136 lines
4.8 KiB
Python
136 lines
4.8 KiB
Python
"""推荐结果 LLM 增强
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扫描完成后异步调用 LLM，为每只推荐股票生成深度分析。
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"""
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import asyncio
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import json
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import logging
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from app.llm.client import chat_completion
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from app.llm.prompts import ENHANCE_SYSTEM_PROMPT, ENHANCE_USER_TEMPLATE
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from app.config import settings
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logger = logging.getLogger(__name__)
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async def enhance_recommendations(result: dict) -> None:
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"""对推荐结果进行 LLM 增强分析（fire-and-forget）"""
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if not settings.deepseek_api_key:
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return
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recommendations = result.get("recommendations", [])
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if not recommendations:
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return
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market_temp = result.get("market_temp")
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hot_sectors = result.get("hot_sectors", [])
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# 构建板块文本
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sectors_text = "\n".join(
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f"- {s.sector_name}: 涨幅{s.pct_change}%, 资金流入{s.capital_inflow}万, "
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f"涨停{s.limit_up_count}家, 热度{s.heat_score}分, 阶段={s.stage}"
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for s in hot_sectors[:5]
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) if hot_sectors else "暂无板块数据"
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# 温度等级
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temp_val = market_temp.temperature if market_temp else 0
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if temp_val >= 60:
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temp_level = "积极"
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elif temp_val >= 30:
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temp_level = "谨慎"
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else:
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temp_level = "低迷"
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enhanced_count = 0
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for rec in recommendations:
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try:
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user_msg = ENHANCE_USER_TEMPLATE.format(
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temperature=market_temp.temperature if market_temp else "N/A",
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temp_level=temp_level,
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up_count=market_temp.up_count if market_temp else 0,
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down_count=market_temp.down_count if market_temp else 0,
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limit_up_count=market_temp.limit_up_count if market_temp else 0,
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max_streak=market_temp.max_streak if market_temp else 0,
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broken_rate=market_temp.broken_rate if market_temp else 0,
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sectors_text=sectors_text,
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name=rec.name,
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ts_code=rec.ts_code,
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sector=rec.sector,
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score=rec.score,
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level=rec.level,
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market_temp_score=rec.market_temp_score,
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sector_score=rec.sector_score,
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capital_score=rec.capital_score,
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technical_score=rec.technical_score,
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position_score=rec.position_score,
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valuation_score=rec.valuation_score,
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signal=rec.signal,
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entry_price=rec.entry_price or "N/A",
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target_price=rec.target_price or "N/A",
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stop_loss=rec.stop_loss or "N/A",
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reasons="；".join(rec.reasons) if rec.reasons else "无",
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)
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messages = [
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{"role": "system", "content": ENHANCE_SYSTEM_PROMPT},
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{"role": "user", "content": user_msg},
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]
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resp = await chat_completion(messages)
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if resp and resp.content:
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rec.llm_analysis = resp.content.strip()
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enhanced_count += 1
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except asyncio.CancelledError:
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logger.warning(f"LLM 增强 {rec.ts_code} 被取消")
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break
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except Exception as e:
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logger.error(f"LLM 增强 {rec.ts_code} 失败: {e}")
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if enhanced_count > 0:
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# 更新数据库
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await _save_llm_analysis_to_db(recommendations)
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# 通过 WebSocket 通知前端
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await _broadcast_llm_ready(recommendations)
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logger.info(f"LLM 增强完成: {enhanced_count}/{len(recommendations)} 条")
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async def _save_llm_analysis_to_db(recommendations: list) -> None:
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"""将 LLM 分析结果更新到数据库"""
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try:
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from app.db.database import get_db
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from sqlalchemy import text
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async with get_db() as db:
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for rec in recommendations:
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if not rec.llm_analysis:
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continue
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await db.execute(
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text(
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"UPDATE recommendations SET llm_analysis = :analysis "
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"WHERE ts_code = :code AND date(created_at) = date('now', 'localtime') "
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"AND scan_session = :session"
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),
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{
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"analysis": rec.llm_analysis,
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"code": rec.ts_code,
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"session": rec.scan_session,
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},
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)
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await db.commit()
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except Exception as e:
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logger.error(f"保存 LLM 分析到数据库失败: {e}")
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async def _broadcast_llm_ready(recommendations: list) -> None:
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"""通过 WebSocket 广播 LLM 分析完成事件"""
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try:
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from app.api.websocket import broadcast_update
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await broadcast_update({
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"type": "llm_analysis_ready",
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"count": len([r for r in recommendations if r.llm_analysis]),
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})
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except Exception as e:
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logger.error(f"广播 LLM 分析完成失败: {e}")
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