AI 및 fastAPI 초기 구조 셋팅

ai-server/backend/ai/embedding.py

@@ -0,0 +1,9 @@
+from langchain_ibm import WatsonxEmbeddings
+from backend.config.settings import settings
+
+watson_embedding = WatsonxEmbeddings(
+    model_id="ibm/granite-embedding-278m-multilingual",
+    url=f"{settings.watsonx_url}",
+    api_key=f"{settings.watsonx_api_key}",
+    project_id=f"{settings.watsonx_project_id}",
+)

ai-server/backend/ai/llm.py

@@ -0,0 +1,17 @@
+from langchain_ibm import ChatWatsonx
+from backend.config.settings import settings
+from langchain_openai import ChatOpenAI
+
+watson_llm = ChatWatsonx(
+    model_id="ibm/granite-4-h-small",
+    url=f"{settings.watsonx_url}",
+    api_key=f"{settings.watsonx_api_key}",
+    project_id=f"{settings.watsonx_project_id}",
+    max_tokens=2000,
+)
+
+hugging_llm = ChatOpenAI(
+    base_url="https://router.huggingface.co/v1",
+    api_key=f"{settings.hf_token}",
+    model_name="Qwen/Qwen3-8B:nscale",
+)

ai-server/backend/config/settings.py

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+from pydantic import Field
+from pydantic_settings import BaseSettings, SettingsConfigDict
+
+class Settings(BaseSettings):
+    model_config = SettingsConfigDict(env_file="backend/.env", extra="ignore")
+    # 사용할 모델
+    watsonx_api_key: str = Field(alias="WATSONX_API_KEY")
+    watsonx_project_id: str = Field(alias="WATSONX_PROJECT_ID")
+    watsonx_url: str = Field(alias="WATSONX_URL")
+    hf_token: str = Field(alias="HF_TOKEN")
+    serper_api_key: str = Field(alias="SERPER_API_KEY")
+
+settings = Settings()

ai-server/backend/main.py

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+from fastapi import FastAPI
+from starlette.staticfiles import StaticFiles
+from contextlib import asynccontextmanager
+
+from backend.routers.stock_router import router as stock_router
+
+from backend.repository.db_init import Base, SessionLocal, engine
+
+# from backend.routers.evalu_router import router as evalu_router
+# from backend.routers.assistant_router import router as assistant_router
+# from backend.routers.call_router import router as call_router
+# from backend.repository.seed import seed_customers
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    print("서버 시작")
+
+    # Base 에 등록된 모든 모델에 테이블 자동 생성
+    Base.metadata.create_all(bind=engine)
+    print("[DB] 테이블 생성 완료 (또는 이미 존재)")
+
+    yield
+    print("서버 종료")
+
+# app = FastAPI()
+# app = FastAPI(title="상담 LLM", version="1.0", lifespan=lifespan)
+app = FastAPI(title="$ Stock AI", version="1.0", lifespan=lifespan, description="""
+    #### $주식 AI 분석 FastAPI + LangGraph + yfinance + ChromaDB
+    
+    ### 주요 기능
+    | 번호 | 기능 | 엔드포인트 |
+    | --- | --- | --- |
+    |1 | 기업 종합 분석 | 'POST /api/stock/analysis'
+    |2 | 투자 의견 조회 | 'GET /api/stock/opinion/{analysis_id}'
+    """,
+)
+
+# static 폴더 지정
+# app.mount("/static", StaticFiles(directory="backend/static"), name="static")
+
+# 라우터 등록
+app.include_router(stock_router)
+# app.include_router(assistant_router)
+# app.include_router(evalu_router)

ai-server/backend/repository/db_init.py

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+from sqlalchemy import create_engine
+from sqlalchemy.orm import DeclarativeBase, sessionmaker
+from pathlib import Path
+
+Path("db").mkdir(parents=True, exist_ok=True)
+
+# echo sql 구문 출력시키는
+engine = create_engine('sqlite:///db/ai.db', echo=True)
+
+# 세션 팩토리 생성
+SessionLocal = sessionmaker(bind=engine, autoflush=False, autocommit=False)
+
+# Base = declarative_Base()
+class Base(DeclarativeBase):
+    pass
+
+# session 을 다른 모듈에서 사용할 수 있도록 제공
+def get_db():
+    db = SessionLocal()
+    try:
+        yield db
+    finally:
+        db.close()