209 lines
6.6 KiB
Python
209 lines
6.6 KiB
Python
"""
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LLM 分析服务 (新架构)
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负责调用 LLM 生成分析报告
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"""
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import os
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from typing import Dict, Optional
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from sqlalchemy.ext.asyncio import AsyncSession
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from sqlalchemy import select
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from google import genai
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from google.genai import types
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from app.models import Company
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from app.services.data_fetcher_service import get_financial_data_from_db
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# Gemini client（懒加载）
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_client: Optional[genai.Client] = None
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def get_genai_client() -> genai.Client:
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"""获取 Gemini API 客户端（单例）"""
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global _client
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if _client is None:
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api_key = os.getenv('GEMINI_API_KEY')
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if not api_key:
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raise ValueError("GEMINI_API_KEY environment variable is not set")
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# Configure httpx proxy globally by monkey-patching
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import httpx
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proxy_url = os.getenv('https_proxy') or os.getenv('HTTPS_PROXY')
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if proxy_url:
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# Monkey-patch httpx to use proxy with SSL verification disabled
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original_init = httpx.Client.__init__
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def patched_init(self, **kwargs):
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kwargs.setdefault('proxy', proxy_url)
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kwargs.setdefault('verify', False)
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return original_init(self, **kwargs)
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httpx.Client.__init__ = patched_init
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_client = genai.Client(api_key=api_key)
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return _client
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async def run_llm_analysis(
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company_id: int,
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data_source: str,
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model: str,
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db: AsyncSession
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) -> Dict:
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"""
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执行 LLM 分析
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Args:
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company_id: 公司ID
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data_source: 数据源
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model: LLM 模型名称
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db: 数据库会话
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Returns:
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包含所有分析内容和 token 使用情况的字典
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"""
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# 1. 获取公司信息
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result = await db.execute(
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select(Company).where(Company.id == company_id)
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)
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company = result.scalar_one()
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# 2. 获取财务数据
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financial_data = await get_financial_data_from_db(
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company_id=company_id,
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data_source=data_source,
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db=db
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)
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# 3. 准备数据上下文
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context = format_financial_data_for_llm(financial_data)
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# 4. 调用 LLM 生成各个部分
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sections = {}
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total_tokens = 0
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tokens_by_section = {}
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# 公司简介
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profile_result = await generate_section(
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model=model,
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company=company,
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section_name='company_profile',
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context=context
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)
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sections['company_profile'] = profile_result['content']
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tokens_by_section['company_profile'] = profile_result['tokens']
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total_tokens += profile_result['tokens']
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# 基本面分析
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fundamental_result = await generate_section(
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model=model,
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company=company,
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section_name='fundamental_analysis',
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context=context
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)
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sections['fundamental_analysis'] = fundamental_result['content']
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tokens_by_section['fundamental_analysis'] = fundamental_result['tokens']
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total_tokens += fundamental_result['tokens']
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# 内部人士分析
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insider_result = await generate_section(
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model=model,
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company=company,
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section_name='insider_analysis',
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context=context
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)
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sections['insider_analysis'] = insider_result['content']
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tokens_by_section['insider_analysis'] = insider_result['tokens']
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total_tokens += insider_result['tokens']
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# 看涨分析
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bullish_result = await generate_section(
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model=model,
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company=company,
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section_name='bullish_analysis',
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context=context
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)
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sections['bullish_analysis'] = bullish_result['content']
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tokens_by_section['bullish_analysis'] = bullish_result['tokens']
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total_tokens += bullish_result['tokens']
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# 看跌分析
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bearish_result = await generate_section(
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model=model,
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company=company,
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section_name='bearish_analysis',
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context=context
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)
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sections['bearish_analysis'] = bearish_result['content']
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tokens_by_section['bearish_analysis'] = bearish_result['tokens']
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total_tokens += bearish_result['tokens']
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return {
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**sections,
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'total_tokens': total_tokens,
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'tokens_by_section': tokens_by_section
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}
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def format_financial_data_for_llm(financial_data: Dict) -> str:
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"""将财务数据格式化为 LLM 上下文"""
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# 简化版本，实际应该更详细地格式化数据
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context = f"""
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公司: {financial_data['company']['company_name']}
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市场: {financial_data['company']['market']}
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代码: {financial_data['company']['symbol']}
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数据源: {financial_data['data_source']}
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财务数据:
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- 利润表记录数: {len(financial_data.get('income_statement', []))}
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- 资产负债表记录数: {len(financial_data.get('balance_sheet', []))}
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- 现金流量表记录数: {len(financial_data.get('cash_flow', []))}
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- 估值数据记录数: {len(financial_data.get('daily_basic', []))}
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"""
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return context
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async def generate_section(
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model: str,
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company: Company,
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section_name: str,
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context: str
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) -> Dict:
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"""
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生成报告的一个部分
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"""
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# 设置 prompt
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prompts = {
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'company_profile': f"请为 {company.company_name} ({company.symbol}) 生成公司简介，包括主营业务、市场地位等。\n\n财务数据:\n{context}",
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'fundamental_analysis': f"请为 {company.company_name} 进行基本面分析，包括财务指标、盈利能力、成长性等。\n\n财务数据:\n{context}",
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'insider_analysis': f"请分析 {company.company_name} 的内部人士交易情况、股权结构等。\n\n财务数据:\n{context}",
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'bullish_analysis': f"请分析 {company.company_name} 的看涨因素和投资亮点。\n\n财务数据:\n{context}",
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'bearish_analysis': f"请分析 {company.company_name} 的风险因素和潜在问题。\n\n财务数据:\n{context}"
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}
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prompt = prompts.get(section_name, f"请分析 {company.company_name}。\n\n{context}")
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try:
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# 获取 Gemini client
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client = get_genai_client()
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# 调用新的 genai API
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response = client.models.generate_content(
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model=model,
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contents=prompt
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)
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# 提取内容和 token 信息
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content = response.text
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# 获取 token 使用情况（新 API 的方式）
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tokens = 0
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if hasattr(response, 'usage_metadata'):
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tokens = response.usage_metadata.total_token_count
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return {
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'content': content,
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'tokens': tokens
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}
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except Exception as e:
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# 如果 LLM 调用失败，返回错误信息
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return {
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'content': f"分析生成失败: {str(e)}",
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'tokens': 0
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}
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