랭그래프 활용한 주식 정보 도출 프로젝트

- 투자 의견 조회
ㄴ 전반적인 주식 판단 기준(ex. 채무, 리스크 등등)을 기준으로 판단하여 도출
- 투자 추천
ex)
{
  "tickers": [
    "NVDA", "GOOGL", "AAPL"
  ],
  "risk_type": "aggressive"
}

project/STOCK_APP/backend/services/stock_service.py

@@ -1,6 +1,12 @@
 from backend.schemas.stock_schemas import TickerInfo
 import yahooquery as yq
 from fastapi import HTTPException
+from backend.graph.stock_graph import build_stock_graph
+from backend.repository.models import StockAnalysis, InvestmentOpinion
+from fastapi.encoders import jsonable_encoder
+from typing import Optional
+from backend.services.score_service import evaluation, get_rating
+from backend.ai.llm import hugging_llm
 
 COMPANY_MAP = {
     "NVDA": {
@@ -115,6 +121,8 @@ class StockService:
                 if alias.lower() in query:
                     return TickerInfo(ticker = ticker, company_name = info["company_name"])
 
+        return None
+
     def _extract_company_keyword(self, query:str):
         """
         query : 엔비디아 분석해줘
@@ -145,4 +153,89 @@ class StockService:
             return TickerInfo(ticker=result["symbol"], company_name=result["longname"])
 
         except Exception:
-            return None
+            return None
+
+    async def analyze(self, query: str, db):
+        # 티커, 회사명 추출
+        ticker_info = await self._extract(query)
+
+        # 그래프 실행
+        stock_graph = build_stock_graph()
+        result = await stock_graph.ainvoke(
+            {
+            "query": query,
+            "ticker": ticker_info.ticker,
+            "company_name": ticker_info.company_name
+            }
+        )
+
+        # 데이터베이스 저장
+        # 테이블과 관련있는 모델 객체 생성
+        entity = StockAnalysis(
+            ticker = result['ticker'],
+            company_name = result['company_name'],
+            analysis_json = jsonable_encoder(result),
+            report = result['report']
+        )
+        db.add(entity)
+        db.commit()
+        db.refresh(entity)
+
+        return {"analysis_id":entity.analysis_id, **result}
+
+    async def opinion_service(self, analysis_id, db):
+        # analysis_id 디비 조회
+        analysis = db.get(StockAnalysis, analysis_id)
+
+        score_result = await evaluation(analysis)
+
+        #
+        total_score = score_result['total_score']
+        rating = get_rating(total_score)
+
+        # AI 투자 의견서
+        opinion = await self.generate_opinion(analysis.report, total_score, rating)
+
+        entity = InvestmentOpinion(
+            analysis_id = analysis_id,
+            opinion = opinion,
+            rating = rating,
+            score = total_score,
+        )
+        db.add(entity)
+        db.commit()
+        db.refresh(entity)
+
+        return {"opinion_id":entity.opinion_id, "analysis_id":analysis_id, "opinion":opinion, "rating":rating, **score_result,}
+
+    async def generate_opinion(self, report, total_score, rating):
+        prompt = f"""
+        당신은 월가의 수석 애널리스트입니다.
+        
+        종합점수 : 
+        {total_score} / 100
+        투자등급 :
+        {rating}
+        
+        다음 기업 분석 보고서를 기반으로 투자 의견서를 작성하세요.
+        {report}
+        
+        반드시 아래 형식으로 작성하세요.
+        1. 투자등급
+        2. 투자근거
+        3. 핵심리스크
+        4. 단기전망
+        5. 장기전망
+        6. 최종의견
+        """
+        result = await hugging_llm.ainvoke(prompt)
+        return result.content
+
+# 싱글톤(객체 하나만 생성) 서비스 인스턴스
+_service:Optional[StockService] = None
+
+def get_stock_service():
+    global _service
+    if _service is None:
+        _service = StockService()
+    return _service