services/rag_pipeline_v2.py

# from retriever.vectordb import search_documents
# from retriever.vectordb_rerank import search_documents
from retriever.vectordb_rerank_law import search_documents as search_law
from retriever.vectordb_rerank_exam import search_documents as search_exam
from generator.prompt_builder import build_prompt
from generator.llm_inference import generate_answer

def rag_pipeline(query: str, top_k: int = 5) -> str:
    """
    1. 사용자 질문으로 관련 문서를 검색
    2. 검색된 문서와 함께 프롬프트 구성
    3. 프롬프트로부터 답변 생성
    """
    # 1. 법령과 문제를 각각 검색
    # context_docs = search_documents(query, top_k=top_k)
    laws_docs = search_law(query, top_k=top_k)
    exam_docs = search_exam(query, top_k=top_k)

    # 2. 프롬프트 구성
    # prompt = build_prompt(query, context_docs)
    prompt = build_prompt(query, laws_docs, exam_docs)

    # 3. LLM으로 문제 생성
    # output = generate_answer(prompt)
    questions = generate_answer(prompt)

    # 4. 결과 저장
    # save_to_exam_vector_db(questions)

    return questions