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https://github.com/hwanny1128/HGZero.git
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feat: init rag service
This commit is contained in:
djeon
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"""
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Vector DB 통합 시스템 FastAPI 애플리케이션
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"""
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from fastapi import FastAPI, HTTPException, Depends
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from fastapi.middleware.cors import CORSMiddleware
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from typing import List, Dict, Any
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import logging
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from pathlib import Path
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from ..models.term import (
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Term,
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TermSearchRequest,
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TermSearchResult,
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TermExplainRequest,
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TermExplanation,
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TermStats
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)
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from ..models.document import (
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DocumentSearchRequest,
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DocumentSearchResult,
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DocumentStats
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)
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from ..models.minutes import (
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MinutesSearchRequest,
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MinutesSearchResult
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)
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from ..db.postgres_vector import PostgresVectorDB
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from ..db.azure_search import AzureAISearchDB
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from ..db.rag_minutes_db import RagMinutesDB
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from ..services.claude_service import ClaudeService
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from ..utils.config import load_config, get_database_url
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from ..utils.embedding import EmbeddingGenerator
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from ..utils.text_processor import extract_nouns_as_query
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# 로깅 설정
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
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)
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logger = logging.getLogger(__name__)
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# FastAPI 앱 생성
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app = FastAPI(
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title="Vector DB 통합 시스템",
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description="회의록 작성 시스템을 위한 Vector DB 기반 용어집 및 관련자료 검색 API",
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version="1.0.0"
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)
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# CORS 설정
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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# 전역 변수 (의존성 주입용)
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_config = None
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_term_db = None
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_doc_db = None
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_rag_minutes_db = None
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_embedding_gen = None
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_claude_service = None
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def get_config():
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"""설정 로드"""
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global _config
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if _config is None:
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config_path = Path(__file__).parent.parent.parent / "config.yaml"
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_config = load_config(str(config_path))
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return _config
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def get_term_db():
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"""용어집 DB 연결"""
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global _term_db
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if _term_db is None:
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config = get_config()
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db_url = get_database_url(config)
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_term_db = PostgresVectorDB(db_url)
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return _term_db
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def get_doc_db():
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"""관련자료 DB 연결"""
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global _doc_db
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if _doc_db is None:
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config = get_config()
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azure_search = config["azure_search"]
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_doc_db = AzureAISearchDB(
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endpoint=azure_search["endpoint"],
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api_key=azure_search["api_key"],
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index_name=azure_search["index_name"],
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api_version=azure_search["api_version"]
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)
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return _doc_db
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def get_rag_minutes_db():
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"""RAG 회의록 DB 연결"""
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global _rag_minutes_db
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if _rag_minutes_db is None:
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config = get_config()
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db_url = get_database_url(config)
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_rag_minutes_db = RagMinutesDB(db_url)
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return _rag_minutes_db
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def get_embedding_gen():
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"""임베딩 생성기"""
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global _embedding_gen
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if _embedding_gen is None:
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config = get_config()
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azure_openai = config["azure_openai"]
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_embedding_gen = EmbeddingGenerator(
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api_key=azure_openai["api_key"],
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endpoint=azure_openai["endpoint"],
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model=azure_openai["embedding_model"],
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dimension=azure_openai["embedding_dimension"],
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api_version=azure_openai["api_version"]
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)
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return _embedding_gen
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def get_claude_service():
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"""Claude 서비스"""
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global _claude_service
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if _claude_service is None:
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config = get_config()
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claude = config["claude"]
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_claude_service = ClaudeService(
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api_key=claude["api_key"],
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model=claude["model"],
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max_tokens=claude["max_tokens"],
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temperature=claude["temperature"]
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)
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return _claude_service
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# ============================================================================
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# 용어집 API
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# ============================================================================
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@app.get("/")
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async def root():
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"""루트 엔드포인트"""
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return {
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"service": "Vector DB 통합 시스템",
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"version": "1.0.0",
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"endpoints": {
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"용어집": "/api/terms/*",
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"관련자료": "/api/documents/*"
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}
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}
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@app.post("/api/terms/search", response_model=List[TermSearchResult])
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async def search_terms(
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request: TermSearchRequest,
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term_db: PostgresVectorDB = Depends(get_term_db),
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embedding_gen: EmbeddingGenerator = Depends(get_embedding_gen)
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):
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"""
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용어 검색 (Hybrid: Keyword + Vector)
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Args:
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request: 검색 요청
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Returns:
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검색 결과 리스트
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"""
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try:
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config = get_config()
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# 명사 추출하여 검색 쿼리 생성
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search_query = extract_nouns_as_query(request.query)
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logger.info(f"검색 쿼리 변환: '{request.query}' → '{search_query}'")
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if request.search_type == "keyword":
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# 키워드 검색
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results = term_db.search_by_keyword(
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query=search_query,
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top_k=request.top_k,
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confidence_threshold=request.confidence_threshold
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)
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elif request.search_type == "vector":
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# 벡터 검색 (임베딩은 원본 쿼리 사용)
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query_embedding = embedding_gen.generate_embedding(search_query)
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results = term_db.search_by_vector(
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query_embedding=query_embedding,
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top_k=request.top_k,
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confidence_threshold=request.confidence_threshold
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)
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else: # hybrid
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# 하이브리드 검색
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keyword_results = term_db.search_by_keyword(
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query=search_query,
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top_k=request.top_k,
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confidence_threshold=request.confidence_threshold
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)
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query_embedding = embedding_gen.generate_embedding(search_query)
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vector_results = term_db.search_by_vector(
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query_embedding=query_embedding,
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top_k=request.top_k,
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confidence_threshold=request.confidence_threshold
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)
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# RRF 통합
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keyword_weight = config["term_glossary"]["search"]["keyword_weight"]
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vector_weight = config["term_glossary"]["search"]["vector_weight"]
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# 간단한 가중합
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results = []
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seen_ids = set()
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for result in keyword_results:
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term_id = result["term"].term_id
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if term_id not in seen_ids:
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result["relevance_score"] *= keyword_weight
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result["match_type"] = "hybrid"
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results.append(result)
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seen_ids.add(term_id)
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for result in vector_results:
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term_id = result["term"].term_id
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if term_id not in seen_ids:
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result["relevance_score"] *= vector_weight
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result["match_type"] = "hybrid"
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results.append(result)
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seen_ids.add(term_id)
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# 점수 기준 재정렬
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results.sort(key=lambda x: x["relevance_score"], reverse=True)
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results = results[:request.top_k]
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# 응답 형식으로 변환
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return [
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TermSearchResult(
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term=result["term"],
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relevance_score=result["relevance_score"],
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match_type=result["match_type"]
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)
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for result in results
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]
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except Exception as e:
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logger.error(f"용어 검색 실패: {str(e)}")
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raise HTTPException(status_code=500, detail=str(e))
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@app.get("/api/terms/{term_id}", response_model=Term)
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async def get_term(
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term_id: str,
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term_db: PostgresVectorDB = Depends(get_term_db)
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):
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"""
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용어 상세 조회
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Args:
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term_id: 용어 ID
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Returns:
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용어 객체
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"""
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try:
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term = term_db.get_term_by_id(term_id)
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if not term:
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raise HTTPException(status_code=404, detail="용어를 찾을 수 없습니다")
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return term
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except HTTPException:
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raise
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except Exception as e:
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logger.error(f"용어 조회 실패: {str(e)}")
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raise HTTPException(status_code=500, detail=str(e))
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@app.post("/api/terms/{term_id}/explain", response_model=TermExplanation)
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async def explain_term(
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term_id: str,
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request: TermExplainRequest,
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term_db: PostgresVectorDB = Depends(get_term_db),
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claude_service: ClaudeService = Depends(get_claude_service)
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):
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"""
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용어 맥락 기반 설명 생성 (Claude AI)
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Args:
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term_id: 용어 ID
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request: 설명 요청
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Returns:
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용어 설명
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"""
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try:
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# 용어 조회
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term = term_db.get_term_by_id(term_id)
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if not term:
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raise HTTPException(status_code=404, detail="용어를 찾을 수 없습니다")
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# Claude AI 호출
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result = claude_service.explain_term(
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term_name=term.term_name,
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definition=term.definition,
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context=term.context,
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meeting_context=request.meeting_context
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)
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return TermExplanation(
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term=term,
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explanation=result["explanation"],
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context_documents=[],
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generated_by=result["generated_by"],
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cached=result["cached"]
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)
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except HTTPException:
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raise
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except Exception as e:
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logger.error(f"용어 설명 생성 실패: {str(e)}")
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raise HTTPException(status_code=500, detail=str(e))
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@app.get("/api/terms/stats", response_model=TermStats)
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async def get_term_stats(term_db: PostgresVectorDB = Depends(get_term_db)):
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"""용어 통계 조회"""
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try:
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stats = term_db.get_stats()
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return TermStats(
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total_terms=stats["total_terms"],
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by_category=stats["by_category"],
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by_source_type={},
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avg_confidence=stats["avg_confidence"]
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)
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except Exception as e:
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logger.error(f"통계 조회 실패: {str(e)}")
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raise HTTPException(status_code=500, detail=str(e))
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# ============================================================================
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# 관련자료 API
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# ============================================================================
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@app.post("/api/documents/search", response_model=List[DocumentSearchResult])
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async def search_documents(
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request: DocumentSearchRequest,
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doc_db: AzureAISearchDB = Depends(get_doc_db),
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embedding_gen: EmbeddingGenerator = Depends(get_embedding_gen)
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):
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"""
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관련 문서 검색 (Hybrid Search + Semantic Ranking)
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Args:
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request: 검색 요청
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Returns:
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검색 결과 리스트
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"""
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try:
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# 쿼리 임베딩 생성
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query_embedding = embedding_gen.generate_embedding(request.query)
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# Hybrid Search 실행
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results = doc_db.hybrid_search(
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query=request.query,
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query_embedding=query_embedding,
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top_k=request.top_k,
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folder=request.folder,
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document_type=request.document_type,
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semantic_ranking=request.semantic_ranking
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)
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# 관련도 임계값 필터링
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filtered_results = [
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r for r in results
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if r["relevance_score"] >= request.relevance_threshold
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]
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# 응답 형식으로 변환
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return [
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DocumentSearchResult(**result)
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for result in filtered_results
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]
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except Exception as e:
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logger.error(f"문서 검색 실패: {str(e)}")
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raise HTTPException(status_code=500, detail=str(e))
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@app.get("/api/documents/stats", response_model=DocumentStats)
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async def get_document_stats(doc_db: AzureAISearchDB = Depends(get_doc_db)):
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"""문서 통계 조회"""
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try:
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stats = doc_db.get_stats()
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return DocumentStats(
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total_documents=stats["total_documents"],
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by_type=stats["by_type"],
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by_domain={},
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total_chunks=stats["total_chunks"]
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)
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except Exception as e:
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logger.error(f"통계 조회 실패: {str(e)}")
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raise HTTPException(status_code=500, detail=str(e))
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# ============================================================================
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# RAG 회의록 API
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# ============================================================================
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@app.post("/api/minutes/search", response_model=List[MinutesSearchResult])
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async def search_related_minutes(
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request: MinutesSearchRequest,
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rag_minutes_db: RagMinutesDB = Depends(get_rag_minutes_db),
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embedding_gen: EmbeddingGenerator = Depends(get_embedding_gen)
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):
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"""
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연관 회의록 검색 (Vector Similarity)
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Args:
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request: 검색 요청
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Returns:
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유사 회의록 리스트
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"""
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try:
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# 쿼리 임베딩 생성
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logger.info(f"회의록 검색 시작: {request.query[:50]}...")
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query_embedding = embedding_gen.generate_embedding(request.query)
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# 벡터 유사도 검색
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results = rag_minutes_db.search_by_vector(
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query_embedding=query_embedding,
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top_k=request.top_k,
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similarity_threshold=request.similarity_threshold
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)
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# 응답 형식으로 변환
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search_results = [
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MinutesSearchResult(
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minutes=result["minutes"],
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similarity_score=result["similarity_score"]
|
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)
|
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for result in results
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]
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logger.info(f"회의록 검색 완료: {len(search_results)}개 결과")
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return search_results
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except Exception as e:
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logger.error(f"회의록 검색 실패: {str(e)}")
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raise HTTPException(status_code=500, detail=str(e))
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||||
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||||
@app.get("/api/minutes/{minutes_id}")
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||||
async def get_minutes(
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||||
minutes_id: str,
|
||||
rag_minutes_db: RagMinutesDB = Depends(get_rag_minutes_db)
|
||||
):
|
||||
"""
|
||||
회의록 상세 조회
|
||||
|
||||
Args:
|
||||
minutes_id: 회의록 ID
|
||||
|
||||
Returns:
|
||||
회의록 객체
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||||
"""
|
||||
try:
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||||
minutes = rag_minutes_db.get_minutes_by_id(minutes_id)
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||||
if not minutes:
|
||||
raise HTTPException(status_code=404, detail="회의록을 찾을 수 없습니다")
|
||||
|
||||
return minutes
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
logger.error(f"회의록 조회 실패: {str(e)}")
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
|
||||
@app.get("/api/minutes/stats")
|
||||
async def get_minutes_stats(rag_minutes_db: RagMinutesDB = Depends(get_rag_minutes_db)):
|
||||
"""회의록 통계 조회"""
|
||||
try:
|
||||
stats = rag_minutes_db.get_stats()
|
||||
return stats
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"통계 조회 실패: {str(e)}")
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import uvicorn
|
||||
uvicorn.run(app, host="0.0.0.0", port=8000)
|
||||
Reference in New Issue
Block a user