selfrelease
7ec4fb0747
- 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
98 lines
2.9 KiB
Python
98 lines
2.9 KiB
Python
"""月报 AI 解析服务。
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使用千问 LLM 从月报原始文本中提取：
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1. 结构化指标数据（营收、现金流、团队等）
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2. AI 摘要
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3. 关注点列表
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"""
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import logging
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from typing import Any
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from app.services.llm_client import llm_client
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logger = logging.getLogger(__name__)
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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，概括企业经营状况和关键变化",
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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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注意：
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- 如果月报内容不足以提取某项指标，对应字段留空或为 0
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- severity 只能是 low/medium/high
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- category 只能是 financial/operational/org/ai_specific
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- 严格输出 JSON，不要包含其他文字"""
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USER_PROMPT_TEMPLATE = """请分析以下 {period} 月报内容：
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<<<USER_INPUT>>>
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{content}
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<<<END_USER_INPUT>>>
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"""
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async def parse_report(
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content: str,
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period_year: int,
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period_month: int,
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) -> dict[str, Any]:
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"""解析月报内容，返回结构化数据。
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Args:
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content: 月报原始文本
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period_year: 报告年份
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period_month: 报告月份
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Returns:
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包含 structured_data / ai_summary / ai_concerns 的字典
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Raises:
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RuntimeError: LLM 调用失败
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"""
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if not content or not content.strip():
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return {
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"structured_data": {},
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"ai_summary": "月报内容为空",
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"ai_concerns": {"items": [], "highlights": []},
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}
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user_prompt = USER_PROMPT_TEMPLATE.format(
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period=f"{period_year}年{period_month}月",
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content=content[:4000], # 限制输入长度
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)
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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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try:
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result = await llm_client.chat_json(messages, temperature=0.3, max_tokens=2000)
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logger.info("月报 AI 解析成功: period=%s-%s", period_year, period_month)
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return result
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except RuntimeError as e:
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logger.error("月报 AI 解析失败: %s", e)
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# 降级：返回空结构
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return {
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"structured_data": {},
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"ai_summary": f"AI 解析失败: {e}",
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"ai_concerns": {"items": [], "highlights": []},
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"fallback_used": True,
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}
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