feat(backend): AI 服务层 — 千问流式 LLM + 月报解析 + 健康度计算 + 风险检测 + Copilot
- LLM 客户端：全部 SSE 流式输出，兼容 OpenAI 接口
- AI 月报解析：SSE 流式端点 POST /reports/{id}/parse
- 健康度计算引擎：四维评分（财务/经营/AI商业化/AI成本）
- 风险自动检测引擎：6 条规则自动检测指标越界
- AI Copilot：SSE 流式对话 POST /copilot/chat
- 权限中间件：角色级 + 字段级权限控制
- 测试：21 个新测试（健康度 8 + 风险检测 8 + 权限 5），总计 64 passed
This commit is contained in:
selfrelease
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"""AI Copilot 路由 — SSE 流式对话。
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为投资人和创始人提供 AI 副驾驶对话能力。
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"""
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import json
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from fastapi import APIRouter, Depends, HTTPException
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from fastapi.responses import StreamingResponse
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from pydantic import BaseModel, Field
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.core.database import get_db
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from app.core.dependencies import get_current_user
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from app.models.user import User
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from app.services.llm_client import llm_client
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router = APIRouter(prefix="/copilot", tags=["copilot"])
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class CopilotMessage(BaseModel):
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"""Copilot 对话请求。"""
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message: str = Field(..., min_length=1, max_length=4000)
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context: dict | None = Field(default=None, description="可选上下文（企业ID/月报ID等）")
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SYSTEM_PROMPT_INVESTOR = """你是 AIPortPilot 投后管理系统的 AI 副驾驶，服务于投资人用户。
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你的职责：
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1. 分析被投企业的经营状况和财务健康度
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2. 识别潜在风险并提供预警建议
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3. 协助撰写投后管理报告和建议
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4. 解答关于企业治理、融资策略的问题
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回答要求：
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- 专业、简洁、有数据支撑
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- 如需引用数据，明确标注来源
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- 对于不确定的信息，坦诚说明
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- 使用中文回答"""
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SYSTEM_PROMPT_FOUNDER = """你是 AIPortPilot 投后管理系统的 AI 副驾驶，服务于创始人用户。
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你的职责：
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1. 协助撰写和优化月报内容
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2. 分析企业经营数据，提供改进建议
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3. 解答融资、团队管理、业务增长等问题
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4. 提供行业趋势和竞品分析参考
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回答要求：
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- 实用、可操作、接地气
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- 关注创始人的实际痛点
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- 使用中文回答"""
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@router.post("/chat")
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async def copilot_chat(
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req: CopilotMessage,
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db: AsyncSession = Depends(get_db),
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user: User = Depends(get_current_user),
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):
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"""AI Copilot 对话（SSE 流式输出）。
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根据用户角色使用不同的 system prompt。
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"""
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system_prompt = (
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SYSTEM_PROMPT_FOUNDER if user.role == "founder" else SYSTEM_PROMPT_INVESTOR
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)
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# 构建上下文
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context_str = ""
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if req.context:
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context_parts = []
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for key, value in req.context.items():
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context_parts.append(f"{key}: {value}")
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context_str = f"\n\n当前上下文：\n" + "\n".join(context_parts)
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messages = [
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{"role": "system", "content": system_prompt + context_str},
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{"role": "user", "content": f"<<<USER_INPUT>>>\n{req.message}\n<<<END_USER_INPUT>>>"},
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]
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async def event_stream():
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"""SSE 流式输出。"""
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try:
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async for token in llm_client.chat_stream(messages, temperature=0.7, max_tokens=2000):
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yield f"data: {json.dumps({'type': 'token', 'content': token})}\n\n"
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yield f"data: {json.dumps({'type': 'done'})}\n\n"
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except Exception as e:
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yield f"data: {json.dumps({'type': 'error', 'message': str(e)})}\n\n"
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return StreamingResponse(
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event_stream(),
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media_type="text/event-stream",
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headers={
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"Cache-Control": "no-cache",
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"Connection": "keep-alive",
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"X-Accel-Buffering": "no",
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},
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)
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@@ -1,15 +1,19 @@
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"""月报路由：CRUD + 提交 + AI 解析占位。"""
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"""月报路由：CRUD + 提交 + AI 解析（SSE 流式）。"""
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import json
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from datetime import datetime, timezone
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from fastapi import APIRouter, Depends, HTTPException, Query, status
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from fastapi.responses import StreamingResponse
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from sqlalchemy import func, select
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.core.database import get_db
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from app.core.dependencies import get_current_user
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from app.models.company import Company
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from app.models.health_score import HealthScore
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from app.models.report import MonthlyReport
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from app.models.risk import RiskEvent
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from app.models.user import User
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from app.schemas.common import ApiResponse, success
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from app.schemas.report import (
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@@ -18,6 +22,9 @@ from app.schemas.report import (
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MonthlyReportResponse,
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MonthlyReportUpdate,
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)
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from app.services.ai_parser import parse_report
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from app.services.health_calculator import calculate_health_score, determine_trend
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from app.services.risk_engine import detect_risks
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router = APIRouter(prefix="/reports", tags=["reports"])
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@@ -204,3 +211,161 @@ async def delete_report(
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await db.delete(report)
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return success(message="删除成功")
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@router.post("/{report_id}/parse")
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async def parse_report_stream(
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report_id: str,
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db: AsyncSession = Depends(get_db),
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user: User = Depends(get_current_user),
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):
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"""AI 解析月报（SSE 流式输出）。
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流式返回解析过程中的 token，最后返回完整结构化结果。
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解析完成后自动计算健康度评分并检测风险事件。
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"""
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result = await db.execute(
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select(MonthlyReport)
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.join(Company, MonthlyReport.company_id == Company.id)
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.where(MonthlyReport.id == report_id, Company.tenant_id == user.tenant_id)
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)
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report = result.scalar_one_or_none()
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if not report:
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raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="月报不存在")
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async def event_stream():
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"""SSE 事件流。"""
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try:
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# 阶段 1：流式输出 AI 解析
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yield f"data: {json.dumps({'type': 'status', 'message': 'AI 解析中...'})}\n\n"
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from app.services.llm_client import llm_client
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system_prompt = """你是投后管理领域的专业分析师。请分析以下企业月报内容，提取结构化信息。
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输出 JSON 格式如下：
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{
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"structured_data": {
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"revenue": {"value": "", "unit": "万元", "yoy_change": "", "note": ""},
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"cash_balance": {"value": "", "unit": "万元", "runway_months": 0, "note": ""},
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"burn_rate": {"value": "", "unit": "万元/月", "trend": "up/stable/down", "note": ""},
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"headcount": {"total": 0, "new_hires": 0, "departures": 0, "note": ""},
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"key_metrics": [{"name": "", "value": "", "change": "", "note": ""}]
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},
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"ai_summary": "一段 100-200 字的月报摘要",
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"ai_concerns": {
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"items": [
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{"category": "financial/operational/org/ai_specific", "severity": "low/medium/high", "description": ""}
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],
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"highlights": ["本期亮点1", "本期亮点2"]
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}
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}
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严格输出 JSON，不要包含 markdown 代码块标记。"""
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user_prompt = f"请分析以下 {report.period_year}年{report.period_month}月 月报内容：\n\n<<<USER_INPUT>>>\n{report.raw_content or '无内容'}\n<<<END_USER_INPUT>>>"
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messages = [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_prompt},
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]
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# 流式收集
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collected = []
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async for token in llm_client.chat_json_stream(messages, temperature=0.3, max_tokens=2000):
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collected.append(token)
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yield f"data: {json.dumps({'type': 'token', 'content': token})}\n\n"
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# 解析完整 JSON
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full_text = "".join(collected).strip()
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if full_text.startswith("```"):
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full_text = full_text.split("\n", 1)[1] if "\n" in full_text else full_text[3:]
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if full_text.endswith("```"):
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full_text = full_text[:-3]
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full_text = full_text.strip()
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parsed = json.loads(full_text)
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# 阶段 2：保存解析结果
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report.structured_data = parsed.get("structured_data", {})
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report.ai_summary = parsed.get("ai_summary", "")
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report.ai_concerns = parsed.get("ai_concerns", {})
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report.status = "ai_parsed"
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await db.flush()
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yield f"data: {json.dumps({'type': 'parsed', 'data': parsed})}\n\n"
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# 阶段 3：计算健康度评分
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yield f"data: {json.dumps({'type': 'status', 'message': '计算健康度评分...'})}\n\n"
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scores = calculate_health_score(parsed.get("structured_data", {}))
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# 查询上期评分判断趋势
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prev_result = await db.execute(
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select(HealthScore)
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.where(HealthScore.company_id == report.company_id)
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.order_by(HealthScore.calculated_at.desc())
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.limit(1)
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)
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prev_score = prev_result.scalar_one_or_none()
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prev_total = prev_score.total_score if prev_score else None
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trend = determine_trend(scores["total_score"], prev_total)
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health = HealthScore(
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company_id=report.company_id,
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total_score=scores["total_score"],
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financial_score=scores["financial_score"],
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operational_score=scores["operational_score"],
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ai_commercial_score=scores["ai_commercial_score"],
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ai_cost_score=scores["ai_cost_score"],
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trend=trend,
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evidence_json={"report_id": report.id, "period": f"{report.period_year}-{report.period_month}"},
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)
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db.add(health)
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await db.flush()
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yield f"data: {json.dumps({'type': 'health_score', 'data': scores, 'trend': trend})}\n\n"
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# 阶段 4：风险自动检测
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yield f"data: {json.dumps({'type': 'status', 'message': '检测风险事件...'})}\n\n"
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risks = detect_risks(parsed.get("structured_data", {}), report.company_id)
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created_risks = []
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for risk_data in risks:
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risk = RiskEvent(
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company_id=risk_data["company_id"],
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type=risk_data["type"],
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severity=risk_data["severity"],
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title=risk_data["title"],
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description=risk_data["description"],
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suggested_action=risk_data["suggested_action"],
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status="open",
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)
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db.add(risk)
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await db.flush()
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created_risks.append({
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"id": risk.id,
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"title": risk_data["title"],
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"severity": risk_data["severity"],
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"type": risk_data["type"],
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})
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yield f"data: {json.dumps({'type': 'risks', 'data': created_risks})}\n\n"
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# 完成
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yield f"data: {json.dumps({'type': 'done', 'message': '解析完成'})}\n\n"
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except json.JSONDecodeError as e:
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yield f"data: {json.dumps({'type': 'error', 'message': f'JSON 解析失败: {e}'})}\n\n"
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except Exception as e:
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yield f"data: {json.dumps({'type': 'error', 'message': str(e)})}\n\n"
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return StreamingResponse(
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event_stream(),
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media_type="text/event-stream",
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headers={
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"Cache-Control": "no-cache",
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"Connection": "keep-alive",
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"X-Accel-Buffering": "no",
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},
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)
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