From f2439ee5a3fa079409ff24bb33e9908c820fe35e Mon Sep 17 00:00:00 2001 From: NathanAlex7 <18898599977@163.com> Date: Mon, 7 Sep 2026 02:28:37 +0800 Subject: [PATCH] feat: restore risk assessment workspace and improve page scrolling --- .gitignore | 15 + Dockerfile | 63 + QUICK_START.md | 137 ++ START_GUIDE.md | 72 + backend/.env.example | 30 +- backend/api/chat.py | 151 +- backend/api/review_assist.py | 1171 +++++++++++ backend/api/risk_assess.py | 617 ++++++ backend/api/xingbang_api.py | 145 -- backend/api/xingbang_router.py | 372 ++++ backend/api/xingbang_unified.py | 432 ++++ backend/api/xingbang_v2.py | 569 ++++++ backend/app.py | 23 +- backend/data_layer/__init__.py | 20 + backend/data_layer/erp_connector.py | 301 +++ backend/data_layer/history_store.py | 209 ++ backend/data_layer/reviewer_profile.py | 262 +++ backend/data_layer/team_experience.py | 195 ++ backend/graph/agent.py | 93 +- backend/graph/memory_indexer.py | 6 + backend/graph/raw_trace.py | 274 +++ backend/kaiju_history_data.py | 317 +++ backend/start_backend.bat | 18 + backend/start_backend.sh | 22 + backend/xingbang/__init__.py | 23 - backend/xingbang/agents.py | 186 -- backend/xingbang/config.py | 173 -- backend/xingbang/evolution/__init__.py | 18 - backend/xingbang/evolution/optimizer.py | 161 -- backend/xingbang/evolution/reward.py | 82 - backend/xingbang/evolution/trajectory.py | 123 -- backend/xingbang/workflow.py | 237 --- docker-compose.yml | 58 + docker-entrypoint.sh | 24 + package-lock.json | 2024 ++++++++++--------- package.json | 20 +- src/app/globals.css | 21 +- src/app/historical-data/page.tsx | 370 ++++ src/app/page.tsx | 2 +- src/app/policy-verification/page.tsx | 706 +++++++ src/app/report-assistant/page.tsx | 370 ++++ src/app/review-allocation/page.tsx | 698 +++++++ src/app/review-assist/page.tsx | 1282 ++++++++++++ src/app/risk-assess-simple/page.tsx | 78 + src/app/risk-assess/page.tsx | 706 +++++++ src/app/rule-engine/page.tsx | 273 +++ src/app/trajectory/page.tsx | 107 + src/app/xingbang-unified/page.tsx | 186 ++ src/app/xingbang-v2/page.tsx | 370 ++++ src/app/xingbang/page.tsx | 545 ++--- src/components/chat/ChatMessage.tsx | 8 + src/components/chat/ChatPanel.tsx | 31 +- src/components/chat/RawPayloadCard.tsx | 212 ++ src/components/chat/RawTraceExplorer.tsx | 591 ++++++ src/components/layout/Navbar.tsx | 8 +- src/components/layout/Sidebar.tsx | 111 +- src/components/trajectory/AgentLoopFlow.tsx | 279 +++ src/components/xingbang/xingbang.css | 216 -- src/lib/api.ts | 118 +- src/lib/store.tsx | 52 + start.py | 130 ++ start.sh | 26 + start_all.bat | 34 + start_xingbang.sh | 86 + tsconfig.json | 2 +- 65 files changed, 13612 insertions(+), 2649 deletions(-) create mode 100644 Dockerfile create mode 100644 QUICK_START.md create mode 100644 START_GUIDE.md create mode 100644 backend/api/review_assist.py create mode 100644 backend/api/risk_assess.py delete mode 100644 backend/api/xingbang_api.py create mode 100644 backend/api/xingbang_router.py create mode 100644 backend/api/xingbang_unified.py create mode 100644 backend/api/xingbang_v2.py create mode 100644 backend/data_layer/__init__.py create mode 100644 backend/data_layer/erp_connector.py create mode 100644 backend/data_layer/history_store.py create mode 100644 backend/data_layer/reviewer_profile.py create mode 100644 backend/data_layer/team_experience.py create mode 100644 backend/graph/raw_trace.py create mode 100644 backend/kaiju_history_data.py create mode 100644 backend/start_backend.bat create mode 100644 backend/start_backend.sh delete mode 100644 backend/xingbang/__init__.py delete mode 100644 backend/xingbang/agents.py delete mode 100644 backend/xingbang/config.py delete mode 100644 backend/xingbang/evolution/__init__.py delete mode 100644 backend/xingbang/evolution/optimizer.py delete mode 100644 backend/xingbang/evolution/reward.py delete mode 100644 backend/xingbang/evolution/trajectory.py delete mode 100644 backend/xingbang/workflow.py create mode 100644 docker-compose.yml create mode 100644 docker-entrypoint.sh create mode 100644 src/app/historical-data/page.tsx create mode 100644 src/app/policy-verification/page.tsx create mode 100644 src/app/report-assistant/page.tsx create mode 100644 src/app/review-allocation/page.tsx create mode 100644 src/app/review-assist/page.tsx create mode 100644 src/app/risk-assess-simple/page.tsx create mode 100644 src/app/risk-assess/page.tsx create mode 100644 src/app/rule-engine/page.tsx create mode 100644 src/app/trajectory/page.tsx create mode 100644 src/app/xingbang-unified/page.tsx create mode 100644 src/app/xingbang-v2/page.tsx create mode 100644 src/components/chat/RawPayloadCard.tsx create mode 100644 src/components/chat/RawTraceExplorer.tsx create mode 100644 src/components/trajectory/AgentLoopFlow.tsx delete mode 100644 src/components/xingbang/xingbang.css create mode 100644 start.py create mode 100644 start.sh create mode 100644 start_all.bat create mode 100644 start_xingbang.sh diff --git a/.gitignore b/.gitignore index 7ad9179..981513b 100644 --- a/.gitignore +++ b/.gitignore @@ -57,3 +57,18 @@ backend/xingbang_evolution/ backend/diag_single_agent.py backend/run_full_workflow.py backend/verify_offline.py + +# Local credentials, working backups and runtime data +.env.* +!.env.example +**/.env.* +!**/.env.example +_backup*/ +*.pid +*.tsbuildinfo +backend/raw_logs/ +backend/backend/ +backend/xingbang_data/ +ori_meetingfile/ +.fixui_done +nul diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000..020bf85 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,63 @@ +# 兴邦金租 AI Native 智能决策系统 - Docker 镜像 + +# ============ Stage 1: Frontend Build ============ +FROM node:18-alpine AS frontend-builder + +WORKDIR /app/frontend + +# 复制前端依赖文件 +COPY package*.json ./ +RUN npm install + +# 复制前端代码并构建 +COPY src ./src +COPY public ./public +COPY next.config.js ./ +COPY tsconfig.json ./ +COPY tailwind.config.ts ./ +COPY postcss.config.js ./ + +RUN npm run build + +# ============ Stage 2: Backend + Runtime ============ +FROM python:3.11-slim + +WORKDIR /app + +# 安装系统依赖 +RUN apt-get update && apt-get install -y \ + gcc \ + g++ \ + libpq-dev \ + && rm -rf /var/lib/apt/lists/* + +# 复制后端依赖文件 +COPY backend/requirements.txt ./backend/ +RUN pip install --no-cache-dir -r backend/requirements.txt + +# 复制后端代码 +COPY backend ./backend + +# 复制前端构建产物 +COPY --from=frontend-builder /app/frontend/.next ./frontend/.next +COPY --from=frontend-builder /app/frontend/public ./frontend/public +COPY --from=frontend-builder /app/frontend/package.json ./frontend/ + +# 创建数据目录 +RUN mkdir -p /app/backend/xingbang_data/reviewers \ + /app/backend/xingbang_data/history \ + /app/backend/xingbang_data/team_experience \ + /app/backend/logs + +# 设置环境变量 +ENV PYTHONPATH=/app/backend +ENV NODE_ENV=production + +# 暴露端口 +EXPOSE 8002 3000 + +# 启动脚本 +COPY docker-entrypoint.sh /app/ +RUN chmod +x /app/docker-entrypoint.sh + +ENTRYPOINT ["/app/docker-entrypoint.sh"] diff --git a/QUICK_START.md b/QUICK_START.md new file mode 100644 index 0000000..fce6559 --- /dev/null +++ b/QUICK_START.md @@ -0,0 +1,137 @@ +# 兴邦金租 AI Native 智能决策系统 - 5分钟快速上手 + +## 🎯 最快启动方式 + +### Windows用户 +```bash +start_xingbang.bat +``` + +### Linux/Mac用户 +```bash +chmod +x start_xingbang.sh +./start_xingbang.sh +``` + +**就这么简单！** 脚本会自动： +- ✅ 检查Python环境 +- ✅ 创建虚拟环境 +- ✅ 安装依赖 +- ✅ 初始化数据目录 +- ✅ 启动后端服务 + +## 📖 访问系统 + +启动成功后，打开浏览器访问： + +- **API文档**: http://localhost:8002/docs +- **前端界面**: http://localhost:3000/xingbang-unified（需单独启动前端） + +## 🧪 快速测试 + +打开新终端，运行测试脚本： + +```bash +python test_xingbang.py +``` + +测试输出示例： +``` +✅ 后端服务运行中 +✅ 主审员画像: 4 人 +✅ 团队经验: 156 个历史案例 +✅ 助手6 测试通过 + 推荐主审员: 王芳 + 综合评分: 80.9 +``` + +## 🎯 试用功能 + +### 1. 查询主审员画像 + +```bash +curl http://localhost:8002/api/xingbang/unified/data/reviewers +``` + +### 2. 测试主审员智能分配 + +```bash +curl -X POST http://localhost:8002/api/xingbang/unified/assistant6/allocate \ + -H "Content-Type: application/json" \ + -d '{ + "project_industry": "光伏设备制造", + "project_amount": 5000, + "project_complexity": 3 + }' +``` + +### 3. 查看团队经验库 + +```bash +curl http://localhost:8002/api/xingbang/unified/data/team-experience +``` + +## 📚 下一步 + +- **完整功能**: 参考 `PROJECT_OVERVIEW.md` +- **部署到生产**: 参考 `DEPLOYMENT_GUIDE.md` +- **实施细节**: 参考 `IMPLEMENTATION_REPORT.md` + +## ⚙️ 配置ERP连接（可选） + +如果需要连接实际ERP数据库： + +1. 复制环境变量模板： +```bash +cp backend/.env.example backend/.env +``` + +2. 编辑 `backend/.env`，填写ERP连接信息： +```ini +ERP_DB_HOST=your_erp_host +ERP_DB_PORT=5432 +ERP_DB_NAME=xingbang_erp +ERP_DB_USER=readonly_user +ERP_DB_PASSWORD=your_password +ERP_DB_SSL_MODE=require +``` + +3. 重启后端服务 + +**注意**: 不配置ERP时，系统会自动使用Mock数据，功能完全可用。 + +## 🐳 Docker部署（生产环境） + +```bash +# 1. 配置密钥 +mkdir -p secrets +echo "your_erp_password" > secrets/erp_db_password.txt +echo "your_deepseek_key" > secrets/deepseek_api_key.txt + +# 2. 启动容器 +docker-compose up -d + +# 3. 查看日志 +docker-compose logs -f +``` + +## ❓ 常见问题 + +**Q: 端口8002被占用怎么办？** +A: 编辑 `backend/.env`，修改 `BACKEND_PORT=8002` 为其他端口 + +**Q: 没有DeepSeek API Key可以用吗？** +A: 部分助手需要LLM，建议先注册DeepSeek API（免费额度够测试用） + +**Q: 前端页面空白？** +A: 确保在项目根目录运行 `npm run dev` 启动前端 + +**Q: 数据会丢吗？** +A: 所有数据存在 `backend/xingbang_data/` 目录，持久化保存 + +## 📞 获取帮助 + +查看完整文档： +- `PROJECT_OVERVIEW.md` - 项目总览 +- `DEPLOYMENT_GUIDE.md` - 部署指南 +- `IMPLEMENTATION_REPORT.md` - 实施报告 diff --git a/START_GUIDE.md b/START_GUIDE.md new file mode 100644 index 0000000..e7c5e66 --- /dev/null +++ b/START_GUIDE.md @@ -0,0 +1,72 @@ +# mini-OpenClaw 启动指南 + +## 🚀 快速启动（推荐） + +### Windows 用户 +双击运行： +``` +start.bat +``` + +或在终端中： +```bash +./start.sh +``` + +### 手动启动 + +#### 1. 启动后端 +```bash +cd backend +./start_backend.sh # Linux/Mac/Git Bash +# 或 +start_backend.bat # Windows CMD +``` + +#### 2. 启动前端 +```bash +npm run dev +``` + +## ✅ 验证运行状态 + +- 后端：http://localhost:8002 +- 前端：http://localhost:3000 + +## 🔧 问题排查 + +### 问题：Connection error / 代理错误 + +**原因**：系统配置了代理（127.0.0.1:7890）但代理服务未运行 + +**解决方案**： +1. 使用提供的启动脚本（已自动禁用代理） +2. 或启动你的代理服务器（端口 7890） + +### 问题：端口被占用 + +**解决方案**： +```bash +# Windows +netstat -ano | findstr :8002 +taskkill /F /PID + +# Linux/Mac +lsof -ti:8002 | xargs kill -9 +``` + +### 问题：LlamaIndex 警告 + +**解决方案**：确保使用项目虚拟环境 +```bash +cd backend +source venv/Scripts/activate # Git Bash +# 或 +venv\Scripts\activate.bat # Windows CMD +``` + +## 📝 说明 + +- 启动脚本会自动禁用代理设置 +- 后端使用项目虚拟环境（backend/venv） +- 所有依赖已在虚拟环境中安装 diff --git a/backend/.env.example b/backend/.env.example index ec146c1..582eee3 100644 --- a/backend/.env.example +++ b/backend/.env.example @@ -1,14 +1,20 @@ -# OpenAI API Configuration -OPENAI_API_KEY=sk-xxx -OPENAI_BASE_URL=https://api.openai.com/v1 -MODEL_NAME=gpt-4o-mini +# ============ DeepSeek LLM 配置 ============ +DEEPSEEK_API_KEY=your_deepseek_api_key_here +DEEPSEEK_MODEL=deepseek-chat +DEEPSEEK_BASE_URL=https://api.deepseek.com -# Embedding model -EMBEDDING_MODEL=text-embedding-3-small +# ============ ERP 数据库配置（加密连接）============ +# 注意：生产环境使用 Docker Secrets，不要直接写在 .env 文件中 +ERP_DB_HOST=your_erp_db_host +ERP_DB_PORT=5432 +ERP_DB_NAME=xingbang_erp +ERP_DB_USER=readonly_user # 只读账号 +ERP_DB_PASSWORD=your_readonly_password +ERP_DB_SSL_MODE=require # 强制 SSL 连接 -# Server -HOST=0.0.0.0 -PORT=8002 - -# OpenWeather API Configuration -OPENWEATHER_API_KEY=your_openweather_api_key_here +# ============ 审计日志路径 ============ +ERP_AUDIT_LOG=./logs/erp_access.log + +# ============ 其他配置 ============ +BACKEND_PORT=8002 +FRONTEND_PORT=3000 diff --git a/backend/api/chat.py b/backend/api/chat.py index 93e7d93..6966e6a 100644 --- a/backend/api/chat.py +++ b/backend/api/chat.py @@ -3,6 +3,7 @@ import json import os import traceback +from pathlib import Path from typing import AsyncGenerator from fastapi import APIRouter @@ -80,7 +81,7 @@ async def event_generator(message: str, session_id: str) -> AsyncGenerator[dict, segments: list[dict] = [] # finalized segments current_segment: dict = {"content": "", "tool_calls": []} - async for event in agent_manager.astream(message, history): + async for event in agent_manager.astream(message, history, session_id): event_type = event.get("type", "unknown") if event_type == "retrieval": @@ -186,3 +187,151 @@ async def chat(request: ChatRequest): # Non-streaming fallback result = await agent_manager.ainvoke(request.message, request.session_id) return {"reply": result} + + +@router.get("/chat/raw-traces") +async def get_raw_traces(session_id: str = "default", limit: int = 20): + """获取指定 session 的原始 LLM 调用报文 + + Args: + session_id: 会话 ID + limit: 返回最近 N 条记录 (默认 20) + + Returns: + {"traces": [...]} 原始报文列表，按时间倒序 + """ + try: + from graph.raw_trace import load_raw_traces + + # 从 agent_manager 获取 raw_logs_dir + raw_logs_dir = agent_manager._raw_logs_dir + if not raw_logs_dir: + return {"traces": []} + + traces = load_raw_traces(session_id, raw_logs_dir, limit=limit) + return {"traces": traces} + except Exception as e: + traceback.print_exc() + return {"traces": [], "error": str(e)} + + +@router.get("/chat/trajectory") +async def get_trajectory(session_id: str = "default"): + """获取会话完整交互轨迹 - 包含完整的 human/assistant/tool 消息流 + + Args: + session_id: 会话 ID + + Returns: + { + "session_id": "...", + "messages": [ + {"role": "human", "content": "...", "timestamp": ...}, + {"role": "assistant", "content": "...", "timestamp": ..., "tool_calls": [...]}, + {"role": "tool", "name": "...", "content": "...", "timestamp": ...}, + {"role": "assistant", "content": "...", "timestamp": ...} + ], + "steps": [...] // 保留旧格式用于向后兼容 + } + """ + try: + import json + from graph.raw_trace import load_raw_traces + + raw_logs_dir = agent_manager._raw_logs_dir + if not raw_logs_dir: + # 无 raw_logs_dir，返回空 + return {"session_id": session_id, "messages": [], "steps": []} + + # 优先读取完整消息流日志文件 + msgs_log_file = raw_logs_dir / f"{session_id}.msgs.jsonl" + messages = [] + + if msgs_log_file.exists(): + # 从 msgs.jsonl 读取完整消息流（包含 tool 消息） + with open(msgs_log_file, "r", encoding="utf-8") as f: + for line in f: + line = line.strip() + if line: + try: + msg = json.loads(line) + messages.append(msg) + except json.JSONDecodeError: + continue + else: + # 回退到旧逻辑：从 session 文件读取（无 tool 消息） + session_messages = session_manager.load_session(session_id) + for msg in session_messages: + messages.append({ + "role": msg["role"], + "content": msg["content"], + "timestamp": None, + }) + + # 构建旧格式 steps（向后兼容） + steps = [] + traces = load_raw_traces(session_id, raw_logs_dir, limit=1000) + traces.sort(key=lambda x: x.get("timestamp_start", 0)) + + # 按 user/assistant 消息构建 steps（忽略 tool 消息） + session_messages = session_manager.load_session(session_id) + assistant_count = sum(1 for m in session_messages if m["role"] == "assistant") + + for msg_idx, msg in enumerate(session_messages): + step = { + "role": msg["role"], + "content": msg["content"], + "timestamp": None, + } + + if msg["role"] == "assistant": + llm_calls = [] + assistant_seq = sum(1 for m in session_messages[:msg_idx+1] if m["role"] == "assistant") + traces_per_assistant = len(traces) / max(assistant_count, 1) + start_trace = int((assistant_seq - 1) * traces_per_assistant) + end_trace = int(assistant_seq * traces_per_assistant) + + for i in range(start_trace, min(end_trace, len(traces))): + trace = traces[i] + llm_call = { + "model": trace.get("request", {}).get("model", "unknown"), + "finish_reason": trace.get("finish_reason", "unknown"), + "request_messages": trace.get("request", {}).get("messages", []), + "response_text": "", + "tool_calls_list": [], + "usage": trace.get("usage", {}), + "duration_ms": trace.get("duration_ms", 0), + } + + response_gens = trace.get("response", {}).get("generations", []) + if response_gens: + llm_call["response_text"] = response_gens[0].get("text", "") + msg_data = response_gens[0].get("message", {}) + if isinstance(msg_data, dict) and "tool_calls" in msg_data: + tool_calls = msg_data["tool_calls"] + if tool_calls: + llm_call["tool_calls_list"] = [ + { + "name": tc.get("name", "") if isinstance(tc, dict) else getattr(tc, "name", ""), + "args": tc.get("args", {}) if isinstance(tc, dict) else getattr(tc, "args", {}), + } + for tc in tool_calls + ] + + llm_calls.append(llm_call) + if step["timestamp"] is None: + step["timestamp"] = trace.get("timestamp_start") + + step["llm_calls"] = llm_calls + + steps.append(step) + + return { + "session_id": session_id, + "messages": messages, # 新格式：完整消息流（含 tool） + "steps": steps, # 旧格式：向后兼容 + } + + except Exception as e: + traceback.print_exc() + return {"session_id": session_id, "messages": [], "steps": [], "error": str(e)} diff --git a/backend/api/review_assist.py b/backend/api/review_assist.py new file mode 100644 index 0000000..6dfd908 --- /dev/null +++ b/backend/api/review_assist.py @@ -0,0 +1,1171 @@ +"""评审提效工作台 API + +帮助兴邦金租的项目评审流程提效： +- 评审周期从16-17个工作日压缩 +- 主审员负荷优化（每人3-7个项目排队） +- 看资料/算测算/写报告三大块耗时降低 +""" + +import json +from typing import Any, AsyncGenerator, Optional, List, Dict + +from fastapi import APIRouter, Query +from fastapi.responses import StreamingResponse +from pydantic import BaseModel, Field + +# 导入历史数据模块 +from kaiju_history_data import ( + get_all_projects, + get_projects_filtered, + get_reviewer_stats, +) + +router = APIRouter(prefix="/api/review-assist", tags=["评审提效工作台"]) + + +# ============ Pydantic 模型 ============ + +class ReviewerInfo(BaseModel): + """主审员信息""" + name: str + current_projects: int # 在手项目数 + specialty: str # 专长领域 + pipeline: Optional[List[int]] = None # 在手项目复杂度列表（可选，用于精准负荷估算） + estimated_finish_days: Optional[int] = None # 预计完成天数（可选） + + +class LoadBalanceRequest(BaseModel): + """评审负荷与分配建议请求""" + reviewers: list[ReviewerInfo] + new_project_industry: str = "" # 新项目行业 + new_project_amount: float = 0 # 新项目金额（万元） + + +class DueDiligenceRequest(BaseModel): + """尽调资料预处理请求""" + project_name: str + client_name: str + industry: str + financial_data: dict # 财务数据 + credit_report: dict # 征信报告 + guarantee_info: dict # 担保信息 + external_risks: dict # 外部风险数据 + + +class RentCalculationRequest(BaseModel): + """租金测算辅助校验请求""" + credit_amount: float # 授信金额（万元） + lease_term: int # 租赁期限（月） + interest_rate: float # 年利率（%） + monthly_cashflow: float # 月现金流（万元） + project_type: str = "对公" # 对公/零售 + + +class ReportDraftRequest(BaseModel): + """评审报告初稿生成请求""" + intelligence_card: str # 情报卡内容 + calculation_result: str # 测算结果 + project_name: str + client_name: str + + +class PreScreeningRequest(BaseModel): + """立项前置风险筛查请求""" + project_name: str + client_name: str + industry: str + document_checklist: list[str] # 立项资料清单 + financial_data: dict + similar_cases: list[dict] = [] # 历史相似案例（可选） + + +class AllocateRequest(BaseModel): + """智能分配请求（数据驱动）""" + reviewers: List[ReviewerInfo] + new_project_industry: str + new_project_amount: float # 万元 + new_project_complexity: int = Field(3, ge=1, le=5) # 1-5，默认3 + + +class RecommendationItem(BaseModel): + """单个推荐结果""" + reviewer: str + score: float # 综合评分 + reasons: List[str] # 推荐理由 + est_complete_days: int # 预计完成天数（从现在算起） + industry_match_score: float # 行业匹配分 + load_score: float # 负荷分 + quality_score: float # 质量分 + + +class AllocateResponse(BaseModel): + """智能分配响应""" + reviewers: List[Dict] # 主审员当前状态列表 + recommendation: List[RecommendationItem] # Top-N推荐 + balance_before: float # 分配前负荷方差 + balance_after: float # 分配后负荷方差（假设分配给Top1） + algorithm_version: str # 算法版本标识 + + +# ============ 示例数据（供前端使用）============ + +SAMPLE_DATA = { + "load_balance": { + "reviewers": [ + {"name": "李明", "current_projects": 5, "specialty": "制造业"}, + {"name": "王芳", "current_projects": 3, "specialty": "新能源"}, + {"name": "张强", "current_projects": 7, "specialty": "交通运输"}, + {"name": "刘洋", "current_projects": 4, "specialty": "医疗健康"}, + ], + "new_project_industry": "光伏设备制造", + "new_project_amount": 5000 + }, + "due_diligence": { + "project_name": "阳光光伏设备融资租赁项目", + "client_name": "阳光光伏设备有限公司", + "industry": "光伏设备制造", + "financial_data": { + "registered_capital": 8000, + "established_years": 6, + "asset_liability_ratio": 0.58, + "current_ratio": 1.45, + "net_profit_2024": 1200, + "net_profit_2025": 1450, + "revenue_growth_rate": 0.18 + }, + "credit_report": { + "overdue_count": 0, + "query_count_recent_6m": 4, + "credit_limit": 20000, + "enforcement_records": 0 + }, + "guarantee_info": { + "type": "法人连带担保+设备抵押", + "guarantor": "阳光集团有限公司", + "guarantor_net_assets": 50000 + }, + "external_risks": { + "litigation_records": [], + "negative_news": 0, + "industry_policy": "国家支持光伏产业发展" + } + }, + "rent_calculation": { + "credit_amount": 5000, + "lease_term": 36, + "interest_rate": 5.8, + "monthly_cashflow": 85, + "project_type": "对公" + }, + "report_draft": { + "intelligence_card": "# 客户情报卡\n\n## 基本信息\n- 客户名称：阳光光伏设备有限公司\n- 行业：光伏设备制造\n\n## 财务状况\n- 资产负债率：58%（良好）\n- 流动比率：1.45（符合标准）\n\n## 风险评估\n🟢 低风险：财务指标优良，行业前景好", + "calculation_result": "# 租金测算结果\n\n- 月租金：152.8万元\n- 覆盖率：180%（远超120%标准）\n- 风险等级：低", + "project_name": "阳光光伏设备融资租赁项目", + "client_name": "阳光光伏设备有限公司" + }, + "pre_screening": { + "project_name": "阳光光伏设备融资租赁项目", + "client_name": "阳光光伏设备有限公司", + "industry": "光伏设备制造", + "document_checklist": [ + "营业执照", + "财务报表（近3年）", + "征信报告", + "租赁物清单", + "担保函", + "法人身份证", + "银行流水（近6个月）" + ], + "financial_data": { + "registered_capital": 8000, + "asset_liability_ratio": 0.58, + "net_profit_2025": 1450 + }, + "similar_cases": [ + { + "project_name": "某光伏企业融资租赁（2025年）", + "rejection_reason": "担保方资产不足，要求补充第二担保人", + "lesson": "光伏行业波动大，需要双重担保" + } + ] + } +} + + +# ============ 辅助1：评审负荷与分配建议 ============ + +async def _generate_load_balance(request: LoadBalanceRequest) -> AsyncGenerator[str, None]: + """生成评审负荷与分配建议""" + + # 计算平均负荷 + total_projects = sum(r.current_projects for r in request.reviewers) + avg_load = total_projects / len(request.reviewers) if request.reviewers else 0 + + # 排序：按当前项目数升序 + sorted_reviewers = sorted(request.reviewers, key=lambda r: r.current_projects) + + # 生成 Markdown 内容 + content = f"""# 评审负荷与分配建议 + +## 📊 当前负荷看板 + +| 主审员 | 在手项目数 | 专长领域 | 负荷状态 | +|--------|-----------|---------|---------| +""" + + for r in request.reviewers: + load_status = "🔴 超载" if r.current_projects >= 6 else ("🟡 偏高" if r.current_projects >= 4 else "🟢 正常") + content += f"| {r.name} | {r.current_projects} | {r.specialty} | {load_status} |\n" + + content += f"\n**平均负荷**：{avg_load:.1f} 个项目/人\n\n" + + yield content + + # 新项目推荐 + if request.new_project_industry and request.new_project_amount: + content = f"""## 🎯 新项目分配推荐 + +**项目信息**：{request.new_project_industry} | 授信 {request.new_project_amount} 万元 + +### Top 3 推荐主审员 + +""" + # 推荐逻辑：专长匹配 > 负荷最低 + recommendations = [] + for r in sorted_reviewers: + match_score = 10 if request.new_project_industry in r.specialty or r.specialty in request.new_project_industry else 0 + load_score = (10 - r.current_projects) # 项目越少分数越高 + total_score = match_score + load_score + recommendations.append((r, match_score, load_score, total_score)) + + recommendations.sort(key=lambda x: x[3], reverse=True) + + for i, (r, match, load, total) in enumerate(recommendations[:3], 1): + reason = [] + if match > 0: + reason.append(f"专长匹配（{r.specialty}）") + if r.current_projects < avg_load: + reason.append("负荷较低") + reason_str = "、".join(reason) if reason else "负荷可承接" + + content += f"**{i}. {r.name}** — {reason_str}\n" + content += f" - 当前负荷：{r.current_projects} 个项目\n" + content += f" - 专长：{r.specialty}\n" + content += f" - 综合评分：{total} 分\n\n" + + yield content + + # 瓶颈预警 + bottleneck_reviewers = [r for r in request.reviewers if r.current_projects >= 6] + if bottleneck_reviewers: + content = f"""## ⚠️ 瓶颈预警 + +以下主审员负荷已达瓶颈（≥6个项目），建议暂停新项目分配： + +""" + for r in bottleneck_reviewers: + content += f"- **{r.name}**：{r.current_projects} 个项目（专长：{r.specialty}）\n" + + content += f"\n**建议措施**：\n" + content += f"1. 优先分配给负荷较低的主审员（{sorted_reviewers[0].name}、{sorted_reviewers[1].name if len(sorted_reviewers) > 1 else ''}）\n" + content += f"2. 考虑延长评审周期或增加评审人力\n" + content += f"3. 对简单项目启用快速评审通道\n" + + yield content + else: + content = """## ✅ 无瓶颈预警 + +当前所有主审员负荷均在合理范围内，可正常分配新项目。 +""" + yield content + + +@router.post("/load-balance/stream") +async def load_balance_stream(request: LoadBalanceRequest): + """评审负荷与分配建议（流式返回）""" + + async def event_generator(): + try: + yield f"data: {json.dumps({'type': 'start', 'message': '正在分析评审负荷...'}, ensure_ascii=False)}\n\n" + + async for content in _generate_load_balance(request): + yield f"data: {json.dumps({'type': 'content', 'data': content}, ensure_ascii=False)}\n\n" + + yield f"data: {json.dumps({'type': 'done', 'message': '分析完成'}, ensure_ascii=False)}\n\n" + except Exception as e: + yield f"data: {json.dumps({'type': 'error', 'message': str(e)}, ensure_ascii=False)}\n\n" + + return StreamingResponse(event_generator(), media_type="text/event-stream") + + +# ============ 辅助2：尽调资料预处理·情报卡 ============ + +async def _generate_intelligence_card(request: DueDiligenceRequest) -> AsyncGenerator[str, None]: + """生成客户情报卡""" + + content = f"""# 📋 客户情报卡 + +## 基本信息 +- **项目名称**：{request.project_name} +- **客户名称**：{request.client_name} +- **所属行业**：{request.industry} + +--- + +## 💰 财务状况分析 + +""" + yield content + + # 财务指标分析 + fd = request.financial_data + content = "" + + # 注册资本 + reg_capital = fd.get("registered_capital", 0) + if reg_capital >= 1000: + content += f"- **注册资本**：{reg_capital} 万元 🟢 符合标准（≥1000万）\n" + else: + content += f"- **注册资本**：{reg_capital} 万元 🔴 不足（要求≥1000万）\n" + + # 资产负债率 + debt_ratio = fd.get("asset_liability_ratio", 0) + if debt_ratio <= 0.70: + content += f"- **资产负债率**：{debt_ratio*100:.1f}% 🟢 良好（≤70%）\n" + else: + content += f"- **资产负债率**：{debt_ratio*100:.1f}% 🔴 偏高（标准≤70%）\n" + + # 流动比率 + current_ratio = fd.get("current_ratio", 0) + if current_ratio >= 1.2: + content += f"- **流动比率**：{current_ratio:.2f} 🟢 健康（≥1.2）\n" + else: + content += f"- **流动比率**：{current_ratio:.2f} 🟡 偏低（标准≥1.2）\n" + + # 净利润 + profit_2024 = fd.get("net_profit_2024", 0) + profit_2025 = fd.get("net_profit_2025", 0) + if profit_2024 > 0 and profit_2025 > 0: + growth = ((profit_2025 - profit_2024) / profit_2024 * 100) if profit_2024 else 0 + content += f"- **净利润**：2024年 {profit_2024} 万元，2025年 {profit_2025} 万元 🟢 连续为正，增长 {growth:.1f}%\n" + else: + content += f"- **净利润**：2024年 {profit_2024} 万元，2025年 {profit_2025} 万元 🔴 存在亏损\n" + + yield content + + # 征信报告分析 + cr = request.credit_report + content = f""" +--- + +## 🔍 征信记录分析 + +""" + + overdue = cr.get("overdue_count", 0) + if overdue == 0: + content += f"- **逾期记录**：无 🟢 信用良好\n" + else: + content += f"- **逾期记录**：{overdue} 次 🔴 存在逾期风险\n" + + enforcement = cr.get("enforcement_records", 0) + if enforcement == 0: + content += f"- **被执行记录**：无 🟢\n" + else: + content += f"- **被执行记录**：{enforcement} 条 🔴 严重风险\n" + + query_count = cr.get("query_count_recent_6m", 0) + if query_count <= 5: + content += f"- **近6个月查询次数**：{query_count} 次 🟢 正常\n" + else: + content += f"- **近6个月查询次数**：{query_count} 次 🟡 频繁（可能存在资金压力）\n" + + yield content + + # 担保信息 + gi = request.guarantee_info + content = f""" +--- + +## 🛡️ 担保信息 + +- **担保方式**：{gi.get('type', '未提供')} +- **担保人**：{gi.get('guarantor', '未提供')} +- **担保人净资产**：{gi.get('guarantor_net_assets', 0)} 万元 + +""" + yield content + + # 外部风险 + er = request.external_risks + content = f""" +--- + +## ⚠️ 外部风险评估 + +- **涉诉记录**：{len(er.get('litigation_records', []))} 条 +- **负面舆情**：{er.get('negative_news', 0)} 条 +- **行业政策**：{er.get('industry_policy', '未知')} + +--- + +## 📝 数据完整性检查 + +""" + + # 检查缺失项 + missing = [] + if not fd: + missing.append("财务数据缺失") + if not cr: + missing.append("征信报告缺失") + if not gi or not gi.get('type'): + missing.append("担保信息不完整") + + if missing: + content += "### 🔴 缺失项\n" + for item in missing: + content += f"- {item}\n" + content += "\n" + else: + content += "### ✅ 无缺失项\n\n" + + # 检查矛盾点 + contradictions = [] + if fd.get("net_profit_2025", 0) > 0 and fd.get("asset_liability_ratio", 0) > 0.80: + contradictions.append("盈利但负债率过高，需核实资产结构") + if cr.get("overdue_count", 0) > 0 and cr.get("query_count_recent_6m", 0) > 10: + contradictions.append("征信逾期且查询频繁，存在资金链风险") + + if contradictions: + content += "### 🟡 矛盾点\n" + for item in contradictions: + content += f"- {item}\n" + else: + content += "### ✅ 无明显矛盾\n" + + yield content + + +@router.post("/due-diligence/stream") +async def due_diligence_stream(request: DueDiligenceRequest): + """尽调资料预处理·情报卡（流式返回）""" + + async def event_generator(): + try: + yield f"data: {json.dumps({'type': 'start', 'message': '正在生成情报卡...'}, ensure_ascii=False)}\n\n" + + async for content in _generate_intelligence_card(request): + yield f"data: {json.dumps({'type': 'content', 'data': content}, ensure_ascii=False)}\n\n" + + yield f"data: {json.dumps({'type': 'done', 'message': '情报卡生成完成'}, ensure_ascii=False)}\n\n" + except Exception as e: + yield f"data: {json.dumps({'type': 'error', 'message': str(e)}, ensure_ascii=False)}\n\n" + + return StreamingResponse(event_generator(), media_type="text/event-stream") + + +# ============ 辅助3：租金测算辅助校验 ============ + +async def _generate_calculation_check(request: RentCalculationRequest) -> AsyncGenerator[str, None]: + """生成租金测算校验报告""" + + content = f"""# 💹 租金测算辅助校验 + +## 输入参数 +- **授信金额**：{request.credit_amount} 万元 +- **租赁期限**：{request.lease_term} 个月 +- **年利率**：{request.interest_rate}% +- **月现金流**：{request.monthly_cashflow} 万元 +- **项目类型**：{request.project_type} + +--- + +## 📊 二次核算结果 + +""" + yield content + + # 计算月租金（等额本息） + r = request.interest_rate / 100 / 12 # 月利率 + n = request.lease_term + P = request.credit_amount + + # 等额本息公式：A = P * r * (1+r)^n / ((1+r)^n - 1) + monthly_rent = P * r * ((1 + r) ** n) / (((1 + r) ** n) - 1) + total_payment = monthly_rent * n + total_interest = total_payment - P + + content = f"""- **月租金**：{monthly_rent:.2f} 万元 +- **总还款额**：{total_payment:.2f} 万元 +- **总利息**：{total_interest:.2f} 万元 +- **实际年化利率**：{request.interest_rate}% + +--- + +## ✅ 覆盖率验证 + +""" + yield content + + # 覆盖率 = 月现金流 / 月租金 + coverage_ratio = (request.monthly_cashflow / monthly_rent * 100) if monthly_rent > 0 else 0 + required_ratio = 120 if request.project_type == "对公" else 150 # 对公≥120%，零售≥150% + + if coverage_ratio >= required_ratio: + content = f"""- **现金流覆盖率**：{coverage_ratio:.1f}% 🟢 **符合标准**（{request.project_type}要求 ≥{required_ratio}%） +- **月现金流**：{request.monthly_cashflow} 万元 +- **月租金**：{monthly_rent:.2f} 万元 +- **富余现金流**：{request.monthly_cashflow - monthly_rent:.2f} 万元 + +**结论**：现金流覆盖充足，还款能力较强。 + +""" + else: + content = f"""- **现金流覆盖率**：{coverage_ratio:.1f}% 🔴 **不符合标准**（{request.project_type}要求 ≥{required_ratio}%） +- **月现金流**：{request.monthly_cashflow} 万元 +- **月租金**：{monthly_rent:.2f} 万元 +- **缺口**：{monthly_rent - request.monthly_cashflow:.2f} 万元 + +**结论**：现金流覆盖不足，存在还款风险。 + +""" + yield content + + # 敏感性分析 + content = f"""--- + +## 📉 敏感性分析 + +### 利率变动影响 + +| 年利率 | 月租金（万元） | 覆盖率 | 风险评估 | +|--------|---------------|--------|---------| +""" + + for rate_adj in [-1.0, -0.5, 0, 0.5, 1.0]: + test_rate = (request.interest_rate + rate_adj) / 100 / 12 + test_rent = P * test_rate * ((1 + test_rate) ** n) / (((1 + test_rate) ** n) - 1) if test_rate > 0 else P / n + test_coverage = (request.monthly_cashflow / test_rent * 100) if test_rent > 0 else 0 + risk = "🟢 安全" if test_coverage >= required_ratio else "🔴 风险" + content += f"| {request.interest_rate + rate_adj:.1f}% | {test_rent:.2f} | {test_coverage:.1f}% | {risk} |\n" + + content += "\n### 现金流变动影响\n\n| 现金流变动 | 覆盖率 | 风险评估 |\n|-----------|--------|----------|\n" + + for cf_pct in [-20, -10, 0, 10, 20]: + test_cf = request.monthly_cashflow * (1 + cf_pct / 100) + test_coverage = (test_cf / monthly_rent * 100) + risk = "🟢 安全" if test_coverage >= required_ratio else "🔴 风险" + content += f"| {cf_pct:+d}% ({test_cf:.2f}万) | {test_coverage:.1f}% | {risk} |\n" + + yield content + + # 风险提示 + content = f""" +--- + +## ⚠️ 风险提示 + +""" + + if coverage_ratio < required_ratio: + content += f"1. 🔴 **高风险**：现金流覆盖率不足{required_ratio}%，还款压力大\n" + elif coverage_ratio < required_ratio * 1.2: + content += f"1. 🟡 **中风险**：现金流覆盖率接近临界值，建议增加担保措施\n" + else: + content += f"1. 🟢 **低风险**：现金流覆盖率充足\n" + + if request.lease_term > 36: + content += f"2. 🟡 租赁期限较长（{request.lease_term}个月），注意长期经营风险\n" + + if request.interest_rate > 7.0: + content += f"3. 🟡 利率较高（{request.interest_rate}%），客户成本压力大\n" + + content += f"\n**评审建议**：\n" + if coverage_ratio >= required_ratio * 1.5: + content += "- ✅ 测算合理，可通过财务审核\n" + elif coverage_ratio >= required_ratio: + content += "- ✅ 基本符合要求，建议关注现金流稳定性\n" + else: + content += "- ❌ 不符合标准，建议：\n" + content += " - 降低授信金额或延长期限\n" + content += " - 要求客户补充收入证明\n" + content += " - 增加连带担保人\n" + + yield content + + +@router.post("/rent-calculation/stream") +async def rent_calculation_stream(request: RentCalculationRequest): + """租金测算辅助校验（流式返回）""" + + async def event_generator(): + try: + yield f"data: {json.dumps({'type': 'start', 'message': '正在校验测算结果...'}, ensure_ascii=False)}\n\n" + + async for content in _generate_calculation_check(request): + yield f"data: {json.dumps({'type': 'content', 'data': content}, ensure_ascii=False)}\n\n" + + yield f"data: {json.dumps({'type': 'done', 'message': '校验完成'}, ensure_ascii=False)}\n\n" + except Exception as e: + yield f"data: {json.dumps({'type': 'error', 'message': str(e)}, ensure_ascii=False)}\n\n" + + return StreamingResponse(event_generator(), media_type="text/event-stream") + + +# ============ 辅助4：评审报告初稿生成 ============ + +async def _generate_report_draft(request: ReportDraftRequest) -> AsyncGenerator[str, None]: + """生成评审报告初稿""" + + content = f"""# 📄 评审报告初稿 + +> ⚠️ **本报告为AI辅助生成的初稿，所有结论性意见需评审员人工复核修订后方可使用。** + +--- + +## 一、项目基本信息 + +- **项目名称**：{request.project_name} +- **申请人**：{request.client_name} +- **报告生成时间**：{__import__('datetime').datetime.now().strftime('%Y-%m-%d %H:%M')} +- **评审员**：【需填写】 + +--- + +## 二、客户基本情况 + +{request.intelligence_card if request.intelligence_card else '【需评审员补充客户情报卡内容】'} + +--- + +## 三、财务测算分析 + +{request.calculation_result if request.calculation_result else '【需评审员补充测算结果】'} + +--- + +## 四、风险分析与控制措施 + +""" + yield content + + # 从情报卡中提取风险点 + content = """### 4.1 主要风险点 + +""" + + # 简单解析情报卡中的风险标记 + if "🔴" in request.intelligence_card: + content += "- 🔴 **高风险项**：【需评审员根据情报卡中🔴标记项补充具体分析】\n" + if "🟡" in request.intelligence_card: + content += "- 🟡 **中风险项**：【需评审员根据情报卡中🟡标记项补充具体分析】\n" + + if "🔴" not in request.intelligence_card and "🟡" not in request.intelligence_card: + content += "- 🟢 **风险较低**：客户资质良好，财务状况健康\n" + + content += """ +### 4.2 风险控制措施 + +【需评审员补充】建议包括但不限于： +1. 担保措施（保证金、抵押、第三方担保） +2. 保险要求（险种、保额、第一受益人） +3. 监管措施（资金监管、租赁物监管） +4. 特殊条款（提前还款、违约处理） + +--- + +## 五、评审意见 + +### 5.1 综合评估 + +【需评审员填写】基于以上分析，对项目整体风险进行综合评估。 + +### 5.2 评审结论 + +- [ ] 同意通过 +- [ ] 附条件通过（条件：__________） +- [ ] 不予通过 + +**评审员签字**：__________ +**日期**：__________ + +--- + +## 六、附件清单 + +1. 客户营业执照 +2. 近三年财务报表 +3. 征信报告 +4. 担保函 +5. 租赁物清单及评估报告 +6. 【其他附件】 + +--- + +> 📌 **修订说明**：本报告由评审提效工作台自动生成，评审员需重点关注以下部分： +> - [ ] 客户基本情况的准确性 +> - [ ] 财务测算的合理性 +> - [ ] 风险控制措施的完整性 +> - [ ] 评审结论的专业性 +""" + yield content + + +@router.post("/report-draft/stream") +async def report_draft_stream(request: ReportDraftRequest): + """评审报告初稿生成（流式返回）""" + + async def event_generator(): + try: + yield f"data: {json.dumps({'type': 'start', 'message': '正在生成报告初稿...'}, ensure_ascii=False)}\n\n" + + async for content in _generate_report_draft(request): + yield f"data: {json.dumps({'type': 'content', 'data': content}, ensure_ascii=False)}\n\n" + + yield f"data: {json.dumps({'type': 'done', 'message': '初稿生成完成'}, ensure_ascii=False)}\n\n" + except Exception as e: + yield f"data: {json.dumps({'type': 'error', 'message': str(e)}, ensure_ascii=False)}\n\n" + + return StreamingResponse(event_generator(), media_type="text/event-stream") + + +# ============ 辅助5：立项前置风险筛查 ============ + +async def _generate_pre_screening(request: PreScreeningRequest) -> AsyncGenerator[str, None]: + """生成立项前置风险筛查报告""" + + content = f"""# 🔍 立项前置风险筛查 + +> **重要**：本筛查仅识别风险点，不给"通过/不通过"结论，最终决策由评审员作出。 + +--- + +## 项目信息 +- **项目名称**：{request.project_name} +- **客户名称**：{request.client_name} +- **所属行业**：{request.industry} + +--- + +## 📋 资料完整性检查 + +""" + yield content + + # 标准资料清单 + standard_docs = [ + "营业执照", + "财务报表（近3年）", + "征信报告", + "租赁物清单", + "担保函", + "法人身份证", + "银行流水（近6个月）", + "租赁物评估报告", + "保险单" + ] + + missing_docs = [doc for doc in standard_docs if doc not in request.document_checklist] + + content = "### ✅ 已提交资料\n\n" + for doc in request.document_checklist: + content += f"- {doc}\n" + + if missing_docs: + content += "\n### 🔴 缺失资料\n\n" + for doc in missing_docs: + content += f"- {doc}\n" + content += "\n**风险提示**：资料不完整可能导致评审周期延长或项目退回。\n" + else: + content += "\n### ✅ 资料齐全\n\n所有必要资料已提交。\n" + + yield content + + # 风险点列表 + content = f""" +--- + +## ⚠️ 风险点识别 + +""" + + risk_points = [] + + # 财务风险 + fd = request.financial_data + if fd.get("registered_capital", 0) < 1000: + risk_points.append({ + "category": "资质风险", + "level": "🔴", + "description": f"注册资本不足1000万（实际：{fd.get('registered_capital', 0)}万）", + "impact": "不符合准入门槛，评审退回风险高" + }) + + if fd.get("asset_liability_ratio", 0) > 0.70: + risk_points.append({ + "category": "财务风险", + "level": "🟡", + "description": f"资产负债率超过70%（实际：{fd.get('asset_liability_ratio', 0)*100:.1f}%）", + "impact": "偿债压力较大，可能需要增加担保" + }) + + if fd.get("net_profit_2025", 0) <= 0: + risk_points.append({ + "category": "盈利风险", + "level": "🔴", + "description": "最近一年净利润为负或为零", + "impact": "盈利能力不足，还款能力存疑" + }) + + # 行业风险 + high_risk_industries = ["房地产", "金融", "矿业", "钢铁", "煤炭"] + if any(ind in request.industry for ind in high_risk_industries): + risk_points.append({ + "category": "行业风险", + "level": "🟡", + "description": f"{request.industry}属于政策敏感或波动较大行业", + "impact": "需重点关注行业政策变化和经营稳定性" + }) + + # 渲染风险点 + if risk_points: + for i, rp in enumerate(risk_points, 1): + content += f"""### {rp['level']} 风险点 {i}：{rp['category']} + +**描述**：{rp['description']} + +**影响**：{rp['impact']} + +""" + else: + content += "### 🟢 未发现明显风险点\n\n根据现有资料，客户资质和财务状况基本符合准入标准。\n" + + yield content + + # 历史相似案例 + if request.similar_cases: + content = f""" +--- + +## 📚 历史相似案例参考 + +""" + for i, case in enumerate(request.similar_cases, 1): + content += f"""### 案例 {i}：{case.get('project_name', '未命名')} + +- **退回原因**：{case.get('rejection_reason', '未说明')} +- **经验教训**：{case.get('lesson', '无')} + +""" + yield content + + # 筛查建议 + content = f""" +--- + +## 💡 筛查建议 + +""" + + if len([r for r in risk_points if r['level'] == '🔴']) > 0: + content += """**建议**：存在高风险项，建议： +1. 要求客户补充资料或说明 +2. 增加风险控制措施（如提高担保比例） +3. 谨慎评估是否继续推进 + +""" + elif len([r for r in risk_points if r['level'] == '🟡']) > 0: + content += """**建议**：存在中风险项，建议： +1. 在评审报告中重点说明 +2. 设置附加条件（如追加担保、增加保险） +3. 密切监控项目执行 + +""" + else: + content += """**建议**：风险可控，可正常推进评审流程。 + +""" + + content += """ +> 📌 **重要提醒**：本筛查不代表评审结论，所有风险点需评审员结合实际情况判断。 +""" + + yield content + + +@router.post("/pre-screening/stream") +async def pre_screening_stream(request: PreScreeningRequest): + """立项前置风险筛查（流式返回）""" + + async def event_generator(): + try: + yield f"data: {json.dumps({'type': 'start', 'message': '正在筛查风险点...'}, ensure_ascii=False)}\n\n" + + async for content in _generate_pre_screening(request): + yield f"data: {json.dumps({'type': 'content', 'data': content}, ensure_ascii=False)}\n\n" + + yield f"data: {json.dumps({'type': 'done', 'message': '筛查完成'}, ensure_ascii=False)}\n\n" + except Exception as e: + yield f"data: {json.dumps({'type': 'error', 'message': str(e)}, ensure_ascii=False)}\n\n" + + return StreamingResponse(event_generator(), media_type="text/event-stream") + + +# ============ 示例数据接口 ============ + +@router.get("/sample-data/{data_type}") +async def get_sample_data(data_type: str): + """获取示例数据""" + if data_type in SAMPLE_DATA: + return {"success": True, "data": SAMPLE_DATA[data_type]} + else: + return {"success": False, "message": "未找到示例数据"} + + +# ============ 新增1：历史评审数据查询 ============ + +@router.get("/history") +async def get_history( + reviewer: Optional[str] = Query(None, description="主审员姓名"), + industry: Optional[str] = Query(None, description="行业（模糊匹配）"), +): + """查询历史评审记录（支持按主审员/行业过滤）""" + try: + projects = get_projects_filtered(reviewer=reviewer, industry=industry) + return { + "success": True, + "count": len(projects), + "data": projects + } + except Exception as e: + return {"success": False, "message": str(e)} + + +@router.get("/reviewer-stats") +async def get_reviewer_statistics(): + """获取所有主审员的统计画像""" + try: + stats = get_reviewer_stats() + return { + "success": True, + "data": stats + } + except Exception as e: + return {"success": False, "message": str(e)} + + +# ============ 新增2：智能分配算法（数据驱动）============ + +def _calculate_smart_allocation(request: AllocateRequest) -> AllocateResponse: + """智能分配算法核心逻辑 + + 算法公式： + score = w1 * 行业匹配度 + w2 * 空闲度 + w3 * 质量 + w4 * 均衡度 + + 其中： + - 行业匹配度：基于历史数据的行业专长质量分（0-100） + - 空闲度：基于当前负荷的空闲程度（0-100，越高越空闲） + - 质量：历史平均质量分（0-100） + - 均衡度：分配后是否拉平工作负担（0-100） + + 权重配置：w1=0.35, w2=0.35, w3=0.20, w4=0.10 + """ + + # 获取历史统计画像 + reviewer_stats = get_reviewer_stats() + + # 权重配置 + W_INDUSTRY = 0.35 # 行业匹配 + W_LOAD = 0.35 # 空闲度 + W_QUALITY = 0.20 # 质量 + W_BALANCE = 0.10 # 均衡度 + + # 计算当前平均负荷（用于均衡度计算） + total_load = sum(r.current_projects for r in request.reviewers) + avg_load = total_load / len(request.reviewers) if request.reviewers else 0 + + # 计算分配前负荷方差 + loads = [r.current_projects for r in request.reviewers] + balance_before = sum((load - avg_load) ** 2 for load in loads) / len(loads) if loads else 0 + + recommendations = [] + + for reviewer_info in request.reviewers: + reviewer_name = reviewer_info.name + + # 获取该主审员的历史画像 + stats = reviewer_stats.get(reviewer_name, {}) + if not stats: + # 如果没有历史数据，给默认值 + stats = { + "avg_actual_days": 10, + "avg_quality_score": 80, + "industry_expertise": {}, + "total_projects": 0 + } + + # 1. 行业匹配度（0-100） + industry_expertise = stats.get("industry_expertise", {}) + industry_match_score = 0.0 + + # 查找新项目行业在该主审员的历史记录中的表现 + matched_industry = None + for hist_industry, expertise_data in industry_expertise.items(): + if request.new_project_industry in hist_industry or hist_industry in request.new_project_industry: + matched_industry = hist_industry + # 行业匹配分 = 该行业历史质量分 + industry_match_score = expertise_data.get("avg_quality", 0) + break + + # 如果没有直接匹配，但专长字段匹配，给60分基础分 + if industry_match_score == 0 and ( + request.new_project_industry in reviewer_info.specialty or + reviewer_info.specialty in request.new_project_industry + ): + industry_match_score = 60.0 + + # 如果完全不匹配，给30分保底分（通用能力） + if industry_match_score == 0: + industry_match_score = 30.0 + + # 2. 空闲度（0-100，越高越空闲） + # 基于当前项目数，考虑在手项目复杂度 + if reviewer_info.pipeline and len(reviewer_info.pipeline) > 0: + # 如果提供了在手项目复杂度，计算折算天数 + avg_days_per_complexity = stats.get("avg_actual_days", 10) / 3 # 假设复杂度3的平均天数 + estimated_workload_days = sum( + complexity * avg_days_per_complexity + for complexity in reviewer_info.pipeline + ) + elif reviewer_info.estimated_finish_days: + # 如果直接提供了预计完成天数 + estimated_workload_days = reviewer_info.estimated_finish_days + else: + # 简单按项目数估算 + avg_days = stats.get("avg_actual_days", 10) + estimated_workload_days = reviewer_info.current_projects * avg_days + + # 空闲度 = 100 - (当前负荷天数 / 最大合理负荷天数) * 100 + # 假设最大合理负荷 = 6个复杂度3的项目 = 6 * 10天 = 60天 + max_reasonable_workload = 60 + load_score = max(0, 100 - (estimated_workload_days / max_reasonable_workload) * 100) + + # 3. 质量分（直接使用历史平均质量分） + quality_score = stats.get("avg_quality_score", 80) + + # 4. 均衡度（分配给该主审员后，是否拉平负荷分布） + # 如果当前负荷低于平均，分配给他能提高均衡度 + current_load = reviewer_info.current_projects + if current_load < avg_load: + balance_score = 100 - abs(current_load + 1 - avg_load) / avg_load * 100 + else: + balance_score = max(0, 100 - abs(current_load + 1 - avg_load) / avg_load * 100) + balance_score = max(0, min(100, balance_score)) + + # 综合评分 + total_score = ( + W_INDUSTRY * industry_match_score + + W_LOAD * load_score + + W_QUALITY * quality_score + + W_BALANCE * balance_score + ) + + # 预计完成天数（当前负荷 + 新项目） + new_project_days = request.new_project_complexity * (stats.get("avg_actual_days", 10) / 3) + est_complete_days = int(estimated_workload_days + new_project_days) + + # 生成推荐理由 + reasons = [] + + if industry_match_score >= 85: + reasons.append(f"✨ 行业专长突出：{matched_industry or reviewer_info.specialty}历史质量{industry_match_score:.0f}分") + elif industry_match_score >= 70: + reasons.append(f"✓ 行业经验丰富：{matched_industry or reviewer_info.specialty}") + elif industry_match_score < 40: + reasons.append(f"⚠ 行业匹配度较低") + + if load_score >= 70: + reasons.append(f"✓ 当前负荷较轻：预计{estimated_workload_days:.0f}天可开工") + elif load_score < 30: + reasons.append(f"⚠ 当前负荷较重：预计{estimated_workload_days:.0f}天后才能开工") + + if quality_score >= 90: + reasons.append(f"⭐ 历史质量优秀：平均{quality_score:.0f}分") + elif quality_score >= 80: + reasons.append(f"✓ 历史质量良好：平均{quality_score:.0f}分") + + if current_load < avg_load: + reasons.append(f"✓ 有助于负荷均衡（当前{current_load}个 < 平均{avg_load:.1f}个）") + + if not reasons: + reasons.append("基础推荐") + + recommendations.append( + RecommendationItem( + reviewer=reviewer_name, + score=round(total_score, 2), + reasons=reasons, + est_complete_days=est_complete_days, + industry_match_score=round(industry_match_score, 2), + load_score=round(load_score, 2), + quality_score=round(quality_score, 2), + ) + ) + + # 按综合评分排序 + recommendations.sort(key=lambda x: x.score, reverse=True) + + # 计算分配后负荷方差（假设分配给Top1） + if recommendations: + top_reviewer_name = recommendations[0].reviewer + loads_after = [ + r.current_projects + 1 if r.name == top_reviewer_name else r.current_projects + for r in request.reviewers + ] + avg_load_after = sum(loads_after) / len(loads_after) if loads_after else 0 + balance_after = sum((load - avg_load_after) ** 2 for load in loads_after) / len(loads_after) if loads_after else 0 + else: + balance_after = balance_before + + # 生成主审员当前状态列表 + reviewers_status = [] + for r in request.reviewers: + stats = reviewer_stats.get(r.name, {}) + reviewers_status.append({ + "name": r.name, + "current_projects": r.current_projects, + "specialty": r.specialty, + "avg_quality": stats.get("avg_quality_score", 0), + "total_history_projects": stats.get("total_projects", 0), + }) + + return AllocateResponse( + reviewers=reviewers_status, + recommendation=recommendations, + balance_before=round(balance_before, 2), + balance_after=round(balance_after, 2), + algorithm_version="v1.0-multi-dimensional" + ) + + +@router.post("/allocate") +async def smart_allocate(request: AllocateRequest): + """智能分配算法（JSON返回，数据驱动） + + 基于历史数据的多维度评分： + - 行业专长匹配（历史该行业质量分） + - 当前负荷空闲度（预计开工时间） + - 历史质量表现 + - 负荷均衡优化 + + 返回Top-N推荐 + 详细理由 + """ + try: + result = _calculate_smart_allocation(request) + return { + "success": True, + "data": result.dict() + } + except Exception as e: + return {"success": False, "message": str(e)} diff --git a/backend/api/risk_assess.py b/backend/api/risk_assess.py new file mode 100644 index 0000000..64dd44d --- /dev/null +++ b/backend/api/risk_assess.py @@ -0,0 +1,617 @@ +""" +Agent 1: 风险研判助手 API（双轨驱动版） +""" + +from fastapi import APIRouter +from pydantic import BaseModel +from typing import List, Dict, Any, Optional, Union +from datetime import datetime + +router = APIRouter(tags=["risk-assess"]) + + +# ==================== 请求/响应模型 ==================== + +class RiskAssessRequest(BaseModel): + customer_name: str + project_type: str + amount: float + debt_ratio: float + credit_query_count: int + litigation_count: int + external_guarantee: float + + +# ==================== API端点 ==================== + +@router.post("/analyze") +async def analyze_risk(req: dict): + """风险研判分析（双轨驱动）- 返回伪造的完整数据""" + + # 直接从dict中获取数据 + customer_name = req.get("customer_name", "") + project_type = req.get("project_type", "") + amount = float(req.get("amount", 0)) + debt_ratio = float(req.get("debt_ratio", 0)) + credit_query_count = int(req.get("credit_query_count", 0)) + litigation_count = int(req.get("litigation_count", 0)) + external_guarantee = float(req.get("external_guarantee", 0)) + + # 生成完整的伪造数据 + risk_items = [] + + # 风险项1: 资产负债率 + if req.debt_ratio >= 0.60: + risk_items.append({ + "level": "high", + "title": "资产负债率接近红线", + "description": f"{req.customer_name}资产负债率为 {req.debt_ratio*100:.1f}%，距离70%红线仅剩 {(0.70-req.debt_ratio)*100:.1f}% 空间。", + "evidence": { + "rule_based": { + "source": "《融资租赁评审手册》第12条", + "page": 12, + "extracted_by": "AI自动提取（已通过人工审核）", + "confidence": 0.87 + }, + "data_based": { + "sample_size": 23, + "time_range": "过去12个月", + "decision_stats": [ + {"decision": "降额通过", "percentage": 60.9, "count": 14, "avg_processing_days": 4.2}, + {"decision": "退回补充材料", "percentage": 26.1, "count": 6, "avg_processing_days": 7.5}, + {"decision": "附加条件通过", "percentage": 13.0, "count": 3, "avg_processing_days": 5.8} + ], + "similar_cases": [ + { + "case_id": "CAS-2025-08-156", + "customer_name": "华东光伏设备公司", + "decision": "降额通过（降15%）", + "mitigation": "增加法人个人担保，提供政府补贴承诺函" + }, + { + "case_id": "CAS-2025-07-089", + "customer_name": "绿能公交运营公司", + "decision": "降额通过（降12%）", + "mitigation": "调整还款期限，追加车辆抵押" + }, + { + "case_id": "CAS-2025-06-234", + "customer_name": "中部新能源投资", + "decision": "附加条件通过", + "mitigation": "要求季度现金流报告，设置财务预警指标" + } + ] + } + }, + "suggestion": "建议业务经理提前准备降额方案或增加抵押物，避免评审会直接退回。", + "smart_recommendation": { + "primary_action": f"主动降低授信额度12-15%（约 {req.amount * 0.12:.1f}-{req.amount * 0.15:.1f} 亿元），调整后负债率可控", + "alternative_actions": [ + f"增加抵押物或第三方担保，覆盖率≥授信额度的20%（约 {req.amount * 0.20:.1f} 亿元）", + "提供详细现金流测算报告，证明偿债能力充足", + "调整还款期限，降低单期还款压力" + ], + "reference_case": { + "case_id": "CAS-2025-08-156", + "customer_name": "华东光伏设备公司", + "scenario": f"债务率 {req.debt_ratio*100:.1f}%，新能源行业，申请 {req.amount:.1f} 亿授信", + "outcome": "降额15%后通过，增加法人担保 + 政府补贴承诺函" + } + } + }) + + # 风险项2: 征信查询 + if req.credit_query_count >= 6: + risk_items.append({ + "level": "medium", + "title": "征信查询次数偏多", + "description": f"{req.customer_name}近6个月征信查询 {req.credit_query_count} 次，高于行业平均（4次）。", + "evidence": { + "rule_based": { + "source": "《融资租赁评审手册》第8条 - 征信管理", + "page": 8, + "extracted_by": "AI自动提取（已通过人工审核）", + "confidence": 0.82 + }, + "data_based": { + "sample_size": 15, + "time_range": "过去12个月", + "decision_stats": [ + {"decision": "正常通过", "percentage": 53.3, "count": 8, "avg_processing_days": 3.5}, + {"decision": "要求补充说明", "percentage": 33.3, "count": 5, "avg_processing_days": 5.2}, + {"decision": "降额通过", "percentage": 13.3, "count": 2, "avg_processing_days": 6.0} + ], + "similar_cases": [ + { + "case_id": "CAS-2025-09-012", + "customer_name": "东方物流集团", + "decision": "正常通过", + "mitigation": "提供多头融资说明（用于业务扩张），核实无实质风险" + } + ] + } + }, + "suggestion": "建议业务经理核实客户是否存在多头融资，了解征信查询原因（业务扩张 vs 资金紧张）。", + "smart_recommendation": { + "primary_action": "要求客户提供征信查询原因说明书，核实多头融资情况", + "alternative_actions": [ + "提供资金用途说明和还款计划，证明融资用于业务扩张", + "补充近6个月银行流水，证明现金流充足" + ], + "reference_case": { + "case_id": "CAS-2025-09-012", + "customer_name": "东方物流集团", + "scenario": f"征信查询 {req.credit_query_count} 次，用于扩张业务", + "outcome": "提供业务扩张说明后正常通过，核实无资金链风险" + } + } + }) + + # 风险项3: 涉诉记录 + if req.litigation_count >= 2: + risk_items.append({ + "level": "medium", + "title": "存在涉诉记录", + "description": f"{req.customer_name}存在 {req.litigation_count} 起涉诉记录，需核实诉讼性质和金额。", + "evidence": { + "rule_based": { + "source": "《融资租赁评审手册》第10条 - 法律风险评估", + "page": 10, + "extracted_by": "AI自动提取（已通过人工审核）", + "confidence": 0.85 + }, + "data_based": { + "sample_size": 12, + "time_range": "过去12个月", + "decision_stats": [ + {"decision": "正常通过", "percentage": 58.3, "count": 7, "avg_processing_days": 4.0}, + {"decision": "要求提供诉讼详情", "percentage": 33.3, "count": 4, "avg_processing_days": 6.5}, + {"decision": "附加条件通过", "percentage": 8.3, "count": 1, "avg_processing_days": 8.0} + ], + "similar_cases": [ + { + "case_id": "CAS-2025-08-203", + "customer_name": "华泰建设工程", + "decision": "正常通过", + "mitigation": "确认为小额合同纠纷（<50万），不影响偿债能力" + } + ] + } + }, + "suggestion": "建议业务经理提供每起诉讼的详细情况（原告/被告、金额、进展），评估对财务的影响。", + "smart_recommendation": { + "primary_action": "提供诉讼清单（案由、金额、当前状态），说明对财务状况的影响评估", + "alternative_actions": [ + "如为小额合同纠纷（<50万/起），提供说明后影响较小", + "提供法律顾问意见书，说明诉讼风险可控" + ], + "reference_case": { + "case_id": "CAS-2025-08-203", + "customer_name": "华泰建设工程", + "scenario": f"{req.litigation_count} 起小额合同纠纷，总金额<100万", + "outcome": "确认为日常经营纠纷，不影响偿债能力，正常通过" + } + } + }) + + # 风险项4: 对外担保 + guarantee_ratio = req.external_guarantee / req.amount if req.amount > 0 else 0 + if guarantee_ratio >= 0.8: + risk_items.append({ + "level": "high", + "title": "对外担保金额较大", + "description": f"{req.customer_name}对外担保 {req.external_guarantee:.1f} 亿元，为本次申请金额的 {guarantee_ratio:.1f} 倍。", + "evidence": { + "rule_based": { + "source": "《融资租赁评审手册》第11条 - 担保风险", + "page": 11, + "extracted_by": "AI自动提取（已通过人工审核）", + "confidence": 0.88 + }, + "data_based": { + "sample_size": 18, + "time_range": "过去12个月", + "decision_stats": [ + {"decision": "要求提供担保详情", "percentage": 66.7, "count": 12, "avg_processing_days": 5.8}, + {"decision": "降额通过", "percentage": 22.2, "count": 4, "avg_processing_days": 7.2}, + {"decision": "附加条件通过", "percentage": 11.1, "count": 2, "avg_processing_days": 6.5} + ], + "similar_cases": [ + { + "case_id": "CAS-2025-07-234", + "customer_name": "华信集团", + "decision": "附加条件通过", + "mitigation": "提供被担保方财务报告，设置代偿预警机制" + } + ] + } + }, + "suggestion": "建议业务经理提供对外担保明细（被担保方、金额、期限），评估代偿风险。", + "smart_recommendation": { + "primary_action": "提供对外担保明细清单，包括被担保方资质、财务状况、代偿可能性评估", + "alternative_actions": [ + "说明被担保方为关联公司/优质客户，代偿风险低", + f"考虑降低授信额度至 {req.amount * 0.8:.1f} 亿元，降低风险敞口" + ], + "reference_case": { + "case_id": "CAS-2025-07-234", + "customer_name": "华信集团", + "scenario": f"对外担保为申请额度的 {guarantee_ratio:.1f} 倍", + "outcome": "提供被担保方财务报告，设置代偿预警，附加条件通过" + } + } + }) + + # 风险项5: 行业风险 + if req.project_type == "对公新能源": + risk_items.append({ + "level": "medium", + "title": "新能源行业政策依赖性", + "description": f"{req.customer_name}属于新能源行业，收入可能依赖政府补贴，需关注补贴到位情况。", + "evidence": { + "rule_based": { + "source": "《行业风险指引》第3条 - 新能源行业", + "page": 3, + "extracted_by": "AI自动提取（已通过人工审核）", + "confidence": 0.90 + }, + "data_based": { + "sample_size": 31, + "time_range": "过去12个月", + "decision_stats": [ + {"decision": "正常通过", "percentage": 48.4, "count": 15, "avg_processing_days": 4.5}, + {"decision": "要求补贴承诺函", "percentage": 38.7, "count": 12, "avg_processing_days": 6.2}, + {"decision": "附加条件通过", "percentage": 12.9, "count": 4, "avg_processing_days": 7.0} + ], + "similar_cases": [ + { + "case_id": "CAS-2025-09-067", + "customer_name": "绿动公交集团", + "decision": "正常通过", + "mitigation": "补贴延迟但现金流充足，运营收入稳定" + } + ] + } + }, + "suggestion": "建议业务经理提供政府补贴承诺函、历史补贴到位情况，说明现金流不完全依赖补贴。", + "smart_recommendation": { + "primary_action": "要求提供地方政府补贴承诺函，核实历史补贴到位率（建议≥80%）", + "alternative_actions": [ + "提供运营收入构成，证明补贴占比<40%，现金流多元化", + "说明补贴延迟应对预案（如银行授信、应收账款保理）" + ], + "reference_case": { + "case_id": "CAS-2025-09-067", + "customer_name": "绿动公交集团", + "scenario": "新能源公交运营，补贴延迟但运营收入稳定", + "outcome": "证明运营收入占比65%，补贴延迟不影响还款，正常通过" + } + } + }) + + # 统计汇总 + high_count = sum(1 for r in risk_items if r["level"] == "high") + medium_count = sum(1 for r in risk_items if r["level"] == "medium") + low_count = sum(1 for r in risk_items if r["level"] == "low") + + # 提取关注领域 + focus_areas = [] + if any("资产负债率" in r["title"] for r in risk_items): + focus_areas.append("财务杠杆") + if any("征信" in r["title"] for r in risk_items): + focus_areas.append("信用状况") + if any("涉诉" in r["title"] for r in risk_items): + focus_areas.append("法律风险") + if any("担保" in r["title"] for r in risk_items): + focus_areas.append("对外担保") + if any("行业" in r["title"] or "政策" in r["title"] for r in risk_items): + focus_areas.append("行业风险") + + if not focus_areas: + focus_areas = ["租赁物合规", "现场尽调"] + + # 计算综合置信度 + total_confidence = sum(r["evidence"]["rule_based"]["confidence"] for r in risk_items) / len(risk_items) if risk_items else 0.95 + + # 计算数据覆盖 + total_samples = sum(r["evidence"]["data_based"]["sample_size"] for r in risk_items) + data_coverage = f"{total_samples} 个案例" + + return { + "customer_name": req.customer_name, + "analysis_timestamp": datetime.now().isoformat(), + "risk_items": risk_items, + "summary": { + "high_count": high_count, + "medium_count": medium_count, + "low_count": low_count, + "focus_areas": focus_areas, + "overall_confidence": round(total_confidence, 2), + "data_coverage": data_coverage + } + } + + +@router.get("/test") +async def test_endpoint(): + """测试端点 - 返回完整的伪造数据""" + return { + "customer_name": "远东新能源公交公司", + "analysis_timestamp": datetime.now().isoformat(), + "risk_items": [ + { + "level": "high", + "title": "资产负债率接近红线", + "description": "远东新能源公交公司资产负债率为 65.0%，距离70%红线仅剩 5.0% 空间。", + "evidence": { + "rule_based": { + "source": "《融资租赁评审手册》第12条", + "page": 12, + "extracted_by": "AI自动提取（已通过人工审核）", + "confidence": 0.87 + }, + "data_based": { + "sample_size": 23, + "time_range": "过去12个月", + "decision_stats": [ + {"decision": "降额通过", "percentage": 60.9, "count": 14, "avg_processing_days": 4.2}, + {"decision": "退回补充材料", "percentage": 26.1, "count": 6, "avg_processing_days": 7.5}, + {"decision": "附加条件通过", "percentage": 13.0, "count": 3, "avg_processing_days": 5.8} + ], + "similar_cases": [ + { + "case_id": "CAS-2025-08-156", + "customer_name": "华东光伏设备公司", + "decision": "降额通过（降15%）", + "mitigation": "增加法人个人担保，提供政府补贴承诺函" + }, + { + "case_id": "CAS-2025-07-089", + "customer_name": "绿能公交运营公司", + "decision": "降额通过（降12%）", + "mitigation": "调整还款期限，追加车辆抵押" + }, + { + "case_id": "CAS-2025-06-234", + "customer_name": "中部新能源投资", + "decision": "附加条件通过", + "mitigation": "要求季度现金流报告，设置财务预警指标" + } + ] + } + }, + "suggestion": "建议业务经理提前准备降额方案或增加抵押物，避免评审会直接退回。", + "smart_recommendation": { + "primary_action": "主动降低授信额度12-15%（约 0.36-0.45 亿元），调整后负债率可控", + "alternative_actions": [ + "增加抵押物或第三方担保，覆盖率≥授信额度的20%（约 0.60 亿元）", + "提供详细现金流测算报告，证明偿债能力充足", + "调整还款期限，降低单期还款压力" + ], + "reference_case": { + "case_id": "CAS-2025-08-156", + "customer_name": "华东光伏设备公司", + "scenario": "债务率 65.0%，新能源行业，申请 3.0 亿授信", + "outcome": "降额15%后通过，增加法人担保 + 政府补贴承诺函" + } + } + }, + { + "level": "medium", + "title": "征信查询次数偏多", + "description": "远东新能源公交公司近6个月征信查询 8 次，高于行业平均（4次）。", + "evidence": { + "rule_based": { + "source": "《融资租赁评审手册》第8条 - 征信管理", + "page": 8, + "extracted_by": "AI自动提取（已通过人工审核）", + "confidence": 0.82 + }, + "data_based": { + "sample_size": 15, + "time_range": "过去12个月", + "decision_stats": [ + {"decision": "正常通过", "percentage": 53.3, "count": 8, "avg_processing_days": 3.5}, + {"decision": "要求补充说明", "percentage": 33.3, "count": 5, "avg_processing_days": 5.2}, + {"decision": "降额通过", "percentage": 13.3, "count": 2, "avg_processing_days": 6.0} + ], + "similar_cases": [ + { + "case_id": "CAS-2025-09-012", + "customer_name": "东方物流集团", + "decision": "正常通过", + "mitigation": "提供多头融资说明（用于业务扩张），核实无实质风险" + }, + { + "case_id": "CAS-2025-07-145", + "customer_name": "鑫盛商贸公司", + "decision": "要求补充说明", + "mitigation": "说明征信查询原因，提供现金流充足证明" + } + ] + } + }, + "suggestion": "建议业务经理核实客户是否存在多头融资，了解征信查询原因（业务扩张 vs 资金紧张）。", + "smart_recommendation": { + "primary_action": "要求客户提供征信查询原因说明书，核实多头融资情况", + "alternative_actions": [ + "提供资金用途说明和还款计划，证明融资用于业务扩张", + "补充近6个月银行流水，证明现金流充足", + "如为临时资金需求，提供订单合同等业务凭证" + ], + "reference_case": { + "case_id": "CAS-2025-09-012", + "customer_name": "东方物流集团", + "scenario": "征信查询 8 次，用于扩张业务", + "outcome": "提供业务扩张说明后正常通过，核实无资金链风险" + } + } + }, + { + "level": "medium", + "title": "存在涉诉记录", + "description": "远东新能源公交公司存在 2 起涉诉记录，需核实诉讼性质和金额。", + "evidence": { + "rule_based": { + "source": "《融资租赁评审手册》第10条 - 法律风险评估", + "page": 10, + "extracted_by": "AI自动提取（已通过人工审核）", + "confidence": 0.85 + }, + "data_based": { + "sample_size": 12, + "time_range": "过去12个月", + "decision_stats": [ + {"decision": "正常通过", "percentage": 58.3, "count": 7, "avg_processing_days": 4.0}, + {"decision": "要求提供诉讼详情", "percentage": 33.3, "count": 4, "avg_processing_days": 6.5}, + {"decision": "附加条件通过", "percentage": 8.3, "count": 1, "avg_processing_days": 8.0} + ], + "similar_cases": [ + { + "case_id": "CAS-2025-08-203", + "customer_name": "华泰建设工程", + "decision": "正常通过", + "mitigation": "确认为小额合同纠纷（<50万），不影响偿债能力" + }, + { + "case_id": "CAS-2025-06-178", + "customer_name": "顺丰供应链公司", + "decision": "要求提供诉讼详情", + "mitigation": "提供案件进展说明，评估财务影响<3%" + } + ] + } + }, + "suggestion": "建议业务经理提供每起诉讼的详细情况（原告/被告、金额、进展），评估对财务的影响。", + "smart_recommendation": { + "primary_action": "提供诉讼清单（案由、金额、当前状态），说明对财务状况的影响评估", + "alternative_actions": [ + "如为小额合同纠纷（<50万/起），提供说明后影响较小", + "如为债务纠纷，需评估败诉风险和财务影响", + "提供法律顾问意见书，说明诉讼风险可控" + ], + "reference_case": { + "case_id": "CAS-2025-08-203", + "customer_name": "华泰建设工程", + "scenario": "2 起小额合同纠纷，总金额<100万", + "outcome": "确认为日常经营纠纷，不影响偿债能力，正常通过" + } + } + }, + { + "level": "high", + "title": "对外担保金额较大", + "description": "远东新能源公交公司对外担保 3.5 亿元，为本次申请金额的 1.2 倍。", + "evidence": { + "rule_based": { + "source": "《融资租赁评审手册》第11条 - 担保风险", + "page": 11, + "extracted_by": "AI自动提取（已通过人工审核）", + "confidence": 0.88 + }, + "data_based": { + "sample_size": 18, + "time_range": "过去12个月", + "decision_stats": [ + {"decision": "要求提供担保详情", "percentage": 66.7, "count": 12, "avg_processing_days": 5.8}, + {"decision": "降额通过", "percentage": 22.2, "count": 4, "avg_processing_days": 7.2}, + {"decision": "附加条件通过", "percentage": 11.1, "count": 2, "avg_processing_days": 6.5} + ], + "similar_cases": [ + { + "case_id": "CAS-2025-07-234", + "customer_name": "华信集团", + "decision": "附加条件通过", + "mitigation": "提供被担保方财务报告，设置代偿预警机制" + }, + { + "case_id": "CAS-2025-09-045", + "customer_name": "宏达投资公司", + "decision": "降额通过（降20%）", + "mitigation": "降低授信以控制担保风险敞口" + } + ] + } + }, + "suggestion": "建议业务经理提供对外担保明细（被担保方、金额、期限），评估代偿风险。", + "smart_recommendation": { + "primary_action": "提供对外担保明细清单，包括被担保方资质、财务状况、代偿可能性评估", + "alternative_actions": [ + "说明被担保方为关联公司/优质客户，代偿风险低", + "提供反担保措施说明（如抵押物、股权质押）", + "考虑降低授信额度至 2.4 亿元，降低风险敞口" + ], + "reference_case": { + "case_id": "CAS-2025-07-234", + "customer_name": "华信集团", + "scenario": "对外担保为申请额度的 1.2 倍", + "outcome": "提供被担保方财务报告，设置代偿预警，附加条件通过" + } + } + }, + { + "level": "medium", + "title": "新能源行业政策依赖性", + "description": "远东新能源公交公司属于新能源行业，收入可能依赖政府补贴，需关注补贴到位情况。", + "evidence": { + "rule_based": { + "source": "《行业风险指引》第3条 - 新能源行业", + "page": 3, + "extracted_by": "AI自动提取（已通过人工审核）", + "confidence": 0.90 + }, + "data_based": { + "sample_size": 31, + "time_range": "过去12个月", + "decision_stats": [ + {"decision": "正常通过", "percentage": 48.4, "count": 15, "avg_processing_days": 4.5}, + {"decision": "要求补贴承诺函", "percentage": 38.7, "count": 12, "avg_processing_days": 6.2}, + {"decision": "附加条件通过", "percentage": 12.9, "count": 4, "avg_processing_days": 7.0} + ], + "similar_cases": [ + { + "case_id": "CAS-2025-08-112", + "customer_name": "晨光新能源科技", + "decision": "要求补贴承诺函", + "mitigation": "地方政府出具补贴承诺函，补贴到位率>85%" + }, + { + "case_id": "CAS-2025-09-067", + "customer_name": "绿动公交集团", + "decision": "正常通过", + "mitigation": "补贴延迟但现金流充足，运营收入稳定" + } + ] + } + }, + "suggestion": "建议业务经理提供政府补贴承诺函、历史补贴到位情况，说明现金流不完全依赖补贴。", + "smart_recommendation": { + "primary_action": "要求提供地方政府补贴承诺函，核实历史补贴到位率（建议≥80%）", + "alternative_actions": [ + "提供运营收入构成，证明补贴占比<40%，现金流多元化", + "说明补贴延迟应对预案（如银行授信、应收账款保理）", + "提供同行业可比公司补贴情况，证明行业普遍性" + ], + "reference_case": { + "case_id": "CAS-2025-09-067", + "customer_name": "绿动公交集团", + "scenario": "新能源公交运营，补贴延迟但运营收入稳定", + "outcome": "证明运营收入占比65%，补贴延迟不影响还款，正常通过" + } + } + } + ], + "summary": { + "high_count": 2, + "medium_count": 3, + "low_count": 0, + "focus_areas": ["财务杠杆", "信用状况", "法律风险", "对外担保", "行业风险"], + "overall_confidence": 0.86, + "data_coverage": "99 个案例" + } + } + + +@router.get("/health") +async def health_check(): + return {"status": "ok", "service": "风险研判助手（双轨驱动版）"} diff --git a/backend/api/xingbang_api.py b/backend/api/xingbang_api.py deleted file mode 100644 index 8ce3785..0000000 --- a/backend/api/xingbang_api.py +++ /dev/null @@ -1,145 +0,0 @@ -"""Xingbang API — 兴邦金租评审工作流 + 自进化模块的 HTTP 接口。 - -端: - POST /api/xingbang/workflow 运行五智能体审批流水线(返回全部产出) - POST /api/xingbang/workflow/stream 流式运行(SSE, 逐步展示) - GET /api/xingbang/evolution 查看自进化洞察 - GET /api/xingbang/trajectories 列出历史轨迹 - GET /api/xingbang/trajectories/{id} 读取单条轨迹 - -复用 mini-OpenClaw 的 AgentManager.llm 与后端基础设施。 -""" - -from __future__ import annotations - -import json -import os -from pathlib import Path -from typing import Any - -from fastapi import APIRouter -from pydantic import BaseModel -from sse_starlette.sse import EventSourceResponse - -from graph.agent import agent_manager -from xingbang.workflow import XingbangWorkflow -from xingbang.config import XINGBANG_CONFIG - -router = APIRouter(prefix="/api/xingbang", tags=["xingbang"]) - - -# 懒加载单例工作流(复用 agent_manager.llm) -_workflow: XingbangWorkflow | None = None - - -def _get_workflow() -> XingbangWorkflow: - global _workflow - if _workflow is None: - if agent_manager._llm is None: - # 若 agent 未初始化, 手动初始化(需要 BASE_DIR) - from pathlib import Path as _P - _workflow = XingbangWorkflow( - llm=None, - base_dir=_P(__file__).resolve().parent.parent, - evolution_enabled=True, - ) - raise RuntimeError("Agent 未初始化, 请先启动后端(会自动初始化 agent_manager)") - _workflow = XingbangWorkflow( - llm=agent_manager._llm, - base_dir=Path(__file__).resolve().parent.parent, - evolution_enabled=True, - ) - return _workflow - - -class WorkflowRequest(BaseModel): - task: str # 客户/项目描述 - customer: str = "" # 客户名(用于轨迹标注) - - -@router.get("/config") -async def get_config(): - """返回兴邦项目配置(数据源、五智能体、档位)。""" - return XINGBANG_CONFIG - - -@router.post("/workflow") -async def run_workflow(req: WorkflowRequest): - """运行完整五智能体审批流水线。""" - wf = _get_workflow() - try: - result = await wf.run(req.task, customer=req.customer) - return result - except RuntimeError as e: - from fastapi import HTTPException - raise HTTPException(status_code=500, detail=str(e)) - - -@router.post("/workflow/stream") -async def run_workflow_stream(req: WorkflowRequest): - """流式运行(SSE), 逐步展示五智能体进度与产出。""" - wf = _get_workflow() - - async def gen(): - async for event in wf.run_stream(req.task, customer=req.customer): - yield { - "event": event.get("event", "event"), - "data": json.dumps( - {k: v for k, v in event.items() if k != "event"}, - ensure_ascii=False, - ), - } - - return EventSourceResponse(gen()) - - -class FeedbackRequest(BaseModel): - trajectory_id: str - feedback: str # "positive" | "negative" - - -@router.post("/evolution/feedback") -async def submit_feedback(req: FeedbackRequest): - """提交用户反馈(Level 2 奖励信号), 追加进轨迹。""" - try: - traj_file = ( - Path(__file__).resolve().parent.parent - / "xingbang_evolution" / "trajectories" / f"{req.trajectory_id}.json" - ) - if not traj_file.exists(): - return {"error": f"轨迹不存在: {req.trajectory_id}"} - data = json.loads(traj_file.read_text(encoding="utf-8")) - data.setdefault("user_feedback", req.feedback) - data["user_feedback"] = req.feedback - traj_file.write_text(json.dumps(data, ensure_ascii=False, indent=2), encoding="utf-8") - return {"status": "ok", "trajectory_id": req.trajectory_id, "feedback": req.feedback} - except Exception as e: - return {"error": str(e)} - - -@router.get("/evolution") -async def get_evolution(): - """查看自进化洞察报告。""" - wf = _get_workflow() - report = wf.evolution.generate_insights() - return {"insights": report, "config": XINGBANG_CONFIG} - - -@router.get("/trajectories") -async def list_trajectories(): - """列出所有已保存轨迹。""" - wf = _get_workflow() - paths = wf.recorder.list_trajectories() - return {"count": len(paths), - "trajectories": [p.stem for p in paths]} - - -@router.get("/trajectories/{trajectory_id}") -async def get_trajectory(trajectory_id: str): - """读取单条轨迹详情。""" - wf = _get_workflow() - traj = wf.recorder.load(trajectory_id) - if not traj: - from fastapi import HTTPException - raise HTTPException(status_code=404, detail="轨迹不存在") - return traj diff --git a/backend/api/xingbang_router.py b/backend/api/xingbang_router.py new file mode 100644 index 0000000..5670f54 --- /dev/null +++ b/backend/api/xingbang_router.py @@ -0,0 +1,372 @@ +""" +兴邦金租智能体系统 API 路由 +基于2026-08-18会议纪要真实痛点设计 +""" + +from fastapi import APIRouter, HTTPException +from fastapi.responses import StreamingResponse +from pydantic import BaseModel +from typing import Optional, Dict, Any +import json +import asyncio +from pathlib import Path + +from graph.agent import agent_manager + +router = APIRouter(prefix="/api/xingbang", tags=["xingbang"]) + + +async def _xingbang_llm(user_prompt: str, system_prompt: str) -> str: + """调用 mini-OpenClaw 核心已初始化的 DeepSeek LLM，构建带差异化 system prompt 的临时 agent。 + + 兼容核心 AgentManager：只复用其 LLM，不依赖不存在的 get_response 等方法。 + 启用后可替换为本地模型(Qwen)以支持"数据不出域"。 + """ + from langchain.agents import create_agent + from langchain_core.messages import HumanMessage + + if agent_manager._llm is None: + from graph.agent import agent_manager as _am + _am.initialize(Path(__file__).resolve().parent.parent) + agent = create_agent( + model=agent_manager._llm, + tools=[], # 兴邦各 Agent 为纯分析角色，暂不绑定核心工具 + system_prompt=system_prompt, + ) + try: + result = await agent.ainvoke({"messages": [HumanMessage(content=user_prompt)]}) + msgs = result.get("messages", []) + for m in reversed(msgs): + if hasattr(m, "content") and m.type == "ai" and m.content: + return m.content + return str(result.get("output", "")) + except Exception as e: # noqa + return f"[Agent 调用失败] {e}" + +# ==================== 请求/响应模型 ==================== + +class WorkflowRequest(BaseModel): + """完整五Agent流水线请求""" + customer_name: str + project_type: str = "对公新能源" # 对公新能源/零售/其他 + amount: float # 申请金额（亿元） + +class AgentRequest(BaseModel): + """单个Agent执行请求""" + agent_type: str # data_gather / risk_screen / case_search / approval_advice / risk_monitor + input_data: Dict[str, Any] + + +# ==================== 五大Agent System Prompts ==================== + +AGENT_PROMPTS = { + "data_gather": """你是【数据汇聚Agent】，负责融资租赁项目的多源数据整理与情报卡生成。 + +**职责：** +1. 从多源异构数据（财报PDF、工商信息、征信、中诚信评级模板、租赁物估值等）整理生成统一的"客户情报卡" +2. 进行数据校验：交叉核对工商信息、财报数据、征信记录的一致性 +3. 标注风险标签：现金流压力、关联交易、对外担保、涉诉情况 +4. 标明"人工待核实项"：数据缺失、矛盾、需现场尽调确认的项 + +**输出格式（Markdown）：** +# 客户情报卡 - {客户名称} + +## 基本信息 +- 注册资本、成立时间、法人、股东结构 +- 主营业务、所属行业 + +## 财务概况 +- 近三年营收/净利润/现金流 +- 资产负债率、流动比率 +- 数据来源：{财报年份} + +## 征信情况 +- 对外担保金额、涉诉记录 +- 信用评级（中诚信模板结果） + +## 租赁物信息 +- 租赁物类型、估值、覆盖率 + +## 风险标签 +- 🔴 高风险：{具体事项} +- 🟡 中风险：{具体事项} + +## 人工待核实项 +- [ ] {事项1} +- [ ] {事项2} + +**原则：只汇聚和标注，不做结论性判断。** +""", + + "risk_screen": """你是【预审参谋Agent】，负责生成风险筛查清单。 + +**核心原则（对齐会议要求）：** +- **给风险点，不给结论**：列出所有潜在风险项及制度依据，但不给"通过/不通过"的结论性建议 +- **降低业务评审对立**：前置风险筛查，让业务部门提前知晓评审会关注的点 +- **标注置信度**：每个风险点标注置信度（高/中/低），基于数据完整性和历史经验 + +**职责：** +1. 基于"客户情报卡"，按公司评审手册逐项筛查风险点 +2. 引用制度条款和历史案例作为依据 +3. 提出"建议核实方向"（不是建议结论） + +**输出格式（Markdown）：** +# 风险筛查清单 - {客户名称} + +## 风险点1：{风险事项} +- **置信度**：⚠️ 高 +- **制度依据**：《评审手册》第X条 - {具体条款} +- **现象描述**：{客户情报卡中的具体数据/情况} +- **建议核实方向**：需现场核实{具体方向}，或要求客户补充{资料} + +## 风险点2：... + +## 汇总 +- 高风险项：{数量} +- 中风险项：{数量} +- 建议重点关注领域：{现金流/担保/租赁物合规/...} + +**不要输出"建议通过/不通过"等结论性意见。** +""", + + "case_search": """你是【经验检索Agent】，负责从历史案例库中检索相似案例。 + +**职责：** +1. 基于客户行业、规模、风险特征检索历史案例（成功+失败） +2. 提取案例的关键决策依据和风险缓释措施 +3. 帮助"拉平评审标准差异"：展示同类风险在历史上的不同评审员如何判断 + +**输出格式（Markdown）：** +# 相似案例检索 - {客户名称} + +## 案例1：{历史客户名称}（{通过/被否}） +- **相似度**：85% +- **项目概况**：{行业、金额、时间} +- **关键风险点**：{当时识别的风险} +- **评审决策**：{通过/被否/附加条件} +- **后续表现**：{是否逾期、结清情况} +- **经验提示**：{对当前项目的参考价值} + +## 案例2：... + +## 经验总结 +- 同行业历史不良率：{%} +- 关键风控措施：{担保增信/租赁物覆盖率/定期检查} + +**目标：让评审员看到"过去怎么做的"，作为当前决策参考。** +""", + + "approval_advice": """你是【审批意见Agent】，负责生成标准化审批参考意见。 + +**职责：** +1. 汇总前三个Agent（数据汇聚、风险筛查、经验检索）的产出 +2. 生成标准化审批参考文档，包含：建议结论、证据引用、风险缓释建议 +3. **明确标注"AI参考意见，最终决策权在评审员"** + +**输出格式（Markdown）：** +# 审批参考意见 - {客户名称} + +> ⚠️ **重要提示**：本意见由AI生成，仅供评审员参考，最终决策权在评审员及审批委员会。评审员可一票否决。 + +## 一、项目概要 +- 客户：{名称} +- 申请金额：{金额}亿元 +- 租赁物：{类型} +- 期限：{年限} + +## 二、综合评估 +- **数据完整性**：✅ 完整 / ⚠️ 部分缺失 +- **主要风险**：{列出Top 3风险点及来源} +- **历史案例参考**：{相似案例结论} + +## 三、AI参考建议 +- **倾向性意见**：🟢 建议通过 / 🟡 建议附加条件通过 / 🔴 建议审慎评估 +- **建议条件**（如通过）： + 1. {风险缓释措施1，如追加担保} + 2. {风险缓释措施2，如提高租赁物覆盖率} +- **重点关注事项**：{评审员需额外核实的点} + +## 四、证据链 +- 数据来源：{财报/征信/工商/中诚信} +- 风险依据：{制度条款引用} +- 历史案例：{案例编号} + +**评审员有权基于独立判断修改或否决本意见。** +""", + + "risk_monitor": """你是【风险监控Agent】，负责审批空窗期与租后动态监控。 + +**职责：** +1. 审批空窗期（立项→上会，约17工作日）：动态监控客户征信、涉诉、舆情变化 +2. 租后阶段：定期检查还款记录、保险覆盖、租赁物状态 +3. 预警触发：发现风险信号时立即推送给评审员/租后管理岗 + +**输出格式（Markdown）：** +# 风险监控报告 - {客户名称} + +## 监控周期 +- 起始：{立项日期} +- 当前：{今日日期} +- 距上会：{剩余天数}工作日 + +## 动态风险扫描 +### 征信变化 +- ✅ 无新增逾期记录 +- 🔴 新增涉诉：{案件概要} [需立即关注] + +### 舆情监控 +- 🟡 负面舆情：{媒体报道/社交媒体} + +### 保险覆盖（租后） +- 保单状态：✅ 有效 / ⚠️ 即将到期 +- 覆盖率：{保额/租赁物价值} = {%} +- 条款检查：{是否足额覆盖风险，如泡水、盗抢等} + +## 预警触发 +- 🚨 高优先级：{需立即处理的风险} +- ⚠️ 中优先级：{需关注的异常} + +## 建议行动 +- [ ] 要求客户说明{具体事项} +- [ ] 现场核查{租赁物/经营状况} +- [ ] 补充{担保/保险} + +**对齐会议痛点："17天动态风险无预警" → 现在有了实时监控。** +""" +} + + +# ==================== 单Agent执行 ==================== + +async def execute_single_agent(agent_type: str, input_data: Dict[str, Any]) -> Dict[str, Any]: + """执行单个Agent""" + if agent_type not in AGENT_PROMPTS: + raise HTTPException(400, f"Unknown agent type: {agent_type}") + + system_prompt = AGENT_PROMPTS[agent_type] + user_prompt = f"""请基于以下输入数据执行任务： + +{json.dumps(input_data, ensure_ascii=False, indent=2)} + +严格按照你的角色职责和输出格式生成报告。""" + + # 调用核心Agent（复用 mini-OpenClaw 的 agent_manager 及其 LLM） + messages = [ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": user_prompt} + ] + + # 复用 mini-OpenClaw 核心已初始化的 DeepSeek LLM，构建带差异化 system prompt 的临时 agent + response = await _xingbang_llm(user_prompt, system_prompt) + + return { + "agent_type": agent_type, + "output": response, + "status": "completed" + } + + +# ==================== 完整流水线（SSE流式） ==================== + +async def run_workflow_stream(customer_name: str, project_type: str, amount: float): + """五Agent流水线 SSE流式执行""" + + # 模拟数据包（实际应从数据库/文件读取） + mock_data = { + "customer_name": customer_name, + "project_type": project_type, + "amount": amount, + "financial_data": { + "revenue_2025": 15.2, + "revenue_2024": 13.8, + "revenue_2023": 12.1, + "net_profit_2025": 1.8, + "cash_flow_2025": 2.3, + "debt_ratio": 0.62 + }, + "credit_info": { + "external_guarantee": 3.5, + "litigation_count": 2, + "credit_rating": "AA-" + }, + "lease_asset": { + "type": "新能源公交车50台", + "valuation": 4.2, + "coverage_ratio": 1.4 + } + } + + # Agent执行顺序 + agents = [ + ("data_gather", "数据汇聚"), + ("risk_screen", "预审参谋"), + ("case_search", "经验检索"), + ("approval_advice", "审批意见"), + ("risk_monitor", "风险监控") + ] + + context = {"initial_data": mock_data} + + for agent_type, agent_name in agents: + # 发送agent_start事件 + yield f"event: agent_start\ndata: {json.dumps({'agent': agent_name, 'type': agent_type}, ensure_ascii=False)}\n\n" + + await asyncio.sleep(0.5) # 模拟处理延迟 + + try: + result = await execute_single_agent(agent_type, context) + context[agent_type] = result["output"] # 传递给下一个Agent + + # 发送agent_end事件 + yield f"event: agent_end\ndata: {json.dumps({'agent': agent_name, 'type': agent_type, 'output': result['output']}, ensure_ascii=False)}\n\n" + + except Exception as e: + yield f"event: error\ndata: {json.dumps({'agent': agent_name, 'error': str(e)}, ensure_ascii=False)}\n\n" + break + + # 完成 + yield f"event: done\ndata: {json.dumps({'status': 'completed'}, ensure_ascii=False)}\n\n" + + +# ==================== API端点 ==================== + +@router.post("/workflow/stream") +async def workflow_stream(req: WorkflowRequest): + """运行完整五Agent流水线（SSE）""" + return StreamingResponse( + run_workflow_stream(req.customer_name, req.project_type, req.amount), + media_type="text/event-stream" + ) + + +@router.post("/agent/execute") +async def execute_agent(req: AgentRequest): + """执行单个Agent""" + result = await execute_single_agent(req.agent_type, req.input_data) + return result + + +@router.get("/config") +async def get_config(): + """获取系统配置""" + return { + "agents": [ + {"id": "data_gather", "name": "数据汇聚Agent", "description": "多源数据整理生成情报卡"}, + {"id": "risk_screen", "name": "预审参谋Agent", "description": "给风险点不给结论，前置筛查"}, + {"id": "case_search", "name": "经验检索Agent", "description": "历史案例检索与经验复用"}, + {"id": "approval_advice", "name": "审批意见Agent", "description": "生成标准化审批参考意见"}, + {"id": "risk_monitor", "name": "风险监控Agent", "description": "审批空窗期+租后动态监控"} + ], + "project_types": ["对公新能源", "对公传统", "零售乘用车", "零售二手车"], + "model_config": { + "provider": "DeepSeek", + "model": "deepseek-v4-flash", + "base_url": "http://49.232.133.151/v1" + } + } + + +@router.get("/health") +async def health_check(): + """健康检查""" + return {"status": "ok", "service": "兴邦智能体系统"} diff --git a/backend/api/xingbang_unified.py b/backend/api/xingbang_unified.py new file mode 100644 index 0000000..ee035c0 --- /dev/null +++ b/backend/api/xingbang_unified.py @@ -0,0 +1,432 @@ +"""兴邦金租智能评审助手 - 统一API（7 个助手完整版） + +基于 2026.8.18 会议痛点的完整解决方案： +1. 立项前置风控助手 🔍 - 业务经理风险自查 +2. 中诚信智能填报助手 📊 - 激活十几万投资的评级模板 +3. 空窗期动态监控助手 🚨 - 17天空窗期风险重扫 +4. 保单条款合规核验助手 🛡️ - OCR+条款解析 +5. 评审报告辅助生成助手 📄 - 看资料+写报告提效 +6. 主审员智能分配助手 🎯 - 多维度负荷优化 +7. AI预审过滤+加速助手 🤖 - 团队经验预判+加速 +""" + +import asyncio +import json +from datetime import datetime +from typing import AsyncGenerator, Dict, List, Optional + +from fastapi import APIRouter, HTTPException +from fastapi.responses import StreamingResponse +from pydantic import BaseModel, Field + +# 导入数据层 +from data_layer.erp_connector import get_erp_connector +from data_layer.history_store import get_history_store +from data_layer.reviewer_profile import get_reviewer_store +from data_layer.team_experience import get_team_experience_store + +router = APIRouter(prefix="/api/xingbang/unified", tags=["兴邦智能评审统一接口"]) + + +# ============ Pydantic 模型 ============ + +class ProjectBasicInfo(BaseModel): + """项目基础信息""" + project_id: str = "" + project_name: str + client_name: str + industry: str + credit_amount: float # 万元 + lease_object: str = "" + lease_value: float = 0 # 万元 + + +class FinancialData(BaseModel): + """财务数据""" + registered_capital: float = 0 + established_years: int = 0 + asset_liability_ratio: float = 0 + current_ratio: float = 0 + quick_ratio: float = 0 + net_profit_2024: float = 0 + net_profit_2025: float = 0 + revenue_growth_rate: float = 0 + + +class CreditReport(BaseModel): + """征信报告""" + overdue_count: int = 0 + query_count_recent_6m: int = 0 + credit_limit: float = 0 + enforcement_records: int = 0 + + +class InsurancePolicyData(BaseModel): + """保单数据""" + policy_number: str + insurance_company: str + policy_type: str + coverage_amount: float + exclusions: List[str] = [] + policy_pdf_path: str = "" + + +# ============ 助手 1：立项前置风控 ============ + +class Assistant1Request(BaseModel): + """立项前置风控请求""" + project: ProjectBasicInfo + financial: FinancialData + credit: CreditReport + + +@router.post("/assistant1/risk-check") +async def assistant1_risk_check(request: Assistant1Request): + """助手1：立项前置风控自查（只给风险点，不给结论）""" + + async def generate(): + try: + # 获取团队经验 + team_exp = get_team_experience_store() + thresholds = team_exp.get_approval_thresholds() + risk_patterns = team_exp.get_risk_patterns(request.project.industry) + red_flags = team_exp.get_industry_red_flags(request.project.industry) + + yield f"data: {json.dumps({'type': 'start'}, ensure_ascii=False)}\n\n" + + # 生成 Markdown 报告 + content = f"""# 立项前置风险自查清单 + +## 📋 项目基本信息 +- **项目名称**：{request.project.project_name} +- **客户名称**：{request.project.client_name} +- **行业**：{request.project.industry} +- **授信金额**：{request.project.credit_amount} 万元 +- **租赁物**：{request.project.lease_object or '未填写'} +- **租赁物价值**：{request.project.lease_value} 万元 + +--- + +## 🔍 风险自查结果 + +""" + yield f"data: {json.dumps({'type': 'content', 'data': content}, ensure_ascii=False)}\n\n" + + # 检查高风险项 + high_risks = [] + medium_risks = [] + low_risks = [] + + # 1. 客户资质检查 + if request.financial.registered_capital < 1000: + high_risks.append("**注册资本不足**：注册资本 {:.0f} 万元 < 1000 万元（标准要求）\n - **建议**：考虑提高授信门槛或要求集团担保".format(request.financial.registered_capital)) + + if request.financial.established_years < 3: + medium_risks.append("**成立时间较短**：成立 {} 年 < 3 年（标准要求）\n - **建议**：补充实控人其他企业经营情况".format(request.financial.established_years)) + + # 2. 财务指标检查 + asset_liability = request.financial.asset_liability_ratio + threshold_al = thresholds.get("资产负债率", {}) + + if asset_liability > threshold_al.get("critical", 0.8): + high_risks.append(f"**资产负债率过高**：{asset_liability:.1%} > {threshold_al.get('critical', 0.8):.0%}（警戒线）\n - **团队共识**：{threshold_al.get('team_consensus', '需要额外担保')}") + elif asset_liability > threshold_al.get("warning", 0.7): + medium_risks.append(f"**资产负债率偏高**：{asset_liability:.1%}（位于 {threshold_al.get('warning', 0.7):.0%}-{threshold_al.get('critical', 0.8):.0%} 区间）\n - **建议**：补充负债结构说明，关注短期偿债压力") + else: + low_risks.append(f"✅ **资产负债率**：{asset_liability:.1%}（良好）") + + current_ratio = request.financial.current_ratio + threshold_cr = thresholds.get("流动比率", {}) + + if current_ratio < threshold_cr.get("critical", 1.0): + high_risks.append(f"**流动比率不足**：{current_ratio:.2f} < {threshold_cr.get('critical', 1.0):.2f}（红线）\n - **团队共识**：{threshold_cr.get('team_consensus', '短期偿债能力堪忧')}") + elif current_ratio < threshold_cr.get("warning", 1.2): + medium_risks.append(f"**流动比率偏低**：{current_ratio:.2f}\n - **建议**：关注应收账款周转情况") + else: + low_risks.append(f"✅ **流动比率**：{current_ratio:.2f}（符合标准）") + + # 3. 征信检查 + if request.credit.overdue_count > 0: + high_risks.append(f"**征信逾期记录**：近 2 年有 {request.credit.overdue_count} 次逾期\n - **建议**：要求客户提供逾期说明及还款证明") + else: + low_risks.append("✅ **征信记录**：近 2 年无逾期（良好）") + + if request.credit.enforcement_records > 0: + high_risks.append(f"**被执行记录**：存在 {request.credit.enforcement_records} 条被执行记录\n - **重要**：评审大概率退回，需先解决执行案件") + else: + low_risks.append("✅ **被执行记录**：无（良好）") + + if request.credit.query_count_recent_6m > 10: + medium_risks.append(f"**征信查询频繁**：近 6 个月查询 {request.credit.query_count_recent_6m} 次\n - **建议**：了解客户融资需求是否过于迫切") + + # 4. 租赁物检查 + if request.project.lease_value > 0: + coverage_ratio = request.project.lease_value / request.project.credit_amount + if coverage_ratio < 1.2: + high_risks.append(f"**租赁物价值不足**：租赁物价值 {request.project.lease_value} 万元，覆盖率 {coverage_ratio:.1%} < 120%（标准要求）\n - **建议**：补充其他担保措施") + else: + low_risks.append(f"✅ **租赁物覆盖率**：{coverage_ratio:.1%}（符合要求）") + + # 5. 行业红线检查 + if red_flags: + content_flags = f"\n### ⚠️ {request.project.industry} 行业常见风险点\n\n" + for flag in red_flags: + content_flags += f"- {flag}\n" + content_flags += "\n**建议**：核实客户是否触及以上风险点\n\n" + yield f"data: {json.dumps({'type': 'content', 'data': content_flags}, ensure_ascii=False)}\n\n" + + # 输出风险项 + if high_risks: + content_high = "### 🔴 高风险项（必须处理，否则评审大概率退回）\n\n" + for i, risk in enumerate(high_risks, 1): + content_high += f"{i}. {risk}\n\n" + yield f"data: {json.dumps({'type': 'content', 'data': content_high}, ensure_ascii=False)}\n\n" + + if medium_risks: + content_medium = "### 🟡 中风险项（建议补充材料或说明）\n\n" + for i, risk in enumerate(medium_risks, 1): + content_medium += f"{i}. {risk}\n\n" + yield f"data: {json.dumps({'type': 'content', 'data': content_medium}, ensure_ascii=False)}\n\n" + + if low_risks: + content_low = "### 🟢 低风险项（符合要求）\n\n" + for risk in low_risks: + content_low += f"- {risk}\n" + content_low += "\n" + yield f"data: {json.dumps({'type': 'content', 'data': content_low}, ensure_ascii=False)}\n\n" + + # 整体评估 + if len(high_risks) == 0 and len(medium_risks) == 0: + suggestion = "✅ **直接提交**：项目风险可控，可直接提交评审" + elif len(high_risks) == 0: + suggestion = "🟡 **补充材料后提交**：建议先处理中风险项，提高通过率" + else: + suggestion = "🔴 **重大调整后提交**：存在高风险项，建议先与客户沟通调整方案" + + content_final = f"""--- + +## 💡 整体评估 + +{suggestion} + +**说明**：本清单基于团队历史评审经验生成，仅供参考，不代表评审结论。最终是否通过由主审员独立判断。 +""" + yield f"data: {json.dumps({'type': 'content', 'data': content_final}, ensure_ascii=False)}\n\n" + yield f"data: {json.dumps({'type': 'done'}, ensure_ascii=False)}\n\n" + + except Exception as e: + yield f"data: {json.dumps({'type': 'error', 'message': str(e)}, ensure_ascii=False)}\n\n" + + return StreamingResponse(generate(), media_type="text/event-stream") + + +# ============ 助手 6：主审员智能分配 ============ + +class Assistant6Request(BaseModel): + """主审员智能分配请求""" + project_industry: str + project_amount: float # 万元 + project_complexity: int = Field(3, ge=1, le=5, description="项目复杂度 1-5") + + +@router.post("/assistant6/allocate") +async def assistant6_allocate(request: Assistant6Request): + """助手6：主审员智能分配（多维度评分）""" + + try: + reviewer_store = get_reviewer_store() + + # 获取推荐列表 + recommendations = reviewer_store.get_recommended_reviewers( + project_industry=request.project_industry, + project_complexity=request.project_complexity, + top_n=3 + ) + + # 获取所有主审员当前状态 + all_profiles = reviewer_store.get_all_profiles() + reviewer_status = [ + { + "name": name, + "current_projects": profile["current_projects"], + "specialty": profile["specialty"], + "workload_capacity": profile["workload_capacity"], + "status": "🔴 超载" if profile["current_projects"] >= profile["workload_capacity"] + else ("🟡 偏高" if profile["current_projects"] >= profile["workload_capacity"] * 0.8 else "🟢 正常") + } + for name, profile in all_profiles.items() + ] + + return { + "reviewers": reviewer_status, + "recommendations": recommendations, + "project_info": { + "industry": request.project_industry, + "amount": request.project_amount, + "complexity": request.project_complexity, + }, + "algorithm_version": "v1.0_multidim_scoring", + } + + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) + + +# ============ 助手 7：AI预审过滤+加速 ============ + +class Assistant7Request(BaseModel): + """AI预审请求""" + project: ProjectBasicInfo + financial: FinancialData + credit: CreditReport + + +@router.post("/assistant7/pre-screen") +async def assistant7_pre_screen(request: Assistant7Request): + """助手7：AI预审过滤（基于团队经验，快速判断是否值得进入正式评审）""" + + async def generate(): + try: + yield f"data: {json.dumps({'type': 'start'}, ensure_ascii=False)}\n\n" + + # 获取团队经验和历史案例 + team_exp = get_team_experience_store() + history_store = get_history_store() + + thresholds = team_exp.get_approval_thresholds() + rejection_reasons = team_exp.get_common_rejection_reasons(top_n=5) + + # 相似案例检索 + similar_cases = history_store.search_similar_cases( + industry=request.project.industry, + credit_amount=request.project.credit_amount, + limit=3 + ) + + content = f"""# AI预审报告（团队经验模式） + +## 📊 项目信息 +- **项目名称**：{request.project.project_name} +- **客户名称**：{request.project.client_name} +- **行业**：{request.project.industry} +- **授信金额**：{request.project.credit_amount} 万元 + +--- + +## 🤖 AI预审判断（基于团队 {team_exp.get_metadata().get('total_cases_analyzed', 0)} 个历史案例） + +""" + yield f"data: {json.dumps({'type': 'content', 'data': content}, ensure_ascii=False)}\n\n" + + # 快速红线检查 + red_line_hit = [] + warning_items = [] + + # 1. 财务红线 + if request.financial.asset_liability_ratio > thresholds.get("资产负债率", {}).get("critical", 0.8): + red_line_hit.append("资产负债率超过红线") + + if request.financial.current_ratio < thresholds.get("流动比率", {}).get("critical", 1.0): + red_line_hit.append("流动比率低于红线") + + # 2. 征信红线 + if request.credit.enforcement_records > 0: + red_line_hit.append(f"存在 {request.credit.enforcement_records} 条被执行记录") + + if request.credit.overdue_count > 2: + red_line_hit.append(f"征信逾期次数过多（{request.credit.overdue_count} 次）") + + # 3. 警告项 + if request.financial.revenue_growth_rate < 0: + warning_items.append("营收负增长") + + if request.credit.query_count_recent_6m > 10: + warning_items.append("征信查询频繁") + + # AI预审结论 + if red_line_hit: + conclusion = "🔴 **不建议进入正式评审**" + reason_text = "\n".join([f"- {item}" for item in red_line_hit]) + content_conclusion = f"""### {conclusion} + +**触及红线项**： +{reason_text} + +**建议**：与客户沟通调整方案，或直接退回立项申请，避免浪费评审资源。 + +""" + elif len(warning_items) >= 2: + conclusion = "🟡 **建议谨慎评审**" + reason_text = "\n".join([f"- {item}" for item in warning_items]) + content_conclusion = f"""### {conclusion} + +**警告项**： +{reason_text} + +**建议**：可以进入正式评审，但需重点关注以上风险点，建议增加尽调深度。 + +""" + else: + conclusion = "🟢 **建议进入正式评审**" + content_conclusion = f"""### {conclusion} + +**初步判断**：项目风险可控，符合基本准入标准，建议正常推进评审流程。 + +""" + + yield f"data: {json.dumps({'type': 'content', 'data': content_conclusion}, ensure_ascii=False)}\n\n" + + # 相似案例参考 + if similar_cases: + content_cases = "\n---\n\n## 📚 相似案例参考\n\n" + for i, case in enumerate(similar_cases, 1): + result_emoji = "✅" if case.get("review_result") == "approved" else "❌" + content_cases += f"""### 案例 {i}：{case.get('project_name', '未知项目')} +- **行业**：{case.get('industry', '未知')} +- **金额**：{case.get('credit_amount', 0)} 万元 +- **评审结果**：{result_emoji} {case.get('review_result', '未知')} +- **关键风险点**：{', '.join([r.get('type', '未知') for r in case.get('identified_risks', [])])} + +""" + yield f"data: {json.dumps({'type': 'content', 'data': content_cases}, ensure_ascii=False)}\n\n" + + # 高频退回原因提示 + content_tips = "\n---\n\n## 💡 团队经验提示（高频退回原因）\n\n" + for reason in rejection_reasons: + content_tips += f"- **{reason['reason']}**（{reason['count']} 次） → {reason['solution']}\n" + + yield f"data: {json.dumps({'type': 'content', 'data': content_tips}, ensure_ascii=False)}\n\n" + + yield f"data: {json.dumps({'type': 'done'}, ensure_ascii=False)}\n\n" + + except Exception as e: + yield f"data: {json.dumps({'type': 'error', 'message': str(e)}, ensure_ascii=False)}\n\n" + + return StreamingResponse(generate(), media_type="text/event-stream") + + +# ============ 数据管理接口 ============ + +@router.get("/data/reviewers") +async def get_all_reviewers(): + """获取所有主审员画像""" + reviewer_store = get_reviewer_store() + return reviewer_store.get_all_profiles() + + +@router.get("/data/team-experience") +async def get_team_experience(): + """获取团队集体经验""" + team_exp = get_team_experience_store() + return { + "approval_thresholds": team_exp.get_approval_thresholds(), + "common_rejections": team_exp.get_common_rejection_reasons(), + "metadata": team_exp.get_metadata(), + } + + +@router.get("/data/history-stats") +async def get_history_statistics(): + """获取历史案例统计""" + history_store = get_history_store() + return history_store.get_statistics() diff --git a/backend/api/xingbang_v2.py b/backend/api/xingbang_v2.py new file mode 100644 index 0000000..c2753c1 --- /dev/null +++ b/backend/api/xingbang_v2.py @@ -0,0 +1,569 @@ +"""兴邦金租智能评审助手 API v2 — 全新设计，推翻旧五智能体框架 + +核心痛点： +1. 中诚信评级模板闲置（线下Excel手工录入） +2. 保单条款覆盖度盲区（只看形式不看内容） +3. 空窗期风险失控（17天立项到上会，期间风险恶化） +4. 评审逻辑因人而异（同一事项30%/60%/90%都可能） +5. 业务与评审对立（风险考量不一样） + +新架构：4个专项助手 +- 立项前置风控助手：业务经理立项前风险自查（只给风险点不给结论） +- 中诚信模板智能填报助手：激活闲置的中诚信模板，自动填充+评级 +- 空窗期动态监控助手：每3天重扫征信/涉诉/舆情，上会前强制重扫 +- 保单条款合规核验助手：OCR+条款解析，核验足额覆盖 +""" + +import asyncio +import json +from datetime import datetime +from pathlib import Path +from typing import AsyncGenerator + +from fastapi import APIRouter, HTTPException +from fastapi.responses import StreamingResponse +from pydantic import BaseModel + +router = APIRouter(prefix="/api/xingbang/v2", tags=["兴邦智能评审助手V2"]) + + +# ============ Pydantic 模型 ============ + +class ProjectData(BaseModel): + """项目基础数据""" + project_name: str + client_name: str + industry: str + credit_amount: float # 授信金额（万元） + lease_object: str # 租赁物 + lease_value: float # 租赁物价值（万元） + financial_data: dict # 财务数据 + credit_report: dict # 征信报告 + litigation_records: list # 涉诉记录 + sentiment_info: dict # 舆情信息 + + +class InsurancePolicy(BaseModel): + """保单数据""" + policy_number: str + insurance_company: str + coverage_amount: float # 保额（万元） + policy_type: str # 险种 + exclusions: list[str] # 除外责任 + policy_pdf_path: str = "" + + +class RiskCheckRequest(BaseModel): + """立项前置风控请求""" + project_data: ProjectData + + +class ZhongchengxinRequest(BaseModel): + """中诚信模板填报请求""" + project_data: ProjectData + + +class MonitoringRequest(BaseModel): + """空窗期监控请求""" + project_id: str + project_name: str + client_name: str + initial_data: ProjectData + + +class PolicyCheckRequest(BaseModel): + """保单核验请求""" + project_name: str + client_name: str + credit_amount: float + lease_value: float + policy: InsurancePolicy + + +# ============ 辅助函数：创建专用 LLM 实例 ============ + +def _create_assistant_llm(system_prompt: str): + """创建专用助手 LLM（复用核心 DeepSeek 配置）""" + import os + from langchain_deepseek import ChatDeepSeek + + return ChatDeepSeek( + model=os.getenv("DEEPSEEK_MODEL", "deepseek-chat"), + api_key=os.getenv("DEEPSEEK_API_KEY"), + api_base=os.getenv("DEEPSEEK_BASE_URL", "https://api.deepseek.com"), + temperature=0.3, # 降低温度，提高稳定性 + streaming=True, + ) + + +# ============ 助手1：立项前置风控助手 ============ + +SYSTEM_PROMPT_RISK_CHECK = """你是兴邦金租的"立项前置风控助手"。 + +**你的职责**： +帮助业务经理在立项前做风险自查，**只标识风险点，不给审批结论**，避免与评审员对立。 + +**工作流程**： +1. 读取项目基础数据（客户、财务、征信、涉诉、舆情） +2. 对照评审手册逐条自查 +3. 输出风险自查清单，标记： + - 🔴 高风险：必须处理，否则评审大概率退回 + - 🟡 中风险：建议补充材料或说明 + - 🟢 低风险：符合要求，可直接提交 + +**评审手册核心要点**（简化版）： +- 客户资质：注册资本≥1000万、成立时间≥3年、行业准入白名单 +- 财务指标：资产负债率≤70%、流动比率≥1.2、净利润连续两年为正 +- 征信记录：近2年无逾期、无被执行记录 +- 租赁物：价值必须≥授信金额×120%、符合适格性要求 +- 涉诉舆情：无重大诉讼、无负面舆情 + +**输出格式**（Markdown）： +```markdown +# 立项前置风险自查清单 + +## 项目基本信息 +- 项目名称：xxx +- 客户名称：xxx +- 授信金额：xxx万元 + +## 风险自查结果 + +### 🔴 高风险项（必须处理） +1. [风险点] 具体描述 + 建议措施 + +### 🟡 中风险项（建议处理） +1. [风险点] 具体描述 + 建议措施 + +### 🟢 低风险项（符合要求） +1. [检查项] 符合标准 + +## 整体评估 +- 建议：[直接提交 / 补充材料后提交 / 重大调整后提交] +``` + +**重要**：不要给"通过/不通过"结论，只客观列出风险点，让业务经理自己决策。 +""" + + +async def _stream_risk_check(project_data: ProjectData) -> AsyncGenerator[str, None]: + """流式生成立项前置风控报告""" + llm = _create_assistant_llm(SYSTEM_PROMPT_RISK_CHECK) + + # 构造用户消息 + user_message = f"""请对以下项目进行立项前置风控自查： + +**项目名称**：{project_data.project_name} +**客户名称**：{project_data.client_name} +**行业**：{project_data.industry} +**授信金额**：{project_data.credit_amount}万元 +**租赁物**：{project_data.lease_object} +**租赁物价值**：{project_data.lease_value}万元 + +**财务数据**： +{json.dumps(project_data.financial_data, ensure_ascii=False, indent=2)} + +**征信报告**： +{json.dumps(project_data.credit_report, ensure_ascii=False, indent=2)} + +**涉诉记录**： +{json.dumps(project_data.litigation_records, ensure_ascii=False, indent=2)} + +**舆情信息**： +{json.dumps(project_data.sentiment_info, ensure_ascii=False, indent=2)} + +请生成风险自查清单。 +""" + + async for chunk in llm.astream(user_message): + if hasattr(chunk, 'content'): + yield chunk.content + + +@router.post("/risk-check/stream") +async def risk_check_stream(request: RiskCheckRequest): + """立项前置风控自查（流式返回）""" + + async def event_generator(): + try: + yield f"data: {json.dumps({'type': 'start', 'message': '正在分析项目风险...'}, ensure_ascii=False)}\n\n" + + async for content in _stream_risk_check(request.project_data): + yield f"data: {json.dumps({'type': 'content', 'data': content}, ensure_ascii=False)}\n\n" + + yield f"data: {json.dumps({'type': 'done', 'message': '风险自查完成'}, ensure_ascii=False)}\n\n" + except Exception as e: + yield f"data: {json.dumps({'type': 'error', 'message': str(e)}, ensure_ascii=False)}\n\n" + + return StreamingResponse(event_generator(), media_type="text/event-stream") + + +# ============ 助手2：中诚信模板智能填报助手 ============ + +SYSTEM_PROMPT_ZHONGCHENGXIN = """你是兴邦金租的"中诚信模板智能填报助手"。 + +**背景**： +兴邦金租花了十几万咨询费购买中诚信评级模型（Excel模板），但因线下手工录入繁琐，一直闲置未用。 + +**你的职责**： +1. 从项目数据自动提取关键指标 +2. 填充中诚信评级模板 +3. 计算评级结果（3A/2A/A/BBB/BB/B/C） +4. 生成评级报告嵌入评审底稿 + +**中诚信评级模型简化版**（泛行业）： +- 财务指标（40%）：资产负债率、流动比率、净资产收益率、营收增长率 +- 经营能力（30%）：行业地位、市场份额、客户集中度、盈利稳定性 +- 偿债能力（20%）：速动比率、利息保障倍数、现金流覆盖率 +- 发展前景（10%）：行业景气度、技术创新能力、政策支持 + +**评级标准**： +- 3A（90-100分）：信用极好，违约风险极低 +- 2A（80-89分）：信用优良，违约风险很低 +- A（70-79分）：信用良好，违约风险较低 +- BBB（60-69分）：信用一般，违约风险中等 +- BB（50-59分）：信用较差，违约风险较高 +- B（40-49分）：信用差，违约风险高 +- C（<40分）：信用极差，违约风险极高 + +**输出格式**（Markdown）： +```markdown +# 中诚信评级报告 + +## 项目基本信息 +- 项目名称：xxx +- 客户名称：xxx +- 行业：xxx + +## 评级指标填充 + +### 一、财务指标（40%） +| 指标 | 数值 | 得分 | +|------|------|------| +| 资产负债率 | xx% | xx分 | +| ... | ... | ... | +**小计**：xx分 + +### 二、经营能力（30%） +... + +### 三、偿债能力（20%） +... + +### 四、发展前景（10%） +... + +## 评级结果 +- **综合得分**：xx分 +- **评级等级**：2A +- **评级说明**：信用优良，违约风险很低 + +## 风险提示 +1. [关键风险点1] +2. [关键风险点2] +``` +""" + + +async def _stream_zhongchengxin_report(project_data: ProjectData) -> AsyncGenerator[str, None]: + """流式生成中诚信评级报告""" + llm = _create_assistant_llm(SYSTEM_PROMPT_ZHONGCHENGXIN) + + user_message = f"""请为以下项目生成中诚信评级报告： + +**项目名称**：{project_data.project_name} +**客户名称**：{project_data.client_name} +**行业**：{project_data.industry} +**授信金额**：{project_data.credit_amount}万元 + +**财务数据**： +{json.dumps(project_data.financial_data, ensure_ascii=False, indent=2)} + +**征信报告**： +{json.dumps(project_data.credit_report, ensure_ascii=False, indent=2)} + +请填充中诚信评级模板并计算评级。 +""" + + async for chunk in llm.astream(user_message): + if hasattr(chunk, 'content'): + yield chunk.content + + +@router.post("/zhongchengxin/stream") +async def zhongchengxin_stream(request: ZhongchengxinRequest): + """中诚信模板智能填报（流式返回）""" + + async def event_generator(): + try: + yield f"data: {json.dumps({'type': 'start', 'message': '正在填充中诚信评级模板...'}, ensure_ascii=False)}\n\n" + + async for content in _stream_zhongchengxin_report(request.project_data): + yield f"data: {json.dumps({'type': 'content', 'data': content}, ensure_ascii=False)}\n\n" + + yield f"data: {json.dumps({'type': 'done', 'message': '中诚信评级完成'}, ensure_ascii=False)}\n\n" + except Exception as e: + yield f"data: {json.dumps({'type': 'error', 'message': str(e)}, ensure_ascii=False)}\n\n" + + return StreamingResponse(event_generator(), media_type="text/event-stream") + + +# ============ 助手3：空窗期动态监控助手 ============ + +SYSTEM_PROMPT_MONITORING = """你是兴邦金租的"空窗期动态监控助手"。 + +**背景**： +立项到上会需要17个工作日，期间客户征信/涉诉/舆情可能恶化，但无人重扫，导致上会时信息已过时。 + +**你的职责**： +1. 对比初始数据与最新数据 +2. 识别异常变化（新增被执行、新增诉讼、舆情负面、信用卡逾期等） +3. 评估风险等级（严重/一般/轻微） +4. 生成红色预警报告 + +**监控维度**： +- 征信变化：新增逾期、新增查询记录、信用卡额度变化 +- 涉诉变化：新增诉讼、新增被执行、执行金额变化 +- 舆情变化：负面新闻、行业风险、关联企业风险 +- 消费行为：淘宝消费骤降、信用卡逾期 + +**风险等级**： +- 🔴 严重：新增被执行/重大诉讼/信用卡逾期，建议暂停审批 +- 🟡 一般：征信查询次数增多/轻微负面舆情，建议重点关注 +- 🟢 轻微：无明显异常 + +**输出格式**（Markdown）： +```markdown +# 空窗期动态监控报告 + +## 项目基本信息 +- 项目名称：xxx +- 监控时间：xxx + +## 异常变化检测 + +### 🔴 严重异常 +1. [异常项] 具体描述 + 建议措施 + +### 🟡 一般异常 +1. [异常项] 具体描述 + +### 🟢 无异常 +- 征信记录：无新增异常 +- 涉诉记录：无新增异常 +- 舆情信息：无新增负面 + +## 监控建议 +- [暂停审批 / 重点关注 / 正常推进] +``` +""" + + +async def _stream_monitoring_report( + initial_data: ProjectData, + latest_data: ProjectData +) -> AsyncGenerator[str, None]: + """流式生成空窗期监控报告""" + llm = _create_assistant_llm(SYSTEM_PROMPT_MONITORING) + + user_message = f"""请对比以下项目的初始数据与最新数据，生成空窗期动态监控报告： + +**项目名称**：{initial_data.project_name} +**客户名称**：{initial_data.client_name} + +**初始数据（立项时）**： +征信报告：{json.dumps(initial_data.credit_report, ensure_ascii=False, indent=2)} +涉诉记录：{json.dumps(initial_data.litigation_records, ensure_ascii=False, indent=2)} +舆情信息：{json.dumps(initial_data.sentiment_info, ensure_ascii=False, indent=2)} + +**最新数据（当前）**： +征信报告：{json.dumps(latest_data.credit_report, ensure_ascii=False, indent=2)} +涉诉记录：{json.dumps(latest_data.litigation_records, ensure_ascii=False, indent=2)} +舆情信息：{json.dumps(latest_data.sentiment_info, ensure_ascii=False, indent=2)} + +请识别异常变化并生成监控报告。 +""" + + async for chunk in llm.astream(user_message): + if hasattr(chunk, 'content'): + yield chunk.content + + +@router.post("/monitoring/stream") +async def monitoring_stream(request: MonitoringRequest): + """空窗期动态监控（流式返回） + + 实际使用中，会定时调用此接口（每3天），传入最新数据 + 这里为演示方便，模拟一次对比 + """ + + # 模拟最新数据（实际应从外部数据源实时抓取） + latest_data = request.initial_data.model_copy() + latest_data.credit_report["overdue_count"] = latest_data.credit_report.get("overdue_count", 0) + 1 + latest_data.litigation_records.append({"case_number": "2026执1234", "amount": 50, "status": "执行中"}) + + async def event_generator(): + try: + yield f"data: {json.dumps({'type': 'start', 'message': '正在对比数据变化...'}, ensure_ascii=False)}\n\n" + + async for content in _stream_monitoring_report(request.initial_data, latest_data): + yield f"data: {json.dumps({'type': 'content', 'data': content}, ensure_ascii=False)}\n\n" + + yield f"data: {json.dumps({'type': 'done', 'message': '监控完成'}, ensure_ascii=False)}\n\n" + except Exception as e: + yield f"data: {json.dumps({'type': 'error', 'message': str(e)}, ensure_ascii=False)}\n\n" + + return StreamingResponse(event_generator(), media_type="text/event-stream") + + +# ============ 助手4：保单条款合规核验助手 ============ + +SYSTEM_PROMPT_POLICY_CHECK = """你是兴邦金租的"保单条款合规核验助手"。 + +**背景**： +兴邦金租要求客户为租赁物投保，保额需覆盖授信金额，但实际只检查"有没有保单"，不细看条款，导致出险时发现保单回不了。 + +**你的职责**： +1. 解析保单关键信息（险种、保额、除外责任） +2. 核验是否符合兴邦要求： + - 保额 ≥ 授信金额（理想120%） + - 险种必须包含：财产一切险/车辆损失险/盗抢险（根据租赁物类型） + - 除外责任不能包含高频风险（如泡水、自然灾害等） +3. 标记风险点 +4. 生成合规核验报告 + +**兴邦保单合规要求**： +- 设备类：财产一切险/工程一切险，保额≥授信×120% +- 车辆类：车损险+盗抢险+三者险，保额≥授信×120% +- 除外责任禁止：战争、核辐射、故意损坏 OK，但泡水、自然灾害、机械故障等高频风险不能除外 + +**输出格式**（Markdown）： +```markdown +# 保单条款合规核验报告 + +## 保单基本信息 +- 保单号：xxx +- 保险公司：xxx +- 险种：xxx +- 保额：xxx万元 + +## 合规核验结果 + +### ✅ 符合项 +1. [核验项] 符合要求 + +### ❌ 不符合项 +1. [核验项] 不符合要求 + 风险说明 + +## 核验结论 +- **是否合规**：[合规 / 不合规] +- **风险等级**：[无风险 / 低风险 / 高风险] +- **处理建议**：[通过 / 要求客户补正保单 / 拒绝] + +## 详细说明 +[具体分析] +``` +""" + + +async def _stream_policy_check( + project_name: str, + client_name: str, + credit_amount: float, + lease_value: float, + policy: InsurancePolicy +) -> AsyncGenerator[str, None]: + """流式生成保单合规核验报告""" + llm = _create_assistant_llm(SYSTEM_PROMPT_POLICY_CHECK) + + required_coverage = credit_amount * 1.2 # 要求120%覆盖 + + user_message = f"""请核验以下保单是否符合兴邦金租要求： + +**项目信息**： +- 项目名称：{project_name} +- 客户名称：{client_name} +- 授信金额：{credit_amount}万元 +- 租赁物价值：{lease_value}万元 +- 要求保额：≥{required_coverage}万元（120%覆盖） + +**保单信息**： +- 保单号：{policy.policy_number} +- 保险公司：{policy.insurance_company} +- 保额：{policy.coverage_amount}万元 +- 险种：{policy.policy_type} +- 除外责任：{json.dumps(policy.exclusions, ensure_ascii=False)} + +请生成合规核验报告。 +""" + + async for chunk in llm.astream(user_message): + if hasattr(chunk, 'content'): + yield chunk.content + + +@router.post("/policy-check/stream") +async def policy_check_stream(request: PolicyCheckRequest): + """保单条款合规核验（流式返回）""" + + async def event_generator(): + try: + yield f"data: {json.dumps({'type': 'start', 'message': '正在核验保单条款...'}, ensure_ascii=False)}\n\n" + + async for content in _stream_policy_check( + request.project_name, + request.client_name, + request.credit_amount, + request.lease_value, + request.policy + ): + yield f"data: {json.dumps({'type': 'content', 'data': content}, ensure_ascii=False)}\n\n" + + yield f"data: {json.dumps({'type': 'done', 'message': '保单核验完成'}, ensure_ascii=False)}\n\n" + except Exception as e: + yield f"data: {json.dumps({'type': 'error', 'message': str(e)}, ensure_ascii=False)}\n\n" + + return StreamingResponse(event_generator(), media_type="text/event-stream") + + +# ============ 综合工作台接口 ============ + +@router.get("/assistants/list") +async def list_assistants(): + """获取所有助手列表""" + return { + "assistants": [ + { + "id": "risk-check", + "name": "立项前置风控助手", + "description": "业务经理立项前风险自查，只给风险点不给结论", + "icon": "🔍", + "pain_point": "业务与评审对立，项目被退回", + "value": "降低退回率，缩短评审周期" + }, + { + "id": "zhongchengxin", + "name": "中诚信模板智能填报助手", + "description": "激活闲置的中诚信模板，自动填充+评级", + "icon": "📊", + "pain_point": "十几万的中诚信模板手工录入太繁琐", + "value": "每个项目省30-60分钟，激活投资" + }, + { + "id": "monitoring", + "name": "空窗期动态监控助手", + "description": "每3天重扫征信/涉诉/舆情，上会前强制重扫", + "icon": "🚨", + "pain_point": "17天空窗期，上会时信息已过时", + "value": "17天风险不落地，避免决策失误" + }, + { + "id": "policy-check", + "name": "保单条款合规核验助手", + "description": "OCR+条款解析，核验足额覆盖", + "icon": "🛡️", + "pain_point": "只看有没有保单，不看条款，出险回不了", + "value": "避免保单风险盲区" + } + ] + } diff --git a/backend/app.py b/backend/app.py index 671b16d..f4455e4 100644 --- a/backend/app.py +++ b/backend/app.py @@ -24,8 +24,11 @@ async def lifespan(app: FastAPI): agent_manager.initialize(BASE_DIR) # Initialize memory indexer for RAG mode - indexer = get_memory_indexer(BASE_DIR) - indexer.rebuild_index() + try: + indexer = get_memory_indexer(BASE_DIR) + indexer.rebuild_index() + except Exception as e: + print(f"⚠️ Memory indexer initialization skipped: {e}") print("✅ mini OpenClaw backend ready") yield @@ -47,7 +50,11 @@ from api.sessions import router as sessions_router from api.tokens import router as tokens_router from api.compress import router as compress_router from api.config_api import router as config_router -from api.xingbang_api import router as xingbang_router +from api.xingbang_router import router as xingbang_router +from api.xingbang_v2 import router as xingbang_v2_router +from api.review_assist import router as review_assist_router +from api.xingbang_unified import router as xingbang_unified_router +from api.risk_assess import router as risk_assess_router app.include_router(chat_router, prefix="/api") app.include_router(files_router, prefix="/api") @@ -56,8 +63,18 @@ app.include_router(tokens_router, prefix="/api") app.include_router(compress_router, prefix="/api") app.include_router(config_router, prefix="/api") app.include_router(xingbang_router) +app.include_router(xingbang_v2_router) +app.include_router(review_assist_router) +app.include_router(xingbang_unified_router) +app.include_router(risk_assess_router, prefix="/api/risk-assess") @app.get("/") async def root(): return {"name": "mini OpenClaw", "version": "0.1.0", "status": "running"} + + +# 支持 `python app.py` 直接启动后端(FastAPI + uvicorn) +if __name__ == "__main__": + import uvicorn + uvicorn.run(app, host="0.0.0.0", port=8002) diff --git a/backend/data_layer/__init__.py b/backend/data_layer/__init__.py new file mode 100644 index 0000000..2040a71 --- /dev/null +++ b/backend/data_layer/__init__.py @@ -0,0 +1,20 @@ +"""兴邦金租数据层模块 + +提供统一的数据访问接口： +- ERP 数据库连接（加密） +- 历史评审案例向量库 +- 主审员画像库 +- 团队集体经验库 +""" + +from .erp_connector import ERPConnector +from .history_store import HistoryStore +from .reviewer_profile import ReviewerProfileStore +from .team_experience import TeamExperienceStore + +__all__ = [ + "ERPConnector", + "HistoryStore", + "ReviewerProfileStore", + "TeamExperienceStore", +] diff --git a/backend/data_layer/erp_connector.py b/backend/data_layer/erp_connector.py new file mode 100644 index 0000000..a54939f --- /dev/null +++ b/backend/data_layer/erp_connector.py @@ -0,0 +1,301 @@ +"""ERP 数据库连接器（加密+只读） + +安全措施： +1. SSL/TLS 加密连接 +2. 只读账号（SELECT 权限） +3. 敏感字段脱敏 +4. 连接池管理 +5. 审计日志 +""" + +import os +import logging +from typing import Dict, List, Optional, Any +from datetime import datetime +import asyncio +from contextlib import asynccontextmanager + +# 使用 asyncpg 作为 PostgreSQL 连接器（如果 ERP 是 PostgreSQL） +# 如果是 MySQL，则使用 aiomysql +# 如果是 Oracle，则使用 cx_Oracle + +logger = logging.getLogger(__name__) + + +class ERPConnector: + """ERP 数据库连接器（加密+只读）""" + + def __init__(self): + self.host = os.getenv("ERP_DB_HOST") + self.port = int(os.getenv("ERP_DB_PORT", "5432")) + self.database = os.getenv("ERP_DB_NAME") + self.user = os.getenv("ERP_DB_USER") # 只读账号 + self.password = os.getenv("ERP_DB_PASSWORD") + self.ssl_mode = os.getenv("ERP_DB_SSL_MODE", "require") + + self._pool = None + self._audit_log_path = os.getenv("ERP_AUDIT_LOG", "./logs/erp_access.log") + + async def initialize(self): + """初始化连接池""" + if self._pool is not None: + return + + try: + # 这里以 asyncpg (PostgreSQL) 为例 + import asyncpg + + self._pool = await asyncpg.create_pool( + host=self.host, + port=self.port, + database=self.database, + user=self.user, + password=self.password, + ssl=self.ssl_mode, + min_size=2, + max_size=10, + command_timeout=30, + ) + logger.info(f"ERP 连接池初始化成功: {self.host}:{self.port}/{self.database}") + except ImportError: + logger.warning("asyncpg 未安装，使用模拟数据模式") + self._pool = "mock" # 标记为模拟模式 + except Exception as e: + logger.error(f"ERP 连接池初始化失败: {e}") + self._pool = "mock" + + async def close(self): + """关闭连接池""" + if self._pool and self._pool != "mock": + await self._pool.close() + self._pool = None + + def _audit_log(self, operation: str, table: str, filters: Dict): + """记录数据访问审计日志""" + timestamp = datetime.now().isoformat() + log_entry = f"[{timestamp}] {operation} | table={table} | filters={filters}\n" + + os.makedirs(os.path.dirname(self._audit_log_path), exist_ok=True) + with open(self._audit_log_path, "a", encoding="utf-8") as f: + f.write(log_entry) + + def _mask_sensitive_fields(self, data: Dict, sensitive_fields: List[str]) -> Dict: + """脱敏敏感字段""" + masked = data.copy() + for field in sensitive_fields: + if field in masked and masked[field]: + value = str(masked[field]) + if len(value) > 4: + masked[field] = value[:2] + "***" + value[-2:] + else: + masked[field] = "***" + return masked + + async def get_project_by_id(self, project_id: str) -> Optional[Dict]: + """根据项目 ID 获取项目基础数据""" + self._audit_log("SELECT", "projects", {"project_id": project_id}) + + if self._pool == "mock": + return self._mock_project_data(project_id) + + async with self._pool.acquire() as conn: + query = """ + SELECT + project_id, project_name, client_name, industry, + credit_amount, lease_object, lease_value, status, + created_at, updated_at + FROM projects + WHERE project_id = $1 + """ + row = await conn.fetchrow(query, project_id) + return dict(row) if row else None + + async def get_client_financial_data(self, client_name: str) -> Dict: + """获取客户财务数据""" + self._audit_log("SELECT", "client_financials", {"client_name": client_name}) + + if self._pool == "mock": + return self._mock_financial_data(client_name) + + async with self._pool.acquire() as conn: + query = """ + SELECT + registered_capital, established_years, + asset_liability_ratio, current_ratio, quick_ratio, + net_profit_2024, net_profit_2025, revenue_growth_rate, + roe, debt_to_equity_ratio + FROM client_financials + WHERE client_name = $1 + ORDER BY fiscal_year DESC + LIMIT 1 + """ + row = await conn.fetchrow(query, client_name) + return dict(row) if row else {} + + async def get_credit_report(self, client_name: str) -> Dict: + """获取征信报告（敏感信息脱敏）""" + self._audit_log("SELECT", "credit_reports", {"client_name": client_name}) + + if self._pool == "mock": + return self._mock_credit_report(client_name) + + async with self._pool.acquire() as conn: + query = """ + SELECT + overdue_count, query_count_recent_6m, + credit_limit, enforcement_records, + last_update_date + FROM credit_reports + WHERE client_name = $1 + ORDER BY last_update_date DESC + LIMIT 1 + """ + row = await conn.fetchrow(query, client_name) + data = dict(row) if row else {} + + # 脱敏：隐藏具体授信额度的前几位 + return self._mask_sensitive_fields(data, ["credit_limit"]) + + async def get_litigation_records(self, client_name: str) -> List[Dict]: + """获取涉诉记录""" + self._audit_log("SELECT", "litigation_records", {"client_name": client_name}) + + if self._pool == "mock": + return self._mock_litigation_records(client_name) + + async with self._pool.acquire() as conn: + query = """ + SELECT + case_number, court, case_type, status, + amount, filing_date, verdict_date + FROM litigation_records + WHERE client_name = $1 + ORDER BY filing_date DESC + LIMIT 10 + """ + rows = await conn.fetch(query, client_name) + return [dict(row) for row in rows] + + async def get_insurance_policies(self, project_id: str) -> List[Dict]: + """获取保单信息""" + self._audit_log("SELECT", "insurance_policies", {"project_id": project_id}) + + if self._pool == "mock": + return self._mock_insurance_policies(project_id) + + async with self._pool.acquire() as conn: + query = """ + SELECT + policy_number, insurance_company, policy_type, + coverage_amount, premium, start_date, end_date, + policy_pdf_url + FROM insurance_policies + WHERE project_id = $1 + ORDER BY start_date DESC + """ + rows = await conn.fetch(query, project_id) + return [dict(row) for row in rows] + + async def get_reviewer_workload(self) -> List[Dict]: + """获取主审员当前工作负荷""" + self._audit_log("SELECT", "reviewer_workload", {}) + + if self._pool == "mock": + return self._mock_reviewer_workload() + + async with self._pool.acquire() as conn: + query = """ + SELECT + reviewer_name, + COUNT(*) as current_projects, + STRING_AGG(industry, ',') as industries + FROM project_reviews + WHERE status IN ('待评审', '评审中') + GROUP BY reviewer_name + ORDER BY current_projects DESC + """ + rows = await conn.fetch(query) + return [dict(row) for row in rows] + + # ============ Mock 数据（用于测试/演示）============ + + def _mock_project_data(self, project_id: str) -> Dict: + """模拟项目数据""" + return { + "project_id": project_id, + "project_name": "阳光光伏设备融资租赁项目", + "client_name": "阳光光伏设备有限公司", + "industry": "光伏设备制造", + "credit_amount": 5000.0, + "lease_object": "光伏组件生产线设备", + "lease_value": 6500.0, + "status": "待评审", + "created_at": datetime.now(), + "updated_at": datetime.now(), + } + + def _mock_financial_data(self, client_name: str) -> Dict: + """模拟财务数据""" + return { + "registered_capital": 8000, + "established_years": 6, + "asset_liability_ratio": 0.58, + "current_ratio": 1.45, + "quick_ratio": 1.2, + "net_profit_2024": 1200, + "net_profit_2025": 1450, + "revenue_growth_rate": 0.18, + "roe": 0.15, + "debt_to_equity_ratio": 1.38, + } + + def _mock_credit_report(self, client_name: str) -> Dict: + """模拟征信报告""" + return { + "overdue_count": 0, + "query_count_recent_6m": 4, + "credit_limit": "20***00", # 已脱敏 + "enforcement_records": 0, + "last_update_date": datetime.now().date(), + } + + def _mock_litigation_records(self, client_name: str) -> List[Dict]: + """模拟涉诉记录""" + return [] + + def _mock_insurance_policies(self, project_id: str) -> List[Dict]: + """模拟保单数据""" + return [ + { + "policy_number": "PICC-2026-001234", + "insurance_company": "中国人保财险", + "policy_type": "财产一切险", + "coverage_amount": 7000.0, + "premium": 35.0, + "start_date": datetime(2026, 1, 1).date(), + "end_date": datetime(2029, 12, 31).date(), + "policy_pdf_url": "/policies/PICC-2026-001234.pdf", + } + ] + + def _mock_reviewer_workload(self) -> List[Dict]: + """模拟主审员负荷""" + return [ + {"reviewer_name": "张强", "current_projects": 7, "industries": "交通运输,制造业"}, + {"reviewer_name": "李明", "current_projects": 5, "industries": "制造业,新能源"}, + {"reviewer_name": "刘洋", "current_projects": 4, "industries": "医疗健康,服务业"}, + {"reviewer_name": "王芳", "current_projects": 3, "industries": "新能源,光伏"}, + ] + + +# 全局单例 +_erp_connector: Optional[ERPConnector] = None + + +async def get_erp_connector() -> ERPConnector: + """获取 ERP 连接器单例""" + global _erp_connector + if _erp_connector is None: + _erp_connector = ERPConnector() + await _erp_connector.initialize() + return _erp_connector diff --git a/backend/data_layer/history_store.py b/backend/data_layer/history_store.py new file mode 100644 index 0000000..d5f16d2 --- /dev/null +++ b/backend/data_layer/history_store.py @@ -0,0 +1,209 @@ +"""历史评审案例向量库 + +功能： +1. 存储历史评审案例（项目特征+评审结果+评审员行为） +2. 相似案例检索（基于向量相似度） +3. 评审员行为学习（团队集体经验，非个人预测） +""" + +import json +import os +from typing import List, Dict, Optional, Tuple +from datetime import datetime +from pathlib import Path +import logging + +logger = logging.getLogger(__name__) + + +class HistoryStore: + """历史评审案例存储（简化版，生产环境建议使用 Chroma/Qdrant）""" + + def __init__(self, data_dir: str = "./xingbang_data/history"): + self.data_dir = Path(data_dir) + self.data_dir.mkdir(parents=True, exist_ok=True) + self.cases_file = self.data_dir / "review_cases.jsonl" + + # 确保文件存在 + if not self.cases_file.exists(): + self.cases_file.touch() + + def add_case(self, case_data: Dict) -> str: + """添加一条历史评审案例""" + case_id = f"case_{datetime.now().strftime('%Y%m%d%H%M%S')}" + + case_record = { + "case_id": case_id, + "timestamp": datetime.now().isoformat(), + **case_data + } + + # 追加写入 JSONL + with open(self.cases_file, "a", encoding="utf-8") as f: + f.write(json.dumps(case_record, ensure_ascii=False) + "\n") + + logger.info(f"案例已保存: {case_id}") + return case_id + + def search_similar_cases( + self, + industry: str, + credit_amount: float, + risk_level: str = None, + limit: int = 5 + ) -> List[Dict]: + """检索相似案例（简化版：基于行业+金额范围）""" + + if not self.cases_file.exists(): + return [] + + similar_cases = [] + + with open(self.cases_file, "r", encoding="utf-8") as f: + for line in f: + if not line.strip(): + continue + + try: + case = json.loads(line) + + # 行业匹配 + industry_match = industry in case.get("industry", "") or case.get("industry", "") in industry + + # 金额范围匹配（±30%） + case_amount = case.get("credit_amount", 0) + amount_match = 0.7 * credit_amount <= case_amount <= 1.3 * credit_amount + + # 风险等级匹配（可选） + risk_match = risk_level is None or case.get("risk_level") == risk_level + + if industry_match and amount_match and risk_match: + similar_cases.append(case) + + except json.JSONDecodeError: + continue + + # 按时间倒序返回最近的案例 + similar_cases.sort(key=lambda x: x.get("timestamp", ""), reverse=True) + return similar_cases[:limit] + + def get_case_by_id(self, case_id: str) -> Optional[Dict]: + """根据 ID 获取案例详情""" + + if not self.cases_file.exists(): + return None + + with open(self.cases_file, "r", encoding="utf-8") as f: + for line in f: + if not line.strip(): + continue + + try: + case = json.loads(line) + if case.get("case_id") == case_id: + return case + except json.JSONDecodeError: + continue + + return None + + def get_team_patterns(self, industry: str = None) -> Dict: + """提取团队集体经验模式（高频风险点、常见处理方式）""" + + if not self.cases_file.exists(): + return {"high_frequency_risks": [], "common_solutions": []} + + risk_counter = {} + solution_counter = {} + + with open(self.cases_file, "r", encoding="utf-8") as f: + for line in f: + if not line.strip(): + continue + + try: + case = json.loads(line) + + # 行业过滤 + if industry and industry not in case.get("industry", ""): + continue + + # 统计高频风险点 + for risk in case.get("identified_risks", []): + risk_key = risk.get("type", "unknown") + risk_counter[risk_key] = risk_counter.get(risk_key, 0) + 1 + + # 统计常见解决方案 + solution = case.get("solution", "") + if solution: + solution_counter[solution] = solution_counter.get(solution, 0) + 1 + + except json.JSONDecodeError: + continue + + # 排序 + high_frequency_risks = sorted( + [{"risk": k, "count": v} for k, v in risk_counter.items()], + key=lambda x: x["count"], + reverse=True + )[:10] + + common_solutions = sorted( + [{"solution": k, "count": v} for k, v in solution_counter.items()], + key=lambda x: x["count"], + reverse=True + )[:10] + + return { + "high_frequency_risks": high_frequency_risks, + "common_solutions": common_solutions, + } + + def get_statistics(self) -> Dict: + """获取统计数据""" + + if not self.cases_file.exists(): + return {"total_cases": 0} + + total_cases = 0 + industry_dist = {} + approval_rate = {"approved": 0, "rejected": 0, "pending": 0} + + with open(self.cases_file, "r", encoding="utf-8") as f: + for line in f: + if not line.strip(): + continue + + try: + case = json.loads(line) + total_cases += 1 + + # 行业分布 + industry = case.get("industry", "未知") + industry_dist[industry] = industry_dist.get(industry, 0) + 1 + + # 通过率 + result = case.get("review_result", "pending") + if result in approval_rate: + approval_rate[result] += 1 + + except json.JSONDecodeError: + continue + + return { + "total_cases": total_cases, + "industry_distribution": industry_dist, + "approval_rate": approval_rate, + } + + +# 全局单例 +_history_store: Optional[HistoryStore] = None + + +def get_history_store() -> HistoryStore: + """获取历史案例存储单例""" + global _history_store + if _history_store is None: + _history_store = HistoryStore() + return _history_store diff --git a/backend/data_layer/reviewer_profile.py b/backend/data_layer/reviewer_profile.py new file mode 100644 index 0000000..e32c86a --- /dev/null +++ b/backend/data_layer/reviewer_profile.py @@ -0,0 +1,262 @@ +"""主审员画像库 + +功能： +1. 存储主审员基础信息（专长、效率、质量评分） +2. 动态更新主审员负荷 +3. 多维度评分（用于智能分配） +""" + +import json +from typing import Dict, List, Optional +from pathlib import Path +from datetime import datetime +import logging + +logger = logging.getLogger(__name__) + + +class ReviewerProfileStore: + """主审员画像存储""" + + def __init__(self, data_dir: str = "./xingbang_data/reviewers"): + self.data_dir = Path(data_dir) + self.data_dir.mkdir(parents=True, exist_ok=True) + self.profiles_file = self.data_dir / "reviewer_profiles.json" + + # 初始化默认数据 + if not self.profiles_file.exists(): + self._init_default_profiles() + + def _init_default_profiles(self): + """初始化默认主审员画像""" + default_profiles = { + "李明": { + "name": "李明", + "specialty": ["制造业", "新能源"], + "current_projects": 5, + "avg_completion_days": 8, + "quality_score": 0.85, # 0-1，基于历史评审质量 + "efficiency_score": 0.78, # 0-1，基于完成速度 + "workload_capacity": 7, # 最大承载项目数 + "industry_experience": { + "制造业": 45, # 历史评审项目数 + "新能源": 32, + "光伏": 18, + }, + "created_at": datetime.now().isoformat(), + "updated_at": datetime.now().isoformat(), + }, + "王芳": { + "name": "王芳", + "specialty": ["新能源", "光伏", "风电"], + "current_projects": 3, + "avg_completion_days": 7, + "quality_score": 0.92, + "efficiency_score": 0.88, + "workload_capacity": 6, + "industry_experience": { + "新能源": 56, + "光伏": 38, + "风电": 22, + }, + "created_at": datetime.now().isoformat(), + "updated_at": datetime.now().isoformat(), + }, + "张强": { + "name": "张强", + "specialty": ["交通运输", "物流", "制造业"], + "current_projects": 7, + "avg_completion_days": 9, + "quality_score": 0.80, + "efficiency_score": 0.72, + "workload_capacity": 8, + "industry_experience": { + "交通运输": 67, + "物流": 43, + "制造业": 28, + }, + "created_at": datetime.now().isoformat(), + "updated_at": datetime.now().isoformat(), + }, + "刘洋": { + "name": "刘洋", + "specialty": ["医疗健康", "服务业", "消费"], + "current_projects": 4, + "avg_completion_days": 8, + "quality_score": 0.88, + "efficiency_score": 0.82, + "workload_capacity": 7, + "industry_experience": { + "医疗健康": 51, + "服务业": 34, + "消费": 29, + }, + "created_at": datetime.now().isoformat(), + "updated_at": datetime.now().isoformat(), + }, + } + + with open(self.profiles_file, "w", encoding="utf-8") as f: + json.dump(default_profiles, f, ensure_ascii=False, indent=2) + + logger.info("主审员画像初始化完成") + + def get_all_profiles(self) -> Dict[str, Dict]: + """获取所有主审员画像""" + with open(self.profiles_file, "r", encoding="utf-8") as f: + return json.load(f) + + def get_profile(self, reviewer_name: str) -> Optional[Dict]: + """获取指定主审员画像""" + profiles = self.get_all_profiles() + return profiles.get(reviewer_name) + + def update_workload(self, reviewer_name: str, current_projects: int): + """更新主审员工作负荷""" + profiles = self.get_all_profiles() + + if reviewer_name in profiles: + profiles[reviewer_name]["current_projects"] = current_projects + profiles[reviewer_name]["updated_at"] = datetime.now().isoformat() + + with open(self.profiles_file, "w", encoding="utf-8") as f: + json.dump(profiles, f, ensure_ascii=False, indent=2) + + logger.info(f"已更新 {reviewer_name} 的负荷: {current_projects} 个项目") + + def update_quality_score(self, reviewer_name: str, quality_score: float): + """更新主审员质量评分""" + profiles = self.get_all_profiles() + + if reviewer_name in profiles: + profiles[reviewer_name]["quality_score"] = quality_score + profiles[reviewer_name]["updated_at"] = datetime.now().isoformat() + + with open(self.profiles_file, "w", encoding="utf-8") as f: + json.dump(profiles, f, ensure_ascii=False, indent=2) + + logger.info(f"已更新 {reviewer_name} 的质量评分: {quality_score:.2f}") + + def calculate_match_score( + self, + reviewer_name: str, + project_industry: str, + project_complexity: int = 3 + ) -> Dict: + """计算主审员与项目的匹配度（多维度评分） + + 评分维度： + - 35% 行业匹配度（专长+历史经验） + - 35% 可用性（当前负荷） + - 20% 质量评分（历史评审质量） + - 10% 团队平衡（避免某人长期超载） + """ + profile = self.get_profile(reviewer_name) + if not profile: + return {"total_score": 0, "breakdown": {}} + + # 1. 行业匹配度（35%） + industry_match = 0 + if any(spec in project_industry or project_industry in spec for spec in profile["specialty"]): + industry_match = 0.8 # 专长匹配 + + # 检查历史经验 + exp_count = 0 + for industry_key in profile["industry_experience"]: + if industry_key in project_industry or project_industry in industry_key: + exp_count = profile["industry_experience"][industry_key] + break + + if exp_count >= 30: + industry_match = 1.0 + elif exp_count >= 10: + industry_match = max(industry_match, 0.6) + elif exp_count > 0: + industry_match = max(industry_match, 0.3) + + industry_score = industry_match * 35 + + # 2. 可用性（35%） + current_load = profile["current_projects"] + capacity = profile["workload_capacity"] + + if current_load >= capacity: + availability = 0.1 # 超载 + elif current_load >= capacity * 0.8: + availability = 0.5 # 偏高 + else: + availability = 1.0 - (current_load / capacity) # 线性递减 + + availability_score = availability * 35 + + # 3. 质量评分（20%） + quality_score = profile["quality_score"] * 20 + + # 4. 团队平衡（10%）- 优先分配给负荷较低的 + all_profiles = self.get_all_profiles() + avg_load = sum(p["current_projects"] for p in all_profiles.values()) / len(all_profiles) + + if current_load < avg_load: + balance_score = 10 + elif current_load == avg_load: + balance_score = 7 + else: + balance_score = max(0, 10 - (current_load - avg_load) * 2) + + # 总分 + total_score = industry_score + availability_score + quality_score + balance_score + + return { + "total_score": round(total_score, 2), + "breakdown": { + "industry_match": round(industry_score, 2), + "availability": round(availability_score, 2), + "quality": round(quality_score, 2), + "balance": round(balance_score, 2), + }, + "estimated_days": profile["avg_completion_days"] + current_load // 2, # 考虑负荷影响 + } + + def get_recommended_reviewers( + self, + project_industry: str, + project_complexity: int = 3, + top_n: int = 3 + ) -> List[Dict]: + """获取推荐主审员列表（Top N）""" + all_profiles = self.get_all_profiles() + + recommendations = [] + for reviewer_name in all_profiles: + match_result = self.calculate_match_score( + reviewer_name, + project_industry, + project_complexity + ) + + profile = all_profiles[reviewer_name] + recommendations.append({ + "reviewer": reviewer_name, + "score": match_result["total_score"], + "breakdown": match_result["breakdown"], + "estimated_days": match_result["estimated_days"], + "current_projects": profile["current_projects"], + "specialty": profile["specialty"], + }) + + # 按总分降序排序 + recommendations.sort(key=lambda x: x["score"], reverse=True) + + return recommendations[:top_n] + + +# 全局单例 +_reviewer_store: Optional[ReviewerProfileStore] = None + + +def get_reviewer_store() -> ReviewerProfileStore: + """获取主审员画像存储单例""" + global _reviewer_store + if _reviewer_store is None: + _reviewer_store = ReviewerProfileStore() + return _reviewer_store diff --git a/backend/data_layer/team_experience.py b/backend/data_layer/team_experience.py new file mode 100644 index 0000000..a3e73de --- /dev/null +++ b/backend/data_layer/team_experience.py @@ -0,0 +1,195 @@ +"""团队集体经验库 + +功能： +1. 学习团队历史评审行为（聚合，非个人） +2. 提取高频风险模式 +3. 为预审助手提供经验支持 +""" + +import json +from typing import Dict, List, Optional +from pathlib import Path +from datetime import datetime +import logging + +logger = logging.getLogger(__name__) + + +class TeamExperienceStore: + """团队集体经验存储""" + + def __init__(self, data_dir: str = "./xingbang_data/team_experience"): + self.data_dir = Path(data_dir) + self.data_dir.mkdir(parents=True, exist_ok=True) + self.experience_file = self.data_dir / "team_patterns.json" + + # 初始化默认经验模式 + if not self.experience_file.exists(): + self._init_default_patterns() + + def _init_default_patterns(self): + """初始化默认团队经验模式""" + default_patterns = { + "risk_patterns": { + "制造业": [ + { + "risk_type": "应收账款集中度过高", + "frequency": 23, + "typical_threshold": "单一客户占比 > 30%", + "team_action": "要求补充第二担保人或增加保证金", + }, + { + "risk_type": "原材料价格波动风险", + "frequency": 18, + "typical_threshold": "大宗商品占成本 > 50%", + "team_action": "要求提供价格锁定合同或套期保值证明", + }, + ], + "新能源": [ + { + "risk_type": "政策依赖度高", + "frequency": 34, + "typical_threshold": "补贴收入 > 营收 20%", + "team_action": "降低授信额度或缩短租赁期限", + }, + { + "risk_type": "技术迭代淘汰风险", + "frequency": 21, + "typical_threshold": "设备技术落后 2 代以上", + "team_action": "要求租赁物残值担保或回购承诺", + }, + ], + "交通运输": [ + { + "risk_type": "油价波动影响", + "frequency": 15, + "typical_threshold": "燃油成本 > 营收 40%", + "team_action": "要求提供运价调整机制证明", + }, + ], + }, + "approval_thresholds": { + "资产负债率": { + "safe": 0.6, + "warning": 0.7, + "critical": 0.8, + "team_consensus": "≤60% 低风险，60-70% 中风险需补充说明，>70% 高风险需额外担保", + }, + "流动比率": { + "safe": 1.5, + "warning": 1.2, + "critical": 1.0, + "team_consensus": "≥1.5 优秀，1.2-1.5 合格，<1.2 需关注短期偿债能力", + }, + "净利润增长率": { + "safe": 0.15, + "warning": 0.05, + "critical": 0.0, + "team_consensus": "持续两年负增长通常不予通过", + }, + }, + "industry_red_flags": { + "光伏": ["产能严重过剩", "欧美双反调查", "地方政府欠补"], + "房地产": ["三道红线踩线", "土地储备不足", "回款周期超18个月"], + "餐饮": ["单店模型未跑通", "加盟商纠纷", "食品安全事故"], + }, + "common_rejection_reasons": [ + {"reason": "担保方资产不足", "count": 45, "solution": "要求补充第二担保人"}, + {"reason": "征信查询次数过多", "count": 32, "solution": "要求客户说明查询原因"}, + {"reason": "涉诉金额过大", "count": 28, "solution": "等待诉讼结果或要求提供和解协议"}, + {"reason": "租赁物适格性存疑", "count": 25, "solution": "要求第三方评估报告"}, + ], + "metadata": { + "last_updated": datetime.now().isoformat(), + "total_cases_analyzed": 156, + "data_source": "2024-2025 年历史评审案例", + }, + } + + with open(self.experience_file, "w", encoding="utf-8") as f: + json.dump(default_patterns, f, ensure_ascii=False, indent=2) + + logger.info("团队经验模式初始化完成") + + def get_risk_patterns(self, industry: str = None) -> List[Dict]: + """获取风险模式（可按行业过滤）""" + with open(self.experience_file, "r", encoding="utf-8") as f: + data = json.load(f) + + risk_patterns = data.get("risk_patterns", {}) + + if industry: + # 精确匹配或模糊匹配 + for key in risk_patterns: + if industry in key or key in industry: + return risk_patterns[key] + return [] + else: + # 返回所有模式 + all_patterns = [] + for patterns in risk_patterns.values(): + all_patterns.extend(patterns) + return all_patterns + + def get_approval_thresholds(self) -> Dict: + """获取团队共识的审批阈值""" + with open(self.experience_file, "r", encoding="utf-8") as f: + data = json.load(f) + + return data.get("approval_thresholds", {}) + + def get_industry_red_flags(self, industry: str) -> List[str]: + """获取行业红线（团队总结的高危信号）""" + with open(self.experience_file, "r", encoding="utf-8") as f: + data = json.load(f) + + industry_flags = data.get("industry_red_flags", {}) + + # 模糊匹配 + for key in industry_flags: + if industry in key or key in industry: + return industry_flags[key] + + return [] + + def get_common_rejection_reasons(self, top_n: int = 10) -> List[Dict]: + """获取高频退回原因及解决方案""" + with open(self.experience_file, "r", encoding="utf-8") as f: + data = json.load(f) + + reasons = data.get("common_rejection_reasons", []) + return reasons[:top_n] + + def update_pattern(self, pattern_type: str, pattern_data: Dict): + """更新经验模式（用于持续学习）""" + with open(self.experience_file, "r", encoding="utf-8") as f: + data = json.load(f) + + # 更新指定类型的模式 + if pattern_type in data: + data[pattern_type].update(pattern_data) + data["metadata"]["last_updated"] = datetime.now().isoformat() + + with open(self.experience_file, "w", encoding="utf-8") as f: + json.dump(data, f, ensure_ascii=False, indent=2) + + logger.info(f"团队经验模式已更新: {pattern_type}") + + def get_metadata(self) -> Dict: + """获取元数据""" + with open(self.experience_file, "r", encoding="utf-8") as f: + data = json.load(f) + + return data.get("metadata", {}) + + +# 全局单例 +_team_experience_store: Optional[TeamExperienceStore] = None + + +def get_team_experience_store() -> TeamExperienceStore: + """获取团队经验存储单例""" + global _team_experience_store + if _team_experience_store is None: + _team_experience_store = TeamExperienceStore() + return _team_experience_store diff --git a/backend/graph/agent.py b/backend/graph/agent.py index 46e8046..45f0813 100644 --- a/backend/graph/agent.py +++ b/backend/graph/agent.py @@ -19,12 +19,17 @@ class AgentManager: self._base_dir: Path | None = None self._tools: list = [] self._llm = None + self._raw_logs_dir: Path | None = None def initialize(self, base_dir: Path) -> None: """Initialize LLM (DeepSeek) and tools. Called once at startup.""" self._base_dir = base_dir self._tools = get_all_tools(base_dir) + # 初始化原始报文日志目录 (base_dir 已是 backend/ 目录) + self._raw_logs_dir = base_dir / "raw_logs" + self._raw_logs_dir.mkdir(exist_ok=True, parents=True) + # Use langchain-deepseek for native DeepSeek support from langchain_deepseek import ChatDeepSeek @@ -70,7 +75,7 @@ class AgentManager: return messages async def astream( - self, message: str, history: list[dict[str, Any]] + self, message: str, history: list[dict[str, Any]], session_id: str = "default" ) -> AsyncGenerator[dict[str, Any], None]: """Stream agent response with token-level and node-level events. @@ -80,6 +85,8 @@ class AgentManager: {"type": "tool_start", "tool": "...", "input": "..."} {"type": "tool_end", "tool": "...", "output": "..."} {"type": "done", "content": "..."} + + Also persists complete message flow to {session}.msgs.jsonl for trajectory. """ # RAG retrieval: inject memory context if enabled rag_mode = get_rag_mode() @@ -101,6 +108,31 @@ class AgentManager: ) rag_context = f"[记忆检索结果]\n{snippets}" + # 为当前 session 创建原始报文回调 + from graph.raw_trace import RawTraceCallback + + raw_callback = RawTraceCallback(session_id, self._raw_logs_dir) + + # 消息流日志文件（记录完整的 human/assistant/tool 消息序列） + import json + import time + msgs_log_file = self._raw_logs_dir / f"{session_id}.msgs.jsonl" + + # 记录 human 消息 + def _append_msg(role: str, content: str, **kwargs): + """追加一条消息到 msgs.jsonl""" + msg_record = { + "role": role, + "content": content, + "timestamp": time.time(), + **kwargs + } + with open(msgs_log_file, "a", encoding="utf-8") as f: + f.write(json.dumps(msg_record, ensure_ascii=False) + "\n") + + _append_msg("human", message) + + # 构建 agent 时传入 callback agent = self._build_agent() # Build messages with optional RAG context appended to history @@ -113,10 +145,13 @@ class AgentManager: full_response = "" tools_just_finished = False + current_assistant_content = "" # 当前 assistant 消息累积 + current_tool_calls = [] # 当前 assistant 的 tool_calls async for event in agent.astream( {"messages": messages}, stream_mode=["messages", "updates"], + config={"callbacks": [raw_callback]}, ): # event is a tuple of (stream_mode, data) when using multiple modes if isinstance(event, tuple): @@ -136,31 +171,65 @@ class AgentManager: yield {"type": "new_response"} tools_just_finished = False full_response += msg.content + current_assistant_content += msg.content yield {"type": "token", "content": msg.content} elif mode == "updates": if isinstance(data, dict): for node_name, node_data in data.items(): - if node_name == "tools" and "messages" in node_data: - for tool_msg in node_data["messages"]: - if hasattr(tool_msg, "name"): - yield { - "type": "tool_end", - "tool": tool_msg.name, - "output": str(tool_msg.content)[:2000], - } - # After all tool results, mark that tools finished - tools_just_finished = True - elif node_name == "model" and "messages" in node_data: + if node_name == "model" and "messages" in node_data: + # 模型节点：记录 assistant 消息和 tool_calls for agent_msg in node_data["messages"]: if hasattr(agent_msg, "tool_calls") and agent_msg.tool_calls: + # 记录 assistant 消息（带 tool_calls） + tool_calls_data = [] for tc in agent_msg.tool_calls: + tool_call_info = { + "name": tc["name"], + "args": tc.get("args", {}) + } + tool_calls_data.append(tool_call_info) + current_tool_calls.append(tool_call_info) yield { "type": "tool_start", "tool": tc["name"], "input": str(tc.get("args", ""))[:1000], } + # 记录带 tool_calls 的 assistant 消息 + _append_msg( + "assistant", + agent_msg.content if hasattr(agent_msg, "content") else "", + tool_calls=tool_calls_data + ) + + elif node_name == "tools" and "messages" in node_data: + # 工具节点：记录每个 ToolMessage 的返回内容 + for tool_msg in node_data["messages"]: + if hasattr(tool_msg, "name"): + tool_output = str(tool_msg.content) + # 记录 tool 消息（完整 content） + _append_msg( + "tool", + tool_output, + name=tool_msg.name, + tool_call_id=getattr(tool_msg, "tool_call_id", None) + ) + yield { + "type": "tool_end", + "tool": tool_msg.name, + "output": tool_output[:2000], # yield 时截断，但完整内容已记录 + } + # After all tool results, mark that tools finished + tools_just_finished = True + + # 记录最终的 assistant 消息（纯回复，无 tool_calls） + if current_assistant_content and not current_tool_calls: + _append_msg("assistant", current_assistant_content) + elif current_assistant_content and current_tool_calls: + # 如果有 tool_calls，记录最后一轮的纯文本回复 + _append_msg("assistant", current_assistant_content) + yield {"type": "done", "content": full_response} async def ainvoke(self, message: str, session_id: str) -> str: diff --git a/backend/graph/memory_indexer.py b/backend/graph/memory_indexer.py index 089d483..483ace8 100644 --- a/backend/graph/memory_indexer.py +++ b/backend/graph/memory_indexer.py @@ -65,6 +65,9 @@ class MemoryIndexer: # 使用 HuggingFace 镜像加速模型下载 if "HF_ENDPOINT" not in os.environ: os.environ["HF_ENDPOINT"] = "https://hf-mirror.com" + # 离线加载已缓存的嵌入模型(避免启动时联网挂起阻塞) + os.environ.setdefault("HF_HUB_OFFLINE", "1") + os.environ.setdefault("TRANSFORMERS_OFFLINE", "1") Settings.embed_model = HuggingFaceEmbedding( model_name=os.getenv("EMBEDDING_MODEL", "BAAI/bge-small-zh"), @@ -112,6 +115,9 @@ class MemoryIndexer: # 使用 HuggingFace 镜像加速模型下载 if "HF_ENDPOINT" not in os.environ: os.environ["HF_ENDPOINT"] = "https://hf-mirror.com" + # 离线加载已缓存的嵌入模型(避免联网挂起阻塞) + os.environ.setdefault("HF_HUB_OFFLINE", "1") + os.environ.setdefault("TRANSFORMERS_OFFLINE", "1") Settings.embed_model = HuggingFaceEmbedding( model_name=os.getenv("EMBEDDING_MODEL", "BAAI/bge-small-zh"), diff --git a/backend/graph/raw_trace.py b/backend/graph/raw_trace.py new file mode 100644 index 0000000..9a61d71 --- /dev/null +++ b/backend/graph/raw_trace.py @@ -0,0 +1,274 @@ +"""原始报文录制 - LangChain 回调处理器，捕获每次 LLM 调用的完整请求/响应""" + +import json +import os +import time +import traceback +import uuid +from pathlib import Path +from typing import Any, Dict, List, Optional + +from langchain_core.callbacks import AsyncCallbackHandler +from langchain_core.messages import BaseMessage +from langchain_core.outputs import LLMResult + + +class RawTraceCallback(AsyncCallbackHandler): + """录制每次 LLM 调用的完整原始请求和响应到 JSONL 文件""" + + def __init__(self, session_id: str, raw_logs_dir: Path): + super().__init__() + self.session_id = session_id + self.raw_logs_dir = raw_logs_dir + self.raw_logs_dir.mkdir(exist_ok=True, parents=True) + + # 当前调用的跟踪信息 (key: run_id, value: trace_data) + self._traces: Dict[str, Dict[str, Any]] = {} + + def _serialize_message(self, msg: BaseMessage) -> Dict[str, Any]: + """将 LangChain 消息序列化为 JSON-safe dict""" + try: + result = { + "type": msg.type, + "content": msg.content if hasattr(msg, "content") else "", + } + # 提取额外字段 + if hasattr(msg, "name"): + result["name"] = msg.name + if hasattr(msg, "tool_calls") and msg.tool_calls: + result["tool_calls"] = msg.tool_calls + if hasattr(msg, "additional_kwargs") and msg.additional_kwargs: + result["additional_kwargs"] = msg.additional_kwargs + return result + except Exception: + return {"type": "unknown", "content": str(msg)} + + async def on_llm_start( + self, + serialized: Dict[str, Any], + prompts: List[str], + *, + run_id: uuid.UUID, + parent_run_id: Optional[uuid.UUID] = None, + tags: Optional[List[str]] = None, + metadata: Optional[Dict[str, Any]] = None, + **kwargs: Any, + ) -> None: + """LLM 调用开始 - 记录请求信息""" + try: + # 提取 messages (如果是 chat model) + messages_raw = kwargs.get("invocation_params", {}).get("messages", []) + messages_serialized = [] + + if messages_raw: + for msg in messages_raw: + if isinstance(msg, BaseMessage): + messages_serialized.append(self._serialize_message(msg)) + elif isinstance(msg, dict): + messages_serialized.append(msg) + else: + messages_serialized.append({"content": str(msg)}) + elif prompts: + # 如果没有 messages，使用 prompts + messages_serialized = [{"type": "human", "content": p} for p in prompts] + + # 提取模型参数 + invocation_params = kwargs.get("invocation_params", {}) + model_name = invocation_params.get("model", "unknown") + temperature = invocation_params.get("temperature", None) + + trace_data = { + "trace_id": str(run_id), + "session_id": self.session_id, + "timestamp_start": time.time(), + "request": { + "model": model_name, + "messages": messages_serialized, + "temperature": temperature, + "invocation_params": { + k: v for k, v in invocation_params.items() + if k not in ["messages", "api_key"] # 过滤敏感信息 + }, + }, + } + + self._traces[str(run_id)] = trace_data + + except Exception: + traceback.print_exc() + + async def on_llm_end( + self, + response: LLMResult, + *, + run_id: uuid.UUID, + parent_run_id: Optional[uuid.UUID] = None, + **kwargs: Any, + ) -> None: + """LLM 调用结束 - 记录响应信息并落盘""" + try: + trace_id = str(run_id) + if trace_id not in self._traces: + return + + trace_data = self._traces[trace_id] + timestamp_end = time.time() + duration_ms = int((timestamp_end - trace_data["timestamp_start"]) * 1000) + + # 提取响应内容 + generations = response.generations[0] if response.generations else [] + response_messages = [] + + # 提取 finish_reason 和 usage (优先从 message.response_metadata，支持流式) + finish_reason = "unknown" + usage_metadata = None + + for gen in generations: + msg_dict = { + "text": gen.text if hasattr(gen, "text") else "", + } + + # 提取 message (AIMessage) + if hasattr(gen, "message") and gen.message: + msg = gen.message + msg_dict["message"] = self._serialize_message(msg) + + # 优先从 message.response_metadata 提取 finish_reason (流式模式) + if hasattr(msg, "response_metadata") and msg.response_metadata: + if "finish_reason" in msg.response_metadata and finish_reason == "unknown": + finish_reason = msg.response_metadata["finish_reason"] + # 提取流式聚合后的 usage_metadata + if "usage_metadata" in msg.response_metadata and not usage_metadata: + usage_metadata = msg.response_metadata["usage_metadata"] + + # 也从 message.usage_metadata 提取（LangChain 新版本） + if hasattr(msg, "usage_metadata") and msg.usage_metadata and not usage_metadata: + usage_metadata = msg.usage_metadata + + # 其次从 generation_info 提取 finish_reason + if finish_reason == "unknown" and hasattr(gen, "generation_info") and gen.generation_info: + if "finish_reason" in gen.generation_info: + finish_reason = gen.generation_info["finish_reason"] + + response_messages.append(msg_dict) + + # 提取 llm_output (包含 usage 等信息) + llm_output = response.llm_output or {} + + trace_data["timestamp_end"] = timestamp_end + trace_data["duration_ms"] = duration_ms + trace_data["response"] = { + "generations": response_messages, + "llm_output": llm_output, + } + + # 提取 token usage：优先使用 usage_metadata（流式），其次 llm_output + if usage_metadata: + # usage_metadata 通常是字典或对象，尝试兼容两种格式 + if isinstance(usage_metadata, dict): + trace_data["usage"] = { + "input_tokens": usage_metadata.get("input_tokens", 0), + "output_tokens": usage_metadata.get("output_tokens", 0), + "total_tokens": usage_metadata.get("total_tokens", 0), + } + else: + # 对象格式（有属性访问） + trace_data["usage"] = { + "input_tokens": getattr(usage_metadata, "input_tokens", 0), + "output_tokens": getattr(usage_metadata, "output_tokens", 0), + "total_tokens": getattr(usage_metadata, "total_tokens", 0), + } + else: + # 回退到 llm_output.token_usage（非流式） + token_usage = llm_output.get("token_usage", {}) + trace_data["usage"] = { + "input_tokens": token_usage.get("prompt_tokens", 0), + "output_tokens": token_usage.get("completion_tokens", 0), + "total_tokens": token_usage.get("total_tokens", 0), + } + + # 最后从 llm_output 兜底 finish_reason（非流式） + if finish_reason == "unknown" and "finish_reason" in llm_output: + finish_reason = llm_output["finish_reason"] + + # 统一字段：finish_reason（向后兼容，保留 stop_reason） + trace_data["finish_reason"] = finish_reason + trace_data["stop_reason"] = finish_reason # 向后兼容旧前端代码 + + # 追加写入 JSONL 文件 + log_file = self.raw_logs_dir / f"{self.session_id}.jsonl" + with open(log_file, "a", encoding="utf-8") as f: + f.write(json.dumps(trace_data, ensure_ascii=False) + "\n") + + # 清理已完成的 trace + del self._traces[trace_id] + + except Exception: + traceback.print_exc() + + async def on_llm_error( + self, + error: Exception, + *, + run_id: uuid.UUID, + parent_run_id: Optional[uuid.UUID] = None, + **kwargs: Any, + ) -> None: + """LLM 调用出错 - 记录错误""" + try: + trace_id = str(run_id) + if trace_id not in self._traces: + return + + trace_data = self._traces[trace_id] + timestamp_end = time.time() + duration_ms = int((timestamp_end - trace_data["timestamp_start"]) * 1000) + + trace_data["timestamp_end"] = timestamp_end + trace_data["duration_ms"] = duration_ms + trace_data["error"] = { + "type": type(error).__name__, + "message": str(error), + } + + # 写入文件 + log_file = self.raw_logs_dir / f"{self.session_id}.jsonl" + with open(log_file, "a", encoding="utf-8") as f: + f.write(json.dumps(trace_data, ensure_ascii=False) + "\n") + + del self._traces[trace_id] + + except Exception: + traceback.print_exc() + + +def load_raw_traces(session_id: str, raw_logs_dir: Path, limit: int = 10) -> List[Dict[str, Any]]: + """读取指定 session 的原始报文记录 + + Args: + session_id: 会话 ID + raw_logs_dir: 原始日志目录 + limit: 返回最近 N 条记录 + + Returns: + 原始报文列表，按时间倒序 + """ + log_file = raw_logs_dir / f"{session_id}.jsonl" + if not log_file.exists(): + return [] + + traces = [] + try: + with open(log_file, "r", encoding="utf-8") as f: + for line in f: + line = line.strip() + if line: + traces.append(json.loads(line)) + + # 按时间倒序，返回最近 limit 条 + traces.sort(key=lambda x: x.get("timestamp_start", 0), reverse=True) + return traces[:limit] + + except Exception: + traceback.print_exc() + return [] diff --git a/backend/kaiju_history_data.py b/backend/kaiju_history_data.py new file mode 100644 index 0000000..b34ae49 --- /dev/null +++ b/backend/kaiju_history_data.py @@ -0,0 +1,317 @@ +"""兴邦金租历史评审数据源（虚拟示例数据，2024-2026年） + +用于智能分配算法的数据驱动决策： +- 主审员产能画像（平均评审天数、行业专长、质量分） +- 历史项目追溯（按主审员/行业查询） +- 多维度分配推荐依据 +""" + +import random +from datetime import datetime, timedelta +from typing import List, Dict, Optional + +# ============ 历史评审项目数据结构 ============ + +class HistoricalProject: + """历史评审项目记录""" + def __init__( + self, + project_id: str, + project_name: str, + industry: str, + credit_amount: float, # 万元 + complexity: int, # 1-5，难度 + planned_days: int, # 计划评审天数 + actual_days: int, # 实际评审天数 + reviewer: str, # 主审员 + outcome: str, # 通过/否决/撤并 + quality_score: int, # 0-100，历史质量分 + review_date: str, # YYYY-MM-DD + ): + self.project_id = project_id + self.project_name = project_name + self.industry = industry + self.credit_amount = credit_amount + self.complexity = complexity + self.planned_days = planned_days + self.actual_days = actual_days + self.reviewer = reviewer + self.outcome = outcome + self.quality_score = quality_score + self.review_date = review_date + + def to_dict(self) -> dict: + return { + "project_id": self.project_id, + "project_name": self.project_name, + "industry": self.industry, + "credit_amount": self.credit_amount, + "complexity": self.complexity, + "planned_days": self.planned_days, + "actual_days": self.actual_days, + "reviewer": self.reviewer, + "outcome": self.outcome, + "quality_score": self.quality_score, + "review_date": self.review_date, + } + + +# ============ 虚拟数据生成（体现真实规律）============ + +def _generate_historical_data() -> List[HistoricalProject]: + """生成2024-2026年虚拟历史评审数据 + + 数据规律设计： + - 李明：制造业专长，效率高（实际天数<计划），质量分85-95 + - 王芳：新能源专长，质量高（90-98），但项目复杂度高时需时较长 + - 张强：交通运输专长，负荷较重（项目多），质量稳定80-90 + - 刘洋：医疗健康专长，细致但速度慢（实际天数略超计划），质量92-98 + - 陈晨：设备租赁专长，全行业覆盖，效率中等，质量78-88 + - 赵磊：环保行业专长，项目少但质量高，88-96 + """ + + projects = [] + project_counter = 1 + + # 主审员配置：(姓名, 主行业, 次要行业, 项目数, 效率倍数, 质量基数, 质量波动) + reviewers_config = [ + ("李明", "制造业", ["装备制造", "机械设备", "汽车零部件"], 22, 0.85, 88, 7), + ("王芳", "新能源", ["光伏设备", "风电设备", "新能源汽车"], 20, 1.05, 92, 6), + ("张强", "交通运输", ["物流运输", "港口码头", "公路客运"], 25, 0.95, 84, 8), + ("刘洋", "医疗健康", ["医疗器械", "医药制造", "医院设备"], 18, 1.15, 94, 4), + ("陈晨", "设备租赁", ["工程机械", "办公设备", "生产设备"], 20, 1.0, 82, 8), + ("赵磊", "环保", ["环保设备", "污水处理", "固废处理"], 16, 1.08, 90, 6), + ] + + # 行业项目名称模板 + industry_templates = { + "制造业": ["精密机械加工设备", "自动化生产线", "数控机床", "模具制造设备"], + "装备制造": ["重型装备", "专用设备", "成套装备"], + "机械设备": ["工业机器人", "包装机械", "印刷设备"], + "汽车零部件": ["汽车配件生产线", "发动机零部件", "车身冲压设备"], + "新能源": ["分布式光伏电站", "储能设备", "充电桩建设"], + "光伏设备": ["光伏组件生产线", "硅片切割设备", "光伏发电项目"], + "风电设备": ["风力发电机组", "风电场建设", "海上风电项目"], + "新能源汽车": ["新能源客车", "电动物流车", "充电设施"], + "交通运输": ["货运车队", "冷链物流车辆", "运输设备更新"], + "物流运输": ["智能仓储设备", "配送车辆", "物流信息系统"], + "港口码头": ["港口装卸设备", "集装箱堆场", "码头机械"], + "公路客运": ["客运班车", "旅游大巴", "公交车辆"], + "医疗健康": ["医学影像设备", "手术器械", "检验检测设备"], + "医疗器械": ["CT设备", "超声设备", "监护仪"], + "医药制造": ["制药生产线", "GMP车间改造", "原料药设备"], + "医院设备": ["病床护理设备", "消毒灭菌设备", "医用气体系统"], + "设备租赁": ["工程机械租赁", "办公设备租赁", "生产设备租赁"], + "工程机械": ["挖掘机", "起重机", "推土机"], + "办公设备": ["复印打印设备", "办公家具", "会议系统"], + "生产设备": ["注塑机", "冲压设备", "焊接设备"], + "环保": ["环保治理设备", "节能改造项目", "清洁能源设备"], + "环保设备": ["废气处理设备", "除尘设备", "脱硫脱硝装置"], + "污水处理": ["污水处理站", "中水回用系统", "污泥处理设备"], + "固废处理": ["垃圾焚烧设备", "危废处理设施", "资源回收系统"], + } + + # 生成每位主审员的历史项目 + start_date = datetime(2024, 1, 1) + end_date = datetime(2026, 8, 31) + + for reviewer_name, main_industry, sub_industries, num_projects, efficiency, quality_base, quality_range in reviewers_config: + + # 该主审员的行业池（主行业占70%，次要行业占30%） + industries_pool = [main_industry] * 7 + sub_industries * 3 + + for i in range(num_projects): + # 随机日期（制造业、新能源集中在2-6月、9-11月） + if main_industry in ["制造业", "新能源", "装备制造"]: + # 集中在业务旺季 + month_choices = [2, 3, 4, 5, 6, 9, 10, 11] + month = random.choice(month_choices) + else: + month = random.randint(1, 12) + + year = random.choice([2024, 2025, 2026]) + if year == 2026 and month > 8: + month = random.randint(1, 8) + + day = random.randint(1, 28) + review_date = datetime(year, month, day) + + # 行业选择（主行业为主） + industry = random.choice(industries_pool) + + # 项目名称 + company_prefix = random.choice([ + "阳光", "华润", "中联", "鑫盛", "宏泰", "瑞丰", "恒通", "盛达", + "金辉", "博瑞", "创新", "天成", "嘉诚", "永安", "兴旺", "腾飞" + ]) + equipment_name = random.choice(industry_templates.get(industry, ["设备租赁项目"])) + project_name = f"{company_prefix}{equipment_name}融资租赁项目" + + # 授信金额（万元）：复杂度越高金额越大 + complexity = random.choices([1, 2, 3, 4, 5], weights=[5, 15, 40, 30, 10])[0] + + if complexity == 1: + credit_amount = random.randint(500, 1500) + elif complexity == 2: + credit_amount = random.randint(1200, 3000) + elif complexity == 3: + credit_amount = random.randint(2500, 6000) + elif complexity == 4: + credit_amount = random.randint(5000, 10000) + else: # 5 + credit_amount = random.randint(8000, 20000) + + # 计划评审天数（复杂度决定） + planned_days = {1: 5, 2: 7, 3: 10, 4: 12, 5: 15}[complexity] + + # 实际评审天数（受主审员效率影响） + actual_days = int(planned_days * efficiency * random.uniform(0.9, 1.1)) + actual_days = max(3, actual_days) # 最少3天 + + # 质量分（受主审员能力+复杂度影响） + # 复杂度高的项目，质量分略有波动 + quality_penalty = (complexity - 3) * 2 if complexity >= 4 else 0 + quality_score = quality_base + random.randint(-quality_range, quality_range) - quality_penalty + quality_score = max(60, min(100, quality_score)) + + # 评审结果（质量分高=通过率高） + if quality_score >= 85: + outcome = random.choices(["通过", "否决", "撤并"], weights=[90, 5, 5])[0] + elif quality_score >= 75: + outcome = random.choices(["通过", "否决", "撤并"], weights=[80, 12, 8])[0] + else: + outcome = random.choices(["通过", "否决", "撤并"], weights=[60, 25, 15])[0] + + # 如果返工（实际天数超出计划较多），质量分降低 + if actual_days > planned_days * 1.3: + quality_score = max(quality_score - 10, 60) + + project = HistoricalProject( + project_id=f"PROJ-{year}-{project_counter:04d}", + project_name=project_name, + industry=industry, + credit_amount=credit_amount, + complexity=complexity, + planned_days=planned_days, + actual_days=actual_days, + reviewer=reviewer_name, + outcome=outcome, + quality_score=quality_score, + review_date=review_date.strftime("%Y-%m-%d"), + ) + + projects.append(project) + project_counter += 1 + + # 按日期排序 + projects.sort(key=lambda p: p.review_date) + + return projects + + +# ============ 全局数据缓存 ============ + +_HISTORICAL_PROJECTS: List[HistoricalProject] = _generate_historical_data() + + +# ============ 查询接口 ============ + +def get_all_projects() -> List[Dict]: + """获取所有历史项目""" + return [p.to_dict() for p in _HISTORICAL_PROJECTS] + + +def get_projects_by_reviewer(reviewer: str) -> List[Dict]: + """按主审员查询历史项目""" + filtered = [p for p in _HISTORICAL_PROJECTS if p.reviewer == reviewer] + return [p.to_dict() for p in filtered] + + +def get_projects_by_industry(industry: str) -> List[Dict]: + """按行业查询历史项目（模糊匹配）""" + filtered = [p for p in _HISTORICAL_PROJECTS if industry in p.industry] + return [p.to_dict() for p in filtered] + + +def get_projects_filtered(reviewer: Optional[str] = None, industry: Optional[str] = None) -> List[Dict]: + """组合过滤查询""" + filtered = _HISTORICAL_PROJECTS + + if reviewer: + filtered = [p for p in filtered if p.reviewer == reviewer] + + if industry: + filtered = [p for p in filtered if industry in p.industry] + + return [p.to_dict() for p in filtered] + + +def get_reviewer_stats() -> Dict[str, Dict]: + """获取所有主审员的统计画像 + + 返回格式： + { + "李明": { + "total_projects": 22, + "avg_actual_days": 8.5, + "avg_quality_score": 88.2, + "industry_expertise": { + "制造业": {"count": 15, "avg_quality": 90.1}, + "装备制造": {"count": 7, "avg_quality": 86.3} + }, + "pass_rate": 0.91 + }, + ... + } + """ + stats = {} + + reviewers = list(set(p.reviewer for p in _HISTORICAL_PROJECTS)) + + for reviewer in reviewers: + projects = [p for p in _HISTORICAL_PROJECTS if p.reviewer == reviewer] + + # 基础统计 + total_projects = len(projects) + avg_actual_days = sum(p.actual_days for p in projects) / total_projects if total_projects > 0 else 0 + avg_quality = sum(p.quality_score for p in projects) / total_projects if total_projects > 0 else 0 + pass_count = len([p for p in projects if p.outcome == "通过"]) + pass_rate = pass_count / total_projects if total_projects > 0 else 0 + + # 行业专长统计 + industry_expertise = {} + for project in projects: + industry = project.industry + if industry not in industry_expertise: + industry_expertise[industry] = {"count": 0, "quality_sum": 0} + industry_expertise[industry]["count"] += 1 + industry_expertise[industry]["quality_sum"] += project.quality_score + + # 计算每个行业的平均质量分 + for industry in industry_expertise: + count = industry_expertise[industry]["count"] + quality_sum = industry_expertise[industry]["quality_sum"] + industry_expertise[industry] = { + "count": count, + "avg_quality": quality_sum / count if count > 0 else 0 + } + + stats[reviewer] = { + "total_projects": total_projects, + "avg_actual_days": round(avg_actual_days, 1), + "avg_quality_score": round(avg_quality, 1), + "industry_expertise": industry_expertise, + "pass_rate": round(pass_rate, 2) + } + + return stats + + +# ============ 初始化时输出统计信息 ============ + +if __name__ == "__main__": + print(f"✅ 已生成 {len(_HISTORICAL_PROJECTS)} 条历史评审记录") + print("\n主审员统计：") + stats = get_reviewer_stats() + for reviewer, data in stats.items(): + print(f" {reviewer}: {data['total_projects']}个项目, 平均{data['avg_actual_days']}天, 质量{data['avg_quality_score']}分") diff --git a/backend/start_backend.bat b/backend/start_backend.bat new file mode 100644 index 0000000..4df4641 --- /dev/null +++ b/backend/start_backend.bat @@ -0,0 +1,18 @@ +@echo off +REM Mini-OpenClaw Backend Startup Script (Windows) +REM 自动禁用代理并启动后端 + +cd /d "%~dp0" + +REM 禁用所有代理设置 +set NO_PROXY=* +set HTTP_PROXY= +set HTTPS_PROXY= +set ALL_PROXY= + +REM 激活虚拟环境 +call venv\Scripts\activate.bat + +REM 启动后端 +echo 🚀 Starting mini-OpenClaw backend (proxy disabled)... +python app.py diff --git a/backend/start_backend.sh b/backend/start_backend.sh new file mode 100644 index 0000000..7ed0a3a --- /dev/null +++ b/backend/start_backend.sh @@ -0,0 +1,22 @@ +#!/bin/bash +# Mini-OpenClaw Backend Startup Script +# 自动禁用代理并启动后端 + +cd "$(dirname "$0")" + +# 禁用所有代理设置 +export NO_PROXY="*" +export no_proxy="*" +unset HTTP_PROXY +unset HTTPS_PROXY +unset http_proxy +unset https_proxy +unset ALL_PROXY +unset all_proxy + +# 激活虚拟环境 +source venv/Scripts/activate + +# 启动后端 +echo "🚀 Starting mini-OpenClaw backend (proxy disabled)..." +python app.py diff --git a/backend/xingbang/__init__.py b/backend/xingbang/__init__.py deleted file mode 100644 index 9734fa1..0000000 --- a/backend/xingbang/__init__.py +++ /dev/null @@ -1,23 +0,0 @@ -"""兴邦金租 AI 评审平台 — 基于 mini-OpenClaw 的项目模块。 - -定位： - 在 mini-OpenClaw 单智能体平台上，扩展出一个可交付的兴邦金租 - AI 评审流水线：五智能体 + 自进化(evolution)模块。挂在 - mini-OpenClaw 后端，复用其 LLM、工具、会话基础设施。 - -五智能体流水线（对应兴邦三档分级审批）： - 1. 数据汇聚官 (data_curator) — 多源异构数据→结构化客户情报卡 - 2. 预审参谋 (risk_scout) — 基于情报卡做尽调初稿+风险分级 - 3. 经验检索 (case_finder) — 历史案例+制度规则 RAG 检索 - 4. 审批顾问 (approval_advisor) — 汇总前序产出→三档分级审批参考 - 5. 风险哨兵 (risk_monitor) — 审批前动态复核，风控信号转变预警 - -自进化(evolution)模块： - 采集每次审批流水线的完整轨迹(trajectory)，计算奖励(reward)， - 从历史中归纳高效工具调用模式，反哺各 agent 的 system prompt。 -""" - -from .workflow import XingbangWorkflow -from .config import XINGBANG_CONFIG - -__all__ = ["XingbangWorkflow", "XINGBANG_CONFIG"] diff --git a/backend/xingbang/agents.py b/backend/xingbang/agents.py deleted file mode 100644 index fd24f5e..0000000 --- a/backend/xingbang/agents.py +++ /dev/null @@ -1,186 +0,0 @@ -"""五智能体定义 — 复用 mini-OpenClaw 的 LLM 与工具，各自带差异化 system prompt。 - -每个智能体 = 一个独立的 LangChain create_agent： - - 共享底层 LLM(DeepSeek) 与读文件工具 - - 差异化的 system prompt(角色、规则、输出约定) - - 专用工具: read_xingbang_data(读兴邦数据包)、read_output(读上游产出) - - 属于同一个全局 AgentManager 编排, 但各 agent 独立 invoke - -轨迹采集: 每个 agent 的每次调用(输入/system/prompt/工具序列/输出)都会被 -evidence 回调记录, 供 evolution 模块使用。 -""" - -from __future__ import annotations - -import json -from pathlib import Path -from typing import Any, Callable - -from langchain.agents import create_agent -from langchain_core.messages import HumanMessage, AIMessage -from langchain_core.tools import tool - -from config import get_rag_mode -from graph.prompt_builder import build_system_prompt -from xingbang.config import agent_specs, XINGBANG_DATA_DIR, DATA_FILES - - -# ── 专用工具:读取兴邦多源数据包 ──────────────────────── -@tool -def read_xingbang_data(filename: str) -> str: - """读取兴邦金租客户的多源数据文件(JSON/CSV/MD/TXT)，返回内容。 - - 可用文件名(需带编号前缀): 01_客户申请信息.json, 02_信用评分卡.json, - 03_风险评级报告.md, 04_授信额度测算.csv, 05_征信记录.json, - 06_司法执行记录.json, 07_舆情风险评分.json, 08_反欺诈模型输出.json, - 09_财务预警模型.csv, 10_财务分析报告.csv, 11_租赁物估值报告.md, - 12_行业调研报告.md, 13_历史审批案例库.json, 14_公司制度要点.md, - 15_银行流水扫描件.txt - """ - if not XINGBANG_DATA_DIR.exists(): - return f"[错误] 兴邦数据目录不存在: {XINGBANG_DATA_DIR}" - path = XINGBANG_DATA_DIR / filename - if not path.exists(): - return f"[错误] 文件不存在: {filename}" - content = path.read_text(encoding="utf-8", errors="ignore") - # 限制长度避免超上下文 - if len(content) > 4000: - content = content[:4000] + "\n...[已截断]" - return content - - -@tool -def read_output(filename: str, base_dir: str = "") -> str: - """读取当前兴邦工作区中上游智能体产出的 Markdown 文件(如 - customer_intel.md, due_diligence.md, case_reference.md)。""" - file_path = Path(base_dir) / filename if base_dir else Path(filename) - if not file_path.exists(): - return f"[错误] 上游产出文件不存在: {filename}" - content = file_path.read_text(encoding="utf-8", errors="ignore") - if len(content) > 8000: - content = content[:8000] + "\n...[已截断]" - return content - - -# ── 单个评审智能体 ───────────────────────────────────── -class XingbangAgent: - """一个兴邦评审智能体: 差异化角色 + 复用 LLM + 工具。""" - - def __init__( - self, - spec, - llm=None, - base_dir: Path | None = None, - output_dir: Path | None = None, - on_evidence: Callable[[str, dict], None] | None = None, - ): - self.spec = spec - self._llm = llm - self._base_dir = base_dir - self._output_dir = output_dir or (base_dir / "xingbang_output" if base_dir else None) - self._on_evidence = on_evidence - self._agent = None - - def _ensure_agent(self): - """构建带专属 system prompt 的独立 agent(惰性)。""" - if self._agent is not None: - return self._agent - tools = [read_xingbang_data] - tools.append(read_output) - prompt = self.spec.system_prompt - # 追加读文件工具说明(需要 base_dir 才能读产出) - if self._base_dir: - prompt += ( - f"\n\n[工作目录] 上游产出文件位于 {self._base_dir}。" - "读取上游产出时使用 read_output 工具并把 filename 设为产出文件名。" - ) - self._agent = create_agent(model=self._llm, tools=tools, system_prompt=prompt) - return self._agent - - def _emit(self, kind: str, data: dict): - if self._on_evidence: - self._on_evidence(kind, data) - - async def run(self, task: str, context: str = "") -> dict: - """执行该智能体任务,返回 {'output': 结果文本, 'file': 保存路径}。""" - self._emit("agent_start", {"agent": self.spec.key, "name": self.spec.name}) - agent = self._ensure_agent() - messages = [] - if context: - messages.append(HumanMessage(content=f"[上游上下文]\n{context}")) - messages.append(HumanMessage(content=task)) - self._emit("prompt", { - "agent": self.spec.key, - "system": self.spec.system_prompt[:500], - "task": task[:300], - }) - import asyncio - max_retries = 4 - backoff = 5 # 初始等待秒 - last_err = None - for attempt in range(max_retries): - try: - result = await agent.ainvoke({"messages": messages}) - final = self._extract_text(result) - break - except Exception as e: # noqa - last_err = e - msg = str(e) - # 对瞬时网络/网关错误重试: 503过载/502坏网关/429限流/连接中断/不完整响应 - retriable = any(k in msg for k in ( - "503", "502", "overloaded", "429", "rate", - "peer closed", "incomplete chunked", "Bad Gateway", - "timeout", "timed out", "Connection", "connection", - "ReadTimeout", "EOF", - )) - if retriable and attempt < max_retries - 1: - await asyncio.sleep(backoff * (2 ** attempt)) - continue - else: - raise # 非瞬时错误, 或重试耗尽 - else: - raise RuntimeError(f"网关过载重试后仍失败: {last_err}") - # 保存产出到文件 - file_path = None - if self._output_dir: - self._output_dir.mkdir(parents=True, exist_ok=True) - file_path = self._output_dir / self.spec.output_file - file_path.write_text(final, encoding="utf-8") - self._emit("agent_end", { - "agent": self.spec.key, - "output_file": str(file_path) if file_path else None, - "output_preview": final[:300], - }) - return {"output": final, "file": file_path} - - @staticmethod - def _extract_text(result: dict) -> str: - messages = result.get("messages", []) - text_parts = [] - for m in messages: - if hasattr(m, "content") and m.type == "ai" and m.content: - text_parts.append(m.content) - if text_parts: - return "\n\n".join(text_parts).strip() - content = result.get("output", "") - return content if content else "[智能体未生成有效输出]" - - -def build_agents( - llm, - base_dir: Path, - output_dir: Path | None = None, - on_evidence: Callable[[str, dict], None] | None = None, - keys: list[str] | None = None, -) -> dict[str, XingbangAgent]: - """按流水线顺序构建五智能体实例。""" - specs = agent_specs() - order = keys or ["data_curator", "risk_scout", "case_finder", - "approval_advisor", "risk_monitor"] - return { - key: XingbangAgent( - spec=specs[key], llm=llm, - base_dir=base_dir, output_dir=output_dir, on_evidence=on_evidence, - ) - for key in order - } diff --git a/backend/xingbang/config.py b/backend/xingbang/config.py deleted file mode 100644 index 0a022cc..0000000 --- a/backend/xingbang/config.py +++ /dev/null @@ -1,173 +0,0 @@ -"""兴邦金租项目配置 — 数据源路径、五智能体定义、分级审批档位。 - -默认数据源指引向兴邦实验数据包(华南远通物流样例): - F:/xingbang_agent/00_数据包/ (15 个多源异构数据文件) -可通过 XINGBANG_DATA_DIR 环境变量覆盖，指向真实业务数据。 - -分级审批档位（对齐兴邦三档分级审批制度）: - 常规档 / 中间档 / 严肃档 -""" - -from __future__ import annotations - -import os -from dataclasses import dataclass, field -from pathlib import Path - -# ── 数据源 ────────────────────────────────────────────── -# 默认指向兴邦实验数据包，可用环境变量 XINGBANG_DATA_DIR 覆盖 -DEFAULT_DATA_DIR = Path("F:/xingbang_agent/00_数据包") -XINGBANG_DATA_DIR = Path( - os.getenv("XINGBANG_DATA_DIR", str(DEFAULT_DATA_DIR)) -) - -# 五智能体内置数据文件(数据汇聚官读取的多源数据) -DATA_FILES: dict[str, str] = { - "申请信息": "01_客户申请信息.json", - "信用评分卡": "02_信用评分卡.json", - "风险评级": "03_风险评级报告.md", - "授信额度": "04_授信额度测算.csv", - "征信记录": "05_征信记录.json", - "司法执行": "06_司法执行记录.json", - "舆情评分": "07_舆情风险评分.json", - "反欺诈": "08_反欺诈模型输出.json", - "财务预警": "09_财务预警模型.csv", - "财报": "10_财务分析报告.csv", - "租赁物估值": "11_租赁物估值报告.md", - "行业调研": "12_行业调研报告.md", - "历史案例库": "13_历史审批案例库.json", - "公司制度": "14_公司制度要点.md", - "银行流水": "15_银行流水扫描件.txt", -} - - -# ── 五智能体定义 ──────────────────────────────────────── -@dataclass -class AgentSpec: - """单个评审智能体的规格""" - - key: str # 标识 - name: str # 中文名 - role: str # 一句话定位 - system_prompt: str # 专属系统提示词(差异化角色) - output_file: str # 产出文件名 - inputs_from: list[str] = field(default_factory=list) # 依赖的上游产出 - - -# 每个 agent 的差异化 system prompt 片段(核心角色约束) -_AGENT_PROMPTS = { - "data_curator": ( - "你是兴邦金租评审流水线的【数据汇聚官】。你的职责是把散落在多源异构数据" - "(JSON/CSV/MD/TXT)中的客户原始数据主动拉齐、清洗、结构化，输出可信、可溯、" - "带置信度的『客户情报卡』。\n" - "规则：\n" - "1. 解析多源数据，统一格式，按客户四维画像(工商/信用司法/舆情/财务/租赁物)归并\n" - "2. 多源冲突(如工商股东vs舆情实控人)要显式标出冲突点,绝不擅自取其一\n" - "3. 风险信号按🔴高/🟡中/🟢低三档分级，每条必须附原始数据依据\n" - "4. 明确列出『未覆盖数据源清单』,绝不编造缺失信息\n" - "5. 输出存为 Markdown 文件。数据不出域,本地处理。" - ), - "risk_scout": ( - "你是兴邦金租评审流水线的【预审参谋】。你的职责是基于上游客户情报卡，" - "产出尽调报告初稿，把风险点做深度分析。\n" - "规则：\n" - "1. 读取上游『客户情报卡』(customer_intel.md)\n" - "2. 对每个风险点做穿透分析:现金流失控、对外担保、资产负债率、涉诉风险、租赁物残值\n" - "3. 正面因素(合同稳定性、增信措施)也要列出\n" - "4. 引用上游的历史案例检索结果(如有)\n" - "5. 输出『尽调报告初稿』,风险按高/中/低分级,每条可溯源" - ), - "case_finder": ( - "你是兴邦金租评审流水线的【经验检索】。你的职责是把沉淀在历史案例和制度规则" - "库中的经验变成可调用的组织知识,回答『以前类似项目怎么批的』。\n" - "规则：\n" - "1. 检索历史案例库(13_历史审批案例库.json)与当前客户最相关的案例\n" - "2. 每个结论必须带案例编号(如H-1103),可追溯\n" - "3. 正面案例和否决/预警案例都要给,辅助全面判断\n" - "4. 结合制度要点(14_公司制度要点.md)给出适用的档位条款\n" - "5. 找不到完全匹配案例时,明确说明差异点,不硬套" - ), - "approval_advisor": ( - "你是兴邦金租评审流水线的【审批顾问】。你的职责是汇总前面所有智能体产出，" - "按三档分级审批制度输出标准化审批参考意见。\n" - "规则：\n" - "1. 读取上游全部产出(情报卡/尽调初稿/历史案例参考)\n" - "2. 按三档制度判定本申请归入:常规档/中间档/严肃档(含判定依据,对照制度条款)\n" - "3. 输出核心风险点(引用溯源)、历史案例支撑(带编号)\n" - "4. 预填流转意见(『建议通过,理由如下,请确认』),人只确认/驳回\n" - "5. 明确标注『本意见为AI参考,最终决策由评审人员确认』\n" - "6. 绝不替代决策,全程留痕可回退" - ), - "risk_monitor": ( - "你是兴邦金租评审流水线的【风险哨兵】。你的职责是审批前的最后一道动态复核，" - "检查关键风控信号是否在评审期间发生变化。\n" - "规则：\n" - "1. 重新读取最新的风控数据(02信用评分/05征信/06司法/07舆情/09财务预警)\n" - "2. 与预审阶段采用的数据对比,检测评分/司法/舆情等关键信号是否转变\n" - "3. 若发现信号转变(如新增执行、评分骤降),必须主动提示『风控结果已转变," - "原预审结论需重新评估』,并给出新档位判定\n" - "4. 若无变化,确认『风控状态稳定,可按原档位推进』\n" - "5. 输出最终风控复核结论" - ), -} - - -def agent_specs() -> dict[str, AgentSpec]: - """返回五个智能体的规格定义(流水线顺序)。""" - specs = { - "data_curator": AgentSpec( - key="data_curator", name="数据汇聚官", role="多源数据→情报卡", - system_prompt=_AGENT_PROMPTS["data_curator"], - output_file="customer_intel.md", - ), - "risk_scout": AgentSpec( - key="risk_scout", name="预审参谋", role="情报卡→尽调初稿", - system_prompt=_AGENT_PROMPTS["risk_scout"], - output_file="due_diligence.md", inputs_from=["customer_intel.md"], - ), - "case_finder": AgentSpec( - key="case_finder", name="经验检索", role="历史案例+规则检索", - system_prompt=_AGENT_PROMPTS["case_finder"], - output_file="case_reference.md", inputs_from=[], - ), - "approval_advisor": AgentSpec( - key="approval_advisor", name="审批顾问", role="汇总→三档审批参考", - system_prompt=_AGENT_PROMPTS["approval_advisor"], - output_file="approval_opinion.md", - inputs_from=["customer_intel.md", "due_diligence.md", "case_reference.md"], - ), - "risk_monitor": AgentSpec( - key="risk_monitor", name="风险哨兵", role="风控动态复核", - system_prompt=_AGENT_PROMPTS["risk_monitor"], - output_file="risk_recheck.md", - inputs_from=["approval_opinion.md"], - ), - } - # 编排顺序:数据汇聚→预审→(经验并列)→审批→风控 - return specs - - -# ── 分级审批档位阈值(对齐 14_公司制度要点.md) ────────── -@dataclass -class ApprovalTiers: - """三档分级审批阈值配置""" - - # 严肃档:额度超阈值 或 重大负面 - serious_amount_wan: float = 5000.0 - # 中间档:额度/评分在阈值内,需 AI 初审+人工确认 - serious_blue_flags: list[str] = field( - default_factory=lambda: ["失信", "被执行", "重大负面", "评分<70"] - ) - # 评分阈值 - low_score_threshold: float = 70.0 - - -XINGBANG_CONFIG: dict = { - "project": "兴邦金租 AI 评审平台", - "version": "0.1.0", - "base": "mini-OpenClaw", - "data_dir": str(XINGBANG_DATA_DIR), - "agents_pipeline": ["data_curator", "risk_scout", "case_finder", - "approval_advisor", "risk_monitor"], - "approval_tiers": ApprovalTiers().__dict__, -} diff --git a/backend/xingbang/evolution/__init__.py b/backend/xingbang/evolution/__init__.py deleted file mode 100644 index 990ce01..0000000 --- a/backend/xingbang/evolution/__init__.py +++ /dev/null @@ -1,18 +0,0 @@ -"""自进化(evolution)模块 — 兴邦金租评审平台的学习与优化子系统。 - -核心思想: 每次审批流水线执行, 采集五智能体的完整轨迹 -(state/action/observation/reward), 从历史轨迹中归纳成功模式, -反哺各智能体的 system prompt, 实现"越用越准"的自进化。 - -分级方案(诚实设计): - - Level 1 轻量(现在能做): 轨迹采集 + 内在奖励 + 失败/成功模式归纳 + prompt 优化 - - Level 2: 用户显式反馈(👍/👎)作为奖励信号(需前端配合) - - Level 3(未来): 专家标注 / LoRA 微调(需 GPU) -本模块实现 Level 1, 接口预留 Level 2/3。 -""" - -from .trajectory import TrajectoryRecorder -from .reward import compute_reward_v1 -from .optimizer import EvolutionEngine - -__all__ = ["TrajectoryRecorder", "compute_reward_v1", "EvolutionEngine"] diff --git a/backend/xingbang/evolution/optimizer.py b/backend/xingbang/evolution/optimizer.py deleted file mode 100644 index 06ade33..0000000 --- a/backend/xingbang/evolution/optimizer.py +++ /dev/null @@ -1,161 +0,0 @@ -"""Evolution Engine — 从历史轨迹中学习, 反哺各智能体 prompt, 实现自进化。 - -闭环(自进化): - 轨迹采集 → 奖励打分 → 归纳成功/失败模式 → 生成进化建议 → - 反哺/追加到各 agent 的系统提示词(可选启用) - -Level 1 实现: - - analyze: 从已保存轨迹中总结高分/低分模式(常见工具序列、步骤分布) - - generate_insights: 生成可读的进化洞察(markdown) - - evolve_prompt: 将洞察追加到对应 agent 的 system prompt(可选) - -轻量自进化的核心: 不编造"强化学习训练", 而是做"基于经验的 prompt 增强" -——把历史中高奖励轨迹用过的工具调用序列、成功策略, 有选择地注入 -未来 agent 的提示词, 让系统越用越聪明。 -""" - -from __future__ import annotations - -import json -from collections import Counter -from pathlib import Path -from typing import Any - -from .reward import compute_reward_v1 -from .trajectory import TrajectoryRecorder - - -class EvolutionEngine: - """自进化引擎(Level 1 轻量版)。""" - - def __init__( - self, - base_dir: Path, - recorder: TrajectoryRecorder, - enabled: bool = True, - ): - self._base_dir = Path(base_dir) - self._recorder = recorder - self.enabled = enabled - self._insights_dir = self._base_dir / "xingbang_evolution" / "insights" - self._insights_dir.mkdir(parents=True, exist_ok=True) - - # ── 分析历史轨迹 ──────────────────────────────────── - def _load_all_trajectories(self) -> list[dict[str, Any]]: - trajs = [] - for p in self._recorder.list_trajectories(): - try: - trajs.append(json.loads(p.read_text(encoding="utf-8"))) - except Exception: - continue - return trajs - - def analyze(self) -> dict[str, Any]: - """分析全部历史轨迹, 返回统计与模式。""" - trajectories = self._load_all_trajectories() - if not trajectories: - return {"count": 0, "message": "尚无轨迹样本,先积累数据"} - - # 计算每条奖励 - scored = [] - for t in trajectories: - r = compute_reward_v1(t) - t["_reward"] = r - scored.append(t) - - scored.sort(key=lambda x: x["_reward"], reverse=True) - high = scored[: max(1, len(scored) // 4)] # 前 25% - low = scored[-max(1, len(scored) // 4):] # 后 25% - - # 归纳高频工具序列 - def agent_tool_sequences(trajs): - counter = Counter() - for t in trajs: - for s in t.get("steps", []): - seq = tuple(s.get("tools", [])) - if seq: - counter[seq] += 1 - return counter.most_common(5) - - high_patterns = { - "seqs": agent_tool_sequences(high), - "avg_steps": sum(len(t["steps"]) for t in high) / max(1, len(high)), - "success_rate": sum(1 for t in high if t["final_result"].get("success")) - / max(1, len(high)), - } - return { - "count": len(trajectories), - "avg_reward": round(sum(t["_reward"] for t in scored) / len(scored), 2), - "high": {"samples": len(high), "patterns": high_patterns}, - "low": { - "samples": len(low), - "avg_steps": sum(len(t["steps"]) for t in low) / max(1, len(low)), - }, - "top_agents_used": self._agent_usage(scored), - } - - def _agent_usage(self, trajs) -> dict: - counter = Counter() - for t in trajs: - for s in t.get("steps", []): - counter[s.get("agent", "?")] += 1 - return dict(counter.most_common()) - - # ── 生成进化洞察 ──────────────────────────────────── - def generate_insights(self) -> str: - """生成可读的 markdown 进化洞察报告。""" - analysis = self.analyze() - if analysis.get("count", 0) == 0: - return "# 自进化洞察\n\n尚无轨迹样本。运行审批流水线积累数据后,系统将自动进化。" - lines = [ - f"# 兴邦 AI 评审·自进化洞察", - f"\n**样本数**: {analysis['count']} 条轨迹 | **平均奖励**: {analysis['avg_reward']}", - "", - "## ✅ 高分轨迹模式 (前 25%)", - ] - hp = analysis["high"] - lines.append(f"- 平均步骤数: {hp['patterns']['avg_steps']:.1f}") - lines.append(f"- 成功率: {hp['patterns']['success_rate']*100:.0f}%") - lines.append("- 高频工具序列:") - for seq, n in hp["patterns"]["seqs"]: - lines.append(f" - {' → '.join(seq)} (出现 {n} 次)") - lines += ["", "## ⚠️ 低分轨迹 (后 25%)", f"- 平均步骤数: {analysis['low']['avg_steps']:.1f}"] - lines += ["", "## 🧠 智能体使用分布", ""] - for agent, count in analysis.get("top_agents_used", {}).items(): - lines.append(f"- {agent}: {count} 次") - lines += [ - "", - "## 进化建议", - "1. 高分轨迹的工具序列可在各 agent 的 system prompt 中作为'推荐工作流'注入", - "2. 步骤数偏多(>5)的 agent 提示词应强调'一次到位,减少反复读取'", - "3. 持续积累轨迹 → 达到一定量后启动更高阶优化(LoRA 微调)", - ] - return "\n".join(lines) - - def save_insights(self) -> Path: - """保存洞察报告到 insights 目录。""" - report = self.generate_insights() - path = self._insights_dir / "evolution_insights.md" - path.write_text(report, encoding="utf-8") - return path - - # ── 反哺 prompt (自进化闭环) ──────────────────────── - def evolve_prompt_suggestion(self, agent_key: str) -> str | None: - """为指定 agent 生成可注入的 prompt 增强建议(基于历史高分轨迹)。 - - 返回一段可追加到该 agent system_prompt 的文本, 若无历史则 None。 - """ - analysis = self.analyze() - if analysis.get("count", 0) == 0: - return None - hp = analysis["high"] - seqs = hp["patterns"]["seqs"] - if not seqs: - return None - top_seq_text = " → ".join(seqs[0][0]) if seqs else "" - suggestion = ( - f"\n\n[自进化推荐 · 基于 {analysis['count']} 条历史轨迹] " - f"高分任务常用工具序列: {top_seq_text}。" - f"请优先复用该模式,减少无效工具往返,一次到位完成任务。" - ) - return suggestion diff --git a/backend/xingbang/evolution/reward.py b/backend/xingbang/evolution/reward.py deleted file mode 100644 index e762e8e..0000000 --- a/backend/xingbang/evolution/reward.py +++ /dev/null @@ -1,82 +0,0 @@ -"""Reward Calculator — 计算一条审批轨迹的奖励分数。 - -诚实分级设计 (对齐兴邦方案): - Level 1 (现在能做,不需要用户交互): - - 任务完成 +10 - - 工具调用成功率 - - 重复调用惩罚 - - 时间惩罚 - Level 2 (需前端加 👍/👎 按钮): - - 用户显式反馈 - Level 3 (需人工标注): - - 专家打分 - -本文件实现 Level 1 + 预留 Level 2/3 参数。 -不做不诚实的信号: 拒绝用"还款表现"当奖励(周期1-3年无法闭环)。 -""" - -from __future__ import annotations -from typing import Any - - -def compute_reward_v1( - trajectory: dict[str, Any], - feedback: str | None = None, - expert_score: float | None = None, -) -> float: - """计算一条轨迹的奖励(Leve1 l 内置指标 + 可选 Level2 反馈 + Level3 专家分)。 - - Args: - trajectory: 轨迹 dict(含 steps 和 final_result) - feedback: Level2, "positive"/"negative" - expert_score: Level3, 1-5 分 - Returns: - float 奖励分 - """ - steps = trajectory.get("steps", []) - final = trajectory.get("final_result", {}) - if not steps: - return 0.0 - - reward = 0.0 - - # 1. 任务完成奖励 - if final.get("success"): - reward += 10.0 - else: - reward -= 10.0 - - # 2. 工具调用成功率(成功工具 / 总工具) - total_tool_calls = 0 - tool_success = 0 - for s in steps: - tools = s.get("tools", []) - total_tool_calls += len(tools) - if tools: - tool_success += 1 # 该步骤至少调了工具视为尽力(简化) - if total_tool_calls > 0: - reward += (tool_success / max(1, total_tool_calls)) * 5.0 - - # 3. 步骤数惩罚(越少越好,常规 5 步流水线 5-15 步为佳) - n_steps = len(steps) - optimal = 5 - step_penalty = abs(n_steps - optimal) * 0.3 - reward -= step_penalty - - # 4. 时间惩罚(超过 600 秒 = 10 分钟开始扣) - duration_ms = final.get("duration_ms", 0) - if duration_ms > 600_000: - reward -= ((duration_ms - 600_000) / 60_000) * 1.0 - - # ── Level 2: 用户反馈 (可选) ── - if feedback == "positive": - reward += 20.0 - elif feedback == "negative": - reward -= 10.0 - - # ── Level 3: 专家打分 (可选, 1-5 归一化到 -10~+10) ── - if expert_score is not None: - reward += (expert_score - 3) * 5.0 - - reward = max(-30.0, min(reward, 50.0)) # 裁剪 - return round(reward, 2) diff --git a/backend/xingbang/evolution/trajectory.py b/backend/xingbang/evolution/trajectory.py deleted file mode 100644 index 6cd05d4..0000000 --- a/backend/xingbang/evolution/trajectory.py +++ /dev/null @@ -1,123 +0,0 @@ -"""Trajectory Recorder — 采集五智能体每次调用的完整轨迹。 - -轨迹格式 (trajectory): -{ - "trajectory_id": "traj_", - "customer": "客户名", - "workflow_id": "...", - "steps": [ - { - "agent": "data_curator", - "input": "任务描述", - "prompt_snippet": "...", - "tools": ["read_xingbang_data", ...], - "output_file": "...", - "output_preview": "...", - "success": true/false, - "time_ms": 1234 - }, ... - ], - "final_result": {...}, - "cumulative_reward": 0.0 -} - -轨迹保存在 /xingbang_evolution/trajectories/*.json。 -""" - -from __future__ import annotations - -import json -import time -import uuid -from pathlib import Path -from typing import Any - - -class TrajectoryRecorder: - """记录并持久化一次评审流水线的完整执行轨迹。""" - - def __init__(self, base_dir: Path, enabled: bool = True): - self._base_dir = Path(base_dir) - self._dir = self._base_dir / "xingbang_evolution" / "trajectories" - self._dir.mkdir(parents=True, exist_ok=True) - self.enabled = enabled - # 当前正在记录的轨迹 - self._current: dict[str, Any] | None = None - # metadata (customer 等) - self._meta: dict = {} - self._start_time: float = 0.0 - - def begin(self, workflow_id: str | None = None, customer: str = "") -> str: - """开始记录一条新轨迹,返回 trajectory_id。""" - self._start_time = time.time() - self._current = { - "trajectory_id": f"traj_{int(time.time())}_{uuid.uuid4().hex[:6]}", - "workflow_id": workflow_id or f"wf_{int(time.time())}", - "customer": customer or "", - "created_at": self._start_time, - "steps": [], - "final_result": {}, - "cumulative_reward": 0.0, - } - return self._current["trajectory_id"] - - def add_step(self, step: dict[str, Any]) -> None: - """追加一步智能体执行记录。""" - if self._current is not None: - self._current["steps"].append(step) - - def finalize(self, success: bool = True, summary: str = "") -> dict[str, Any]: - """结束记录,保存轨迹到磁盘,返回轨迹。""" - if self._current is None: - return {} - self._current["final_result"] = { - "success": success, - "summary": summary, - "duration_ms": int((time.time() - self._start_time) * 1000), - "total_steps": len(self._current["steps"]), - } - # 记录成功/失败 - trajectory = self._current - self._save(trajectory) - self._current = None - return trajectory - - def _save(self, trajectory: dict[str, Any]) -> Path: - path = self._dir / f"{trajectory['trajectory_id']}.json" - path.write_text( - json.dumps(trajectory, ensure_ascii=False, indent=2), - encoding="utf-8", - ) - return path - - def set_reward(self, reward: float) -> None: - """计算奖励后写回当前轨迹并重新落盘(进化闭环数据完整)。""" - if self._current is not None: - self._current["cumulative_reward"] = reward - # 若已有 trajectory_id, 覆盖落盘(full path 已知) - tid = self._current.get("trajectory_id") - if tid: - self._save(self._current) - - def rewrite(self, trajectory: dict[str, Any]) -> Path: - """写回一条已存在的轨迹(非当前进行中的), 返回路径。""" - return self._save(trajectory) - - # ── 从调用侧采集 ──────────────────────────────────── - def on_evidence(self, kind: str, data: dict) -> None: - """订阅 agents 的 evidence 回调,累积成本步记录。""" - if not self.enabled or self._current is None: - return - if kind == "agent_start": - buffered = [] - self._current["_buffered"] = buffered - # 这一步先不展开, 由 workflow 显式调用 add_step 更为清晰 - - def list_trajectories(self) -> list[Path]: - return sorted(self._dir.glob("*.json")) - - def load(self, trajectory_id: str) -> dict[str, Any]: - path = self._dir / f"{trajectory_id}.json" - if path.exists(): - return json.loads(path.read_text(encoding="utf-8")) - return {} diff --git a/backend/xingbang/workflow.py b/backend/xingbang/workflow.py deleted file mode 100644 index 85df112..0000000 --- a/backend/xingbang/workflow.py +++ /dev/null @@ -1,237 +0,0 @@ -"""XingbangWorkflow — 五智能体审批流水线 + 自进化闭环。 - -执行流程: - data_curator → risk_scout → case_finder → approval_advisor → risk_monitor - (case_finder 与 risk_scout 可并行, 此处先串行保证可追溯) - -每个 agent 的产出写入输出目录, 供下游读取; 同时全程记录 -轨迹到 evolution 模块, 跑完后计算奖励并更新进化洞察。 - -API 风格: run() 返回完整结果; 也可用 run_stream() 逐步 yield 进度。 -""" - -from __future__ import annotations - -import time -import uuid -from pathlib import Path -from typing import Any, AsyncGenerator - -from xingbang.config import agent_specs -from xingbang.agents import build_agents, XingbangAgent -from xingbang.evolution.trajectory import TrajectoryRecorder -from xingbang.evolution.reward import compute_reward_v1 -from xingbang.evolution.optimizer import EvolutionEngine - - -class XingbangWorkflow: - """兴邦五智能体审批工作流。""" - - def __init__( - self, - llm, - base_dir: Path, - output_dir: Path | None = None, - evolution_enabled: bool = True, - ): - self._base_dir = Path(base_dir) - self._output_dir = output_dir or (self._base_dir / "xingbang_output") - self._output_dir.mkdir(parents=True, exist_ok=True) - - # 自进化模块 - self.evolution_enabled = evolution_enabled - self._recorder = TrajectoryRecorder(self._base_dir, enabled=evolution_enabled) - self._evolution = EvolutionEngine(self._base_dir, self._recorder, enabled=evolution_enabled) - - # 五智能体(注入证据回调 → 轨迹) - self._agents: dict[str, XingbangAgent] = build_agents( - llm=llm, - base_dir=self._output_dir, - output_dir=self._output_dir, - on_evidence=self._on_evidence, - ) - - # 流水线顺序 - self._pipeline = ["data_curator", "risk_scout", "case_finder", - "approval_advisor", "risk_monitor"] - self._results: dict[str, dict] = {} - - def _on_evidence(self, kind: str, data: dict): - # 轨迹采集(保持简单: 在 workflow 层显式 add_step) - pass - - @property - def recorder(self) -> TrajectoryRecorder: - return self._recorder - - @property - def evolution(self) -> EvolutionEngine: - return self._evolution - - # ── 单次评审 ──────────────────────────────────────── - async def run(self, task: str, customer: str = "") -> dict[str, Any]: - """运行完整五智能体审批流水线,返回各步产出 + 轨迹 + 奖励。""" - workflow_id = f"wf_{int(time.time())}_{uuid.uuid4().hex[:4]}" - traj_id = self._recorder.begin(workflow_id=workflow_id, customer=customer) - - # 第一步任务描述 - tasks = self._build_tasks(task) - final_output = "" - summary_parts = [] - - for key in self._pipeline: - agent = self._agents[key] - agent_task = tasks.get(key, task) - - # 收集上游上下文(该 agent 依赖的产出) - ctx = self._collect_context(key, agent_task) - - t0 = time.time() - try: - result = await agent.run(agent_task, context=ctx) - success = True - output = result["output"] - file = result.get("file") - except Exception as e: # noqa - success = False - output = f"[执行失败] {e}" - file = None - - elapsed_ms = int((time.time() - t0) * 1000) - - # 记录这一步到轨迹 - step = { - "agent": key, - "name": agent.spec.name, - "input": agent_task[:300], - "tools": ["read_xingbang_data", "read_output"], - "output_file": str(file) if file else None, - "output_preview": output[:200], - "success": success, - "time_ms": elapsed_ms, - } - self._recorder.add_step(step) - self._results[key] = {"output": output, "file": file, "success": success} - summary_parts.append(f"[{key}] {'✅成功' if success else '❌失败'}") - final_output = output # 最后一环(风险哨兵)作为总结论 - - # 收尾: 计算奖励 + 保存进化洞察 - trajectory = self._recorder.finalize( - success=all(self._results[k]["success"] for k in self._pipeline), - summary=" | ".join(summary_parts), - ) - reward = compute_reward_v1(trajectory) - trajectory["cumulative_reward"] = reward - self._recorder.rewrite(trajectory) # 写回含 reward 轨迹, 进化数据完整 - insights_path = self._evolution.save_insights() - - return { - "workflow_id": workflow_id, - "trajectory_id": traj_id, - "customer": customer, - "reward": reward, - "results": { - k: { - kk: (str(vv) if kk == "file" and vv is not None else vv) - for kk, vv in v.items() - if kk in ("output", "file", "success") - } - for k, v in self._results.items() - }, - "final_summary": summary_parts, - "evolution_insights_file": str(insights_path), - } - - # ── 逐步流式(供前端展示进度) ─────────────────────── - async def run_stream(self, task: str, customer: str = "") -> AsyncGenerator[dict, None]: - """逐步 yield 每步进度与产出,便于前端 SSE 展示。""" - workflow_id = f"wf_{int(time.time())}_{uuid.uuid4().hex[:4]}" - traj_id = self._recorder.begin(workflow_id=workflow_id, customer=customer) - tasks = self._build_tasks(task) - - for key in self._pipeline: - agent = self._agents[key] - agent_task = tasks.get(key, task) - ctx = self._collect_context(key, agent_task) - yield {"event": "agent_start", "agent": key, "name": agent.spec.name} - - t0 = time.time() - try: - result = await agent.run(agent_task, context=ctx) - success = True - output = result["output"] - file = result.get("file") - except Exception as e: # noqa - success = False - output = f"[执行失败] {e}" - file = None - elapsed_ms = int((time.time() - t0) * 1000) - - step = { - "agent": key, "name": agent.spec.name, "input": agent_task[:300], - "tools": ["read_xingbang_data", "read_output"], - "output_file": str(file) if file else None, - "output_preview": output[:200], "success": success, "time_ms": elapsed_ms, - } - self._recorder.add_step(step) - self._results[key] = {"output": output, "file": file, "success": success} - yield {"event": "agent_end", "agent": key, "name": agent.spec.name, - "success": success, "output": output, "file": str(file) if file else None} - - trajectory = self._recorder.finalize(success=True) - reward = compute_reward_v1(trajectory) - trajectory["cumulative_reward"] = reward - self._recorder.rewrite(trajectory) # 写回含 reward 轨迹, 进化数据完整 - insights_path = self._evolution.save_insights() - yield {"event": "done", "workflow_id": workflow_id, - "trajectory_id": traj_id, "reward": reward, - "insights_file": str(insights_path)} - - # ── 辅助 ──────────────────────────────────────────── - def _build_tasks(self, user_task: str) -> dict[str, str]: - """为每个智能体构造针对性的任务描述。""" - return { - "data_curator": ( - f"请读取兴邦数据包中的多源数据, 为客户『{user_task}』(申请评审) " - f"生成结构化客户情报卡, 保存到 customer_intel.md。" - ), - "risk_scout": ( - f"基于上游 customer_intel.md, 对客户『{user_task}』做尽调分析, " - f"生成尽调报告初稿, 保存到 due_diligence.md。" - ), - "case_finder": ( - f"检索历史案例库与制度规则, 为『{user_task}』这类项目找出最相关案例, " - f"生成历史案例参考, 保存到 case_reference.md。" - ), - "approval_advisor": ( - f"汇总情报卡/尽调/历史案例, 按三档分级制度为『{user_task}』" - f"输出审批参考意见, 保存到 approval_opinion.md。" - ), - "risk_monitor": ( - f"审批前动态复核最新风控数据, 为『{user_task}』输出风控复核结论, " - f"保存到 risk_recheck.md。" - ), - } - - def _collect_context(self, agent_key: str, task: str) -> str: - """收集该 agent 依赖的上游产出作为上下文。""" - spec = agent_specs()[agent_key] - parts = [] - for dep in spec.inputs_from: - res = self._results.get(self._dep_owner(dep)) - if res and res.get("output"): - parts.append(f"[{dep}]\n{res['output'][:2000]}") - if parts: - return "\n\n".join(parts) - return "" - - def _dep_owner(self, output_file: str) -> str: - """根据产出文件名反查产生它的 agent key。""" - mapping = { - "customer_intel.md": "data_curator", - "due_diligence.md": "risk_scout", - "case_reference.md": "case_finder", - "approval_opinion.md": "approval_advisor", - "risk_recheck.md": "risk_monitor", - } - return mapping.get(output_file, "data_curator") diff --git a/docker-compose.yml b/docker-compose.yml new file mode 100644 index 0000000..90d0f3e --- /dev/null +++ b/docker-compose.yml @@ -0,0 +1,58 @@ +version: '3.8' + +services: + xingbang-app: + build: + context: . + dockerfile: Dockerfile + container_name: xingbang-ai-native + ports: + - "8002:8002" # 后端 API + - "3000:3000" # 前端 UI + environment: + - DEEPSEEK_API_KEY=${DEEPSEEK_API_KEY} + - DEEPSEEK_MODEL=${DEEPSEEK_MODEL:-deepseek-chat} + - DEEPSEEK_BASE_URL=${DEEPSEEK_BASE_URL:-https://api.deepseek.com} + - ERP_DB_HOST=${ERP_DB_HOST} + - ERP_DB_PORT=${ERP_DB_PORT:-5432} + - ERP_DB_NAME=${ERP_DB_NAME} + - ERP_DB_USER=${ERP_DB_USER} + - ERP_DB_PASSWORD=${ERP_DB_PASSWORD} + - ERP_DB_SSL_MODE=${ERP_DB_SSL_MODE:-require} + volumes: + - xingbang-data:/app/backend/xingbang_data + - xingbang-logs:/app/backend/logs + secrets: + - erp_db_password + - deepseek_api_key + restart: unless-stopped + networks: + - xingbang-network + + # （可选）如果需要本地测试数据库 + # postgres: + # image: postgres:15-alpine + # container_name: xingbang-erp-mock + # environment: + # - POSTGRES_DB=xingbang_erp + # - POSTGRES_USER=readonly_user + # - POSTGRES_PASSWORD_FILE=/run/secrets/erp_db_password + # volumes: + # - postgres-data:/var/lib/postgresql/data + # networks: + # - xingbang-network + +volumes: + xingbang-data: + xingbang-logs: + # postgres-data: + +networks: + xingbang-network: + driver: bridge + +secrets: + erp_db_password: + file: ./secrets/erp_db_password.txt + deepseek_api_key: + file: ./secrets/deepseek_api_key.txt diff --git a/docker-entrypoint.sh b/docker-entrypoint.sh new file mode 100644 index 0000000..15f1339 --- /dev/null +++ b/docker-entrypoint.sh @@ -0,0 +1,24 @@ +#!/bin/bash +set -e + +echo "🚀 启动兴邦金租 AI Native 智能决策系统" + +# 等待 ERP 数据库就绪（如果配置了） +if [ ! -z "$ERP_DB_HOST" ]; then + echo "⏳ 等待 ERP 数据库连接..." + until pg_isready -h $ERP_DB_HOST -p $ERP_DB_PORT -U $ERP_DB_USER; do + echo "数据库未就绪，2秒后重试..." + sleep 2 + done + echo "✅ ERP 数据库已连接" +fi + +# 启动后端（后台） +echo "🔧 启动后端服务 (端口 8002)..." +cd /app/backend +python app.py & + +# 启动前端（前台） +echo "🌐 启动前端服务 (端口 3000)..." +cd /app/frontend +npm start diff --git a/package-lock.json b/package-lock.json index 72177f1..3f163b3 100644 --- a/package-lock.json +++ b/package-lock.json @@ -11,9 +11,9 @@ "@monaco-editor/react": "^4.6.0", "clsx": "^2.1.0", "lucide-react": "^0.400.0", - "next": "^14.2.0", - "react": "^18.3.0", - "react-dom": "^18.3.0", + "next": "^14.1.0", + "react": "^18.2.0", + "react-dom": "^18.2.0", "react-markdown": "^9.0.0", "remark-gfm": "^4.0.0" }, @@ -30,9 +30,9 @@ } }, "node_modules/@alloc/quick-lru": { - "version": "5.2.0", - "resolved": "https://registry.npmjs.org/@alloc/quick-lru/-/quick-lru-5.2.0.tgz", - "integrity": "sha512-UrcABB+4bUrFABwbluTIBErXwvbsU/V7TZWfmbgJfbkwiBuziS9gxdODUyuiecfdGQ85jglMW6juS3+z5TsKLw==", + "version": "5.3.0", + "resolved": "https://registry.npmmirror.com/@alloc/quick-lru/-/quick-lru-5.3.0.tgz", + "integrity": "sha512-U4+70Pc5ZS9osnCBCE5Jha/ciHM+Yp+CNMNC/7HvYbNRk1Ldd+f7qO65W5qfhu/TCv+/ozljlXXe9Nj8419DMA==", "dev": true, "license": "MIT", "engines": { @@ -43,21 +43,21 @@ } }, "node_modules/@emnapi/core": { - "version": "1.8.1", - "resolved": "https://registry.npmjs.org/@emnapi/core/-/core-1.8.1.tgz", - "integrity": "sha512-AvT9QFpxK0Zd8J0jopedNm+w/2fIzvtPKPjqyw9jwvBaReTTqPBk9Hixaz7KbjimP+QNz605/XnjFcDAL2pqBg==", + "version": "1.10.0", + "resolved": "https://registry.npmmirror.com/@emnapi/core/-/core-1.10.0.tgz", + "integrity": "sha512-yq6OkJ4p82CAfPl0u9mQebQHKPJkY7WrIuk205cTYnYe+k2Z8YBh11FrbRG/H6ihirqcacOgl2BIO8oyMQLeXw==", "dev": true, "license": "MIT", "optional": true, "dependencies": { - "@emnapi/wasi-threads": "1.1.0", + "@emnapi/wasi-threads": "1.2.1", "tslib": "^2.4.0" } }, "node_modules/@emnapi/runtime": { - "version": "1.8.1", - "resolved": "https://registry.npmjs.org/@emnapi/runtime/-/runtime-1.8.1.tgz", - "integrity": "sha512-mehfKSMWjjNol8659Z8KxEMrdSJDDot5SXMq00dM8BN4o+CLNXQ0xH2V7EchNHV4RmbZLmmPdEaXZc5H2FXmDg==", + "version": "1.10.0", + "resolved": "https://registry.npmmirror.com/@emnapi/runtime/-/runtime-1.10.0.tgz", + "integrity": "sha512-ewvYlk86xUoGI0zQRNq/mC+16R1QeDlKQy21Ki3oSYXNgLb45GV1P6A0M+/s6nyCuNDqe5VpaY84BzXGwVbwFA==", "dev": true, "license": "MIT", "optional": true, @@ -66,9 +66,9 @@ } }, "node_modules/@emnapi/wasi-threads": { - "version": "1.1.0", - "resolved": "https://registry.npmjs.org/@emnapi/wasi-threads/-/wasi-threads-1.1.0.tgz", - "integrity": "sha512-WI0DdZ8xFSbgMjR1sFsKABJ/C5OnRrjT06JXbZKexJGrDuPTzZdDYfFlsgcCXCyf+suG5QU2e/y1Wo2V/OapLQ==", + "version": "1.2.1", + "resolved": "https://registry.npmmirror.com/@emnapi/wasi-threads/-/wasi-threads-1.2.1.tgz", + "integrity": "sha512-uTII7OYF+/Mes/MrcIOYp5yOtSMLBWSIoLPpcgwipoiKbli6k322tcoFsxoIIxPDqW01SQGAgko4EzZi2BNv2w==", "dev": true, "license": "MIT", "optional": true, @@ -77,9 +77,9 @@ } }, "node_modules/@eslint-community/eslint-utils": { - "version": "4.9.1", - "resolved": "https://registry.npmjs.org/@eslint-community/eslint-utils/-/eslint-utils-4.9.1.tgz", - "integrity": "sha512-phrYmNiYppR7znFEdqgfWHXR6NCkZEK7hwWDHZUjit/2/U0r6XvkDl0SYnoM51Hq7FhCGdLDT6zxCCOY1hexsQ==", + "version": "4.10.1", + "resolved": "https://registry.npmmirror.com/@eslint-community/eslint-utils/-/eslint-utils-4.10.1.tgz", + "integrity": "sha512-cuadcxVFE8sDK6iWJbs8Sn0av2Nrh2QSGQhVlBW9AaAHqHwjWsZHT8LJ4hFGPh7ASBV2deFdM7H/DPjulmh8rg==", "dev": true, "license": "MIT", "dependencies": { @@ -97,7 +97,7 @@ }, "node_modules/@eslint-community/regexpp": { "version": "4.12.2", - "resolved": "https://registry.npmjs.org/@eslint-community/regexpp/-/regexpp-4.12.2.tgz", + "resolved": "https://registry.npmmirror.com/@eslint-community/regexpp/-/regexpp-4.12.2.tgz", "integrity": "sha512-EriSTlt5OC9/7SXkRSCAhfSxxoSUgBm33OH+IkwbdpgoqsSsUg7y3uh+IICI/Qg4BBWr3U2i39RpmycbxMq4ew==", "dev": true, "license": "MIT", @@ -107,7 +107,7 @@ }, "node_modules/@eslint/eslintrc": { "version": "2.1.4", - "resolved": "https://registry.npmjs.org/@eslint/eslintrc/-/eslintrc-2.1.4.tgz", + "resolved": "https://registry.npmmirror.com/@eslint/eslintrc/-/eslintrc-2.1.4.tgz", "integrity": "sha512-269Z39MS6wVJtsoUl10L60WdkhJVdPG24Q4eZTH3nnF6lpvSShEK3wQjDX9JRWAUPvPh7COouPpU9IrqaZFvtQ==", "dev": true, "license": "MIT", @@ -131,7 +131,7 @@ }, "node_modules/@eslint/js": { "version": "8.57.1", - "resolved": "https://registry.npmjs.org/@eslint/js/-/js-8.57.1.tgz", + "resolved": "https://registry.npmmirror.com/@eslint/js/-/js-8.57.1.tgz", "integrity": "sha512-d9zaMRSTIKDLhctzH12MtXvJKSSUhaHcjV+2Z+GK+EEY7XKpP5yR4x+N3TAcHTcu963nIr+TMcCb4DBCYX1z6Q==", "dev": true, "license": "MIT", @@ -141,7 +141,7 @@ }, "node_modules/@humanwhocodes/config-array": { "version": "0.13.0", - "resolved": "https://registry.npmjs.org/@humanwhocodes/config-array/-/config-array-0.13.0.tgz", + "resolved": "https://registry.npmmirror.com/@humanwhocodes/config-array/-/config-array-0.13.0.tgz", "integrity": "sha512-DZLEEqFWQFiyK6h5YIeynKx7JlvCYWL0cImfSRXZ9l4Sg2efkFGTuFf6vzXjK1cq6IYkU+Eg/JizXw+TD2vRNw==", "deprecated": "Use @eslint/config-array instead", "dev": true, @@ -157,7 +157,7 @@ }, "node_modules/@humanwhocodes/module-importer": { "version": "1.0.1", - "resolved": "https://registry.npmjs.org/@humanwhocodes/module-importer/-/module-importer-1.0.1.tgz", + 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"node_modules/yocto-queue": { "version": "0.1.0", - "resolved": "https://registry.npmjs.org/yocto-queue/-/yocto-queue-0.1.0.tgz", + "resolved": "https://registry.npmmirror.com/yocto-queue/-/yocto-queue-0.1.0.tgz", "integrity": "sha512-rVksvsnNCdJ/ohGc6xgPwyN8eheCxsiLM8mxuE/t/mOVqJewPuO1miLpTHQiRgTKCLexL4MeAFVagts7HmNZ2Q==", "dev": true, "license": "MIT", @@ -7649,7 +7863,7 @@ }, "node_modules/zwitch": { "version": "2.0.4", - "resolved": "https://registry.npmjs.org/zwitch/-/zwitch-2.0.4.tgz", + "resolved": "https://registry.npmmirror.com/zwitch/-/zwitch-2.0.4.tgz", "integrity": "sha512-bXE4cR/kVZhKZX/RjPEflHaKVhUVl85noU3v6b8apfQEc1x4A+zBxjZ4lN8LqGd6WZ3dl98pY4o717VFmoPp+A==", "license": "MIT", "funding": { diff --git a/package.json b/package.json index 8058fb6..ac76ee7 100644 --- a/package.json +++ b/package.json @@ -9,24 +9,24 @@ "lint": "next lint" }, "dependencies": { - "next": "^14.2.0", - "react": "^18.3.0", - "react-dom": "^18.3.0", - "react-markdown": "^9.0.0", - "remark-gfm": "^4.0.0", "@monaco-editor/react": "^4.6.0", + "clsx": "^2.1.0", "lucide-react": "^0.400.0", - "clsx": "^2.1.0" + "next": "^14.1.0", + "react": "^18.2.0", + "react-dom": "^18.2.0", + "react-markdown": "^9.0.0", + "remark-gfm": "^4.0.0" }, "devDependencies": { "@types/node": "^20.0.0", "@types/react": "^18.3.0", "@types/react-dom": "^18.3.0", - "typescript": "^5.4.0", - "tailwindcss": "^3.4.0", - "postcss": "^8.4.0", "autoprefixer": "^10.4.0", "eslint": "^8.0.0", - "eslint-config-next": "^14.2.0" + "eslint-config-next": "^14.2.0", + "postcss": "^8.4.0", + "tailwindcss": "^3.4.0", + "typescript": "^5.4.0" } } diff --git a/src/app/globals.css b/src/app/globals.css index 03529ec..eb8426c 100644 --- a/src/app/globals.css +++ b/src/app/globals.css @@ -11,7 +11,8 @@ --accent-orange: #ff6723; } -html, body { height: 100%; overflow: hidden; } +html { height: 100%; overflow-y: auto; } +body { min-height: 100%; } body { color: var(--text-primary); @@ -50,10 +51,20 @@ body { } /* ── Scrollbar ──────────────────────────────────────── */ -::-webkit-scrollbar { width: 5px; height: 5px; } -::-webkit-scrollbar-track { background: transparent; } -::-webkit-scrollbar-thumb { background: rgba(0,0,0,0.10); border-radius: 10px; } -::-webkit-scrollbar-thumb:hover { background: rgba(0,0,0,0.18); } +::-webkit-scrollbar { width: 14px; height: 14px; } +::-webkit-scrollbar-track { background: #e8ecf2; border-radius: 10px; } +::-webkit-scrollbar-thumb { + background: #8796ad; + border: 3px solid #e8ecf2; + border-radius: 10px; + min-height: 40px; +} +::-webkit-scrollbar-thumb:hover { background: #596f91; } +::-webkit-scrollbar-thumb:active { background: var(--accent); } + +@supports not selector(::-webkit-scrollbar) { + * { scrollbar-width: auto; scrollbar-color: #8796ad #e8ecf2; } +} /* ── Scroll shadow overlay (top/bottom) ─────────────── */ .scroll-shadow { diff --git a/src/app/historical-data/page.tsx b/src/app/historical-data/page.tsx new file mode 100644 index 0000000..5cd64c9 --- /dev/null +++ b/src/app/historical-data/page.tsx @@ -0,0 +1,370 @@ +"use client"; + +import { useState } from "react"; +import { Database, Search, Filter, Calendar, TrendingUp, CheckCircle, XCircle, AlertCircle } from "lucide-react"; + +interface HistoricalCase { + case_id: string; + customer_name: string; + project_type: string; + amount: number; + debt_ratio: number; + credit_query_count: number; + litigation_count: number; + decision: string; + decision_date: string; + processing_days: number; + mitigation_measures: string; + final_outcome: string; + risk_level: "high" | "medium" | "low"; +} + +export default function HistoricalDataPage() { + const [searchTerm, setSearchTerm] = useState(""); + const [selectedDecision, setSelectedDecision] = useState("all"); + const [selectedRiskLevel, setSelectedRiskLevel] = useState("all"); + const [dateRange, setDateRange] = useState("12"); + + // 模拟历史案例数据 + const [cases] = useState([ + { + case_id: "CAS-2025-08-156", + customer_name: "华东光伏设备公司", + project_type: "新能源设备", + amount: 2.8, + debt_ratio: 0.68, + credit_query_count: 7, + litigation_count: 1, + decision: "降额通过", + decision_date: "2025-08-15", + processing_days: 4, + mitigation_measures: "降低授信额度15%，增加法人个人担保，提供政府补贴承诺函", + final_outcome: "已放款，回款正常", + risk_level: "medium" + }, + { + case_id: "CAS-2025-07-089", + customer_name: "绿能公交运营公司", + project_type: "对公新能源", + amount: 3.5, + debt_ratio: 0.72, + credit_query_count: 9, + litigation_count: 0, + decision: "降额通过", + decision_date: "2025-07-22", + processing_days: 6, + mitigation_measures: "降低授信额度12%，调整还款期限，追加车辆抵押", + final_outcome: "已放款，回款正常", + risk_level: "high" + }, + { + case_id: "CAS-2025-06-234", + customer_name: "中部新能源投资", + project_type: "新能源投资", + amount: 4.2, + debt_ratio: 0.65, + credit_query_count: 6, + litigation_count: 2, + decision: "附加条件通过", + decision_date: "2025-06-18", + processing_days: 5, + mitigation_measures: "要求季度现金流报告，设置财务预警指标，增加母公司担保", + final_outcome: "已放款，回款正常", + risk_level: "high" + }, + { + case_id: "CAS-2025-08-201", + customer_name: "南方物流运输公司", + project_type: "物流运输", + amount: 1.8, + debt_ratio: 0.58, + credit_query_count: 4, + litigation_count: 0, + decision: "直接通过", + decision_date: "2025-08-28", + processing_days: 2, + mitigation_measures: "无需额外措施", + final_outcome: "已放款，回款正常", + risk_level: "low" + }, + { + case_id: "CAS-2025-07-145", + customer_name: "东部制造业集团", + project_type: "设备制造", + amount: 5.5, + debt_ratio: 0.75, + credit_query_count: 12, + litigation_count: 3, + decision: "退回", + decision_date: "2025-07-10", + processing_days: 8, + mitigation_measures: "要求补充材料后重新申请", + final_outcome: "客户未重新申请", + risk_level: "high" + }, + { + case_id: "CAS-2025-06-098", + customer_name: "西部能源开发公司", + project_type: "能源开发", + amount: 6.0, + debt_ratio: 0.62, + credit_query_count: 5, + litigation_count: 1, + decision: "附加条件通过", + decision_date: "2025-06-05", + processing_days: 7, + mitigation_measures: "增加土地使用权抵押，提供项目收益权质押", + final_outcome: "已放款，回款正常", + risk_level: "medium" + }, + { + case_id: "CAS-2025-08-178", + customer_name: "北方建筑工程公司", + project_type: "建筑工程", + amount: 3.2, + debt_ratio: 0.70, + credit_query_count: 8, + litigation_count: 2, + decision: "降额通过", + decision_date: "2025-08-20", + processing_days: 5, + mitigation_measures: "降低授信额度20%，要求项目方连带担保", + final_outcome: "已放款，回款正常", + risk_level: "high" + }, + { + case_id: "CAS-2025-07-167", + customer_name: "中部科技创新公司", + project_type: "科技研发", + amount: 2.0, + debt_ratio: 0.55, + credit_query_count: 3, + litigation_count: 0, + decision: "直接通过", + decision_date: "2025-07-28", + processing_days: 3, + mitigation_measures: "无需额外措施", + final_outcome: "已放款，回款正常", + risk_level: "low" + } + ]); + + const decisions = ["all", "直接通过", "降额通过", "附加条件通过", "退回"]; + const riskLevels = ["all", "high", "medium", "low"]; + + const filteredCases = cases.filter(c => { + const matchesSearch = c.customer_name.toLowerCase().includes(searchTerm.toLowerCase()) || + c.case_id.toLowerCase().includes(searchTerm.toLowerCase()); + const matchesDecision = selectedDecision === "all" || c.decision === selectedDecision; + const matchesRisk = selectedRiskLevel === "all" || c.risk_level === selectedRiskLevel; + return matchesSearch && matchesDecision && matchesRisk; + }); + + // 统计数据 + const stats = { + total: cases.length, + approved: cases.filter(c => c.decision.includes("通过")).length, + rejected: cases.filter(c => c.decision === "退回").length, + avgProcessingDays: (cases.reduce((sum, c) => sum + c.processing_days, 0) / cases.length).toFixed(1) + }; + + const getDecisionBadge = (decision: string) => { + if (decision.includes("直接通过")) { + return {decision}; + } else if (decision.includes("通过")) { + return {decision}; + } else if (decision === "退回") { + return {decision}; + } + return {decision}; + }; + + const getRiskBadge = (level: string) => { + switch (level) { + case "high": return 高风险; + case "medium": return 中风险; + case "low": return 低风险; + default: return null; + } + }; + + return ( +
+ {/* Header */} +
+
+
+

+ 💾 历史数据分析 +

+

案例数据查询与分析（轨道2：数据驱动）

+
+
+
+ + {/* 统计卡片 */} +
+
+
{stats.total}
+
历史案例总数
+
+
+
{stats.approved}
+
通过案例
+
+
+
{stats.rejected}
+
退回案例
+
+
+
{stats.avgProcessingDays}
+
平均处理天数
+
+
+ + {/* 搜索和筛选 */} +
+
+ {/* 搜索框 */} +
+ + setSearchTerm(e.target.value)} + className="w-full pl-10 pr-4 py-2 bg-slate-900 border border-slate-700 rounded-lg text-white focus:outline-none focus:border-blue-500" + /> +
+ + {/* 筛选器 */} +
+ {/* 决策筛选 */} +
+ 决策结果: +
+ {decisions.map(dec => ( + + ))} +
+
+ + {/* 风险等级筛选 */} +
+ 风险等级: +
+ {riskLevels.map(level => ( + + ))} +
+
+ + {/* 时间范围 */} +
+ + +
+
+ +
+ 找到 {filteredCases.length} 个匹配案例 +
+
+
+ + {/* 案例列表 */} +
+ {filteredCases.length === 0 ? ( +
+ 未找到匹配的案例 +
+ ) : ( + filteredCases.map(case_ => ( +
+ {/* 标题行 */} +
+
+
+ {case_.case_id} + {getDecisionBadge(case_.decision)} + {getRiskBadge(case_.risk_level)} +
+

{case_.customer_name}

+

{case_.project_type}

+
+
+
{case_.amount.toFixed(1)}亿
+
{case_.decision_date}
+
+
+ + {/* 关键指标 */} +
+
+
资产负债率
+
{(case_.debt_ratio * 100).toFixed(0)}%
+
+
+
征信查询
+
{case_.credit_query_count}次
+
+
+
涉诉记录
+
{case_.litigation_count}起
+
+
+
处理时长
+
{case_.processing_days}天
+
+
+ + {/* 风控措施 */} +
+
风控措施
+
{case_.mitigation_measures}
+
+ + {/* 最终结果 */} +
+
最终结果
+
{case_.final_outcome}
+
+
+ )) + )} +
+
+ ); +} diff --git a/src/app/page.tsx b/src/app/page.tsx index 306a081..ab245f2 100644 --- a/src/app/page.tsx +++ b/src/app/page.tsx @@ -41,7 +41,7 @@ function MainLayout() { {/* Content area — flexbox layout with resizable panels */} -
+
{/* Left sidebar */}
; + exclusions: Array<{ + clause: string; + risk_level: "high" | "medium" | "low"; + description: string; + }>; + overall_result: "compliant" | "non_compliant" | "needs_review"; + recommendations: string[]; +} + +interface ReportTemplate { + id: string; + name: string; + description: string; + sections: string[]; +} + +export default function PolicyVerificationPage() { + const [activeTab, setActiveTab] = useState<"upload" | "results" | "templates">("upload"); + const [selectedFile, setSelectedFile] = useState(null); + const [selectedTemplate, setSelectedTemplate] = useState("TPL-POLICY-001"); + + const [checkForm, setCheckForm] = useState({ + customer_name: "远东新能源公交公司", + lease_amount: 8000, + project_type: "新能源设备" + }); + + const [loading, setLoading] = useState(false); + const [checkResult, setCheckResult] = useState(null); + const [generatedReport, setGeneratedReport] = useState(null); + + // 报告模板 + const [templates] = useState([ + { + id: "TPL-POLICY-001", + name: "标准保单核验报告", + description: "适用于常规设备和车辆的保单核验", + sections: ["保单基本信息", "合规性检查", "除外责任分析", "风险评估", "核验结论", "整改建议"] + }, + { + id: "TPL-POLICY-002", + name: "新能源专项保单核验", + description: "针对新能源设备的专项核验报告", + sections: ["保单基本信息", "新能源特殊险种", "合规性检查", "政策补贴覆盖", "除外责任分析", "核验结论"] + }, + { + id: "TPL-POLICY-003", + name: "大额设备保单核验", + description: "5000万以上大额设备的详细核验", + sections: ["保单基本信息", "保额充足性分析", "多保险公司协同", "合规性检查", "除外责任分析", "风险评估", "核验结论"] + }, + { + id: "TPL-POLICY-004", + name: "快速核验简报", + description: "低风险项目的快速核验简报", + sections: ["保单基本信息", "合规性检查", "核验结论"] + } + ]); + + const handleFileChange = (e: React.ChangeEvent) => { + if (e.target.files && e.target.files[0]) { + setSelectedFile(e.target.files[0]); + } + }; + + const handlePolicyCheck = async () => { + if (!selectedFile) { + alert("请先上传保单PDF文件"); + return; + } + + setLoading(true); + setCheckResult(null); + setGeneratedReport(null); + + try { + // 模拟OCR识别和保单核验 + await new Promise(resolve => setTimeout(resolve, 2500)); + + const mockResult: PolicyCheckResult = { + policy_id: "POL-2026-001", + customer_name: checkForm.customer_name, + check_timestamp: new Date().toLocaleString('zh-CN'), + policy_info: { + insurance_company: "中国人民财产保险股份有限公司", + policy_number: "PICC-FZL-20260801-0156", + coverage_types: ["设备损失险", "盗抢险", "第三者责任险", "营业中断险"], + coverage_amount: 9600, + lease_amount: checkForm.lease_amount, + coverage_ratio: (9600 / checkForm.lease_amount) * 100 + }, + compliance_items: [ + { + category: "保额覆盖", + item: "保额 ≥ 授信金额 × 120%", + status: "pass", + detail: `保额9600万元 ≥ 授信${checkForm.lease_amount}万元 × 120% = ${(checkForm.lease_amount * 1.2).toFixed(0)}万元` + }, + { + category: "险种完整性", + item: "必备险种齐全", + status: "pass", + detail: "设备损失险、盗抢险、第三者责任险均已投保" + }, + { + category: "保险期限", + item: "保险期限覆盖租赁期", + status: "pass", + detail: "保险期限36个月，租赁期36个月，完全覆盖" + }, + { + category: "第一受益人", + item: "兴邦金租为第一受益人", + status: "pass", + detail: "保单明确载明：第一受益人为兴邦融资租赁有限公司" + }, + { + category: "除外责任", + item: "高频风险不得除外", + status: "fail", + detail: "保单除外责任包含「泡水」和「自然灾害」，属于高频风险，不应除外" + }, + { + category: "保费支付", + item: "保费按期支付证明", + status: "warning", + detail: "仅提供首期保费缴纳凭证，后续保费需持续跟踪" + } + ], + exclusions: [ + { + clause: "因泡水、浸水导致的设备损失", + risk_level: "high", + description: "极端天气下泡水风险较高，尤其是户外设备。此条款将导致泡水事故无法理赔，风险敞口较大。" + }, + { + clause: "因自然灾害（台风、暴雨、洪水）导致的损失", + risk_level: "high", + description: "自然灾害属于不可抗力但发生概率较高，此条款将导致重大自然灾害损失无法理赔。" + }, + { + clause: "战争、核辐射、恐怖袭击", + risk_level: "low", + description: "此类风险发生概率极低，属于常规除外责任，可接受。" + }, + { + clause: "故意损坏、违法使用", + risk_level: "low", + description: "此类风险属于人为因素，常规除外责任，可接受。" + } + ], + overall_result: "non_compliant", + recommendations: [ + "【必须整改】要求保险公司删除「泡水」除外责任条款，或增加泡水附加险", + "【必须整改】要求保险公司删除「自然灾害」除外责任条款，或增加自然灾害附加险", + "【建议跟踪】建立保费缴纳监控机制，每期保费到期前提醒客户缴纳", + "【风险提示】如客户无法调整保单条款，建议在合同中明确：保单除外责任导致的损失由客户承担" + ] + }; + + setCheckResult(mockResult); + setActiveTab("results"); + } catch (error) { + alert("保单核验失败"); + } finally { + setLoading(false); + } + }; + + const handleGenerateReport = () => { + if (!checkResult) return; + + const template = templates.find(t => t.id === selectedTemplate)!; + let reportContent = ""; + + reportContent += `# ${checkResult.customer_name} - 保单合规核验报告\n\n`; + reportContent += `**报告模板**: ${template.name}\n`; + reportContent += `**核验时间**: ${checkResult.check_timestamp}\n`; + reportContent += `**保单编号**: ${checkResult.policy_info.policy_number}\n`; + reportContent += `**核验结果**: ${checkResult.overall_result === "compliant" ? "✅ 合规" : checkResult.overall_result === "non_compliant" ? "❌ 不合规" : "⚠️ 需要复核"}\n\n`; + reportContent += `---\n\n`; + + template.sections.forEach(section => { + switch (section) { + case "保单基本信息": + reportContent += `## 一、保单基本信息\n\n`; + reportContent += `| 项目 | 内容 |\n|------|------|\n`; + reportContent += `| 保险公司 | ${checkResult.policy_info.insurance_company} |\n`; + reportContent += `| 保单号 | ${checkResult.policy_info.policy_number} |\n`; + reportContent += `| 投保险种 | ${checkResult.policy_info.coverage_types.join("、")} |\n`; + reportContent += `| 保险金额 | ${checkResult.policy_info.coverage_amount}万元 |\n`; + reportContent += `| 授信金额 | ${checkResult.policy_info.lease_amount}万元 |\n`; + reportContent += `| 覆盖比例 | ${checkResult.policy_info.coverage_ratio.toFixed(1)}% |\n\n`; + break; + + case "合规性检查": + reportContent += `## 二、合规性检查\n\n`; + checkResult.compliance_items.forEach((item, idx) => { + const icon = item.status === "pass" ? "✅" : item.status === "fail" ? "❌" : "⚠️"; + reportContent += `### ${idx + 1}. ${icon} ${item.item}\n\n`; + reportContent += `**检查类别**: ${item.category}\n\n`; + reportContent += `**检查结果**: ${item.status === "pass" ? "通过" : item.status === "fail" ? "不通过" : "需关注"}\n\n`; + reportContent += `**详细说明**: ${item.detail}\n\n`; + }); + break; + + case "除外责任分析": + reportContent += `## 三、除外责任条款分析\n\n`; + const highRiskExclusions = checkResult.exclusions.filter(e => e.risk_level === "high"); + const mediumRiskExclusions = checkResult.exclusions.filter(e => e.risk_level === "medium"); + const lowRiskExclusions = checkResult.exclusions.filter(e => e.risk_level === "low"); + + if (highRiskExclusions.length > 0) { + reportContent += `### 🔴 高风险除外责任（${highRiskExclusions.length}项）\n\n`; + highRiskExclusions.forEach((exc, idx) => { + reportContent += `**${idx + 1}. ${exc.clause}**\n\n`; + reportContent += `${exc.description}\n\n`; + }); + } + + if (mediumRiskExclusions.length > 0) { + reportContent += `### 🟡 中风险除外责任（${mediumRiskExclusions.length}项）\n\n`; + mediumRiskExclusions.forEach((exc, idx) => { + reportContent += `**${idx + 1}. ${exc.clause}**\n\n`; + reportContent += `${exc.description}\n\n`; + }); + } + + if (lowRiskExclusions.length > 0) { + reportContent += `### 🟢 低风险除外责任（${lowRiskExclusions.length}项，可接受）\n\n`; + lowRiskExclusions.forEach((exc, idx) => { + reportContent += `- ${exc.clause}\n`; + }); + reportContent += `\n`; + } + break; + + case "风险评估": + reportContent += `## 四、风险评估\n\n`; + const failedItems = checkResult.compliance_items.filter(i => i.status === "fail"); + const warningItems = checkResult.compliance_items.filter(i => i.status === "warning"); + reportContent += `**不合规项**: ${failedItems.length}项\n\n`; + reportContent += `**关注项**: ${warningItems.length}项\n\n`; + reportContent += `**高风险除外**: ${checkResult.exclusions.filter(e => e.risk_level === "high").length}项\n\n`; + reportContent += `**综合评估**: ${checkResult.overall_result === "compliant" ? "保单符合公司要求，可以接受" : checkResult.overall_result === "non_compliant" ? "保单存在重大不合规项，建议要求客户整改" : "保单基本合规，但存在需要关注的项目"}\n\n`; + break; + + case "核验结论": + reportContent += `## ${template.sections.indexOf(section) + 1}. 核验结论\n\n`; + if (checkResult.overall_result === "compliant") { + reportContent += `✅ **结论**: 保单合规，符合兴邦金租保单管理要求\n\n`; + } else if (checkResult.overall_result === "non_compliant") { + reportContent += `❌ **结论**: 保单不合规，存在以下重大问题：\n\n`; + checkResult.compliance_items.filter(i => i.status === "fail").forEach(item => { + reportContent += `- ${item.item}: ${item.detail}\n`; + }); + reportContent += `\n`; + } else { + reportContent += `⚠️ **结论**: 保单基本合规，但需要关注以下事项\n\n`; + } + break; + + case "整改建议": + case "核验建议": + reportContent += `## ${template.sections.indexOf(section) + 1}. 整改建议\n\n`; + checkResult.recommendations.forEach((rec, idx) => { + reportContent += `${idx + 1}. ${rec}\n\n`; + }); + break; + + case "新能源特殊险种": + reportContent += `## 二、新能源特殊险种核验\n\n`; + reportContent += `- 新能源设备特殊险种覆盖情况\n`; + reportContent += `- 电池、充电设施专项保险\n`; + reportContent += `- 技术更新换代风险覆盖\n\n`; + break; + + case "政策补贴覆盖": + reportContent += `## 三、政策补贴覆盖情况\n\n`; + reportContent += `- 政府补贴延迟风险保险\n`; + reportContent += `- 补贴政策变更风险覆盖\n\n`; + break; + + case "保额充足性分析": + reportContent += `## 二、保额充足性深度分析\n\n`; + reportContent += `- 大额设备分段保险情况\n`; + reportContent += `- 累计风险敞口分析\n`; + reportContent += `- 保额动态调整机制\n\n`; + break; + + case "多保险公司协同": + reportContent += `## 三、多保险公司协同核验\n\n`; + reportContent += `- 多家保险公司分担情况\n`; + reportContent += `- 主次保险责任划分\n`; + reportContent += `- 理赔协调机制\n\n`; + break; + } + }); + + reportContent += `---\n\n`; + reportContent += `**核验人**: AI智能核验系统\n\n`; + reportContent += `**复核人**: [待人工复核]\n\n`; + reportContent += `**核验日期**: ${checkResult.check_timestamp}\n`; + + setGeneratedReport(reportContent); + }; + + const getStatusBadge = (status: string) => { + switch (status) { + case "pass": + return 通过; + case "fail": + return 不通过; + case "warning": + return 需关注; + default: + return null; + } + }; + + const getRiskBadge = (level: string) => { + switch (level) { + case "high": + return 高风险; + case "medium": + return 中风险; + case "low": + return 低风险; + default: + return null; + } + }; + + return ( +
+ {/* Header */} +
+
+
+

+ 保单条款智能核验 +

+

OCR识别 + 条款解析 + 合规核验 + 报告生成

+
+
+
+ + {/* Tab Navigation */} +
+ + + +
+ + {/* Upload Tab */} + {activeTab === "upload" && ( +
+ {/* Project Info */} +
+

项目信息

+
+
+ + setCheckForm({ ...checkForm, customer_name: e.target.value })} + className="w-full px-3 py-2 bg-slate-900 border border-slate-700 rounded-lg text-white focus:outline-none focus:border-green-500" + /> +
+
+ + setCheckForm({ ...checkForm, lease_amount: parseFloat(e.target.value) })} + className="w-full px-3 py-2 bg-slate-900 border border-slate-700 rounded-lg text-white focus:outline-none focus:border-green-500" + /> +
+
+ + setCheckForm({ ...checkForm, project_type: e.target.value })} + className="w-full px-3 py-2 bg-slate-900 border border-slate-700 rounded-lg text-white focus:outline-none focus:border-green-500" + /> +
+
+
+ + {/* Upload Section */} +
+

上传保单PDF

+ +
+ + + +
+ + +
+
+ )} + + {/* Results Tab */} + {activeTab === "results" && checkResult && ( +
+ {/* Summary Card */} +
+
+
+

{checkResult.customer_name}

+

核验时间: {checkResult.check_timestamp}

+
+
+
+ {checkResult.overall_result === "compliant" ? "✅" : + checkResult.overall_result === "non_compliant" ? "❌" : "⚠️"} +
+
+ {checkResult.overall_result === "compliant" ? "合规" : + checkResult.overall_result === "non_compliant" ? "不合规" : "需复核"} +
+
+
+ +
+
+
保险金额
+
{checkResult.policy_info.coverage_amount}万
+
+
+
授信金额
+
{checkResult.policy_info.lease_amount}万
+
+
+
覆盖比例
+
{checkResult.policy_info.coverage_ratio.toFixed(1)}%
+
+
+
高风险除外
+
{checkResult.exclusions.filter(e => e.risk_level === "high").length}项
+
+
+
+ + {/* Compliance Items */} +
+

+ + 合规性检查结果 +

+
+ {checkResult.compliance_items.map((item, idx) => ( +
+
+
+
+ {getStatusBadge(item.status)} + {item.item} +
+
检查类别: {item.category}
+
+
+

{item.detail}

+
+ ))} +
+
+ + {/* Exclusions */} +
+

+ + 除外责任条款分析 +

+
+ {checkResult.exclusions.map((exc, idx) => ( +
+
+ {getRiskBadge(exc.risk_level)} + {exc.clause} +
+

{exc.description}

+
+ ))} +
+
+ + {/* Recommendations */} +
+

整改建议

+
    + {checkResult.recommendations.map((rec, idx) => ( +
  • + {idx + 1}. + {rec} +
    • + ))} +
+
+ + {/* Generate Report */} +
+

生成核验报告

+ +
+ + +
+ + + + {generatedReport && ( +
+
+

报告预览

+
+ + +
+
+ 
+                  {generatedReport}
+
+
+ )} +
+
+ )} + + {/* Templates Tab */} + {activeTab === "templates" && ( +
+
+

核验报告模板库

+
+ + {templates.map(template => ( +
+
+
+
+ +

{template.name}

+ {template.id} +
+

{template.description}

+
+
+ +
+ {template.sections.map((section, idx) => ( + + {section} + + ))} +
+
+ ))} +
+ )} +
+ ); +} diff --git a/src/app/report-assistant/page.tsx b/src/app/report-assistant/page.tsx new file mode 100644 index 0000000..0dd5535 --- /dev/null +++ b/src/app/report-assistant/page.tsx @@ -0,0 +1,370 @@ +"use client"; + +import { useState } from "react"; +import { FileText, Download, Eye, Edit, Plus, Trash2, CheckSquare, Square } from "lucide-react"; + +interface ReportTemplate { + id: string; + name: string; + description: string; + sections: string[]; + created_at: string; + usage_count: number; +} + +interface GeneratedReport { + template_name: string; + generated_at: string; + content: { + title: string; + sections: Array<{ + name: string; + content: string; + }>; + }; +} + +export default function ReportAssistantPage() { + const [activeTab, setActiveTab] = useState<"generate" | "templates">("generate"); + const [selectedTemplate, setSelectedTemplate] = useState(""); + const [projectInfo, setProjectInfo] = useState({ + customer_name: "远东新能源公交公司", + project_type: "对公新能源", + amount: 3.0, + risk_level: "medium" + }); + + const [loading, setLoading] = useState(false); + const [generatedReport, setGeneratedReport] = useState(null); + + // 模拟报告模板数据 + const [templates] = useState([ + { + id: "TPL-001", + name: "标准风险评审报告", + description: "适用于常规项目的风险评审报告，包含完整的风险分析和建议", + sections: ["项目概况", "风险识别", "财务分析", "法律合规性", "综合评审意见", "审批建议"], + created_at: "2025-01-15", + usage_count: 156 + }, + { + id: "TPL-002", + name: "新能源项目专项报告", + description: "针对新能源行业的专项评审报告，关注政策补贴和行业风险", + sections: ["项目概况", "行业政策分析", "技术可行性", "财务测算", "风险评估", "审批建议"], + created_at: "2025-02-10", + usage_count: 89 + }, + { + id: "TPL-003", + name: "大额授信专项报告", + description: "5亿元以上大额授信项目的详细评审报告", + sections: ["项目概况", "企业背景调查", "深度财务分析", "抵押物评估", "风险缓释措施", "总部审批建议"], + created_at: "2025-01-20", + usage_count: 34 + }, + { + id: "TPL-004", + name: "快速审批简报", + description: "低风险项目的快速审批简报，简化流程", + sections: ["项目基本信息", "快速风险评估", "审批建议"], + created_at: "2025-02-01", + usage_count: 203 + } + ]); + + const handleGenerateReport = async () => { + if (!selectedTemplate) { + alert("请先选择报告模板"); + return; + } + + setLoading(true); + setGeneratedReport(null); + + try { + // 模拟生成报告 + await new Promise(resolve => setTimeout(resolve, 2000)); + + const template = templates.find(t => t.id === selectedTemplate)!; + + const mockReport: GeneratedReport = { + template_name: template.name, + generated_at: new Date().toLocaleString('zh-CN'), + content: { + title: `${projectInfo.customer_name} - ${template.name}`, + sections: template.sections.map(sectionName => ({ + name: sectionName, + content: generateMockSectionContent(sectionName, projectInfo) + })) + } + }; + + setGeneratedReport(mockReport); + } catch (error) { + alert("生成报告失败"); + } finally { + setLoading(false); + } + }; + + const generateMockSectionContent = (sectionName: string, info: any): string => { + switch (sectionName) { + case "项目概况": + return `客户名称：${info.customer_name}\n项目类型：${info.project_type}\n授信金额：${info.amount}亿元\n风险等级：${info.risk_level === 'high' ? '高风险' : info.risk_level === 'medium' ? '中风险' : '低风险'}\n\n本项目为${info.project_type}类型融资租赁项目，客户${info.customer_name}申请授信金额${info.amount}亿元。经初步评估，项目风险等级为${info.risk_level === 'high' ? '高风险' : info.risk_level === 'medium' ? '中风险' : '低风险'}。`; + + case "风险识别": + case "风险评估": + return `基于双轨驱动分析（规则引擎+历史数据），识别出以下主要风险点：\n\n1. 财务风险：资产负债率接近行业警戒线，需关注偿债能力\n2. 信用风险：征信查询次数偏高，需核实融资需求真实性\n3. 法律风险：存在未结案涉诉记录，需评估潜在影响\n\n历史数据显示，类似项目的通过率为73%，其中60%需要附加风控措施。`; + + case "财务分析": + case "深度财务分析": + return `关键财务指标分析：\n\n• 资产负债率：65%（行业平均60%）\n• 流动比率：1.2（良好）\n• 净资产收益率：8.5%（中等）\n• 现金流覆盖倍数：1.8倍\n\n综合评估：财务状况整体可控，但需关注债务结构优化。`; + + case "审批建议": + case "综合评审意见": + return `综合评审意见：\n\n建议：附加条件通过\n\n理由：\n1. 客户基本面良好，行业前景明朗\n2. 财务指标整体可控，但需加强风险缓释\n3. 历史同类案例成功率较高\n\n附加条件：\n• 建议降低授信额度10-15%\n• 要求增加抵押物或第三方担保\n• 设置季度财务监控机制`; + + case "行业政策分析": + return `${info.project_type}行业政策环境：\n\n• 国家政策支持力度大，补贴政策稳定\n• 地方政府配套措施完善\n• 行业准入门槛提高，竞争格局改善\n• 需关注补贴退坡风险`; + + case "技术可行性": + return `技术方案评估：\n\n• 技术路线成熟，市场验证充分\n• 设备供应商资质良好\n• 运营团队经验丰富\n• 维护保养体系完善`; + + default: + return `${sectionName}相关内容...\n\n[此部分内容由AI根据项目实际情况和历史数据自动生成]`; + } + }; + + return ( +
+ {/* Header */} +
+
+
+

+ 报告助手 +

+

智能报告生成与模板管理

+
+
+
+ + {/* Tab Navigation */} +
+ + +
+ + {/* Generate Report Tab */} + {activeTab === "generate" && ( +
+ {/* Project Info Form */} +
+

项目信息

+
+
+ + setProjectInfo({ ...projectInfo, customer_name: e.target.value })} + className="w-full px-3 py-2 bg-slate-900 border border-slate-700 rounded-lg text-white focus:outline-none focus:border-purple-500" + /> +
+
+ + setProjectInfo({ ...projectInfo, project_type: e.target.value })} + className="w-full px-3 py-2 bg-slate-900 border border-slate-700 rounded-lg text-white focus:outline-none focus:border-purple-500" + /> +
+
+ + setProjectInfo({ ...projectInfo, amount: parseFloat(e.target.value) })} + className="w-full px-3 py-2 bg-slate-900 border border-slate-700 rounded-lg text-white focus:outline-none focus:border-purple-500" + /> +
+
+ + +
+
+
+ + {/* Template Selection */} +
+

选择报告模板

+
+ {templates.map(template => ( +
setSelectedTemplate(template.id)} + className={`p-4 rounded-lg border-2 cursor-pointer transition-all ${ + selectedTemplate === template.id + ? "border-purple-500 bg-purple-900/30" + : "border-slate-700 bg-slate-900/50 hover:border-slate-600" + }`} + > +
+
+ {selectedTemplate === template.id ? ( + + ) : ( + + )} +

{template.name}

+
+ {template.id} +
+

{template.description}

+
+ 使用次数: {template.usage_count} + 创建: {template.created_at} +
+
+ 章节: {template.sections.join(" · ")} +
+
+ ))} +
+ + +
+ + {/* Generated Report */} + {generatedReport && ( +
+
+
+

{generatedReport.content.title}

+

生成时间: {generatedReport.generated_at}

+
+
+ + +
+
+ +
+ {generatedReport.content.sections.map((section, idx) => ( +
+

{section.name}

+

+ {section.content} +

+
+ ))} +
+
+ )} +
+ )} + + {/* Templates Tab */} + {activeTab === "templates" && ( +
+
+

报告模板库

+ +
+ +
+ {templates.map(template => ( +
+
+
+
+ +

{template.name}

+ {template.id} +
+

{template.description}

+
+
+ + +
+
+ +
+ {template.sections.map((section, idx) => ( + + {section} + + ))} +
+ +
+ 使用次数: {template.usage_count} + 创建时间: {template.created_at} +
+
+ ))} +
+
+ )} +
+ ); +} diff --git a/src/app/review-allocation/page.tsx b/src/app/review-allocation/page.tsx new file mode 100644 index 0000000..56c917e --- /dev/null +++ b/src/app/review-allocation/page.tsx @@ -0,0 +1,698 @@ +"use client"; + +import { useState } from "react"; +import { Users, TrendingUp, AlertTriangle, Calculator, Target, History, CheckCircle } from "lucide-react"; + +interface Reviewer { + id: string; + name: string; + specialty: string[]; + current_projects: number; + workload_capacity: number; + avg_processing_days: number; + quality_score: number; + status: "available" | "busy" | "full"; +} + +interface Recommendation { + reviewer: string; + score: number; + breakdown: { + history_match: number; + workload: number; + specialty: number; + quality: number; + }; + estimated_days: number; + reason: string; +} + +interface KeyIndicator { + name: string; + value: string; + unit: string; + warning?: string; + historical_avg?: string; +} + +export default function ReviewAllocationPage() { + const [activeTab, setActiveTab] = useState<"allocation" | "calculation">("allocation"); + + // 智能调配表单 + const [allocationForm, setAllocationForm] = useState({ + project_name: "远东新能源公交项目", + project_industry: "新能源", + project_amount: 3.0, + project_complexity: 3, + risk_level: "medium" + }); + + const [allocationResult, setAllocationResult] = useState<{ + reviewers: Reviewer[]; + recommendations: Recommendation[]; + } | null>(null); + + const [loadingAllocation, setLoadingAllocation] = useState(false); + + // 关键指标测算表单 + const [calculationForm, setCalculationForm] = useState({ + project_name: "远东新能源公交项目", + revenue_year1: 5000, + revenue_year2: 6000, + revenue_year3: 6500, + cost_ratio: 0.65, + depreciation: 800, + interest_rate: 0.045, + loan_amount: 25000, + irr_target: 0.08 + }); + + const [calculationResult, setCalculationResult] = useState<{ + indicators: KeyIndicator[]; + warnings: string[]; + historical_insights: string[]; + } | null>(null); + + const [loadingCalculation, setLoadingCalculation] = useState(false); + + // 处理智能调配 + const handleAllocation = async () => { + setLoadingAllocation(true); + setAllocationResult(null); + + try { + await new Promise(resolve => setTimeout(resolve, 1500)); + + // 模拟评审员数据 + const mockReviewers: Reviewer[] = [ + { + id: "REV-001", + name: "张建国", + specialty: ["新能源", "制造业", "大额授信"], + current_projects: 3, + workload_capacity: 5, + avg_processing_days: 4.2, + quality_score: 9.2, + status: "available" + }, + { + id: "REV-002", + name: "李明华", + specialty: ["新能源", "交通运输", "设备租赁"], + current_projects: 2, + workload_capacity: 5, + avg_processing_days: 3.8, + quality_score: 8.9, + status: "available" + }, + { + id: "REV-003", + name: "王晓红", + specialty: ["建筑工程", "房地产", "基础设施"], + current_projects: 5, + workload_capacity: 5, + avg_processing_days: 5.5, + quality_score: 8.5, + status: "full" + }, + { + id: "REV-004", + name: "赵强", + specialty: ["科技研发", "信息技术", "医疗器械"], + current_projects: 4, + workload_capacity: 5, + avg_processing_days: 4.8, + quality_score: 9.0, + status: "busy" + } + ]; + + // 智能推荐（根据行业匹配、工作负荷、历史质量） + const mockRecommendations: Recommendation[] = [ + { + reviewer: "李明华", + score: 92.5, + breakdown: { + history_match: 95, + workload: 90, + specialty: 98, + quality: 89 + }, + estimated_days: 4, + reason: "专长高度匹配（新能源+交通运输），当前负荷最轻，历史同类项目处理速度快" + }, + { + reviewer: "张建国", + score: 88.3, + breakdown: { + history_match: 88, + workload: 80, + specialty: 95, + quality: 92 + }, + estimated_days: 5, + reason: "新能源领域经验丰富，质量评分最高，适合处理复杂项目" + }, + { + reviewer: "赵强", + score: 65.2, + breakdown: { + history_match: 45, + workload: 60, + specialty: 40, + quality: 90 + }, + estimated_days: 6, + reason: "专长不完全匹配，但质量稳定，可作为备选" + } + ]; + + setAllocationResult({ + reviewers: mockReviewers, + recommendations: mockRecommendations + }); + + } catch (error) { + alert("智能调配失败"); + } finally { + setLoadingAllocation(false); + } + }; + + // 处理关键指标测算 + const handleCalculation = async () => { + setLoadingCalculation(true); + setCalculationResult(null); + + try { + await new Promise(resolve => setTimeout(resolve, 1500)); + + // 模拟计算关键指标 + const totalRevenue = calculationForm.revenue_year1 + calculationForm.revenue_year2 + calculationForm.revenue_year3; + const totalCost = totalRevenue * calculationForm.cost_ratio; + const interestExpense = calculationForm.loan_amount * calculationForm.interest_rate; + const netProfit = totalRevenue - totalCost - calculationForm.depreciation * 3 - interestExpense * 3; + const irr = ((netProfit / calculationForm.loan_amount) / 3); + + const mockIndicators: KeyIndicator[] = [ + { + name: "项目内部收益率 (IRR)", + value: (irr * 100).toFixed(2), + unit: "%", + warning: irr < calculationForm.irr_target ? `低于目标IRR ${(calculationForm.irr_target * 100).toFixed(1)}%` : undefined, + historical_avg: "8.5%" + }, + { + name: "净现值 (NPV)", + value: (netProfit - calculationForm.loan_amount * 0.05).toFixed(0), + unit: "万元", + historical_avg: "3200万元" + }, + { + name: "投资回收期", + value: (calculationForm.loan_amount / (totalRevenue / 3)).toFixed(1), + unit: "年", + warning: (calculationForm.loan_amount / (totalRevenue / 3)) > 5 ? "回收期较长" : undefined, + historical_avg: "4.2年" + }, + { + name: "资产负债率", + value: ((calculationForm.loan_amount / (calculationForm.loan_amount * 1.5)) * 100).toFixed(1), + unit: "%", + historical_avg: "62%" + }, + { + name: "利息保障倍数", + value: ((totalRevenue - totalCost) / (interestExpense * 3)).toFixed(2), + unit: "倍", + warning: ((totalRevenue - totalCost) / (interestExpense * 3)) < 2 ? "偿债能力偏弱" : undefined, + historical_avg: "3.5倍" + }, + { + name: "现金流覆盖率", + value: (1.8).toFixed(2), + unit: "倍", + historical_avg: "2.1倍" + } + ]; + + const mockWarnings: string[] = []; + const mockInsights: string[] = []; + + if (irr < calculationForm.irr_target) { + mockWarnings.push("⚠️ 项目IRR低于目标值，建议调整收入预测或降低成本"); + } + + if ((calculationForm.loan_amount / (totalRevenue / 3)) > 5) { + mockWarnings.push("⚠️ 投资回收期超过5年，需评估长期风险"); + } + + mockInsights.push("📊 根据历史数据，新能源行业项目平均IRR为8.5%，当前项目处于行业中位水平"); + mockInsights.push("💡 类似规模项目的成功案例中，70%采用了分期投资策略以降低风险"); + mockInsights.push("🎯 建议关注：补贴政策变化、设备折旧率、运营成本控制"); + + setCalculationResult({ + indicators: mockIndicators, + warnings: mockWarnings, + historical_insights: mockInsights + }); + + } catch (error) { + alert("指标测算失败"); + } finally { + setLoadingCalculation(false); + } + }; + + const getStatusBadge = (status: string) => { + switch (status) { + case "available": + return 可用; + case "busy": + return 繁忙; + case "full": + return 已满; + default: + return null; + } + }; + + return ( +
+ {/* Header */} +
+
+
+

+ 评审调配助手 +

+

智能评审员分配 + 关键指标测算

+
+
+
+ + {/* Tab Navigation */} +
+ + +
+ + {/* 智能调配 Tab */} + {activeTab === "allocation" && ( +
+ {/* 项目信息 */} +
+

项目信息

+
+
+ + setAllocationForm({ ...allocationForm, project_name: e.target.value })} + className="w-full px-3 py-2 bg-slate-900 border border-slate-700 rounded-lg text-white focus:outline-none focus:border-orange-500" + /> +
+
+ + setAllocationForm({ ...allocationForm, project_industry: e.target.value })} + className="w-full px-3 py-2 bg-slate-900 border border-slate-700 rounded-lg text-white focus:outline-none focus:border-orange-500" + /> +
+
+ + setAllocationForm({ ...allocationForm, project_amount: parseFloat(e.target.value) })} + className="w-full px-3 py-2 bg-slate-900 border border-slate-700 rounded-lg text-white focus:outline-none focus:border-orange-500" + /> +
+
+ + setAllocationForm({ ...allocationForm, project_complexity: parseInt(e.target.value) })} + className="w-full px-3 py-2 bg-slate-900 border border-slate-700 rounded-lg text-white focus:outline-none focus:border-orange-500" + /> +
+
+ + +
+
+ + +
+ + {/* 调配结果 */} + {allocationResult && ( + <> + {/* 评审员看板 */} +
+

+ + 评审员工作负荷看板 +

+
+ {allocationResult.reviewers.map(reviewer => ( +
+
+
+

{reviewer.name}

+ {reviewer.id} +
+ {getStatusBadge(reviewer.status)} +
+ +
+
+ 在手项目: + {reviewer.current_projects}/{reviewer.workload_capacity} +
+
+ 平均处理: + {reviewer.avg_processing_days}天 +
+
+ 质量评分: + {reviewer.quality_score}/10 +
+
+
专长领域:
+
+ {reviewer.specialty.map((spec, idx) => ( + {spec} + ))} +
+
+
+
+ ))} +
+
+ + {/* Top 3 推荐 */} +
+

+ + Top 3 推荐评审员 +

+
+ {allocationResult.recommendations.map((rec, idx) => ( +
+
+
+
+ {idx + 1} +
+
+

{rec.reviewer}

+

预计 {rec.estimated_days} 天完成

+
+
+
+
{rec.score.toFixed(1)}
+
综合评分
+
+
+ +
+
推荐理由
+
{rec.reason}
+
+ +
+
+
{rec.breakdown.history_match}
+
历史匹配
+
+
+
{rec.breakdown.workload}
+
负荷状态
+
+
+
{rec.breakdown.specialty}
+
专长匹配
+
+
+
{rec.breakdown.quality}
+
质量评分
+
+
+
+ ))} +
+
+ + )} +
+ )} + + {/* 关键指标测算 Tab */} + {activeTab === "calculation" && ( +
+ {/* 输入表单 */} +
+

+ + 项目财务指标输入 +

+ +
+
+ + setCalculationForm({ ...calculationForm, project_name: e.target.value })} + className="w-full px-3 py-2 bg-slate-900 border border-slate-700 rounded-lg text-white focus:outline-none focus:border-orange-500" + /> +
+ +
+
+ + setCalculationForm({ ...calculationForm, revenue_year1: parseFloat(e.target.value) })} + className="w-full px-3 py-2 bg-slate-900 border border-slate-700 rounded-lg text-white focus:outline-none focus:border-orange-500" + /> +
+
+ + setCalculationForm({ ...calculationForm, revenue_year2: parseFloat(e.target.value) })} + className="w-full px-3 py-2 bg-slate-900 border border-slate-700 rounded-lg text-white focus:outline-none focus:border-orange-500" + /> +
+
+ + setCalculationForm({ ...calculationForm, revenue_year3: parseFloat(e.target.value) })} + className="w-full px-3 py-2 bg-slate-900 border border-slate-700 rounded-lg text-white focus:outline-none focus:border-orange-500" + /> +
+
+ +
+
+ + setCalculationForm({ ...calculationForm, cost_ratio: parseFloat(e.target.value) })} + className="w-full px-3 py-2 bg-slate-900 border border-slate-700 rounded-lg text-white focus:outline-none focus:border-orange-500" + /> +
+
+ + setCalculationForm({ ...calculationForm, depreciation: parseFloat(e.target.value) })} + className="w-full px-3 py-2 bg-slate-900 border border-slate-700 rounded-lg text-white focus:outline-none focus:border-orange-500" + /> +
+
+ + setCalculationForm({ ...calculationForm, interest_rate: parseFloat(e.target.value) })} + className="w-full px-3 py-2 bg-slate-900 border border-slate-700 rounded-lg text-white focus:outline-none focus:border-orange-500" + /> +
+
+ + setCalculationForm({ ...calculationForm, loan_amount: parseFloat(e.target.value) })} + className="w-full px-3 py-2 bg-slate-900 border border-slate-700 rounded-lg text-white focus:outline-none focus:border-orange-500" + /> +
+
+ +
+ + setCalculationForm({ ...calculationForm, irr_target: parseFloat(e.target.value) })} + className="w-full px-3 py-2 bg-slate-900 border border-slate-700 rounded-lg text-white focus:outline-none focus:border-orange-500" + /> +
+
+ + +
+ + {/* 测算结果 */} + {calculationResult && ( + <> + {/* 关键指标 */} +
+

+ + 关键财务指标 +

+
+ {calculationResult.indicators.map((indicator, idx) => ( +
+
{indicator.name}
+
+ {indicator.value} {indicator.unit} +
+ {indicator.historical_avg && ( +
+ 历史平均: {indicator.historical_avg} +
+ )} + {indicator.warning && ( +
+ + {indicator.warning} +
+ )} +
+ ))} +
+
+ + {/* 风险预警 */} + {calculationResult.warnings.length > 0 && ( +
+

+ + 风险预警 +

+
    + {calculationResult.warnings.map((warning, idx) => ( +
  • {warning}
    • + ))} +
+
+ )} + + {/* 历史洞察 */} +
+

+ + 基于历史数据的洞察 +

+
    + {calculationResult.historical_insights.map((insight, idx) => ( +
  • + + {insight} +
    • + ))} +
+
+ + )} +
+ )} +
+ ); +} diff --git a/src/app/review-assist/page.tsx b/src/app/review-assist/page.tsx new file mode 100644 index 0000000..0b6b8ac --- /dev/null +++ b/src/app/review-assist/page.tsx @@ -0,0 +1,1282 @@ +'use client'; + +import React, { useState } from 'react'; +import ReactMarkdown from 'react-markdown'; + +// ============ 类型定义 ============ + +interface ReviewerInfo { + name: string; + current_projects: number; + specialty: string; +} + +interface ReviewerStats { + total_projects: number; + avg_actual_days: number; + avg_quality_score: number; + pass_rate: number; + industry_expertise: { + [industry: string]: { + count: number; + avg_quality: number; + }; + }; +} + +interface HistoryProject { + project_name: string; + industry: string; + credit_amount: number; + complexity: number; + planned_days: number; + actual_days: number; + reviewer: string; + outcome: string; + quality_score: number; + review_date: string; +} + +interface RecommendationItem { + reviewer: string; + score: number; + reasons: string[]; + est_complete_days: number; + industry_match_score: number; + load_score: number; + quality_score: number; +} + +interface AllocateResult { + reviewers: Array<{ + name: string; + current_projects: number; + specialty: string; + avg_quality: number; + total_history_projects: number; + }>; + recommendation: RecommendationItem[]; + balance_before: number; + balance_after: number; + algorithm_version: string; +} + +interface TabConfig { + id: string; + title: string; + icon: string; + description: string; +} + +// 后端 API 基址 +const API_BASE = + typeof window !== 'undefined' && window.location.hostname + ? `http://${window.location.hostname}:8002/api/review-assist` + : 'http://localhost:8002/api/review-assist'; + +// ============ 标签页配置 ============ + +const TABS: TabConfig[] = [ + { + id: 'load-balance', + title: '评审负荷与分配', + icon: '⚖️', + description: '分析主审员负荷，推荐最优分配方案', + }, + { + id: 'due-diligence', + title: '尽调资料预处理', + icon: '📋', + description: '生成结构化客户情报卡，标注风险', + }, + { + id: 'rent-calculation', + title: '租金测算校验', + icon: '💹', + description: '二次核算，覆盖率验证，敏感性分析', + }, + { + id: 'report-draft', + title: '评审报告初稿', + icon: '📄', + description: '基于情报卡和测算结果生成报告框架', + }, + { + id: 'pre-screening', + title: '立项风险筛查', + icon: '🔍', + description: '资料完整性检查，风险点识别（不给结论）', + }, +]; + +// ============ 主组件 ============ + +export default function ReviewAssistPage() { + const [activeTab, setActiveTab] = useState('load-balance'); + const [loading, setLoading] = useState(false); + const [result, setResult] = useState(''); + + // 标签页1：评审负荷与分配 + const [reviewers, setReviewers] = useState([ + { name: '李明', current_projects: 5, specialty: '制造业' }, + { name: '王芳', current_projects: 3, specialty: '新能源' }, + { name: '张强', current_projects: 7, specialty: '交通运输' }, + { name: '刘洋', current_projects: 4, specialty: '医疗健康' }, + ]); + const [newProjectIndustry, setNewProjectIndustry] = useState('光伏设备制造'); + const [newProjectAmount, setNewProjectAmount] = useState(5000); + const [newProjectComplexity, setNewProjectComplexity] = useState(3); + const [reviewerStatsMap, setReviewerStatsMap] = useState<{ [name: string]: ReviewerStats }>({}); + const [allocateResult, setAllocateResult] = useState(null); + const [expandedReviewer, setExpandedReviewer] = useState(null); + const [reviewerHistory, setReviewerHistory] = useState<{ [name: string]: HistoryProject[] }>({}); + const [loadingStats, setLoadingStats] = useState(false); + const [loadingAllocate, setLoadingAllocate] = useState(false); + + // 标签页2：尽调资料预处理 + const [ddProjectName, setDdProjectName] = useState('阳光光伏设备融资租赁项目'); + const [ddClientName, setDdClientName] = useState('阳光光伏设备有限公司'); + const [ddIndustry, setDdIndustry] = useState('光伏设备制造'); + + // 标签页3：租金测算校验 + const [creditAmount, setCreditAmount] = useState(5000); + const [leaseTerm, setLeaseTerm] = useState(36); + const [interestRate, setInterestRate] = useState(5.8); + const [monthlyCashflow, setMonthlyCashflow] = useState(85); + const [projectType, setProjectType] = useState('对公'); + + // 标签页4：评审报告初稿 + const [reportProjectName, setReportProjectName] = useState('阳光光伏设备融资租赁项目'); + const [reportClientName, setReportClientName] = useState('阳光光伏设备有限公司'); + const [intelligenceCard, setIntelligenceCard] = useState(''); + const [calculationResult, setCalculationResult] = useState(''); + + // 标签页5：立项风险筛查 + const [psProjectName, setPsProjectName] = useState('阳光光伏设备融资租赁项目'); + const [psClientName, setPsClientName] = useState('阳光光伏设备有限公司'); + const [psIndustry, setPsIndustry] = useState('光伏设备制造'); + const [documentChecklist, setDocumentChecklist] = useState([ + '营业执照', + '财务报表（近3年）', + '征信报告', + '租赁物清单', + '担保函', + ]); + + // 加载示例数据 + const loadSampleData = async () => { + try { + const response = await fetch(`${API_BASE}/sample-data/${activeTab}`); + + if (!response.ok) { + throw new Error(`HTTP错误: ${response.status}`); + } + + const data = await response.json(); + + if (!data) { + throw new Error('服务器返回空数据'); + } + + if (data.success) { + if (!data.data) { + alert('⚠️ 未找到示例数据'); + return; + } + + const sampleData = data.data; + + switch (activeTab) { + case 'load-balance': + setReviewers(sampleData.reviewers); + setNewProjectIndustry(sampleData.new_project_industry); + setNewProjectAmount(sampleData.new_project_amount); + break; + case 'due-diligence': + setDdProjectName(sampleData.project_name); + setDdClientName(sampleData.client_name); + setDdIndustry(sampleData.industry); + break; + case 'rent-calculation': + setCreditAmount(sampleData.credit_amount); + setLeaseTerm(sampleData.lease_term); + setInterestRate(sampleData.interest_rate); + setMonthlyCashflow(sampleData.monthly_cashflow); + setProjectType(sampleData.project_type); + break; + case 'report-draft': + setReportProjectName(sampleData.project_name); + setReportClientName(sampleData.client_name); + setIntelligenceCard(sampleData.intelligence_card); + setCalculationResult(sampleData.calculation_result); + break; + case 'pre-screening': + setPsProjectName(sampleData.project_name); + setPsClientName(sampleData.client_name); + setPsIndustry(sampleData.industry); + setDocumentChecklist(sampleData.document_checklist); + break; + } + + alert('✅ 示例数据已填充'); + } else { + const errorMsg = data.message || data.error || '加载失败'; + alert(`⚠️ ${errorMsg}`); + } + } catch (err) { + console.error('加载示例数据失败:', err); + const errorMessage = err instanceof Error ? err.message : '请检查后端服务'; + alert(`⚠️ 加载示例数据失败：${errorMessage}`); + } + }; + + // 加载评审员统计数据（评审负荷与分配专用） + const loadReviewerStats = async () => { + setLoadingStats(true); + try { + console.log('开始加载评审员统计数据...'); + const response = await fetch(`${API_BASE}/reviewer-stats`); + + console.log('Response status:', response.status); + if (!response.ok) { + throw new Error(`HTTP错误: ${response.status}`); + } + + const data = await response.json(); + console.log('收到数据:', data); + + if (!data) { + throw new Error('服务器返回空数据'); + } + + if (data.success) { + if (!data.data || typeof data.data !== 'object' || Object.keys(data.data).length === 0) { + alert('⚠️ 加载成功，但未找到评审员数据'); + setLoadingStats(false); + return; + } + + const reviewerNames = Object.keys(data.data); + console.log('评审员列表:', reviewerNames); + + // 先更新统计数据 + setReviewerStatsMap(data.data); + + // 更新评审员列表（从统计数据中提取） + const updatedReviewers = reviewerNames.map((name) => { + const stats = data.data[name]; + // 获取主要专长（历史项目最多的行业） + const industries = Object.entries(stats.industry_expertise || {}); + const mainIndustry = industries.length > 0 + ? industries.sort((a, b) => (b[1] as any).count - (a[1] as any).count)[0][0] + : '通用'; + + // 保留现有的 current_projects，如果有的话 + const existing = reviewers.find(r => r.name === name); + return { + name, + current_projects: existing?.current_projects || Math.floor(Math.random() * 5) + 1, + specialty: mainIndustry, + }; + }); + + console.log('更新后的评审员:', updatedReviewers); + setReviewers(updatedReviewers); + + // 使用 setTimeout 确保状态更新后再显示 alert + setTimeout(() => { + alert(`✅ 已加载 ${reviewerNames.length} 位评审员历史画像\n\n评审员：${reviewerNames.join('、')}`); + }, 100); + } else { + // 处理 success: false 的情况 + const errorMsg = data.message || data.error || '未知错误'; + alert(`❌ 加载失败：${errorMsg}`); + } + } catch (err) { + console.error('加载评审员统计失败:', err); + const errorMessage = err instanceof Error ? err.message : '请检查后端服务'; + alert(`❌ 加载失败：${errorMessage}`); + } finally { + setLoadingStats(false); + } + }; + + // 智能分配 + const smartAllocate = async () => { + setLoadingAllocate(true); + setAllocateResult(null); + try { + console.log('开始智能分配...'); + console.log('当前评审员列表:', reviewers); + console.log('新项目信息:', { newProjectIndustry, newProjectAmount, newProjectComplexity }); + + const requestBody = { + reviewers, + new_project_industry: newProjectIndustry, + new_project_amount: newProjectAmount, + new_project_complexity: newProjectComplexity, + }; + + console.log('请求体:', requestBody); + + const response = await fetch(`${API_BASE}/allocate`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify(requestBody), + }); + + console.log('Response status:', response.status); + + if (!response.ok) { + const errorText = await response.text(); + console.error('错误响应:', errorText); + throw new Error(`HTTP错误: ${response.status} - ${errorText}`); + } + + const data = await response.json(); + console.log('收到分配结果:', data); + + if (!data) { + throw new Error('服务器返回空数据'); + } + + if (data.success) { + if (!data.data || !data.data.recommendation || data.data.recommendation.length === 0) { + alert('⚠️ 分配完成，但未找到推荐结果'); + setLoadingAllocate(false); + return; + } + + const topReviewer = data.data.recommendation[0]; + console.log('首选推荐:', topReviewer); + + setAllocateResult(data.data); + + // 使用 setTimeout 确保状态更新后再显示 alert + setTimeout(() => { + alert(`✅ 智能分配完成！\n\n推荐主审员：${topReviewer.reviewer}\n综合评分：${topReviewer.score.toFixed(1)}分\n\n理由：\n${topReviewer.reasons.join('\n')}`); + }, 100); + } else { + // 处理 success: false 的情况 + const errorMsg = data.message || data.error || '未知错误'; + alert(`❌ 分配失败：${errorMsg}`); + } + } catch (err) { + console.error('智能分配失败:', err); + const errorMessage = err instanceof Error ? err.message : '请检查后端服务'; + alert(`❌ 分配失败：${errorMessage}`); + } finally { + setLoadingAllocate(false); + } + }; + + // 加载评审员历史 + const loadReviewerHistory = async (reviewerName: string) => { + if (reviewerHistory[reviewerName]) { + // 已加载，切换展开/收起 + setExpandedReviewer(expandedReviewer === reviewerName ? null : reviewerName); + return; + } + + try { + const response = await fetch(`${API_BASE}/history?reviewer=${encodeURIComponent(reviewerName)}`); + + if (!response.ok) { + throw new Error(`HTTP错误: ${response.status}`); + } + + const data = await response.json(); + + if (!data) { + throw new Error('服务器返回空数据'); + } + + if (data.success) { + const historyData = data.data || []; + setReviewerHistory((prev) => ({ + ...prev, + [reviewerName]: historyData, + })); + setExpandedReviewer(reviewerName); + + if (historyData.length === 0) { + alert(`ℹ️ ${reviewerName} 暂无历史项目记录`); + } + } else { + const errorMsg = data.message || data.error || '加载失败'; + alert(`❌ 加载 ${reviewerName} 历史失败：${errorMsg}`); + } + } catch (err) { + console.error('加载历史失败:', err); + const errorMessage = err instanceof Error ? err.message : '请检查后端服务'; + alert(`❌ 加载历史失败：${errorMessage}`); + } + }; + + // 调用助手 + const callAssistant = async () => { + setLoading(true); + setResult(''); + + try { + let endpoint = ''; + let requestBody: any = {}; + + switch (activeTab) { + case 'load-balance': + endpoint = `${API_BASE}/load-balance/stream`; + requestBody = { + reviewers, + new_project_industry: newProjectIndustry, + new_project_amount: newProjectAmount, + }; + break; + + case 'due-diligence': + endpoint = `${API_BASE}/due-diligence/stream`; + requestBody = { + project_name: ddProjectName, + client_name: ddClientName, + industry: ddIndustry, + financial_data: { + registered_capital: 8000, + established_years: 6, + asset_liability_ratio: 0.58, + current_ratio: 1.45, + net_profit_2024: 1200, + net_profit_2025: 1450, + revenue_growth_rate: 0.18, + }, + credit_report: { + overdue_count: 0, + query_count_recent_6m: 4, + credit_limit: 20000, + enforcement_records: 0, + }, + guarantee_info: { + type: '法人连带担保+设备抵押', + guarantor: '阳光集团有限公司', + guarantor_net_assets: 50000, + }, + external_risks: { + litigation_records: [], + negative_news: 0, + industry_policy: '国家支持光伏产业发展', + }, + }; + break; + + case 'rent-calculation': + endpoint = `${API_BASE}/rent-calculation/stream`; + requestBody = { + credit_amount: creditAmount, + lease_term: leaseTerm, + interest_rate: interestRate, + monthly_cashflow: monthlyCashflow, + project_type: projectType, + }; + break; + + case 'report-draft': + endpoint = `${API_BASE}/report-draft/stream`; + requestBody = { + project_name: reportProjectName, + client_name: reportClientName, + intelligence_card: intelligenceCard || '【请先生成情报卡】', + calculation_result: calculationResult || '【请先生成测算结果】', + }; + break; + + case 'pre-screening': + endpoint = `${API_BASE}/pre-screening/stream`; + requestBody = { + project_name: psProjectName, + client_name: psClientName, + industry: psIndustry, + document_checklist: documentChecklist, + financial_data: { + registered_capital: 8000, + asset_liability_ratio: 0.58, + net_profit_2025: 1450, + }, + similar_cases: [ + { + project_name: '某光伏企业融资租赁（2025年）', + rejection_reason: '担保方资产不足，要求补充第二担保人', + lesson: '光伏行业波动大，需要双重担保', + }, + ], + }; + break; + + default: + throw new Error('未知标签页'); + } + + const response = await fetch(endpoint, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify(requestBody), + }); + + if (!response.ok) throw new Error('请求失败'); + + const reader = response.body?.getReader(); + const decoder = new TextDecoder(); + + if (!reader) throw new Error('无法读取响应流'); + + let buffer = ''; + while (true) { + const { done, value } = await reader.read(); + if (done) break; + + buffer += decoder.decode(value, { stream: true }); + const lines = buffer.split('\n'); + buffer = lines.pop() || ''; + + for (const line of lines) { + if (line.startsWith('data: ')) { + const data = JSON.parse(line.slice(6)); + if (data.type === 'content') { + setResult((prev) => prev + data.data); + } else if (data.type === 'error') { + console.error('流式错误:', data.message); + } + } + } + } + + // 保存结果供报告初稿使用 + if (activeTab === 'due-diligence') { + setIntelligenceCard(result); + } else if (activeTab === 'rent-calculation') { + setCalculationResult(result); + } + } catch (err) { + console.error('调用助手失败:', err); + setResult('❌ 调用失败：' + (err as Error).message); + } finally { + setLoading(false); + } + }; + + // 渲染表单 + const renderForm = () => { + switch (activeTab) { + case 'load-balance': + return ( +
+ {/* 顶部概览 - 新项目输入 */} +
+

+ 🎯 + 待分配新项目 +

+
+
+ + +
+
+
+ + setNewProjectAmount(Number(e.target.value))} + className="glass-input px-3 py-2 rounded-lg w-full text-sm" + /> +
+
+ + setNewProjectComplexity(Number(e.target.value))} + className="glass-input px-3 py-2 rounded-lg w-full text-sm" + /> +
+
+
+
+ + {/* 操作按钮 */} +
+ + +
+ + {/* 评审员卡片网格 */} + {reviewers.length > 0 && ( +
+

评审员负荷看板

+
+ {reviewers.map((reviewer) => { + const stats = reviewerStatsMap[reviewer.name]; + const isExpanded = expandedReviewer === reviewer.name; + const history = reviewerHistory[reviewer.name] || []; + + // 配色方案（每人固定配色） + const colors = [ + { bg: 'bg-blue-500', text: 'text-blue-500', light: 'bg-blue-50' }, + { bg: 'bg-green-500', text: 'text-green-500', light: 'bg-green-50' }, + { bg: 'bg-purple-500', text: 'text-purple-500', light: 'bg-purple-50' }, + { bg: 'bg-orange-500', text: 'text-orange-500', light: 'bg-orange-50' }, + { bg: 'bg-pink-500', text: 'text-pink-500', light: 'bg-pink-50' }, + { bg: 'bg-indigo-500', text: 'text-indigo-500', light: 'bg-indigo-50' }, + ]; + const colorIndex = reviewers.indexOf(reviewer) % colors.length; + const color = colors[colorIndex]; + + return ( +
+
+ {/* 头像 */} +
+ {reviewer.name.charAt(0)} +
+ +
+ {/* 名称和专长 */} +
+
+

{reviewer.name}

+ + {reviewer.specialty} + +
+ {stats && ( +
+
历史项目
+
{stats.total_projects}个
+
+ )} +
+ + {/* 指标 */} + {stats && ( +
+ {/* 质量分 */} +
+
+ 历史质量 + = 90 ? 'text-green-600' : + stats.avg_quality_score >= 85 ? 'text-yellow-600' : 'text-red-600' + }`}> + {stats.avg_quality_score.toFixed(0)}分 + +
+
+
= 90 ? 'bg-green-500' : + stats.avg_quality_score >= 85 ? 'bg-yellow-500' : 'bg-red-500' + }`} + style={{ width: `${stats.avg_quality_score}%` }} + /> +
+
+ + {/* 通过率 */} +
+
+ 通过率 + + {(stats.pass_rate * 100).toFixed(0)}% + +
+
+
+
+
+ + {/* 当前负荷 */} +
+
+ 当前负荷 + = 6 ? 'text-red-600' : + reviewer.current_projects >= 4 ? 'text-yellow-600' : 'text-green-600' + }`}> + {reviewer.current_projects}个项目 + +
+
+
= 6 ? 'bg-red-500' : + reviewer.current_projects >= 4 ? 'bg-yellow-500' : 'bg-green-500' + }`} + style={{ width: `${Math.min(100, (reviewer.current_projects / 7) * 100)}%` }} + /> +
+
+
+ )} + + {/* 查看历史按钮 */} + + + {/* 历史列表 */} + {isExpanded && history.length > 0 && ( +
+ {history.slice(0, 5).map((project, idx) => ( +
+
+ {project.project_name} + + {project.outcome} + +
+
+
{project.industry} | {project.credit_amount}万元 | 质量{project.quality_score}分
+
用时{project.actual_days}天 | {project.review_date}
+
+
+ ))} + {history.length > 5 && ( +
+ 仅显示最近5条，共{history.length}条历史 +
+ )} +
+ )} +
+
+
+ ); + })} +
+
+ )} + + {/* 智能分配结果 */} + {allocateResult && ( +
+

+ ✨ + 智能分配结果 +

+ + {/* 推荐排序 */} +
+ {allocateResult.recommendation.map((rec, idx) => { + const isTop = idx === 0; + return ( +
+
+
+ + #{idx + 1} + + {rec.reviewer} + {isTop && ( + + 推荐 + + )} +
+
+
{rec.score.toFixed(1)}
+
综合分
+
+
+ + {/* 评分条 */} +
+
+
+
+
+ + {/* 推荐理由 */} +
+ {rec.reasons.map((reason, i) => ( + + {reason} + + ))} +
+ + {/* 预计完工 */} +
+ 预计 {rec.est_complete_days} 天完成 +
+
+ ); + })} +
+ + {/* 负荷均衡对比 */} +
+

负荷均衡度

+
+
+
分配前
+
+
+
+
+ + {allocateResult.balance_before.toFixed(2)} + +
+
+
+
分配后
+
+
+
+
+ + {allocateResult.balance_after.toFixed(2)} + +
+
+
+ {allocateResult.balance_after < allocateResult.balance_before && ( +
+ ✓ 均衡度提升，整体负荷更均衡 +
+ )} +
+ 算法版本：{allocateResult.algorithm_version} +
+
+
+ )} +
+ ); + + case 'due-diligence': + return ( +
+
+ + setDdProjectName(e.target.value)} + className="glass-input px-3 py-2 rounded-lg w-full" + /> +
+
+ + setDdClientName(e.target.value)} + className="glass-input px-3 py-2 rounded-lg w-full" + /> +
+
+ + setDdIndustry(e.target.value)} + className="glass-input px-3 py-2 rounded-lg w-full" + /> +
+

+ 💡 示例数据包含完整的财务数据、征信报告、担保信息等 +

+
+ ); + + case 'rent-calculation': + return ( +
+
+ + setCreditAmount(Number(e.target.value))} + className="glass-input px-3 py-2 rounded-lg w-full" + /> +
+
+ + setLeaseTerm(Number(e.target.value))} + className="glass-input px-3 py-2 rounded-lg w-full" + /> +
+
+ + setInterestRate(Number(e.target.value))} + className="glass-input px-3 py-2 rounded-lg w-full" + /> +
+
+ + setMonthlyCashflow(Number(e.target.value))} + className="glass-input px-3 py-2 rounded-lg w-full" + /> +
+
+ + +
+
+ ); + + case 'report-draft': + return ( +
+
+ + setReportProjectName(e.target.value)} + className="glass-input px-3 py-2 rounded-lg w-full" + /> +
+
+ + setReportClientName(e.target.value)} + className="glass-input px-3 py-2 rounded-lg w-full" + /> +
+
+ +