369 lines
15 KiB
Python
369 lines
15 KiB
Python
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"""
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AI分析服务 - 多AI分层分析
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"""
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import json
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import asyncio
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from typing import List, Dict, Optional
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import httpx
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from sqlalchemy.ext.asyncio import AsyncSession
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from sqlalchemy import select
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from app.config import settings
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from app.utils.logger import logger
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from app.models import Report, ExtractedContent, AnalysisResult, AnalysisStatus
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class AIAnalyzer:
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"""AI分析服务"""
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def __init__(self):
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self.api_url = settings.AI_API_URL
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self.api_key = settings.AI_API_KEY
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self.model = settings.AI_MODEL
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self.parallel_count = settings.AI_PARALLEL_COUNT
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self.timeout = httpx.Timeout(600.0) # 增加到10分钟超时
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# 复用 client 以提升性能
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self.client = httpx.AsyncClient(timeout=self.timeout)
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async def __del__(self):
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await self.client.aclose()
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async def call_ai(self, prompt: str, system_prompt: str = None) -> Dict:
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"""调用AI接口（带重试机制） - 复用Client"""
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messages = []
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if system_prompt:
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messages.append({"role": "system", "content": system_prompt})
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messages.append({"role": "user", "content": prompt})
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headers = {
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"Authorization": f"Bearer {self.api_key}",
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"Content-Type": "application/json"
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}
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payload = {
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"model": self.model,
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"messages": messages,
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"temperature": 0.3,
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"max_tokens": 4096
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}
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max_retries = 3
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retry_delays = [2, 5, 10]
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for attempt in range(max_retries + 1):
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try:
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# 使用复用的 client
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response = await self.client.post(
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self.api_url,
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headers=headers,
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json=payload
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)
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if response.status_code == 200:
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data = response.json()
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content = data["choices"][0]["message"]["content"]
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tokens = data.get("usage", {}).get("total_tokens", 0)
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return {"success": True, "content": content, "tokens": tokens}
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else:
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error_msg = f"AI调用失败: {response.status_code} {response.text}"
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logger.error(error_msg)
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if response.status_code < 500:
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return {"success": False, "error": response.text}
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raise Exception(error_msg)
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except Exception as e:
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if attempt < max_retries:
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delay = retry_delays[attempt]
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logger.warning(f"AI调用异常: {e}，{delay}秒后重试...")
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await asyncio.sleep(delay)
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else:
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logger.error(f"AI调用异常: {e}")
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return {"success": False, "error": str(e)}
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async def analyze_section(self, section: ExtractedContent, company_name: str) -> Dict:
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"""分析单个章节 - 定制化电商+战略扩展+数字化视角"""
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keyword = section.section_keyword
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# 判断内容类型
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is_financial = any(kw in keyword for kw in ["资产负债表", "利润表", "现金流量表", "财务数据", "财务指标"])
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is_operation = any(kw in keyword for kw in ["主营业务", "经营情况", "收入", "成本", "毛利率", "费用", "研发"])
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is_market = any(kw in keyword for kw in ["行业", "市场", "竞争", "份额"])
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# 系统提示词
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system_prompt = """你是一位服务于亿级电商眼镜商家（全平台、多品牌）的战略顾问。
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客户现状：主营太阳镜/运动/光学镜，年收1亿。正在进行渠道扩张（分销）和数字化转型。
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你的任务：基于上市公司年报原文，为客户提供实战参考和战略情报。
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【核心准则】：
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1. **身份定位**：你是顾问，要提供不仅是数据，更是业务视角的洞察。
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2. **证据至上**：你的所有结论必须直接引自或推导自原文事实。
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3. **拒绝脑补**：严禁任何形式的脱离原文的“反向推导”、猜测或幻觉。
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4. **诚实声明**：如果关键信息缺失，请直接指出，严禁臆测。"""
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if is_financial:
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analysis_prompt = f"""作为顾问，请研读{company_name}的财务数据片段，结合客户（年收1亿且在转型）的需求进行分析：
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---
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{section.content[:10000]}
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---
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任务：
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1. **真实IT底色**：从报表中找出确凿的系统/软件/研发投入。
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2. **经营稳定性**：通过存货和利润数据，分析其作为龙头的真实盈利质量。
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3. **投入产出比**：分析其费用结构是否合理。
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注意：仅分析原文，绝不编造。"""
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elif is_operation:
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analysis_prompt = f"""作为顾问，请从{company_name}的经营描述中，拆解其对客户有价值的实战战术：
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---
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{section.content[:10000]}
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---
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任务：
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1. **真实数字化**：原文中是否明确提到了使用的系统？解决了什么业务问题？
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2. **渠道体系**：其分销/加盟/直营模式的真实描述是什么？
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3. **品类逻辑**：他们在什么品类上投入最重？
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注意：严禁强行关联数字化。"""
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elif is_market:
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analysis_prompt = f"""作为顾问，请分析{company_name}眼中的市场局局势，为客户的战略布局提供方向：
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---
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{section.content[:8000]}
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---
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任务：
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1. **核心壁垒**：大厂公开承认的竞争优势是什么？
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2. **行业机会**：原文中他们重点看好哪些细分增长点？
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注意：仅提炼策略。"""
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else:
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analysis_prompt = f"""作为顾问，分析{company_name}关于「{keyword}」的真实战略图谋：
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---
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{section.content[:8000]}
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---
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任务：
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1. **明确投向**：未来大厂明确要投入资源解决的问题是什么？
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2. **战略排序**：他们的重心是在“求快”还是“求稳”？
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注意：必须基于原文描述。"""
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result = await self.call_ai(analysis_prompt, system_prompt)
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return {
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"section_name": section.section_name,
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"keyword": section.section_keyword,
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"analysis": result.get("content", ""),
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"tokens": result.get("tokens", 0),
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"success": result.get("success", False),
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"content_type": "financial" if is_financial else ("operation" if is_operation else ("market" if is_market else "strategy"))
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}
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async def summarize_analyses(self, analyses: List[Dict], company_name: str, report_title: str) -> Dict:
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"""汇总分析 - 生成《战略扩张与数字化内参》"""
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system_prompt = """你是一位专注于零售品牌增长的战略顾问。
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客户是年收1亿的电商眼镜品牌，正处于“拓展分销”、“拓新品类”和“数字化转型”的关键期。
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请基于上市公司年报，为客户提供一份具备战略高度的扩张与升级指南。"""
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analyses_text = ""
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# 财务类情报
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fin_insights = [a['analysis'] for a in analyses if a.get('success') and a.get('content_type') == 'financial']
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analyses_text += "【财务情报】\n" + "\n".join(fin_insights) + "\n\n"
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# 运营类情报
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op_insights = [a['analysis'] for a in analyses if a.get('success') and a.get('content_type') == 'operation']
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analyses_text += "【运营实战】\n" + "\n".join(op_insights) + "\n\n"
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# 市场/战略情报
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mkt_insights = [a['analysis'] for a in analyses if a.get('success') and a.get('content_type') in ['market', 'strategy']]
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analyses_text += "【战略情报】\n" + "\n".join(mkt_insights)
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prompt = f"""以下是从{company_name}《{report_title}》提炼出的关键情报：
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{analyses_text}
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请合成一份**《高密度实战内参》**。
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【内参结构】：
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## 📊 关键指标对标局
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## 🛠 数字化实战方案
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## 🌍 全渠道与分销战法
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## 🎯 新机会雷达
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## 💡 顾问最终建议（3条）"""
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result = await self.call_ai(prompt, system_prompt)
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return {
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"summary": result.get("content", ""),
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"tokens": result.get("tokens", 0),
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"success": result.get("success", False)
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}
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async def analyze_report(self, db: AsyncSession, report: Report, force: bool = False) -> bool:
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"""分析完整报告"""
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if report.is_analyzed and not force:
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logger.info(f"报告已分析: {report.title}")
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return True
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# 更新状态
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report.analysis_status = AnalysisStatus.ANALYZING.value
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await db.commit()
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# 获取提取的内容
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stmt = select(ExtractedContent).where(ExtractedContent.report_id == report.id)
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result = await db.execute(stmt)
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contents = result.scalars().all()
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if not contents:
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logger.info(f"报告无提取内容，尝试自动提取: {report.title}")
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from app.services.pdf_extractor import pdf_extractor
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try:
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contents = await pdf_extractor.extract_and_save(db, report)
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if not contents:
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report.analysis_status = AnalysisStatus.FAILED.value
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await db.commit()
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return False
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except Exception as e:
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logger.error(f"自动提取异常: {e}")
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report.analysis_status = AnalysisStatus.FAILED.value
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await db.commit()
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return False
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from app.models import Company
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company_stmt = select(Company).where(Company.id == report.company_id)
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company_result = await db.execute(company_stmt)
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company = company_result.scalar_one_or_none()
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company_name = company.short_name or company.company_name if company else "未知公司"
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logger.info(f"开始分析报告: {report.title}, 共 {len(contents)} 个章节")
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# 清除旧结果
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try:
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from sqlalchemy import delete
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stmt = delete(AnalysisResult).where(AnalysisResult.report_id == report.id)
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await db.execute(stmt)
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await db.commit()
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except Exception as e:
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logger.error(f"清除旧结果失败: {e}")
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# 使用 Worker 队列模式而非 gather
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queue = asyncio.Queue()
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for i, c in enumerate(contents):
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queue.put_nowait((i, c))
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valid_results = []
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# 准备一个专门用于增量保存的锁，防止 session 并发提交冲突
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db_lock = asyncio.Lock()
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async def worker():
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while not queue.empty():
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try:
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# 使用 get_nowait 避免在此处挂起等待
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idx, content = queue.get_nowait()
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except asyncio.QueueEmpty:
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break
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try:
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# 主动让出，确保不阻塞主线程
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await asyncio.sleep(0.5)
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logger.info(f"Worker处理章节 [{idx+1}/{len(contents)}]: {content.section_name}")
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res = await self.analyze_section(content, company_name)
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if res.get("success"):
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async with db_lock:
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try:
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from app.database import AsyncSessionLocal
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async with AsyncSessionLocal() as session:
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analysis = AnalysisResult(
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report_id=report.id,
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analysis_type="section",
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section_name=res["section_name"],
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ai_model=self.model,
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summary=res["analysis"],
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token_count=res["tokens"],
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is_final=False
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)
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session.add(analysis)
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await session.commit()
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valid_results.append(res)
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except Exception as e:
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logger.error(f"章节增量保存失败: {e}")
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except Exception as e:
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logger.error(f"Worker异常: {e}")
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finally:
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queue.task_done()
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# 启动 Consumer Workers
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workers = [asyncio.create_task(worker()) for _ in range(self.parallel_count)]
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await asyncio.gather(*workers)
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# 汇总分析
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report.analysis_status = AnalysisStatus.SUMMARIZING.value
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await db.commit()
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if valid_results:
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|
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summary = await self.summarize_analyses(valid_results, company_name, report.title)
|
|||
|
|
if summary.get("success"):
|
|||
|
|
final_analysis = AnalysisResult(
|
|||
|
|
report_id=report.id,
|
|||
|
|
analysis_type="summary",
|
|||
|
|
section_name="综合分析",
|
|||
|
|
ai_model=self.model,
|
|||
|
|
summary=summary["summary"],
|
|||
|
|
token_count=summary.get("tokens", 0),
|
|||
|
|
is_final=True
|
|||
|
|
)
|
|||
|
|
db.add(final_analysis)
|
|||
|
|
|
|||
|
|
# 更新报告状态
|
|||
|
|
report.is_analyzed = True
|
|||
|
|
report.analysis_status = AnalysisStatus.COMPLETED.value
|
|||
|
|
await db.commit()
|
|||
|
|
return True
|
|||
|
|
|
|||
|
|
async def analyze_with_quality_check(
|
|||
|
|
self,
|
|||
|
|
db: AsyncSession,
|
|||
|
|
report: Report,
|
|||
|
|
enable_quality_check: bool = False
|
|||
|
|
) -> bool:
|
|||
|
|
"""带质量检查的分析（可选启用多评判师评估）"""
|
|||
|
|
result = await self.analyze_report(db, report)
|
|||
|
|
|
|||
|
|
if result and enable_quality_check:
|
|||
|
|
try:
|
|||
|
|
from app.services.quality_evaluator import quality_evaluator
|
|||
|
|
|
|||
|
|
# 获取刚生成的汇总
|
|||
|
|
stmt = select(AnalysisResult).where(
|
|||
|
|
AnalysisResult.report_id == report.id,
|
|||
|
|
AnalysisResult.analysis_type == "summary"
|
|||
|
|
)
|
|||
|
|
from sqlalchemy import select
|
|||
|
|
result = await db.execute(stmt)
|
|||
|
|
summary_result = result.scalar_one_or_none()
|
|||
|
|
|
|||
|
|
if summary_result:
|
|||
|
|
passed, evaluation = await quality_evaluator.evaluate_and_decide(
|
|||
|
|
db, summary_result
|
|||
|
|
)
|
|||
|
|
logger.info(f"质量评估: 通过={passed}, 评分={evaluation.get('avg_score', 0)}")
|
|||
|
|
|
|||
|
|
if not passed:
|
|||
|
|
logger.warning(f"质量不达标，建议人工复核: {report.title}")
|
|||
|
|
|
|||
|
|
except ImportError:
|
|||
|
|
logger.warning("质量评估模块未加载")
|
|||
|
|
|
|||
|
|
return result
|
|||
|
|
|
|||
|
|
|
|||
|
|
ai_analyzer = AIAnalyzer()
|
|||
|
|
|