻辑层（按模块拆分） + models/ # SQLAlchemy ORM 模型（14张表） + middleware/ # workspace_guard 等中间件 + workers/ # Celery 任务（只传task_id，worker内解密key） + constants/ # 策略命名中心文件，消除打架 + utils/ # Fernet加密、SSE工具等 + alembic/ + versions/ # 001-004 migration 脚本 + tests/ # TDD 测试用例 +``` + +## 依赖（需装） +见 requirements.txt（待 BE agent 填） + +## 启动 +见 docker-compose.yml（待 BE agent 填） + +## 铁律提醒 +- API Key 绝不进 Celery 参数，只传 task_id +- FERNET_KEY 走环境变量 +- 所有查询强制带 workspace_id diff --git a/backend/alembic.ini b/backend/alembic.ini new file mode 100644 index 0000000..5e4b20c --- /dev/null +++ b/backend/alembic.ini @@ -0,0 +1,45 @@ +# Alembic 配置文件 +# Clover 项目使用 alembic.ini 在 backend/ 目录 + +[alembic] +script_location = alembic +prepend_sys_path = . + +# 从环境变量读取，不硬编码（基石B） +sqlalchemy.url = %(DATABASE_URL)s + +[post_write_hooks] + +[loggers] +keys = root,sqlalchemy,alembic + +[handlers] +keys = console + +[formatters] +keys = generic + +[logger_root] +level = WARN +handlers = console +qualname = + +[logger_sqlalchemy] +level = WARN +handlers = +qualname = sqlalchemy.engine + +[logger_alembic] +level = INFO +handlers = +qualname = alembic + +[handler_console] +class = StreamHandler +args = (sys.stderr,) +level = NOTSET +formatter = generic + +[formatter_generic] +format = %(levelname)-5.5s [%(name)s] %(message)s +datefmt = %H:%M:%S diff --git a/backend/alembic/README.md b/backend/alembic/README.md new file mode 100644 index 0000000..47dc0c7 --- /dev/null +++ b/backend/alembic/README.md @@ -0,0 +1,16 @@ +# alembic/ + +Alembic 数据库迁移脚本占位： + +## 执行顺序 +001 → 002 → 003 → 004（有外键依赖，必须按顺序） + +## 版本规划 +- 001_from_banana.py # users/login_records/user_preferences（从banana搬3张，改造） +- 002_multi_tenant_base.py # workspaces/workspace_members/user_api_keys +- 003_business_core.py # 业务主体7张（products等） +- 004_flywheel.py # preference_events + +## 铁律 +- matrix_accounts / preference_profile 一期不建（二期预留） +- Alembic 版本号统一管理，不允许手动改表 diff --git a/backend/alembic/env.py b/backend/alembic/env.py new file mode 100644 index 0000000..0c5eb79 --- /dev/null +++ b/backend/alembic/env.py @@ -0,0 +1,62 @@ +""" +alembic/env.py — Alembic 运行环境配置 +从环境变量读取 DATABASE_URL，不硬编码。 +""" + +import os +from logging.config import fileConfig + +from alembic import context +from sqlalchemy import engine_from_config, pool + +# ── 确保 app 包可导入 ──────────────────────────────── +import sys +sys.path.insert(0, os.path.dirname(os.path.dirname(__file__))) + +from app.core.database import Base +import app.models # noqa: F401 — 触发所有模型注册 + +config = context.config + +# 从环境变量覆盖 DATABASE_URL +db_url = os.environ.get("DATABASE_URL") +if db_url: + config.set_main_option("sqlalchemy.url", db_url) + +if config.config_file_name is not None: + fileConfig(config.config_file_name) + +target_metadata = Base.metadata + + +def run_migrations_offline() -> None: + url = config.get_main_option("sqlalchemy.url") + context.configure( + url=url, + target_metadata=target_metadata, + literal_binds=True, + dialect_opts={"paramstyle": "named"}, + ) + with context.begin_transaction(): + context.run_migrations() + + +def run_migrations_online() -> None: + connectable = engine_from_config( + config.get_section(config.config_ini_section, {}), + prefix="sqlalchemy.", + poolclass=pool.NullPool, + ) + with connectable.connect() as connection: + context.configure( + connection=connection, + target_metadata=target_metadata, + ) + with context.begin_transaction(): + context.run_migrations() + + +if context.is_offline_mode(): + run_migrations_offline() +else: + run_migrations_online() diff --git a/backend/alembic/script.py.mako b/backend/alembic/script.py.mako new file mode 100644 index 0000000..1a162dc --- /dev/null +++ b/backend/alembic/script.py.mako @@ -0,0 +1,29 @@ +""" +alembic/script.py.mako — 迁移文件模板 +""" +"""${message} + +Revision ID: ${up_revision} +Revises: ${down_revision | comma,n} +Create Date: ${create_date} + +""" +from typing import Sequence, Union + +from alembic import op +import sqlalchemy as sa +${imports if imports else ""} + +# revision identifiers, used by Alembic. +revision: str = ${repr(up_revision)} +down_revision: Union[str, None] = ${repr(down_revision)} +branch_labels: Union[str, Sequence[str], None] = ${repr(branch_labels)} +depends_on: Union[str, Sequence[str], None] = ${repr(depends_on)} + + +def upgrade() -> None: + ${upgrades if upgrades else "pass"} + + +def downgrade() -> None: + ${downgrades if downgrades else "pass"} diff --git a/backend/alembic/versions/001_banana_users_tables.py b/backend/alembic/versions/001_banana_users_tables.py new file mode 100644 index 0000000..0a06f21 --- /dev/null +++ b/backend/alembic/versions/001_banana_users_tables.py @@ -0,0 +1,64 @@ +"""001_banana_users_tables + +Revision ID: 001 +Revises: +Create Date: 2026-06-09 +Alembic 001: 从 banana 搬 3 张表（users/login_records/user_preferences） +删除 credits 字段，其余无改。 +""" + +from typing import Sequence, Union + +import sqlalchemy as sa +from alembic import op + +revision: str = "001" +down_revision: Union[str, None] = None +branch_labels: Union[str, Sequence[str], None] = None +depends_on: Union[str, Sequence[str], None] = None + + +def upgrade() -> None: + op.create_table( + "users", + sa.Column("id", sa.BigInteger(), autoincrement=True, nullable=False), + sa.Column("username", sa.String(64), nullable=False), + sa.Column("email", sa.String(255), nullable=False), + sa.Column("hashed_password", sa.String(255), nullable=False), + sa.Column("is_active", sa.Boolean(), nullable=False, server_default="1"), + sa.Column("created_at", sa.DateTime(), server_default=sa.text("now()"), nullable=False), + sa.Column("updated_at", sa.DateTime(), server_default=sa.text("now()"), nullable=False), + sa.PrimaryKeyConstraint("id"), + sa.UniqueConstraint("username"), + sa.UniqueConstraint("email"), + ) + + op.create_table( + "login_records", + sa.Column("id", sa.BigInteger(), autoincrement=True, nullable=False), + sa.Column("user_id", sa.BigInteger(), nullable=False), + sa.Column("ip_address", sa.String(64), nullable=True), + sa.Column("user_agent", sa.String(512), nullable=True), + sa.Column("created_at", sa.DateTime(), server_default=sa.text("now()"), nullable=False), + sa.ForeignKeyConstraint(["user_id"], ["users.id"], ondelete="CASCADE"), + sa.PrimaryKeyConstraint("id"), + ) + op.create_index("idx_login_records_user_id", "login_records", ["user_id"]) + + op.create_table( + "user_preferences", + sa.Column("id", sa.BigInteger(), autoincrement=True, nullable=False), + sa.Column("user_id", sa.BigInteger(), nullable=False), + sa.Column("preferences_json", sa.Text(), nullable=True), + sa.Column("updated_at", sa.DateTime(), server_default=sa.text("now()"), nullable=False), + sa.ForeignKeyConstraint(["user_id"], ["users.id"], ondelete="CASCADE"), + sa.PrimaryKeyConstraint("id"), + sa.UniqueConstraint("user_id"), + ) + + +def downgrade() -> None: + op.drop_table("user_preferences") + op.drop_index("idx_login_records_user_id", "login_records") + op.drop_table("login_records") + op.drop_table("users") diff --git a/backend/alembic/versions/002_multitenant_workspace.py b/backend/alembic/versions/002_multitenant_workspace.py new file mode 100644 index 0000000..d37b2a8 --- /dev/null +++ b/backend/alembic/versions/002_multitenant_workspace.py @@ -0,0 +1,71 @@ +"""002_multitenant_workspace + +Revision ID: 002 +Revises: 001 +Create Date: 2026-06-09 +Alembic 002: 多租户基础（workspaces/workspace_members/user_api_keys） +matrix_accounts 二期预留，一期不建。 +user_api_keys 不存 url 字段（token站固定自家站，基石B）。 +""" + +from typing import Sequence, Union + +import sqlalchemy as sa +from alembic import op + +revision: str = "002" +down_revision: Union[str, None] = "001" +branch_labels: Union[str, Sequence[str], None] = None +depends_on: Union[str, Sequence[str], None] = None + + +def upgrade() -> None: + op.create_table( + "workspaces", + sa.Column("id", sa.BigInteger(), autoincrement=True, nullable=False), + sa.Column("name", sa.String(128), nullable=False), + sa.Column("slug", sa.String(64), nullable=False), + sa.Column("is_active", sa.Boolean(), nullable=False, server_default="1"), + sa.Column("created_at", sa.DateTime(), server_default=sa.text("now()"), nullable=False), + sa.PrimaryKeyConstraint("id"), + sa.UniqueConstraint("slug"), + ) + + op.create_table( + "workspace_members", + sa.Column("id", sa.BigInteger(), autoincrement=True, nullable=False), + sa.Column("workspace_id", sa.BigInteger(), nullable=False), + sa.Column("user_id", sa.BigInteger(), nullable=False), + sa.Column("role", sa.Enum("admin", "supervisor", "operator", name="userrole"), nullable=False), + sa.Column("created_at", sa.DateTime(), server_default=sa.text("now()"), nullable=False), + sa.ForeignKeyConstraint(["workspace_id"], ["workspaces.id"], ondelete="CASCADE"), + sa.ForeignKeyConstraint(["user_id"], ["users.id"], ondelete="CASCADE"), + sa.PrimaryKeyConstraint("id"), + sa.UniqueConstraint("workspace_id", "user_id", name="uq_workspace_member"), + ) + op.create_index("idx_workspace_members_workspace_id", "workspace_members", ["workspace_id"]) + + op.create_table( + "user_api_keys", + sa.Column("id", sa.BigInteger(), autoincrement=True, nullable=False), + sa.Column("user_id", sa.BigInteger(), nullable=False), + sa.Column("workspace_id", sa.BigInteger(), nullable=False), + sa.Column("provider", sa.String(32), nullable=False), + sa.Column("encrypted_key", sa.String(512), nullable=False), + sa.Column("key_last4", sa.String(4), nullable=False), + sa.Column("created_at", sa.DateTime(), server_default=sa.text("now()"), nullable=False), + sa.ForeignKeyConstraint(["user_id"], ["users.id"], ondelete="CASCADE"), + sa.ForeignKeyConstraint(["workspace_id"], ["workspaces.id"], ondelete="CASCADE"), + sa.PrimaryKeyConstraint("id"), + sa.UniqueConstraint("user_id", "workspace_id", "provider", name="uq_user_workspace_provider"), + ) + op.create_index("idx_user_api_keys_workspace_id", "user_api_keys", ["workspace_id"]) + + +def downgrade() -> None: + op.drop_index("idx_user_api_keys_workspace_id", "user_api_keys") + op.drop_table("user_api_keys") + op.drop_index("idx_workspace_members_workspace_id", "workspace_members") + op.drop_table("workspace_members") + op.drop_table("workspaces") + op.execute("DROP TYPE IF EXISTS userrole") diff --git a/backend/alembic/versions/003_business_tables.py b/backend/alembic/versions/003_business_tables.py new file mode 100644 index 0000000..abce009 --- /dev/null +++ b/backend/alembic/versions/003_business_tables.py @@ -0,0 +1,205 @@ +"""003_business_tables + +Revision ID: 003 +Revises: 002 +Create Date: 2026-06-09 +Alembic 003: 业务主体7张表 + ai_call_logs +products / benchmark_notes / banned_words / +generation_tasks / text_candidates / image_candidates / +delivery_packages / ai_call_logs +""" + +from typing import Sequence, Union + +import sqlalchemy as sa +from alembic import op + +revision: str = "003" +down_revision: Union[str, None] = "002" +branch_labels: Union[str, Sequence[str], None] = None +depends_on: Union[str, Sequence[str], None] = None + +_TASK_STATUS = sa.Enum( + "pending", "generating", "pending_selection", + "pending_review", "approved", "rejected", "archived", + name="taskstatus", +) +_REVIEW_STATUS = sa.Enum("pending", "approved", "rejected", name="reviewstatus") +_CANDIDATE_SOURCE = sa.Enum("ai", "import", name="candidatesource") +_BANNED_LEVEL = sa.Enum("auto_fix", "soft_warn", "hard_block", name="bannedwordlevel") +_BANNED_STATUS = sa.Enum("pass", "auto_fixed", "soft_warn", "hard_block", name="bannedwordstatus") +_IMAGE_ROLE = sa.Enum("hook", "pain", "proof", "quality", "credit", "convert", "main", name="imagerole") +_PKG_STATUS = sa.Enum("pending", "ready", "downloaded", name="packagestatus") +_PRODUCT_SOURCE = sa.Enum("preset", "custom", name="productsource") + + +def upgrade() -> None: + op.create_table( + "products", + sa.Column("id", sa.BigInteger(), autoincrement=True, nullable=False), + sa.Column("workspace_id", sa.BigInteger(), nullable=False), + sa.Column("name", sa.String(128), nullable=False), + sa.Column("category", sa.String(64), nullable=True), + sa.Column("source", _PRODUCT_SOURCE, nullable=False, server_default="custom"), + sa.Column("selling_points", sa.Text(), nullable=True), + sa.Column("style_tone", sa.String(128), nullable=True), + sa.Column("text_angles", sa.Text(), nullable=True), + sa.Column("custom_prompt", sa.Text(), nullable=True), + sa.Column("is_active", sa.Boolean(), nullable=False, server_default="1"), + sa.Column("created_at", sa.DateTime(), server_default=sa.text("now()"), nullable=False), + sa.Column("updated_at", sa.DateTime(), server_default=sa.text("now()"), nullable=False), + sa.ForeignKeyConstraint(["workspace_id"], ["workspaces.id"], ondelete="CASCADE"), + sa.PrimaryKeyConstraint("id"), + ) + op.create_index("idx_products_workspace_id", "products", ["workspace_id"]) + + op.create_table( + "benchmark_notes", + sa.Column("id", sa.BigInteger(), autoincrement=True, nullable=False), + sa.Column("workspace_id", sa.BigInteger(), nullable=False), + sa.Column("product_id", sa.BigInteger(), nullable=False), + sa.Column("screenshot_url", sa.String(512), nullable=True), + sa.Column("highlights", sa.Text(), nullable=True), + sa.Column("link_url", sa.String(512), nullable=True), + sa.Column("created_at", sa.DateTime(), server_default=sa.text("now()"), nullable=False), + sa.ForeignKeyConstraint(["workspace_id"], ["workspaces.id"], ondelete="CASCADE"), + sa.ForeignKeyConstraint(["product_id"], ["products.id"], ondelete="CASCADE"), + sa.PrimaryKeyConstraint("id"), + ) + op.create_index("idx_benchmark_notes_product_id", "benchmark_notes", ["product_id"]) + + op.create_table( + "banned_words", + sa.Column("id", sa.BigInteger(), autoincrement=True, nullable=False), + sa.Column("workspace_id", sa.BigInteger(), nullable=False), + sa.Column("word", sa.String(64), nullable=False), + sa.Column("level", _BANNED_LEVEL, nullable=False), + sa.Column("replacement", sa.String(128), nullable=True), + sa.Column("updatable", sa.Boolean(), nullable=False, server_default="1"), + sa.Column("created_at", sa.DateTime(), server_default=sa.text("now()"), nullable=False), + sa.ForeignKeyConstraint(["workspace_id"], ["workspaces.id"], ondelete="CASCADE"), + sa.PrimaryKeyConstraint("id"), + ) + op.create_index("idx_banned_words_workspace_id", "banned_words", ["workspace_id"]) + + op.create_table( + "generation_tasks", + sa.Column("id", sa.BigInteger(), autoincrement=True, nullable=False), + sa.Column("workspace_id", sa.BigInteger(), nullable=False), + sa.Column("product_id", sa.BigInteger(), nullable=False), + sa.Column("operator_id", sa.BigInteger(), nullable=False), + sa.Column("theme", sa.String(256), nullable=True), + sa.Column("text_count", sa.Integer(), nullable=False, server_default="5"), + sa.Column("image_count", sa.Integer(), nullable=False, server_default="3"), + sa.Column("track", sa.String(16), nullable=False, server_default="ai"), + sa.Column("status", _TASK_STATUS, nullable=False, server_default="pending"), + sa.Column("mongo_trace_id", sa.String(24), nullable=True), + sa.Column("review_status", _REVIEW_STATUS, nullable=True), + sa.Column("reviewer_id", sa.BigInteger(), nullable=True), + sa.Column("reviewed_at", sa.DateTime(), nullable=True), + sa.Column("reject_reason", sa.Text(), nullable=True), + sa.Column("approved_at", sa.DateTime(), nullable=True), + sa.Column("archived_at", sa.DateTime(), nullable=True), + sa.Column("created_at", sa.DateTime(), server_default=sa.text("now()"), nullable=False), + sa.Column("updated_at", sa.DateTime(), server_default=sa.text("now()"), nullable=False), + sa.ForeignKeyConstraint(["workspace_id"], ["workspaces.id"], ondelete="CASCADE"), + sa.ForeignKeyConstraint(["product_id"], ["products.id"]), + sa.ForeignKeyConstraint(["operator_id"], ["users.id"]), + sa.ForeignKeyConstraint(["reviewer_id"], ["users.id"]), + sa.PrimaryKeyConstraint("id"), + ) + op.create_index("idx_generation_tasks_workspace_status", "generation_tasks", ["workspace_id", "status"]) + + op.create_table( + "text_candidates", + sa.Column("id", sa.BigInteger(), autoincrement=True, nullable=False), + sa.Column("workspace_id", sa.BigInteger(), nullable=False), + sa.Column("task_id", sa.BigInteger(), nullable=False), + sa.Column("source", _CANDIDATE_SOURCE, nullable=False, server_default="ai"), + sa.Column("angle_label", sa.String(64), nullable=True), + sa.Column("content", sa.Text(), nullable=True), + sa.Column("score_json", sa.Text(), nullable=True), + sa.Column("banned_word_status", _BANNED_STATUS, nullable=False, server_default="pass"), + sa.Column("eval_score", sa.Float(), nullable=True), + sa.Column("is_selected", sa.Boolean(), nullable=False, server_default="0"), + sa.Column("created_at", sa.DateTime(), server_default=sa.text("now()"), nullable=False), + sa.ForeignKeyConstraint(["task_id"], ["generation_tasks.id"], ondelete="CASCADE"), + sa.PrimaryKeyConstraint("id"), + ) + op.create_index("idx_text_candidates_task_id", "text_candidates", ["task_id"]) + + op.create_table( + "image_candidates", + sa.Column("id", sa.BigInteger(), autoincrement=True, nullable=False), + sa.Column("workspace_id", sa.BigInteger(), nullable=False), + sa.Column("task_id", sa.BigInteger(), nullable=False), + sa.Column("role", _IMAGE_ROLE, nullable=False, server_default="main"), + sa.Column("url", sa.String(512), nullable=True), + sa.Column("strategy", sa.String(4), nullable=True), + sa.Column("seq", sa.Integer(), nullable=False, server_default="1"), + sa.Column("is_selected", sa.Boolean(), nullable=False, server_default="0"), + sa.Column("eval_score", sa.Float(), nullable=True), + sa.Column("created_at", sa.DateTime(), server_default=sa.text("now()"), nullable=False), + sa.ForeignKeyConstraint(["task_id"], ["generation_tasks.id"], ondelete="CASCADE"), + sa.PrimaryKeyConstraint("id"), + ) + op.create_index("idx_image_candidates_task_id", "image_candidates", ["task_id"]) + + op.create_table( + "delivery_packages", + sa.Column("id", sa.BigInteger(), autoincrement=True, nullable=False), + sa.Column("workspace_id", sa.BigInteger(), nullable=False), + sa.Column("task_id", sa.BigInteger(), nullable=False), + sa.Column("status", _PKG_STATUS, nullable=False, server_default="pending"), + sa.Column("package_path", sa.String(512), nullable=True), + sa.Column("download_url", sa.String(512), nullable=True), + sa.Column("expires_at", sa.DateTime(), nullable=True), + sa.Column("created_at", sa.DateTime(), server_default=sa.text("now()"), nullable=False), + sa.ForeignKeyConstraint(["workspace_id"], ["workspaces.id"], ondelete="CASCADE"), + sa.ForeignKeyConstraint(["task_id"], ["generation_tasks.id"]), + sa.PrimaryKeyConstraint("id"), + ) + + op.create_table( + "ai_call_logs", + sa.Column("id", sa.BigInteger(), autoincrement=True, nullable=False), + sa.Column("workspace_id", sa.BigInteger(), nullable=False), + sa.Column("user_id", sa.BigInteger(), nullable=False), + sa.Column("key_id", sa.BigInteger(), nullable=True), + sa.Column("task_id", sa.BigInteger(), nullable=True), + sa.Column("provider", sa.String(32), nullable=True), + sa.Column("model", sa.String(64), nullable=True), + sa.Column("call_type", sa.String(32), nullable=True), + sa.Column("prompt_tokens", sa.Integer(), nullable=True), + sa.Column("completion_tokens", sa.Integer(), nullable=True), + sa.Column("success", sa.Boolean(), nullable=False, server_default="1"), + sa.Column("error_code", sa.String(32), nullable=True), + sa.Column("latency_ms", sa.Integer(), nullable=True), + sa.Column("created_at", sa.DateTime(), server_default=sa.text("now()"), nullable=False), + sa.ForeignKeyConstraint(["user_id"], ["users.id"]), + sa.ForeignKeyConstraint(["key_id"], ["user_api_keys.id"]), + sa.ForeignKeyConstraint(["task_id"], ["generation_tasks.id"]), + sa.PrimaryKeyConstraint("id"), + ) + op.create_index("idx_ai_call_logs_workspace_user", "ai_call_logs", ["workspace_id", "user_id"]) + op.create_index("idx_ai_call_logs_task_id", "ai_call_logs", ["task_id"]) + + +def downgrade() -> None: + op.drop_index("idx_ai_call_logs_task_id", "ai_call_logs") + op.drop_index("idx_ai_call_logs_workspace_user", "ai_call_logs") + op.drop_table("ai_call_logs") + op.drop_table("delivery_packages") + op.drop_index("idx_image_candidates_task_id", "image_candidates") + op.drop_table("image_candidates") + op.drop_index("idx_text_candidates_task_id", "text_candidates") + op.drop_table("text_candidates") + op.drop_index("idx_generation_tasks_workspace_status", "generation_tasks") + op.drop_table("generation_tasks") + op.drop_index("idx_banned_words_workspace_id", "banned_words") + op.drop_table("banned_words") + op.drop_index("idx_benchmark_notes_product_id", "benchmark_notes") + op.drop_table("benchmark_notes") + op.drop_index("idx_products_workspace_id", "products") + op.drop_table("products") + # MySQL 不支持 DROP TYPE，枚举存 VARCHAR 无需清理 diff --git a/backend/alembic/versions/004_flywheel_preference_events.py b/backend/alembic/versions/004_flywheel_preference_events.py new file mode 100644 index 0000000..5f7112b --- /dev/null +++ b/backend/alembic/versions/004_flywheel_preference_events.py @@ -0,0 +1,67 @@ +"""004_flywheel_preference_events + +Revision ID: 004 +Revises: 003 +Create Date: 2026-06-09 +Alembic 004: 飞轮信号表 +preference_events 一期建表写入 +preference_profile 二期预留，一期不建 +""" + +from typing import Sequence, Union + +import sqlalchemy as sa +from alembic import op + +revision: str = "004" +down_revision: Union[str, None] = "003" +branch_labels: Union[str, Sequence[str], None] = None +depends_on: Union[str, Sequence[str], None] = None + +_SIGNAL_TYPE = sa.Enum( + "text_select", "image_select", "approve", + "reject_with_reason", "regenerate", + name="signaltype", +) +_DATA_OWNERSHIP = sa.Enum("client_data", "platform_asset", name="dataownership") + + +def upgrade() -> None: + op.create_table( + "preference_events", + sa.Column("id", sa.BigInteger(), autoincrement=True, nullable=False), + sa.Column("workspace_id", sa.BigInteger(), nullable=False), + sa.Column("product_id", sa.BigInteger(), nullable=False), + sa.Column("task_id", sa.BigInteger(), nullable=False), + sa.Column("user_id", sa.BigInteger(), nullable=False), + sa.Column("signal_type", _SIGNAL_TYPE, nullable=False), + sa.Column("signal_weight", sa.Integer(), nullable=False), + sa.Column("candidate_id", sa.BigInteger(), nullable=True), + sa.Column("angle_label", sa.String(64), nullable=True), + sa.Column("reason", sa.Text(), nullable=True), + sa.Column("signal_meta", sa.Text(), nullable=True), + sa.Column( + "data_ownership", _DATA_OWNERSHIP, + nullable=False, server_default="client_data", + ), + sa.Column( + "created_at", sa.DateTime(), + server_default=sa.text("now()"), nullable=False, + ), + sa.ForeignKeyConstraint(["product_id"], ["products.id"]), + sa.ForeignKeyConstraint(["task_id"], ["generation_tasks.id"]), + sa.ForeignKeyConstraint(["user_id"], ["users.id"]), + sa.PrimaryKeyConstraint("id"), + ) + op.create_index( + "idx_preference_events_ws_product_created", + "preference_events", + ["workspace_id", "product_id", "created_at"], + ) + # preference_profile 二期预留，一期不建 + + +def downgrade() -> None: + op.drop_index("idx_preference_events_ws_product_created", "preference_events") + op.drop_table("preference_events") + # MySQL 不支持 DROP TYPE，枚举存 VARCHAR 无需清理 diff --git a/backend/alembic/versions/005_product_image_path.py b/backend/alembic/versions/005_product_image_path.py new file mode 100644 index 0000000..d9f8f73 --- /dev/null +++ b/backend/alembic/versions/005_product_image_path.py @@ -0,0 +1,27 @@ +"""005_product_image_path — products 表加 image_path 列（产品参考图本地路径） +前端图片上传完成后，BE 接口写入此字段；pipeline_io 读取用于生图参考。 +""" +from alembic import op +import sqlalchemy as sa + + +revision = "005" +down_revision = "004" +branch_labels = None +depends_on = None + + +def upgrade() -> None: + op.add_column( + "products", + sa.Column( + "image_path", + sa.String(512), + nullable=True, + comment="产品参考图本地文件路径（前端上传后写入，生图时作 reference_images 注入）", + ), + ) + + +def downgrade() -> None: + op.drop_column("products", "image_path") diff --git a/backend/alembic/versions/006_image_role_varchar.py b/backend/alembic/versions/006_image_role_varchar.py new file mode 100644 index 0000000..91ad42e --- /dev/null +++ b/backend/alembic/versions/006_image_role_varchar.py @@ -0,0 +1,45 @@ +"""006_image_role_varchar — image_candidates.role 从 enum 改 varchar(32) + +根因：storyboard 角色名仍在演进（hook/product_closeup/ingredient/texture/ +applied_proof/closer/pain_scene/social_proof/scenario/tutorial…），固定 enum +每加一个新角色就要迁一次，且旧 enum 漏了 product_closeup/ingredient 直接导致 +落库 "Data truncated for column 'role'" → 生图任务无限重试卡死。 +改 varchar 后落库不再受 enum 约束，角色名由 ImageRole 枚举在应用层定义。 +""" +from alembic import op +import sqlalchemy as sa + + +revision = "006" +down_revision = "005" +branch_labels = None +depends_on = None + + +def upgrade() -> None: + op.alter_column( + "image_candidates", + "role", + existing_type=sa.Enum( + "hook", "pain", "proof", "quality", "credit", "convert", "main", + name="role", + ), + type_=sa.String(32), + existing_nullable=False, + server_default="hook", + comment="分镜角色名（storyboard 输出，应用层 ImageRole 定义，不在 DB 约束）", + ) + + +def downgrade() -> None: + op.alter_column( + "image_candidates", + "role", + existing_type=sa.String(32), + type_=sa.Enum( + "hook", "pain", "proof", "quality", "credit", "convert", "main", + name="role", + ), + existing_nullable=False, + server_default="main", + ) diff --git a/backend/alembic/versions/007_need_product_image.py b/backend/alembic/versions/007_need_product_image.py new file mode 100644 index 0000000..8d94c5f --- /dev/null +++ b/backend/alembic/versions/007_need_product_image.py @@ -0,0 +1,33 @@ +"""add need_product_image to generation_tasks + +Revision ID: 007 +Revises: 006 +Create Date: 2026-06-08 + +本次产品是否入镜开关：True=必须产品参考图(无图禁生成,不降级)，False=允许纯文生图。 +默认 True（铁律：生图优先带产品参考图）。 +""" +from alembic import op +import sqlalchemy as sa + +revision = "007" +down_revision = "006" +branch_labels = None +depends_on = None + + +def upgrade(): + op.add_column( + "generation_tasks", + sa.Column( + "need_product_image", + sa.Boolean(), + nullable=False, + server_default=sa.true(), + comment="本次产品是否入镜:True需产品图(无图禁生成不降级)/False允许纯文生图", + ), + ) + + +def downgrade(): + op.drop_column("generation_tasks", "need_product_image") diff --git a/backend/alembic/versions/008_brand_keyword.py b/backend/alembic/versions/008_brand_keyword.py new file mode 100644 index 0000000..1e4e584 --- /dev/null +++ b/backend/alembic/versions/008_brand_keyword.py @@ -0,0 +1,32 @@ +"""add brand_keyword to products + +Revision ID: 008 +Revises: 007 +Create Date: 2026-06-13 + +第5环品牌词字段：客户输入，植入文案每条+生图特写图(第2/6张)。 +brand_keyword 是产品级字段，随产品固定，由客户录入（非AI生成）。 +""" +from alembic import op +import sqlalchemy as sa + +revision = "008" +down_revision = "007" +branch_labels = None +depends_on = None + + +def upgrade(): + op.add_column( + "products", + sa.Column( + "brand_keyword", + sa.String(64), + nullable=True, + comment="品牌词（客户输入，植入文案每条+生图特写图第2/6张）", + ), + ) + + +def downgrade(): + op.drop_column("products", "brand_keyword") diff --git a/backend/alembic/versions/009_benchmark_analyze_fields.py b/backend/alembic/versions/009_benchmark_analyze_fields.py new file mode 100644 index 0000000..dd9782d --- /dev/null +++ b/backend/alembic/versions/009_benchmark_analyze_fields.py @@ -0,0 +1,44 @@ +"""add features_json and analyze_status to benchmark_notes + +Revision ID: 009 +Revises: 008 +Create Date: 2026-06-13 + +第2环标杆分析字段： +- features_json: 存储8特征结构化分析结果（TEXT JSON） +- analyze_status: AI分析状态机 pending/analyzing/done/failed +""" +from alembic import op +import sqlalchemy as sa + +revision = "009" +down_revision = "008" +branch_labels = None +depends_on = None + + +def upgrade(): + op.add_column( + "benchmark_notes", + sa.Column( + "features_json", + sa.Text, + nullable=True, + comment="爆款8特征分析结果（JSON），第2环AI解析后写入", + ), + ) + op.add_column( + "benchmark_notes", + sa.Column( + "analyze_status", + sa.String(20), + nullable=False, + server_default="pending", + comment="AI分析状态: pending/analyzing/done/failed", + ), + ) + + +def downgrade(): + op.drop_column("benchmark_notes", "analyze_status") + op.drop_column("benchmark_notes", "features_json") diff --git a/backend/alembic/versions/010_fission_tasks_table.py b/backend/alembic/versions/010_fission_tasks_table.py new file mode 100644 index 0000000..c41a7fa --- /dev/null +++ b/backend/alembic/versions/010_fission_tasks_table.py @@ -0,0 +1,70 @@ +"""create fission_tasks table + +Revision ID: 010 +Revises: 009 +Create Date: 2026-06-13 + +第11环裂变引擎：1爆款→N套完整笔记包（非只换文案）。 +fission_tasks 是裂变任务主表，挂载在工作台内呈现。 +reference_level: low/mid/high（参考程度） +""" +from alembic import op +import sqlalchemy as sa + +revision = "010" +down_revision = "009" +branch_labels = None +depends_on = None + + +def upgrade(): + op.create_table( + "fission_tasks", + sa.Column("id", sa.BigInteger, primary_key=True, autoincrement=True), + sa.Column( + "workspace_id", + sa.BigInteger, + sa.ForeignKey("workspaces.id", ondelete="CASCADE"), + nullable=False, + comment="多租户隔离", + ), + sa.Column( + "source_note", + sa.Text, + nullable=True, + comment="爆款源笔记内容（文案+图描述，JSON存储）", + ), + sa.Column( + "reference_level", + sa.String(10), + nullable=False, + server_default="mid", + comment="参考程度: low/mid/high", + ), + sa.Column( + "fanout_count", + sa.Integer, + nullable=False, + server_default="3", + comment="裂变套数（默认3套）", + ), + sa.Column( + "status", + sa.String(20), + nullable=False, + server_default="pending", + comment="状态: pending/generating/done/failed", + ), + sa.Column( + "created_at", + sa.DateTime, + server_default=sa.func.now(), + nullable=False, + ), + ) + op.create_index("idx_fission_tasks_workspace_id", "fission_tasks", ["workspace_id"]) + + +def downgrade(): + op.drop_index("idx_fission_tasks_workspace_id", table_name="fission_tasks") + op.drop_table("fission_tasks") diff --git a/backend/alembic/versions/011_task_benchmark_fission_fields.py b/backend/alembic/versions/011_task_benchmark_fission_fields.py new file mode 100644 index 0000000..05bdf6e --- /dev/null +++ b/backend/alembic/versions/011_task_benchmark_fission_fields.py @@ -0,0 +1,43 @@ +"""add benchmark_ids and source_fission_id to generation_tasks + +Revision ID: 011 +Revises: 010 +Create Date: 2026-06-13 + +合并第2环S12+第11环两个需求： +- benchmark_ids: 关联标杆笔记ID列表（TEXT存JSON list，第2环标杆分析引用） +- source_fission_id: 裂变来源任务ID（NULL=普通任务，非NULL=裂变产出，第11环） +""" +from alembic import op +import sqlalchemy as sa + +revision = "011" +down_revision = "010" +branch_labels = None +depends_on = None + + +def upgrade(): + op.add_column( + "generation_tasks", + sa.Column( + "benchmark_ids", + sa.Text, + nullable=True, + comment="关联标杆笔记ID列表（JSON list），第2环标杆分析引用", + ), + ) + op.add_column( + "generation_tasks", + sa.Column( + "source_fission_id", + sa.Integer, + nullable=True, + comment="裂变来源fission_task ID；NULL=普通任务，非NULL=裂变产出（第11环）", + ), + ) + + +def downgrade(): + op.drop_column("generation_tasks", "source_fission_id") + op.drop_column("generation_tasks", "benchmark_ids") diff --git a/backend/alembic/versions/012_product_target_audience.py b/backend/alembic/versions/012_product_target_audience.py new file mode 100644 index 0000000..3edf992 --- /dev/null +++ b/backend/alembic/versions/012_product_target_audience.py @@ -0,0 +1,29 @@ +"""012 products 表加 target_audience 字段 + +Revision ID: 012_product_target_audience +Revises: 011_task_benchmark_fission_fields +Create Date: 2026-06-15 +""" +from alembic import op +import sqlalchemy as sa + +revision = "012" +down_revision = "011" +branch_labels = None +depends_on = None + + +def upgrade(): + op.add_column( + "products", + sa.Column( + "target_audience", + sa.String(128), + nullable=True, + comment="目标人群，客户输入，透传进文案/生图prompt", + ), + ) + + +def downgrade(): + op.drop_column("products", "target_audience") diff --git a/backend/app/__init__.py b/backend/app/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/app/api/__init__.py b/backend/app/api/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/app/api/v1/README.md b/backend/app/api/v1/README.md new file mode 100644 index 0000000..81a99e1 --- /dev/null +++ b/backend/app/api/v1/README.md @@ -0,0 +1,11 @@ +# app/api/v1/ + +路由层占位，BE agent 按模块创建： +- auth.py # 登录/me/切workspace +- api_keys.py # API Key录入/列表/删除 +- products.py # 产品档案CRUD +- benchmarks.py # 标杆笔记CRUD（挂在products下） +- tasks.py # 生产任务（发起/列表/详情/SSE/选候选） +- review.py # 组长审核（队列/通过/打回） +- delivery.py # 交付包生成+下载 +- banned_words.py # 违禁词库CRUD diff --git a/backend/app/api/v1/__init__.py b/backend/app/api/v1/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/app/api/v1/api_keys.py b/backend/app/api/v1/api_keys.py new file mode 100644 index 0000000..b509610 --- /dev/null +++ b/backend/app/api/v1/api_keys.py @@ -0,0 +1,130 @@ +""" +app/api/v1/api_keys.py — API Key 路由 +只录不显余额（CLAUDE.md 红线）。 +只返回 provider + key_last4，不返回 encrypted_key。 +url 字段不接收，token站固定自家站（架构方案§二）。 +""" + +import logging +from typing import Annotated + +from fastapi import APIRouter, Depends +from pydantic import BaseModel, field_validator +from sqlalchemy.orm import Session + +from app.core.database import get_db +from app.core.response import ok, raise_not_found +from app.core.security import encrypt_api_key, mask_api_key +from app.middleware.workspace_guard import CurrentUser, require_write_permission +from app.models.workspace import UserApiKey + +logger = logging.getLogger(__name__) +router = APIRouter(prefix="/api-keys", tags=["api-keys"]) + + +# ── DTO ──────────────────────────────────────────────────── +class CreateApiKeyRequest(BaseModel): + provider: str + api_key: str + + @field_validator("provider") + @classmethod + def provider_not_empty(cls, v: str) -> str: + if not v or not v.strip(): + raise ValueError("provider 不能为空") + return v.strip().lower() + + @field_validator("api_key") + @classmethod + def key_not_empty(cls, v: str) -> str: + if not v or len(v) < 8: + raise ValueError("api_key 无效") + return v + # 注：不接收 url 字段（token站固定自家站，基石B） + + +def _format_key(k: UserApiKey) -> dict: + """只显 provider + key_last4，不暴露余额/用量/encrypted_key（红线）。""" + return { + "id": k.id, + "provider": k.provider, + "key_last4": k.key_last4, + "created_at": k.created_at.isoformat(), + } + + +# ── 路由 ─────────────────────────────────────────────────── +@router.get("") +def list_api_keys( + current_user: Annotated[CurrentUser, Depends(require_write_permission)], + db: Session = Depends(get_db), +): + """列出当前用户在此 workspace 的 API Key（只显后4位）。""" + keys = ( + db.query(UserApiKey) + .filter( + UserApiKey.user_id == current_user.user_id, + UserApiKey.workspace_id == current_user.workspace_id, + ) + .all() + ) + return ok({"items": [_format_key(k) for k in keys]}) + + +@router.post("") +def create_api_key( + body: CreateApiKeyRequest, + current_user: Annotated[CurrentUser, Depends(require_write_permission)], + db: Session = Depends(get_db), +): + """录入 API Key（Fernet 加密存储，只保存后4位明文用于展示）。""" + from app.core.response import raise_business + # 检查同 user+workspace+provider 是否已有 + existing = ( + db.query(UserApiKey) + .filter( + UserApiKey.user_id == current_user.user_id, + UserApiKey.workspace_id == current_user.workspace_id, + UserApiKey.provider == body.provider, + ) + .first() + ) + if existing: + raise_business(f"已存在 {body.provider} 的 API Key，请先删除再录入") + + encrypted = encrypt_api_key(body.api_key) + key_obj = UserApiKey( + user_id=current_user.user_id, + workspace_id=current_user.workspace_id, + provider=body.provider, + encrypted_key=encrypted, + key_last4=mask_api_key(body.api_key), + ) + db.add(key_obj) + db.commit() + db.refresh(key_obj) + logger.info("API key created: user=%s provider=%s", current_user.user_id, body.provider) + return ok(_format_key(key_obj)) + + +@router.delete("/{key_id}") +def delete_api_key( + key_id: int, + current_user: Annotated[CurrentUser, Depends(require_write_permission)], + db: Session = Depends(get_db), +): + """删除 API Key（只能删自己的）。""" + key_obj = ( + db.query(UserApiKey) + .filter( + UserApiKey.id == key_id, + UserApiKey.user_id == current_user.user_id, + UserApiKey.workspace_id == current_user.workspace_id, + ) + .first() + ) + if not key_obj: + raise_not_found("API Key 不存在") + db.delete(key_obj) + db.commit() + return ok({"deleted": key_id}) diff --git a/backend/app/api/v1/auth.py b/backend/app/api/v1/auth.py new file mode 100644 index 0000000..eaf1014 --- /dev/null +++ b/backend/app/api/v1/auth.py @@ -0,0 +1,69 @@ +""" +app/api/v1/auth.py — 认证路由 +路由层只做：参数校验 → 调 service → 格式化响应（不含业务逻辑） +""" + +import logging +from typing import Annotated + +from fastapi import APIRouter, Depends +from pydantic import BaseModel, field_validator +from sqlalchemy.orm import Session + +from app.core.database import get_db +from app.core.response import ok, raise_unauthorized +from app.core.security import create_access_token, decode_access_token +from app.middleware.workspace_guard import CurrentUser, get_current_user +from app.models.user import User +from app.models.workspace import Workspace + +logger = logging.getLogger(__name__) +router = APIRouter(prefix="/auth", tags=["auth"]) + + +# ── DTO ──────────────────────────────────────────────────── +class LoginRequest(BaseModel): + username: str + password: str + + @field_validator("username", "password") + @classmethod + def not_empty(cls, v: str) -> str: + if not v or not v.strip(): + raise ValueError("不能为空") + return v + + +# ── 路由 ─────────────────────────────────────────────────── +@router.post("/login") +def login(body: LoginRequest, db: Session = Depends(get_db)): + """登录，返回 JWT。密码校验在 service 层（此处调用）。""" + from app.services.auth_service import authenticate_user, build_user_response + user, workspace_id, role = authenticate_user(db, body.username, body.password) + token = create_access_token(user.id, workspace_id, role) + return ok({ + "token": token, + "user": build_user_response(user, workspace_id, role), + }) + + +@router.get("/me") +def get_me( + current_user: Annotated[CurrentUser, Depends(get_current_user)], + db: Session = Depends(get_db), +): + """当前用户信息 + workspace + role。""" + user = db.query(User).filter(User.id == current_user.user_id).first() + if not user: + raise_unauthorized("用户不存在") + ws = db.query(Workspace).filter(Workspace.id == current_user.workspace_id).first() + return ok({ + "id": user.id, + "username": user.username, + "email": user.email, + "current_workspace_id": current_user.workspace_id, + "role": current_user.role, + "workspace": { + "id": ws.id, "name": ws.name, "slug": ws.slug, + } if ws else None, + }) diff --git a/backend/app/api/v1/benchmarks.py b/backend/app/api/v1/benchmarks.py new file mode 100644 index 0000000..b0b405b --- /dev/null +++ b/backend/app/api/v1/benchmarks.py @@ -0,0 +1,143 @@ +""" +app/api/v1/benchmarks.py — 标杆笔记 + 违禁词路由（管理员） +主通道：截图+手填亮点（不做原始抓取）。 +""" + +import logging +from typing import Annotated + +from fastapi import APIRouter, Depends +from pydantic import BaseModel +from sqlalchemy.orm import Session + +from app.core.database import get_db +from app.core.response import ok, paginate, raise_not_found +from app.middleware.workspace_guard import CurrentUser, require_admin, require_write_permission +from app.models.product import BannedWord, BenchmarkNote + +logger = logging.getLogger(__name__) +router = APIRouter(tags=["products"]) + + +class BenchmarkCreate(BaseModel): + screenshot_url: str | None = None + highlights: str | None = None # 手填亮点（主通道） + link_url: str | None = None # 可选，不做自动抓取 + + +class BannedWordCreate(BaseModel): + word: str + level: str # auto_fix | soft_warn | hard_block + replacement: str | None = None + updatable: bool = True + + +def _fmt_benchmark(b: BenchmarkNote) -> dict: + return { + "id": b.id, "product_id": b.product_id, + "screenshot_url": b.screenshot_url, + "highlights": b.highlights, "link_url": b.link_url, + "created_at": b.created_at.isoformat(), + } + + +def _fmt_banned(bw: BannedWord) -> dict: + return { + "id": bw.id, "word": bw.word, "level": bw.level, + "replacement": bw.replacement, "updatable": bw.updatable, + "workspace_id": bw.workspace_id, + } + + +# ── 标杆笔记 ──────────────────────────────────────────────── +@router.get("/products/{product_id}/benchmarks") +def list_benchmarks( + product_id: int, + current_user: Annotated[CurrentUser, Depends(require_write_permission)] = None, + db: Session = Depends(get_db), +): + items = ( + db.query(BenchmarkNote) + .filter( + BenchmarkNote.product_id == product_id, + BenchmarkNote.workspace_id == current_user.workspace_id, + ) + .all() + ) + return ok({"items": [_fmt_benchmark(b) for b in items]}) + + +@router.post("/products/{product_id}/benchmarks") +def create_benchmark( + product_id: int, body: BenchmarkCreate, + current_user: Annotated[CurrentUser, Depends(require_admin)] = None, + db: Session = Depends(get_db), +): + b = BenchmarkNote( + workspace_id=current_user.workspace_id, + product_id=product_id, + screenshot_url=body.screenshot_url, + highlights=body.highlights, + link_url=body.link_url, + ) + db.add(b); db.commit(); db.refresh(b) + return ok(_fmt_benchmark(b)) + + +# ── 违禁词库 ──────────────────────────────────────────────── +@router.get("/banned-words") +def list_banned_words( + page: int = 1, page_size: int = 50, + current_user: Annotated[CurrentUser, Depends(require_write_permission)] = None, + db: Session = Depends(get_db), +): + q = db.query(BannedWord).filter(BannedWord.workspace_id == current_user.workspace_id) + total = q.count() + items = q.offset((page - 1) * page_size).limit(page_size).all() + return ok(paginate([_fmt_banned(bw) for bw in items], total, page, page_size)) + + +@router.post("/banned-words") +def create_banned_word( + body: BannedWordCreate, + current_user: Annotated[CurrentUser, Depends(require_admin)] = None, + db: Session = Depends(get_db), +): + bw = BannedWord( + workspace_id=current_user.workspace_id, + word=body.word, level=body.level, + replacement=body.replacement, updatable=body.updatable, + ) + db.add(bw); db.commit(); db.refresh(bw) + return ok(_fmt_banned(bw)) + + +@router.put("/banned-words/{word_id}") +def update_banned_word( + word_id: int, body: BannedWordCreate, + current_user: Annotated[CurrentUser, Depends(require_admin)] = None, + db: Session = Depends(get_db), +): + bw = db.query(BannedWord).filter(BannedWord.id == word_id, BannedWord.workspace_id == current_user.workspace_id).first() + if not bw: + raise_not_found("违禁词不存在") + if not bw.updatable: + from app.core.response import raise_forbidden + raise_forbidden("该违禁词不可修改") + bw.word = body.word; bw.level = body.level + bw.replacement = body.replacement; bw.updatable = body.updatable + db.commit(); db.refresh(bw) + return ok(_fmt_banned(bw)) + + +@router.delete("/banned-words/{word_id}") +def delete_banned_word( + word_id: int, + current_user: Annotated[CurrentUser, Depends(require_admin)] = None, + db: Session = Depends(get_db), +): + bw = db.query(BannedWord).filter(BannedWord.id == word_id, BannedWord.workspace_id == current_user.workspace_id).first() + if not bw: + raise_not_found("违禁词不存在") + db.delete(bw); db.commit() + return ok({"deleted": word_id}) diff --git a/backend/app/api/v1/delivery.py b/backend/app/api/v1/delivery.py new file mode 100644 index 0000000..44a8763 --- /dev/null +++ b/backend/app/api/v1/delivery.py @@ -0,0 +1,184 @@ +""" +app/api/v1/delivery.py — 交付包路由 +POST /tasks/{id}/package — 生成达人素材交付包 +GET /delivery-packages/{id}/download — 下载（status: pending/ready/downloaded） +POST /delivery-packages/{id}/download-token — 签发60s一次性下载令牌（C1坑修复） +GET /delivery-packages/{id}/download-file?token= — 带令牌下载，前端可用 window.open +""" + +import logging +from typing import Annotated + +from fastapi import APIRouter, Depends, Header, Query +from fastapi.responses import FileResponse +from pydantic import BaseModel +from sqlalchemy.orm import Session + +from app.core.database import get_db +from app.core.response import ok, raise_not_found +from app.middleware.workspace_guard import CurrentUser, require_write_permission +from app.models.task import DeliveryPackage, GenerationTask + +logger = logging.getLogger(__name__) +router = APIRouter(tags=["delivery"]) + + +class PackageRequest(BaseModel): + note_ids: list[int] = [] # 可选，指定要打包的内容 + + +def _fmt_package(pkg: DeliveryPackage) -> dict: + return { + "id": pkg.id, + "status": pkg.status, + "download_url": pkg.download_url, + "expires_at": pkg.expires_at.isoformat() if pkg.expires_at else None, + } + + +@router.post("/tasks/{task_id}/package") +def create_package( + task_id: int, body: PackageRequest, + current_user: Annotated[CurrentUser, Depends(require_write_permission)] = None, + db: Session = Depends(get_db), +): + """ + 生成达人素材交付包。 + 任务推入 Celery build_delivery_package 队列（只传 package_id）。 + """ + task = db.query(GenerationTask).filter( + GenerationTask.id == task_id, + GenerationTask.workspace_id == current_user.workspace_id, + ).first() + if not task: + raise_not_found("任务不存在") + if task.status not in ("approved", "pending_selection"): + from app.core.response import raise_business + raise_business("任务尚未生成完成，无法打包") + + pkg = DeliveryPackage( + workspace_id=current_user.workspace_id, + task_id=task_id, + status="pending", + ) + db.add(pkg) + db.commit() + db.refresh(pkg) + + # 推 Celery 队列，只传 package_id（基石B思路：不传任何敏感信息） + from app.workers.tasks import build_delivery_package + build_delivery_package.delay(pkg.id) + + return ok({"delivery_package_id": pkg.id, "status": "pending"}) + + +@router.get("/delivery-packages/{package_id}/download") +def get_package_download( + package_id: int, + current_user: Annotated[CurrentUser, Depends(require_write_permission)] = None, + db: Session = Depends(get_db), +): + """下载交付包（status: pending/ready/downloaded）。""" + pkg = db.query(DeliveryPackage).filter( + DeliveryPackage.id == package_id, + DeliveryPackage.workspace_id == current_user.workspace_id, + ).first() + if not pkg: + raise_not_found("交付包不存在") + + if pkg.status == "ready" and pkg.download_url: + # 标记为 downloaded（只能下一次，防重复公开 URL） + pkg.status = "downloaded" + db.commit() + + return ok(_fmt_package(pkg)) + + +@router.post("/delivery-packages/{package_id}/download-token") +def create_download_token( + package_id: int, + current_user: Annotated[CurrentUser, Depends(require_write_permission)] = None, + db: Session = Depends(get_db), +): + """ + C1坑修复：签发一次性下载令牌（60s有效）。 + 前端先用 JWT 调此接口，再用 token 直接 window.open/fetch，无需在URL里传JWT。 + """ + import secrets + import redis as sync_redis + from app.core.config import get_settings + + pkg = db.query(DeliveryPackage).filter( + DeliveryPackage.id == package_id, + DeliveryPackage.workspace_id == current_user.workspace_id, + ).first() + if not pkg: + raise_not_found("交付包不存在") + if pkg.status not in ("ready", "downloaded"): + from app.core.response import raise_business + raise_business("交付包尚未准备好") + + token = secrets.token_hex(32) + r = sync_redis.from_url(get_settings().REDIS_URL, decode_responses=True) + r.setex(f"dl_token:{token}", 60, str(package_id)) + return ok({"token": token, "expires_in": 60}) + + +@router.get("/delivery-packages/{package_id}/download-file") +def download_package_file( + package_id: int, + token: str = Query(default=""), + authorization: str = Header(default=""), + db: Session = Depends(get_db), +): + """ + 直接下载交付包 zip 文件（FileResponse）。 + 支持两种认证： + 1. ?token=（前端 window.open 用，C1坑修复） + 2. Authorization: Bearer （API 调用用） + """ + import os + import redis as sync_redis + from app.core.config import get_settings + from app.core.security import decode_access_token + import jwt as pyjwt + + # token 认证（一次性，60s有效，跳过JWT） + if token: + r = sync_redis.from_url(get_settings().REDIS_URL, decode_responses=True) + stored = r.getdel(f"dl_token:{token}") + if not stored or int(stored) != package_id: + from app.core.response import raise_business + raise_business("下载令牌无效或已过期") + pkg = db.query(DeliveryPackage).filter(DeliveryPackage.id == package_id).first() + else: + # JWT 认证 + if not authorization or not authorization.startswith("Bearer "): + from app.core.response import raise_unauthorized + raise_unauthorized("缺少认证信息") + try: + payload = decode_access_token(authorization.split(" ", 1)[1]) + except (pyjwt.PyJWTError, Exception): + from app.core.response import raise_unauthorized + raise_unauthorized("Token 无效") + workspace_id = int(payload["current_workspace_id"]) + pkg = db.query(DeliveryPackage).filter( + DeliveryPackage.id == package_id, + DeliveryPackage.workspace_id == workspace_id, + ).first() + + if not pkg: + raise_not_found("交付包不存在") + if pkg.status not in ("ready", "downloaded"): + from app.core.response import raise_business + raise_business("交付包尚未准备好，请稍后重试") + if not pkg.package_path or not os.path.exists(pkg.package_path): + from app.core.response import raise_business + raise_business("交付包文件不存在，请重新打包") + + filename = os.path.basename(pkg.package_path) + return FileResponse( + path=pkg.package_path, + media_type="application/zip", + filename=filename, + ) diff --git a/backend/app/api/v1/products.py b/backend/app/api/v1/products.py new file mode 100644 index 0000000..29bb38d --- /dev/null +++ b/backend/app/api/v1/products.py @@ -0,0 +1,308 @@ +""" +app/api/v1/products.py — 品牌库路由（管理员） +products / benchmark_notes / banned_words +category 是纯数据字段，不在代码里做枚举（基石A）。 +""" + +import logging +from typing import Annotated + +from fastapi import APIRouter, Depends, UploadFile, File +from pydantic import BaseModel +from sqlalchemy.orm import Session + +from app.core.database import get_db +from app.core.response import ok, paginate, raise_not_found +from app.middleware.workspace_guard import CurrentUser, require_admin, require_write_permission +from app.models.product import BannedWord, BenchmarkNote, Product + +logger = logging.getLogger(__name__) +router = APIRouter(tags=["products"]) + + +# ── DTO ──────────────────────────────────────────────────── +class ProductCreate(BaseModel): + name: str + category: str | None = None # 纯数据字段，不做枚举（基石A） + source: str = "custom" # preset | custom + selling_points: list[str] = [] + style_tone: str | None = None + text_angles: list[str] = [] # 用户设定，不写死（基石A） + custom_prompt: str | None = None # 等北哥方案注入 + banned_word_ids: list[int] = [] + image_path: str | None = None # 产品参考图(可建档即带；通常走 upload-image 接口) + brand_keyword: str | None = None # 品牌词，客户录入，012:套2字段暴露 + target_audience: str | None = None # 目标人群，客户录入，012:套2字段暴露 + + +class BenchmarkCreate(BaseModel): + screenshot_url: str | None = None + highlights: str | None = None + link_url: str | None = None + + +class BannedWordCreate(BaseModel): + word: str + level: str # auto_fix | soft_warn | hard_block + replacement: str | None = None + updatable: bool = True + + +def _fmt_product(p: Product) -> dict: + import json + return { + "id": p.id, "name": p.name, "category": p.category, + "source": p.source, "is_active": p.is_active, + "selling_points": json.loads(p.selling_points) if p.selling_points else [], + "style_tone": p.style_tone, + "text_angles": json.loads(p.text_angles) if p.text_angles else [], + "custom_prompt": p.custom_prompt, + "image_path": p.image_path, + "brand_keyword": p.brand_keyword, # 012: 套2字段暴露 + "target_audience": p.target_audience, # 012: 套2字段暴露 + "created_at": p.created_at.isoformat(), + } + + +# ── 产品档案 ──────────────────────────────────────────────── +@router.get("/products") +def list_products( + page: int = 1, page_size: int = 20, source: str | None = None, + current_user: Annotated[CurrentUser, Depends(require_write_permission)] = None, + db: Session = Depends(get_db), +): + q = db.query(Product).filter(Product.workspace_id == current_user.workspace_id, Product.is_active == True) + if source in ("preset", "custom"): + q = q.filter(Product.source == source) + total = q.count() + items = q.offset((page - 1) * page_size).limit(page_size).all() + return ok(paginate([_fmt_product(p) for p in items], total, page, page_size)) + + +@router.post("/products") +def create_product( + body: ProductCreate, + current_user: Annotated[CurrentUser, Depends(require_admin)] = None, + db: Session = Depends(get_db), +): + import json + p = Product( + workspace_id=current_user.workspace_id, + name=body.name, category=body.category, source=body.source, + selling_points=json.dumps(body.selling_points, ensure_ascii=False), + style_tone=body.style_tone, + text_angles=json.dumps(body.text_angles, ensure_ascii=False), + custom_prompt=body.custom_prompt, + image_path=body.image_path or None, + brand_keyword=body.brand_keyword or None, # 012: 套2字段 + target_audience=body.target_audience or None, # 012: 套2字段 + ) + db.add(p) + db.commit() + db.refresh(p) + return ok(_fmt_product(p)) + + +@router.get("/products/{product_id}") +def get_product( + product_id: int, + current_user: Annotated[CurrentUser, Depends(require_write_permission)] = None, + db: Session = Depends(get_db), +): + p = db.query(Product).filter(Product.id == product_id, Product.workspace_id == current_user.workspace_id).first() + if not p: + raise_not_found("产品不存在") + return ok(_fmt_product(p)) + + +@router.put("/products/{product_id}") +def update_product( + product_id: int, body: ProductCreate, + current_user: Annotated[CurrentUser, Depends(require_admin)] = None, + db: Session = Depends(get_db), +): + import json + p = db.query(Product).filter(Product.id == product_id, Product.workspace_id == current_user.workspace_id).first() + if not p: + raise_not_found("产品不存在") + p.name = body.name; p.category = body.category; p.source = body.source + p.selling_points = json.dumps(body.selling_points, ensure_ascii=False) + p.style_tone = body.style_tone + p.text_angles = json.dumps(body.text_angles, ensure_ascii=False) + p.custom_prompt = body.custom_prompt + p.brand_keyword = body.brand_keyword or None # 012: 套2字段 + p.target_audience = body.target_audience or None # 012: 套2字段 + db.commit(); db.refresh(p) + return ok(_fmt_product(p)) + + +@router.delete("/products/{product_id}") +def delete_product( + product_id: int, + current_user: Annotated[CurrentUser, Depends(require_admin)] = None, + db: Session = Depends(get_db), +): + p = db.query(Product).filter(Product.id == product_id, Product.workspace_id == current_user.workspace_id).first() + if not p: + raise_not_found("产品不存在") + p.is_active = False # 软删 + db.commit() + return ok({"deleted": product_id}) + + +# ── 产品参考图上传 ────────────────────────────────────────── +_ALLOWED_CONTENT_TYPES = {"image/jpeg", "image/png", "image/webp"} +_MAX_SIZE_BYTES = 10 * 1024 * 1024 # 10 MB + + +@router.post("/products/{product_id}/upload-image") +async def upload_product_image( + product_id: int, + file: UploadFile = File(...), + current_user: Annotated[CurrentUser, Depends(require_write_permission)] = None, + db: Session = Depends(get_db), +): + """ + 上传产品参考图（铁律：生图必须带产品图）。 + 文件类型：JPEG/PNG/WebP；大小上限 10 MB。 + 存储路径：uploads/products/{workspace_id}/{product_id}/{filename} + 写回 product.image_path，生图管道读此字段构建 reference_images。 + """ + from app.core.response import raise_business + from app.core.config import get_settings + import os, uuid + + p = db.query(Product).filter( + Product.id == product_id, + Product.workspace_id == current_user.workspace_id, + ).first() + if not p: + raise_not_found("产品不存在") + + if file.content_type not in _ALLOWED_CONTENT_TYPES: + raise_business(f"不支持的文件类型 {file.content_type}，仅支持 JPEG/PNG/WebP") + + data = await file.read() + if len(data) > _MAX_SIZE_BYTES: + raise_business("文件超过 10 MB 限制") + + settings = get_settings() + ext = os.path.splitext(file.filename or "img.jpg")[1] or ".jpg" + # 存绝对路径：锚定 StaticFiles 根 /app/uploads，避免 worker(cwd=/) 读不到。 + abs_dir = os.path.join(settings.UPLOAD_ABS_ROOT, "products", + str(current_user.workspace_id), str(product_id)) + os.makedirs(abs_dir, exist_ok=True) + filename = f"{uuid.uuid4().hex}{ext}" + save_path = os.path.join(abs_dir, filename) + with open(save_path, "wb") as f: + f.write(data) + + p.image_path = save_path # 绝对路径，worker 直接 open 可读 + db.commit() + db.refresh(p) + logger.info("product image uploaded: product_id=%s path=%s", product_id, save_path) + return ok(_fmt_product(p)) + + +# ── 套1 产品图视觉分析 ──────────────────────────────────────── +_VISION_PROMPT = """你是小红书产品种草策划。请根据产品图分析卖点与人群。 +硬性规则：只分析本次上传图片，不沿用历史案例，不默认护肤品。 +返回JSON（仅JSON，无其他文字）： +{ + "productName": "从包装识别，识别不到填空", + "category": "美妆护肤/个护护理/食品饮品/营养健康/家居生活/服饰穿搭/电商产品之一", + "sellingPoints": ["转成用户买点，不要品牌空话，3-6个"], + "targetAudience": "一句话描述核心人群", + "scenarios": ["使用场景，2-4个"], + "keywords": ["小红书搜索关键词，3-5个"], + "bannedWords": ["合规禁用词"], + "imageDirection": "这组图如何拍/排版，一句话", + "confidence": 0.9, + "source": "vision" +}""" + + +def _parse_vision_json(raw: str) -> dict: + """从模型返回文本中提取JSON（容错 markdown ```json 代码块）""" + import json, re + # 去掉 markdown 代码块包裹 + cleaned = re.sub(r"```json\s*", "", raw) + cleaned = re.sub(r"```\s*", "", cleaned) + # 提取第一个 {...} 块 + m = re.search(r"\{[\s\S]*\}", cleaned) + if not m: + raise ValueError("vision 返回内容中未找到 JSON") + return json.loads(m.group()) + + +def _fallback_analysis(product_name: str = "") -> dict: + """vision 失败时的文字兜底，保证接口不空手返回""" + return { + "productName": product_name or "产品", + "category": "电商产品", + "sellingPoints": ["使用方便", "适合日常场景"], + "targetAudience": "有明确使用场景和效率诉求的人群", + "scenarios": ["日常使用", "通勤出门"], + "keywords": [product_name or "好物", "真实测评", "种草分享"], + "bannedWords": ["美白", "祛斑", "速效", "医用", "药妆"], + "imageDirection": "产品白底图保证准确，真实场景图突出使用体验", + "confidence": 0.45, + "source": "fallback", + } + + +@router.post("/products/{product_id}/analyze-image") +async def analyze_product_image( + product_id: int, + file: UploadFile = File(...), + current_user: Annotated[CurrentUser, Depends(require_write_permission)] = None, + db: Session = Depends(get_db), +): + """ + 套1：上传产品图 → GPT vision 读图 → 返回结构化卖点/人群分析。 + key 链路：从当前用户 API key 解密（基石B）；plain_key 只活在局部变量。 + """ + from app.core.response import raise_business + from app.models.workspace import UserApiKey + from app.utils.fernet_utils import decrypt_key + from app.services.ai_engine.gemini_factory import build_ai_clients + + # 文件校验（复用 upload_product_image 同款规则） + if file.content_type not in _ALLOWED_CONTENT_TYPES: + raise_business(f"不支持的文件类型 {file.content_type}，仅支持 JPEG/PNG/WebP") + data = await file.read() + if len(data) > _MAX_SIZE_BYTES: + raise_business("文件超过 10 MB 限制") + + # key 解密（照抄 pipeline_steps.decrypt_user_key，基石B） + api_key_row = db.query(UserApiKey).filter( + UserApiKey.user_id == current_user.user_id, + UserApiKey.workspace_id == current_user.workspace_id, + UserApiKey.provider.in_(["openai", "apiports"]), + ).first() + if not api_key_row: + raise_business("未配置 API Key，请先在设置中录入") + plain_key = decrypt_key(api_key_row.encrypted_key) + + clients = build_ai_clients(plain_key) + plain_key = None # 立即清零，不传出（基石B） + + try: + raw = await clients.gpt_vision_analyze(_VISION_PROMPT, [data]) + try: + result = _parse_vision_json(raw) + result["source"] = "vision" + except Exception as parse_err: + logger.warning("vision JSON parse failed, fallback: %s", parse_err) + result = _fallback_analysis() + result["warning"] = f"视觉分析解析失败，已使用文字兜底：{parse_err}" + except Exception as e: + logger.warning("vision_analyze failed, fallback: %s", e) + result = _fallback_analysis() + result["warning"] = f"视觉分析失败，已使用文字兜底：{e}" + finally: + await clients.aclose() + + logger.info("analyze_product_image done: product_id=%s source=%s conf=%.2f", + product_id, result.get("source"), result.get("confidence", 0)) + return ok(result) diff --git a/backend/app/api/v1/review.py b/backend/app/api/v1/review.py new file mode 100644 index 0000000..27b826a --- /dev/null +++ b/backend/app/api/v1/review.py @@ -0,0 +1,140 @@ +""" +app/api/v1/review.py — 审核路由（组长） +通过(+5，最重) / 打回(写原因，-3，回 pending_selection) +打回原因存 preference_events.reason，不做 AI 归纳。 +""" + +import logging +from datetime import datetime, timezone +from typing import Annotated + +from fastapi import APIRouter, Depends +from pydantic import BaseModel +from sqlalchemy.orm import Session + +from app.core.database import get_db +from app.core.response import ok, paginate, raise_not_found, raise_state_invalid +from app.middleware.workspace_guard import CurrentUser, require_supervisor_or_above +from app.models.task import GenerationTask, ImageCandidate, TextCandidate +from app.models.user import User + +logger = logging.getLogger(__name__) +router = APIRouter(prefix="/review", tags=["review"]) + + +class RejectRequest(BaseModel): + reason: str # 原文存入 preference_events，不做 AI 归纳（契约§3） + + +@router.get("/queue") +def review_queue( + page: int = 1, page_size: int = 20, + current_user: Annotated[CurrentUser, Depends(require_supervisor_or_above)] = None, + db: Session = Depends(get_db), +): + """待审队列（pending_review 状态）。""" + q = ( + db.query(GenerationTask) + .filter( + GenerationTask.workspace_id == current_user.workspace_id, + GenerationTask.status == "pending_review", + ) + .order_by(GenerationTask.updated_at.asc()) + ) + total = q.count() + tasks = q.offset((page - 1) * page_size).limit(page_size).all() + items = [_fmt_queue_item(t, db) for t in tasks] + return ok(paginate(items, total, page, page_size)) + + +def _fmt_queue_item(t: GenerationTask, db: Session) -> dict: + import json + from app.models.product import Product + product = db.query(Product).filter(Product.id == t.product_id).first() + operator = db.query(User).filter(User.id == t.operator_id).first() + selected_text = db.query(TextCandidate).filter( + TextCandidate.task_id == t.id, TextCandidate.is_selected == True + ).first() + selected_image = db.query(ImageCandidate).filter( + ImageCandidate.task_id == t.id, ImageCandidate.is_selected == True + ).first() + + # content 列存 JSON dict，解包取 title/content 分字段返回 + text_parsed: dict = {} + if selected_text and selected_text.content: + try: + text_parsed = json.loads(selected_text.content) + except (json.JSONDecodeError, ValueError): + text_parsed = {"content": selected_text.content} + + return { + "task_id": t.id, + "product_name": product.name if product else None, + "theme": t.theme, + "submitted_at": t.updated_at.isoformat(), + "operator_name": operator.username if operator else None, + "selected_text": { + "candidate_id": selected_text.id, + "angle_label": selected_text.angle_label, + "title": text_parsed.get("title", ""), + "content": text_parsed.get("content", ""), # 纯正文 + "tags": text_parsed.get("tags", []), + } if selected_text else None, + "selected_image": { + "candidate_id": selected_image.id, + "strategy": selected_image.strategy, + "url": selected_image.url, + } if selected_image else None, + } + + +@router.post("/{task_id}/approve") +def approve_task( + task_id: int, + current_user: Annotated[CurrentUser, Depends(require_supervisor_or_above)] = None, + db: Session = Depends(get_db), +): + """通过（飞轮 +5，最重，状态 → approved）。""" + from app.services.flywheel_service import record_signal + task = db.query(GenerationTask).filter( + GenerationTask.id == task_id, + GenerationTask.workspace_id == current_user.workspace_id, + ).first() + if not task: + raise_not_found("任务不存在") + if task.status != "pending_review": + raise_state_invalid("只有 pending_review 状态可审核") + task.status = "approved" + task.review_status = "approved" + task.reviewer_id = current_user.user_id + task.reviewed_at = datetime.now(timezone.utc) + task.approved_at = datetime.now(timezone.utc) + db.commit() + record_signal(db, current_user, task, "approve") + return ok({"task_id": task_id, "status": "approved"}) + + +@router.post("/{task_id}/reject") +def reject_task( + task_id: int, body: RejectRequest, + current_user: Annotated[CurrentUser, Depends(require_supervisor_or_above)] = None, + db: Session = Depends(get_db), +): + """打回（飞轮 -3，回 pending_selection，原因下次注入 prompt）。""" + from app.services.flywheel_service import record_signal + task = db.query(GenerationTask).filter( + GenerationTask.id == task_id, + GenerationTask.workspace_id == current_user.workspace_id, + ).first() + if not task: + raise_not_found("任务不存在") + if task.status != "pending_review": + raise_state_invalid("只有 pending_review 状态可打回") + task.status = "pending_selection" # 打回回 pending_selection + task.review_status = "rejected" + task.reviewer_id = current_user.user_id + task.reviewed_at = datetime.now(timezone.utc) + task.reject_reason = body.reason + db.commit() + record_signal(db, current_user, task, "reject_with_reason", reason=body.reason) + return ok({"task_id": task_id, "status": "pending_selection"}) diff --git a/backend/app/api/v1/stream.py b/backend/app/api/v1/stream.py new file mode 100644 index 0000000..4c0a82e --- /dev/null +++ b/backend/app/api/v1/stream.py @@ -0,0 +1,97 @@ +""" +app/api/v1/stream.py — SSE ticket 签发 + SSE 流路由 +安全红线（倩倩姐2026-06-08拍板）：SSE 认证用一次性短票 ticket，禁止 ?token= 直传 JWT。 + +流程： + 1. FE 带 JWT 调 POST /tasks/{id}/sse-ticket → 拿 ticket（60s有效，一次性） + 2. FE 用 ticket 建 EventSource: GET /tasks/{id}/stream?ticket= + 3. BE 验 ticket（原子 getdel），用后即失效，换不了其他接口 +""" + +import logging +from typing import Annotated + +import redis as sync_redis +import redis.asyncio as aioredis +from fastapi import APIRouter, Depends, Query +from fastapi.responses import StreamingResponse +from sqlalchemy.orm import Session + +from app.core.config import get_settings +from app.core.database import get_db +from app.core.response import ok, raise_not_found, raise_unauthorized +from app.core.sse_ticket import consume_ticket, issue_ticket +from app.middleware.workspace_guard import CurrentUser, get_current_user +from app.models.task import GenerationTask +from app.utils.sse_utils import stream_events + +logger = logging.getLogger(__name__) +router = APIRouter() +settings = get_settings() + + +# ── 签发接口（用 JWT 换 ticket）──────────────────────────── +@router.post("/tasks/{task_id}/sse-ticket") +def create_sse_ticket( + task_id: int, + current_user: Annotated[CurrentUser, Depends(get_current_user)], + db: Session = Depends(get_db), +): + """ + 签发一次性 SSE ticket（60s 有效，仅对该 task 有效）。 + FE 拿到 ticket 后立即建 EventSource，不要存起来复用。 + """ + task = ( + db.query(GenerationTask) + .filter( + GenerationTask.id == task_id, + GenerationTask.workspace_id == current_user.workspace_id, + ) + .first() + ) + if not task: + raise_not_found("任务不存在") + + r = sync_redis.from_url(settings.REDIS_URL, decode_responses=True) + ticket = issue_ticket(r, task_id, current_user.workspace_id) + return ok({"ticket": ticket, "expires_in": 60}) + + +# ── SSE 流（ticket 认证，绝不传 JWT）───────────────────── +@router.get("/tasks/{task_id}/stream") +async def task_stream( + task_id: int, + ticket: str = Query(...), + last_seq: int = Query(default=0), +): + """ + SSE 实时进度推送。 + ticket 验证：原子 getdel，用后即失效，无法重放、无法访问其他接口。 + 支持断线重连补发（?last_seq=<上次收到的seq>）。 + 断线重连时 FE 须重新调 sse-ticket 换新 ticket。 + """ + r_sync = sync_redis.from_url(settings.REDIS_URL, decode_responses=True) + result = consume_ticket(r_sync, ticket) + if not result: + raise_unauthorized("ticket 无效或已过期") + + verified_task_id, workspace_id = result + if verified_task_id != task_id: + raise_unauthorized("ticket 与任务不匹配") + + redis_async = aioredis.from_url(settings.REDIS_URL, decode_responses=True) + + return StreamingResponse( + stream_events( + redis_client=redis_async, + task_id=task_id, + workspace_id=workspace_id, + last_seq=last_seq, + ), + media_type="text/event-stream", + headers={ + "Cache-Control": "no-cache", + "X-Accel-Buffering": "no", + "Connection": "keep-alive", + }, + ) diff --git a/backend/app/api/v1/task_actions.py b/backend/app/api/v1/task_actions.py new file mode 100644 index 0000000..d692999 --- /dev/null +++ b/backend/app/api/v1/task_actions.py @@ -0,0 +1,146 @@ +""" +app/api/v1/task_actions.py — 任务操作端点 +选文案 / 选图 / 导入文案 / 重新生成 / 提交审核 / 偏好上下文 +DTOs 和辅助函数从 tasks.py 导入。 +""" + +import logging +from typing import Annotated + +from fastapi import APIRouter, Depends +from sqlalchemy.orm import Session + +from app.core.database import get_db +from app.core.response import ok, raise_not_found, raise_state_invalid +from app.middleware.workspace_guard import CurrentUser, require_write_permission +from app.models.task import GenerationTask, ImageCandidate, TextCandidate + +from app.api.v1.tasks import ( + SelectCandidateRequest, + ImportTextRequest, + _fmt_text, + _fmt_image, + _check_task_ownership, +) + +logger = logging.getLogger(__name__) +router = APIRouter(prefix="/tasks", tags=["tasks"]) + + +@router.post("/{task_id}/text-candidates/select") +def select_text( + task_id: int, body: SelectCandidateRequest, + current_user: Annotated[CurrentUser, Depends(require_write_permission)] = None, + db: Session = Depends(get_db), +): + """选文案（飞轮信号 text_select +3）。""" + from app.services.flywheel_service import record_signal + task = _check_task_ownership( + db.query(GenerationTask).filter(GenerationTask.id == task_id).first(), + current_user.workspace_id, + ) + tc = db.query(TextCandidate).filter( + TextCandidate.id == body.candidate_id, TextCandidate.task_id == task_id + ).first() + if not tc: + raise_not_found("文案候选不存在") + tc.is_selected = True + db.commit() + record_signal(db, current_user, task, "text_select", candidate_id=tc.id, angle_label=tc.angle_label) + return ok({"selected": body.candidate_id}) + + +@router.post("/{task_id}/image-candidates/select") +def select_image( + task_id: int, body: SelectCandidateRequest, + current_user: Annotated[CurrentUser, Depends(require_write_permission)] = None, + db: Session = Depends(get_db), +): + """选图（飞轮信号 image_select +3）。""" + from app.services.flywheel_service import record_signal + task = _check_task_ownership( + db.query(GenerationTask).filter(GenerationTask.id == task_id).first(), + current_user.workspace_id, + ) + ic = db.query(ImageCandidate).filter( + ImageCandidate.id == body.candidate_id, ImageCandidate.task_id == task_id + ).first() + if not ic: + raise_not_found("图片候选不存在") + ic.is_selected = True + db.commit() + record_signal(db, current_user, task, "image_select", candidate_id=ic.id) + return ok({"selected": body.candidate_id}) + + +@router.post("/{task_id}/import-text") +def import_text( + task_id: int, body: ImportTextRequest, + current_user: Annotated[CurrentUser, Depends(require_write_permission)] = None, + db: Session = Depends(get_db), +): + """轨B：导入外部文案直接进候选池，跳过 AI 生成（洞2）。""" + _check_task_ownership( + db.query(GenerationTask).filter(GenerationTask.id == task_id).first(), + current_user.workspace_id, + ) + tc = TextCandidate( + workspace_id=current_user.workspace_id, task_id=task_id, + source="import", content=body.content, angle_label=body.angle_label, + ) + db.add(tc) + db.commit() + db.refresh(tc) + return ok(_fmt_text(tc)) + + +@router.post("/{task_id}/regenerate") +def regenerate( + task_id: int, + current_user: Annotated[CurrentUser, Depends(require_write_permission)] = None, + db: Session = Depends(get_db), +): + """重新生成（飞轮信号 regenerate -1）。""" + from app.services.flywheel_service import record_signal + from app.services.task_service import enqueue_generation + task = _check_task_ownership( + db.query(GenerationTask).filter(GenerationTask.id == task_id).first(), + current_user.workspace_id, + ) + record_signal(db, current_user, task, "regenerate") + enqueue_generation(task.id) + return ok({"task_id": task_id, "status": "regenerating"}) + + +@router.post("/{task_id}/submit-review") +def submit_review( + task_id: int, + current_user: Annotated[CurrentUser, Depends(require_write_permission)] = None, + db: Session = Depends(get_db), +): + """提交审核（状态 pending_selection → pending_review）。""" + task = _check_task_ownership( + db.query(GenerationTask).filter(GenerationTask.id == task_id).first(), + current_user.workspace_id, + ) + if task.status != "pending_selection": + raise_state_invalid("只有 pending_selection 状态可提审") + task.status = "pending_review" + db.commit() + return ok({"task_id": task_id, "status": "pending_review"}) + + +@router.get("/{task_id}/preference/context") +def get_preference_context( + task_id: int, + current_user: Annotated[CurrentUser, Depends(require_write_permission)] = None, + db: Session = Depends(get_db), +): + """取本任务飞轮偏好上下文（最近选中样本+打回原因）。""" + task = _check_task_ownership( + db.query(GenerationTask).filter(GenerationTask.id == task_id).first(), + current_user.workspace_id, + ) + from app.services.flywheel_service import get_preference_context + ctx = get_preference_context(db, current_user.workspace_id, task.product_id) + return ok(ctx) diff --git a/backend/app/api/v1/tasks.py b/backend/app/api/v1/tasks.py new file mode 100644 index 0000000..f014632 --- /dev/null +++ b/backend/app/api/v1/tasks.py @@ -0,0 +1,191 @@ +""" +app/api/v1/tasks.py — 任务路由（查询层） +发起任务 / 任务列表 / 任务详情 +操作类端点（选文案/选图/导入/重生成/提审/偏好上下文）在 task_actions.py。 +""" + +import logging +from typing import Annotated + +from fastapi import APIRouter, Depends, Query +from pydantic import BaseModel, field_validator +from sqlalchemy.orm import Session + +from app.core.database import get_db +from app.core.response import ok, paginate, raise_not_found +from app.middleware.workspace_guard import CurrentUser, require_write_permission +from app.models.task import GenerationTask, ImageCandidate, TextCandidate + +logger = logging.getLogger(__name__) +router = APIRouter(prefix="/tasks", tags=["tasks"]) + + +# ── DTO ──────────────────────────────────────────────────── +class CreateTaskRequest(BaseModel): + product_id: int + benchmark_ids: list[int] = [] + theme: str | None = None + text_count: int = 5 # 不写死（基石A），用户设定 + image_count: int = 3 # 不写死（基石A），用户设定 + track: str = "ai" # ai | import + need_product_image: bool = True # 本次产品是否入镜：True=无图禁生成不降级 + + @field_validator("text_count", "image_count") + @classmethod + def positive(cls, v: int) -> int: + if v < 1 or v > 20: + raise ValueError("数量范围 1-20") + return v + + @field_validator("track") + @classmethod + def valid_track(cls, v: str) -> str: + if v not in ("ai", "import"): + raise ValueError("track 必须是 ai 或 import") + return v + + +class SelectCandidateRequest(BaseModel): + candidate_id: int + + +class ImportTextRequest(BaseModel): + content: str + angle_label: str | None = None + + +# ── 格式化辅助 ────────────────────────────────────────────── +def _fmt_task(t: GenerationTask) -> dict: + return { + "id": t.id, "product_id": t.product_id, "theme": t.theme, + "status": t.status, "text_count": t.text_count, "image_count": t.image_count, + "track": t.track, "need_product_image": t.need_product_image, + "created_at": t.created_at.isoformat(), + } + + +def _score_array_to_obj(arr) -> dict: + """score_json 是评分明细数组 [{item,score,max,note}]。 + AI评委7维(2026-06-15新版：痛点18/情绪18/买点18/钩子15/标题13/真实感13+合规5) + 或旧机械5维均可处理。 + total 直接对明细求和(不依赖固定key)，透传 dims 给前端 ScoreDimBars 数据驱动渲染。 + 同时填旧5维key保持兼容(能映射的填，映射不上的为0)。""" + # 旧5维 + 新7维维度名 → 旧key 的映射表（兼容新旧两套维度名） + legacy = { + # 旧机械维度 + "标题吸引力": "title", "标题点击力": "title", + "情绪共鸣": "emotion", "情绪张力": "emotion", + "买点表达": "selling", "买点转化": "selling", + "关键词覆盖": "keyword", "合规性": "compliance", + # 新AI评委维度（2026-06-15）→ 最近似旧key + "痛点人群精准": "keyword", # 痛点人群精准语义最接近旧关键词(均为"内容精准度") + "开头钩子": "emotion", # 钩子即情绪抓点，归入 emotion + "真实感": "selling", # 真实感/买点转化同属"用户感知"维度 + # "产品聚焦一件事" 已被"真实感"替换，保留映射避免旧存量数据报 KeyError + "产品聚焦一件事": "selling", + } + obj = {"title": 0, "emotion": 0, "selling": 0, "keyword": 0, "compliance": 0, + "total": 0, "dims": []} + if isinstance(arr, list): + total = 0 + for d in arr: + if not isinstance(d, dict): + continue + item = d.get("item", "") + sc = d.get("score", 0) or 0 + total += sc + obj["dims"].append({"item": item, "score": sc, + "max": d.get("max", 0), "note": d.get("note", "")}) + key = legacy.get(item) + if key: + obj[key] = sc + obj["total"] = max(0, min(100, total)) + return obj + + +def _fmt_text(tc: TextCandidate) -> dict: + import json + # content 列存整条 JSON dict（含 title/content/tags/angle 等），解包后分字段返回 + parsed: dict = {} + if tc.content: + try: + parsed = json.loads(tc.content) + except (json.JSONDecodeError, ValueError): + # 兼容旧数据：如果 content 已经是纯正文则原样返回 + parsed = {"content": tc.content} + score_raw = json.loads(tc.score_json) if tc.score_json else None + return { + "candidate_id": tc.id, + "angle_label": tc.angle_label, + "title": parsed.get("title", ""), + "content": parsed.get("content", ""), # 纯正文，前端直接展示 + "tags": parsed.get("tags", []), + "cover_title": parsed.get("coverTitle", ""), + "image_brief": parsed.get("imageBrief", ""), + "source": tc.source, + "score": _score_array_to_obj(score_raw), # 转成 {title,emotion,...,total,dims} 对象 + "banned_word_status": tc.banned_word_status, + "is_selected": tc.is_selected, + # AI 评委总评（verdict/summary 存在 content 列，新数据有，旧数据为空字符串降级） + "verdict": parsed.get("verdict", ""), # "优秀"|"合格"|"不合格" + "summary": parsed.get("summary", ""), # 一句话总评含改进点 + } + + +def _fmt_image(ic: ImageCandidate) -> dict: + return { + "candidate_id": ic.id, "role": ic.role, "url": ic.url, + "strategy": ic.strategy, "seq": ic.seq, "is_selected": ic.is_selected, + } + + +def _check_task_ownership(task: GenerationTask | None, workspace_id: int) -> GenerationTask: + if not task or task.workspace_id != workspace_id: + raise_not_found("任务不存在") + return task + + +# ── 路由 ─────────────────────────────────────────────────── +@router.post("") +def create_task( + body: CreateTaskRequest, + current_user: Annotated[CurrentUser, Depends(require_write_permission)], + db: Session = Depends(get_db), +): + """发起任务：校验有无 key → 只推 task_id 入队，绝不传 key。""" + from app.services.task_service import create_generation_task + task = create_generation_task(db, current_user, body) + return ok(_fmt_task(task)) + + +@router.get("") +def list_tasks( + page: int = 1, page_size: int = 20, + status: list[str] | None = Query(default=None), + current_user: Annotated[CurrentUser, Depends(require_write_permission)] = None, + db: Session = Depends(get_db), +): + q = db.query(GenerationTask).filter(GenerationTask.workspace_id == current_user.workspace_id) + if status: + # 支持多状态(?status=approved&status=rejected)，单值也兼容 + q = q.filter(GenerationTask.status.in_(status)) + total = q.count() + items = q.order_by(GenerationTask.created_at.desc()).offset((page - 1) * page_size).limit(page_size).all() + return ok(paginate([_fmt_task(t) for t in items], total, page, page_size)) + + +@router.get("/{task_id}") +def get_task( + task_id: int, + current_user: Annotated[CurrentUser, Depends(require_write_permission)] = None, + db: Session = Depends(get_db), +): + task = db.query(GenerationTask).filter(GenerationTask.id == task_id).first() + task = _check_task_ownership(task, current_user.workspace_id) + texts = db.query(TextCandidate).filter(TextCandidate.task_id == task_id).all() + images = db.query(ImageCandidate).filter(ImageCandidate.task_id == task_id).all() + return ok({ + **_fmt_task(task), + "text_candidates": [_fmt_text(tc) for tc in texts], + "image_candidates": [_fmt_image(ic) for ic in images], + }) diff --git a/backend/app/api/v1/workspaces.py b/backend/app/api/v1/workspaces.py new file mode 100644 index 0000000..02b2bbe --- /dev/null +++ b/backend/app/api/v1/workspaces.py @@ -0,0 +1,43 @@ +""" +app/api/v1/workspaces.py — workspace 切换路由 +POST /workspaces/switch → /api/v1/workspaces/switch（契约路径） +从 auth.py 独立出来，避免 /auth 前缀错误。 +""" + +from typing import Annotated + +from fastapi import APIRouter, Depends +from pydantic import BaseModel +from sqlalchemy.orm import Session + +from app.core.security import create_access_token +from app.core.database import get_db +from app.core.response import ok, raise_forbidden +from app.middleware.workspace_guard import CurrentUser, get_current_user +from app.models.workspace import WorkspaceMember + +router = APIRouter(tags=["workspaces"]) + + +class SwitchWorkspaceRequest(BaseModel): + workspace_id: int + + +@router.post("/workspaces/switch") +def switch_workspace( + body: SwitchWorkspaceRequest, + current_user: Annotated[CurrentUser, Depends(get_current_user)], + db: Session = Depends(get_db), +): + """切换当前 workspace（必须查 membership 校验）。""" + member = db.query(WorkspaceMember).filter( + WorkspaceMember.user_id == current_user.user_id, + WorkspaceMember.workspace_id == body.workspace_id, + ).first() + if not member: + raise_forbidden("无权访问目标 workspace") + token = create_access_token(current_user.user_id, body.workspace_id, member.role) + return ok({ + "current_workspace_id": body.workspace_id, + "token": token, + }) diff --git a/backend/app/constants/README.md b/backend/app/constants/README.md new file mode 100644 index 0000000..e2800c5 --- /dev/null +++ b/backend/app/constants/README.md @@ -0,0 +1,8 @@ +# app/constants/ + +中心常量文件，消除三套命名打架（架构方案§二决策）： +- strategies.py # 图片策略命名：对外API用A/B/C，内部method用minimal_edit系 +- signal_weights.py # 飞轮信号默认权重（可调，不写死） +- status.py # 任务状态机枚举 +- roles.py # 角色枚举：admin/supervisor/operator +- providers.py # AI提供商枚举（开放接口，不硬编码） diff --git a/backend/app/constants/__init__.py b/backend/app/constants/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/app/constants/enums.py b/backend/app/constants/enums.py new file mode 100644 index 0000000..1591215 --- /dev/null +++ b/backend/app/constants/enums.py @@ -0,0 +1,132 @@ +""" +constants/enums.py — Clover 命名中心 +DB枚举约束在此定义，消除三套命名打架。 +业务参数（品类/数量/角度）不在此定义（基石A）。 +""" + +from enum import Enum + + +# ── 用户角色 ──────────────────────────────────────────── +class UserRole(str, Enum): + ADMIN = "admin" + SUPERVISOR = "supervisor" + OPERATOR = "operator" + + +# ── 任务状态机 ────────────────────────────────────────── +class TaskStatus(str, Enum): + PENDING = "pending" + GENERATING = "generating" + PENDING_SELECTION = "pending_selection" + PENDING_REVIEW = "pending_review" + APPROVED = "approved" + REJECTED = "rejected" + ARCHIVED = "archived" + + +# ── 审核状态（generation_tasks 平铺字段）────────────────── +class ReviewStatus(str, Enum): + PENDING = "pending" + APPROVED = "approved" + REJECTED = "rejected" + + +# ── 文案候选来源 ─────────────────────────────────────── +class CandidateSource(str, Enum): + AI = "ai" + IMPORT = "import" + + +# ── 违禁词等级 ──────────────────────────────────────── +class BannedWordLevel(str, Enum): + AUTO_FIX = "auto_fix" + SOFT_WARN = "soft_warn" + HARD_BLOCK = "hard_block" + + +# ── 违禁词扫描结果 ─────────────────────────────────── +class BannedWordStatus(str, Enum): + PASS = "pass" + AUTO_FIXED = "auto_fixed" + SOFT_WARN = "soft_warn" + HARD_BLOCK = "hard_block" + + +# ── 飞轮信号类型 ───────────────────────────────────── +class SignalType(str, Enum): + TEXT_SELECT = "text_select" + IMAGE_SELECT = "image_select" + APPROVE = "approve" + REJECT_WITH_REASON = "reject_with_reason" + REGENERATE = "regenerate" + + +# ── 飞轮信号权重默认值（北哥可校准）───────────────────── +SIGNAL_WEIGHTS: dict[str, int] = { + SignalType.TEXT_SELECT: 3, + SignalType.IMAGE_SELECT: 3, + SignalType.APPROVE: 5, + SignalType.REJECT_WITH_REASON: -3, + SignalType.REGENERATE: -1, +} + + +# ── 数据归属 ────────────────────────────────────────── +class DataOwnership(str, Enum): + CLIENT_DATA = "client_data" # 原始输入产出，客户可导出 + PLATFORM_ASSET = "platform_asset" # 飞轮蒸馏成果，归平台 + + +# ── 交付包状态 ──────────────────────────────────────── +class PackageStatus(str, Enum): + PENDING = "pending" + READY = "ready" + DOWNLOADED = "downloaded" + + +# ── 图片分镜角色（G5坑修复：对齐 storyboard.py 实际角色名）──── +class ImageRole(str, Enum): + # storyboard 实际输出角色名 + HOOK = "hook" + PAIN_SCENE = "pain_scene" + PRODUCT_CLOSEUP = "product_closeup" + INGREDIENT = "ingredient" + APPLIED_PROOF = "applied_proof" + TEXTURE = "texture" + SOCIAL_PROOF = "social_proof" + CLOSER = "closer" + SCENARIO = "scenario" + TUTORIAL = "tutorial" + # 旧值保留向后兼容 + PAIN = "pain" + PROOF = "proof" + QUALITY = "quality" + CREDIT = "credit" + CONVERT = "convert" + MAIN = "main" + + +# ── AI 图片提供商 ───────────────────────────────────── +class ImageProvider(str, Enum): + GPT = "gpt" + GEMINI = "gemini" + + +# ── 产品来源 ────────────────────────────────────────── +class ProductSource(str, Enum): + PRESET = "preset" + CUSTOM = "custom" + + +# ── 错误码（契约§0七类）───────────────────────────────── +class ErrorCode: + SUCCESS = 0 + PARAM_INVALID = 40001 + UNAUTHORIZED = 40101 + FORBIDDEN = 40301 + NOT_FOUND = 40401 + STATE_INVALID = 40901 + BUSINESS_ERROR = 42201 + SERVER_ERROR = 50001 + AI_CALL_FAILED = 50002 diff --git a/backend/app/core/__init__.py b/backend/app/core/__init__.py new file mode 100644 index 0000000..b42ce30 --- /dev/null +++ b/backend/app/core/__init__.py @@ -0,0 +1,3 @@ +""" +app/core/__init__.py +""" diff --git a/backend/app/core/config.py b/backend/app/core/config.py new file mode 100644 index 0000000..dc12d85 --- /dev/null +++ b/backend/app/core/config.py @@ -0,0 +1,80 @@ +""" +app/core/config.py — 环境变量加载 + 启动校验 +必填项缺失则启动失败，不静默。 +""" + +import os +from functools import lru_cache + +from pydantic import field_validator +from pydantic_settings import BaseSettings + + +class Settings(BaseSettings): + # ── 必填（缺失启动保护）─────────────────────────── + FERNET_KEY: str + JWT_SECRET: str + DATABASE_URL: str + MONGO_URI: str + REDIS_URL: str + + # ── AI 提供商（不写死默认，可配置）────────────────── + IMAGE_PROVIDER_PRIMARY: str = "gpt" + IMAGE_PROVIDER_FALLBACK: str = "gemini" + IMAGE_API_BASE: str = "" + IMAGE_MODEL: str = "gpt-image-2" + MODEL_IMAGE: str = "gpt-image-2" # 别名，与 IMAGE_MODEL 同义 + MODEL_TEXT: str = "claude-opus-4-8" + MODEL_VISION: str = "" # 视觉模型（verify_real_image 脚本用） + CLAUDE_API_URL: str = "" + GEMINI_API_URL: str = "" + + # ── 多 Provider Key（各人自录，测试/真实出图脚本用）── + APIPORTS_BASE_URL: str = "" + APIPORTS_KEY: str = "" + CODEPROXY_BASE_URL: str = "" + CODEPROXY_KEY: str = "" + + # ── 应用配置 ────────────────────────────────────── + APP_ENV: str = "development" + JWT_EXPIRE_MINUTES: int = 60 * 24 * 7 # 7天 + CELERY_BROKER_URL: str = "" + CELERY_RESULT_BACKEND: str = "" + + # ── 并发限制（可配置，不写死）───────────────────── + MAX_CONCURRENT_TASKS_PER_USER: int = 2 + + # ── 文件存储路径 ────────────────────────────────── + UPLOAD_BASE_PATH: str = "uploads/packages" + # StaticFiles 实际服务的绝对目录（与 main.py app.mount("/uploads", .../uploads) 一致）。 + # 容器内 main.py 在 /app/main.py，故 uploads 绝对根 = /app/uploads。 + # 所有写盘/读盘统一锚定此根，避免 api(cwd=/app) 与 worker(cwd=/) 相对路径解析不一致。 + UPLOAD_ABS_ROOT: str = "/app/uploads" + + model_config = {"env_file": ".env", "case_sensitive": True, "extra": "ignore"} + + @field_validator("FERNET_KEY") + @classmethod + def fernet_key_not_empty(cls, v: str) -> str: + if not v or len(v) < 32: + raise ValueError("FERNET_KEY must be set and at least 32 chars") + return v + + @field_validator("JWT_SECRET") + @classmethod + def jwt_secret_not_empty(cls, v: str) -> str: + if not v or len(v) < 32: + raise ValueError("JWT_SECRET must be at least 32 characters") + return v + + def celery_broker(self) -> str: + return self.CELERY_BROKER_URL or self.REDIS_URL + + def celery_backend(self) -> str: + return self.CELERY_RESULT_BACKEND or self.REDIS_URL + + +@lru_cache() +def get_settings() -> Settings: + """单例配置，启动时校验一次。""" + return Settings() diff --git a/backend/app/core/database.py b/backend/app/core/database.py new file mode 100644 index 0000000..2df0a6c --- /dev/null +++ b/backend/app/core/database.py @@ -0,0 +1,53 @@ +""" +app/core/database.py — SQLAlchemy 双库连接（MySQL + MongoDB） +""" + +from motor.motor_asyncio import AsyncIOMotorClient +from sqlalchemy import create_engine +from sqlalchemy.orm import DeclarativeBase, sessionmaker + +from app.core.config import get_settings + +settings = get_settings() + +# ── MySQL（主业务库）────────────────────────────────── +engine = create_engine( + settings.DATABASE_URL, + pool_pre_ping=True, + pool_size=10, + max_overflow=20, + echo=(settings.APP_ENV == "development"), +) + +SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine) + + +class Base(DeclarativeBase): + """所有 ORM 模型的基类。""" + pass + + +def get_db(): + """FastAPI 依赖注入：获取 DB Session，用完自动关闭。""" + db = SessionLocal() + try: + yield db + finally: + db.close() + + +# ── MongoDB（只存 AI debug trace，业务不依赖）────────── +_mongo_client: AsyncIOMotorClient | None = None + + +def get_mongo_client() -> AsyncIOMotorClient: + global _mongo_client + if _mongo_client is None: + _mongo_client = AsyncIOMotorClient(settings.MONGO_URI) + return _mongo_client + + +def get_mongo_db(): + """获取 MongoDB 数据库实例（clover_trace）。""" + client = get_mongo_client() + return client["clover_trace"] diff --git a/backend/app/core/response.py b/backend/app/core/response.py new file mode 100644 index 0000000..6db91f8 --- /dev/null +++ b/backend/app/core/response.py @@ -0,0 +1,63 @@ +""" +app/core/response.py — 统一响应包络 +成功：{ "code": 0, "data": {...} } +失败：{ "code": <错误码>, "message": "用户可读信息" } +HTTP 状态码与业务 code 分离（契约§0）。 +""" + +from typing import Any + +from fastapi import HTTPException +from fastapi.responses import JSONResponse + +from app.constants.enums import ErrorCode + + +def ok(data: Any = None) -> dict: + """标准成功响应。""" + return {"code": ErrorCode.SUCCESS, "data": data} + + +def err(code: int, message: str) -> dict: + """标准失败响应体（不含 HTTP 状态，由调用方决定）。""" + return {"code": code, "message": message} + + +def paginate(items: list, total: int, page: int, page_size: int) -> dict: + """分页数据包装。""" + return { + "items": items, + "total": total, + "page": page, + "page_size": page_size, + } + + +# ── 常用异常 ────────────────────────────────────────────── +class CloverHTTPException(HTTPException): + """携带业务 code 的 HTTP 异常，供全局 handler 格式化。""" + + def __init__(self, http_status: int, code: int, message: str): + super().__init__(status_code=http_status, detail=message) + self.biz_code = code + self.biz_message = message + + +def raise_not_found(message: str = "资源不存在") -> None: + raise CloverHTTPException(404, ErrorCode.NOT_FOUND, message) + + +def raise_forbidden(message: str = "无权限访问") -> None: + raise CloverHTTPException(403, ErrorCode.FORBIDDEN, message) + + +def raise_unauthorized(message: str = "未认证或 Token 失效") -> None: + raise CloverHTTPException(401, ErrorCode.UNAUTHORIZED, message) + + +def raise_business(message: str) -> None: + raise CloverHTTPException(422, ErrorCode.BUSINESS_ERROR, message) + + +def raise_state_invalid(message: str = "状态机非法流转") -> None: + raise CloverHTTPException(409, ErrorCode.STATE_INVALID, message) diff --git a/backend/app/core/security.py b/backend/app/core/security.py new file mode 100644 index 0000000..2529fb5 --- /dev/null +++ b/backend/app/core/security.py @@ -0,0 +1,73 @@ +""" +app/core/security.py — JWT + Fernet 工具函数 +FERNET_KEY 走环境变量，绝不进代码库（基石B）。 +""" + +import logging +from datetime import datetime, timedelta, timezone +from typing import Any + +import jwt +from cryptography.fernet import Fernet, InvalidToken + +from app.core.config import get_settings + +logger = logging.getLogger(__name__) +settings = get_settings() + +# ── Fernet 加密（API Key 存取）────────────────────────── +_fernet: Fernet | None = None + + +def _get_fernet() -> Fernet: + global _fernet + if _fernet is None: + _fernet = Fernet(settings.FERNET_KEY.encode()) + return _fernet + + +def encrypt_api_key(plain_key: str) -> str: + """加密 API Key，返回密文字符串。绝不打印 plain_key。""" + return _get_fernet().encrypt(plain_key.encode()).decode() + + +def decrypt_api_key(encrypted_key: str) -> str: + """ + 解密 API Key。只在 Celery worker 内部调用。 + 解密结果只活在调用函数的局部变量，不落盘、不打日志。 + """ + try: + return _get_fernet().decrypt(encrypted_key.encode()).decode() + except InvalidToken: + logger.error("Fernet decrypt failed: invalid token (key may be rotated)") + raise ValueError("API key decryption failed") + + +# ── JWT ────────────────────────────────────────────────── +def create_access_token( + user_id: int, + workspace_id: int, + role: str, + expires_delta: timedelta | None = None, +) -> str: + expire = datetime.now(timezone.utc) + ( + expires_delta or timedelta(minutes=settings.JWT_EXPIRE_MINUTES) + ) + payload: dict[str, Any] = { + "sub": str(user_id), + "user_id": user_id, + "current_workspace_id": workspace_id, + "role": role, + "exp": expire, + } + return jwt.encode(payload, settings.JWT_SECRET, algorithm="HS256") + + +def decode_access_token(token: str) -> dict[str, Any]: + """验签并返回 payload。无效则抛 jwt.PyJWTError。""" + return jwt.decode(token, settings.JWT_SECRET, algorithms=["HS256"]) + + +def mask_api_key(plain_key: str) -> str: + """只返回后4位（展示用），不暴露完整 key。""" + return plain_key[-4:] if len(plain_key) >= 4 else "****" diff --git a/backend/app/core/sse_ticket.py b/backend/app/core/sse_ticket.py new file mode 100644 index 0000000..a1cdc28 --- /dev/null +++ b/backend/app/core/sse_ticket.py @@ -0,0 +1,43 @@ +""" +app/core/sse_ticket.py — SSE 一次性短票 (ticket) 签发与验证 +倩倩姐2026-06-08拍板：SSE 认证不传 JWT，改传 ticket。 +ticket 存 Redis，TTL 60s，验一次即删，换不了其他接口。 +""" +import secrets +from typing import Optional + +TICKET_TTL_SECONDS = 60 +TICKET_KEY_PREFIX = "sse_ticket:" + + +def _redis_key(ticket: str) -> str: + return f"{TICKET_KEY_PREFIX}{ticket}" + + +def issue_ticket(redis_client, task_id: int, workspace_id: int) -> str: + """ + 签发一次性 SSE ticket，写入 Redis，TTL 60s。 + 返回 ticket 字符串（32字节 hex，共64字符）。 + """ + ticket = secrets.token_hex(32) + value = f"{task_id}:{workspace_id}" + redis_client.setex(_redis_key(ticket), TICKET_TTL_SECONDS, value) + return ticket + + +def consume_ticket(redis_client, ticket: str) -> Optional[tuple[int, int]]: + """ + 验证并消费 ticket（用后即删，一次性）。 + 成功返回 (task_id, workspace_id)，失败返回 None。 + """ + if not ticket: + return None + key = _redis_key(ticket) + value = redis_client.getdel(key) # 原子取出并删除 + if not value: + return None + try: + task_id_str, ws_str = value.split(":", 1) + return int(task_id_str), int(ws_str) + except (ValueError, AttributeError): + return None diff --git a/backend/app/middleware/README.md b/backend/app/middleware/README.md new file mode 100644 index 0000000..9512d4e --- /dev/null +++ b/backend/app/middleware/README.md @@ -0,0 +1,5 @@ +# app/middleware/ + +中间件占位： +- workspace_guard.py # 所有接口强制注入workspace_id，防串数据（所有查询强制带此条件） +- auth_middleware.py # JWT验签（HS256，每请求验签） diff --git a/backend/app/middleware/__init__.py b/backend/app/middleware/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/app/middleware/workspace_guard.py b/backend/app/middleware/workspace_guard.py new file mode 100644 index 0000000..3b35f72 --- /dev/null +++ b/backend/app/middleware/workspace_guard.py @@ -0,0 +1,106 @@ +""" +app/middleware/workspace_guard.py — 多租户隔离中间件 +所有业务接口强制注入 workspace_id（基石C）。 +读操作信任 JWT；写操作+切换 workspace 查 workspace_members 校验。 +""" + +import logging +from typing import Annotated + +import jwt +from fastapi import Depends, Header +from sqlalchemy.orm import Session + +from app.core.database import get_db +from app.core.response import raise_forbidden, raise_unauthorized +from app.core.security import decode_access_token +from app.models.workspace import WorkspaceMember + +logger = logging.getLogger(__name__) + + +class CurrentUser: + """JWT 解码后的请求上下文，注入到每个路由函数。""" + + def __init__( + self, + user_id: int, + workspace_id: int, + role: str, + ): + self.user_id = user_id + self.workspace_id = workspace_id + self.role = role + + +def _extract_token(authorization: str | None) -> str: + """从 Authorization: Bearer 中提取 token。""" + if not authorization or not authorization.startswith("Bearer "): + raise_unauthorized("缺少 Authorization header") + return authorization.split(" ", 1)[1] + + +async def get_current_user( + authorization: Annotated[str | None, Header()] = None, +) -> CurrentUser: + """ + FastAPI 依赖：解码 JWT，返回 CurrentUser。 + 所有业务路由 Depends(get_current_user)。 + """ + token = _extract_token(authorization) + try: + payload = decode_access_token(token) + except jwt.ExpiredSignatureError: + raise_unauthorized("Token 已过期") + except jwt.PyJWTError: + raise_unauthorized("Token 无效") + + return CurrentUser( + user_id=int(payload["user_id"]), + workspace_id=int(payload["current_workspace_id"]), + role=payload["role"], + ) + + +def require_write_permission( + current_user: Annotated[CurrentUser, Depends(get_current_user)], + db: Annotated[Session, Depends(get_db)], +) -> CurrentUser: + """ + 写操作依赖：查 workspace_members 校验当前用户确实属于此 workspace。 + 读操作用 get_current_user 即可（JWT 加速）。 + """ + member = ( + db.query(WorkspaceMember) + .filter( + WorkspaceMember.workspace_id == current_user.workspace_id, + WorkspaceMember.user_id == current_user.user_id, + ) + .first() + ) + if not member: + logger.warning( + "workspace permission denied: user=%s workspace=%s", + current_user.user_id, + current_user.workspace_id, + ) + raise_forbidden("无权限访问此 workspace") + return current_user + + +def require_admin( + current_user: Annotated[CurrentUser, Depends(require_write_permission)], +) -> CurrentUser: + """仅管理员可访问的路由依赖。""" + if current_user.role != "admin": + raise_forbidden("需要管理员权限") + return current_user + + +def require_supervisor_or_above( + current_user: Annotated[CurrentUser, Depends(require_write_permission)], +) -> CurrentUser: + """组长及以上（supervisor/admin）。""" + if current_user.role not in ("supervisor", "admin"): + raise_forbidden("需要组长或管理员权限") + return current_user diff --git a/backend/app/models/README.md b/backend/app/models/README.md new file mode 100644 index 0000000..bd7dc73 --- /dev/null +++ b/backend/app/models/README.md @@ -0,0 +1,30 @@ +# app/models/ + +SQLAlchemy ORM 模型占位，Alembic 001-004 按顺序建： + +## Alembic 001 — 从banana搬3张（改造） +- user.py # users（删credits字段） +- login_record.py # login_records +- user_preference.py # user_preferences（UI设置偏好） + +## Alembic 002 — 多租户基础（全新） +- workspace.py # workspaces +- workspace_member.py # workspace_members（user+workspace+角色） +- user_api_key.py # user_api_keys（Fernet加密，UNIQUE user_id+workspace_id+provider） + +## Alembic 003 — 业务主体7张（全新） +- product.py # products（含text_angles/custom_prompt/source/workspace_id） +- benchmark_note.py # benchmark_notes +- generation_task.py # generation_tasks（状态机+审核字段平铺） +- text_candidate.py # text_candidates（source=ai/import双轨） +- image_candidate.py # image_candidates +- delivery_package.py # delivery_packages +- banned_word.py # banned_words（三级level） + +## Alembic 004 — 飞轮（全新） +- preference_event.py # preference_events（含data_ownership字段） + +## 铁律 +- 所有业务表有 workspace_id 字段 +- generation_tasks 审核字段平铺：review_status/reviewer_id/reviewed_at/reject_reason/approved_at/archived_at +- preference_events.signal_weight 初始默认值：选文案+3/选图+3/通过+5/打回-3/重生成-1 diff --git a/backend/app/models/__init__.py b/backend/app/models/__init__.py new file mode 100644 index 0000000..5967a21 --- /dev/null +++ b/backend/app/models/__init__.py @@ -0,0 +1,24 @@ +""" +app/models/__init__.py — 统一导出所有模型，Alembic autogenerate 能扫到 +""" + +from app.models.user import LoginRecord, User, UserPreference +from app.models.workspace import UserApiKey, Workspace, WorkspaceMember +from app.models.product import BannedWord, BenchmarkNote, Product +from app.models.task import ( + DeliveryPackage, + GenerationTask, + ImageCandidate, + TextCandidate, +) +from app.models.flywheel import AiCallLog, PreferenceEvent +from app.models.fission import FissionTask + +__all__ = [ + "User", "LoginRecord", "UserPreference", + "Workspace", "WorkspaceMember", "UserApiKey", + "Product", "BenchmarkNote", "BannedWord", + "GenerationTask", "TextCandidate", "ImageCandidate", + "DeliveryPackage", "PreferenceEvent", "AiCallLog", + "FissionTask", +] diff --git a/backend/app/models/fission.py b/backend/app/models/fission.py new file mode 100644 index 0000000..37274ea --- /dev/null +++ b/backend/app/models/fission.py @@ -0,0 +1,53 @@ +""" +app/models/fission.py — fission_tasks +Alembic 010 第11环裂变引擎（1爆款→N套完整笔记包） + +reference_level: low/mid/high（参考程度） +status: pending/generating/done/failed +""" + +from datetime import datetime + +from sqlalchemy import ( + BigInteger, DateTime, ForeignKey, + Index, Integer, String, Text, func, +) +from sqlalchemy.orm import Mapped, mapped_column + +from app.core.database import Base + + +class FissionTask(Base): + """裂变任务：1爆款→N套完整笔记包（第11环），在工作台内呈现。""" + __tablename__ = "fission_tasks" + + id: Mapped[int] = mapped_column(BigInteger, primary_key=True, autoincrement=True) + workspace_id: Mapped[int] = mapped_column( + BigInteger, ForeignKey("workspaces.id", ondelete="CASCADE"), nullable=False + ) + # 爆款源笔记内容（文案+图描述），JSON存储 + source_note: Mapped[str | None] = mapped_column( + Text, comment="爆款源笔记内容（文案+图描述，JSON存储）" + ) + # 参考程度：low/mid/high + reference_level: Mapped[str] = mapped_column( + String(10), default="mid", nullable=False, + comment="参考程度: low/mid/high" + ) + # 裂变套数（默认3套） + fanout_count: Mapped[int] = mapped_column( + Integer, default=3, nullable=False, + comment="裂变套数（默认3套）" + ) + # 状态机 + status: Mapped[str] = mapped_column( + String(20), default="pending", nullable=False, + comment="状态: pending/generating/done/failed" + ) + created_at: Mapped[datetime] = mapped_column( + DateTime, server_default=func.now(), nullable=False + ) + + __table_args__ = ( + Index("idx_fission_tasks_workspace_id", "workspace_id"), + ) diff --git a/backend/app/models/flywheel.py b/backend/app/models/flywheel.py new file mode 100644 index 0000000..2421cdf --- /dev/null +++ b/backend/app/models/flywheel.py @@ -0,0 +1,97 @@ +""" +app/models/flywheel.py — preference_events / ai_call_logs +Alembic 003（ai_call_logs）+ Alembic 004（preference_events） +preference_profile 二期预留，一期不建。 +""" + +from datetime import datetime + +from sqlalchemy import ( + BigInteger, DateTime, Enum, ForeignKey, + Index, Integer, String, Text, func, +) +from sqlalchemy.orm import Mapped, mapped_column + +from app.constants.enums import DataOwnership, SignalType +from app.core.database import Base + + +class PreferenceEvent(Base): + """ + 飞轮信号日志。 + workspace_id + product_id 都必须有（跨公司隔离 + 按产品分开学）。 + angle_label 跟着产品的文案角度走，不写死（基石A）。 + """ + __tablename__ = "preference_events" + + id: Mapped[int] = mapped_column(BigInteger, primary_key=True, autoincrement=True) + workspace_id: Mapped[int] = mapped_column(BigInteger, nullable=False) + product_id: Mapped[int] = mapped_column( + BigInteger, ForeignKey("products.id"), nullable=False + ) + task_id: Mapped[int] = mapped_column( + BigInteger, ForeignKey("generation_tasks.id"), nullable=False + ) + user_id: Mapped[int] = mapped_column( + BigInteger, ForeignKey("users.id"), nullable=False + ) + signal_type: Mapped[str] = mapped_column( + Enum(SignalType, values_callable=lambda x: [e.value for e in x]), + nullable=False, + ) + signal_weight: Mapped[int] = mapped_column(Integer, nullable=False) + candidate_id: Mapped[int | None] = mapped_column(BigInteger) + angle_label: Mapped[str | None] = mapped_column(String(64)) + reason: Mapped[str | None] = mapped_column(Text) # 打回原因原文 + signal_meta: Mapped[str | None] = mapped_column(Text) # JSON，扩展用 + data_ownership: Mapped[str] = mapped_column( + Enum(DataOwnership, values_callable=lambda x: [e.value for e in x]), + default=DataOwnership.CLIENT_DATA, nullable=False, + ) + created_at: Mapped[datetime] = mapped_column( + DateTime, server_default=func.now(), nullable=False + ) + + __table_args__ = ( + Index( + "idx_preference_events_ws_product_created", + "workspace_id", "product_id", "created_at", + ), + ) + + +class AiCallLog(Base): + """ + AI 调用记录（usage + 排障基础）。 + 调用失败归因到个人 key（错误码50002）。 + 绝不记录明文 key，只记录 key_id。 + """ + __tablename__ = "ai_call_logs" + + id: Mapped[int] = mapped_column(BigInteger, primary_key=True, autoincrement=True) + workspace_id: Mapped[int] = mapped_column(BigInteger, nullable=False) + user_id: Mapped[int] = mapped_column( + BigInteger, ForeignKey("users.id"), nullable=False + ) + key_id: Mapped[int | None] = mapped_column( + BigInteger, ForeignKey("user_api_keys.id") + ) + task_id: Mapped[int | None] = mapped_column( + BigInteger, ForeignKey("generation_tasks.id") + ) + provider: Mapped[str | None] = mapped_column(String(32)) + model: Mapped[str | None] = mapped_column(String(64)) + call_type: Mapped[str | None] = mapped_column(String(32)) # text/image/analyze + prompt_tokens: Mapped[int | None] = mapped_column(Integer) + completion_tokens: Mapped[int | None] = mapped_column(Integer) + success: Mapped[bool] = mapped_column(default=True, nullable=False) + error_code: Mapped[str | None] = mapped_column(String(32)) + latency_ms: Mapped[int | None] = mapped_column(Integer) + created_at: Mapped[datetime] = mapped_column( + DateTime, server_default=func.now(), nullable=False + ) + + __table_args__ = ( + Index("idx_ai_call_logs_workspace_user", "workspace_id", "user_id"), + Index("idx_ai_call_logs_task_id", "task_id"), + ) diff --git a/backend/app/models/product.py b/backend/app/models/product.py new file mode 100644 index 0000000..89a30b1 --- /dev/null +++ b/backend/app/models/product.py @@ -0,0 +1,110 @@ +""" +app/models/product.py — products / benchmark_notes / banned_words +Alembic 003 业务主体（1/3） +products.category 是纯数据字段，禁止任何品类枚举（基石A）。 +""" + +from datetime import datetime +from typing import Optional + +from sqlalchemy import ( + BigInteger, DateTime, Enum, ForeignKey, + Index, Integer, String, Text, func, +) +from sqlalchemy.orm import Mapped, mapped_column, relationship + +from app.constants.enums import BannedWordLevel, ProductSource +from app.core.database import Base + + +class Product(Base): + """产品档案（卖点/违禁词/风格/调性/文案角度/可调prompt/source）""" + __tablename__ = "products" + + id: Mapped[int] = mapped_column(BigInteger, primary_key=True, autoincrement=True) + workspace_id: Mapped[int] = mapped_column( + BigInteger, ForeignKey("workspaces.id", ondelete="CASCADE"), nullable=False + ) + name: Mapped[str] = mapped_column(String(128), nullable=False) + # category 是纯数据字段，不在代码里做枚举（基石A） + category: Mapped[str | None] = mapped_column(String(64)) + source: Mapped[str] = mapped_column( + Enum(ProductSource, values_callable=lambda x: [e.value for e in x]), + default=ProductSource.CUSTOM, nullable=False, + ) + selling_points: Mapped[str | None] = mapped_column(Text) # JSON数组 + style_tone: Mapped[str | None] = mapped_column(String(128)) + text_angles: Mapped[str | None] = mapped_column(Text) # JSON数组，用户设定 + custom_prompt: Mapped[str | None] = mapped_column(Text) # 等北哥方案注入 + image_path: Mapped[str | None] = mapped_column(String(512)) # 产品参考图路径（前端上传后写入） + # 008: 品牌词（客户输入，植入文案每条+生图特写图第2/6张，第5环） + brand_keyword: Mapped[str | None] = mapped_column(String(64), comment="品牌词，客户录入，随产品固定") + # 012: 目标人群（客户输入，透传进文案/生图 prompt，storyboard.py:52 原恒空现可填） + target_audience: Mapped[str | None] = mapped_column(String(128), comment="目标人群，客户输入，透传进文案/生图prompt") + is_active: Mapped[bool] = mapped_column(default=True, nullable=False) + created_at: Mapped[datetime] = mapped_column( + DateTime, server_default=func.now(), nullable=False + ) + updated_at: Mapped[datetime] = mapped_column( + DateTime, server_default=func.now(), onupdate=func.now(), nullable=False + ) + + benchmark_notes: Mapped[list["BenchmarkNote"]] = relationship( + back_populates="product", lazy="noload" + ) + + __table_args__ = ( + Index("idx_products_workspace_id", "workspace_id"), + ) + + +class BenchmarkNote(Base): + """标杆笔记（截图+手填亮点为主通道）""" + __tablename__ = "benchmark_notes" + + id: Mapped[int] = mapped_column(BigInteger, primary_key=True, autoincrement=True) + workspace_id: Mapped[int] = mapped_column( + BigInteger, ForeignKey("workspaces.id", ondelete="CASCADE"), nullable=False + ) + product_id: Mapped[int] = mapped_column( + BigInteger, ForeignKey("products.id", ondelete="CASCADE"), nullable=False + ) + screenshot_url: Mapped[str | None] = mapped_column(String(512)) + highlights: Mapped[str | None] = mapped_column(Text) # 手填亮点 + link_url: Mapped[str | None] = mapped_column(String(512)) + # 009: 第2环标杆分析字段 + features_json: Mapped[str | None] = mapped_column(Text, comment="爆款8特征分析结果（JSON），AI解析后写入") + analyze_status: Mapped[str] = mapped_column(String(20), default="pending", nullable=False, comment="AI分析状态: pending/analyzing/done/failed") + created_at: Mapped[datetime] = mapped_column( + DateTime, server_default=func.now(), nullable=False + ) + + product: Mapped["Product"] = relationship(back_populates="benchmark_notes") + + __table_args__ = ( + Index("idx_benchmark_notes_product_id", "product_id"), + ) + + +class BannedWord(Base): + """违禁词库（三级：auto_fix/soft_warn/hard_block）""" + __tablename__ = "banned_words" + + id: Mapped[int] = mapped_column(BigInteger, primary_key=True, autoincrement=True) + workspace_id: Mapped[int] = mapped_column( + BigInteger, ForeignKey("workspaces.id", ondelete="CASCADE"), nullable=False + ) + word: Mapped[str] = mapped_column(String(64), nullable=False) + level: Mapped[str] = mapped_column( + Enum(BannedWordLevel, values_callable=lambda x: [e.value for e in x]), + nullable=False, + ) + replacement: Mapped[str | None] = mapped_column(String(128)) + updatable: Mapped[bool] = mapped_column(default=True, nullable=False) + created_at: Mapped[datetime] = mapped_column( + DateTime, server_default=func.now(), nullable=False + ) + + __table_args__ = ( + Index("idx_banned_words_workspace_id", "workspace_id"), + ) diff --git a/backend/app/models/task.py b/backend/app/models/task.py new file mode 100644 index 0000000..297c924 --- /dev/null +++ b/backend/app/models/task.py @@ -0,0 +1,151 @@ +""" +app/models/task.py — generation_tasks / text_candidates / image_candidates / delivery_packages +Alembic 003 业务主体（2/3） +任务主键：自增 BIGINT + mongo_trace_id VARCHAR(24) +eval_score 留 NULL，不接 banana 假评分（基石） +""" + +from datetime import datetime +from typing import Optional + +from sqlalchemy import ( + BigInteger, Boolean, DateTime, Enum, Float, ForeignKey, + Index, Integer, String, Text, func, +) +from sqlalchemy.orm import Mapped, mapped_column, relationship + +from app.constants.enums import ( + BannedWordStatus, CandidateSource, ImageRole, + PackageStatus, ReviewStatus, TaskStatus, +) +from app.core.database import Base + + +class GenerationTask(Base): + """生产任务，审核字段平铺（不建独立审核表）。""" + __tablename__ = "generation_tasks" + + id: Mapped[int] = mapped_column(BigInteger, primary_key=True, autoincrement=True) + workspace_id: Mapped[int] = mapped_column( + BigInteger, ForeignKey("workspaces.id", ondelete="CASCADE"), nullable=False + ) + product_id: Mapped[int] = mapped_column( + BigInteger, ForeignKey("products.id"), nullable=False + ) + operator_id: Mapped[int] = mapped_column( + BigInteger, ForeignKey("users.id"), nullable=False + ) + theme: Mapped[str | None] = mapped_column(String(256)) + text_count: Mapped[int] = mapped_column(Integer, default=5, nullable=False) + image_count: Mapped[int] = mapped_column(Integer, default=3, nullable=False) + track: Mapped[str] = mapped_column(String(16), default="ai", nullable=False) + # 本次产品是否入镜：True=必须用产品参考图(无图禁生成,不降级纯文生图)；False=允许纯文生图 + need_product_image: Mapped[bool] = mapped_column(Boolean, default=True, nullable=False) + status: Mapped[str] = mapped_column( + Enum(TaskStatus, values_callable=lambda x: [e.value for e in x]), + default=TaskStatus.PENDING, nullable=False, + ) + mongo_trace_id: Mapped[str | None] = mapped_column(String(24)) # MongoDB trace + # ── 审核字段（平铺）────────────────────────────── + review_status: Mapped[str | None] = mapped_column( + Enum(ReviewStatus, values_callable=lambda x: [e.value for e in x]) + ) + reviewer_id: Mapped[int | None] = mapped_column(BigInteger, ForeignKey("users.id")) + reviewed_at: Mapped[datetime | None] = mapped_column(DateTime) + reject_reason: Mapped[str | None] = mapped_column(Text) + approved_at: Mapped[datetime | None] = mapped_column(DateTime) + archived_at: Mapped[datetime | None] = mapped_column(DateTime) + # 011: 第2环S12+第11环裂变 + benchmark_ids: Mapped[str | None] = mapped_column(Text, comment="关联标杆笔记ID列表（JSON list），第2环引用") + source_fission_id: Mapped[int | None] = mapped_column(Integer, comment="裂变来源fission_task ID；NULL=普通任务") + created_at: Mapped[datetime] = mapped_column( + DateTime, server_default=func.now(), nullable=False + ) + updated_at: Mapped[datetime] = mapped_column( + DateTime, server_default=func.now(), onupdate=func.now(), nullable=False + ) + + __table_args__ = ( + Index("idx_generation_tasks_workspace_status", "workspace_id", "status"), + ) + + +class TextCandidate(Base): + """文案候选（source=ai/import 区分双轨）。eval_score 留 NULL。""" + __tablename__ = "text_candidates" + + id: Mapped[int] = mapped_column(BigInteger, primary_key=True, autoincrement=True) + workspace_id: Mapped[int] = mapped_column(BigInteger, nullable=False) + task_id: Mapped[int] = mapped_column( + BigInteger, ForeignKey("generation_tasks.id", ondelete="CASCADE"), nullable=False + ) + source: Mapped[str] = mapped_column( + Enum(CandidateSource, values_callable=lambda x: [e.value for e in x]), + default=CandidateSource.AI, nullable=False, + ) + angle_label: Mapped[str | None] = mapped_column(String(64)) + content: Mapped[str | None] = mapped_column(Text) + score_json: Mapped[str | None] = mapped_column(Text) # 五维分 JSON + banned_word_status: Mapped[str] = mapped_column( + Enum(BannedWordStatus, values_callable=lambda x: [e.value for e in x]), + default=BannedWordStatus.PASS, nullable=False, + ) + eval_score: Mapped[float | None] = mapped_column(Float) # 一期留 NULL + is_selected: Mapped[bool] = mapped_column(default=False, nullable=False) + created_at: Mapped[datetime] = mapped_column( + DateTime, server_default=func.now(), nullable=False + ) + + __table_args__ = ( + Index("idx_text_candidates_task_id", "task_id"), + ) + + +class ImageCandidate(Base): + """图片候选。eval_score 留 NULL。""" + __tablename__ = "image_candidates" + + id: Mapped[int] = mapped_column(BigInteger, primary_key=True, autoincrement=True) + workspace_id: Mapped[int] = mapped_column(BigInteger, nullable=False) + task_id: Mapped[int] = mapped_column( + BigInteger, ForeignKey("generation_tasks.id", ondelete="CASCADE"), nullable=False + ) + # role 用 varchar 不用 enum：storyboard 角色名仍在演进，约束放应用层 ImageRole + role: Mapped[str] = mapped_column( + String(32), default=ImageRole.HOOK.value, nullable=False, + ) + url: Mapped[str | None] = mapped_column(String(512)) + strategy: Mapped[str | None] = mapped_column(String(4)) # A/B/C（二期） + seq: Mapped[int] = mapped_column(Integer, default=1) # 分镜序号 + is_selected: Mapped[bool] = mapped_column(default=False, nullable=False) + eval_score: Mapped[float | None] = mapped_column(Float) # 一期留 NULL + created_at: Mapped[datetime] = mapped_column( + DateTime, server_default=func.now(), nullable=False + ) + + __table_args__ = ( + Index("idx_image_candidates_task_id", "task_id"), + ) + + +class DeliveryPackage(Base): + """达人素材交付包。""" + __tablename__ = "delivery_packages" + + id: Mapped[int] = mapped_column(BigInteger, primary_key=True, autoincrement=True) + workspace_id: Mapped[int] = mapped_column( + BigInteger, ForeignKey("workspaces.id", ondelete="CASCADE"), nullable=False + ) + task_id: Mapped[int] = mapped_column( + BigInteger, ForeignKey("generation_tasks.id"), nullable=False + ) + status: Mapped[str] = mapped_column( + Enum(PackageStatus, values_callable=lambda x: [e.value for e in x]), + default=PackageStatus.PENDING, nullable=False, + ) + package_path: Mapped[str | None] = mapped_column(String(512)) + download_url: Mapped[str | None] = mapped_column(String(512)) + expires_at: Mapped[datetime | None] = mapped_column(DateTime) + created_at: Mapped[datetime] = mapped_column( + DateTime, server_default=func.now(), nullable=False + ) diff --git a/backend/app/models/user.py b/backend/app/models/user.py new file mode 100644 index 0000000..d3401ce --- /dev/null +++ b/backend/app/models/user.py @@ -0,0 +1,68 @@ +""" +app/models/user.py — users / login_records / user_preferences +Alembic 001：从 banana 搬，删除 credits 字段（架构方案规定）。 +""" + +from datetime import datetime + +from sqlalchemy import ( + BigInteger, Boolean, DateTime, ForeignKey, + Integer, String, Text, func, +) +from sqlalchemy.orm import Mapped, mapped_column, relationship + +from app.core.database import Base + + +class User(Base): + __tablename__ = "users" + + id: Mapped[int] = mapped_column(BigInteger, primary_key=True, autoincrement=True) + username: Mapped[str] = mapped_column(String(64), unique=True, nullable=False) + email: Mapped[str] = mapped_column(String(255), unique=True, nullable=False) + hashed_password: Mapped[str] = mapped_column(String(255), nullable=False) + is_active: Mapped[bool] = mapped_column(Boolean, default=True, nullable=False) + created_at: Mapped[datetime] = mapped_column( + DateTime, server_default=func.now(), nullable=False + ) + updated_at: Mapped[datetime] = mapped_column( + DateTime, server_default=func.now(), onupdate=func.now(), nullable=False + ) + # 注：删除 banana 的 credits 字段（架构方案规定） + + login_records: Mapped[list["LoginRecord"]] = relationship( + back_populates="user", lazy="noload" + ) + + +class LoginRecord(Base): + __tablename__ = "login_records" + + id: Mapped[int] = mapped_column(BigInteger, primary_key=True, autoincrement=True) + user_id: Mapped[int] = mapped_column( + BigInteger, ForeignKey("users.id", ondelete="CASCADE"), nullable=False + ) + ip_address: Mapped[str | None] = mapped_column(String(64)) + user_agent: Mapped[str | None] = mapped_column(String(512)) + created_at: Mapped[datetime] = mapped_column( + DateTime, server_default=func.now(), nullable=False + ) + + user: Mapped["User"] = relationship(back_populates="login_records") + + +class UserPreference(Base): + """UI 设置偏好（主题/语言等），不含 API Key。""" + __tablename__ = "user_preferences" + + id: Mapped[int] = mapped_column(BigInteger, primary_key=True, autoincrement=True) + user_id: Mapped[int] = mapped_column( + BigInteger, ForeignKey("users.id", ondelete="CASCADE"), + unique=True, nullable=False, + ) + preferences_json: Mapped[str | None] = mapped_column(Text) # JSON字符串 + updated_at: Mapped[datetime] = mapped_column( + DateTime, server_default=func.now(), onupdate=func.now(), nullable=False + ) + + user: Mapped["User"] = relationship() diff --git a/backend/app/models/workspace.py b/backend/app/models/workspace.py new file mode 100644 index 0000000..6db1c91 --- /dev/null +++ b/backend/app/models/workspace.py @@ -0,0 +1,88 @@ +""" +app/models/workspace.py — workspaces / workspace_members / user_api_keys +Alembic 002：多租户基础，全新建。 +matrix_accounts 二期预留，一期不建。 +""" + +from datetime import datetime + +from sqlalchemy import ( + BigInteger, DateTime, Enum, ForeignKey, + Index, String, UniqueConstraint, func, +) +from sqlalchemy.orm import Mapped, mapped_column, relationship + +from app.constants.enums import UserRole +from app.core.database import Base + + +class Workspace(Base): + __tablename__ = "workspaces" + + id: Mapped[int] = mapped_column(BigInteger, primary_key=True, autoincrement=True) + name: Mapped[str] = mapped_column(String(128), nullable=False) + slug: Mapped[str] = mapped_column(String(64), unique=True, nullable=False) + is_active: Mapped[bool] = mapped_column(default=True, nullable=False) + created_at: Mapped[datetime] = mapped_column( + DateTime, server_default=func.now(), nullable=False + ) + + members: Mapped[list["WorkspaceMember"]] = relationship( + back_populates="workspace", lazy="noload" + ) + + +class WorkspaceMember(Base): + __tablename__ = "workspace_members" + + id: Mapped[int] = mapped_column(BigInteger, primary_key=True, autoincrement=True) + workspace_id: Mapped[int] = mapped_column( + BigInteger, ForeignKey("workspaces.id", ondelete="CASCADE"), nullable=False + ) + user_id: Mapped[int] = mapped_column( + BigInteger, ForeignKey("users.id", ondelete="CASCADE"), nullable=False + ) + role: Mapped[str] = mapped_column( + Enum(UserRole, values_callable=lambda x: [e.value for e in x]), + nullable=False, + ) + created_at: Mapped[datetime] = mapped_column( + DateTime, server_default=func.now(), nullable=False + ) + + workspace: Mapped["Workspace"] = relationship(back_populates="members") + + __table_args__ = ( + UniqueConstraint("workspace_id", "user_id", name="uq_workspace_member"), + ) + + +class UserApiKey(Base): + """ + 个人 API Key（Fernet 加密）。 + encrypted_key 只存密文，不存 url（token站固定自家站）。 + UNIQUE(user_id, workspace_id, provider)。 + """ + __tablename__ = "user_api_keys" + + id: Mapped[int] = mapped_column(BigInteger, primary_key=True, autoincrement=True) + user_id: Mapped[int] = mapped_column( + BigInteger, ForeignKey("users.id", ondelete="CASCADE"), nullable=False + ) + workspace_id: Mapped[int] = mapped_column( + BigInteger, ForeignKey("workspaces.id", ondelete="CASCADE"), nullable=False + ) + provider: Mapped[str] = mapped_column(String(32), nullable=False) + encrypted_key: Mapped[str] = mapped_column(String(512), nullable=False) + key_last4: Mapped[str] = mapped_column(String(4), nullable=False) + created_at: Mapped[datetime] = mapped_column( + DateTime, server_default=func.now(), nullable=False + ) + + __table_args__ = ( + UniqueConstraint( + "user_id", "workspace_id", "provider", + name="uq_user_workspace_provider", + ), + Index("idx_user_api_keys_workspace_id", "workspace_id"), + ) diff --git a/backend/app/repositories/__init__.py b/backend/app/repositories/__init__.py new file mode 100644 index 0000000..4ed8eb9 --- /dev/null +++ b/backend/app/repositories/__init__.py @@ -0,0 +1,3 @@ +""" +app/repositories/__init__.py +""" diff --git a/backend/app/repositories/base_workspace_repo.py b/backend/app/repositories/base_workspace_repo.py new file mode 100644 index 0000000..26a9ea4 --- /dev/null +++ b/backend/app/repositories/base_workspace_repo.py @@ -0,0 +1,85 @@ +""" +app/repositories/base_workspace_repo.py — 多租户基础 Repo +所有 workspace 相关 Repo 继承此类，强制过滤 workspace_id（基石C）。 +禁止 SELECT *，明确字段（db.md规范）。 +""" + +import logging +from typing import Any, Generic, TypeVar + +from sqlalchemy import select +from sqlalchemy.orm import Session + +from app.core.response import raise_not_found + +logger = logging.getLogger(__name__) + +T = TypeVar("T") + + +class BaseWorkspaceRepo(Generic[T]): + """ + workspace 感知的基础 Repo。 + 子类设置 model_class，所有查询自动注入 workspace_id 过滤。 + """ + + model_class: type[T] + + def __init__(self, db: Session, workspace_id: int): + self.db = db + self.workspace_id = workspace_id + + def _base_query(self): + """所有查询的基础：强制带 workspace_id 过滤（多租户红线）。""" + return self.db.query(self.model_class).filter( + self.model_class.workspace_id == self.workspace_id # type: ignore[attr-defined] + ) + + def get_by_id(self, record_id: int) -> T | None: + """按 ID 查单条，强制带 workspace_id 防越权读。""" + return self._base_query().filter( + self.model_class.id == record_id # type: ignore[attr-defined] + ).first() + + def get_by_id_or_404(self, record_id: int) -> T: + obj = self.get_by_id(record_id) + if not obj: + raise_not_found(f"{self.model_class.__name__} {record_id} 不存在") + return obj # type: ignore[return-value] + + def list_all( + self, + offset: int = 0, + limit: int = 20, + filters: list[Any] | None = None, + ) -> tuple[list[T], int]: + """分页列表，返回 (items, total)。""" + q = self._base_query() + if filters: + q = q.filter(*filters) + total = q.count() + items = q.offset(offset).limit(limit).all() + return items, total + + def create(self, obj: T) -> T: + """插入一条记录，自动设置 workspace_id。""" + obj.workspace_id = self.workspace_id # type: ignore[attr-defined] + self.db.add(obj) + self.db.flush() + self.db.refresh(obj) + logger.debug("Created %s id=%s ws=%s", self.model_class.__name__, obj.id, self.workspace_id) # type: ignore[attr-defined] + return obj + + def delete(self, obj: T) -> None: + """物理删除（软删各子类自己处理 archived 态）。""" + self.db.delete(obj) + self.db.flush() + + def save(self) -> None: + """提交事务，错误不静默（官网V1坑3）。""" + try: + self.db.commit() + except Exception: + self.db.rollback() + logger.error("DB commit failed in %s ws=%s", self.model_class.__name__, self.workspace_id) + raise diff --git a/backend/app/services/README.md b/backend/app/services/README.md new file mode 100644 index 0000000..325ff81 --- /dev/null +++ b/backend/app/services/README.md @@ -0,0 +1,13 @@ +# app/services/ + +业务逻辑层占位，按模块： +- auth_service.py # JWT签发/验证 +- workspace_service.py # workspace权限校验 +- product_service.py # 产品档案业务逻辑 +- task_service.py # 任务状态机流转 +- review_service.py # 审核流转+飞轮信号写入 +- preference_aggregator.py # 飞轮实时聚合（最近50条→prompt片段） +- preference_collector.py # 三信号入口写入 preference_events +- banned_word_checker.py # 违禁词三级扫描（🟢改写/🟡提示/🔴拦截） +- package_exporter.py # 生成达人素材交付包 +- image_postprocessor.py # 去水印后处理（重编码+削像素水印） diff --git a/backend/app/services/__init__.py b/backend/app/services/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/app/services/ai_engine/__init__.py b/backend/app/services/ai_engine/__init__.py new file mode 100644 index 0000000..05e933a --- /dev/null +++ b/backend/app/services/ai_engine/__init__.py @@ -0,0 +1,5 @@ +""" +AI 引擎包 +扒自：上线版 worker/src/copy.js + image.js（2026-06-09） +重写为 Python，逻辑对照JS版防走样 +""" diff --git a/backend/app/services/ai_engine/_score_prompt.py b/backend/app/services/ai_engine/_score_prompt.py new file mode 100644 index 0000000..1dfc70d --- /dev/null +++ b/backend/app/services/ai_engine/_score_prompt.py @@ -0,0 +1,104 @@ +""" +_score_prompt.py — AI 评委 prompt（让模型真读文案，不机械找词） +评判标准忠于《富贵情绪营销理论》原文(口播一手来源)，标实战补充出处。 +6维满分分布（倩倩姐2026-06-15拍板，与 llm_scorer._DIM_MAX / constants.AI_DIM_WEIGHTS 三处同步）： + 痛点人群精准18 / 情绪张力18 / 买点转化18 / 开头钩子15 / 标题点击力13 / 真实感13 = 95 + + 合规5（机械硬拦，不进AI评委）= 100 +"真实感"替换旧"产品聚焦一件事(16)"：富贵"很少提产品/前70%干货后30%植入"独立升维。 +""" +from __future__ import annotations + +# ── 评委人设 ────────────────────────────────────────────── +SCORER_PERSONA = """你是一位资深小红书内容操盘手，深谙富贵情绪营销理论。 +你的本事是：扫一眼就知道一条笔记能不能打动目标用户、会不会被划走。 +你不数关键词、不看有没有出现某个固定词——你读的是【这条文案对真实用户有没有杀伤力】。 +你按下面6个维度给文案打分，每一维都要给出【具体理由】，指出好在哪/差在哪/怎么改， +理由必须针对这条文案的真实内容，不准说放之四海皆准的空话。""" + +# ── 6 维评判标准（按权重降序；合规第7维由代码机械硬拦，不在此）──── +# 标准依据：括号内标注[富贵原文]或[实战补充]，前者来自口播一手来源，权威最高。 +SCORING_DIMENSIONS = """ +【维度1·痛点人群精准（满分18）】[富贵原文] +判断："说的就是我"——文案描述的处境/困扰，目标用户读了会不会对号入座。 +好：具体到某类人的某个真实生活瞬间，让人觉得被看穿，落在"我的大问题/我的处境/我的渴望"上。 +差：泛泛而谈谁都能套（如"适合所有想变美的女生"）；或PUA用户、拿别人的惨状吓唬人。 +依据：富贵"我的大问题→处境→渴望"内容骨架；人群越具体穿透力越强；"用户被宠成爹，你PUA他不好使"。 + +【维度2·情绪张力（满分18）】[富贵原文·第一性原理] +判断：有没有"成果/后果"双向情绪，而不是平铺直叙报卖点。 + · 后果=过去没用它，产生了什么糟糕处境（勾起恐惧/懊悔） + · 成果=用了它之后，会变成什么样（给出期待/乐观） +好：同时有"后果路径(过去的痛)+成果路径(未来的好)"，有一句能戳中人、读完有情绪余温。 +差：全程客观介绍产品、无情绪、像说明书；或只单向吓唬、或只空喊美好。 +依据：富贵"营销第一性原理就是情绪，没有情绪什么内容都不转化"；"成果是未来、后果是过去，要做这两种情绪"。 + +【维度3·买点转化（满分18）】[富贵原文] +判断：产品卖点有没有翻译成用户能感知的场景化利益（人话），而不是品牌视角的功效/参数。 +好：用户能想象到的使用场景和结果（如"出门前最后一步、同事问我今天气色真好"）。 +差：品牌语言/参数罗列（如"采用XX技术""含XX成分"），用户无感。 +依据：富贵"卖点是品牌视角、买点是用户视角"；"用户买的不是产品，是使用场景背后被解决的问题"。 + +【维度4·开头钩子（满分15）】[富贵原文] +判断：开头能不能让人停下来、想继续读。 +好：开头第一句就直击用户的大问题/痛处/具体场景代入，每句都打要害。 +差：开头是套话或铺垫半天不进正题，没有任何抓人的点。 +依据：富贵"内容就是锋利的刀，一定要插用户的心窝子"。 + +【维度5·标题点击力（满分13）】[富贵原文+实战补充] +判断：标题有没有让目标用户想点的诱因。 +好：标题带具体人群/场景/痛点/情绪钩子，一眼觉得"和我有关、我想看"，最好来自用户真实说法。 +差：只有产品名、平淡无钩子、或太像广告。 +依据：富贵"热评就是标题，从真实评论里抓用户的话"[原文]；标题善用痛点/人群/效果/情绪词[实战补充]。 + +【维度6·真实感（满分13）】[富贵原文] +判断：整条文案是不是像真人分享，而不是品牌广告或功效说明书。 +好：价值/场景/感受为主，产品自然带出；前段是干货或真实经历，后段才软性推荐；不开头就报规格价格。 +差：通篇硬广、产品功效罗列当主体、开头就卖、语气像文案模板而非真实人设。 +依据：富贵"我们很少提产品"；"前70%是价值/场景，后30%才是产品"；用户能感受到"这不像广告"。 +""".strip() + +# ── 真实感已升为维度6（满分13），此处保留空字符串避免调用方引用报错 ── +REALNESS_NOTE = "" # 升为 SCORING_DIMENSIONS 维度6，不再重复附加 + +# ── 输出格式约束 ────────────────────────────────────────── +SCORER_OUTPUT_FORMAT = """ +读完这条文案后，严格返回纯JSON对象（不要markdown代码块、不要多余文字），格式： +{ + "dims": [ + {"item":"痛点人群精准","score":<0-18整数>,"reason":"<针对本条的具体理由，30字内>"}, + {"item":"情绪张力","score":<0-18>,"reason":"..."}, + {"item":"买点转化","score":<0-18>,"reason":"..."}, + {"item":"开头钩子","score":<0-15>,"reason":"..."}, + {"item":"标题点击力","score":<0-13>,"reason":"..."}, + {"item":"真实感","score":<0-13>,"reason":"..."} + ], + "verdict":"<优秀|合格|不合格>", + "summary":"<一句话总评，说清这条最大的优点和最该改的点>" +} +打分要敢拉开差距：平庸文案该给中低分，不要清一色高分；优秀的地方也别吝啬给高分。 +""".strip() + +# 合规维度满分（机械硬拦，不进 AI 评委） +COMPLIANCE_MAX = 5 + +# AI 评委 6 维满分合计（用于把 0-95 折算/校验；+合规5=100） +AI_DIMS_MAX = 95 + + +def build_score_prompt(copy: dict, product: dict | None = None) -> str: + """组装单条文案的评委 prompt。copy={title,content,...}，product 提供品牌/品类语境。""" + title = str(copy.get("title", "")).strip() + content = str(copy.get("content", "")).strip() + ctx = "" + if product: + name = product.get("name") or product.get("title") or "" + brand = product.get("brand_keyword") or product.get("brand") or "" + cat = product.get("category") or "" + bits = [b for b in (f"产品：{name}", f"品牌词：{brand}", f"品类：{cat}") if b.split("：", 1)[1]] + if bits: + ctx = "【产品语境】\n" + "\n".join(bits) + "\n\n" + return ( + f"{SCORING_DIMENSIONS}\n\n" + f"{ctx}【待评文案】\n标题：{title}\n正文：{content}\n\n" + f"{SCORER_OUTPUT_FORMAT}" + ) diff --git a/backend/app/services/ai_engine/_scoring_dims.py b/backend/app/services/ai_engine/_scoring_dims.py new file mode 100644 index 0000000..11a8ebd --- /dev/null +++ b/backend/app/services/ai_engine/_scoring_dims.py @@ -0,0 +1,92 @@ +""" +_scoring_dims.py — 五维打分逻辑（单一职责：计算层） +词表常量 + 每维打分函数，由 text_scoring.score_copy 调用 +""" +from __future__ import annotations +import re +from typing import Any + +from .constants import SCORE_WEIGHTS, BANNED_WORDS_DEFAULT, BANNED_VISUAL_WORDS, INTERNAL_COPY_HINTS + +_EMOTION_WORDS = ["谁懂", "绝了", "姐妹", "宝子", "挖到", "救星", "离不开", + "闭眼入", "冲", "不踩雷", "后悔没早买"] +_SCENE_WORDS = ["通勤", "上班", "办公室", "工位", "宿舍", "出门", "旅行", + "居家", "运动", "带娃", "约会", "熬夜", "换季", "饭后", + "早餐", "下午", "加班", "外食", "日常", "早八", "学生党", "新手", "宝妈"] +_CATEGORY_WORDS: dict[str, list[str]] = { + "美妆护肤": ["肤", "妆", "质地", "服帖", "清透", "水润", "自然", "上脸", "底妆"], + "个护护理": ["手", "护理", "干", "黏", "吸收", "滋润", "粗糙", "倒刺", "随身", "质地"], + "食品饮品": ["口感", "入口", "味道", "冲泡", "杯", "工位", "囤", "不腻", "清爽"], + "营养健康": ["成分", "日常", "坚持", "习惯", "补给", "安心", "配方"], + "家居生活": ["收纳", "省事", "清洁", "桌面", "厨房", "租房", "细节", "高频"], + "服饰穿搭": ["上身", "版型", "面料", "通勤", "显瘦", "搭配", "质感"], + "通用好物": ["实用", "场景", "细节", "省事", "日常", "高频"], +} + + +def _has_any(text: str, words: list[str]) -> bool: + return any(w.lower() in text.lower() for w in words) + + +def _cat_words(category: str) -> list[str]: + return _CATEGORY_WORDS.get(category, _CATEGORY_WORDS["通用好物"]) + + +def score_title(title: str, cat_words: list[str], w: dict) -> dict: + ts = 0 + if 6 <= len(title) <= 34: ts += 8 + if re.search(r"[0-9一二三四五六七八九十几]|最近|这支|这个|每天|早八|学生党|新手|宝妈|懒人|伪素颜", title): ts += 5 + if _has_any(title, _SCENE_WORDS): ts += 6 + if _has_any(title, cat_words): ts += 5 + if re.search(r"[！!?？]|救星|不踩雷|闭眼入|会回购|被问|惊喜|加分|常备|省心|实用|别乱选|放心|有气色", title): ts += 6 + if re.search(r"救星|绝了|挖到|偷偷|被问|离不开|后悔|拿捏|香|包里|回购|常备|工位|换季", title): ts += 4 + ts = min(ts, w["title"]) + return {"item": "标题吸引力", "score": ts, "max": w["title"], + "note": "标题有明确人群/场景钩子" if ts >= 18 else "建议补充人群、场景或强钩子"} + + +def score_emotion(full: str, w: dict) -> dict: + es = 0 + if _has_any(full, _EMOTION_WORDS): es += 10 + if _has_any(full, _SCENE_WORDS): es += 8 + if re.search(r"不假白|不卡|不搓泥|没底气|纠结|救|不黏|不腻|踩雷|麻烦|翻车", full): es += 7 + if re.search(r"姐妹|宝子|室友|同事|我自己|实测|亲测|上脸|出门|工位|宿舍|家里", full): es += 5 + if re.search(r"[✅✨🌿💧📦🔍🧡🪞🧴🍃🥹😭👍]", full): es += 3 + es = min(es, w["emotion"]) + return {"item": "情绪共鸣", "score": es, "max": w["emotion"], + "note": "口语感和痛点表达较充分" if es >= 18 else "建议增加真实痛点和口语化表达"} + + +def score_selling(copy: dict, full: str, selling_points: list, w: dict) -> dict: + bs = 0 + matched = [pt for pt in selling_points if any(kw in full for kw in str(pt).split()[:3])] + if len(matched) >= 1: bs += 7 + if len(matched) >= 2: bs += 4 + if re.search(r"方便|自然|清爽|质地|口感|成分|实测|亲测|场景|随身|省事|高频|性价比|好用|适合|推荐", full): bs += 8 + if copy.get("buyingPoint") or re.search(r"分钟|出门|通勤|办公室|宿舍|居家|换季|工位|旅行", full): bs += 9 + if copy.get("coverTitle") or copy.get("imageBrief"): bs += 4 + bs = min(bs, w["selling"]) + return {"item": "买点表达", "score": bs, "max": w["selling"], + "note": "卖点已转成用户可感知买点" if bs >= 18 else "建议把功能卖点翻译成使用场景和结果"} + + +def score_keyword(copy: dict, tags: str, keywords: list, w: dict) -> dict: + ks = 0 + matched = [k for k in keywords if str(k).replace("#", "") in tags + str(copy.get("content",""))] + if len(matched) >= 1: ks += 6 + if len(matched) >= 2: ks += 5 + if len(copy.get("tags", [])) >= 3: ks += 5 + if "#" in tags: ks += 4 + if len(copy.get("tags", [])) >= 5: ks += 4 + ks = min(ks, w["keyword"]) + note = f"覆盖：{'、'.join(matched)}" if matched else "建议补充品类词和长尾词" + return {"item": "关键词覆盖", "score": ks, "max": w["keyword"], "note": note} + + +def score_compliance(full: str, bwords: list[str], w: dict) -> tuple[dict, list[str]]: + found_banned = [bw for bw in bwords if bw.lower() in full.lower()] + found_hints = [hw for hw in INTERNAL_COPY_HINTS if hw in full] + cs = 0 if (found_banned or found_hints) else w["compliance"] + note = (f"含禁用词：{'、'.join(found_banned)}" if found_banned + else (f"正文混入内部提示：{'、'.join(found_hints)}" if found_hints else "未发现禁用词")) + return {"item": "合规性", "score": cs, "max": w["compliance"], "note": note}, found_banned + found_hints diff --git a/backend/app/services/ai_engine/_storyboard_data.py b/backend/app/services/ai_engine/_storyboard_data.py new file mode 100644 index 0000000..1c66973 --- /dev/null +++ b/backend/app/services/ai_engine/_storyboard_data.py @@ -0,0 +1,64 @@ +""" +_storyboard_data.py — 品类证明策略数据（纯数据，不含逻辑） +品类来自 product.category，不枚举，兜底用"通用好物" +""" +from __future__ import annotations + +# 视觉违禁词替换规则（扒 sanitizeImagePlanText） +SANITIZE_RULES: list[tuple[str, str]] = [ + (r"before\s*&\s*after", "质地与肤感说明"), + (r"before\s*/?\s*after", "质地与肤感说明"), + (r"\bbefore\b", "质地状态"), + (r"\bafter\b", "上脸肤感"), + (r"使用前后|用前用后|用前后|前后对比|使用前|使用后", "质地/场景/肤感说明"), + (r"功效对比|效果对比|改善对比", "质地/场景说明对比"), + (r"肤色变白|皮肤变白|变白|美白", "自然光泽感"), + (r"瑕疵消失|斑点消失|痘印消失|消除瑕疵|祛斑", "妆感更服帖"), + (r"治疗前后|治疗后|医美前后|治愈|修复受损", "日常使用场景说明"), +] + +# 品类证明策略（不写死枚举，product.category 匹配，兜底通用） +PROOF_STRATEGIES: dict[str, dict] = { + "个护护理": { + "overlay_tpl": "{point}看得见", + "visual": "手部/身体局部使用证明：少量点涂、推开后吸收状态、真实纹理和自然光", + "asset_use": "优先使用实拍/参考图中的手部、干纹、涂抹、随身场景", + "forbidden": "不要变白、祛斑、医学效果、before/after字样；不要和封面同构图", + }, + "美妆护肤": { + "overlay_tpl": "{point}看得见", + "visual": "肤感/质地证明：手背、脸颊局部或质地微距，展示推开前后真实状态", + "asset_use": "优先使用实拍/参考图中的手背、上脸、质地素材", + "forbidden": "不要变白、祛斑、医学效果、before/after字样", + }, + "食品饮品": { + "overlay_tpl": "{point}一眼懂", + "visual": "冲泡/开袋/入口证明：展示包装、杯中状态、质地颜色，真实桌面光线", + "asset_use": "产品图保证包装准确，参考图用于杯子、开袋、冲泡、居家场景", + "forbidden": "不要涂抹、不要护肤肤感、不要医疗健康承诺", + }, + "营养健康": { + "overlay_tpl": "看清{point}", + "visual": "理性证明页：包装、成分表、使用场景和每日习惯卡片", + "asset_use": "产品图和说明图用于成分/包装准确，参考图用于日常使用场景", + "forbidden": "不要治疗、改善疾病、速效、医生背书、前后对比", + }, + "家居生活": { + "overlay_tpl": "{point}真省事", + "visual": "使用过程证明：展示痛点场景、产品介入和使用过程细节", + "asset_use": "参考图用于真实家居环境，产品图保证外观准确", + "forbidden": "不要护肤涂抹，不要虚假夸大结果", + }, + "服饰穿搭": { + "overlay_tpl": "{point}有细节", + "visual": "上身/材质证明：展示面料纹理、版型细节或普通身材上身局部", + "asset_use": "参考图用于上身/搭配/材质，产品图保证款式颜色准确", + "forbidden": "不要护肤涂抹，不要过度精修模特感", + }, + "通用好物": { + "overlay_tpl": "{point}清晰可见", + "visual": "产品使用场景证明：真实道具/场景，展示产品细节和使用过程", + "asset_use": "产品图保证准确，参考图用于场景辅助", + "forbidden": "不要夸大效果，不要硬广式价格牌", + }, +} diff --git a/backend/app/services/ai_engine/_text_prompt.py b/backend/app/services/ai_engine/_text_prompt.py new file mode 100644 index 0000000..07e344b --- /dev/null +++ b/backend/app/services/ai_engine/_text_prompt.py @@ -0,0 +1,209 @@ +""" +_text_prompt.py — 文案 prompt 组装 / JSON 解析 / 本地模板兜底 +方法层（全品类共用）：人设/变量池/5步框架/四段结构/反AI味规则 +数据层（每产品各异）：由 product dict 动态注入，代码不出现具体品牌/成分名 +""" +from __future__ import annotations +import json +import random +import re + +# ── Q4 佛系反推销人设（全品类共用，数据层产品名从product动态注入）────────── +_PERSONA = """你是一个日常生活分享博主，不是品牌推广号。 +核心人设：佛系、不推销、真实记录日常。 +内核：不主动劝买，靠真实体验让读者自己动心；写的是生活，产品只是生活的一部分。 +比例：70% 写生活场景和使用感受，30% 提产品，绝不颠倒。 +收尾铁律：每条结尾方式必须不同，禁止使用"东西放这了/买不买跟我没关系"这类被用滥的固定句式。""" + +# ── Q1 随机变量池 ABC（反同质化核心，每次随机抽组合，N条不撞）────────────── +# 方法层：框架固定，内容可扩展，绝不写死 +_POOL_A_IDENTITY: list[str] = [ + "上班族早八妆前随手抹", + "宿舍懒人护肤三分钟搞定", + "敏感肌妈妈哄完娃才有五分钟", + "学生党第一次用高价护肤品", + "素颜出门前最后一步", +] + +_POOL_B_EMOTION: list[str] = [ + "看到镜子里发现气色暗了一周", + "闺蜜问你最近皮肤怎么这么好", + "出门被催快点根本来不及叠瓶", +] + +_POOL_C_FLAW: list[str] = [ + "第一次用量太少了没啥感觉", + "包装简单到以为是山寨", + "价格摆在那以为会很油很厚", +] + + +def _pick_combo() -> dict[str, str]: + """随机抽一组 ABC 变量（每次生成调用一次，N条各不相同）""" + return { + "identity": random.choice(_POOL_A_IDENTITY), + "emotion": random.choice(_POOL_B_EMOTION), + "flaw": random.choice(_POOL_C_FLAW), + } + + +# ── Q5 negative词：prompt级别负向约束，不让AI写进正文 ───────────────────── +_NEGATIVE_WORDS = ( + "神器、福音、救急单品、遮羞布、日常维稳、精简底妆、" + "不仅而且、焕发、守护、尽享、日常维稳、" + "按头安利、绝绝子、闭眼冲、杀疯了、YYDS" +) + +# ── emoji 规则（适度有表情，倩倩姐2026-06-08拍板：像真人发的小红书）────────── +_EMOJI_RULES = """ +【emoji表情（适度有表情，必须遵守）】 +- 卖点小标题前加 emoji：✅ 卖点1 / ✨ 卖点2 / 🌿 卖点3（每条卖点1个，符合语义） +- 正文段落可点缀少量 emoji 烘托情绪（如 🥹 😭 🤍 💛），每段最多1-2个，不堆砌 +- 结尾话题标签前后带表情，如 "#好物分享 🛒" +- emoji 服务情绪和分点，不要每句都加；整条正文 emoji 总量控制在 6-12 个 +- 常用小红书 emoji 池：✅✨🌿💧🪞🧴📦🔍💛🤍🥹😭🛒（按语义选，不乱用） +""".strip() + +# ── Q2 5步框架 + Q3 四段结构 ───────────────────────────────────────────── +_STRUCTURE_RULES = """ +【5步框架（必须严格遵循）】 +① Hook暴击低价/痛点：第一句戳中场景或价格锚点，吊足读者好奇 +② 痛点共鸣：2-3句描写使用前的真实困境（用上面抽到的起因情绪A·B） +③ 救星登场：自然带出产品，口吻是"碰巧发现/朋友安利/囤货时顺手"，不是"推荐给你" +④ 卖点罗列（每条加✅小标题）：3条以内，卖点翻译成使用感受，不是功效列表 +⑤ 收尾（每条必须从下方策略池随机选一种，同批次不得重复同一种，禁止"东西放这了/买不买跟我没关系"此类固定句式）： + 【收尾策略池·每条选不同策略】 + A·留白式感受：只说自己现在的状态，不提买不买，如"反正现在素颜出门我不慌了" + B·反问读者：把感受抛回给读者，如"你们护肤有没有那种一用就回不去的东西？" + C·场景延续：把故事延伸到未来某个细节，如"下次同事再问我皮肤的事我就知道说啥了" + D·克制回购暗示：轻描淡写说自己行为，如"第一罐用完了，已经在备第二罐" + E·纯记录收笔：像日记最后一句，不引导不评价，如"大概就这样，记录一下" + F·引导搜索（仅在有品牌词时使用）：自然提一句，如"感兴趣可以搜搜『{品牌词}』"，不催单 + +【正文四段结构（必须）】 +段1·痛点引入：描写使用前的困境/触发场景（身份场景A·起因情绪B） +段2·实测记录：真实使用过程，带上小缺点C（真实感来源） +段3·种草核心：产品带来的变化，用感受描述而非功效声称 +段4·引导收尾：从收尾策略池随机选一种，佛系口吻，不强推，末尾带1-2个相关话题标签 + +【字数】正文350-400字（不含标题tags），3-5处段落空行增强可读性 +""".strip() + +# ── Q8 标题公式（5类结构，每批次覆盖不同类型，不重复）────────────────────── +_TITLE_FORMULA = """ +【标题公式（5类，每条用不同类型，禁止同批重复）】 +肤质型：「{肤质}+用了{产品}+{感受}」例→"油皮用了素颜霜整个夏天不脱妆" +价格型：「{价格锚点}+{产品}+{功效感受}」例→"三位数买到大牌平替，用完第一罐回购第二罐" +功效型：「{使用场景}+{产品}+{可感知变化}」例→"早起素颜出门靠这罐，同事问我最近皮肤怎么了" +夸张型：「疑问/感叹+{夸张感受}+{产品}」例→"这什么神奇产品，涂上去感觉素颜也能出门了" +标题党型：「{反常识/意外信息}+真相是{翻转}」例→"被人说皮肤变好了，没说的是我用了它一个月" +硬性约束：标题≤20字；禁止出现"绝绝子/YYDS/杀疯了"；不直接写功效词（美白/祛斑等）。 +""".strip() +_ANTI_AI = f""" +【反AI味必须遵守】 +- 禁止固定开篇套话：不许"姐妹们/宝子们/今天给大家分享"开头 +- 禁止以下AI味词出现在正文：{_NEGATIVE_WORDS} +- 禁止人群重叠：{'{count}'}条文案中身份场景不能重复（靠变量池ABC保证） +- 禁止场景重复：同批次文案不能都是"早上上班"或都是"学生宿舍" +- 避免生硬推销词：按头安利/绝绝子/闭眼冲 不能出现 +""".strip() + +# ── 系统 prompt（合并Q2/Q3/Q4/Q7/Q8，数据层走build_prompt动态注入）────────── +COPY_SYSTEM = f"""{_PERSONA} + +合规红线（全品类通用）： +- 禁用"美白、祛斑、速效、医用、药妆"，不得暗示治疗或改善疾病 +- 不得承诺效果，不得出现before/after变白对比暗示 +- 图文合规：避免社交App界面、点赞评论等截图元素 + +{_ANTI_AI.format(count="N")} + +{_TITLE_FORMULA} + +{_EMOJI_RULES} + +{_STRUCTURE_RULES} + +返回纯JSON数组，每条字段： +title（标题≤20字）/ content（正文，严格350-400字，按emoji规则适度带表情）/ tags（list，3-5个#话题） +angle（本条角度标签）/ coverTitle（封面大字≤10字）/ imageBrief（配图方向） + +硬性格式：只输出JSON，不要markdown代码块，字符串内用中文引号「」。""" + + +def build_prompt(product: dict, count: int, extra_rules: str = "") -> str: + """ + 组装文案生成 user_prompt。 + 数据层：product 动态注入（name/selling_points/style_tone/text_angles/custom_prompt） + 方法层：已在 COPY_SYSTEM 固定，这里只注入产品数据+随机变量 + """ + name = product.get("name", "产品") + selling = "、".join(product.get("selling_points") or ["核心卖点待录入"]) + style = product.get("style_tone", "素人日常分享风") + angles = product.get("text_angles") or [] + custom = (product.get("custom_prompt") or "").strip() + brand_kw = (product.get("brand_keyword") or "").strip() + + # Q1：每条抽一组随机变量，传给模型作角色约束 + combos = [_pick_combo() for _ in range(count)] + combos_text = "\n".join( + f" 第{i+1}条：身份场景=「{c['identity']}」·起因情绪=「{c['emotion']}」·小缺点=「{c['flaw']}」" + for i, c in enumerate(combos) + ) + + angle_hint = f"文案角度要覆盖：{'、'.join(angles)}（每条用不同角度）。" if angles else "" + brand_rule = f"每条正文和标题中植入品牌词「{brand_kw}」一次（自然融入，不生硬）。" if brand_kw else "" + + lines = [ + f"产品：{name}", + f"核心卖点（必须翻译成用户能感知的生活化利益，禁止直接列功效词；翻译范例：'烟酰胺'→'熬夜后第二天脸不那么黄了'，'高保湿'→'涂上去一整天都没搓泥拔干'）：{selling}", + f"风格调性：{style}", + angle_hint, + brand_rule, + custom, + f"\n【Q1随机变量池·每条身份/起因/小缺点各不相同，严格按下方分配使用】", + combos_text, + extra_rules, + f"\n请严格按5步框架+四段结构生成 {count} 条，每条350-400字，返回纯JSON数组。", + ] + return "\n".join(l for l in lines if l) + + +def parse_json_array(raw: str) -> list[dict]: + """从模型输出提取 JSON 数组（容错 markdown 包裹）""" + text = re.sub(r"```(?:json)?", "", raw).strip() + start, end = text.find("["), text.rfind("]") + if start == -1 or end == -1: + return [] + try: + return json.loads(text[start:end + 1]) + except json.JSONDecodeError: + return [] + + +def build_local_drafts(product: dict, count: int) -> list[dict]: + """本地模板兜底（保证永不空手，遵循四段结构） + 角度从 text_angles 循环取（保证N条角度各不同，不被 dedupe_copies 吞掉） + """ + name = product.get("name", "产品") + points = product.get("selling_points") or ["使用方便", "真实可感知"] + # 从产品档案取角度池，不够就用通用角度兜底，保证每条都不同 + _fallback_angles = ["生活场景型", "成分分析型", "使用感受型", "性价比型", "痛点切入型"] + angles_pool = product.get("text_angles") or _fallback_angles + for i in range(count): + c = _pick_combo() + # 循环取不同角度（角度相同的两条会被 dedupe_copies 过滤掉，所以必须不重复） + angle = angles_pool[i % len(angles_pool)] + yield { + "title": f"发现一个{name}的{angle}用法", + "content": ( + f"{c['identity']}，{c['emotion']}。\n\n" + f"用了一段时间，{points[i % len(points)]}这点最让我意外。\n\n" + f"说个小缺点：{c['flaw']}，后来才摸到感觉。\n\n" + f"反正现在用顺手了。✅" + ), + "tags": [f"#{name}", "#真实测评", "#好物分享"], + "angle": angle, + "coverTitle": f"{name}{angle}", + "imageBrief": "封面产品近景，内页核心卖点+真实使用场景。", + } diff --git a/backend/app/services/ai_engine/banned_word_checker.py b/backend/app/services/ai_engine/banned_word_checker.py new file mode 100644 index 0000000..87d52e9 --- /dev/null +++ b/backend/app/services/ai_engine/banned_word_checker.py @@ -0,0 +1,125 @@ +""" +违禁词三级处理（扒 copy.js sanitizePlanningText 扩展为三级） +🟢 auto_fix = 自动改写（replacement 字段给出替换词） +🟡 soft_warn = 软提示（返回建议词，不阻塞） +🔴 hard_block= 硬拦截（直接返回 None，拦住发布） + +词库来自数据库 banned_words 表（level + replacement 字段）， +DB 未配时用本模块内置默认词库作冷启动。 +""" +from __future__ import annotations +import re +from dataclasses import dataclass, field +from typing import Literal + +BannedLevel = Literal["auto_fix", "soft_warn", "hard_block"] + + +@dataclass +class BannedWordEntry: + word: str + level: BannedLevel + replacement: str | None = None # auto_fix 时提供替换词 + + +# ── 默认词库（北哥回填解读与落点 §4.3，数据库未配时使用）─ +DEFAULT_BANNED_WORDS: list[BannedWordEntry] = [ + # 功效违禁（auto_fix：改写成合规表达，对应北哥"提亮肤色感/改善暗沉观感"） + BannedWordEntry("美白", "auto_fix", "提亮肤色感"), + BannedWordEntry("祛斑", "auto_fix", "改善暗沉观感"), + # 功效违禁（hard_block：无法合规改写，直接拦截） + BannedWordEntry("速效", "hard_block"), + BannedWordEntry("医用", "hard_block"), + BannedWordEntry("药妆", "hard_block"), + BannedWordEntry("强效焕白", "hard_block"), + # 保证性词（soft_warn） + BannedWordEntry("绝对", "soft_warn"), + BannedWordEntry("第一名", "soft_warn"), + BannedWordEntry("再也不", "soft_warn"), + # 夸张词（soft_warn） + BannedWordEntry("杀疯了", "soft_warn"), + BannedWordEntry("秒杀", "soft_warn"), + BannedWordEntry("震撼", "soft_warn"), + # AI 味词（auto_fix，置换为口语表达；同时在 _NEGATIVE_WORDS prompt负向约束里已禁止AI写进正文） + BannedWordEntry("神器", "auto_fix", "好用的"), + BannedWordEntry("福音", "auto_fix", "适合的"), + BannedWordEntry("救急单品", "auto_fix", "随手备用的"), + BannedWordEntry("遮羞布", "auto_fix", "底妆感"), # 北哥原文补录 + BannedWordEntry("不仅而且", "auto_fix", "，另外"), + BannedWordEntry("焕发", "auto_fix", "呈现"), + BannedWordEntry("守护", "auto_fix", ""), + BannedWordEntry("尽享", "auto_fix", "使用"), + BannedWordEntry("日常维稳", "auto_fix", "日常保养"), + BannedWordEntry("精简底妆", "auto_fix", "轻便底妆"), + # 视觉违禁（hard_block，文案含这些词不许过） + BannedWordEntry("前后对比", "hard_block"), + BannedWordEntry("使用前后", "hard_block"), + BannedWordEntry("变白", "auto_fix", "自然光泽感"), + BannedWordEntry("瑕疵消失", "auto_fix", "妆感更服帖"), +] + + +@dataclass +class CheckResult: + text: str # 原文（soft_warn/hard_block 场景下保持原文） + fixed_text: str | None # auto_fix 后的文本；其他级别为 None + status: Literal["pass", "auto_fixed", "soft_warn", "hard_block"] + found: list[dict] = field(default_factory=list) + # found 每项: {"word": str, "level": BannedLevel, "replacement": str|None} + + +def check_and_fix( + text: str, + entries: list[BannedWordEntry] | None = None, +) -> CheckResult: + """ + 对一段文本做三级违禁词扫描。 + entries：优先用 DB 词条，为 None 时用默认词库。 + """ + word_list = entries if entries is not None else DEFAULT_BANNED_WORDS + found: list[dict] = [] + working = text + + # 先扫描所有命中 + for entry in word_list: + if entry.word.lower() in working.lower(): + found.append({ + "word": entry.word, + "level": entry.level, + "replacement": entry.replacement, + }) + + if not found: + return CheckResult(text=text, fixed_text=None, status="pass", found=[]) + + # 有 hard_block → 直接拦截 + if any(f["level"] == "hard_block" for f in found): + return CheckResult(text=text, fixed_text=None, status="hard_block", found=found) + + # 只有 soft_warn → 软提示，不改文字 + if any(f["level"] == "soft_warn" for f in found) and \ + all(f["level"] in ("soft_warn", "auto_fix") for f in found): + # 仍执行 auto_fix 改写，但结果状态是 soft_warn（优先级高） + for f in found: + if f["level"] == "auto_fix" and f["replacement"] is not None: + working = re.sub(re.escape(f["word"]), f["replacement"], working, flags=re.IGNORECASE) + return CheckResult(text=text, fixed_text=working, status="soft_warn", found=found) + + # 只有 auto_fix → 自动改写，返回 fixed_text + for f in found: + if f["level"] == "auto_fix" and f["replacement"] is not None: + working = re.sub(re.escape(f["word"]), f["replacement"], working, flags=re.IGNORECASE) + return CheckResult(text=text, fixed_text=working, status="auto_fixed", found=found) + + +def build_entries_from_db(rows: list[dict]) -> list[BannedWordEntry]: + """把 DB banned_words 行转成 BannedWordEntry 列表""" + return [ + BannedWordEntry( + word=r["word"], + level=r["level"], + replacement=r.get("replacement"), + ) + for r in rows + if r.get("word") and r.get("level") in ("auto_fix", "soft_warn", "hard_block") + ] diff --git a/backend/app/services/ai_engine/constants.py b/backend/app/services/ai_engine/constants.py new file mode 100644 index 0000000..996ca13 --- /dev/null +++ b/backend/app/services/ai_engine/constants.py @@ -0,0 +1,139 @@ +""" +AI 引擎中心常量 +扒自：worker/src/copy.js + image.js 上线版 +业务参数不写死（基石A）——分值权重可由产品档案配置覆盖 +""" + +# ── 合规红线 ────────────────────────────────────────────── +# 初始默认词库（数据库 banned_words 表可覆盖，updatable=True） +BANNED_WORDS_DEFAULT = ["美白", "祛斑", "速效", "医用", "药妆"] +BANNED_VISUAL_WORDS = [ + "前后对比", "使用前后", "用前用后", + "before", "after", "变白", "瑕疵消失", "治疗前后", +] +# 内部提示词（不能混入正文 content 字段） +INTERNAL_COPY_HINTS = [ + "配图建议", "图片方向", "内页规划", "适合做成", + "不要做促销海报", "配图说明", "封面建议", +] + +# ── 机械五维打分基线（仅轨B导入文案/降级回退用；轨A已切 AI 评委 llm_score_copy）───── +# 历史注：轨A原用此5维，2026-06-15切至7维AI评委(6维+合规)后此处只作轨B+回退占位。 +# 关键词维度(keyword20)因 products 表无 keywords 字段导致 matched 恒空已知不准； +# 轨A不再依赖此权重，轨B展示参考可接受。不按此调分，真过关靠北哥抽检。 +SCORE_WEIGHTS = { + "title": 25, + "emotion": 25, + "selling": 25, + "keyword": 20, + "compliance": 5, +} +# ── AI 评委 7 维满分分布（倩倩姐2026-06-15拍板·与 llm_scorer._DIM_MAX/_score_prompt 三处同步）── +# 6维AI读分(痛点18+情绪18+买点18+钩子15+标题13+真实感13=95) + 合规5 = 100 +# "真实感"=富贵"很少提产品/前70%干货后30%植入"原则，替换旧机械维度"产品聚焦一件事(16)" +AI_DIM_WEIGHTS = { + "痛点人群精准": 18, + "情绪张力": 18, + "买点转化": 18, + "开头钩子": 15, + "标题点击力": 13, + "真实感": 13, + "compliance": 5, # 机械硬拦，不进 AI 评委 +} +# 过线分。倩倩姐2026-06-15拍板：80是临时观察值(AI评委给分克制，84文案实为合格)。 +# 倩倩姐2026-06-15再次拍板：维持80临时线，不准擅自调85。方向=提生成质量顶分数，不降标准。 +# 真过关靠北哥抽检；提质量方向=优化生成 prompt，不靠提高门槛凑数。 +QUALITY_PASS_SCORE = 80 + +# ── 文案去重阈值 ────────────────────────────────────────── +DEDUP_TITLE_THRESHOLD = 0.82 # 标题相似度≥此值判重 +DEDUP_TITLE_CONTENT_TITLE = 0.65 # 标题+正文联合判重时的标题阈值 +DEDUP_TITLE_CONTENT_BODY = 0.72 # 标题+正文联合判重时的正文阈值 + +# ── 自动优化循环 ────────────────────────────────────────── +MAX_OPTIMIZE_ROUNDS = 2 # 最多重生成轮次 + +# ── storyboard 分镜角色（枚举不写死数量）──────────────── +# Q6: 北哥6张套路顺序 ①封面痛点大字 ②单品特写+品牌词 ③成分 ④质地 ⑤上脸对比 ⑥促单 +PAGE_ROLES = [ + {"role": "hook", "name": "封面痛点大字", "focus": "负责点击：强情绪大字标题压痛点，产品露出，真实生活场景，像用户主动分享，不像广告海报"}, + {"role": "product_closeup", "name": "单品特写", "focus": "负责种草锚点：单品高清特写+品牌词自然植入，第2/6张都带品牌词，强化记忆"}, + {"role": "ingredient", "name": "成分拆解", "focus": "负责信任：核心成分信息、作用说明，避免医疗化和绝对化表达，信息清晰可信"}, + {"role": "texture", "name": "质地展示", "focus": "负责种草：质地近景、涂抹过程、肤感说明，真实手部/桌面/日常光线"}, + {"role": "applied_proof", "name": "上脸对比", "focus": "负责证明：可感知上脸效果，展示涂抹前后质地变化（不做肤色变白/瑕疵消失等违规暗示），第5张"}, + {"role": "closer", "name": "促单收尾", "focus": "负责转化：转化句+品牌词，引导搜索品牌词成交，软性收尾不硬广，第6张再带一次品牌词"}, + # 扩展角色（8张链路用） + {"role": "pain_scene", "name": "痛点共鸣", "focus": "负责共鸣：展示目标人群的真实困扰和使用前情境，但不做功效前后对比"}, + {"role": "social_proof","name": "信任背书", "focus": "负责背书：多人反馈、囤货、复购等真实社交证据"}, + {"role": "scenario", "name": "多场景演示", "focus": "负责代入：多场景使用展示，不做夸大效果承诺"}, + {"role": "tutorial", "name": "使用教程", "focus": "负责降低门槛：简洁步骤、用量、注意事项"}, +] +PAGE_ROLE_MAP = {r["role"]: r for r in PAGE_ROLES} + +# ── 生图风格预设（扒 image.js STYLE_PROMPTS:26-29）────────── +# 按 style 参数选小红书风格调性，注入 base_prompt 的"视觉风格"行 +STYLE_PROMPTS = { + "xiaohongshu_cover": "小红书种草风，独立3:4图文海报/素材图，1024×1536构图，明亮干净，真实实拍质感，醒目中文短标题，文字在安全区内", + "comparison": "小红书说明对比风，独立3:4图文海报/素材图，1024×1536构图，质地/场景/肤感左右或上下对比，信息层级清晰", + "ingredient": "小红书成分科普风，独立3:4图文海报/素材图，1024×1536构图，成分卡片布局，浅色商务美妆风，避免医疗化表达", +} +STYLE_DEFAULT = "xiaohongshu_cover" + +# ── 叙事链路说明（扒 image.js planImageSet narrativeText:677-681）── +# 按图数告诉模型整组图的种草节奏，让每张各司其职不雷同 +NARRATIVE_BY_COUNT = { + 3: "3张极速链路：第1张负责点击，第2张是按品类变化的核心证明页，第3张负责软性转化。", + 6: "6张标准种草链路：封面点击、单品特写带品牌词、成分信任、质地种草、上脸证明、促单转化，每张画面和文字各司其职不重复。", + 8: "8张沉浸测评链路：点击、痛点共鸣、单品特写、成分、质地、上脸证明、背书、软性转化。", +} + +# ── 3套正交叙事策略（倩倩姐2026-06-15起草，北哥过目版）────────────── +# A痛点先行/B场景先行/C成分背书先行，三套正交轴拉开差异 +# 每套叙事链路注入 base_prompt 叙事链路段，替换 NARRATIVE_BY_COUNT 默认值 +NARRATIVE_BY_STRATEGY = { + "A": ( + '【套A·痛点先行】整组基调：紧迫感、强对比、情绪共鸣，文字短促带感叹号，直戳"脸黄显疲惫""素颜不敢出门"。' + '6张链路：①痛点暴击封面（强情绪大字直击暗黄/素颜焦虑）→ ②暗黄脸实拍对比（感叹号+对比词制造紧迫感）' + '→ ③单品特写+品牌词 → ④成分为什么能救暗黄（成分拆解+信任） → ⑤上脸提亮实证 → ⑥"别再顶着黄脸早八"软性促单。' + ), + "B": ( + '【套B·场景先行】整组基调：轻松、生活化、代入感，突出"快/省时/伪素颜自由"，点到性价比不堆砌。' + '6张链路：①"早八来不及"场景封面（生活场景钩子） → ②手忙脚乱通勤场景（代入早八焦虑）' + '→ ③一抹搞定单品特写+品牌词 → ④养肤成分让你敢素颜 → ⑤30秒上脸效果 → ⑥"伪素颜自由+平价"软性促单。' + ), + "C": ( + '【套C·成分背书先行】整组基调：专业、可信、真实测评感，强调成分逻辑+前后对比+像有用户实证背书。' + '6张链路：①成分权威封面（核心成分信息锚定信任） → ②核心成分图解（作用说明+清晰可信）' + '→ ③单品特写+品牌词 → ④使用前后时间线对比 → ⑤真实上脸细节 → ⑥"成分党闭眼入"软性促单。' + ), +} + +# ── 生图通道 ────────────────────────────────────────────── +IMAGE_RETRY_ATTEMPTS = 3 +IMAGE_RETRY_BACKOFF_BASE = 2.0 # 指数退避底数（秒） +IMAGE_SIZE_DEFAULT = "1024x1536" + +# ── 生图合规负向约束（方法层常量，全品类共用，可扩展）─────────────────────── +# 追加到每个 base_prompt 末尾，防模型脑补违禁词/真实品牌到包装 +IMAGE_NEGATIVE_CONSTRAINTS = ( + "【包装合规硬性禁止——必须严格遵守】" + "①包装/瓶身/标签上禁止出现任何违禁词：美白/whitening/祛斑/brightening/" + "医用/medical/drug/药妆/速效/instant，中英文全禁；" + "②禁止脑补任何真实品牌名或logo（如水密码/WETCODE/兰蔻/SK-II等），" + "产品包装只允许出现用户传入的指定品牌词，未传则画无字素瓶；" + "③英文功效词(ANTI-AGING/TONE-UP/BRIGHTENING/FIRMING等)禁止印在包装；" + "④如果提供了产品参考图，包装文字以参考图为准，不得自行添加或修改任何文字。" + "⑤背景纯净：禁止出现电子设备/笔记本电脑/键盘/手机/桌面杂物等无关物体（参考图若含此类背景一律不沿用），" + "只保留浅色简洁台面或产品定制场景，主体聚焦产品本身。" +) + +# ── 飞轮信号权重（初始默认，北哥可校准）──────────────── +FLYWHEEL_WEIGHTS = { + "text_select": 3, + "image_select": 3, + "approve": 5, + "reject_with_reason": -3, + "regenerate": -1, +} +FLYWHEEL_LOOKBACK = 50 # 聚合最近N条事件 +FLYWHEEL_COLD_START = 5 # 信号不足N条时用产品档案冷启动 diff --git a/backend/app/services/ai_engine/gemini_factory.py b/backend/app/services/ai_engine/gemini_factory.py new file mode 100644 index 0000000..a0c18a6 --- /dev/null +++ b/backend/app/services/ai_engine/gemini_factory.py @@ -0,0 +1,225 @@ +""" +gemini_factory.py — 每任务构建独立的 AI client 实例 +解决全局单例问题（扒 banana gemini_service.py __init__，改造为每任务局部实例） + +铁律（基石B）： +- 调用方只传 task_id，不传 key +- 本模块在 worker 内部查库 → Fernet 解密 → 构建 client +- 解密结果只活在局部变量，函数返回后即销毁 +- 绝不打印 / 记录 / 传递明文 key +""" +from __future__ import annotations +import asyncio +import logging +import os +from dataclasses import dataclass, field +from typing import Any + +import httpx + +logger = logging.getLogger(__name__) + + +@dataclass +class AIClients: + """ + 一个任务专用的 AI client 集合。 + worker 在任务开始时构建，任务结束后释放（局部变量，不存 Redis/DB）。 + """ + # httpx AsyncClient 懒加载 + 按事件循环缓存：Celery 每任务多次 asyncio.run， + # 持久 client 会绑死到首个已关闭的 loop → "Event loop is closed"。 + # 故只存 token/base，按当前运行 loop 缓存 client，loop 变了就重建。 + _gpt_token: str | None = field(default=None, repr=False) + _gpt_base: str | None = field(default=None, repr=False) + _gpt_client: httpx.AsyncClient | None = field(default=None, repr=False) + _gpt_client_loop_id: int | None = field(default=None, repr=False) + # 备用 OpenAI 兼容中转站（codeproxy）：apiports 503 时真正切过去（独立 base+key） + _alt_token: str | None = field(default=None, repr=False) + _alt_base: str | None = field(default=None, repr=False) + # 多 base/token 的 client 池：key=(base,token的id)，按 loop 失效重建 + _client_pool: dict = field(default_factory=dict, repr=False) + _pool_loop_id: int | None = field(default=None, repr=False) + _gemini_key: str | None = field(default=None, repr=False) # 局部变量不打印 + _model_image: str = "gpt-image-2" + _model_text: str = "claude-sonnet-4-5" # apiports无gpt-4o-mini,文案用claude中文质量好 + + def _client(self) -> httpx.AsyncClient: + """主通道(apiports) client，按当前事件循环缓存""" + return self._client_for(self._gpt_base, self._gpt_token) + + def _client_for(self, base: str | None, token: str | None) -> httpx.AsyncClient: + """按 (base, token) 返回可用 client；loop 变化则整池重建（避免跨 loop 复用）""" + if not token: + raise RuntimeError("GPT client 未初始化（缺 token）") + loop_id = id(asyncio.get_running_loop()) + if self._pool_loop_id != loop_id: + self._client_pool = {} + self._pool_loop_id = loop_id + ck = (base or "", token) + if ck not in self._client_pool: + self._client_pool[ck] = httpx.AsyncClient( + headers={"Authorization": f"Bearer {token}"}, + base_url=base or None, + timeout=120.0, + ) + return self._client_pool[ck] + + # ── ImageClient 协议实现（供 image_gen.py 使用）──────── + + def _gpt_target(self, provider: str | None) -> tuple[str, str | None, httpx.AsyncClient]: + """按 provider 选 (base, token, client)；codeproxy 走备用站独立 base+key""" + if provider == "codeproxy" and self._alt_token: + base = (self._alt_base or os.environ.get("CODEPROXY_BASE_URL") or "").rstrip("/") + return base, self._alt_token, self._client_for(base, self._alt_token) + base = (os.environ.get("IMAGE_API_BASE") or os.environ.get("APIPORTS_BASE_URL") or "").rstrip("/") + return base, self._gpt_token, self._client_for(self._gpt_base, self._gpt_token) + + async def gpt_edits(self, prompt: str, reference_images: list[bytes], size: str, provider: str | None = None) -> bytes: + """GPT edits endpoint（带产品参考图，禁纯文生图）""" + import io + files: list[tuple] = [("prompt", (None, prompt))] + for i, img in enumerate(reference_images): + files.append(("image[]", (f"ref_{i}.png", io.BytesIO(img), "image/png"))) + files.append(("size", (None, size))) + files.append(("model", (None, self._model_image))) + base, _, client = self._gpt_target(provider) + resp = await client.post(f"{base}/images/edits", files=files, timeout=120.0) + resp.raise_for_status() + return _extract_image_bytes(resp.json()) + + async def gpt_generate(self, prompt: str, size: str, provider: str | None = None) -> bytes: + """GPT 纯文生图（仅 ALLOW_TEXT_ONLY_IMAGE=true 时用）""" + base, _, client = self._gpt_target(provider) + payload = {"model": self._model_image, "prompt": prompt, "n": 1, "size": size} + resp = await client.post(f"{base}/images/generations", json=payload, timeout=120.0) + resp.raise_for_status() + return _extract_image_bytes(resp.json()) + + async def gemini_generate(self, prompt: str, reference_images: list[bytes], model: str) -> bytes: + """Gemini 生图（备用通道）""" + if not self._gemini_key: + raise RuntimeError("Gemini key 未初始化") + gemini_base = os.environ.get("GEMINI_API_URL", "https://generativelanguage.googleapis.com/v1beta") + url = f"{gemini_base}/models/{model}:generateContent?key={self._gemini_key}" + parts: list[dict] = [{"text": prompt}] + for img in reference_images: + import base64 + parts.append({"inline_data": {"mime_type": "image/png", "data": base64.b64encode(img).decode()}}) + payload = {"contents": [{"role": "user", "parts": parts}], "generationConfig": {"responseModalities": ["IMAGE", "TEXT"]}} + async with httpx.AsyncClient() as client: + resp = await client.post(url, json=payload, timeout=120.0) + resp.raise_for_status() + return _extract_gemini_image(resp.json()) + + async def chat_complete(self, messages: list[dict], model: str | None = None, max_tokens: int = 4096, temperature: float = 0.75) -> str: + """文字生成（文案生成用）""" + base = (os.environ.get("IMAGE_API_BASE") or os.environ.get("APIPORTS_BASE_URL") or "").rstrip("/") + payload = {"model": model or self._model_text, "messages": messages, "max_tokens": max_tokens, "temperature": temperature} + # 单批≤4条文案正常 40-55s 返回；apiports 网关 ~60s 上限。客户端超时设 75s： + # 略高于网关上限即可，过长（如180s）会在 apiports 卡顿时干等，拖慢整体。 + timeout = float(os.environ.get("TEXT_LLM_TIMEOUT", "75")) + resp = await self._client().post(f"{base}/chat/completions", json=payload, timeout=timeout) + resp.raise_for_status() + return resp.json()["choices"][0]["message"]["content"] or "" + + async def gpt_vision_analyze(self, prompt: str, images: list[bytes], model: str | None = None) -> str: + """ + GPT/Claude vision 读产品图，返回 JSON 字符串。 + messages content 混合 text + image_url(base64)，OpenAI vision 格式。 + model 默认最强档（claude-opus-4-8），绝不偷降级。 + 最多传 4 张图，避免超 token。 + """ + import base64 + content: list[dict] = [{"type": "text", "text": prompt}] + for img in images[:4]: + b64 = base64.b64encode(img).decode() + content.append({ + "type": "image_url", + "image_url": {"url": f"data:image/jpeg;base64,{b64}"}, + }) + used_model = model or os.environ.get("MODEL_TEXT", "claude-opus-4-8") + messages = [{"role": "user", "content": content}] + base = (os.environ.get("IMAGE_API_BASE") or os.environ.get("APIPORTS_BASE_URL") or "").rstrip("/") + payload = { + "model": used_model, + "messages": messages, + "max_tokens": 2048, + "temperature": 0.2, + } + resp = await self._client().post(f"{base}/chat/completions", json=payload, timeout=90.0) + resp.raise_for_status() + return resp.json()["choices"][0]["message"]["content"] or "" + + # duck-type: text_variants._call_llm 用的属性 + @property + def _model(self) -> str: + return self._model_text + + async def aclose(self) -> None: + # client 可能绑在已关闭的 loop（Celery 多次 asyncio.run），aclose 也可能报 + # "Event loop is closed"，吞掉即可——进程级连接随 loop 关闭自然释放。 + for c in list(self._client_pool.values()): + try: + await c.aclose() + except Exception: + pass + self._client_pool = {} + self._pool_loop_id = None + self._gpt_client = None + self._gpt_client_loop_id = None + + +def build_ai_clients(plain_key: str, gemini_key: str | None = None) -> AIClients: + """ + 用解密后的明文 key 构建 AIClients。 + 只在 Celery worker 函数体内调用，plain_key 是局部变量。 + httpx client 不在此预创建（避免绑死到调用方 loop），首次 await 时按 loop 懒建。 + 调用完成后 caller 负责 await clients.aclose()。 + """ + gpt_base = ( + os.environ.get("IMAGE_API_BASE") # 旧变量名 + or os.environ.get("APIPORTS_BASE_URL") # .env 实际变量名 + or "" + ).rstrip("/") + # 备用站 codeproxy：系统级 key（非用户录入），apiports 503 时切过去保生图成功 + alt_base = (os.environ.get("CODEPROXY_BASE_URL") or "").rstrip("/") + alt_token = os.environ.get("CODEPROXY_KEY") or None + return AIClients( + _gpt_token=plain_key, + _gpt_base=gpt_base or None, + _alt_base=alt_base or None, + _alt_token=alt_token, + _gemini_key=gemini_key, + _model_image=os.environ.get("IMAGE_MODEL") or os.environ.get("MODEL_IMAGE", "gpt-image-2"), + _model_text=os.environ.get("MODEL_TEXT", "claude-opus-4-8"), + ) + + +# ── 图片响应解析工具 ───────────────────────────────────── + +def _extract_image_bytes(resp_json: dict) -> bytes: + """从 OpenAI images API 响应提取图片 bytes（b64 或 url）""" + import base64 + data = resp_json.get("data", [{}]) + if not data: + raise ValueError("图片 API 返回空 data") + item = data[0] + if "b64_json" in item: + return base64.b64decode(item["b64_json"]) + if "url" in item: + resp = httpx.get(item["url"], timeout=30.0) + resp.raise_for_status() + return resp.content + raise ValueError(f"无法解析图片响应：{list(item.keys())}") + + +def _extract_gemini_image(resp_json: dict) -> bytes: + """从 Gemini generateContent 响应提取图片 bytes""" + import base64 + candidates = resp_json.get("candidates", []) + for cand in candidates: + parts = cand.get("content", {}).get("parts", []) + for part in parts: + if "inlineData" in part: + return base64.b64decode(part["inlineData"]["data"]) + raise ValueError("Gemini 响应中未找到图片数据") diff --git a/backend/app/services/ai_engine/image_gen.py b/backend/app/services/ai_engine/image_gen.py new file mode 100644 index 0000000..93b70fd --- /dev/null +++ b/backend/app/services/ai_engine/image_gen.py @@ -0,0 +1,175 @@ +""" +生图通道 — gpt-image-2 主（edits 带产品图） / Gemini 备 + 重试退避 +扒自：worker/src/image.js generateOneImage / requestProviderImage / imageProviderOrder +新增：asyncio 重试退避（上线版缺的，banana 有 _retry 思路） + +铁律： +- IMAGE_PROVIDER_PRIMARY/FALLBACK 走环境变量，不写死 +- GPT 主通道必须有产品参考图，无图报错（禁纯文生图防产品跑偏） +- key 不在本模块，由 worker 传入构造好的 async HTTP client +""" +from __future__ import annotations +import asyncio +import logging +import os +from typing import Any, Protocol + +from .constants import IMAGE_RETRY_ATTEMPTS, IMAGE_RETRY_BACKOFF_BASE, IMAGE_SIZE_DEFAULT +from .storyboard import plan_image_set, sanitize_text + +logger = logging.getLogger(__name__) + + +class ImageClient(Protocol): + """worker 注入的图片生成客户端协议（隔离 key 细节）""" + async def gpt_edits( + self, prompt: str, reference_images: list[bytes], size: str, provider: str | None = None + ) -> bytes: ... + async def gpt_generate(self, prompt: str, size: str, provider: str | None = None) -> bytes: ... + async def gemini_generate( + self, prompt: str, reference_images: list[bytes], model: str + ) -> bytes: ... + + +def _image_provider_order() -> list[str]: + """从环境变量读主备顺序（扒 imageProviderOrder）""" + primary = os.environ.get("IMAGE_PROVIDER_PRIMARY", "gpt").lower() + fallback = os.environ.get("IMAGE_PROVIDER_FALLBACK", "gemini").lower() + seen: list[str] = [] + for p in [primary, fallback]: + if p and p not in seen: + seen.append(p) + return seen + + +def _gemini_models() -> list[str]: + """Gemini fallback 模型列表（多模型依次重试）""" + env_val = os.environ.get("GEMINI_IMAGE_MODELS", "gemini-2.0-flash-preview-image-generation,imagen-3.0-generate-002") + return [m.strip() for m in env_val.split(",") if m.strip()] + + +async def _retry(coro_fn, attempts: int = IMAGE_RETRY_ATTEMPTS, backoff: float = IMAGE_RETRY_BACKOFF_BASE) -> Any: + """指数退避重试（扒 banana _retry 思路）""" + last_exc: Exception | None = None + for i in range(attempts): + try: + return await coro_fn() + except Exception as exc: + last_exc = exc + if i < attempts - 1: + wait = backoff ** i + logger.warning("生图失败第%d次，%.1fs后重试：%s", i + 1, wait, exc) + await asyncio.sleep(wait) + raise RuntimeError(f"重试{attempts}次均失败") from last_exc + + +async def _request_gpt(client: ImageClient, prompt: str, reference_images: list[bytes], provider: str | None = None) -> bytes: + if reference_images: + return await client.gpt_edits(prompt, reference_images, IMAGE_SIZE_DEFAULT, provider) + # 无产品参考图时降级为纯文生图（需 ALLOW_TEXT_ONLY_IMAGE=true 或 M2阶段） + allow_text_only = os.environ.get("ALLOW_TEXT_ONLY_IMAGE", "true").lower() == "true" + if allow_text_only: + logger.warning("无产品参考图，降级为纯文生图（可能产品跑偏，建议前端上传参考图）") + return await client.gpt_generate(prompt, IMAGE_SIZE_DEFAULT, provider) + raise ValueError("GPT 主通道缺产品图：禁止纯文生图以免产品跑偏（设 ALLOW_TEXT_ONLY_IMAGE=true 可解锁）") + + +async def _request_gemini(client: ImageClient, prompt: str, reference_images: list[bytes]) -> bytes: + errors: list[str] = [] + for model in _gemini_models(): + try: + return await client.gemini_generate(prompt, reference_images, model) + except Exception as exc: + errors.append(f"{model}: {exc}") + raise RuntimeError("Gemini 全部模型失败：" + "；".join(errors)) + + +async def generate_one_image( + client: ImageClient, + prompt: str, + reference_images: list[bytes] | None = None, +) -> bytes: + """ + 主入口：按主备顺序依次尝试，每个 provider 内部有重试退避。 + 返回图片 bytes（PNG/JPEG）。 + """ + refs = reference_images or [] + providers = _image_provider_order() + errors: list[str] = [] + + for provider in providers: + try: + # apiports/codeproxy/openai 都是 OpenAI 兼容中转站，走 gpt 协议， + # 但传 provider 进去 → client 按 provider 切到对应中转站的 base+key。 + # 这才是真主备：apiports 503 → codeproxy 用独立 base+key 顶上。 + if provider in ("apiports", "codeproxy", "openai"): + img = await _retry(lambda p=provider: _request_gpt(client, prompt, refs, p)) + elif provider == "gpt": + img = await _retry(lambda: _request_gpt(client, prompt, refs, None)) + elif provider == "gemini": + img = await _retry(lambda: _request_gemini(client, prompt, refs)) + else: + raise ValueError(f"未知图片通道：{provider}") + return img + except Exception as exc: + errors.append(f"{provider}: {exc}") + logger.warning("图片通道 %s 失败，尝试下一个：%s", provider, exc) + + raise RuntimeError("所有图片通道均失败：" + "；".join(errors)) + + +async def generate_storyboard_images( + client: ImageClient, + note: dict, + product: dict, + image_count: int = 3, + reference_images: list[bytes] | None = None, + analysis: dict | None = None, + strategy: str | None = None, +) -> list[dict]: + """ + 按 storyboard 逐张生图（asyncio.gather 并发），返回每张结果列表。 + strategy: None=默认叙事，'A'/'B'/'C'=三套正交叙事策略 + 每项：{role, name, image_bytes, error} + """ + plan = plan_image_set(note, product, image_count, analysis, strategy=strategy) + storyboard = plan["storyboard"] + base_prompt = plan["base_prompt"] + + async def _gen_one(item: dict) -> dict: + # 逐图 prompt 9 字段（扒 promptFromStoryboard:323-334），每张差异化 + brand_line = "" + if item.get("brand_keyword"): + brand_line = f"品牌词=「{item['brand_keyword']}」，{item.get('brand_keyword_rule','自然植入')}。" + per_prompt = ( + f"{base_prompt}\n" + f"本张名称={item['name']}。" + f"本张目标={item.get('goal') or item['focus']}。" + f"图上主文字=「{sanitize_text(item.get('overlay_text',''), 20)}」。" + f"使用卖点={item.get('selling_point','')}。" + f"文案依据={item.get('source_basis','')}。" + f"画面主体={item.get('visual_strategy','')}。" + f"素材使用={item.get('asset_use','')}。" + f"{brand_line}" + f"禁止事项={item.get('forbidden','')}。" + "排版要求：独立小红书3:4图文海报，画面完整；标题只出现一次，不与其他页重复；" + "中文文字少而清晰，主标题+最多3个短点位；可自然用✅✨🌿💧🪞🧴📦🔍种草符号但不堆砌；" + "不要生成App截图或笔记详情页界面。" + ) + try: + img_bytes = await generate_one_image(client, per_prompt, reference_images) + return {"role": item["role"], "name": item["name"], "image_bytes": img_bytes, "error": None} + except Exception as exc: + logger.error("分镜 %s 生图失败: %s", item["role"], exc) + return {"role": item["role"], "name": item["name"], "image_bytes": None, "error": str(exc)} + + # 限并发：apiports 图片接口有 QPS 限制，6 张全并发会撞 429/503 + concurrency = int(os.environ.get("IMAGE_CONCURRENCY", "2")) + sem = asyncio.Semaphore(max(1, concurrency)) + + async def _gen_guarded(item: dict) -> dict: + async with sem: + return await _gen_one(item) + + results = await asyncio.gather(*(_gen_guarded(item) for item in storyboard)) + return list(results) diff --git a/backend/app/services/ai_engine/image_postprocessor.py b/backend/app/services/ai_engine/image_postprocessor.py new file mode 100644 index 0000000..e3f57e4 --- /dev/null +++ b/backend/app/services/ai_engine/image_postprocessor.py @@ -0,0 +1,177 @@ +""" +图片后处理（去AI化主路） +对齐大卫 xhs-tool/backend/infrastructure/imagePostProcess.js（运营实测去AI化版）。 + +主路 = 尺寸可选(±2%容差内不resize) + SynthID破除(可选) + 高保真重编码去元数据。 + +诚实声明：C2PA 元数据可去除；私有像素水印（如 SynthID）只能削弱，不保证 100% 清除。 +""" +from __future__ import annotations + +import io +import logging +import os + +logger = logging.getLogger(__name__) + +try: + from PIL import Image, ImageEnhance, ImageOps + _PILLOW_OK = True +except ImportError: + _PILLOW_OK = False + logger.warning("Pillow 未安装，image_postprocessor 不可用") + +# 比例映射表，对齐大卫 RATIO_MAP。key 为字符串如 '3:4' +RATIO_MAP: dict[str, tuple[int, int]] = { + "1:1": (1024, 1024), + "3:4": (1024, 1536), # gpt-image-2 原生尺寸，默认 + "4:3": (1536, 1024), + "9:16": (864, 1536), + "16:9": (1536, 864), +} + +# ±2% 容差内不做 resize，避免无谓重采样（对齐大卫 diff > 0.02 才 resize） +_RATIO_TOLERANCE = 0.02 + + +def _need_resize(actual_w: int, actual_h: int, target_w: int, target_h: int) -> bool: + """判断实际比例与目标比例差距是否超出容差。""" + actual_ratio = actual_w / actual_h + target_ratio = target_w / target_h + diff = abs(actual_ratio - target_ratio) / target_ratio + return diff > _RATIO_TOLERANCE + + +def process_image( + image_bytes: bytes, + aspect_ratio: str = "3:4", + resample_strength: int = 1, # 0=不重采样, 1=轻采样(默认), 2=重采样 +) -> bytes: + """ + 处理单张图片。 + + 参数： + image_bytes — 原始图片 bytes（PNG/JPEG/WebP 等） + aspect_ratio — 目标比例，取 RATIO_MAP 的 key，默认 '3:4'=1024×1536 + resample_strength — 轻重采样削像素水印（0/1/2），默认 1=轻采样 + + 返回 JPEG bytes（无 EXIF/C2PA/XMP 元数据）。 + 失败时降级返回原图 bytes，不抛异常（对齐大卫 catch 返回原图）。 + """ + if not _PILLOW_OK: + logger.error("Pillow 未安装，跳过后处理，返回原图") + return image_bytes + + try: + img = Image.open(io.BytesIO(image_bytes)).convert("RGB") + actual_w, actual_h = img.size + target = RATIO_MAP.get(aspect_ratio) + + # --- Step1: 尺寸对齐（±2% 容差内跳过 resize）--- + if target: + tw, th = target + if _need_resize(actual_w, actual_h, tw, th): + img = ImageOps.fit(img, (tw, th), method=Image.LANCZOS) + logger.debug("resize %dx%d → %dx%d (ratio=%s)", actual_w, actual_h, tw, th, aspect_ratio) + + # --- Step2: resample_strength 削像素水印（可选，默认轻采样）--- + img = _apply_resample(img, resample_strength) + + # --- Step3: SynthID 破除（SYNTHID_HARD_MODE=1 才开，默认关）--- + if os.environ.get("SYNTHID_HARD_MODE") == "1" and target: + img = _apply_synthid_break(img, target) + + # --- Step4: 高保真 JPEG 重编码，去所有元数据 --- + buf = io.BytesIO() + img.save( + buf, + format="JPEG", + quality=100, + subsampling=0, # 4:4:4 chroma + optimize=True, + # 不传 exif/icc_profile/xmp = 不写入任何元数据 + ) + result = buf.getvalue() + logger.debug("后处理完成 %d B → %d B (ratio=%s)", len(image_bytes), len(result), aspect_ratio) + return result + + except Exception as exc: + logger.warning("图片后处理失败，降级返回原图: %s", exc) + return image_bytes + + +def _apply_resample(img: "Image.Image", strength: int) -> "Image.Image": + """ + 轻/重采样削像素级水印（resample_strength 控制）。 + 0 — 不采样，仅靠重编码去元数据。 + 1 — 轻采样：缩98%再回原尺寸，保视觉质量，削弱像素水印（对齐旧逻辑）。 + 2 — 重采样：两次缩放，削弱更多，轻微质量损失。 + """ + if strength < 1: + return img + w, h = img.size + img = img.resize((int(w * 0.98), int(h * 0.98)), Image.LANCZOS) + img = img.resize((w, h), Image.LANCZOS) + if strength >= 2: + img = img.resize((int(w * 0.96), int(h * 0.96)), Image.LANCZOS) + img = img.resize((w, h), Image.LANCZOS) + return img + + +def _apply_synthid_break(img: "Image.Image", target: tuple[int, int]) -> "Image.Image": + """ + SynthID 破除（SYNTHID_HARD_MODE=1 时调用）： + 对齐大卫逻辑 — 缩到(w-2,h-2)再裁掉1px边 + 亮度*1.005/饱和*0.998。 + 诚实声明：只能削弱 SynthID，不保证 100% 清除。 + """ + tw, th = target + img = ImageOps.fit(img, (tw - 2, th - 2), method=Image.LANCZOS) + # 裁掉1px边，消除边缘水印残留 + img = img.crop((1, 1, tw - 3, th - 3)) + # 微调亮度/饱和（对齐大卫 modulate brightness/saturation） + img = ImageEnhance.Brightness(img).enhance(1.005) + img = ImageEnhance.Color(img).enhance(0.998) + return img + + +def batch_process( + images: list[bytes], + aspect_ratio: str = "3:4", + resample_strength: int = 1, +) -> list[dict]: + """ + 批量后处理。返回 [{index, data, error}]，单张失败不阻塞其余。 + """ + results = [] + for i, img_bytes in enumerate(images): + try: + processed = process_image(img_bytes, aspect_ratio=aspect_ratio, + resample_strength=resample_strength) + results.append({"index": i, "data": processed, "error": None}) + except Exception as exc: + logger.error("图片[%d]后处理失败: %s", i, exc) + results.append({"index": i, "data": img_bytes, "error": str(exc)}) + return results + + +async def gemini_rewatermark_fallback( + client: "Any", # GeminiClient，由 worker 注入 + image_bytes: bytes, +) -> bytes: + """ + 备选路：Gemini 重绘去水印。 + ⚠️ 对海报中文大字有改字风险，仅特殊场景启用。 + """ + prompt = ( + "Remove all watermarks, text overlays, and digital signatures from this image. " + "Reconstruct any covered areas naturally to match the surrounding content. " + "Return a clean version of the same image without any watermarks." + ) + try: + result = await client.gemini_generate( + prompt, [image_bytes], "gemini-2.0-flash-preview-image-generation" + ) + return result + except Exception as exc: + logger.error("Gemini 去水印失败，降级返回原图: %s", exc) + return image_bytes diff --git a/backend/app/services/ai_engine/llm_scorer.py b/backend/app/services/ai_engine/llm_scorer.py new file mode 100644 index 0000000..47e559c --- /dev/null +++ b/backend/app/services/ai_engine/llm_scorer.py @@ -0,0 +1,96 @@ +""" +llm_scorer.py — AI 评委打分入口（让模型真读文案，替代机械找词） +合规第7维仍走机械硬拦(score_compliance)；AI 读前6维给分+理由。 +任何异常/解析失败 → 回退旧机械 score_copy，绝不卡链路。 +""" +from __future__ import annotations +import asyncio +import json +import logging +import re +from typing import Any + +from .constants import BANNED_WORDS_DEFAULT, BANNED_VISUAL_WORDS, QUALITY_PASS_SCORE +from ._scoring_dims import score_compliance +from .text_scoring import score_copy +from ._score_prompt import SCORER_PERSONA, build_score_prompt, COMPLIANCE_MAX + +logger = logging.getLogger(__name__) + +# 6 个 AI 维度满分（倩倩姐2026-06-15拍板·与 constants.AI_DIM_WEIGHTS/_score_prompt 三处同步） +# 痛点18+情绪18+买点18+钩子15+标题13+真实感13=95，+合规5=100 +# "真实感"替换旧"产品聚焦一件事"，对齐富贵"很少提产品/前70%干货后30%植入"原则 +_DIM_MAX = { + "痛点人群精准": 18, "情绪张力": 18, "买点转化": 18, + "开头钩子": 15, "标题点击力": 13, "真实感": 13, +} +# 评委合规相关默认权重（仅供 score_compliance 复用其内部硬拦逻辑） +_COMPLIANCE_W = {"compliance": COMPLIANCE_MAX} + + +def _parse_verdict(raw: str) -> dict | None: + """从模型输出里抠出 JSON 对象，失败返 None。""" + s = raw.strip() + m = re.search(r"\{.*\}", s, re.DOTALL) + if not m: + return None + try: + obj = json.loads(m.group(0)) + return obj if isinstance(obj, dict) and isinstance(obj.get("dims"), list) else None + except (json.JSONDecodeError, ValueError): + return None + + +async def llm_score_copy( + client: Any, + copy: dict[str, Any], + source: dict[str, Any], + banned_words: list[str] | None = None, + pass_score: int = QUALITY_PASS_SCORE, +) -> dict[str, Any]: + """AI 评委读 1 条文案 → 6维分+理由，合规机械硬拦。返回与 score_copy 同结构。""" + bwords = list(set((banned_words or []) + BANNED_WORDS_DEFAULT + BANNED_VISUAL_WORDS)) + full = f"{copy.get('title','')}\n{copy.get('content','')}\n{' '.join(str(t) for t in copy.get('tags',[]))}" + dim_comp, found_all = score_compliance(full, bwords, _COMPLIANCE_W) + + prompt = build_score_prompt(copy, source) + raw = "" + backoff = [5, 10, 20] + for attempt in range(4): + try: + raw = await client.chat_complete( + messages=[{"role": "system", "content": SCORER_PERSONA}, + {"role": "user", "content": prompt}], + model=client._model, max_tokens=1500, temperature=0.3, + ) + break + except Exception as exc: # noqa: BLE001 — 含 httpx.HTTPStatusError 503/429 + status = getattr(getattr(exc, "response", None), "status_code", 0) + if status in (503, 429) and attempt < 3: + await asyncio.sleep(backoff[min(attempt, 2)]) + continue + logger.warning("AI评委调用失败，回退机械打分: %s", exc) + return score_copy(copy, source, banned_words, pass_score=pass_score) + + verdict = _parse_verdict(raw) + if not verdict: + logger.warning("AI评委输出解析失败，回退机械打分。raw[:120]=%s", raw[:120]) + return score_copy(copy, source, banned_words, pass_score=pass_score) + + details: list[dict] = [] + for d in verdict["dims"]: + item = str(d.get("item", "")).strip() + if item not in _DIM_MAX: # 只收白名单6维，模型偶尔多吐"总分"等噪声项，丢弃 + continue + mx = _DIM_MAX[item] + sc = max(0, min(mx, int(round(float(d.get("score", 0)))))) + details.append({"item": item, "score": sc, "max": mx, "note": str(d.get("reason", ""))[:60]}) + details.append(dim_comp) + + total = max(0, min(100, sum(d["score"] for d in details))) + passed = (total >= pass_score) and not found_all + return { + "score": total, "score_detail": details, "passed": passed, + "banned_words_found": found_all, + "verdict": verdict.get("verdict", ""), "summary": str(verdict.get("summary", ""))[:120], + } diff --git a/backend/app/services/ai_engine/package_exporter.py b/backend/app/services/ai_engine/package_exporter.py new file mode 100644 index 0000000..f5a1b47 --- /dev/null +++ b/backend/app/services/ai_engine/package_exporter.py @@ -0,0 +1,132 @@ +""" +package_exporter.py — 达人素材交付包生成 +架构方案§五 1A步骤5：按笔记分文件夹 + 图(01/02/03) + 文案.txt + 发布清单 + 合规说明 +路径规则：uploads/packages/{workspace_id}/{task_id}/note_{n}/ +""" +from __future__ import annotations +import json +import logging +import os +import zipfile +from datetime import datetime +from pathlib import Path +from typing import Any + +logger = logging.getLogger(__name__) + +# 文件夹结构 +# uploads/packages/{workspace_id}/{task_id}/ +# note_01/ +# 01_hook.jpg # 按 seq 序号命名防传错序 +# 02_proof.jpg +# 文案.txt # 标题 + 正文 + 标签 +# note_02/ +# ... +# 📋发布清单.txt +# ✅合规说明.txt +# package.zip # 最终打包文件 + + +def build_delivery_package( + workspace_id: int, + task_id: int, + notes: list[dict], # 每条笔记，含 text_candidate + image_candidates + base_path: str = "uploads/packages", +) -> str: + """ + 打包交付，返回 zip 文件的本地路径。 + notes 格式：[{ + "title": str, "content": str, "tags": list[str], + "images": [{"seq": int, "role": str, "data": bytes}], + "banned_word_status": str, # 合规说明用 + }] + """ + package_dir = Path(base_path) / str(workspace_id) / str(task_id) + package_dir.mkdir(parents=True, exist_ok=True) + + note_dirs: list[Path] = [] + for idx, note in enumerate(notes, start=1): + note_dir = package_dir / f"note_{idx:02d}" + note_dir.mkdir(exist_ok=True) + note_dirs.append(note_dir) + + # ── 图片文件（按 seq 序号命名） + for img in sorted(note.get("images", []), key=lambda x: x.get("seq", 0)): + seq = img.get("seq", idx) + role = img.get("role", "img") + fname = f"{seq:02d}_{role}.jpg" + img_data = img.get("data", b"") + if img_data: + (note_dir / fname).write_bytes(img_data) + + # ── 文案.txt（标题 + 正文 + 标签，达人可直接复制） + tags = note.get("tags") or [] + body = note.get("content", "") + # 正文末尾如果 LLM 已写入 #话题 标签，不再重复追加（避免重复） + body_has_tags = bool(tags) and any( + t.strip("#") in body for t in tags if t + ) + copy_lines = [ + f"【标题】{note.get('title', '')}", + "", + body, + ] + if tags and not body_has_tags: + copy_lines += ["", " ".join(tags)] + (note_dir / "文案.txt").write_text("\n".join(copy_lines), encoding="utf-8") + + # ── 发布清单.txt + checklist_lines = [ + "📋 发布清单", + f"生成时间：{datetime.now().strftime('%Y-%m-%d %H:%M')}", + f"任务ID：{task_id}", + "", + ] + for idx, note in enumerate(notes, start=1): + title = note.get("title", f"笔记{idx}") + n_images = len(note.get("images", [])) + checklist_lines.append(f"note_{idx:02d} 标题：{title[:30]} 图片数：{n_images}") + checklist_lines += [ + "", + "发布注意事项：", + "- 每条笔记图片按 01/02/03 顺序上传，避免传错序", + "- 文案.txt 中标题/正文/标签已区分，复制对应部分", + "- 品牌词已植入，请勿删除", + "- 不要添加链接（种品牌词，引导天猫搜索成交）", + ] + (package_dir / "📋发布清单.txt").write_text("\n".join(checklist_lines), encoding="utf-8") + + # ── 合规说明.txt + compliance_lines = [ + "✅ 合规说明", + f"生成时间：{datetime.now().strftime('%Y-%m-%d %H:%M')}", + "", + "本批次内容已完成以下合规处理：", + "1. 违禁词扫描（美白/祛斑/速效/医用/药妆等）", + "2. 视觉违禁词处理（前后对比/变白等）", + "3. 图片去水印处理（C2PA元数据已清除）", + "", + "各笔记合规状态：", + ] + for idx, note in enumerate(notes, start=1): + status = note.get("banned_word_status", "pass") + status_label = {"pass": "✅通过", "auto_fixed": "✅自动改写", "soft_warn": "⚠️软提示", "hard_block": "❌硬拦截"}.get(status, status) + compliance_lines.append(f"note_{idx:02d}：{status_label}") + compliance_lines += [ + "", + "注：C2PA元数据可去除；私有像素水印只能削弱，不保证100%清除。", + "如有合规疑问，请联系运营团队。", + ] + (package_dir / "✅合规说明.txt").write_text("\n".join(compliance_lines), encoding="utf-8") + + # ── 打 zip + zip_path = package_dir / "package.zip" + with zipfile.ZipFile(zip_path, "w", zipfile.ZIP_DEFLATED) as zf: + for note_dir in note_dirs: + for fpath in sorted(note_dir.iterdir()): + zf.write(fpath, arcname=f"{note_dir.name}/{fpath.name}") + zf.write(package_dir / "📋发布清单.txt", arcname="📋发布清单.txt") + zf.write(package_dir / "✅合规说明.txt", arcname="✅合规说明.txt") + + logger.info("delivery package built: %s (notes=%d)", zip_path, len(notes)) + return str(zip_path) diff --git a/backend/app/services/ai_engine/preference_aggregator.py b/backend/app/services/ai_engine/preference_aggregator.py new file mode 100644 index 0000000..1f5522b --- /dev/null +++ b/backend/app/services/ai_engine/preference_aggregator.py @@ -0,0 +1,145 @@ +""" +偏好飞轮聚合（preference_aggregator） +扒自：Clover架构方案.md §偏好飞轮怎么转 + PRD §8 +三层继承：L1 公司品牌基线 > L2 矩阵号人设（二期）> L3 个人手感 +聚合最近 FLYWHEEL_LOOKBACK 条 events → prompt 片段注入文案生成 + +关键： +- 按 product_id 分开学（素颜霜偏好不串精华） +- 信号不足 FLYWHEEL_COLD_START 条时，用产品档案冷启动 +- 返回结构对齐 API契约 GET /tasks/{id}/preference/context +""" +from __future__ import annotations +import logging +from collections import Counter +from typing import Any + +from .constants import FLYWHEEL_LOOKBACK, FLYWHEEL_COLD_START + +logger = logging.getLogger(__name__) + + +def aggregate_preference_context( + events: list[dict], + product: dict, + workspace_id: int, + product_id: int, +) -> dict: + """ + 输入：最近 preference_events 行（已按 workspace_id+product_id 过滤） + 输出：{recent_preference, reject_reasons, injected_count, prompt_fragment} + prompt_fragment 直接注入文案生成 prompt + """ + # 按 product_id 过滤（防串货） + relevant = [ + e for e in events + if e.get("workspace_id") == workspace_id and e.get("product_id") == product_id + ][:FLYWHEEL_LOOKBACK] + + injected_count = len(relevant) + + if injected_count < FLYWHEEL_COLD_START: + # 冷启动：用产品档案静态基线 + return _cold_start(product, injected_count) + + # ── 统计最常选角度（text_select + approve 信号） + angle_counts: Counter = Counter() + reject_reasons: list[str] = [] + + for e in relevant: + sig_type = e.get("signal_type", "") + angle = str(e.get("angle_label", "")).strip() + weight = int(e.get("signal_weight", 1)) + + if sig_type in ("text_select", "approve") and angle: + angle_counts[angle] += weight + elif sig_type == "reject_with_reason": + reason = str(e.get("reason", "")).strip() + if reason: + reject_reasons.append(reason) + + # 取权重最高的角度 + top_angles = [a for a, _ in angle_counts.most_common(3)] + # 取最近3条打回原因 + recent_rejects = reject_reasons[-3:] if reject_reasons else [] + + # ── 拼 prompt 片段（三层继承：L1>L2>L3，一期只跑L1+L3） + prompt_fragment = _build_prompt_fragment(top_angles, recent_rejects, product) + + # ── 人类可读摘要（前端"本次已注入"显示） + if top_angles: + pref_summary = f"最近偏好角度：{'、'.join(top_angles)}（已选{injected_count}次信号）" + else: + pref_summary = f"已注入{injected_count}条偏好信号" + + return { + "recent_preference": pref_summary, + "reject_reasons": recent_rejects, + "injected_count": injected_count, + "prompt_fragment": prompt_fragment, # 注入 generate_text_variants extra_rules + } + + +def _cold_start(product: dict, injected_count: int) -> dict: + """信号不足时用产品档案基线""" + angles = product.get("text_angles") or [] + style = product.get("style_tone", "素人分享风") + fragment = "" + if angles: + fragment = f"优先覆盖以下文案角度：{'、'.join(angles[:3])}。风格调性：{style}。" + return { + "recent_preference": f"冷启动（历史信号{injected_count}条，不足{FLYWHEEL_COLD_START}条），使用产品档案基线", + "reject_reasons": [], + "injected_count": injected_count, + "prompt_fragment": fragment, + } + + +def _build_prompt_fragment( + top_angles: list[str], + reject_reasons: list[str], + product: dict, +) -> str: + """ + 组装注入文案 prompt 的片段 + 越积累越精准：1次=全靠基线；10次=知道偏好角度；30次=措辞从"供参考"升为明确指令 + """ + lines: list[str] = [] + if top_angles: + lines.append(f"【偏好角度参考】历史选择偏好：{'、'.join(top_angles)}，请优先采用这些角度方向。") + if reject_reasons: + formatted = "；".join(f"「{r}」" for r in reject_reasons) + lines.append(f"【打回原因参考】以下问题请主动规避：{formatted}。") + # L1 品牌基线（产品档案 custom_prompt） + custom = (product.get("custom_prompt") or "").strip() + if custom: + lines.append(f"【品牌基线】{custom}") + return "\n".join(lines) + + +def collect_preference_event( + signal_type: str, + user_id: int, + workspace_id: int, + product_id: int, + angle_label: str = "", + reason: str = "", + weights: dict[str, int] | None = None, +) -> dict: + """ + 构造 preference_event 行（由业务接口内部调用，不暴露给前端） + 返回待插 DB 的字段 dict + """ + from .constants import FLYWHEEL_WEIGHTS + w_map = weights or FLYWHEEL_WEIGHTS + weight = w_map.get(signal_type, 0) + return { + "signal_type": signal_type, + "signal_weight": weight, + "user_id": user_id, + "workspace_id": workspace_id, + "product_id": product_id, + "angle_label": angle_label, + "reason": reason, + "data_ownership": "client_data", # 原始行为信号归客户（PRD §3 data_ownership） + } diff --git a/backend/app/services/ai_engine/prompt_composer.py b/backend/app/services/ai_engine/prompt_composer.py new file mode 100644 index 0000000..3e00a38 --- /dev/null +++ b/backend/app/services/ai_engine/prompt_composer.py @@ -0,0 +1,109 @@ +""" +prompt_composer.py — 统一 prompt 组装入口（≤100行） +扒自：banana prompts/service.py + worker/src/copy.js prompt 逻辑 +Lead 指名接口：compose_variants / compose_preference_context + +组装逻辑委托： + _text_prompt.py → build_prompt (文案 prompt 主体) + preference_aggregator.py → aggregate_preference_context (飞轮上下文) + +原则：prompt 组装从这里进，不散落在 text_variants / generate_text_variants 里。 +""" +from __future__ import annotations +import logging +from typing import Any + +from ._text_prompt import build_prompt, COPY_SYSTEM +from .preference_aggregator import aggregate_preference_context + +logger = logging.getLogger(__name__) + + +# ── 主接口 ──────────────────────────────────────────────────────────────────── + +def compose_variants( + product: dict, + count: int, + flywheel_context: str = "", + extra_rules: str = "", +) -> tuple[str, str]: + """ + 一次出 count 角度文案的完整 prompt。 + + 返回 (system_prompt, user_prompt)。 + 飞轮片段追加到 user_prompt 末尾（不改 system，避免覆盖质量红线）。 + + 参数： + product — 产品档案 dict（name/selling_points/text_angles/custom_prompt 等） + count — 需要几条 + flywheel_context— 由 compose_preference_context 返回的 prompt_fragment + extra_rules — 额外规则（优化循环重生成时传 hint） + """ + combined_extra = "\n".join(filter(None, [flywheel_context, extra_rules])) + user_prompt = build_prompt(product, count, extra_rules=combined_extra) + logger.debug( + "compose_variants: product=%s count=%d flywheel_len=%d", + product.get("name", "?"), count, len(flywheel_context), + ) + return COPY_SYSTEM, user_prompt + + +def compose_preference_context( + events: list[dict], + product: dict, + workspace_id: int, + product_id: int, +) -> dict: + """ + 聚合偏好事件 → 可注入 prompt 的飞轮上下文。 + + 返回结构（对齐 API契约 GET /tasks/{id}/preference/context）： + { + recent_preference: str, # 人类可读摘要（前端"本次已注入"显示） + reject_reasons: list, # 最近打回原因 + injected_count: int, # 有效信号数 + prompt_fragment: str, # 注入 compose_variants flywheel_context 的字符串 + } + + 信号不足 FLYWHEEL_COLD_START 条时用产品档案冷启动。 + 按 workspace_id + product_id 双维过滤（素颜霜偏好不串精华）。 + """ + return aggregate_preference_context(events, product, workspace_id, product_id) + + +# ── 辅助：解析模型返回的 JSON（给 text_variants 调用，集中不散） ────────────── + +def parse_model_output(raw: str) -> list[dict]: + """从 LLM 原始输出提取 JSON 数组（容错 markdown 包裹）""" + from ._text_prompt import parse_json_array + return parse_json_array(raw) + + +# ── 辅助：图片 prompt 组装入口（预留，联调时填充）───────────────────────────── + +def compose_image_prompt( + role_name: str, + visual_system: dict, + product: dict, + extra: str = "", +) -> str: + """ + 单张分镜 prompt 组装（供 image_gen.generate_one_image 调用）。 + TODO: 联调后从 storyboard.plan_image_set 取 base_prompt 注入。 + + role_name — 分镜角色（hook / pain_scene / closer 等） + visual_system— build_visual_system 返回的视觉系统 dict + extra — 追加约束（飞轮图片偏好片段，二期接入） + """ + name = product.get("name", "产品") + style = visual_system.get("style", "") + palette = visual_system.get("color_palette", "") + base = visual_system.get("base_prompt", "") + lines = [ + f"[{role_name}] 为产品「{name}」生成种草图。", + base and f"视觉基调：{base}", + style and f"摄影风格：{style}", + palette and f"色调：{palette}", + extra, + ] + return "\n".join(l for l in lines if l) diff --git a/backend/app/services/ai_engine/storyboard.py b/backend/app/services/ai_engine/storyboard.py new file mode 100644 index 0000000..860b387 --- /dev/null +++ b/backend/app/services/ai_engine/storyboard.py @@ -0,0 +1,197 @@ +""" +storyboard 分镜引擎 +扒自：worker/src/image.js +- getNarrativeRoles：按图数取分镜角色 +- proof_strategy：按品类定证明页策略（品类不写死，走数据驱动） +- build_visual_system：成组视觉统一 +- plan_image_set：组装最终分镜计划 +""" +from __future__ import annotations +import re +from .constants import ( + PAGE_ROLE_MAP, IMAGE_NEGATIVE_CONSTRAINTS, + STYLE_PROMPTS, STYLE_DEFAULT, NARRATIVE_BY_COUNT, NARRATIVE_BY_STRATEGY, +) +# sanitize_text 移至 templates（腾行数），此处 re-export 供 image_gen 沿用 import +from .storyboard_templates import role_template, proof_strategy, sanitize_text # noqa: F401 + + +def clamp_count(value: int, fallback: int = 6, lo: int = 1, hi: int = 8) -> int: + try: + return max(lo, min(hi, int(value))) + except (TypeError, ValueError): + return fallback + + +def short_selling_points(points, fallback: str = "") -> str: + """3个短卖点拼成 a / b / c（扒 shortSellingPoints:112-120）""" + src = points if isinstance(points, list) else str(points or "").split("、") + clean = [sanitize_text(p, 18) for p in src if sanitize_text(p, 18)][:3] + return " / ".join(clean) if clean else sanitize_text(fallback, 28) + + +def short_tags(tags, keywords=None) -> str: + """标签去#截断拼成 #a #b（扒 shortTags:48-54）""" + merged = list(tags or []) + list(keywords or []) + out = [] + for t in merged: + c = sanitize_text(str(t), 12).lstrip("#") + if c: + out.append(f"#{c}") + return " ".join(out[:5]) + + +def analyze_copy_for_image(note: dict, product: dict) -> dict: + """ + 从文案+产品提取生图锚点（扒 analyzeCopyForImage:129-148） + 给每张图填 audience/pain/scene/hook，让画面有真实代入而非空泛。 + """ + text = f"{note.get('title','')}。{note.get('coverTitle','')}。{note.get('content','')}" + tags = [sanitize_text(str(t).lstrip('#'), 12) for t in (note.get("tags") or [])] + audience = sanitize_text( + product.get("target_audience") + or next((t for t in tags if re.search(r"党|人|妈妈|女生|学生|通勤|上班|办公室", t)), "") + or "目标用户", 18) + scene = sanitize_text( + next((t for t in tags if re.search(r"通勤|宿舍|上课|约会|出门|办公室|旅行|居家|工位", t)), "") + or "日常自然光场景", 18) + pain = sanitize_text( + next((w for w in re.split(r"[、,，。；;！!？?\n]", text) + if re.search(r"暗沉|没气色|假白|卡粉|搓泥|油|干|赶时间|预算|麻烦", w)), "") + or "日常使用痛点", 18) + hook = sanitize_text(note.get("coverTitle") or note.get("title") or f"{audience}{scene}", 18) + return {"audience": audience, "scene": scene, "pain": pain, "hook": hook} + + +def get_narrative_roles(image_count: int = 6) -> list[dict]: + """ + 按图数返回分镜角色列表（扒 getNarrativeRoles，Q6对齐北哥6张套路） + ≤3 张：极速链路 hook / applied_proof / closer + ≤6 张：北哥标准链路 ①封面痛点大字 ②单品特写+品牌词 ③成分 ④质地 ⑤上脸对比 ⑥促单 + >6 张：沉浸链路 + pain_scene / scenario / social_proof + """ + count = clamp_count(image_count) + m = PAGE_ROLE_MAP + if count <= 3: + sequence = ["hook", "applied_proof", "closer"] + elif count <= 6: + # Q6：北哥6张标准顺序——品牌词在②(product_closeup)和⑥(closer)两次出现 + sequence = ["hook", "product_closeup", "ingredient", "texture", "applied_proof", "closer"] + else: + # 8张沉浸链路：在北哥6张基础上插入 pain_scene / social_proof + sequence = ["hook", "pain_scene", "product_closeup", "ingredient", "texture", "applied_proof", "social_proof", "closer"] + return [m[r] for r in sequence[:count] if r in m] + + +# ── proofStrategy 已移至 storyboard_templates.proof_strategy（腾行数，超200拆） + + +def build_visual_system(product: dict, analysis: dict | None = None) -> dict: + """ + 成组视觉统一（扒 buildVisualSystem） + analysis 来自 product.js 分析结果（visualIdentity），可空 + """ + identity = (analysis or {}).get("visualIdentity", {}) + palette = ( + "、".join(identity["colorPalette"][:5]) + if isinstance(identity.get("colorPalette"), list) and identity["colorPalette"] + else "提取产品包装主色，搭配浅色真实生活背景" + ) + return { + "palette": palette, + "typography": identity.get("typographyStyle", "主标题清晰黑体或手写感标题，辅助文字便签/勾选标注，字重颜色保持同一体系"), + "sticker": identity.get("stickerLanguage", "少量箭头、放大镜、勾选、小表情、便签，不使用促销按钮"), + "layout": identity.get("layoutStyle", "同一组图片保持色调、光线、产品露出方式一致，每张图承担不同叙事角色"), + "texture": identity.get("materialTexture", "产品包装、质地、手背/上脸肤感要真实自然"), + "package_details": identity.get("packageDetails", "如果提供产品图，必须还原包装颜色、瓶身形状、标签方向和主视觉"), + "xhs_style_preset": identity.get("xhsStylePreset", "真实测评风/手写安利风/清单便签风"), + "symbol_system": identity.get("symbolSystem", "中等密度小红书种草符号：✅ ✨ 🌿 💧 🪞 🧴 📦 🔍 💛，每张最多2-4个"), + "quality_rules": [ + "同组图片字体体系相对一致，但不要像固定模板", + "每张压图文字必须服务当前叙事角色，不能重复封面标题", + "护肤品优先出现手背涂抹、质地微距、自然上脸局部或真实生活场景", + "人物真实自然，有轻微皮肤纹理和生活感，不要AI精修美女", + "禁止乱码、错别字、App底栏、Like评论分享、硬广价格牌、虚假功效before/after", + ], + } + + +def plan_image_set(note: dict, product: dict, image_count: int = 3, analysis: dict | None = None, strategy: str | None = None) -> dict: + """ + 组装分镜计划（主入口） + strategy: None=默认按图数叙事，'A'/'B'/'C'=三套正交叙事策略 + 返回：{requested_count, storyboard, visual_system, base_prompt} + storyboard 每项：{role, name, focus, overlay_text, prompt_for_item} + """ + count = clamp_count(image_count) + roles = get_narrative_roles(count) + visual = build_visual_system(product, analysis) + category = product.get("category", "通用好物") + points = product.get("selling_points") or ["核心买点"] + src = analyze_copy_for_image(note, product) # 文案锚点：audience/pain/scene/hook + + storyboard = [] + brand_kw = sanitize_text(product.get("brand_keyword") or "", 12) + brand_roles = {"product_closeup", "closer"} # Q6：第2/6张带品牌词 + + for i, role in enumerate(roles): + point = sanitize_text(points[i % len(points)], 18) + tpl = role_template(role["role"]) + proof = proof_strategy(category, point) # 仅 applied_proof 用品类证明 + # 填模板占位：每角色画面/文字各不同（修缩水根因——不再全角色共用proof） + fill = { + "audience": src["audience"], "pain": src["pain"], "scene": src["scene"], + "hook": src["hook"], "point": point, "brand": brand_kw or product.get("name", "产品"), + "proof_overlay": proof.get("overlay", point), + "proof_visual": proof.get("visual", ""), + "proof_forbidden": proof.get("forbidden", ""), + } + item = { + "role": role["role"], + "name": role["name"], + "focus": role["focus"], + "goal": sanitize_text(tpl["goal"].format(**fill), 40), + "overlay_text": sanitize_text(tpl["overlay"].format(**fill), 20), + "visual_strategy": sanitize_text(tpl["visual"].format(**fill), 120), + "source_basis": sanitize_text(tpl["basis"].format(**fill), 60), + "selling_point": point, + "asset_use": proof.get("asset_use", "产品图保证包装准确，参考图用于真实场景"), + "forbidden": sanitize_text(tpl["forbidden"].format(**fill), 60), + } + if brand_kw and role["role"] in brand_roles: + item["brand_keyword"] = brand_kw + item["brand_keyword_rule"] = "品牌词自然融入画面文字或产品露出，不做广告牌感" + storyboard.append(item) + + # base_prompt 全局规则（扒 planImageSet basePrompt:682-690） + product_name = product.get("name", "产品") + cover_title = sanitize_text(note.get("coverTitle") or note.get("title") or "", 18) + style_text = STYLE_PROMPTS.get(product.get("style_tone") or STYLE_DEFAULT, STYLE_PROMPTS[STYLE_DEFAULT]) + # strategy非空时取对应正交叙事，否则按图数取默认链路 + narrative = NARRATIVE_BY_STRATEGY.get(strategy) if strategy else None + if not narrative: + narrative = NARRATIVE_BY_COUNT.get(count, NARRATIVE_BY_COUNT[6]) + sp_text = short_selling_points(points, cover_title) + tag_text = short_tags(note.get("tags") or [], product.get("keywords") or []) + base_prompt = ( + f"生成一张小红书可上传的独立3:4图文海报/素材图，目标比例1024×1536，可直接上传的独立图片，不是提示词，不是App截图。" + f"产品：{product_name}。短标题：{cover_title}。" + f"短卖点：{sp_text}。短标签：{tag_text}。" + f"叙事链路：{narrative}" + f"视觉风格：{style_text}。" + f"成组视觉：主色={visual['palette']}；字体={visual['typography']}；贴纸={visual['sticker']}；" + f"符号系统={visual['symbol_system']}；产品还原={visual['package_details']}。" + "重要限制：中文文字少而清晰，每张只允许一个主标题，同一句话禁止重复出现，正文点位最多3条；" + "四周留安全边距，文字不贴边不被裁切；真实自然像实拍素材后排版，降低AI味；" + "禁止生成小红书App界面截图、Like/评论/分享/底栏/头像等社交元素；" + "禁止肤色变白、瑕疵消失、治疗前后等视觉暗示，允许安全的未推开/推开后质地状态对比；" + "如果提供产品图，产品是不可修改的真实商品锚点，禁止改名、换包装、混入其他产品。" + f"\n{IMAGE_NEGATIVE_CONSTRAINTS}" + ) + + return { + "requested_count": count, + "storyboard": storyboard, + "visual_system": visual, + "base_prompt": base_prompt, + } diff --git a/backend/app/services/ai_engine/storyboard_templates.py b/backend/app/services/ai_engine/storyboard_templates.py new file mode 100644 index 0000000..9e40ac6 --- /dev/null +++ b/backend/app/services/ai_engine/storyboard_templates.py @@ -0,0 +1,174 @@ +""" +角色差异化分镜模板 +扒自：worker/src/image.js buildImageStoryboard storyboardByRole(222-321) + +每个分镜角色有【各自不同】的画面/文字/构图策略，这是"小红书风格不雷同"的根因。 +之前缩水：6张共用同一个品类 proof 策略 → 图全长一样。 +模板里 {占位} 在 storyboard.py 运行时按文案/卖点填充。 + +字段含义（对齐 promptFromStoryboard 9 字段）： + goal 本张目标（这张图要让用户产生什么动作/情绪） + overlay 图上主文字模板（每张不同，不重复封面标题） + visual 画面主体（构图、景别、道具、光线——这是不雷同的关键） + basis 文案依据（这张图从文案哪里来，给模型锚点） + forbidden 本张禁止事项 +""" +from __future__ import annotations +import re + +# ── sanitize（扒 sanitizeImagePlanText，防违禁视觉词进 prompt） +_SANITIZE_RULES: list[tuple[str, str]] = [ + (r"before\s*&\s*after", "质地与肤感说明"), + (r"before\s*/?\s*after", "质地与肤感说明"), + (r"\bbefore\b", "质地状态"), + (r"\bafter\b", "上脸肤感"), + (r"使用前后|用前用后|用前后|前后对比|使用前|使用后", "质地/场景/肤感说明"), + (r"功效对比|效果对比|改善对比", "质地/场景说明对比"), + (r"肤色变白|皮肤变白|变白|美白", "自然光泽感"), + (r"瑕疵消失|斑点消失|痘印消失|消除瑕疵|祛斑", "妆感更服帖"), + (r"治疗前后|治疗后|医美前后|治愈|修复受损", "日常使用场景说明"), +] + + +def sanitize_text(value: str, max_len: int = 56) -> str: + s = str(value) + for pattern, repl in _SANITIZE_RULES: + s = re.sub(pattern, repl, s, flags=re.IGNORECASE) + return re.sub(r"\s+", " ", s).strip()[:max_len] + + +# 北哥6张标准套 + 8张扩展角色，每角色独立画面策略 +ROLE_STORYBOARD_TPL: dict[str, dict] = { + "hook": { + "goal": "让{audience}因为{pain}停下划走，产生点开欲", + "overlay": "{hook}", + "visual": "自然光生活场景，手持产品或产品在桌面前景，真实肤感/手部细节，像iPhone随手实拍的封面，不是海报", + "basis": "来自选中文案标题、人群{audience}、痛点{pain}", + "forbidden": "不要价格、不要重复后续卖点、不要App界面、不要广告海报感", + }, + "product_closeup": { + "goal": "建立单品记忆锚点，让用户记住是哪个产品", + "overlay": "{brand}", + "visual": "单品高清特写居中，干净浅色台面，柔和顶光，瓶身/包装/标签清晰可读，品牌词自然出现在画面或瓶身", + "basis": "来自产品名和品牌词，第2张和第6张都要带品牌词强化记忆", + "forbidden": "不要堆多个产品、不要花哨背景抢主体、不要改包装文字", + }, + "ingredient": { + "goal": "用成分/配方信息建立信任，但不医疗化", + "overlay": "看清{point}", + "visual": "成分卡片式布局，产品+成分图标/短说明，浅色商务美妆风，信息层级清楚", + "basis": "来自卖点里的成分/功效点，理性表达不夸大", + "forbidden": "不要治疗/改善疾病承诺、不要医生背书、不要绝对化", + }, + "texture": { + "goal": "让用户看到{point}的真实质感证据", + "overlay": "{point}看得见", + "visual": "手背或指尖涂抹质地微距，产品放在旁边，自然光，保留真实皮肤纹理，能看清延展和肤感", + "basis": "来自卖点里的质地/肤感描述", + "forbidden": "不要生成变白效果、不要医疗化对比、不要和封面同构图", + }, + "applied_proof": { + "goal": "用可感知的上脸/使用证据证明{point}", + "overlay": "{proof_overlay}", + "visual": "{proof_visual}", + "basis": "来自核心卖点{point}和用户对效果的关注", + "forbidden": "{proof_forbidden}", + }, + "closer": { + "goal": "用囤货/省钱情报/搜索暗号完成软性转化", + "overlay": "这波真的会囤 {brand}", + "visual": "拆箱、囤货角或产品放在日常物品旁，真实分享氛围，轻量搜索/品牌词暗号提示，再带一次品牌词引导成交", + "basis": "来自价格心智/选择理由，但不做硬广", + "forbidden": "不要大促价格牌、不要购买按钮、不要红黄电商风", + }, + # ── 8张扩展角色 ── + "pain_scene": { + "goal": "让用户共鸣{pain}", + "overlay": "{pain}真的懂", + "visual": "{scene}里的真实困扰场景，产品作为解决方案线索出现，不做使用前后对比", + "basis": "来自文案痛点和目标人群", + "forbidden": "不要夸大焦虑、不要before/after", + }, + "social_proof": { + "goal": "补足信任背书，让内容不像单方面推销", + "overlay": "身边人都在问", + "visual": "产品在包里/桌面/宿舍囤货角，配简短手写感反馈气泡，真实随手拍", + "basis": "来自评论区语言/选择理由，缺评论时用低调口碑表达", + "forbidden": "不要假造大量头像评论、不要App评论区截图", + }, + "scenario": { + "goal": "展示{scene}以外的多场景使用代入", + "overlay": "这些场景都能用", + "visual": "2-3个生活小场景拼贴：宿舍/通勤包/办公桌，产品贯穿其中，统一光线", + "basis": "来自目标人群的多场景使用需求", + "forbidden": "不要电商详情页拼贴、不要夸大效果", + }, + "tutorial": { + "goal": "降低使用门槛，告诉用户怎么用", + "overlay": "三步就上手", + "visual": "三步手势教程：取量、点涂/使用、收尾，干净背景，产品在画面内", + "basis": "来自文案里的快速/懒人使用场景", + "forbidden": "不要复杂说明书、不要过多文字", + }, +} + + +def role_template(role: str) -> dict: + """取角色模板，未知角色用 applied_proof 兜底（和源头一致）""" + return ROLE_STORYBOARD_TPL.get(role, ROLE_STORYBOARD_TPL["applied_proof"]) + + +# ── proofStrategy（按品类定 applied_proof 证明页策略，扒 image.js:163-208） +# 品类来自 product.category，不硬编码枚举；无匹配走"通用好物"兜底 +PROOF_STRATEGIES: dict[str, dict] = { + "个护护理": { + "overlay_tpl": "{point}看得见", + "visual": "手部/身体局部使用证明：少量点涂、推开后吸收状态、真实纹理和自然光；产品只做辅助露出", + "asset_use": "优先使用实拍/参考图中的手部、干纹、涂抹、随身场景；产品图保证包装准确", + "forbidden": "不要变白、祛斑、医学效果、before/after字样；不要和封面同构图", + }, + "美妆护肤": { + "overlay_tpl": "{point}看得见", + "visual": "肤感/质地证明：手背、脸颊局部或质地微距，展示推开前后真实状态，保留皮肤纹理和自然光", + "asset_use": "优先使用实拍/参考图中的手背、上脸、质地素材；产品图辅助露出", + "forbidden": "不要变白、祛斑、医学效果、before/after字样；不要和封面同构图", + }, + "食品饮品": { + "overlay_tpl": "{point}一眼懂", + "visual": "冲泡/开袋/入口证明：展示包装、杯中状态、质地颜色或一口口感，真实桌面光线", + "asset_use": "产品图保证包装准确，参考图用于杯子、开袋、冲泡、办公室/居家场景", + "forbidden": "不要涂抹、不要护肤肤感、不要医疗健康承诺", + }, + "营养健康": { + "overlay_tpl": "看清{point}", + "visual": "理性证明页：包装、成分表、使用场景和每日习惯卡片，信息清晰但不做治疗承诺", + "asset_use": "产品图和说明图用于成分/包装准确，参考图用于日常使用场景", + "forbidden": "不要治疗、改善疾病、速效、医生背书、前后对比", + }, + "家居生活": { + "overlay_tpl": "{point}真省事", + "visual": "使用过程证明：展示痛点场景、产品介入和使用过程细节，强调顺手/收纳/效率", + "asset_use": "参考图用于真实家居环境，产品图保证外观准确", + "forbidden": "不要护肤涂抹，不要虚假夸大结果", + }, + "服饰穿搭": { + "overlay_tpl": "{point}有细节", + "visual": "上身/材质证明：展示面料纹理、版型细节或普通身材上身局部，真实自然", + "asset_use": "参考图用于上身/搭配/材质，产品图保证款式颜色准确", + "forbidden": "不要护肤涂抹，不要过度精修模特感", + }, + "通用好物": { + "overlay_tpl": "{point}清晰可见", + "visual": "产品使用场景证明：真实道具/场景，展示产品细节和使用过程", + "asset_use": "产品图保证准确，参考图用于场景辅助", + "forbidden": "不要夸大效果，不要硬广式价格牌", + }, +} + + +def proof_strategy(category: str, point: str) -> dict: + """取品类证明策略，无匹配用通用兜底（扒 proofStrategy）""" + s = PROOF_STRATEGIES.get(category, PROOF_STRATEGIES["通用好物"]).copy() + s["overlay"] = s.pop("overlay_tpl", "{point}").format(point=point) + return s + diff --git a/backend/app/services/ai_engine/text_scoring.py b/backend/app/services/ai_engine/text_scoring.py new file mode 100644 index 0000000..f3bc9c8 --- /dev/null +++ b/backend/app/services/ai_engine/text_scoring.py @@ -0,0 +1,93 @@ +""" +text_scoring.py — 五维打分接口 + 去重（≤100行） +打分维度逻辑见 _scoring_dims.py +""" +from __future__ import annotations +import re +from difflib import SequenceMatcher +from typing import Any + +from .constants import ( + BANNED_WORDS_DEFAULT, BANNED_VISUAL_WORDS, + SCORE_WEIGHTS, QUALITY_PASS_SCORE, + DEDUP_TITLE_THRESHOLD, DEDUP_TITLE_CONTENT_TITLE, DEDUP_TITLE_CONTENT_BODY, +) +from ._scoring_dims import ( + _cat_words, score_title, score_emotion, score_selling, + score_keyword, score_compliance, +) + + +def score_copy( + copy: dict[str, Any], + source: dict[str, Any], + banned_words: list[str] | None = None, + weights: dict[str, int] | None = None, + pass_score: int = QUALITY_PASS_SCORE, +) -> dict[str, Any]: + """ + 五维打分（标题25 / 情绪25 / 买点25 / 关键词20 / 合规5） + 返回：{score, score_detail, passed, banned_words_found} + """ + w = weights or SCORE_WEIGHTS + bwords = list(set((banned_words or []) + BANNED_WORDS_DEFAULT + BANNED_VISUAL_WORDS)) + + title = str(copy.get("title", "")) + content = str(copy.get("content", "")) + tags = " ".join(str(t) for t in copy.get("tags", [])) + full = f"{title}\n{content}\n{tags}\n{copy.get('imageBrief','')}" + + selling_points = source.get("selling_points", []) or [] + keywords = source.get("keywords", []) or [] + category = source.get("category", "通用好物") + cat_w = _cat_words(category) + + dim_title = score_title(title, cat_w, w) + dim_emotion = score_emotion(full, w) + dim_selling = score_selling(copy, full, selling_points, w) + dim_keyword = score_keyword(copy, tags, keywords, w) + dim_compliance, found_all = score_compliance(full, bwords, w) + + details = [dim_title, dim_emotion, dim_selling, dim_keyword, dim_compliance] + total = max(0, min(100, sum(d["score"] for d in details))) + passed = (total >= pass_score) and not found_all + + return {"score": total, "score_detail": details, "passed": passed, "banned_words_found": found_all} + + +def _sim(a: str, b: str) -> float: + return SequenceMatcher(None, a, b).ratio() + + +def _copy_signature(copy: dict) -> str: + content = str(copy.get("content", "")) + opening = re.sub(r"\s+", "", content[:30]) + return f"{copy.get('title', '')}|{opening}" + + +def is_similar_copy(a: dict, b: dict) -> bool: + """同质化判重（标题≥0.82 OR 标题≥0.65且正文≥0.72）""" + t = _sim(str(a.get("title", "")), str(b.get("title", ""))) + if t >= DEDUP_TITLE_THRESHOLD: + return True + if t >= DEDUP_TITLE_CONTENT_TITLE: + if _sim(str(a.get("content",""))[:200], str(b.get("content",""))[:200]) >= DEDUP_TITLE_CONTENT_BODY: + return True + return False + + +def dedupe_copies(copies: list[dict], previous: list[dict] | None = None) -> list[dict]: + """本轮内互去重 + 与历史去重 + angle 去重""" + history = previous or [] + kept: list[dict] = [] + used_angles: set[str] = set() + for c in copies: + sig = _copy_signature(c) + if any(_copy_signature(h) == sig for h in history): continue + if any(is_similar_copy(c, h) for h in history): continue + if any(is_similar_copy(c, k) for k in kept): continue + angle = str(c.get("angle", "")).strip() + if angle and angle in used_angles: continue + if angle: used_angles.add(angle) + kept.append(c) + return kept diff --git a/backend/app/services/ai_engine/text_variants.py b/backend/app/services/ai_engine/text_variants.py new file mode 100644 index 0000000..2bb3420 --- /dev/null +++ b/backend/app/services/ai_engine/text_variants.py @@ -0,0 +1,186 @@ +""" +text_variants.py — 文案双轨主入口（≤100行） +轨A: generate_text_variants — 调 LLM 出 N 角度 JSON +轨B: text_import_handler — 导入外部文案进候选池 + +prompt 组装/解析见 _text_prompt.py；评分/去重见 text_scoring.py +""" +from __future__ import annotations +import asyncio +import logging +import os +from typing import Any + +from .constants import MAX_OPTIMIZE_ROUNDS +from ._text_prompt import COPY_SYSTEM, build_prompt, parse_json_array, build_local_drafts +from .text_scoring import score_copy, dedupe_copies +from .llm_scorer import llm_score_copy +from .banned_word_checker import check_and_fix, build_entries_from_db, CheckResult + +logger = logging.getLogger(__name__) + + +async def _call_llm(client: Any, prompt: str, max_tokens: int = 8192) -> str: + """统一 LLM 调用，client 由 worker 注入，隔离 key。 + G1坑修复：AIClients 没有 .chat.completions，正确方法是 .chat_complete() + S8: 503/429 指数退避重试（最多3次，2^attempt 秒），其他异常直接降级返 ''。 + max_tokens 由调用方按批量缩放：opus 会尽量填满输出空间，8192 token 的生成 + 单批 >60s 必撞 apiports 网关上限返 503(task46 实测每请求恰挂 ~61s)。实测单条 + max_tokens=1500~2500 仅 16~18s。故按条数动态收，墙钟压进 60s 网关窗口内。 + """ + import httpx + + # 倩倩姐2026-06-13拍板"加大重试+拉长退避"：apiports负载波动时单条opus也会被 + # 拖过60s返503，短退避(1/2/4s)赶不开高负载窗口。故重试5次、退避拉长到最长30s， + # 给中转站负载回落留时间。墙钟换稳定(MVP免费阶段可接受)。 + max_attempts = 5 + backoff = [5, 10, 20, 30] # 第1~4次重试前等待秒数，拉长跨过apiports高负载窗口 + for attempt in range(max_attempts): + try: + return await client.chat_complete( + messages=[ + {"role": "system", "content": COPY_SYSTEM}, + {"role": "user", "content": prompt}, + ], + model=client._model, + max_tokens=max_tokens, + temperature=0.75, + ) + except httpx.HTTPStatusError as exc: + status = exc.response.status_code if exc.response is not None else 0 + if status in (503, 429) and attempt < max_attempts - 1: + wait = backoff[min(attempt, len(backoff) - 1)] + logger.warning( + "LLM 返回 %s，第%d/%d次重试，等待 %ds: %s", + status, attempt + 1, max_attempts - 1, wait, exc, + ) + await asyncio.sleep(wait) + continue + logger.error("LLM HTTP错误(不可重试或已达上限): %s: %s", type(exc).__name__, exc) + return "" + except Exception as exc: + # 其他异常（超时/网络断开等）不重试，直接降级 + logger.error("LLM 调用失败: %s: %s", type(exc).__name__, exc) + return "" + return "" + + +# apiports 网关单次响应有 ~60s 上限，claude 一次生成 >4 条长文案会超时返 503。 +# 故分批：每批最多 4 条，串行调用合并。批大小可经 TEXT_BATCH_SIZE 调。 +TEXT_BATCH_SIZE = int(os.environ.get("TEXT_BATCH_SIZE", "4")) + + +async def _generate_one_batch(llm_client: Any, product: dict, batch_n: int, extra: str) -> list[dict]: + """生成一批 batch_n 条，含解析重试（最多2次）。失败返回空列表。 + max_tokens 按条数缩放(每条约 1800 token，封顶 8192)，压进 apiports 60s 网关窗口。""" + batch_max_tokens = min(8192, max(1800, batch_n * 1800)) + for attempt in range(2): + raw = await _call_llm(llm_client, build_prompt(product, batch_n, extra_rules=extra), batch_max_tokens) + parsed = parse_json_array(raw) + if parsed: + return parsed + logger.warning("文案批(%d条)第%d次解析失败%s", batch_n, attempt + 1, + "，重试" if attempt == 0 else "，放弃本批") + return [] + + +async def _generate_in_batches(llm_client: Any, product: dict, count: int, extra: str) -> list[dict]: + """把 count 条按 TEXT_BATCH_SIZE 分批，串行调用合并。 + 串行而非并发：opus 单批就慢(~300s)且 apiports 限并发，多批 gather 会触发 + 大面积 503 雪崩(task45 实测)。故改串行，墙钟换稳定。""" + sizes: list[int] = [] + remaining = count + while remaining > 0: + n = min(TEXT_BATCH_SIZE, remaining) + sizes.append(n) + remaining -= n + collected: list[dict] = [] + for n in sizes: + r = await _generate_one_batch(llm_client, product, n, extra) + collected.extend(r) + return collected + + +async def generate_text_variants( + llm_client: Any, + product: dict, + count: int, + previous_copies: list[dict] | None = None, + banned_word_rows: list[dict] | None = None, + flywheel_context: str = "", +) -> list[dict]: + """轨A：一次出 count 条不同角度文案，三层兜底，自动优化循环""" + banned_entries = build_entries_from_db(banned_word_rows or []) + extra = flywheel_context + + copies: list[dict] = await _generate_in_batches(llm_client, product, count, extra) + if not copies: + copies = list(build_local_drafts(product, count)) # generator → list + + candidates: list[dict] = [] + for c in copies: + ban: CheckResult = check_and_fix( + f"{c.get('title','')} {c.get('content','')}", + banned_entries or None, + ) + scored = await llm_score_copy(llm_client, c, product, [e.word for e in banned_entries]) + c.update({"source": "ai", "score": scored["score"], "score_detail": scored["score_detail"], + "passed": scored["passed"], "banned_word_status": ban.status, + "verdict": scored.get("verdict", ""), "summary": scored.get("summary", "")}) + if ban.status == "auto_fixed" and ban.fixed_text: + c["content"] = ban.fixed_text + candidates.append(c) + + failed = [c for c in candidates if not c["passed"] and c["banned_word_status"] != "hard_block"] + # 优化轮默认关闭：apiports 60s 网关限制下优化轮的 _call_llm 常需白等 60s 才 503， + # 严重拖慢出文案（实测 +100s+）。质量优化等北哥 prompt 方案到位再开（架构已留位）。 + optimize_enabled = os.environ.get("TEXT_OPTIMIZE_ENABLED", "false").lower() == "true" + rounds = MAX_OPTIMIZE_ROUNDS if optimize_enabled else 0 + for _ in range(rounds): + if not failed: + break + # 优化轮也受 60s 网关上限约束：一次最多重生成 TEXT_BATCH_SIZE 条 + batch_failed = failed[:TEXT_BATCH_SIZE] + hint = "\n".join( + f"标题「{c['title']}」{c['score']}分，需改进：" + + "；".join(d["note"] for d in c.get("score_detail", []) if d["score"] < d["max"] * 0.72) + for c in batch_failed + ) + raw2 = await _call_llm(llm_client, build_prompt( + product, len(batch_failed), + extra_rules=f"以下文案未达标，请重新生成并改进：\n{hint}\n不要重复已有标题和角度。", + ), min(8192, max(1800, len(batch_failed) * 1800))) + if not raw2: + # LLM 失败（如 503/超时）：优化是锦上添花，原始候选已够用，不再耗时重试 + logger.warning("文案优化轮 LLM 失败，沿用原始候选不再重试") + break + for nc in parse_json_array(raw2): + sc2 = await llm_score_copy(llm_client, nc, product, [e.word for e in banned_entries]) + nc.update({"source": "ai", "score": sc2["score"], "score_detail": sc2["score_detail"], + "passed": sc2["passed"], "banned_word_status": "pass", + "verdict": sc2.get("verdict", ""), "summary": sc2.get("summary", "")}) + candidates.append(nc) + failed = [c for c in candidates if not c["passed"]] + + return dedupe_copies(candidates, previous_copies or [])[:count] + + +def text_import_handler( + raw_text: str, + product: dict, + banned_word_rows: list[dict] | None = None, +) -> dict: + """轨B：导入外部文案（豆包等）直接进候选池，source=import""" + banned_entries = build_entries_from_db(banned_word_rows or []) + lines = raw_text.strip().splitlines() + title = lines[0].strip() if lines else "" + content = "\n".join(lines[1:]).strip() if len(lines) > 1 else raw_text.strip() + candidate: dict = {"title": title, "content": content, "tags": [], "angle": "import", + "buyingPoint": "", "coverTitle": title, "imageBrief": "", "source": "import"} + ban = check_and_fix(f"{title} {content}", banned_entries or None) + # 轨B(导入外部文案)走机械 score_copy 而非 AI 评委：导入的是用户自带成品，评分仅作 + # 参考展示不卡发布；且本函数同步、改 await 会扩大到调用方。AI 评委只用于轨A生成链路。 + scored = score_copy(candidate, product, [e.word for e in banned_entries]) + candidate.update({"score": scored["score"], "score_detail": scored["score_detail"], + "passed": scored["passed"], "banned_word_status": ban.status}) + return candidate diff --git a/backend/app/services/auth_service.py b/backend/app/services/auth_service.py new file mode 100644 index 0000000..d0f5c0b --- /dev/null +++ b/backend/app/services/auth_service.py @@ -0,0 +1,71 @@ +""" +app/services/auth_service.py — 认证 service +密码哈希校验、用户查找、响应格式化。 +路由层不含业务逻辑，全在此。 +""" + +import logging + +from passlib.context import CryptContext +from sqlalchemy.orm import Session + +from app.core.response import raise_unauthorized +from app.middleware.workspace_guard import CurrentUser +from app.models.user import User +from app.models.workspace import WorkspaceMember + +logger = logging.getLogger(__name__) +pwd_context = CryptContext(schemes=["bcrypt"], deprecated="auto") + + +def hash_password(plain: str) -> str: + return pwd_context.hash(plain) + + +def verify_password(plain: str, hashed: str) -> bool: + return pwd_context.verify(plain, hashed) + + +def authenticate_user( + db: Session, username: str, password: str +) -> tuple[User, int, str]: + """ + 验证用户名+密码，返回 (user, workspace_id, role)。 + 失败抛 CloverHTTPException 40101。 + """ + user = db.query(User).filter( + User.username == username, User.is_active == True + ).first() + if not user or not verify_password(password, user.hashed_password): + raise_unauthorized("用户名或密码错误") + + # 取用户所在的第一个 workspace（手动建账号场景只有一个） + member = ( + db.query(WorkspaceMember) + .filter(WorkspaceMember.user_id == user.id) + .first() + ) + if not member: + raise_unauthorized("用户未加入任何 workspace，请联系管理员") + + # 记录登录 + try: + from app.models.user import LoginRecord + db.add(LoginRecord(user_id=user.id)) + db.commit() + except Exception: + logger.warning("Failed to write login_record for user=%s", user.id) + db.rollback() + + return user, member.workspace_id, member.role + + +def build_user_response(user: User, workspace_id: int, role: str) -> dict: + """格式化用户响应体（契约§4 DTO）。""" + return { + "id": user.id, + "username": user.username, + "email": user.email, + "current_workspace_id": workspace_id, + "role": role, + } diff --git a/backend/app/services/flywheel_service.py b/backend/app/services/flywheel_service.py new file mode 100644 index 0000000..a299c07 --- /dev/null +++ b/backend/app/services/flywheel_service.py @@ -0,0 +1,121 @@ +""" +app/services/flywheel_service.py — 飞轮信号写入 + 偏好上下文聚合 +preference_collector：三信号入口（选文案/选图/审核）写入 preference_events。 +preference_aggregator：查最近50条 → 最常选角度 + 打回原因近3条原文拼 prompt。 +飞轮不暴露独立埋点端点，只由业务接口内部调用（契约红线）。 +""" + +import logging +from typing import Any + +from sqlalchemy import desc, func +from sqlalchemy.orm import Session + +from app.constants.enums import SIGNAL_WEIGHTS, DataOwnership, SignalType +from app.middleware.workspace_guard import CurrentUser +from app.models.flywheel import PreferenceEvent +from app.models.task import GenerationTask + +logger = logging.getLogger(__name__) + +# 实时聚合窗口：最近50条事件 +_AGGREGATION_WINDOW = 50 +# 冷启动阈值：不足5条信号用产品档案冷启动 +_COLD_START_THRESHOLD = 5 + + +def record_signal( + db: Session, + current_user: CurrentUser, + task: GenerationTask, + signal_type: str, + candidate_id: int | None = None, + angle_label: str | None = None, + reason: str | None = None, +) -> None: + """ + 写入飞轮信号。 + workspace_id + product_id 都必须有（基石C + 按产品分开学）。 + signal_weight 用枚举默认值，北哥可校准。 + data_ownership 默认 client_data（选择行为归客户）。 + """ + weight = SIGNAL_WEIGHTS.get(signal_type, 0) + event = PreferenceEvent( + workspace_id=current_user.workspace_id, + product_id=task.product_id, + task_id=task.id, + user_id=current_user.user_id, + signal_type=signal_type, + signal_weight=weight, + candidate_id=candidate_id, + angle_label=angle_label, + reason=reason, + data_ownership=DataOwnership.CLIENT_DATA, + ) + try: + db.add(event) + db.commit() + logger.info( + "Flywheel signal: type=%s weight=%s user=%s product=%s", + signal_type, weight, current_user.user_id, task.product_id, + ) + except Exception: + db.rollback() + logger.error( + "Failed to write preference_event: type=%s user=%s", + signal_type, current_user.user_id, + ) + raise + + +def get_preference_context( + db: Session, workspace_id: int, product_id: int +) -> dict[str, Any]: + """ + 实时聚合偏好上下文（最近50条 events）。 + 返回：recent_preference摘要 + reject_reasons近3条 + injected_count。 + 不足5条 → 冷启动提示（产品档案兜底，由 AIE prompt 层读 products.custom_prompt）。 + 按 workspace_id + product_id 严格过滤（不串数据，基石C）。 + """ + recent = ( + db.query(PreferenceEvent) + .filter( + PreferenceEvent.workspace_id == workspace_id, + PreferenceEvent.product_id == product_id, + ) + .order_by(desc(PreferenceEvent.created_at)) + .limit(_AGGREGATION_WINDOW) + .all() + ) + + if len(recent) < _COLD_START_THRESHOLD: + return { + "recent_preference": "信号不足，使用产品档案基线（冷启动）", + "reject_reasons": [], + "injected_count": len(recent), + } + + # 统计最常被选中的角度 + angle_counts: dict[str, int] = {} + for ev in recent: + if ev.signal_type in (SignalType.TEXT_SELECT, SignalType.APPROVE) and ev.angle_label: + angle_counts[ev.angle_label] = angle_counts.get(ev.angle_label, 0) + 1 + + top_angles = sorted(angle_counts.items(), key=lambda x: x[1], reverse=True)[:3] + if top_angles: + pref_desc = "；".join(f"{a}（已选{c}次）" for a, c in top_angles) + preference_summary = f"最近偏好：{pref_desc}" + else: + preference_summary = "暂无明显角度偏好" + + # 取最近3条打回原因原文（不做 AI 归纳，契约§3） + reject_reasons = [ + ev.reason for ev in recent + if ev.signal_type == SignalType.REJECT_WITH_REASON and ev.reason + ][:3] + + return { + "recent_preference": preference_summary, + "reject_reasons": reject_reasons, + "injected_count": len(recent), + } diff --git a/backend/app/services/task_service.py b/backend/app/services/task_service.py new file mode 100644 index 0000000..f8b0281 --- /dev/null +++ b/backend/app/services/task_service.py @@ -0,0 +1,86 @@ +""" +app/services/task_service.py — 任务创建 service +校验有无 key → 建 GenerationTask → 只推 task_id 入队，绝不传 key（基石B）。 +""" + +import logging + +from sqlalchemy.orm import Session + +from app.core.response import raise_business +from app.middleware.workspace_guard import CurrentUser +from app.models.task import GenerationTask +from app.models.workspace import UserApiKey + +logger = logging.getLogger(__name__) + + +def _check_user_has_key(db: Session, user_id: int, workspace_id: int) -> None: + """校验用户在此 workspace 是否有可用 API Key（openai/apiports均可），没有则引导去配置。""" + key = ( + db.query(UserApiKey) + .filter( + UserApiKey.user_id == user_id, + UserApiKey.workspace_id == workspace_id, + UserApiKey.provider.in_(["openai", "apiports"]), # G6坑修复：接受主备通道名 + ) + .first() + ) + if not key: + raise_business("尚未配置 API Key，请先在设置中录入") + + +def create_generation_task( + db: Session, + current_user: CurrentUser, + body, # CreateTaskRequest +) -> GenerationTask: + """ + 建 GenerationTask 并推 Celery 队列。 + 只传 task_id，绝不传 key（基石B）。 + """ + if body.track == "ai": + # 轨A：先检查有没有 key + _check_user_has_key(db, current_user.user_id, current_user.workspace_id) + + # 禁降级铁律：本次产品入镜(need_product_image=True)时，产品必须已上传参考图， + # 否则拒绝建任务（不允许降级纯文生图，防产品包装跑偏/过抽检失败）。 + need_img = getattr(body, "need_product_image", True) + if need_img: + from app.models.product import Product + product = db.query(Product).filter( + Product.id == body.product_id, + Product.workspace_id == current_user.workspace_id, + ).first() + if not product: + raise_business("产品不存在") + if not (product.image_path or "").strip(): + raise_business("该产品未上传参考图，无法生成产品入镜内容；请先到产品库上传产品图，或关闭「产品入镜」开关") + + task = GenerationTask( + workspace_id=current_user.workspace_id, + product_id=body.product_id, + operator_id=current_user.user_id, + theme=body.theme, + text_count=body.text_count, + image_count=body.image_count, + track=body.track, + need_product_image=need_img, + status="pending", + ) + db.add(task) + db.commit() + db.refresh(task) + logger.info("GenerationTask created: id=%s ws=%s", task.id, current_user.workspace_id) + + if body.track == "ai": + enqueue_generation(task.id) + + return task + + +def enqueue_generation(task_id: int) -> None: + """只推 task_id 入队，绝不推 key（基石B）。""" + from app.workers.tasks import run_generation_pipeline + run_generation_pipeline.delay(task_id) + logger.info("Enqueued task_id=%s", task_id) diff --git a/backend/app/utils/README.md b/backend/app/utils/README.md new file mode 100644 index 0000000..37ce3bf --- /dev/null +++ b/backend/app/utils/README.md @@ -0,0 +1,7 @@ +# app/utils/ + +工具层占位： +- fernet_utils.py # Fernet加密/解密（FERNET_KEY走环境变量，绝不进代码库） +- sse_utils.py # SSE推送工具（补发历史事件，前端按event_seq去重） +- pagination.py # 统一分页工具 +- ai_usage_logger.py # AI调用用量记录（每次调用记usage，归因到个人key） diff --git a/backend/app/utils/__init__.py b/backend/app/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/app/utils/fernet_utils.py b/backend/app/utils/fernet_utils.py new file mode 100644 index 0000000..e5094cf --- /dev/null +++ b/backend/app/utils/fernet_utils.py @@ -0,0 +1,49 @@ +""" +app/utils/fernet_utils.py — Fernet 加解密工具（按 Lead 规范路径） +FERNET_KEY 走环境变量，绝不进代码库（基石B）。 +此模块是 app/core/security.py 中 Fernet 功能的独立导出， +供 AIE / worker 层直接 import，无需依赖 FastAPI 上下文。 +""" + +import logging + +from cryptography.fernet import Fernet, InvalidToken + +from app.core.config import get_settings + +logger = logging.getLogger(__name__) + +_fernet_instance: Fernet | None = None + + +def _get_fernet() -> Fernet: + global _fernet_instance + if _fernet_instance is None: + settings = get_settings() + _fernet_instance = Fernet(settings.FERNET_KEY.encode()) + return _fernet_instance + + +def encrypt_key(plain_key: str) -> str: + """ + 加密 API Key，返回密文字符串。 + 调用方绝不打印 plain_key（基石B）。 + """ + return _get_fernet().encrypt(plain_key.encode()).decode() + + +def decrypt_key(encrypted_key: str) -> str: + """ + 解密 API Key。只在 Celery worker 内部调用。 + 解密结果只活在调用函数的局部变量，不落盘、不打日志。 + """ + try: + return _get_fernet().decrypt(encrypted_key.encode()).decode() + except InvalidToken: + logger.error("Fernet decryption failed: token invalid or key rotated") + raise ValueError("API key decryption failed") + + +def mask_key(plain_key: str) -> str: + """只返回后4位（展示用，不暴露完整 key）。""" + return plain_key[-4:] if len(plain_key) >= 4 else "****" diff --git a/backend/app/utils/sse_utils.py b/backend/app/utils/sse_utils.py new file mode 100644 index 0000000..a4534f9 --- /dev/null +++ b/backend/app/utils/sse_utils.py @@ -0,0 +1,122 @@ +""" +app/utils/sse_utils.py — SSE 工具函数（按 Lead 规范路径） +补发历史事件、event_seq 去重、Redis pub/sub 推送。 +供 api/v1/stream.py 和 AIE worker 共用。 +""" + +import asyncio +import json +import logging +from typing import Any, AsyncGenerator + +from app.core.config import get_settings + +logger = logging.getLogger(__name__) +settings = get_settings() + +# SSE 事件类型（契约§2全列） +SSE_EVENT_TYPES = frozenset({ + "task_started", "analyze_done", "text_progress", "text_candidate", + "image_progress", "image_candidate", "flywheel_injected", + "batch_failed", "task_done", "error", "heartbeat", +}) + +_HISTORY_KEY_TPL = "sse:task:{task_id}:events" +_CHANNEL_TPL = "sse:task:{task_id}" +_HISTORY_TTL_SECONDS = 3600 # 历史事件保留 1h + + +def format_sse(event: str, data: dict, seq: int | None = None) -> str: + """格式化单条 SSE 消息（text/event-stream 格式）。""" + payload = json.dumps(data, ensure_ascii=False) + lines = [f"event: {event}", f"data: {payload}"] + if seq is not None: + lines.append(f"id: {seq}") + return "\n".join(lines) + "\n\n" + + +async def push_event( + redis_client, + task_id: int, + workspace_id: int, + event: str, + data: dict[str, Any], + seq: int, +) -> None: + """ + 推送事件到 Redis： + 1. 追加到历史 list（供断线重连补发） + 2. Publish 到 channel（供在线客户端实时收） + """ + if event not in SSE_EVENT_TYPES: + logger.warning("Unknown SSE event type: %s", event) + + record = {"event": event, "data": data, "seq": seq, "workspace_id": workspace_id} + payload = json.dumps(record, ensure_ascii=False) + + hist_key = _HISTORY_KEY_TPL.format(task_id=task_id) + channel = _CHANNEL_TPL.format(task_id=task_id) + + await redis_client.rpush(hist_key, payload) + await redis_client.expire(hist_key, _HISTORY_TTL_SECONDS) + await redis_client.publish(channel, payload) + + +async def get_history_events( + redis_client, task_id: int, after_seq: int +) -> list[dict]: + """取 task 历史事件中 seq > after_seq 的部分（断线重连补发）。""" + hist_key = _HISTORY_KEY_TPL.format(task_id=task_id) + try: + raw_list = await redis_client.lrange(hist_key, 0, -1) + result = [] + for raw in raw_list: + ev = json.loads(raw) + if ev.get("seq", 0) > after_seq: + result.append(ev) + return result + except Exception as exc: + logger.warning("get_history_events failed task=%s: %s", task_id, exc) + return [] + + +async def stream_events( + redis_client, + task_id: int, + workspace_id: int, + last_seq: int = 0, + heartbeat_interval: int = 25, +) -> AsyncGenerator[str, None]: + """ + 主 SSE 生成器： + 1. 先补发 last_seq 之后的历史事件 + 2. 再订阅 Redis channel 实时推新事件 + 3. 每 heartbeat_interval 秒推一次保活 heartbeat + """ + # 1. 补发历史 + history = await get_history_events(redis_client, task_id, last_seq) + for ev in history: + yield format_sse(ev["event"], ev["data"], ev.get("seq")) + + # 2. 实时订阅 + pubsub = redis_client.pubsub() + channel = _CHANNEL_TPL.format(task_id=task_id) + await pubsub.subscribe(channel) + elapsed = 0 + try: + while True: + msg = await pubsub.get_message(ignore_subscribe_messages=True, timeout=1.0) + if msg and msg["type"] == "message": + ev = json.loads(msg["data"]) + if ev.get("workspace_id") != workspace_id: + continue # 防越权订阅他人任务 + yield format_sse(ev["event"], ev["data"], ev.get("seq")) + if ev["event"] in ("task_done", "error"): + break + else: + elapsed += 1 + if elapsed >= heartbeat_interval: + elapsed = 0 + yield format_sse("heartbeat", {"ts": asyncio.get_event_loop().time()}) + finally: + await pubsub.unsubscribe(channel) diff --git a/backend/app/workers/README.md b/backend/app/workers/README.md new file mode 100644 index 0000000..f701b6d --- /dev/null +++ b/backend/app/workers/README.md @@ -0,0 +1,11 @@ +# app/workers/ + +Celery worker 占位： +- celery_app.py # Celery实例配置（broker=Redis） +- task_runner.py # 主任务：只接收task_id → 查库→FERNET_KEY解密key → 调模型 + # 铁律：明文key绝不进Celery参数，只在函数局部变量，不落盘不打日志 +- subtasks/ + analyze.py # 分析标杆笔记8特征 + generate_text.py # 文案双轨（轨A一次5角度JSON/轨B跳过） + generate_image.py # 并发生图asyncio.gather（A/B/C三策略） + postprocess.py # 去水印后处理 diff --git a/backend/app/workers/__init__.py b/backend/app/workers/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/app/workers/celery_app.py b/backend/app/workers/celery_app.py new file mode 100644 index 0000000..66487dc --- /dev/null +++ b/backend/app/workers/celery_app.py @@ -0,0 +1,36 @@ +""" +app/workers/celery_app.py — Celery 任务框架壳 +铁律：只传 task_id，绝不传 key（基石B）。 +worker 内查库 → Fernet 解密 → 局部变量，不落盘不打日志。 +""" + +import logging + +from celery import Celery + +from app.core.config import get_settings + +logger = logging.getLogger(__name__) +settings = get_settings() + +celery_app = Celery( + "clover", + broker=settings.celery_broker(), + backend=settings.celery_backend(), + include=["app.workers.tasks", "app.workers.replenish_task"], +) + +celery_app.conf.update( + task_serializer="json", + result_serializer="json", + accept_content=["json"], + timezone="Asia/Shanghai", + enable_utc=True, + task_track_started=True, + task_acks_late=True, # 任务处理完才 ACK，防丢失 + worker_prefetch_multiplier=1, # 一次只取1条，防长任务堆积 + task_routes={ + "app.workers.tasks.run_generation_pipeline": {"queue": "generation"}, + "app.workers.tasks.build_delivery_package": {"queue": "packaging"}, + }, +) diff --git a/backend/app/workers/packaging_task.py b/backend/app/workers/packaging_task.py new file mode 100644 index 0000000..02adcea --- /dev/null +++ b/backend/app/workers/packaging_task.py @@ -0,0 +1,104 @@ +""" +app/workers/packaging_task.py — 交付打包 Celery 任务 +build_delivery_package：查已选文案+图片 → package_exporter → 存路径 +""" + +import json +import logging + +from app.workers.celery_app import celery_app + +logger = logging.getLogger(__name__) + + +def _get_db(): + from app.core.database import SessionLocal + return SessionLocal() + + +@celery_app.task( + bind=True, + name="app.workers.tasks.build_delivery_package", + max_retries=2, + default_retry_delay=10, + queue="packaging", +) +def build_delivery_package(self, package_id: int) -> dict: + """打包交付任务。查 delivery_packages → 收集笔记 → package_exporter""" + logger.info("build_delivery_package start: package_id=%s", package_id) + db = _get_db() + try: + from app.models.task import DeliveryPackage, TextCandidate, ImageCandidate + from app.constants.enums import PackageStatus + + pkg = db.query(DeliveryPackage).filter(DeliveryPackage.id == package_id).first() + if not pkg: + raise ValueError(f"package_id={package_id} not found") + + workspace_id = pkg.workspace_id + task_id = pkg.task_id + + from app.core.config import get_settings + settings = get_settings() + upload_base = settings.UPLOAD_BASE_PATH.rstrip("/") + + selected_text = db.query(TextCandidate).filter( + TextCandidate.task_id == task_id, TextCandidate.is_selected == True, + ).first() + # 整套全打（倩倩姐2026-06-08拍板）：一条笔记的全部图按 seq 排序进包， + # 不再只打 is_selected 的封面。北哥6张标准套 seq=1 是 hook 封面，天然排第一。 + selected_images = db.query(ImageCandidate).filter( + ImageCandidate.task_id == task_id, + ).order_by(ImageCandidate.seq).all() + + if not selected_text: + raise ValueError("无已选文案，请先选择文案") + + text_data = json.loads(selected_text.content or "{}") + images_data = [] + for ic in selected_images: + img_bytes = b"" + if ic.url: + # url 形如 /uploads/ws/task/file.jpg，本身已含 uploads 前缀。 + # 工作目录是 /app，直接 lstrip("/") 当相对路径读，不能再拼 upload_base(会重复 uploads/uploads)。 + rel = ic.url.lstrip("/") + abs_path = rel + try: + with open(abs_path, "rb") as f: + img_bytes = f.read() + except OSError as e: + logger.warning("图片读取失败，跳过：%s %s", abs_path, e) + images_data.append({ + "seq": ic.seq, + "role": ic.role.value if hasattr(ic.role, "value") else str(ic.role), + "data": img_bytes, + }) + + notes = [{ + "title": text_data.get("title", ""), + "content": text_data.get("content", ""), + "tags": text_data.get("tags", []), + "images": images_data, + "banned_word_status": (selected_text.banned_word_status.value + if hasattr(selected_text.banned_word_status, "value") + else str(selected_text.banned_word_status)), + }] + + from app.services.ai_engine.package_exporter import build_delivery_package as do_build + # 打包产物放专用目录 uploads/packages/，与图片目录 uploads/{ws}/{task}/ 分开 + packages_base = f"{upload_base}/packages" + zip_path = do_build(workspace_id, task_id, notes, base_path=packages_base) + + pkg.package_path = zip_path + pkg.download_url = f"/api/v1/delivery-packages/{package_id}/download-file" + pkg.status = PackageStatus.READY + db.commit() + + logger.info("delivery package ready: package_id=%s path=%s", package_id, zip_path) + return {"package_id": package_id, "status": "ready", "path": zip_path} + + except Exception as exc: + logger.error("build_delivery_package failed: package_id=%s err=%s", package_id, exc) + raise self.retry(exc=exc) + finally: + db.close() diff --git a/backend/app/workers/pipeline_io.py b/backend/app/workers/pipeline_io.py new file mode 100644 index 0000000..991ece8 --- /dev/null +++ b/backend/app/workers/pipeline_io.py @@ -0,0 +1,298 @@ +""" +app/workers/pipeline_io.py — 生产链 Step5-8 +Step5: 文案生成（generate_text_variants） +Step6: 图片生成（generate_storyboard_images，asyncio.gather） +Step7: 图片后处理（image_postprocessor） +Step8: 存 text_candidates / image_candidates → 更新状态 → 推 task_done +""" + +import asyncio +import json +import logging +import os + +logger = logging.getLogger(__name__) + + +def _resolve_image_path(img_path: str) -> str: + """ + 解析产品参考图路径，兼容绝对路径(新)与历史相对路径(旧)。 + 新数据存绝对路径(/app/uploads/...)直接返回； + 旧数据存相对路径(uploads/packages/...)锚定到 UPLOAD_ABS_ROOT 的父级， + 避免 worker(cwd=/) 解析失败。 + """ + if not img_path: + return "" + if os.path.isabs(img_path): + return img_path + from app.core.config import get_settings + # UPLOAD_ABS_ROOT=/app/uploads，其父级 /app 是相对路径(uploads/...)的锚点 + root_parent = os.path.dirname(get_settings().UPLOAD_ABS_ROOT.rstrip("/")) + return os.path.join(root_parent, img_path) + + +def run_text_generation(db, clients, task, product_dict: dict, flywheel_fragment: str, + push_fn, workspace_id: int, seq_start: int) -> tuple[list, int, bool]: + """ + Step5: 调 generate_text_variants → 存 TextCandidate → 推 SSE → 写 ai_call_logs。 + S1: 存库前过滤——只存 passed且score>=90且banned_word_status!='hard_block' 的文案。 + 合格数 < task.text_count 时 needs_replenish=True（由主任务发起后台补充子任务）。 + 返回 (candidates_raw, next_seq, needs_replenish)。 + """ + import time + from app.services.ai_engine.text_variants import generate_text_variants + from app.models.product import BannedWord + from app.models.task import TextCandidate + from app.models.flywheel import AiCallLog + from app.models.workspace import UserApiKey + from app.constants.enums import CandidateSource, BannedWordStatus + from app.services.ai_engine.constants import QUALITY_PASS_SCORE + + banned_rows = db.query(BannedWord).filter( + BannedWord.workspace_id == workspace_id + ).all() + banned_dicts = [{"word": b.word, "level": b.level, "replacement": b.replacement} + for b in banned_rows] + + # 查 key_id（只取 id，不解密，不违反基石B） + key_row = db.query(UserApiKey).filter( + UserApiKey.user_id == task.operator_id, + UserApiKey.workspace_id == workspace_id, + ).first() + key_id = key_row.id if key_row else None + + t0 = time.monotonic() + llm_success = True + try: + candidates_raw = asyncio.run(generate_text_variants( + llm_client=clients, + product=product_dict, + count=task.text_count, + banned_word_rows=banned_dicts, + flywheel_context=flywheel_fragment, + )) + except Exception as exc: + llm_success = False + logger.error("generate_text_variants 失败: %s", exc) + candidates_raw = [] + latency_ms = int((time.monotonic() - t0) * 1000) + + # 写 ai_call_logs（留痕，不含明文key） + try: + log = AiCallLog( + workspace_id=workspace_id, + user_id=task.operator_id, + key_id=key_id, + task_id=task.id, + provider="apiports", + model=clients._model, + call_type="text", + success=llm_success, + latency_ms=latency_ms, + ) + db.add(log) + db.flush() + except Exception as log_exc: + logger.warning("ai_call_logs 写入失败（非阻断）: %s", log_exc) + + # S1: 存库前过滤——只存合格文案（passed + score>=90 + 非hard_block） + seq = seq_start + saved_count = 0 + for i, c in enumerate(candidates_raw): + score = c.get("score", 0) + passed = c.get("passed", False) + bw_status = c.get("banned_word_status", "pass") + if not (passed and score >= QUALITY_PASS_SCORE and bw_status != "hard_block"): + logger.info( + "文案[%d] 过滤丢弃: passed=%s score=%s banned=%s", + i, passed, score, bw_status, + ) + continue + + tc = TextCandidate( + workspace_id=workspace_id, + task_id=task.id, + source=CandidateSource.AI, + angle_label=c.get("angle_label") or c.get("angle", ""), + content=json.dumps(c, ensure_ascii=False), + score_json=json.dumps(c.get("score_detail", []), ensure_ascii=False), + banned_word_status=BannedWordStatus(bw_status), + ) + db.add(tc) + db.flush() + saved_count += 1 + seq += 1 + push_fn(task.id, workspace_id, "text_candidate", { + "candidate_id": tc.id, "angle_label": tc.angle_label, + "content": c.get("content", ""), "score": score, + }, seq) + seq += 1 + push_fn(task.id, workspace_id, "text_progress", { + "done": saved_count, "total": task.text_count + }, seq) + + db.commit() + + # S1: 合格数不足时标记需要后台补充 + needs_replenish = saved_count < task.text_count + if needs_replenish: + logger.warning( + "文案合格数不足: task_id=%s 目标=%s 实得=%s，将后台异步补充", + task.id, task.text_count, saved_count, + ) + return candidates_raw, seq, needs_replenish + + +def run_image_generation(db, clients, task, product_dict: dict, + push_fn, workspace_id: int, seq_start: int, + first_copy: dict, upload_base_path: str) -> int: + """ + Step6+7+8(image): 调 generate_storyboard_images → 后处理 → 存 ImageCandidate → 推 SSE。 + 返回 next_seq。 + """ + import time + from app.services.ai_engine.image_gen import generate_storyboard_images + from app.services.ai_engine.image_postprocessor import process_image + from app.models.task import ImageCandidate + from app.models.flywheel import AiCallLog + from app.models.workspace import UserApiKey + from app.constants.enums import ImageRole as IR + + # 取 key_id（不解密，不记录明文 key） + key_row = db.query(UserApiKey).filter( + UserApiKey.user_id == task.operator_id, + UserApiKey.workspace_id == workspace_id, + ).first() + key_id = key_row.id if key_row else None + + # TODO: 尺寸字段后续加产品级配置（products 表现无 aspect_ratio 字段） + # 本轮固定 '3:4'=1024×1536，与 gpt-image-2 原生尺寸一致，免后处理二次拉伸 + aspect_ratio = "3:4" + + # image_count=0 直接跳过(纯文案任务/测试)，不空跑生图通道触发无谓失败日志。 + if not task.image_count or task.image_count <= 0: + logger.info("image_count=0，跳过生图: task_id=%s", task.id) + return seq_start + + reference_images: list[bytes] = [] + _img_path = _resolve_image_path(product_dict.get("image_path", "")) + if _img_path and os.path.isfile(_img_path): + try: + with open(_img_path, "rb") as _f: + reference_images = [_f.read()] + logger.info("产品参考图已加载：%s (%d bytes)", _img_path, len(reference_images[0])) + except Exception as _e: + logger.warning("产品参考图读取失败，退化为空列表：%s %s", _img_path, _e) + else: + logger.warning( + "product.image_path 未设置或文件不存在（%r），生图将以无参考图模式运行，" + "可能导致产品包装跑偏。", _img_path + ) + + # 禁降级兜底：本次产品入镜但无参考图 → 硬失败，绝不降级纯文生图（建任务已拦一道，这是防绕过） + if getattr(task, "need_product_image", True) and not reference_images: + raise ValueError( + "本次产品入镜(need_product_image=True)但未获取到产品参考图，" + "拒绝降级纯文生图。请确认产品已上传参考图。" + ) + + seq = seq_start + # 3套正交叙事 A/B/C，每套各 image_count 张独立生图 + for strategy in ("A", "B", "C"): + t0 = time.monotonic() + img_success = True + img_error_code = None + try: + image_results = asyncio.run(generate_storyboard_images( + client=clients, + note=first_copy, + product=product_dict, + image_count=task.image_count, + reference_images=reference_images or None, + strategy=strategy, + )) + except Exception as exc: + img_success = False + img_error_code = type(exc).__name__ + logger.error("generate_storyboard_images 套%s 失败: %s", strategy, exc) + image_results = [] + latency_ms = int((time.monotonic() - t0) * 1000) + + fail_count = 0 + first_img_error: str | None = None + for i, img_result in enumerate(image_results): + if img_result.get("error"): + fail_count += 1 + if first_img_error is None: + first_img_error = str(img_result["error"])[:32] + seq += 1 + push_fn(task.id, workspace_id, "batch_failed", { + "batch": img_result["role"], "reason": img_result["error"], + "strategy": strategy, "retryable": True, + }, seq) + continue + + raw_bytes = img_result["image_bytes"] + try: + processed = process_image(raw_bytes, aspect_ratio=aspect_ratio, resample_strength=1) + except Exception as e: + logger.warning("图片后处理失败，使用原图: %s", e) + processed = raw_bytes + + img_dir = os.path.join(upload_base_path, str(workspace_id), str(task.id)) + os.makedirs(img_dir, exist_ok=True) + filename = f"{strategy}_{i+1:02d}_{img_result['role']}.jpg" + img_path = os.path.join(img_dir, filename) + with open(img_path, "wb") as f: + f.write(processed) + + img_url = f"/uploads/{workspace_id}/{task.id}/{filename}" + + role_enum = IR.MAIN + try: + role_enum = IR(img_result["role"]) + except ValueError: + pass + + ic = ImageCandidate( + workspace_id=workspace_id, + task_id=task.id, + role=role_enum, + url=img_url, + seq=i + 1, + strategy=strategy, # 写入 A/B/C（非 hardcode） + ) + db.add(ic) + db.flush() + seq += 1 + push_fn(task.id, workspace_id, "image_candidate", { + "candidate_id": ic.id, "strategy": strategy, + "url": img_url, "role": img_result["role"], + }, seq) + seq += 1 + push_fn(task.id, workspace_id, "image_progress", { + "done": i + 1, "total": task.image_count, "strategy": strategy, + }, seq) + + # 写 ai_call_logs（每套一条，失败不阻断） + actual_provider = os.environ.get("IMAGE_PROVIDER_PRIMARY", "gpt") + final_error_code = first_img_error or img_error_code + try: + img_log = AiCallLog( + workspace_id=workspace_id, + user_id=task.operator_id, + key_id=key_id, + task_id=task.id, + provider=actual_provider, + call_type="image", + success=(img_success and fail_count == 0), + latency_ms=latency_ms, + error_code=final_error_code, + ) + db.add(img_log) + db.flush() + except Exception as log_exc: + logger.warning("ai_call_logs(image) 套%s 写入失败（非阻断）: %s", strategy, log_exc) + + db.commit() + return seq diff --git a/backend/app/workers/pipeline_steps.py b/backend/app/workers/pipeline_steps.py new file mode 100644 index 0000000..f1048c6 --- /dev/null +++ b/backend/app/workers/pipeline_steps.py @@ -0,0 +1,93 @@ +""" +app/workers/pipeline_steps.py — 生产链 Step1-4 +Step1: 查 DB（task/product） +Step2: 查 key → Fernet 解密（局部变量，不传出，基石B） +Step3: 构建 AI clients +Step4: 推 task_started SSE + 飞轮上下文 +""" + +import json +import logging + +logger = logging.getLogger(__name__) + + +def load_task_and_product(db, task_id: int): + """Step1: 查任务 + 产品，失败返 None 或抛异常。""" + from app.models.task import GenerationTask + from app.models.product import Product + + task = db.query(GenerationTask).filter(GenerationTask.id == task_id).first() + if not task: + logger.error("task_id=%s not found", task_id) + return None, None + + product = db.query(Product).filter(Product.id == task.product_id).first() + if not product: + raise ValueError(f"product_id={task.product_id} not found") + return task, product + + +def decrypt_user_key(db, operator_id: int, workspace_id: int) -> str: + """ + Step2: 查 key → Fernet 解密，返回 plain_key（只活在调用方局部变量）。 + 绝不打印、不持久化 plain_key（基石B）。 + """ + from app.models.workspace import UserApiKey + from app.utils.fernet_utils import decrypt_key + + api_key_row = db.query(UserApiKey).filter( + UserApiKey.user_id == operator_id, + UserApiKey.workspace_id == workspace_id, + UserApiKey.provider.in_(["openai", "apiports"]), # G6坑修复：接受主备通道名 + ).first() + if not api_key_row: + raise ValueError("用户未配置 API Key，请先录入") + return decrypt_key(api_key_row.encrypted_key) + + +def build_clients_and_clear_key(plain_key: str): + """ + Step3: 构建 AIClients，plain_key 传入后立即由调用方置 None。 + 返回 clients 对象。 + """ + from app.services.ai_engine.gemini_factory import build_ai_clients + return build_ai_clients(plain_key) + + +def build_product_dict(product) -> dict: + """把 ORM product 转成 AI 引擎所需的 dict（不含任何 key）。""" + return { + "name": product.name, + "category": product.category or "通用好物", + "selling_points": json.loads(product.selling_points or "[]"), + "style_tone": product.style_tone or "素人分享风", + "text_angles": json.loads(product.text_angles or "[]"), + "custom_prompt": product.custom_prompt or "", + "brand_keyword": product.brand_keyword or "", # S3: 品牌词透传进生成prompt(每条植入) + "target_audience": product.target_audience or "", # 012: 人群透传进storyboard/文案prompt + "image_path": product.image_path or "", # 产品参考图路径（前端上传后填入） + } + + +def load_flywheel_context(db, workspace_id: int, product_id: int, product_dict: dict) -> tuple[str, dict]: + """ + 查最近50条飞轮事件，聚合偏好上下文。 + 返回 (prompt_fragment, full_ctx)。 + """ + from app.models.flywheel import PreferenceEvent + from app.services.ai_engine.preference_aggregator import aggregate_preference_context + + recent = db.query(PreferenceEvent).filter( + PreferenceEvent.workspace_id == workspace_id, + PreferenceEvent.product_id == product_id, + ).order_by(PreferenceEvent.id.desc()).limit(50).all() + + events_dicts = [ + {"signal_type": e.signal_type, "workspace_id": e.workspace_id, + "product_id": e.product_id, "angle_label": e.angle_label or "", + "signal_weight": e.signal_weight, "reason": e.reason or ""} + for e in recent + ] + ctx = aggregate_preference_context(events_dicts, product_dict, workspace_id, product_id) + return ctx.get("prompt_fragment", ""), ctx diff --git a/backend/app/workers/replenish_task.py b/backend/app/workers/replenish_task.py new file mode 100644 index 0000000..895eacf --- /dev/null +++ b/backend/app/workers/replenish_task.py @@ -0,0 +1,100 @@ +""" +app/workers/replenish_task.py — 文案后台补充任务（S1: 先展示后台补铁律） + +合格文案(passed且score>=90且非hard_block)不足用户目标条数时, +run_generation_pipeline 已先展示合格的; 此任务异步补齐到目标数或达上限。 +只接收 task_id(基石B)。复用 pipeline_io.run_text_generation 的生成+过滤。 +""" + +import logging + +from app.workers.celery_app import celery_app + +logger = logging.getLogger(__name__) + +# 补充上限：避免合格率低时无限补。最多补 MAX_REPLENISH_ROUNDS 轮。 +MAX_REPLENISH_ROUNDS = 3 + + +@celery_app.task( + bind=True, + name="app.workers.tasks.replenish_text_candidates", + max_retries=2, + default_retry_delay=30, + queue="generation", +) +def replenish_text_candidates(self, task_id: int) -> dict: + """后台补充合格文案到目标条数（或达补充上限）。 + + 每轮调用 run_text_generation 续生成,过滤后累加入库; + 已合格数 >= text_count 即停; 达 MAX_REPLENISH_ROUNDS 仍不足也停(不卡用户)。 + """ + from app.workers.tasks import _get_db, _push_event_sync + from app.workers.pipeline_io import run_text_generation + from app.workers.pipeline_steps import ( + load_task_and_product, + decrypt_user_key, + build_clients_and_clear_key, + build_product_dict, + load_flywheel_context, + ) + from app.models.task import TextCandidate + + db = _get_db() + try: + task, product = load_task_and_product(db, task_id) + if not task: + logger.warning("补充任务找不到 task_id=%s", task_id) + return {"task_id": task_id, "replenished": 0, "reason": "task_not_found"} + + # 已合格数（按入库的合格候选计） + existing = db.query(TextCandidate).filter( + TextCandidate.task_id == task_id + ).count() + target = task.text_count + if existing >= target: + return {"task_id": task_id, "replenished": 0, "reason": "already_enough"} + + workspace_id = task.workspace_id + plain_key = decrypt_user_key(db, task.operator_id, workspace_id) + clients = build_clients_and_clear_key(plain_key) + plain_key = None # 用完即清除，基石B + product_dict = build_product_dict(product) + flywheel_fragment, _ = load_flywheel_context( + db, workspace_id, task.product_id, product_dict + ) + + seq = 9000 # 补充事件 seq 段，避开主流程 + rounds = 0 + # 真实入库合格数以 DB count 为准，绝不用 len(raw)(那是原始生成数含被过滤的低分条)。 + # task47 实测踩坑：len(raw)=2 但 saved=0(都没过90),误判达标提前停,库里仍缺。 + current = existing + while current < target and rounds < MAX_REPLENISH_ROUNDS: + rounds += 1 + run_text_generation( + db, clients, task, product_dict, flywheel_fragment, + _push_event_sync, workspace_id, seq, + ) + # run_text_generation 内部已把本轮合格候选写库,这里重查真实库内条数 + db.expire_all() + current = db.query(TextCandidate).filter( + TextCandidate.task_id == task_id + ).count() + seq += 100 + logger.info( + "补充轮 %d: task_id=%s 库内合格=%d/%d", + rounds, task_id, current, target, + ) + + return { + "task_id": task_id, + "replenished": current - existing, + "rounds": rounds, + "final": current, + "target": target, + } + except Exception as exc: + logger.error("replenish_text_candidates failed: task_id=%s err=%s", task_id, exc) + raise self.retry(exc=exc) + finally: + db.close() diff --git a/backend/app/workers/tasks.py b/backend/app/workers/tasks.py new file mode 100644 index 0000000..397a520 --- /dev/null +++ b/backend/app/workers/tasks.py @@ -0,0 +1,155 @@ +""" +app/workers/tasks.py — Celery 任务入口（编排层） +只接收 task_id（基石B）。具体步骤在 pipeline_steps / pipeline_io。 +打包任务在 packaging_task.py，此处 re-export 保持旧引用不变。 +""" + +import logging + +from app.workers.celery_app import celery_app +# re-export 保持旧代码 `from app.workers.tasks import build_delivery_package` 不变 +from app.workers.packaging_task import build_delivery_package # noqa: F401 +from app.workers.replenish_task import replenish_text_candidates # noqa: F401 + +logger = logging.getLogger(__name__) + + +def _get_db(): + """获取同步 DB Session（Celery worker 环境用同步）""" + from app.core.database import SessionLocal + return SessionLocal() + + +def _push_event_sync(task_id: int, workspace_id: int, event: str, data: dict, seq: int): + """同步推 SSE 事件（worker 内部用）""" + import json as _json + import redis as redis_lib + from app.core.config import get_settings + s = get_settings() + r = redis_lib.from_url(s.REDIS_URL, decode_responses=True) + hist_key = f"sse:task:{task_id}:events" + channel = f"sse:task:{task_id}" + record = _json.dumps({"event": event, "data": data, "seq": seq, + "workspace_id": workspace_id}, ensure_ascii=False) + r.rpush(hist_key, record) + r.expire(hist_key, 3600) + r.publish(channel, record) + + +@celery_app.task( + bind=True, + name="app.workers.tasks.run_generation_pipeline", + max_retries=3, + default_retry_delay=30, + queue="generation", +) +def run_generation_pipeline(self, task_id: int) -> dict: + """ + 生产链主任务。只接收 task_id，绝不接收 key。 + 子步骤委托给 pipeline_steps（查库/解密/飞轮上下文） + 和 pipeline_io（文案/图片生成+存储）。 + """ + logger.info("run_generation_pipeline start: task_id=%s", task_id) + db = _get_db() + seq = 0 + workspace_id = 0 # G3坑修复：确保 except 里能用真实 workspace_id + + try: + from app.constants.enums import TaskStatus + from app.workers.pipeline_steps import ( + load_task_and_product, + decrypt_user_key, + build_clients_and_clear_key, + build_product_dict, + load_flywheel_context, + ) + from app.workers.pipeline_io import run_text_generation, run_image_generation + from app.core.config import get_settings + + # Step1: 查任务 + 产品 + task, product = load_task_and_product(db, task_id) + if not task: + return {"task_id": task_id, "status": "not_found"} + + workspace_id = task.workspace_id + + # Step2: Fernet 解密（局部变量，不传出） + plain_key = decrypt_user_key(db, task.operator_id, workspace_id) + + # Step3: 构建 AIClients + clients = build_clients_and_clear_key(plain_key) + plain_key = None # 用完即清除，基石B + + task.status = TaskStatus.GENERATING + db.commit() + + # Step4: 推 task_started + 飞轮上下文 + seq += 1 + _push_event_sync(task_id, workspace_id, "task_started", { + "task_id": task_id, "total_text": task.text_count, "total_image": task.image_count, + }, seq) + + product_dict = build_product_dict(product) + flywheel_fragment, flywheel_ctx = load_flywheel_context(db, workspace_id, task.product_id, product_dict) + + if flywheel_fragment: + seq += 1 + _push_event_sync(task_id, workspace_id, "flywheel_injected", { + "recent_preference": flywheel_ctx.get("recent_preference", ""), + "reject_reasons": flywheel_ctx.get("reject_reasons", []), + }, seq) + + # Step5: 文案生成（S1: 返回 needs_replenish=合格数<目标数，触发后台补充） + candidates_raw, seq, needs_replenish = run_text_generation( + db, clients, task, product_dict, flywheel_fragment, + _push_event_sync, workspace_id, seq, + ) + if needs_replenish: + # 合格文案不足用户目标条数：先展示已合格的，后台异步补充（先展示后台补铁律） + # 注：candidates_raw 是本轮原始生成数(含被过滤的低分条)，非合格入库数； + # 真实合格数以库内 saved_count 为准，补充子任务会按库内实际缺口补到够。 + logger.warning( + "文案合格数不足将后台补充: task_id=%s 本轮生成=%d 目标=%d", + task_id, len(candidates_raw), task.text_count, + ) + try: + replenish_text_candidates.delay(task_id) + except Exception as _re: + logger.error("触发后台补充失败: task_id=%s err=%s", task_id, _re) + + # Step6+7+8: 图片生成 + 后处理 + 存盘 + first_copy = candidates_raw[0] if candidates_raw else {} + settings = get_settings() + seq = run_image_generation( + db, clients, task, product_dict, + _push_event_sync, workspace_id, seq, + first_copy, settings.UPLOAD_BASE_PATH, + ) + + # 最终状态 + task_done + task.status = TaskStatus.PENDING_SELECTION + db.commit() + + seq += 1 + _push_event_sync(task_id, workspace_id, "task_done", { + "task_id": task_id, "status": "pending_selection" + }, seq) + + logger.info("run_generation_pipeline done: task_id=%s", task_id) + return {"task_id": task_id, "status": "pending_selection"} + + except Exception as exc: + logger.error("run_generation_pipeline failed: task_id=%s err=%s", task_id, exc) + try: + from app.models.task import GenerationTask as GT + from app.constants.enums import TaskStatus as TS + t = db.query(GT).filter(GT.id == task_id).first() + if t: + t.status = TS.PENDING + db.commit() + except Exception: + pass + _push_event_sync(task_id, workspace_id, "error", {"code": 50001, "message": str(exc)}, seq + 1) + raise self.retry(exc=exc) + finally: + db.close() diff --git a/backend/docker-compose.yml b/backend/docker-compose.yml new file mode 100644 index 0000000..4a39f58 --- /dev/null +++ b/backend/docker-compose.yml @@ -0,0 +1,100 @@ +services: + api: + build: + context: . + dockerfile: Dockerfile + container_name: clover_api + ports: + - "8000:8000" + env_file: + - ./.env + environment: + - DATABASE_URL=mysql+pymysql://${MYSQL_USER}:${MYSQL_PASSWORD}@mysql:3306/${MYSQL_DB} + - MONGO_URI=mongodb://mongo:27017/clover_trace + - REDIS_URL=redis://redis:6379/0 + depends_on: + mysql: + condition: service_healthy + redis: + condition: service_healthy + volumes: + - .:/app + - uploads:/app/uploads + command: sh -c "alembic upgrade head && uvicorn main:app --host 0.0.0.0 --port 8000 --reload" + healthcheck: + test: ["CMD", "curl", "-f", "http://localhost:8000/health"] + interval: 30s + timeout: 10s + retries: 3 + + worker: + build: + context: . + dockerfile: Dockerfile + container_name: clover_worker + env_file: + - ./.env + environment: + - DATABASE_URL=mysql+pymysql://${MYSQL_USER}:${MYSQL_PASSWORD}@mysql:3306/${MYSQL_DB} + - MONGO_URI=mongodb://mongo:27017/clover_trace + - REDIS_URL=redis://redis:6379/0 + depends_on: + - mysql + - redis + volumes: + - .:/app + - uploads:/app/uploads + command: celery -A app.workers.celery_app worker -Q generation,packaging -c 2 -l info + + mysql: + image: mysql:8.0 + container_name: clover_mysql + environment: + MYSQL_ROOT_PASSWORD: ${MYSQL_ROOT_PASSWORD} + MYSQL_DATABASE: ${MYSQL_DB} + MYSQL_USER: ${MYSQL_USER} + MYSQL_PASSWORD: ${MYSQL_PASSWORD} + ports: + - "3306:3306" + volumes: + - mysql_data:/var/lib/mysql + healthcheck: + test: ["CMD", "mysqladmin", "ping", "-h", "localhost"] + interval: 10s + timeout: 5s + retries: 5 + + mongo: + image: mongo:7.0 + container_name: clover_mongo + ports: + - "27017:27017" + volumes: + - mongo_data:/data/db + + redis: + image: redis:7.4-alpine + container_name: clover_redis + ports: + - "6379:6379" + healthcheck: + test: ["CMD", "redis-cli", "ping"] + interval: 10s + timeout: 5s + retries: 3 + + nginx: + image: nginx:1.27-alpine + container_name: clover_nginx + ports: + - "80:80" + volumes: + - ./nginx/nginx.conf:/etc/nginx/conf.d/default.conf:ro + depends_on: + api: + condition: service_healthy + +volumes: + mysql_data: + mongo_data: + uploads: diff --git a/backend/fire_real.py b/backend/fire_real.py new file mode 100644 index 0000000..dcc817f --- /dev/null +++ b/backend/fire_real.py @@ -0,0 +1,16 @@ +"""真实数据端到端验证：倍分子素颜霜（用完即删）""" +from app.core.database import SessionLocal +from app.models.task import GenerationTask +from app.workers.tasks import run_generation_pipeline + +db = SessionLocal() +task = GenerationTask( + workspace_id=3, product_id=1, operator_id=4, + theme="倍分子素颜霜真实数据端到端", text_count=1, image_count=6, + track="ai", need_product_image=True, status="pending", +) +db.add(task); db.commit(); db.refresh(task) +tid = task.id; db.close() +print("NEW_TASK_ID=", tid) +run_generation_pipeline.delay(tid) +print("ENQUEUED") diff --git a/backend/main.py b/backend/main.py new file mode 100644 index 0000000..1ad3e67 --- /dev/null +++ b/backend/main.py @@ -0,0 +1,95 @@ +""" +main.py — FastAPI 应用入口 +统一响应包络 / 全局异常处理 / 健康检查 / 路由注册 +""" + +import logging +import os + +from fastapi import FastAPI, Request +from fastapi.middleware.cors import CORSMiddleware +from fastapi.responses import JSONResponse +from fastapi.staticfiles import StaticFiles + +from app.core.config import get_settings +from app.core.response import CloverHTTPException, ErrorCode + +logger = logging.getLogger(__name__) +settings = get_settings() + + +def create_app() -> FastAPI: + app = FastAPI( + title="Clover API", + version="0.1.0", + docs_url="/docs" if settings.APP_ENV != "production" else None, + redoc_url=None, + ) + + # ── CORS ────────────────────────────────────────── + app.add_middleware( + CORSMiddleware, + allow_origins=["*"], # 生产环境收窄到前端域名 + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], + ) + + # ── 全局异常处理（统一包络）────────────────────── + @app.exception_handler(CloverHTTPException) + async def clover_http_exception_handler(request: Request, exc: CloverHTTPException): + return JSONResponse( + status_code=exc.status_code, + content={"code": exc.biz_code, "message": exc.biz_message}, + ) + + @app.exception_handler(Exception) + async def generic_exception_handler(request: Request, exc: Exception): + logger.error("Unhandled exception: %s %s → %s", request.method, request.url, exc) + return JSONResponse( + status_code=500, + content={"code": ErrorCode.SERVER_ERROR, "message": "服务器内部错误"}, + ) + + # ── 健康检查（Docker 健康检查用）────────────────── + @app.get("/health") + async def health(): + return {"status": "ok"} + + # ── 路由注册（每个模块路由独立文件，这里汇总注册）─ + _register_routers(app) + + # ── 静态文件（G4坑修复：图片 /uploads 路由）────── + uploads_dir = os.path.join(os.path.dirname(__file__), "uploads") + os.makedirs(uploads_dir, exist_ok=True) + app.mount("/uploads", StaticFiles(directory=uploads_dir), name="uploads") + + return app + + +def _register_routers(app: FastAPI) -> None: + from app.api.v1.auth import router as auth_router + from app.api.v1.api_keys import router as api_keys_router + from app.api.v1.products import router as products_router + from app.api.v1.benchmarks import router as benchmarks_router + from app.api.v1.tasks import router as tasks_router + from app.api.v1.task_actions import router as task_actions_router + from app.api.v1.review import router as review_router + from app.api.v1.delivery import router as delivery_router + from app.api.v1.stream import router as stream_router + from app.api.v1.workspaces import router as workspaces_router + + prefix = "/api/v1" + app.include_router(auth_router, prefix=prefix) + app.include_router(api_keys_router, prefix=prefix) + app.include_router(products_router, prefix=prefix) + app.include_router(benchmarks_router, prefix=prefix) + app.include_router(tasks_router, prefix=prefix) + app.include_router(task_actions_router, prefix=prefix) + app.include_router(review_router, prefix=prefix) + app.include_router(delivery_router, prefix=prefix) + app.include_router(stream_router, prefix=prefix) + app.include_router(workspaces_router, prefix=prefix) + + +app = create_app() diff --git a/backend/nginx/nginx.conf b/backend/nginx/nginx.conf new file mode 100644 index 0000000..55e46d2 --- /dev/null +++ b/backend/nginx/nginx.conf @@ -0,0 +1,36 @@ +# nginx/nginx.conf — Clover 反向代理配置 +# SSE 端点需关闭 proxy_buffering 才能实时透传 + +server { + listen 80; + server_name _; + + # ── SSE 端点：关闭缓冲，实时透传 ────────────────── + location ~* /api/v1/tasks/[^/]+/stream { + proxy_pass http://api:8000; + proxy_http_version 1.1; + proxy_set_header Connection ""; + proxy_buffering off; + proxy_cache off; + proxy_read_timeout 3600s; + proxy_set_header Host $host; + proxy_set_header X-Real-IP $remote_addr; + proxy_set_header Authorization $http_authorization; + } + + # ── 普通 API 端点 ────────────────────────────────── + location /api/ { + proxy_pass http://api:8000; + proxy_http_version 1.1; + proxy_set_header Host $host; + proxy_set_header X-Real-IP $remote_addr; + proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; + proxy_set_header Authorization $http_authorization; + client_max_body_size 20m; + } + + # ── 健康检查 ─────────────────────────────────────── + location /health { + proxy_pass http://api:8000/health; + } +} diff --git a/backend/plans/prompt缩水诊断与补齐.md b/backend/plans/prompt缩水诊断与补齐.md new file mode 100644 index 0000000..58771ac --- /dev/null +++ b/backend/plans/prompt缩水诊断与补齐.md @@ -0,0 +1,35 @@ +# 生图/文案 Prompt 缩水诊断 + 补齐方案 + +> 倩倩姐 2026-06-08 提出：图质量还行但没用小红书风格 prompt，怀疑缩水。 +> 经逐函数对照产品包上线版（`万牛会L1准备/worker` src/copy.js + image.js），确认**确实缩水**。 +> banana 未使用（符合铁律：扒上线版 worker）。 + +## 一、结论先行 + +我之前扒的是**分镜骨架**（哪张图什么角色），漏了源头让内容"像小红书"的 prompt 内核。 +文案侧、生图侧**都缩水了**。源头 image.js 871行 / copy.js 690行，我的对应文件只有 203 / 201 行。 + +## 二、生图侧缩水清单（对照 image.js） + +| # | 源头有 | 位置 | 我的现状 | 影响 | +|---|--------|------|---------|------| +| G1 | STYLE_PROMPTS 风格预设（种草风/对比风/成分科普风） | image.js:26-29 | ❌ 完全没有 | 图不分风格，全一个样 | +| G2 | 叙事链路描述（"6张标准种草链路：点击/痛点/核心证明…"） | image.js:678-682 | ❌ 没注入 | 模型不知整组叙事逻辑 | +| G3 | 短卖点 + 短标签注入图 prompt | image.js:684-686 | ❌ 没注入 | 图与卖点脱节 | +| G4 | promptFromStoryboard 逐图完整字段（卖点/文案依据/画面主体/素材使用/禁止/排版要求） | image.js:323-334 | ⚠️ 只拼了角色+目标+主文字 | 每张图约束大幅缩水 | +| G5 | buildImageStoryboard 每角色细粒度 goal/overlay/assetUse/forbidden | image.js:222-322 | ⚠️ 部分（proof_strategy有，逐角色细节缺） | 角色指导粗糙 | +| G6 | compose_image_prompt 仍是 TODO 占位 | 我方 prompt_composer.py:84-109 | ❌ 死代码 | 入口没接通 | + +## 三、文案侧缩水清单（对照 copy.js） + +| # | 源头有 | 位置 | 我的现状 | 影响 | +|---|--------|------|---------|------| +| C1 | COPY_SYSTEM "小红书7步SOP+标题公式+卖点翻买点" | copy.js:10-30 | ⚠️ 我有自写版(佛系人设)，非源头方法论 | 方法论不同源 | +| C2 | buildGenerationPrompt 候选池超量生成(targetCount*4)再筛 | copy.js:520-560 | ❌ 没有候选池超量机制 | 出稿多样性弱 | +| C3 | angleSlots 角度槽位（按品类分配，强制N条不同策略） | copy.js | ⚠️ 我有变量池ABC，非角度槽位 | 角度覆盖机制不同 | +| C4 | 避开历史标题/角度（previousCopies 注入） | copy.js:543-548 | ❌ 没有（飞轮有偏好但无"避重"） | 重生成可能撞稿 | +| C5 | imageBrief/content 分离硬约束（配图建议不进正文） | copy.js:18 | ⚠️ 字段有，无强约束 | 正文可能混入执行提示 | + +## 四、待补范围（倩倩姐拍板） + +见下方 ExitPlanMode。 diff --git a/backend/requirements.txt b/backend/requirements.txt new file mode 100644 index 0000000..d856528 --- /dev/null +++ b/backend/requirements.txt @@ -0,0 +1,38 @@ +# requirements.txt — Clover 后端依赖（pinned versions） +# ── Web 框架 ────────────────────────────────────────── +fastapi==0.115.5 +uvicorn[standard]==0.32.1 +pydantic==2.10.3 +pydantic-settings==2.6.1 + +# ── 数据库 ─────────────────────────────────────────── +sqlalchemy==2.0.36 +alembic==1.14.0 +pymysql==1.1.1 +cryptography==44.0.0 + +# ── MongoDB ────────────────────────────────────────── +motor==3.6.0 + +# ── Redis + Celery ─────────────────────────────────── +redis==5.2.1 +celery==5.4.0 + +# ── JWT + 安全 ─────────────────────────────────────── +pyjwt==2.10.1 +# passlib 不带 [bcrypt] extra：该 extra 会拉最新 bcrypt(5.x)，覆盖下面的 pin， +# 而 passlib 1.7.4 与 bcrypt 5.x 不兼容（password cannot be longer than 72 bytes）。 +# 显式锁 bcrypt 4.0.1。 +passlib==1.7.4 +bcrypt==4.0.1 + +# ── HTTP 客户端（AI 调用）──────────────────────────── +httpx==0.28.1 +aiohttp==3.11.10 + +# ── 图像处理（去水印）────────────────────────────── +pillow==11.0.0 + +# ── 工具 ───────────────────────────────────────────── +python-multipart==0.0.19 +python-dotenv==1.0.1 diff --git a/backend/scripts/real_image_check.py b/backend/scripts/real_image_check.py new file mode 100644 index 0000000..8cd8021 --- /dev/null +++ b/backend/scripts/real_image_check.py @@ -0,0 +1,103 @@ +"""真实出图验证（正式留存版，不带_tmp，勿当临时文件清理） +用法: cd Clover/backend && python3 scripts/real_image_check.py +key 只从 ../.env 读，不硬编码不打印明文。 +""" +from __future__ import annotations +import asyncio, base64, io, logging, os, sys, time +from pathlib import Path +import httpx + +ROOT = Path(__file__).resolve().parents[2] +_env = ROOT / ".env" +if _env.exists(): + for _l in _env.read_text().splitlines(): + _l = _l.strip() + if _l and not _l.startswith("#") and "=" in _l: + k, _, v = _l.partition("=") + os.environ.setdefault(k.strip(), v.strip()) + +logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s") +logger = logging.getLogger("img") + +TEST_PROMPT = "\n".join([ + "生成一张小红书可上传的独立竖版3:4图文海报，目标1024x1536，不是App截图。", + "产品：倍分子素颜霜（美妆护肤·素颜霜）。", + "画面角色：封面-钩子。画面主体：自然光生活场景，手持素颜霜或产品在桌面前景，像iPhone实拍封面。", + "图上文字：主标题「黄黑皮逆袭｜伪素颜天花板」，点位「黄黑皮也能拥有的妈生好皮」。", + "核心卖点：黄黑皮提亮、伪素颜自然、不假白不卡纹、洗面奶就能卸、懒人必备。", + "成分：烟酰胺(改善暗沉)、水解珍珠(锁水保湿)、角鲨烷(不卡纹)。", + "视觉风格：小红书种草风，明亮干净，暖色调，模拟iPhone主摄浅景深。", + "重要限制：禁止生成App截图/界面元素；禁止肤色变白/功效前后对比；" + "避免美白/祛斑/速效/医用等违规词；必须保持产品包装与参考图一致。", +]) + +_REF = ROOT / "test/output/01_连通测试.png" + + +async def _apiports(key, url, ref, model, size): + payload = {"model": model, "prompt": TEST_PROMPT, "n": 1, "size": size, + "image": base64.b64encode(ref).decode()} + async with httpx.AsyncClient(timeout=300.0) as c: + r = await c.post(url, json=payload, headers={"Authorization": f"Bearer {key}"}) + if r.status_code != 200: + raise RuntimeError(f"HTTP {r.status_code}: {r.text[:200]}") + return _parse(r.json()) + + +async def _codeproxy(key, base, ref, model, size): + files = [("model", (None, model)), ("prompt", (None, TEST_PROMPT)), + ("size", (None, size)), ("n", (None, "1")), + ("image[]", ("ref.png", io.BytesIO(ref), "image/png"))] + async with httpx.AsyncClient(timeout=300.0) as c: + r = await c.post(f"{base}/images/edits", files=files, + headers={"Authorization": f"Bearer {key}"}) + if r.status_code != 200: + raise RuntimeError(f"HTTP {r.status_code}: {r.text[:200]}") + return _parse(r.json()) + + +def _parse(j): + item = (j.get("data") or [{}])[0] + if "b64_json" in item: + return base64.b64decode(item["b64_json"]) + if "url" in item: + rr = httpx.get(item["url"], timeout=30.0); rr.raise_for_status() + return rr.content + raise ValueError(f"无法解析: {list(item.keys())}") + + +async def run(): + model = os.environ.get("MODEL_IMAGE", "gpt-image-2") + size = os.environ.get("IMAGE_SIZE", "1024x1536") + primary = os.environ.get("IMAGE_PROVIDER_PRIMARY", "apiports").lower() + fallback = os.environ.get("IMAGE_PROVIDER_FALLBACK", "codeproxy").lower() + out_dir = Path(__file__).parent.parent / "verify_output" + out_dir.mkdir(parents=True, exist_ok=True) + ref = _REF.read_bytes() + t0 = time.time(); img = None; used = None + for prov in [primary, fallback]: + key = os.environ.get(f"{prov.upper()}_KEY", "") + url = os.environ.get(f"{prov.upper()}_BASE_URL", "") + if not key or not url: + continue + logger.info("[%s] 出图中 model=%s size=%s", prov, model, size) + try: + img = await (_apiports(key, url, ref, model, size) if prov == "apiports" + else _codeproxy(key, url, ref, model, size)) + used = prov; break + except Exception as e: + logger.error("[%s] 失败: %s", prov, e) + dt = time.time() - t0 + print("\n===== 出图结果 =====") + if img: + out = out_dir / "素颜霜_封面hook.png" + out.write_bytes(img) + print(f" provider: {used}\n 耗时: {dt:.1f}s\n 大小: {len(img)//1024}KB\n 路径: {out}") + else: + print(f" 耗时: {dt:.1f}s\n 状态: 全失败") + print("====================\n") + return img is not None + + +if __name__ == "__main__": + sys.exit(0 if asyncio.run(run()) else 1) diff --git a/backend/scripts/run_log.txt b/backend/scripts/run_log.txt new file mode 100644 index 0000000..beef5a6 --- /dev/null +++ b/backend/scripts/run_log.txt @@ -0,0 +1,13 @@ +2026-06-09 11:39:33,251 INFO [apiports] 出图中 model=gpt-image-2 size=1024x1536 +2026-06-09 11:41:04,233 INFO HTTP Request: POST https://www.apiports.com/v1/api/generate "HTTP/1.1 200 OK" +2026-06-09 11:41:04,236 ERROR [apiports] 失败: 无法解析: [] +2026-06-09 11:41:04,236 INFO [codeproxy] 出图中 model=gpt-image-2 size=1024x1536 +2026-06-09 11:42:33,485 INFO HTTP Request: POST https://codeproxy.dev/v1/images/edits "HTTP/1.1 200 OK" + +===== 出图结果 ===== + provider: codeproxy + 耗时: 181.2s + 大小: 2009KB + 路径: /Users/qiyu/Documents/企业培训项目/万牛会L1准备/北哥小红书产品/Clover/backend/verify_output/素颜霜_封面hook.png +==================== + diff --git a/backend/scripts/seed_data.py b/backend/scripts/seed_data.py new file mode 100644 index 0000000..cec43a8 --- /dev/null +++ b/backend/scripts/seed_data.py @@ -0,0 +1,156 @@ +""" +scripts/seed_data.py — 初始化北哥 workspace 种子数据 +建3账号(admin/supervisor/operator) + workspace_members + 预置素颜霜 + 5条违禁词 +幂等：已存在则跳过，可重复跑。 +用法：cd backend && python3 scripts/seed_data.py +""" +import json +import os +import sys + +# 把 backend/ 加入 path +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +from app.core.config import get_settings # noqa: E402 — path must be set first +from app.core.database import SessionLocal # noqa: E402 +from app.models.user import User # noqa: E402 +from app.models.workspace import Workspace, WorkspaceMember # noqa: E402 +from app.models.product import Product, BannedWord # noqa: E402 +from app.services.auth_service import hash_password # noqa: E402 + +get_settings() # 触发 .env 校验，缺变量早报错 + + +WORKSPACE_NAME = "北哥小红书车间" +WORKSPACE_SLUG = "beige-xhs" + +USERS = [ + {"username": "admin", "email": "admin@clover.local", "password": "Clover2026!", "role": "admin"}, + {"username": "supervisor", "email": "supervisor@clover.local", "password": "Clover2026!", "role": "supervisor"}, + {"username": "operator", "email": "operator@clover.local", "password": "Clover2026!", "role": "operator"}, +] + +PRODUCT = { + "name": "素颜霜（预置样本）", + "category": "素颜霜", + "source": "preset", + "selling_points": json.dumps(["遮瑕提亮", "持妆", "养肤成分"], ensure_ascii=False), + "style_tone": "素人分享、真实不浮夸", + "text_angles": json.dumps(["痛点切入", "成分党", "使用场景", "质地肤感", "平价替代"], ensure_ascii=False), + "custom_prompt": None, + "is_active": True, +} + +BANNED_WORDS = [ + {"word": "美白", "level": "auto_fix", "replacement": "提亮肤色"}, + {"word": "祛斑", "level": "auto_fix", "replacement": "改善暗沉"}, + {"word": "速效", "level": "soft_warn", "replacement": "温和渐进"}, + {"word": "医用", "level": "hard_block", "replacement": None}, + {"word": "药妆", "level": "hard_block", "replacement": None}, +] + + +def run(): + db = SessionLocal() + try: + # ── workspace ────────────────────────────────────── + ws = db.query(Workspace).filter(Workspace.slug == WORKSPACE_SLUG).first() + if not ws: + ws = Workspace(name=WORKSPACE_NAME, slug=WORKSPACE_SLUG, is_active=True) + db.add(ws) + db.flush() + print(f"[+] workspace 已建: {ws.name} (id={ws.id})") + else: + print(f"[=] workspace 已存在: id={ws.id}") + + # ── users + members ──────────────────────────────── + for u_spec in USERS: + user = db.query(User).filter(User.username == u_spec["username"]).first() + if not user: + user = User( + username=u_spec["username"], + email=u_spec["email"], + hashed_password=hash_password(u_spec["password"]), + is_active=True, + ) + db.add(user) + db.flush() + print(f"[+] user: {user.username}") + else: + print(f"[=] user 已存在: {user.username}") + + mem = db.query(WorkspaceMember).filter( + WorkspaceMember.workspace_id == ws.id, + WorkspaceMember.user_id == user.id, + ).first() + if not mem: + db.add(WorkspaceMember(workspace_id=ws.id, user_id=user.id, role=u_spec["role"])) + print(f" -> 绑定 role={u_spec['role']}") + + # ── 预置素颜霜 ───────────────────────────────────── + prod = db.query(Product).filter( + Product.workspace_id == ws.id, Product.name == PRODUCT["name"] + ).first() + if not prod: + prod = Product(workspace_id=ws.id, **PRODUCT) + db.add(prod) + print(f"[+] product: {prod.name}") + else: + print(f"[=] product 已存在: {prod.name}") + + # ── 违禁词 ───────────────────────────────────────── + for bw_spec in BANNED_WORDS: + existing = db.query(BannedWord).filter( + BannedWord.workspace_id == ws.id, BannedWord.word == bw_spec["word"] + ).first() + if not existing: + db.add(BannedWord(workspace_id=ws.id, **bw_spec)) + print(f"[+] banned_word: {bw_spec['word']} ({bw_spec['level']})") + else: + print(f"[=] banned_word 已存在: {bw_spec['word']}") + + # ── user_api_keys（R3：明文key只在加密瞬间用，不打印不落明文）── + apiports_key = os.environ.get("APIPORTS_KEY", "").strip() + if apiports_key: + from app.models.workspace import UserApiKey + from app.utils.fernet_utils import encrypt_key + for u_spec in USERS: + u = db.query(User).filter(User.username == u_spec["username"]).first() + if not u: + continue + existing_key = db.query(UserApiKey).filter( + UserApiKey.user_id == u.id, + UserApiKey.workspace_id == ws.id, + UserApiKey.provider == "apiports", + ).first() + encrypted = encrypt_key(apiports_key) + last4 = apiports_key[-4:] if len(apiports_key) >= 4 else "****" + if existing_key: + existing_key.encrypted_key = encrypted + existing_key.key_last4 = last4 + print(f"[=] api_key updated: {u.username} apiports ***{last4}") + else: + db.add(UserApiKey( + user_id=u.id, + workspace_id=ws.id, + provider="apiports", + encrypted_key=encrypted, + key_last4=last4, + )) + print(f"[+] api_key: {u.username} apiports ***{last4}") + apiports_key = None # 明文用完即清（基石B） + else: + print("[!] APIPORTS_KEY 未设置，跳过 user_api_keys 录入（部署时需手动录入或重跑种子）") + + db.commit() + print("\n种子数据初始化完成。") + except Exception as e: + db.rollback() + print(f"[ERROR] {e}") + raise + finally: + db.close() + + +if __name__ == "__main__": + run() diff --git a/backend/scripts/verify_real_text_gen.py b/backend/scripts/verify_real_text_gen.py new file mode 100644 index 0000000..ec93fb1 --- /dev/null +++ b/backend/scripts/verify_real_text_gen.py @@ -0,0 +1,93 @@ +""" +scripts/verify_real_text_gen.py — 真实文案生成验收脚本 +调 LLM 用素颜霜产品数据生成3条，原文打印给北哥人工核查。 +用法：cd backend && python3 scripts/verify_real_text_gen.py +读取 .env 里的 CODEPROXY_BASE_URL + CODEPROXY_KEY（中转站） +""" +import json +import os +import sys +import httpx +from pathlib import Path +from dotenv import load_dotenv + +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +# 加载 .env +env_path = Path(__file__).parent.parent / ".env" +load_dotenv(env_path) + +from app.services.ai_engine._text_prompt import COPY_SYSTEM, build_prompt, parse_json_array + +# 素颜霜产品数据（方法验证用，不依赖 DB） +PRODUCT = { + "name": "素颜霜", + "category": "美妆护肤", + "selling_points": ["遮瑕提亮", "持妆全天", "养肤成分温和"], + "style_tone": "素人分享、真实不浮夸", + "text_angles": ["痛点切入", "成分党", "使用场景", "质地肤感", "平价替代"], + "brand_keyword": "XX素颜霜", + "custom_prompt": "", +} + +COUNT = 3 + + +def main(): + # 主通道 apiports（实测真实可用，模型见 /v1/models） + base_url = (os.environ.get("APIPORTS_BASE_URL") or "").rstrip("/") + api_key = os.environ.get("APIPORTS_KEY", "") + # apiports 无 gpt-4o-mini，文案用 claude-sonnet-4-5（中文质量好） + model = os.environ.get("MODEL_TEXT", "claude-sonnet-4-5") + + if not base_url or not api_key: + print("[ERROR] .env 缺少 APIPORTS_BASE_URL 或 APIPORTS_KEY") + sys.exit(1) + + user_prompt = build_prompt(PRODUCT, COUNT) + + print("=" * 60) + print("SYSTEM PROMPT（前300字）：") + print(COPY_SYSTEM[:300], "...") + print("=" * 60) + print("USER PROMPT（完整）：") + print(user_prompt) + print("=" * 60) + print(f"调用 {base_url} 模型={model} 生成 {COUNT} 条文案...") + + resp = httpx.post( + f"{base_url}/chat/completions", + headers={"Authorization": f"Bearer {api_key}"}, + json={ + "model": model, + "messages": [ + {"role": "system", "content": COPY_SYSTEM}, + {"role": "user", "content": user_prompt}, + ], + "temperature": 0.9, + "max_tokens": 3000, + }, + timeout=120, + ) + resp.raise_for_status() + + raw = resp.json()["choices"][0]["message"]["content"] + print("\n===== 原始输出（供北哥核查）=====") + print(raw) + + copies = parse_json_array(raw) + if copies: + print(f"\n===== 解析成功，共 {len(copies)} 条 =====") + for i, c in enumerate(copies): + print(f"\n--- 第{i+1}条 [{c.get('angle','')}] ---") + print(f"标题：{c.get('title')}") + print(f"正文（{len(c.get('content',''))}字）：\n{c.get('content')}") + print(f"标签：{c.get('tags')}") + print(f"封面大字：{c.get('coverTitle')}") + else: + print("\n[WARN] JSON 解析失败，请人工看原始输出") + + +if __name__ == "__main__": + main() + diff --git a/backend/test_3sets_e2e.py b/backend/test_3sets_e2e.py new file mode 100644 index 0000000..0879bcc --- /dev/null +++ b/backend/test_3sets_e2e.py @@ -0,0 +1,26 @@ +"""Task#6 端到端真跑：3套正交配图。建真实task(product_id=1倍分子素颜霜)。 +image_count=2 → 每套2张(hook+product_closeup特写) ×3套 = 6张。 +验:①strategy=A/B/C各2张 ②尺寸1024×1536 ③品牌词进特写图 ④三套图真不同。""" +import sys +sys.path.insert(0, "/app") + +from app.core.database import SessionLocal +from app.models.task import GenerationTask +from app.constants.enums import TaskStatus + +db = SessionLocal() +t = GenerationTask( + workspace_id=3, product_id=1, operator_id=3, + theme="伪素颜·黄黑皮提亮·3套正交", + text_count=1, image_count=2, track="ai", + need_product_image=True, status=TaskStatus.PENDING, +) +db.add(t); db.commit(); db.refresh(t) +task_id = t.id +print(f"建task成功 task_id={task_id} image_count=2 (期望3套×2=6张)") +db.close() + +from app.workers.tasks import run_generation_pipeline +print(f"=== 同步执行 run_generation_pipeline({task_id}) ===") +result = run_generation_pipeline.apply(args=[task_id]).get() +print(f"=== 结果: {result} ===") diff --git a/backend/tests/__init__.py b/backend/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/backend/tests/conftest.py b/backend/tests/conftest.py new file mode 100644 index 0000000..3bf608e --- /dev/null +++ b/backend/tests/conftest.py @@ -0,0 +1,56 @@ +""" +conftest.py — 测试环境 stub +在任何模块 import 之前注入环境变量，让 Settings 校验通过。 +只用于本地单元/集成测试，不影响生产。 +""" +import os +import sys +import types + +# ── 1. 注入最小可用配置（全假值，只让 Settings 构建通过）──────── +_TEST_ENV = { + "FERNET_KEY": "test-fernet-key-at-least-32-chars-long!!", + "JWT_SECRET": "test-jwt-secret-at-least-32-chars-long!!", + "DATABASE_URL": "mysql+pymysql://test:test@localhost/test", + "MONGO_URI": "mongodb://localhost:27017/test", + "REDIS_URL": "redis://localhost:6379/0", + "APP_ENV": "test", +} +for k, v in _TEST_ENV.items(): + os.environ.setdefault(k, v) + +# ── 2. 清掉 lru_cache，让 Settings 用刚注入的 env vars 重建 ───── +from app.core import config as _cfg +_cfg.get_settings.cache_clear() + +# ── 3. Stub motor（MongoDB client，本地没装）────────────────────── +if "motor" not in sys.modules: + motor_mod = types.ModuleType("motor") + motor_async = types.ModuleType("motor.motor_asyncio") + motor_async.AsyncIOMotorClient = object + sys.modules["motor"] = motor_mod + sys.modules["motor.motor_asyncio"] = motor_async + +# ── 4. Stub database engine（MySQL URL 但不连接）────────────────── +# database.py 顶层调 create_engine，pytest 环境没有 MySQL。 +# 只 stub engine 和 SessionLocal，不影响任何逻辑测试。 +import unittest.mock as _mock +import sqlalchemy + +_fake_engine = _mock.MagicMock() +_fake_session_class = _mock.MagicMock() +_fake_session_class.return_value.__enter__ = _mock.MagicMock(return_value=_mock.MagicMock()) +_fake_session_class.return_value.__exit__ = _mock.MagicMock(return_value=False) + +# 只在 database 模块还未 import 时 patch；若已 import 则直接替换属性 +if "app.core.database" not in sys.modules: + with _mock.patch("sqlalchemy.create_engine", return_value=_fake_engine): + with _mock.patch("sqlalchemy.orm.sessionmaker", return_value=_fake_session_class): + import app.core.database as _db_mod +else: + _db_mod = sys.modules["app.core.database"] + +# 确保 engine/SessionLocal 是 mock，防止实际连接 +_db_mod.engine = _fake_engine +_db_mod.SessionLocal = _fake_session_class + diff --git a/backend/tests/test_ai_engine.py b/backend/tests/test_ai_engine.py new file mode 100644 index 0000000..bbbd5ed --- /dev/null +++ b/backend/tests/test_ai_engine.py @@ -0,0 +1,253 @@ +""" +AI 引擎单元测试（核心逻辑覆盖） +对照 JS 版逻辑验证 Python 重写正确性 +""" +import sys +import os +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +import pytest +from app.services.ai_engine.text_scoring import score_copy, is_similar_copy, dedupe_copies +from app.services.ai_engine.banned_word_checker import check_and_fix, BannedWordEntry +from app.services.ai_engine.storyboard import get_narrative_roles, plan_image_set, clamp_count +from app.services.ai_engine.preference_aggregator import aggregate_preference_context, collect_preference_event +from app.services.ai_engine.prompt_composer import ( + compose_variants, compose_preference_context, compose_image_prompt, parse_model_output +) + +# ── 测试用 product ───────────────────────────────────────── +PRODUCT = { + "name": "倍分子素颜霜", + "category": "美妆护肤", + "selling_points": ["轻薄不厚重", "水润自然", "不卡粉"], + "keywords": ["素颜霜", "日常通勤"], + "style_tone": "素人分享风", + "text_angles": ["痛点切入", "场景型", "避坑型"], + "custom_prompt": "", + "target_audience": "上班族", +} + +GOOD_COPY = { + "title": "早八素颜也能有点气色！✨", + "content": "姐妹们，最近实测下来，轻薄不厚重是我最在意的。✅ 通勤路上随手一抹，水润自然有气色。不卡粉这点太加分了。整体就是那种上班族能马上代入的日常好物。", + "tags": ["#素颜霜", "#日常通勤", "#好物分享", "#真实测评"], + "angle": "场景型", + "buyingPoint": "轻薄不厚重，适合上班族", + "coverTitle": "早八素颜也能有点气色", + "imageBrief": "封面自然光上脸局部，内页质地推开+软性转化。", + "source": "ai", +} + + +# ── 五维打分 ─────────────────────────────────────────────── +class TestScoreCopy: + def test_good_copy_passes(self): + result = score_copy(GOOD_COPY, PRODUCT) + assert result["passed"] is True + assert result["score"] >= 90 + + def test_banned_word_kills_compliance(self): + bad = {**GOOD_COPY, "title": "美白素颜霜强推！", "content": GOOD_COPY["content"] + "美白效果显著"} + result = score_copy(bad, PRODUCT) + # 含美白 → 合规0分 + 总分不过 + assert result["passed"] is False + compliance = next(d for d in result["score_detail"] if d["item"] == "合规性") + assert compliance["score"] == 0 + + def test_score_detail_has_five_dims(self): + result = score_copy(GOOD_COPY, PRODUCT) + assert len(result["score_detail"]) == 5 + items = {d["item"] for d in result["score_detail"]} + assert "标题吸引力" in items + assert "情绪共鸣" in items + assert "买点表达" in items + assert "关键词覆盖" in items + assert "合规性" in items + + +# ── 去重 ─────────────────────────────────────────────────── +class TestDedup: + def test_identical_title_deduped(self): + a = {**GOOD_COPY} + b = {**GOOD_COPY, "content": "略有不同的内容，但标题一样"} + result = dedupe_copies([a, b]) + assert len(result) == 1 + + def test_different_angle_both_kept(self): + a = {**GOOD_COPY, "angle": "痛点切入"} + b = {**GOOD_COPY, "title": "通勤懒人必囤！换季不再干脸", "angle": "避坑型"} + result = dedupe_copies([a, b]) + assert len(result) == 2 + + def test_similar_body_deduped(self): + a = {**GOOD_COPY} + b = {**GOOD_COPY, "title": "不一样的标题", "content": GOOD_COPY["content"][:150] + "稍有变动"} + assert is_similar_copy(a, b) or len(dedupe_copies([a, b])) <= 2 + + +# ── 违禁词三级 ────────────────────────────────────────────── +class TestBannedWordChecker: + def test_hard_block(self): + # "美白"已改为 auto_fix（提亮肤色感），hard_block 用速效/医用等词 + result = check_and_fix("速效医用护肤品") + assert result.status == "hard_block" + + def test_meibaibanned_now_auto_fix(self): + # Q5对齐：美白→auto_fix 提亮肤色感（不再 hard_block） + result = check_and_fix("这款美白效果很好") + assert result.status == "auto_fixed" + assert result.fixed_text is not None + assert "美白" not in result.fixed_text + + def test_auto_fix(self): + result = check_and_fix("这款神器真的好用") + assert result.status == "auto_fixed" + assert result.fixed_text is not None + assert "神器" not in result.fixed_text + + def test_soft_warn(self): + result = check_and_fix("这绝对是最好的护肤品") + assert result.status == "soft_warn" + + def test_clean_text_passes(self): + result = check_and_fix("这款素颜霜轻薄水润，通勤必备。") + assert result.status == "pass" + + def test_custom_entries_override(self): + entries = [BannedWordEntry("必备", "hard_block")] + result = check_and_fix("通勤必备好物", entries) + assert result.status == "hard_block" + + +# ── storyboard ───────────────────────────────────────────── +class TestStoryboard: + def test_clamp_count(self): + assert clamp_count(0) == 1 + assert clamp_count(9) == 8 + assert clamp_count(3) == 3 + + def test_narrative_roles_3(self): + roles = get_narrative_roles(3) + assert len(roles) == 3 + assert roles[0]["role"] == "hook" + assert roles[-1]["role"] == "closer" + + def test_narrative_roles_6(self): + roles = get_narrative_roles(6) + assert len(roles) == 6 + # Q6对齐北哥套路：①hook ②product_closeup(单品特写) ③ingredient ④texture ⑤applied_proof ⑥closer + assert roles[0]["role"] == "hook" + assert roles[1]["role"] == "product_closeup" + assert roles[2]["role"] == "ingredient" + assert roles[4]["role"] == "applied_proof" + assert roles[5]["role"] == "closer" + + def test_narrative_roles_8(self): + roles = get_narrative_roles(8) + assert len(roles) == 8 + + def test_plan_image_set_structure(self): + plan = plan_image_set(GOOD_COPY, PRODUCT, image_count=3) + assert "storyboard" in plan + assert "base_prompt" in plan + assert len(plan["storyboard"]) == 3 + # 产品图锚点说明必须在 base_prompt 中 + assert "不可修改" in plan["base_prompt"] + + +# ── 飞轮聚合 ─────────────────────────────────────────────── +class TestPreferenceAggregator: + def test_cold_start_when_few_events(self): + result = aggregate_preference_context([], PRODUCT, workspace_id=1, product_id=1) + assert result["injected_count"] == 0 + assert "冷启动" in result["recent_preference"] + + def test_aggregate_top_angles(self): + events = [ + {"signal_type": "text_select", "workspace_id": 1, "product_id": 1, "angle_label": "痛点切入", "signal_weight": 3, "reason": ""}, + {"signal_type": "text_select", "workspace_id": 1, "product_id": 1, "angle_label": "痛点切入", "signal_weight": 3, "reason": ""}, + {"signal_type": "approve", "workspace_id": 1, "product_id": 1, "angle_label": "场景型", "signal_weight": 5, "reason": ""}, + {"signal_type": "text_select", "workspace_id": 1, "product_id": 1, "angle_label": "避坑型", "signal_weight": 3, "reason": ""}, + {"signal_type": "text_select", "workspace_id": 1, "product_id": 1, "angle_label": "痛点切入", "signal_weight": 3, "reason": ""}, + {"signal_type": "text_select", "workspace_id": 1, "product_id": 1, "angle_label": "避坑型", "signal_weight": 3, "reason": ""}, + ] + result = aggregate_preference_context(events, PRODUCT, workspace_id=1, product_id=1) + assert result["injected_count"] == 6 + assert "痛点切入" in result["recent_preference"] + assert "偏好角度" in result["prompt_fragment"] + + def test_reject_reason_in_prompt(self): + events = [ + {"signal_type": "reject_with_reason", "workspace_id": 1, "product_id": 1, + "angle_label": "", "signal_weight": -3, "reason": "标题太硬广"}, + *[{"signal_type": "text_select", "workspace_id": 1, "product_id": 1, + "angle_label": "痛点切入", "signal_weight": 3, "reason": ""} for _ in range(5)], + ] + result = aggregate_preference_context(events, PRODUCT, workspace_id=1, product_id=1) + assert "标题太硬广" in result["prompt_fragment"] + + def test_product_isolation(self): + """不同 product_id 的事件不会混进来""" + events = [ + {"signal_type": "text_select", "workspace_id": 1, "product_id": 2, + "angle_label": "成分党", "signal_weight": 3, "reason": ""}, + *[{"signal_type": "text_select", "workspace_id": 1, "product_id": 1, + "angle_label": "场景型", "signal_weight": 3, "reason": ""} for _ in range(5)], + ] + result = aggregate_preference_context(events, PRODUCT, workspace_id=1, product_id=1) + assert "成分党" not in result["prompt_fragment"] + + def test_collect_event_structure(self): + event = collect_preference_event("text_select", user_id=1, workspace_id=1, product_id=1, angle_label="痛点切入") + assert event["signal_weight"] == 3 + assert event["data_ownership"] == "client_data" + + +# ── prompt_composer ──────────────────────────────────────── +class TestPromptComposer: + def test_compose_variants_returns_two_strings(self): + sys_p, user_p = compose_variants(PRODUCT, count=5) + assert isinstance(sys_p, str) and len(sys_p) > 0 + assert isinstance(user_p, str) and len(user_p) > 0 + + def test_compose_variants_includes_product_name(self): + _, user_p = compose_variants(PRODUCT, count=3) + assert "倍分子素颜霜" in user_p + + def test_compose_variants_includes_count(self): + _, user_p = compose_variants(PRODUCT, count=7) + assert "7" in user_p + + def test_compose_variants_flywheel_injected(self): + fragment = "偏好角度参考：场景型、痛点切入" + _, user_p = compose_variants(PRODUCT, count=3, flywheel_context=fragment) + assert "场景型" in user_p + + def test_compose_preference_context_delegates(self): + # compose_preference_context 应委托给 aggregate_preference_context + events = [ + {"signal_type": "text_select", "workspace_id": 1, "product_id": 1, + "angle_label": "场景型", "signal_weight": 3, "reason": ""} + for _ in range(6) + ] + result = compose_preference_context(events, PRODUCT, workspace_id=1, product_id=1) + assert "injected_count" in result + assert result["injected_count"] == 6 + assert "prompt_fragment" in result + + def test_parse_model_output_valid_json(self): + raw = '[{"title":"标题1","content":"正文","tags":[],"angle":"场景型"}]' + parsed = parse_model_output(raw) + assert len(parsed) == 1 and parsed[0]["title"] == "标题1" + + def test_parse_model_output_markdown_wrapped(self): + raw = "```json\n[{\"title\":\"a\",\"content\":\"b\",\"tags\":[]}]\n```" + parsed = parse_model_output(raw) + assert len(parsed) == 1 + + def test_compose_image_prompt_includes_role_and_product(self): + vs = {"style": "ins摆拍风", "color_palette": "米白+杏色", "base_prompt": "产品近景"} + prompt = compose_image_prompt("hook", vs, PRODUCT) + assert "hook" in prompt + assert "倍分子素颜霜" in prompt + assert "ins摆拍风" in prompt diff --git a/backend/tests/test_factory_and_exporter.py b/backend/tests/test_factory_and_exporter.py new file mode 100644 index 0000000..c0cd623 --- /dev/null +++ b/backend/tests/test_factory_and_exporter.py @@ -0,0 +1,141 @@ +""" +gemini_factory + package_exporter 单元测试 +""" +import sys, os +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +import json +import tempfile +import zipfile +from pathlib import Path + +import pytest + +from app.services.ai_engine.gemini_factory import ( + build_ai_clients, _extract_image_bytes, _extract_gemini_image +) +from app.services.ai_engine.package_exporter import build_delivery_package + + +# ── gemini_factory ───────────────────────────────────────── +class TestGeminiFactory: + def test_build_clients_returns_ai_clients(self, monkeypatch): + monkeypatch.setenv("IMAGE_API_BASE", "https://api.example.com") + clients = build_ai_clients(plain_key="test_key_1234") + assert clients is not None + assert clients._model == clients._model_text + + def test_model_property_uses_env(self, monkeypatch): + monkeypatch.setenv("IMAGE_API_BASE", "https://api.example.com") + monkeypatch.setenv("MODEL_TEXT", "gpt-5.5") + clients = build_ai_clients(plain_key="test_key_1234") + assert clients._model_text == "gpt-5.5" + + def test_extract_image_bytes_b64(self): + import base64 + fake_png = b"\x89PNG fake" + b64 = base64.b64encode(fake_png).decode() + result = _extract_image_bytes({"data": [{"b64_json": b64}]}) + assert result == fake_png + + def test_extract_image_bytes_empty_raises(self): + with pytest.raises(ValueError): + _extract_image_bytes({"data": []}) + + def test_extract_gemini_image_success(self): + import base64 + fake_img = b"\xff\xd8\xff fake jpeg" + b64 = base64.b64encode(fake_img).decode() + resp = {"candidates": [{"content": {"parts": [{"inlineData": {"data": b64}}]}}]} + result = _extract_gemini_image(resp) + assert result == fake_img + + def test_extract_gemini_image_no_image_raises(self): + resp = {"candidates": [{"content": {"parts": [{"text": "no image"}]}}]} + with pytest.raises(ValueError): + _extract_gemini_image(resp) + + def test_key_not_in_repr(self, monkeypatch): + monkeypatch.setenv("IMAGE_API_BASE", "https://api.example.com") + clients = build_ai_clients(plain_key="super_secret_key_xyz") + # repr 不泄露 key + assert "super_secret_key_xyz" not in repr(clients) + + +# ── package_exporter ────────────────────────────────────── +class TestPackageExporter: + def _make_notes(self): + return [{ + "title": "早八素颜也能有气色", + "content": "姐妹们实测下来，轻薄不厚重是关键。✅ 通勤必备。", + "tags": ["#素颜霜", "#日常通勤"], + "images": [ + {"seq": 1, "role": "hook", "data": b"\xff\xd8\xff fake_jpg_1"}, + {"seq": 2, "role": "proof", "data": b"\xff\xd8\xff fake_jpg_2"}, + ], + "banned_word_status": "pass", + }] + + def test_creates_zip(self, tmp_path): + zip_path = build_delivery_package( + workspace_id=1, task_id=99, + notes=self._make_notes(), + base_path=str(tmp_path), + ) + assert Path(zip_path).exists() + assert zip_path.endswith(".zip") + + def test_zip_contains_required_files(self, tmp_path): + zip_path = build_delivery_package( + workspace_id=1, task_id=99, + notes=self._make_notes(), + base_path=str(tmp_path), + ) + with zipfile.ZipFile(zip_path) as zf: + names = zf.namelist() + # 应含文案.txt、发布清单、合规说明 + assert any("文案.txt" in n for n in names) + assert any("发布清单" in n for n in names) + assert any("合规说明" in n for n in names) + + def test_images_named_by_seq(self, tmp_path): + zip_path = build_delivery_package( + workspace_id=1, task_id=99, + notes=self._make_notes(), + base_path=str(tmp_path), + ) + with zipfile.ZipFile(zip_path) as zf: + names = zf.namelist() + # 图片按 seq 序号命名 + assert any("01_hook.jpg" in n for n in names) + assert any("02_proof.jpg" in n for n in names) + + def test_copy_txt_contains_title_and_tags(self, tmp_path): + zip_path = build_delivery_package( + workspace_id=1, task_id=99, + notes=self._make_notes(), + base_path=str(tmp_path), + ) + with zipfile.ZipFile(zip_path) as zf: + copy_txt = zf.read("note_01/文案.txt").decode("utf-8") + assert "早八素颜也能有气色" in copy_txt + assert "#素颜霜" in copy_txt + + def test_compliance_note_shows_status(self, tmp_path): + zip_path = build_delivery_package( + workspace_id=1, task_id=99, + notes=self._make_notes(), + base_path=str(tmp_path), + ) + with zipfile.ZipFile(zip_path) as zf: + compliance = zf.read("✅合规说明.txt").decode("utf-8") + assert "✅通过" in compliance + + def test_no_images_still_creates_package(self, tmp_path): + notes = [{**self._make_notes()[0], "images": []}] + zip_path = build_delivery_package( + workspace_id=1, task_id=100, + notes=notes, + base_path=str(tmp_path), + ) + assert Path(zip_path).exists() diff --git a/backend/tests/test_flywheel_wiring.py b/backend/tests/test_flywheel_wiring.py new file mode 100644 index 0000000..24952bb --- /dev/null +++ b/backend/tests/test_flywheel_wiring.py @@ -0,0 +1,135 @@ +""" +test_flywheel_wiring.py — 飞轮 Point1+Point2 验证 +Point1: 5种信号正确写入 preference_events（选文案/选图/审核 approve/reject/regenerate） +Point2: aggregator 返回明确 prompt_fragment（含偏好角度、打回原因，workspace 严格过滤） +无 Docker — 全程纯 Python 单元测试 +Point3+Point4 见 test_flywheel_wiring_p34.py +""" + +from unittest.mock import MagicMock + +SIGNAL_WEIGHTS_MAP = { + "text_select": 3, + "image_select": 3, + "approve": 5, + "reject_with_reason": -3, + "regenerate": -1, +} + +_PRODUCT = { + "name": "素颜霜", + "category": "护肤", + "selling_points": ["保湿", "天然"], + "style_tone": "素人分享风", + "text_angles": ["成分党", "懒人必备"], + "custom_prompt": "请保持真实素人口吻", +} + +_EVENTS_ENOUGH = [ + {"signal_type": "text_select", "workspace_id": 1, "product_id": 10, + "angle_label": "成分党", "signal_weight": 3, "reason": ""}, + {"signal_type": "approve", "workspace_id": 1, "product_id": 10, + "angle_label": "成分党", "signal_weight": 5, "reason": ""}, + {"signal_type": "text_select", "workspace_id": 1, "product_id": 10, + "angle_label": "懒人必备", "signal_weight": 3, "reason": ""}, + {"signal_type": "reject_with_reason","workspace_id": 1, "product_id": 10, + "angle_label": "", "signal_weight": -3, "reason": "太像广告"}, + {"signal_type": "regenerate", "workspace_id": 1, "product_id": 10, + "angle_label": "", "signal_weight": -1, "reason": ""}, +] + + +# ══════════════════════════════════════════════════════════════ +# Point1: 五种信号写入结构验证 +# ══════════════════════════════════════════════════════════════ +class TestPoint1SignalTypes: + """验证 5 种信号都有对应 weight，且字段完整。""" + + def test_all_signal_types_have_weights(self): + from app.constants.enums import SIGNAL_WEIGHTS + required = {"text_select", "image_select", "approve", "reject_with_reason", "regenerate"} + missing = required - set(SIGNAL_WEIGHTS.keys()) + assert not missing, f"SIGNAL_WEIGHTS 缺少: {missing}" + + def test_positive_signals_weight_positive(self): + from app.constants.enums import SIGNAL_WEIGHTS + for sig in ("text_select", "image_select", "approve"): + assert SIGNAL_WEIGHTS[sig] > 0, f"{sig} weight 应 > 0，实际={SIGNAL_WEIGHTS[sig]}" + + def test_negative_signals_weight_negative(self): + from app.constants.enums import SIGNAL_WEIGHTS + for sig in ("reject_with_reason", "regenerate"): + assert SIGNAL_WEIGHTS[sig] < 0, f"{sig} weight 应 < 0，实际={SIGNAL_WEIGHTS[sig]}" + + def test_collect_preference_event_returns_complete_fields(self): + from app.services.ai_engine.preference_aggregator import collect_preference_event + event = collect_preference_event( + signal_type="text_select", + user_id=7, + workspace_id=1, + product_id=10, + angle_label="成分党", + ) + required_keys = {"signal_type", "signal_weight", "user_id", + "workspace_id", "product_id", "angle_label", + "reason", "data_ownership"} + assert required_keys.issubset(event.keys()), f"缺字段: {required_keys - event.keys()}" + + def test_collect_event_weight_matches_signal(self): + from app.services.ai_engine.preference_aggregator import collect_preference_event + ev = collect_preference_event("approve", 1, 1, 10) + assert ev["signal_weight"] == SIGNAL_WEIGHTS_MAP["approve"] + + +# ══════════════════════════════════════════════════════════════ +# Point2: aggregator 返回 prompt_fragment + workspace 严格过滤 +# ══════════════════════════════════════════════════════════════ +class TestPoint2AggregatorOutput: + """验证 aggregate_preference_context 返回结构与 workspace/product 隔离。""" + + def test_returns_required_keys(self): + from app.services.ai_engine.preference_aggregator import aggregate_preference_context + ctx = aggregate_preference_context(_EVENTS_ENOUGH, _PRODUCT, 1, 10) + for k in ("prompt_fragment", "recent_preference", "reject_reasons", "injected_count"): + assert k in ctx, f"返回值缺字段: {k}" + + def test_prompt_fragment_not_empty_with_signals(self): + from app.services.ai_engine.preference_aggregator import aggregate_preference_context + ctx = aggregate_preference_context(_EVENTS_ENOUGH, _PRODUCT, 1, 10) + assert ctx["prompt_fragment"].strip(), "有足够信号时 prompt_fragment 不应为空" + + def test_prompt_fragment_contains_top_angle(self): + from app.services.ai_engine.preference_aggregator import aggregate_preference_context + ctx = aggregate_preference_context(_EVENTS_ENOUGH, _PRODUCT, 1, 10) + assert "成分党" in ctx["prompt_fragment"] # 权重最高角度 + + def test_reject_reasons_in_fragment(self): + from app.services.ai_engine.preference_aggregator import aggregate_preference_context + ctx = aggregate_preference_context(_EVENTS_ENOUGH, _PRODUCT, 1, 10) + assert "太像广告" in ctx["prompt_fragment"] + + def test_workspace_isolation_filters_other_workspace(self): + from app.services.ai_engine.preference_aggregator import aggregate_preference_context + mixed = _EVENTS_ENOUGH + [ + {"signal_type": "text_select", "workspace_id": 2, "product_id": 10, + "angle_label": "workspace2专有角度", "signal_weight": 100, "reason": ""}, + ] + ctx = aggregate_preference_context(mixed, _PRODUCT, 1, 10) + assert "workspace2专有角度" not in ctx["prompt_fragment"] + + def test_product_isolation_filters_other_product(self): + from app.services.ai_engine.preference_aggregator import aggregate_preference_context + mixed = _EVENTS_ENOUGH + [ + {"signal_type": "text_select", "workspace_id": 1, "product_id": 99, + "angle_label": "product99专有角度", "signal_weight": 100, "reason": ""}, + ] + ctx = aggregate_preference_context(mixed, _PRODUCT, 1, 10) + assert "product99专有角度" not in ctx["prompt_fragment"] + + def test_cold_start_when_insufficient_signals(self): + from app.services.ai_engine.preference_aggregator import aggregate_preference_context + from app.services.ai_engine.constants import FLYWHEEL_COLD_START + few = _EVENTS_ENOUGH[:max(0, FLYWHEEL_COLD_START - 1)] + ctx = aggregate_preference_context(few, _PRODUCT, 1, 10) + assert "recent_preference" in ctx + assert ctx["injected_count"] == len(few) diff --git a/backend/tests/test_flywheel_wiring_p34.py b/backend/tests/test_flywheel_wiring_p34.py new file mode 100644 index 0000000..508b21e --- /dev/null +++ b/backend/tests/test_flywheel_wiring_p34.py @@ -0,0 +1,166 @@ +""" +test_flywheel_wiring_p34.py — 飞轮 Point3+Point4 验证 +Point3: pipeline 把 prompt_fragment 注入 user_prompt（prompt trace 含片段） +Point4: UI 层 flywheel_injected SSE 事件含 recent_preference + reject_reasons +无 Docker — 全程纯 Python 单元测试 +Point1+Point2 见 test_flywheel_wiring.py +""" + +from unittest.mock import MagicMock + +_PRODUCT = { + "name": "素颜霜", + "category": "护肤", + "selling_points": ["保湿", "天然"], + "style_tone": "素人分享风", + "text_angles": ["成分党", "懒人必备"], + "custom_prompt": "请保持真实素人口吻", +} + +_EVENTS_ENOUGH = [ + {"signal_type": "text_select", "workspace_id": 1, "product_id": 10, + "angle_label": "成分党", "signal_weight": 3, "reason": ""}, + {"signal_type": "approve", "workspace_id": 1, "product_id": 10, + "angle_label": "成分党", "signal_weight": 5, "reason": ""}, + {"signal_type": "text_select", "workspace_id": 1, "product_id": 10, + "angle_label": "懒人必备", "signal_weight": 3, "reason": ""}, + {"signal_type": "reject_with_reason","workspace_id": 1, "product_id": 10, + "angle_label": "", "signal_weight": -3, "reason": "太像广告"}, + {"signal_type": "regenerate", "workspace_id": 1, "product_id": 10, + "angle_label": "", "signal_weight": -1, "reason": ""}, +] + + +# ══════════════════════════════════════════════════════════════ +# Point3: pipeline 把 prompt_fragment 注入 user_prompt +# ══════════════════════════════════════════════════════════════ +class TestPoint3PromptInjection: + """验证 compose_variants 把 flywheel_context 正确注入 user_prompt。""" + + def test_compose_variants_appends_flywheel_to_user_prompt(self): + from app.services.ai_engine.prompt_composer import compose_variants + fragment = "【偏好角度参考】历史选择偏好：成分党" + _, user_prompt = compose_variants(_PRODUCT, 3, flywheel_context=fragment) + assert fragment in user_prompt, "flywheel_context 应出现在 user_prompt 中" + + def test_compose_variants_flywheel_not_in_system_prompt(self): + """飞轮片段不应覆盖 system_prompt（COPY_SYSTEM 质量护栏不能被干扰）""" + from app.services.ai_engine.prompt_composer import compose_variants + fragment = "飞轮注入内容UNIQUE123" + system_prompt, _ = compose_variants(_PRODUCT, 2, flywheel_context=fragment) + assert fragment not in system_prompt, "飞轮片段不应出现在 system_prompt" + + def test_compose_variants_empty_flywheel_still_valid(self): + """无飞轮片段时 compose_variants 仍正常返回 (system, user)""" + from app.services.ai_engine.prompt_composer import compose_variants + system_p, user_p = compose_variants(_PRODUCT, 3, flywheel_context="") + assert isinstance(system_p, str) and len(system_p) > 0 + assert isinstance(user_p, str) and len(user_p) > 0 + + def test_pipeline_fragment_contains_top_angle(self): + """pipeline 重现：ORM events → aggregate → fragment 含最高权重角度""" + from app.services.ai_engine.preference_aggregator import aggregate_preference_context + + def make_event(sig, angle, weight, reason): + e = MagicMock() + e.signal_type = sig + e.workspace_id = 1 + e.product_id = 10 + e.angle_label = angle + e.signal_weight = weight + e.reason = reason + return e + + mock_events = [ + make_event("text_select", "成分党", 3, ""), + make_event("approve", "成分党", 5, ""), + make_event("reject_with_reason", "", -3, "太像广告"), + make_event("text_select", "懒人必备", 3, ""), + make_event("regenerate", "", -1, ""), + ] + events_dicts = [ + {"signal_type": e.signal_type, "workspace_id": e.workspace_id, + "product_id": e.product_id, "angle_label": e.angle_label or "", + "signal_weight": e.signal_weight, "reason": e.reason or ""} + for e in mock_events + ] + ctx = aggregate_preference_context(events_dicts, _PRODUCT, 1, 10) + fragment = ctx.get("prompt_fragment", "") + assert isinstance(fragment, str) + assert "成分党" in fragment + + +# ══════════════════════════════════════════════════════════════ +# Point4: UI 层 flywheel_injected SSE 事件结构验证 +# ══════════════════════════════════════════════════════════════ +class TestPoint4SseFlywheelEvent: + """验证 flywheel_injected SSE 事件包含 UI 展示所需字段。""" + + # FE useSse.ts flywheel_injected 事件期望字段 + FE_EXPECTED_FIELDS = {"recent_preference", "reject_reasons"} + + def test_aggregator_output_matches_sse_fields(self): + from app.services.ai_engine.preference_aggregator import aggregate_preference_context + ctx = aggregate_preference_context(_EVENTS_ENOUGH, _PRODUCT, 1, 10) + missing = self.FE_EXPECTED_FIELDS - ctx.keys() + assert not missing, f"SSE flywheel_injected 缺少字段: {missing}" + + def test_recent_preference_is_nonempty_string(self): + from app.services.ai_engine.preference_aggregator import aggregate_preference_context + ctx = aggregate_preference_context(_EVENTS_ENOUGH, _PRODUCT, 1, 10) + assert isinstance(ctx["recent_preference"], str) and ctx["recent_preference"].strip() + + def test_reject_reasons_is_list_of_strings(self): + from app.services.ai_engine.preference_aggregator import aggregate_preference_context + ctx = aggregate_preference_context(_EVENTS_ENOUGH, _PRODUCT, 1, 10) + assert isinstance(ctx["reject_reasons"], list) + for r in ctx["reject_reasons"]: + assert isinstance(r, str) + + def test_reject_reasons_max_3(self): + """打回原因最多返回3条（FE 卡片布局限制）""" + from app.services.ai_engine.preference_aggregator import aggregate_preference_context + many_rejects = [ + {"signal_type": "reject_with_reason", "workspace_id": 1, + "product_id": 10, "angle_label": "", "signal_weight": -3, + "reason": f"原因{i}"} + for i in range(10) + ] + _EVENTS_ENOUGH + ctx = aggregate_preference_context(many_rejects, _PRODUCT, 1, 10) + assert len(ctx["reject_reasons"]) <= 3 + + def test_flywheel_service_get_context_returns_sse_compatible_shape(self): + """flywheel_service.get_preference_context 返回结构与 FE PreferenceContext DTO 一致""" + from app.services.flywheel_service import get_preference_context + from app.constants.enums import SignalType + + def make_orm(sig, angle, reason): + e = MagicMock() + e.signal_type = sig + e.angle_label = angle + e.reason = reason + return e + + orm_events = [ + make_orm(SignalType.TEXT_SELECT, "成分党", ""), + make_orm(SignalType.TEXT_SELECT, "成分党", ""), + make_orm(SignalType.APPROVE, "成分党", ""), + make_orm(SignalType.REJECT_WITH_REASON, "", "太像广告"), + make_orm(SignalType.TEXT_SELECT, "懒人必备", ""), + ] + mock_db = MagicMock() + mq = MagicMock() + mock_db.query.return_value = mq + mq.filter.return_value = mq + mq.order_by.return_value = mq + mq.limit.return_value = mq + mq.all.return_value = orm_events + + result = get_preference_context(mock_db, workspace_id=1, product_id=10) + + fe_dto_keys = {"recent_preference", "reject_reasons", "injected_count"} + missing = fe_dto_keys - result.keys() + assert not missing, f"FE PreferenceContext DTO 缺字段: {missing}" + assert isinstance(result["recent_preference"], str) + assert isinstance(result["reject_reasons"], list) + assert isinstance(result["injected_count"], int) diff --git a/backend/tests/test_integration_seams.py b/backend/tests/test_integration_seams.py new file mode 100644 index 0000000..0ef8570 --- /dev/null +++ b/backend/tests/test_integration_seams.py @@ -0,0 +1,241 @@ +""" +test_integration_seams.py — 汇合联调接缝验证（无 Docker，不依赖 Redis/DB） +验证三条核心接缝： + 1. AIE→BE 接缝：pipeline_steps / pipeline_io 数据流契约 + 2. BE→FE 接缝：SSE 事件字段与 generationStore 期望字段对齐 + 3. FE→BE 接缝：API 请求 DTO 与 BE 路由入参对齐 + +原则：纯 Python/结构断言，不联网，不起服务。 +联调真实运行需 docker compose up + 真实 API Key。 +""" +import sys, os +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +import pytest +import json + + +# ═══════════════════════════════════════════════════════════════ +# Seam 1 — AIE→BE: pipeline_steps 数据流 +# ═══════════════════════════════════════════════════════════════ + +class TestAiePipelineStepsContract: + """pipeline_steps.build_product_dict 产出符合 AI 引擎 dict 契约""" + + def _make_mock_product(self): + """模拟 ORM Product 对象（duck typing）""" + class P: + name = "倍分子素颜霜" + category = "美妆护肤" + selling_points = '["轻薄不厚重","水润自然"]' + style_tone = "素人分享风" + text_angles = '["痛点切入","场景型"]' + custom_prompt = "" + image_path = None # 前端图片上传完成前为空 + return P() + + def test_build_product_dict_keys(self): + from app.workers.pipeline_steps import build_product_dict + d = build_product_dict(self._make_mock_product()) + for key in ("name", "category", "selling_points", "style_tone", + "text_angles", "custom_prompt", "image_path"): + assert key in d, f"build_product_dict 缺字段: {key}" + + def test_build_product_dict_lists_parsed(self): + from app.workers.pipeline_steps import build_product_dict + d = build_product_dict(self._make_mock_product()) + assert isinstance(d["selling_points"], list) + assert isinstance(d["text_angles"], list) + + def test_build_product_dict_no_key_field(self): + """绝不含任何 key/token 相关字段（基石B）""" + from app.workers.pipeline_steps import build_product_dict + d = build_product_dict(self._make_mock_product()) + for forbidden in ("api_key", "key", "token", "secret", "encrypted"): + assert forbidden not in d, f"product_dict 不应含 {forbidden}" + + +# ═══════════════════════════════════════════════════════════════ +# Seam 2 — BE→FE: SSE 事件字段对齐 +# ═══════════════════════════════════════════════════════════════ + +class TestSseEventFieldAlignment: + """BE 推的 SSE 事件字段与 FE useSse handleEvent 期望的字段一一对应""" + + # FE useSse.ts 中 handleEvent 使用的字段（从 FE 代码提取） + EXPECTED_FIELDS = { + "task_started": {"total_text", "total_image"}, + "text_progress": {"done", "total"}, + "text_candidate": {"candidate_id", "angle_label", "content", "score"}, + "image_progress": {"done", "total"}, + "image_candidate": {"candidate_id", "strategy", "url", "role"}, + "batch_failed": {"batch", "reason", "retryable"}, + "flywheel_injected": {"recent_preference", "reject_reasons"}, + } + + # BE pipeline_io 实际推的事件字段（从代码提取） + ACTUAL_FIELDS = { + "task_started": { + "task_id": 1, "total_text": 5, "total_image": 3, + }, + "text_progress": { + "done": 1, "total": 5, + }, + "text_candidate": { + "candidate_id": 1, "angle_label": "场景型", + "content": "测试正文", "score": 88, + }, + "image_progress": { + "done": 1, "total": 3, + }, + "image_candidate": { + "candidate_id": 1, "strategy": "A", "url": "/uploads/ws/1/1/01_hook.jpg", + "role": "hook", + }, + "batch_failed": { + "batch": "hook", "reason": "502", "retryable": True, + }, + "flywheel_injected": { + "recent_preference": "最近偏好角度：场景型", + "reject_reasons": [], + }, + } + + def test_all_fe_required_fields_present(self): + """FE 期望的每个字段都在 BE 实际推的 data 中""" + for event_type, required_fields in self.EXPECTED_FIELDS.items(): + actual = self.ACTUAL_FIELDS.get(event_type, {}) + for field in required_fields: + assert field in actual, ( + f"SSE 事件 [{event_type}] 缺字段 [{field}]，FE 会拿到 undefined" + ) + + def test_no_extra_pii_in_sse_events(self): + """SSE 事件不含 key/token/password 等敏感字段""" + for event_type, data in self.ACTUAL_FIELDS.items(): + for field in data: + assert "key" not in field.lower() and "token" not in field.lower(), ( + f"SSE 事件 [{event_type}] 含敏感字段 [{field}]" + ) + + +# ═══════════════════════════════════════════════════════════════ +# Seam 3 — FE→BE: CreateTaskRequest DTO 对齐 +# ═══════════════════════════════════════════════════════════════ + +class TestCreateTaskRequestContract: + """FE tasks/new/page.tsx 发的请求体与 BE CreateTaskRequest 契约对齐（独立 DTO 验证，不依赖 motor）""" + + @pytest.fixture(autouse=True) + def _stub_motor(self, monkeypatch): + """database.py 顶层 import motor 在测试环境可能缺失，mock 掉避免 ModuleNotFoundError""" + import sys + import types + if "motor" not in sys.modules: + motor_mod = types.ModuleType("motor") + motor_async = types.ModuleType("motor.motor_asyncio") + motor_async.AsyncIOMotorClient = object + sys.modules["motor"] = motor_mod + sys.modules["motor.motor_asyncio"] = motor_async + + def _get_dto(self): + from app.api.v1.tasks import CreateTaskRequest + return CreateTaskRequest + + def test_create_task_required_fields(self): + Req = self._get_dto() + req = Req(product_id=1, theme="今日主题", text_count=8, image_count=5, track="ai") + assert req.product_id == 1 + assert req.theme == "今日主题" + assert req.track == "ai" + + def test_count_validation(self): + import pydantic + Req = self._get_dto() + with pytest.raises(pydantic.ValidationError): + Req(product_id=1, text_count=0, track="ai") # 0 < 1 + with pytest.raises(pydantic.ValidationError): + Req(product_id=1, text_count=21, track="ai") # 21 > 20 + + def test_track_validation(self): + import pydantic + Req = self._get_dto() + with pytest.raises(pydantic.ValidationError): + Req(product_id=1, track="auto") # 非法 track + + def test_import_track_no_key_check(self): + """轨B import 字段校验通过（业务层 key 检查在 service，不在 DTO）""" + Req = self._get_dto() + req = Req(product_id=1, track="import") + assert req.track == "import" + + +# ═══════════════════════════════════════════════════════════════ +# Seam 4 — SSE ticket 流程 +# ═══════════════════════════════════════════════════════════════ + +class TestSseTicket: + """ticket 签发/消费/防重放逻辑（用 mock redis）""" + + def _make_mock_redis(self): + store: dict = {} + + class MockRedis: + def setex(self, key, ttl, value): + store[key] = value + + def getdel(self, key): + return store.pop(key, None) + + return MockRedis(), store + + def test_issue_and_consume(self): + from app.core.sse_ticket import issue_ticket, consume_ticket + r, _ = self._make_mock_redis() + ticket = issue_ticket(r, task_id=42, workspace_id=7) + result = consume_ticket(r, ticket) + assert result == (42, 7) + + def test_consume_once_only(self): + from app.core.sse_ticket import issue_ticket, consume_ticket + r, _ = self._make_mock_redis() + ticket = issue_ticket(r, task_id=1, workspace_id=1) + consume_ticket(r, ticket) # 第一次消费 + result = consume_ticket(r, ticket) # 第二次 → None + assert result is None + + def test_invalid_ticket_returns_none(self): + from app.core.sse_ticket import consume_ticket + r, _ = self._make_mock_redis() + assert consume_ticket(r, "fake-ticket") is None + + def test_empty_ticket_returns_none(self): + from app.core.sse_ticket import consume_ticket + r, _ = self._make_mock_redis() + assert consume_ticket(r, "") is None + + +# ═══════════════════════════════════════════════════════════════ +# Seam 5 — SSE sse_utils 格式化 +# ═══════════════════════════════════════════════════════════════ + +class TestSseFormat: + """format_sse 输出符合 text/event-stream 规范，FE EventSource 能解析""" + + def test_format_contains_event_and_data(self): + from app.utils.sse_utils import format_sse + out = format_sse("text_candidate", {"candidate_id": 1, "score": 92}, seq=3) + assert "event: text_candidate" in out + assert "data:" in out + assert "id: 3" in out + + def test_format_ends_with_double_newline(self): + from app.utils.sse_utils import format_sse + out = format_sse("task_done", {"status": "pending_selection"}) + assert out.endswith("\n\n") + + def test_data_is_valid_json(self): + from app.utils.sse_utils import format_sse + out = format_sse("heartbeat", {"ts": 1234567.0}) + data_line = [l for l in out.split("\n") if l.startswith("data:")][0] + json.loads(data_line[len("data:"):].strip()) # 不应抛异常 diff --git a/backend/verify_output/素颜霜_封面hook.png b/backend/verify_output/素颜霜_封面hook.png new file mode 100644 index 0000000..36e1f0d Binary files /dev/null and b/backend/verify_output/素颜霜_封面hook.png differ diff --git a/banana扒包与取长补短.md b/banana扒包与取长补短.md new file mode 100644 index 0000000..fe7fc7a --- /dev/null +++ b/banana扒包与取长补短.md @@ -0,0 +1,107 @@ +# banana 扒包 + 产品包✖️banana 取长补短 + +> 目的：决定 Clover 的技术地基——banana 哪些能用、产品包哪些能用、怎么结合。 +> 铁律：好的保留，不好的优化升级。 +> 扒包日期：2026-06-05｜扒包人：CC + +--- + +## 一、banana 是什么（比记忆里强很多） + +它本身就是一个**完整的"小红书AI种草笔记生成平台"**，不是空骨架。 +技术栈：Next.js14前端 + FastAPI后端 + Celery队列 + MySQL(关系) + MongoDB(文档) + Redis(队列/SSE) + Docker6容器。 + +### 已完成且✅验证的能力 +- **认证**：注册/登录/JWT刷新/设备记录（auth完整） +- **任务流**：创建→Celery异步→SSE实时进度推送→结果展示（核心链路通） +- **积分系统**：余额/充值/流水/趋势（MVP模拟） +- **素材库**：CRUD/收藏/brand_tone字段 +- **上传**：magic number校验+扩展名白名单+路径穿越防护（安全已加固） +- **前端10页**：登录/概览/创建/历史/任务结果(SSE流)/图片编辑/最终效果/素材库/积分/个人中心 +- **AI抽象层**：AIProvider Protocol，mock/gemini可切换（架构支持插拔） + +### 半成品/待完成（PROGRESS.md说的） +- brand_tone 的 Alembic迁移没做 +- 前端交互细节没打磨 +- 端到端没完整自测；积分扣费没集成；AI图片持久化待做 +- 无测试覆盖、无CI/CD + +--- + +## 二、banana 的 AI 内核有多厚（决定取舍的关键） + +gemini_service.py 779行，能力清单： +- analyze_content 产品/参考分析（出overall_score） +- generate_text / generate_text_stream 文案生成（流式） +- generate_images / _generate_single_image 生图 +- **evaluate_schema 方案评分**（对应产品包打分，但只有1个总分，**比产品包五维浅**） +- edit_image 对话式改图 / apply_quick_action 快捷操作 +- **remove_watermark 去水印**（✅洞3刚需，banana已内置！） +- **extract_white_bg 白底图提取**（产品图标准化，加分项） +- _retry 重试退避（✅产品包缺的，banana有） +- gemini_image_client.generate_with_urls 带图URL生图（≈edits能力） + +⚠️ 关键短板：**banana的AI内核全绑Gemini**，没有产品包的"换角度/品类策略/去重/自动优化循环"那套业务深度。 +- banana打分=1个总分；产品包打分=五维(标题/情绪/买点/关键词/合规) +- banana无去重、无6品类话术、无storyboard 10角色分镜、无自动优化循环 +- banana的prompt在gemini_service里写死（但有prompt_template_doc的MongoDB集合，预留了可调位） + +### banana去水印实现真相（要标注） +remove_watermark 做法=**让Gemini重绘一张去水印图**(prompt: Remove all watermarks...reconstruct naturally)。 +- 优点：连像素级SynthID都重绘掉，比re-encode彻底 +- ⚠️风险：重绘会改变文字/构图，**对小红书海报的中文大标题是风险**(可能改字/糊字) +- 结论：可作为"去水印通道之一"，但海报类要测；洞3的re-encode/transcode削水印是更安全的"无损"方案，二者可分级用 + +--- + +## 三、取长补短对比表（核心结论） + +| 能力维度 | 产品包 | banana | Clover怎么取 | +|---------|--------|--------|-------------| +| **文案业务内核**(打分/优化/去重/品类/角度) | ✅强(五维+循环+去重+6品类) | ⚠️弱(1个总分) | **取产品包** | +| **storyboard分镜**(10角色+品类证明) | ✅强 | ❌无 | **取产品包** | +| **违禁词三级+自动改写** | ✅强 | ❌无 | **取产品包** | +| **爆款裂变+参考度调节** | ✅强 | ❌无 | **取产品包** | +| **工程底座**(认证/队列/SSE/Docker) | ❌无(无状态Worker) | ✅强 | **取banana** | +| **多租户/JWT/用户体系** | ❌无 | ✅有(差workspace层) | **取banana,补租户层** | +| **异步队列**(解决生图4min阻塞) | ❌无(同步) | ✅Celery+SSE | **取banana** | +| **数据库/存档** | ❌无 | ✅MySQL+MongoDB | **取banana,补delivery_packages** | +| **去水印** | ❌无(洞3要新建) | ✅remove_watermark | **取banana,海报类加re-encode安全档** | +| **白底图/产品图标准化** | ⚠️product.js分析 | ✅extract_white_bg | **两者结合** | +| **重试退避** | ❌无(baseline证明需要) | ✅_retry | **取banana** | +| **AI Provider抽象**(可插拔) | ❌写死 | ✅Protocol | **取banana** | +| **生图主模型** | Gemini主/GPT备 | 全Gemini | **改:gpt-image-2主/Gemini备** | +| **可调prompt** | ⚠️prompt写死在代码 | ⚠️有MongoDB集合预留位 | **取banana预留位,做成北哥可调** | +| **积分计费** | ❌无 | ✅有(MVP模拟) | **MVP免费,先关闭,留接口** | +| **前端** | demo展示页(不复用) | 10页生产级 | **取banana骨架,按前端设计参考图.png改5步流程** | + +--- + +## 四、结论：Clover 全新建，从两源各扒零件重建 + +**🔴 架构定性（倩倩姐2026-06-05拍板，已落 Clover/CLAUDE.md）：** +- Clover 是**全新独立项目**，在 `Clover/` 下从头建立。 +- banana 和产品包都是**只读素材源**，只复制不修改，绝不边盖边修。 +- 需要哪个零件 → 复制出来 → 在 Clover 里结合本项目迭代/优化/重建。 +- ~~以banana为底座~~（说法作废，那是边盖边修的错误架构）。 + +**从产品包扒（复制JS算法→Clover里重写Python）：** +- 文案五维打分/自动优化循环/去重 / storyboard 10角色分镜 / 6品类话术 / 违禁词三级自动改写 / 爆款裂变+参考度调节 + +**从banana扒（复制Python模块→Clover里改造）：** +- FastAPI+Celery+SSE 异步骨架 / JWT认证 / 双库模型范式 / AIProvider抽象层 / remove_watermark+extract_white_bg / _retry重试 / 上传安全加固 / 前端Next.js组件范式 + +**Clover自己新建（两源都没有）：** +- workspace多租户层 / delivery_packages存档打包+导出 / preference_events飞轮 / token-url收口 / banned_words可配置表 / 按"前端设计参考图.png"的5步流程前端 + +**改造点：** +- 生图主模型改 gpt-image-2 主、Gemini 备；生图接口改 edits 优先 +- banana去水印(Gemini重绘)对海报中文标题有改字风险 → 加 re-encode 无损安全档分级用 +- 积分计费 MVP 免费先关闭，留接口 +- 可调prompt：参考banana的prompt_template_doc预留位，做成北哥可调，等北哥样本注入 + +### ⚠️ 待定决策（给倩倩姐）— 已拍板 +1. ~~JS→Python重写 vs JS微服务~~ → **拍板：Python重写融进Clover**（统一栈），但 banana/产品包文件夹只读复制，不在其上改。 +2. ~~banana半成品先跑通验证~~ → **作废**：不基于banana迭代，所以不存在"先跑通banana"。banana只读，扒模块时该模块逻辑要在Clover里重新验证。 + + diff --git a/frontend/README.md b/frontend/README.md new file mode 100644 index 0000000..336c05f --- /dev/null +++ b/frontend/README.md @@ -0,0 +1,24 @@ +# Clover Frontend + +Next.js（React）— 5屏骨架 + +## 目录规划 +``` +frontend/src/ + app/ # Next.js App Router + components/ # 可复用组件 + lib/ # API client（按 openapi.yaml 生成） + types/ # TypeScript 类型 +``` + +## 5屏 +1. 管理配置台 — 产品档案/标杆笔记/违禁词/API Key录入 +2. 开任务 — 选产品+标杆+今天主题+设定数量，发起生成 +3. 挑文案+挑图 — SSE实时看生成进度，8选3文案/挑图，显示"本次已注入" +4. 确认预览 — 提交审核 +5. 审核台（组长） — 待审队列/通过/打回写原因 + +## 前端红线（不得违反） +- 不展示 Token 余额/用量 +- 不做积分中心/billing 页 +- 飞轮偏好信号不暴露独立 POST 给前端（防伪造） diff --git a/frontend/next-env.d.ts b/frontend/next-env.d.ts new file mode 100644 index 0000000..4f11a03 --- /dev/null +++ b/frontend/next-env.d.ts @@ -0,0 +1,5 @@ +/// +/// + +// NOTE: This file should not be edited +// see https://nextjs.org/docs/basic-features/typescript for more information. diff --git a/frontend/next.config.js b/frontend/next.config.js new file mode 100644 index 0000000..c288791 --- /dev/null +++ b/frontend/next.config.js @@ -0,0 +1,24 @@ +/** @type {import('next').NextConfig} */ +const nextConfig = { + reactStrictMode: true, + images: { + domains: ['localhost'], + }, + async rewrites() { + const apiBase = process.env.NEXT_PUBLIC_API_BASE_URL || 'http://localhost:8000'; + return [ + { + source: '/api/:path*', + destination: `${apiBase}/api/:path*`, + }, + { + // 图片等静态资源：后端 /uploads 由 StaticFiles 提供，前端代理过去 + // 否则 会请求前端3000端口导致404破图 + source: '/uploads/:path*', + destination: `${apiBase}/uploads/:path*`, + }, + ]; + }, +}; + +module.exports = nextConfig; diff --git a/frontend/package-lock.json b/frontend/package-lock.json new file mode 100644 index 0000000..a63cdac --- /dev/null +++ b/frontend/package-lock.json @@ -0,0 +1,6222 @@ +{ + "name": "clover-frontend", + "version": "0.1.0", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": 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"devDependencies": { + "@types/node": "^20.14.0", + "@types/react": "^18.3.3", + "@types/react-dom": "^18.3.0", + "autoprefixer": "^10.4.19", + "eslint": "^8.57.0", + "eslint-config-next": "14.2.0", + "postcss": "^8.4.38", + "tailwindcss": "^3.4.4", + "typescript": "^5.4.5" + } +} diff --git a/frontend/postcss.config.js b/frontend/postcss.config.js new file mode 100644 index 0000000..12a703d --- /dev/null +++ b/frontend/postcss.config.js @@ -0,0 +1,6 @@ +module.exports = { + plugins: { + tailwindcss: {}, + autoprefixer: {}, + }, +}; diff --git a/frontend/src/app/config/page.tsx b/frontend/src/app/config/page.tsx new file mode 100644 index 0000000..5ed16e2 --- /dev/null +++ b/frontend/src/app/config/page.tsx @@ -0,0 +1,58 @@ +'use client'; +/** + * 屏1 配置台（管理员 /config） + * 产品库 / 标杆笔记 / 违禁词 / API Key 录入 + * 🔴 红线：key 只录不显余额（不做 Token 用量展示） + */ +import { useState } from 'react'; +import { ProductsTab } from '@/components/config/ProductsTab'; +import { BenchmarksTab } from '@/components/config/BenchmarksTab'; +import { BannedWordsTab } from '@/components/config/BannedWordsTab'; +import { ApiKeysTab } from '@/components/config/ApiKeysTab'; + +type TabKey = 'products' | 'benchmarks' | 'banned_words' | 'api_keys'; + +const TABS: { key: TabKey; label: string }[] = [ + { key: 'products', label: '产品档案' }, + { key: 'benchmarks', label: '标杆笔记' }, + { key: 'banned_words', label: '违禁词库' }, + { key: 'api_keys', label: 'API Key' }, +]; + +export default function ConfigPage() { + const [activeTab, setActiveTab] = useState('products'); + + return ( +
+

配置台

+ + {/* Tab 导航 */} +
+ {TABS.map((tab) => ( + + ))} +
+ + {/* Tab 内容 */} +
+ {activeTab === 'products' && } + {activeTab === 'benchmarks' && } + {activeTab === 'banned_words' && } + {activeTab === 'api_keys' && } +
+
+ ); +} diff --git a/frontend/src/app/dashboard/page.tsx b/frontend/src/app/dashboard/page.tsx new file mode 100644 index 0000000..c28626f --- /dev/null +++ b/frontend/src/app/dashboard/page.tsx @@ -0,0 +1,51 @@ +'use client'; +/** + * /dashboard — 工作台（各角色登录后的中转落地页） + * 按角色展示对应入口 + */ +import Link from 'next/link'; +import { useAuthStore } from '@/stores/authStore'; + +export default function DashboardPage() { + const { user, role } = useAuthStore(); + + return ( +
+

+ 你好，{user?.username ?? '朋友'} +

+

+ {role === 'admin' && '你是管理员，可管理产品配置和审核'} + {role === 'supervisor' && '你是组长，负责审核待发笔记'} + {role === 'operator' && '今天要生成什么主题？'} +

+ +
+ {role !== 'supervisor' && ( + + )} + {(role === 'supervisor' || role === 'admin') && ( + + )} + {role === 'admin' && ( + + )} + +
+
+ ); +} + +function DashCard({ href, icon, label, desc }: { + href: string; icon: string; label: string; desc: string; +}) { + return ( + + +

{label}

+

{desc}

+ + ); +} diff --git a/frontend/src/app/globals.css b/frontend/src/app/globals.css new file mode 100644 index 0000000..49851c9 --- /dev/null +++ b/frontend/src/app/globals.css @@ -0,0 +1,8 @@ +@tailwind base; +@tailwind components; +@tailwind utilities; + +/* 最小宽度 1280px，不做移动端 */ +body { + min-width: 1280px; +} diff --git a/frontend/src/app/history/components.tsx b/frontend/src/app/history/components.tsx new file mode 100644 index 0000000..ed64600 --- /dev/null +++ b/frontend/src/app/history/components.tsx @@ -0,0 +1,174 @@ +'use client'; +/** + * HistoryTable / HistorySkeleton / Pagination / DownloadButton + * 历史归档页子组件（按约束拆出 ≤100行） + * + * C1修复：下载走 fetch+blob，不用 window.open。 + * window.open 无法附带 Authorization header，会 401。 + * 改法：api.getBlob 拿二进制流 → URL.createObjectURL → a.click → revoke。 + */ +import { useState } from 'react'; +import Link from 'next/link'; +import { clsx } from 'clsx'; +import { TaskListItem } from '@/types'; +import { api } from '@/lib/api'; + +const STATUS_LABEL: Record = { + approved: { text: '已通过', color: 'text-green-600 bg-green-50' }, + archived: { text: '已归档', color: 'text-gray-500 bg-gray-100' }, + rejected: { text: '被打回', color: 'text-red-500 bg-red-50' }, +}; + +// --- DownloadButton --- 触发打包→轮询→下载 zip +function DownloadButton({ taskId }: { taskId: number }) { + const [state, setState] = useState<'idle' | 'packaging' | 'done' | 'error'>('idle'); + const [pkgId, setPkgId] = useState(null); + + async function handleDownload() { + setState('packaging'); + try { + // 1. 触发打包 + const pkg = await api.post<{ delivery_package_id: number }>(`/api/v1/tasks/${taskId}/package`, {}); + const id = pkg.delivery_package_id; + setPkgId(id); + // 2. 轮询 status（最多20次，每2s） + for (let i = 0; i < 20; i++) { + await new Promise((r) => setTimeout(r, 2000)); + const info = await api.get<{ status: string }>(`/api/v1/delivery-packages/${id}/download`); + if (info.status === 'ready' || info.status === 'downloaded') { + // 3. fetch+blob 下载（带 Authorization header，避免 window.open 401） + const blob = await api.getBlob(`/api/v1/delivery-packages/${id}/download-file`); + const url = URL.createObjectURL(blob); + const a = document.createElement('a'); + a.href = url; + a.download = `delivery-${id}.zip`; + document.body.appendChild(a); + a.click(); + document.body.removeChild(a); + URL.revokeObjectURL(url); + setState('done'); + return; + } + } + setState('error'); + } catch { setState('error'); } + } + + if (state === 'done') return 已下载; + if (state === 'error') return 失败，刷新重试; + return ( + + ); +} + +// --- HistoryTable --- +export function HistoryTable({ tasks }: { tasks: TaskListItem[] }) { + return ( +
+ + + + + + + + + + + + + {tasks.map((task, i) => ( + + + + + + + + + ))} + +
任务状态文案图片创建时间操作
+ {task.theme} + + + {STATUS_LABEL[task.status]?.text ?? task.status} + + {task.text_count} 条{task.image_count} 张 + {new Date(task.created_at).toLocaleDateString('zh-CN')} + +
+ + 查看 + + {task.status === 'approved' && } +
+
+
+ ); +} + +// --- HistorySkeleton --- +export function HistorySkeleton() { + return ( +
+ {Array.from({ length: 6 }).map((_, i) => ( +
+
+
+
+
+
+
+
+ ))} +
+ ); +} + +// --- Pagination --- +export function Pagination({ + page, total, onChange, +}: { page: number; total: number; onChange: (p: number) => void }) { + return ( +
+ + + 第 {page} / {total} 页 + + +
+ ); +} diff --git a/frontend/src/app/history/page.tsx b/frontend/src/app/history/page.tsx new file mode 100644 index 0000000..0e59f1a --- /dev/null +++ b/frontend/src/app/history/page.tsx @@ -0,0 +1,95 @@ +'use client'; +/** + * 历史归档 /history + * 角色：熟手 + 组长（管理员也能进） + * 数据：GET /api/v1/tasks?status=approved,archived,rejected + */ +import { useEffect, useState, useCallback } from 'react'; +import { api } from '@/lib/api'; +import { TaskListItem, PaginatedResponse } from '@/types'; +import { getErrorAction } from '@/types/errors'; +import { clsx } from 'clsx'; +import { HistoryTable, HistorySkeleton, Pagination } from './components'; + +const HISTORY_STATUSES = ['approved', 'archived', 'rejected']; +const STATUS_LABELS: Record = { + approved: '已通过', archived: '已归档', rejected: '被打回', +}; +const PAGE_SIZE = 20; + +export default function HistoryPage() { + const [tasks, setTasks] = useState([]); + const [total, setTotal] = useState(0); + const [page, setPage] = useState(1); + const [loading, setLoading] = useState(true); + const [error, setError] = useState(null); + const [statusFilter, setStatusFilter] = useState('all'); + + const fetchHistory = useCallback(async (p: number, status: string) => { + setLoading(true); + setError(null); + try { + const params = new URLSearchParams({ page: String(p), page_size: String(PAGE_SIZE) }); + if (status !== 'all') params.append('status', status); + else HISTORY_STATUSES.forEach((s) => params.append('status', s)); + const res = await api.get>(`/api/v1/tasks?${params}`); + setTasks(res.items); + setTotal(res.total); + } catch (e: unknown) { + const ae = e as { code?: number }; + setError(getErrorAction(ae.code ?? 50001).message); + } finally { + setLoading(false); + } + }, []); + + useEffect(() => { fetchHistory(page, statusFilter); }, [page, statusFilter, fetchHistory]); + + const totalPages = Math.ceil(total / PAGE_SIZE); + + return ( +
+
+

历史归档

+ 共 {total} 条 +
+ + {/* 状态筛选 */} +
+ {[{ key: 'all', label: '全部' }, ...HISTORY_STATUSES.map((s) => ({ + key: s, label: STATUS_LABELS[s], + }))].map((opt) => ( + + ))} +
+ + {error && ( +
+ {error} + +
+ )} + + {loading ? : tasks.length === 0 ? ( +
暂无历史记录
+ ) : ( + + )} + + {totalPages > 1 && } +
+ ); +} diff --git a/frontend/src/app/layout.tsx b/frontend/src/app/layout.tsx new file mode 100644 index 0000000..9efe792 --- /dev/null +++ b/frontend/src/app/layout.tsx @@ -0,0 +1,49 @@ +import type { Metadata } from 'next'; +import './globals.css'; +import { AuthGuard } from '@/components/AuthGuard'; +import { Sidebar } from '@/components/layout/Sidebar'; + +export const metadata: Metadata = { + title: '龙石小红书内容生产平台', + description: '小红书内容生产工具', +}; + +export default function RootLayout({ + children, +}: { + children: React.ReactNode; +}) { + return ( + + + +
+ {/* 顶部 Header */} +
+

+ 龙石小红书内容生产平台 +

+ +
+ +
+ +
+ {children} +
+
+
+ + + + ); +} + +/** 右上角当前用户展示（Server Component 不可用 Store，Client 组件内联） */ +function UserBadge() { + // 通过 Client 组件获取 store 数据，此处为 Server Component 占位 + return ; +} + +// 内联 Client 组件，避免额外文件 +import UserBadgeClient from '@/components/layout/UserBadge'; diff --git a/frontend/src/app/login/page.tsx b/frontend/src/app/login/page.tsx new file mode 100644 index 0000000..66bc08a --- /dev/null +++ b/frontend/src/app/login/page.tsx @@ -0,0 +1,86 @@ +'use client'; +// /login — 登录页，按角色跳转首页（admin→/config, supervisor→/review, operator→/tasks/new） +import { useState } from 'react'; +import { useRouter } from 'next/navigation'; +import { useAuthStore } from '@/stores/authStore'; +import { api } from '@/lib/api'; +import { LoginRequest, LoginResponse, ApiError } from '@/types/index'; +import { getErrorAction } from '@/types/errors'; + +const INPUT_CLS = 'w-full border border-border-default rounded-lg px-3 py-2 text-sm focus:outline-none focus:border-brand-orange transition-colors'; + +export default function LoginPage() { + const router = useRouter(); + const { login, role } = useAuthStore(); + const [form, setForm] = useState({ username: '', password: '' }); + const [error, setError] = useState(''); + const [loading, setLoading] = useState(false); + + async function handleSubmit(e: React.FormEvent) { + e.preventDefault(); + if (!form.username || !form.password) { + setError('请填写用户名和密码'); + return; + } + setLoading(true); + setError(''); + try { + const data = await api.post('/api/v1/auth/login', form); + login(data.token, data.user); + // 按角色跳对应首页 + const dest = + data.user.role === 'admin' + ? '/config' + : data.user.role === 'supervisor' + ? '/review' + : '/tasks/new'; + router.replace(dest); + } catch (err) { + const apiErr = err as ApiError; + const action = getErrorAction(apiErr?.code); + setError(apiErr?.message || action.message); + } finally { + setLoading(false); + } + } + + return ( +
+
+
+ +

龙石内容生产平台

+

小红书内容工具

+
+ +
+
+ + setForm((f) => ({ ...f, username: e.target.value }))} + className={INPUT_CLS} aria-required="true" /> +
+ +
+ + setForm((f) => ({ ...f, password: e.target.value }))} + className={INPUT_CLS} aria-required="true" /> +
+ + {error && ( +
+ {error} +
+ )} + + +
+
+
+ ); +} diff --git a/frontend/src/app/page.tsx b/frontend/src/app/page.tsx new file mode 100644 index 0000000..5521c34 --- /dev/null +++ b/frontend/src/app/page.tsx @@ -0,0 +1,9 @@ +import { redirect } from 'next/navigation'; + +/** + * 根路由 / → 重定向到 /dashboard + * AuthGuard 会根据登录态再跳到对应首页 + */ +export default function RootPage() { + redirect('/dashboard'); +} diff --git a/frontend/src/app/review/page.tsx b/frontend/src/app/review/page.tsx new file mode 100644 index 0000000..1560076 --- /dev/null +++ b/frontend/src/app/review/page.tsx @@ -0,0 +1,98 @@ +'use client'; +// 屏5 组长审核台：通过(+5) / 打回必填原因(-3) +import { useEffect, useState } from 'react'; +import { api } from '@/lib/api'; +import { ReviewQueueItem, RejectRequest, PaginatedResponse, ApiError } from '@/types/index'; +import { RejectModal } from '@/components/review/RejectModal'; +import { ReviewCard, ReviewSkeleton } from '@/components/review/ReviewCard'; +import { getErrorAction } from '@/types/errors'; + +export default function ReviewPage() { + const [queue, setQueue] = useState([]); + const [loading, setLoading] = useState(false); + const [rejectTarget, setRejectTarget] = useState(null); + const [toast, setToast] = useState<{ message: string; type: 'success' | 'error' } | null>(null); + + useEffect(() => { loadQueue(); }, []); + + async function loadQueue() { + setLoading(true); + try { + const data = await api.get>('/api/v1/review/queue'); + setQueue(data.items); + } finally { + setLoading(false); + } + } + + function showToast(message: string, type: 'success' | 'error') { + setToast({ message, type }); + setTimeout(() => setToast(null), 3000); + } + + async function handleApprove(taskId: number) { + try { + await api.post(`/api/v1/review/${taskId}/approve`); + setQueue((q) => q.filter((item) => item.task_id !== taskId)); + showToast('已通过，进入成品库', 'success'); + } catch (err) { + const apiErr = err as ApiError; + showToast(apiErr?.message || getErrorAction(apiErr?.code).message, 'error'); + } + } + + async function handleReject(taskId: number, reason: string) { + const req: RejectRequest = { reason }; + await api.post(`/api/v1/review/${taskId}/reject`, req); + setQueue((q) => q.filter((item) => item.task_id !== taskId)); + showToast('已打回，任务回到挑选状态', 'success'); + } + + return ( +
+
+

+ 待审笔记{queue.length > 0 && （{queue.length}）} +

+
+ + {/* Toast 提示 */} + {toast && ( +
+ {toast.message} +
+ )} + + {loading ? ( + + ) : queue.length === 0 ? ( +
+ + 暂无待审笔记 +
+ ) : ( +
+ {queue.map((item) => ( + handleApprove(item.task_id)} + onReject={() => setRejectTarget(item.task_id)} + /> + ))} +
+ )} + +
+ 通过的笔记进入成品库，运营可导出文案+图+标签，人工去小红书/达人处发布。 +
+ + {rejectTarget !== null && ( + handleReject(rejectTarget, reason)} + onClose={() => setRejectTarget(null)} /> + )} +
+ ); +} diff --git a/frontend/src/app/tasks/[id]/confirm/page.tsx b/frontend/src/app/tasks/[id]/confirm/page.tsx new file mode 100644 index 0000000..b241e24 --- /dev/null +++ b/frontend/src/app/tasks/[id]/confirm/page.tsx @@ -0,0 +1,91 @@ +'use client'; +// 屏4.5 确认预览：提交审核 / 重新生成（飞轮-1） +import { useRouter, useParams } from 'next/navigation'; +import { ProgressBar } from '@/components/layout/ProgressBar'; +import { ConfirmActionBar } from '@/components/tasks/ConfirmActionBar'; +import { ConfirmPreviewImages } from '@/components/tasks/ConfirmPreviewImages'; +import { useTaskStore } from '@/stores/taskStore'; +import { useGenerationStore } from '@/stores/generationStore'; +import { api } from '@/lib/api'; +import { useState } from 'react'; + +export default function ConfirmPage() { + const params = useParams<{ id: string }>(); + const taskId = params?.id ? Number(params.id) : null; + const router = useRouter(); + const { selectedTextIds, selectedImageIds } = useTaskStore(); + // imageCandidates 平铺（契约 image_candidate 事件无 batch 字段） + const { textCandidates, imageCandidates } = useGenerationStore(); + const [submitting, setSubmitting] = useState(false); + const [regenerating, setRegenerating] = useState(false); + + const selectedTexts = textCandidates.filter((c) => selectedTextIds.includes(c.candidate_id)); + const selectedImages = imageCandidates.filter((c) => selectedImageIds.includes(c.candidate_id)); + + async function handleSubmitReview() { + if (!taskId) return; + setSubmitting(true); + try { + await api.post(`/api/v1/tasks/${taskId}/submit-review`); + router.push('/review'); + } finally { + setSubmitting(false); + } + } + + async function handleRegenerate() { + if (!taskId) return; + setRegenerating(true); + try { + await api.post(`/api/v1/tasks/${taskId}/regenerate`); + router.push(`/tasks/${taskId}/text`); + } finally { + setRegenerating(false); + } + } + + return ( +
+ + +
+

确认预览

+ +
+ {/* 已选文案 */} +
+

已选文案

+ {selectedTexts.length === 0 ? ( +

暂无选中文案

+ ) : ( +
+ {selectedTexts.map((c) => ( +
+ {c.angle_label} +

+ {c.content} +

+ 总分 {c.score.total} +
+ ))} +
+ )} +
+ + {/* 已选图 */} + +
+ + {/* 操作栏 */} + 0} + onSubmit={handleSubmitReview} + onRegenerate={handleRegenerate} + /> +
+
+ ); +} diff --git a/frontend/src/app/tasks/[id]/images/page.tsx b/frontend/src/app/tasks/[id]/images/page.tsx new file mode 100644 index 0000000..1b51795 --- /dev/null +++ b/frontend/src/app/tasks/[id]/images/page.tsx @@ -0,0 +1,86 @@ +'use client'; +// 屏4 挑图：0/N异步电影 + 平铺选图 + 单批重试（一期=整体重生） + 能离开 +import { useRouter, useParams } from 'next/navigation'; +import { ProgressBar } from '@/components/layout/ProgressBar'; +import { FlywheelBannerFromSse } from '@/components/FlywheelBanner'; +import { ImageProgressCardSkeleton } from '@/components/tasks/ImageProgressCard'; +import { ImageProgressHeader } from '@/components/tasks/ImageProgressHeader'; +import { ImageActionBar } from '@/components/tasks/ImageActionBar'; +import { SseStatusBar } from '@/components/tasks/SseStatusBar'; +import { useSse } from '@/hooks/useSse'; +import { useGenerationStore } from '@/stores/generationStore'; +import { useTaskStore } from '@/stores/taskStore'; +import { api } from '@/lib/api'; + +export default function ImageSelectionPage() { + const params = useParams<{ id: string }>(); + const taskId = params?.id ? Number(params.id) : null; + const router = useRouter(); + + const { imageCandidates, imageDone, imageTotal, failedBatches, flywheelInjected } = useGenerationStore(); + const { selectedImageIds, toggleImageSelection } = useTaskStore(); + const { connectionStatus, reconnectAttempt } = useSse(taskId); + + const pendingCount = imageTotal - imageDone; + const allDone = imageTotal > 0 && pendingCount === 0; + + async function handleRetry(_batchRole: string) { + // 一期 regenerate = 整体重生，契约无 batch 字段，不发 body + // 二期按批重生时再扩展，_batchRole 目前仅作入参占位（UI 提示用） + if (!taskId) return; + await api.post(`/api/v1/tasks/${taskId}/regenerate`); + } + + async function handleProceedToConfirm() { + if (!taskId || selectedImageIds.length === 0) return; + for (const candidateId of selectedImageIds) { + await api.post(`/api/v1/tasks/${taskId}/image-candidates/select`, { candidate_id: candidateId }); + } + router.push(`/tasks/${taskId}/confirm`); + } + + return ( +
+ + {flywheelInjected && ( + + )} + +
+ + {/* 图片网格（谁好谁先冒） */} +
+ {imageCandidates.length === 0 + ? Array.from({ length: 5 }).map((_, i) => ) + : imageCandidates.map((c) => ( +
toggleImageSelection(c.candidate_id)} + className={`relative rounded-xl overflow-hidden border cursor-pointer transition-all + ${selectedImageIds.includes(c.candidate_id) ? 'border-brand-orange ring-2 ring-brand-orange' : 'border-border-default hover:border-brand-orange/50'}`} + aria-label={`图片 ${c.role}，${selectedImageIds.includes(c.candidate_id) ? '已选中' : '未选中'}`} + > + {c.role} +

{c.role}

+
+ ))} +
+ {/* 失败批次提示 */} + {failedBatches.length > 0 && allDone && ( +
+ 有 {failedBatches.length} 批没出来。 + {failedBatches.filter((b) => b.retryable).map((b) => ( + + ))} +
+ )} + {imageCandidates.length > 0 && ( + + )} +
+
+ ); +} diff --git a/frontend/src/app/tasks/[id]/text/page.tsx b/frontend/src/app/tasks/[id]/text/page.tsx new file mode 100644 index 0000000..6994460 --- /dev/null +++ b/frontend/src/app/tasks/[id]/text/page.tsx @@ -0,0 +1,94 @@ +'use client'; +// 屏3 挑文案：SSE实时+五维分+多选+飞轮隐形 +import { useEffect } from 'react'; +import { useRouter, useParams } from 'next/navigation'; +import { ProgressBar } from '@/components/layout/ProgressBar'; +import { FlywheelBannerFromSse } from '@/components/FlywheelBanner'; +import { TextCandidateCard, TextCandidateCardSkeleton } from '@/components/tasks/TextCandidateCard'; +import { SseStatusBar } from '@/components/tasks/SseStatusBar'; +import { useSse } from '@/hooks/useSse'; +import { useGenerationStore } from '@/stores/generationStore'; +import { useTaskStore } from '@/stores/taskStore'; +import { api } from '@/lib/api'; + +export default function TextSelectionPage() { + const params = useParams<{ id: string }>(); + const taskId = params?.id ? Number(params.id) : null; + const router = useRouter(); + + const { textCandidates, textDone, textTotal, flywheelInjected } = useGenerationStore(); + const { selectedTextIds, toggleTextSelection, setCurrentTask } = useTaskStore(); + const { connectionStatus, reconnectAttempt } = useSse(taskId); + + useEffect(() => { + if (taskId) setCurrentTask(taskId, 'generating'); + }, [taskId]); + + async function handleProceedToImages() { + if (!taskId || selectedTextIds.length === 0) return; + for (const candidateId of selectedTextIds) { + await api.post(`/api/v1/tasks/${taskId}/text-candidates/select`, { candidate_id: candidateId }); + } + router.push(`/tasks/${taskId}/images`); + } + + const isGenerating = textDone < textTotal || textTotal === 0; + const showSkeleton = textCandidates.length === 0 && isGenerating; + + return ( +
+ + {flywheelInjected && ( + + )} + +
+
+

+ 挑文案{textTotal > 0 && 已生成 {textDone}/{textTotal} 条} +

+ +
+ + {/* SSE 进度提示（生成中且无内容时） */} + {showSkeleton && ( +
+ 正在生成文案… 预计 15-30 秒，好了会更新，可先去忙别的 +
+ )} + + {/* 文案卡网格（谁好谁先冒） */} +
+ {showSkeleton + ? Array.from({ length: 4 }).map((_, i) => ) + : textCandidates.map((c) => ( + + ))} +
+ + {/* 底部操作栏 */} + {textCandidates.length > 0 && ( +
+ + 已选 {selectedTextIds.length} 条 + + +
+ )} +
+
+ ); +} diff --git a/frontend/src/app/tasks/new/page.tsx b/frontend/src/app/tasks/new/page.tsx new file mode 100644 index 0000000..361005c --- /dev/null +++ b/frontend/src/app/tasks/new/page.tsx @@ -0,0 +1,99 @@ +'use client'; +/** + * 屏2 开新任务（熟手 /tasks/new） + * 使用 NewTaskForm + ConfigPreviewPanel + RecentTasksBar + FlywheelBanner + */ +import { useEffect, useState } from 'react'; +import { useRouter } from 'next/navigation'; +import { ProgressBar } from '@/components/layout/ProgressBar'; +import { FlywheelBanner } from '@/components/FlywheelBanner'; +import { ConfigPreviewPanel } from '@/components/tasks/ConfigPreviewPanel'; +import { RecentTasksBar } from '@/components/tasks/RecentTasksBar'; +import { NewTaskForm } from '@/components/tasks/NewTaskForm'; +import { api } from '@/lib/api'; +import { Product, CreateTaskRequest } from '@/types/index'; +import { PaginatedResponse } from '@/types/index'; +import { usePreferenceStore } from '@/stores/preferenceStore'; +import { getErrorAction } from '@/types/errors'; +import { ApiError } from '@/types/index'; + +export default function NewTaskPage() { + const router = useRouter(); + const { context } = usePreferenceStore(); + const [products, setProducts] = useState([]); + const [selectedProduct, setSelectedProduct] = useState(null); + const [form, setForm] = useState>({ + benchmark_ids: [], + theme: '', + text_count: 8, + image_count: 6, + track: 'ai', + need_product_image: true, + }); + const [submitting, setSubmitting] = useState(false); + const [error, setError] = useState(''); + + useEffect(() => { loadProducts(); }, []); + + async function loadProducts() { + try { + const data = await api.get>('/api/v1/products'); + setProducts(data.items); + if (data.items.length > 0) setSelectedProduct(data.items[0]); + } catch { + // ignore + } + } + + async function handleSubmit(e: React.FormEvent, track: 'ai' | 'import') { + e.preventDefault(); + if (!selectedProduct) { setError('请选择产品'); return; } + if (!form.theme.trim()) { setError('请填写今天主题'); return; } + // 禁降级：产品入镜但该产品没传参考图 → 拦在前端，引导去上传 + if (form.need_product_image && !selectedProduct.image_path) { + setError('该产品还没上传参考图，无法生成产品入镜内容。请先到「配置-产品库」上传产品图，或关闭下方「产品入镜」开关。'); + return; + } + setSubmitting(true); + setError(''); + try { + const req: CreateTaskRequest = { product_id: selectedProduct.id, ...form, track }; + const data = await api.post<{ id: number }>('/api/v1/tasks', req); + router.push(`/tasks/${data.id}/text`); + } catch (err) { + const apiErr = err as ApiError; + setError(apiErr?.message || getErrorAction(apiErr?.code).message); + } finally { + setSubmitting(false); + } + } + + return ( +
+ + {context && } + +
+ {/* 主表单区 */} +
+

开新任务

+ setForm((f) => ({ ...f, ...patch }))} + submitting={submitting} + error={error} + onSubmitAi={(e) => handleSubmit(e, 'ai')} + onSubmitImport={(e) => handleSubmit(e, 'import')} + /> +
+
+ + {/* 右侧配置预览面板 */} + {selectedProduct && } +
+
+ ); +} diff --git a/frontend/src/components/AuthGuard.tsx b/frontend/src/components/AuthGuard.tsx new file mode 100644 index 0000000..2cc4f1a --- /dev/null +++ b/frontend/src/components/AuthGuard.tsx @@ -0,0 +1,76 @@ +'use client'; +/** + * AuthGuard — 路由守卫 + * - 未登录 → /login + * - 越权（组长访问 /config，非管理员等）→ /dashboard + * 扒 banana AuthGuard 范式，全新重建 + */ +import { useEffect } from 'react'; +import { useRouter, usePathname } from 'next/navigation'; +import { useAuthStore } from '@/stores/authStore'; +import { UserRole } from '@/types/index'; + +// 路由 → 最低所需角色 +const ROUTE_ROLES: Record = { + '/config': ['admin'], + '/review': ['admin', 'supervisor'], + '/history': ['admin', 'supervisor', 'operator'], + '/tasks/new': ['admin', 'operator'], + '/dashboard': ['admin', 'supervisor', 'operator'], +}; + +function getRequiredRoles(pathname: string): UserRole[] | null { + // 精确匹配或前缀匹配（如 /tasks/[id]/text） + if (ROUTE_ROLES[pathname]) return ROUTE_ROLES[pathname]; + for (const [prefix, roles] of Object.entries(ROUTE_ROLES)) { + if (pathname.startsWith(prefix + '/')) return roles; + } + return null; // 公开路由（/login 等） +} + +interface AuthGuardProps { + children: React.ReactNode; +} + +export function AuthGuard({ children }: AuthGuardProps) { + const router = useRouter(); + const pathname = usePathname(); + const { isAuthenticated, role, isLoading, fetchMe, endLoading } = useAuthStore(); + + useEffect(() => { + // 首屏校验：有 token 且 store 未初始化 → 拉 me；无 token → 结束 loading 让守卫放行判断 + const token = typeof window !== 'undefined' ? localStorage.getItem('clover_token') : null; + if (token && !isAuthenticated && isLoading) { + fetchMe(); + } else if (!token && isLoading) { + endLoading(); + } + }, [isAuthenticated, isLoading, fetchMe, endLoading]); + + useEffect(() => { + if (isLoading) return; + + // 公开路由直接放行 + if (pathname === '/login') return; + + if (!isAuthenticated) { + router.replace('/login'); + return; + } + + const required = getRequiredRoles(pathname); + if (required && role && !required.includes(role)) { + router.replace('/dashboard'); + } + }, [isAuthenticated, role, isLoading, pathname, router]); + + if (isLoading) { + return ( +
+
加载中…
+
+ ); + } + + return <>{children}; +} diff --git a/frontend/src/components/FlywheelBanner.tsx b/frontend/src/components/FlywheelBanner.tsx new file mode 100644 index 0000000..8a15808 --- /dev/null +++ b/frontend/src/components/FlywheelBanner.tsx @@ -0,0 +1,65 @@ +'use client'; +/** + * FlywheelBanner — 飞轮注入提示横幅 + * 飞轮隐形：无"训练AI"按钮，只在生成时显示"本次已注入" + * 对应 flywheel_injected SSE 事件 + GET /preference/context + */ +import { PreferenceContext } from '@/types/index'; + +interface FlywheelBannerProps { + context: PreferenceContext; +} + +export function FlywheelBanner({ context }: FlywheelBannerProps) { + if (!context.injected_count && !context.recent_preference) return null; + + return ( +
+ +
+ 本次已注入： + {context.recent_preference && ( + {context.recent_preference} + )} + {context.reject_reasons?.length > 0 && ( + + · 打回原因：{context.reject_reasons.slice(0, 2).join('；')} + + )} +
+
+ ); +} + +/** + * FlywheelBannerFromSse — SSE flywheel_injected 事件版本 + */ +interface FlywheelBannerFromSseProps { + recentPreference: string; + rejectReasons: string[]; +} + +export function FlywheelBannerFromSse({ recentPreference, rejectReasons }: FlywheelBannerFromSseProps) { + return ( +
+ +
+ 本次已注入： + {recentPreference && {recentPreference}} + {rejectReasons?.length > 0 && ( + + · 打回原因：{rejectReasons.slice(0, 2).join('；')} + + )} +
+
+ ); +} diff --git a/frontend/src/components/config/ApiKeysTab.tsx b/frontend/src/components/config/ApiKeysTab.tsx new file mode 100644 index 0000000..33bb89b --- /dev/null +++ b/frontend/src/components/config/ApiKeysTab.tsx @@ -0,0 +1,98 @@ +'use client'; +// ApiKeysTab — API Key 录入（只录不显余额，CLAUDE.md红线） +import { useEffect, useState } from 'react'; +import { api } from '@/lib/api'; +import { ApiKey, CreateApiKeyRequest, PaginatedResponse, ApiError } from '@/types/index'; +import { getErrorAction } from '@/types/errors'; + +export function ApiKeysTab() { + const [keys, setKeys] = useState([]); + const [form, setForm] = useState({ provider: 'openai', api_key: '' }); + const [loading, setLoading] = useState(false); + const [error, setError] = useState(''); + const [saving, setSaving] = useState(false); + + useEffect(() => { loadKeys(); }, []); + + async function loadKeys() { + setLoading(true); + try { + const data = await api.get>('/api/v1/api-keys'); + setKeys(data.items); + } catch { /* silent */ } finally { setLoading(false); } + } + + async function handleAdd(e: React.FormEvent) { + e.preventDefault(); + if (!form.api_key.trim()) { setError('请填写 API Key'); return; } + setSaving(true); setError(''); + try { + await api.post('/api/v1/api-keys', form); + setForm({ provider: 'openai', api_key: '' }); + await loadKeys(); + } catch (err) { + const apiErr = err as ApiError; + setError(apiErr?.message || getErrorAction(apiErr?.code).message); + } finally { setSaving(false); } + } + + async function handleDelete(id: number) { + if (!confirm('确认删除此 Key？')) return; + try { + await api.delete(`/api/v1/api-keys/${id}`); + setKeys((ks) => ks.filter((k) => k.id !== id)); + } catch { /* ignore */ } + } + + return ( +
+

+ API Key 由各人自录，余额和用量请到中转站后台查看。 +

+
+
+ + +
+
+ + setForm((f) => ({ ...f, api_key: e.target.value }))} + placeholder="sk-..." + className="w-full border border-border-default rounded-lg px-3 py-2 text-sm focus:outline-none focus:border-brand-orange" + aria-label="输入 API Key" + /> +
+ +
+ {error &&

{error}

} + {loading ? ( +
加载中…
+ ) : ( +
    + {keys.map((k) => ( +
  • + + {k.provider} + …{k.key_last4} + + +
    • + ))} + {keys.length === 0 && ( +
  • 暂无 Key，请先录入
    • + )} +
+ )} +
+ ); +} diff --git a/frontend/src/components/config/BannedWordsTab.tsx b/frontend/src/components/config/BannedWordsTab.tsx new file mode 100644 index 0000000..4b82413 --- /dev/null +++ b/frontend/src/components/config/BannedWordsTab.tsx @@ -0,0 +1,90 @@ +'use client'; +/** + * BannedWordsTab — 违禁词库管理（三级可调） + */ +import { useEffect, useState } from 'react'; +import { api } from '@/lib/api'; +import { BannedWord, CreateBannedWordRequest, BannedWordLevel } from '@/types/index'; +import { PaginatedResponse } from '@/types/index'; +import { BannedWordsTable } from './BannedWordsTable'; + +export function BannedWordsTab() { + const [words, setWords] = useState([]); + const [loading, setLoading] = useState(false); + const [form, setForm] = useState({ + word: '', level: 'soft_warn', replacement: null, updatable: true, + }); + const [saving, setSaving] = useState(false); + + useEffect(() => { loadWords(); }, []); + + async function loadWords() { + setLoading(true); + try { + const data = await api.get>('/api/v1/banned-words'); + setWords(data.items); + } finally { setLoading(false); } + } + + async function handleAdd(e: React.FormEvent) { + e.preventDefault(); + if (!form.word.trim()) return; + setSaving(true); + try { + await api.post('/api/v1/banned-words', form); + setForm({ word: '', level: 'soft_warn', replacement: null, updatable: true }); + await loadWords(); + } finally { setSaving(false); } + } + + async function handleDelete(id: number) { + if (!confirm('确认删除此违禁词？')) return; + await api.delete(`/api/v1/banned-words/${id}`); + setWords((ws) => ws.filter((w) => w.id !== id)); + } + + return ( +
+

+ 三级：自动改写（替换）/ 软提示（生成后提醒）/ 硬拦截（生成前阻止） +

+
+
+ + setForm((f) => ({ ...f, word: e.target.value }))} + className="border border-border-default rounded-lg px-3 py-2 text-sm focus:outline-none focus:border-brand-orange w-32" + aria-required="true" + /> +
+
+ + +
+
+ + setForm((f) => ({ ...f, replacement: e.target.value || null }))} + placeholder="如：提亮" + className="border border-border-default rounded-lg px-3 py-2 text-sm focus:outline-none focus:border-brand-orange w-28" + /> +
+ +
+ {loading ? ( +
加载中…
+ ) : ( + + )} +
+ ); +} diff --git a/frontend/src/components/config/BannedWordsTable.tsx b/frontend/src/components/config/BannedWordsTable.tsx new file mode 100644 index 0000000..88adbe3 --- /dev/null +++ b/frontend/src/components/config/BannedWordsTable.tsx @@ -0,0 +1,54 @@ +'use client'; +/** + * BannedWordsTable — 违禁词列表表格 + */ +import { BannedWord, BannedWordLevel } from '@/types/index'; + +const LEVEL_LABELS: Record = { + auto_fix: { label: '🟢 自动改写', color: 'text-status-success bg-green-50' }, + soft_warn: { label: '🟡 软提示', color: 'text-status-warning bg-yellow-50' }, + hard_block: { label: '🔴 硬拦截', color: 'text-status-error bg-red-50' }, +}; + +interface BannedWordsTableProps { + words: BannedWord[]; + onDelete: (id: number) => void; +} + +export function BannedWordsTable({ words, onDelete }: BannedWordsTableProps) { + return ( + + + + + + + + + + + {words.map((w) => ( + + + + + + + ))} + {words.length === 0 && ( + + )} + +
词级别替换词操作
{w.word} + + {LEVEL_LABELS[w.level].label} + + {w.replacement ?? '—'} + {w.updatable && ( + + )} +
暂无违禁词
+ ); +} diff --git a/frontend/src/components/config/BenchmarkForm.tsx b/frontend/src/components/config/BenchmarkForm.tsx new file mode 100644 index 0000000..b631b04 --- /dev/null +++ b/frontend/src/components/config/BenchmarkForm.tsx @@ -0,0 +1,66 @@ +'use client'; +/** + * BenchmarkForm — 标杆笔记上传表单 + */ +import { useState } from 'react'; +import { api } from '@/lib/api'; +import { CreateBenchmarkRequest } from '@/types/index'; + +interface BenchmarkFormProps { + productId: number; + onSaved: () => void; + onCancel: () => void; +} + +export function BenchmarkForm({ productId, onSaved, onCancel }: BenchmarkFormProps) { + const [form, setForm] = useState({ + screenshot_url: '', highlights: '', link_url: null, + }); + const [saving, setSaving] = useState(false); + + async function handleSave(e: React.FormEvent) { + e.preventDefault(); + if (!form.screenshot_url || !form.highlights) return; + setSaving(true); + try { + await api.post(`/api/v1/products/${productId}/benchmarks`, form); + onSaved(); + } finally { + setSaving(false); + } + } + + return ( +
+

上传标杆笔记

+
+ + setForm((f) => ({ ...f, screenshot_url: e.target.value }))} + placeholder="https://..." + className="w-full border border-border-default rounded-lg px-3 py-2 text-sm focus:outline-none focus:border-brand-orange" + required aria-required="true" + /> +
+
+ +