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LangGraph로 구현하는 Self-Correcting RAG 시스템

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이번 실습에서는 LangGraph를 활용하여 스스로 품질을 검증하고 개선하는 고급 RAG 시스템을 만듭니다.
\n각 단계마다 품질을 체크하고, 문제가 있으면 자동으로 수정하는 시스템입니다!

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목차

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0. 환경 설정

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필요한 라이브러리 설치

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pip install langgraph langchain langchain-openai langchain-community chromadb openai python-dotenv

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company_info.json

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[\n  {\n    \"id\": \"company_overview\",\n    \"topic\": \"회사 소개\",\n    \"content\": \"테크노바는 2020년에 설립된 AI 기반 솔루션을 제공하는 스타트업입니다. 서울 강남구에 본사를 두고 있으며, 현재 직원 수는 약 50명입니다.\"\n  },\n  {\n    \"id\": \"main_product\",\n    \"topic\": \"주력 제품\",\n    \"content\": \"우리의 주력 제품은 'SmartAssist'라는 AI 챗봇 플랫폼입니다. 고객 서비스 자동화와 업무 효율성 향상을 위한 솔루션을 제공합니다.\"\n  },\n  {\n    \"id\": \"ceo_info\",\n    \"topic\": \"대표이사\",\n    \"content\": \"김철수 대표는 카이스트에서 인공지능을 전공했으며, 이전에 네이버에서 AI 연구원으로 근무했습니다. 10년 이상의 AI 개발 경험을 보유하고 있습니다.\"\n  },\n  {\n    \"id\": \"office_location\",\n    \"topic\": \"사무실 위치\",\n    \"content\": \"본사는 서울특별시 강남구 테헤란로 123에 위치해 있습니다. 지하철 2호선 강남역에서 도보 5분 거리이며, 15층에 사무실이 있습니다.\"\n  },\n  {\n    \"id\": \"work_hours\",\n    \"topic\": \"근무 시간\",\n    \"content\": \"근무 시간은 오전 9시부터 오후 6시까지이며, 코어타임은 오전 10시부터 오후 4시까지입니다. 주 5일 근무제를 시행하고 있습니다.\"\n  },\n  {\n    \"id\": \"benefits\",\n    \"topic\": \"복지 제도\",\n    \"content\": \"직원들에게 최신형 맥북 프로와 듀얼 모니터를 제공하며, 점심 식대 지원, 간식 무제한 제공, 연 1회 워크샵, 도서 구입비 지원 등의 복지를 제공합니다.\"\n  },\n  {\n    \"id\": \"tech_stack\",\n    \"topic\": \"기술 스택\",\n    \"content\": \"백엔드는 Python FastAPI와 Django를 사용하고, 프론트엔드는 React와 TypeScript를 사용합니다. AI 모델 학습에는 PyTorch를 주로 사용합니다.\"\n  },\n  {\n    \"id\": \"team_structure\",\n    \"topic\": \"팀 구조\",\n    \"content\": \"회사는 크게 개발팀, 디자인팀, 영업팀, 마케팅팀으로 구성되어 있습니다. 개발팀이 가장 크며 약 30명의 엔지니어가 근무하고 있습니다.\"\n  },\n  {\n    \"id\": \"funding\",\n    \"topic\": \"투자 유치\",\n    \"content\": \"2023년에 시리즈 A 라운드에서 50억원의 투자를 유치했습니다. 주요 투자사는 카카오벤처스와 네이버 D2SF입니다.\"\n  },\n  {\n    \"id\": \"clients\",\n    \"topic\": \"주요 고객사\",\n    \"content\": \"현재 삼성전자, LG전자, 현대자동차 등 대기업을 포함해 총 100여개의 고객사를 보유하고 있습니다.\"\n  }\n]

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라이브러리 Import

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import os\nimport json\nfrom dotenv import load_dotenv\nfrom typing import TypedDict, Literal\n\n# LangChain 관련\nfrom langchain_openai import ChatOpenAI, OpenAIEmbeddings\nfrom langchain_community.vectorstores import Chroma\nfrom langchain.schema import Document\n\n# LangGraph 관련\nfrom langgraph.graph import StateGraph, END\n\n# 환경 변수 로드\nload_dotenv()\n\nprint(\"✅ 환경 설정 완료!\")

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1. Self-Correcting RAG 개요

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일반 RAG vs Self-Correcting RAG

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일반 RAG (3강):

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질문 → 검색 → 답변 생성 → 끝

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Self-Correcting RAG (4강):

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질문 → 질문 평가 → ❌ → 질문 재작성 → 다시 평가\n         ↓ ✅\n       검색 → 검색 평가 → ❌ → 쿼리 재작성 → 다시 검색\n         ↓ ✅\n     답변 생성 → 답변 평가 → ❌ → 문제 진단 → 해당 단계 복귀\n         ↓ ✅\n       최종 답변

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일반 RAG의 한계

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1. 부정확한 검색

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문제: “우리 회사 휴가 정책은?” → 근무시간 문서 검색됨
원인: 질문이 모호하거나 임베딩 매칭 실패
결과: 잘못된 문서 기반 답변

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2. 관련 없는 문서 검색

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문제: 질문과 무관한 문서가 상위 검색됨
원인: 키워드는 유사하지만 맥락이 다름
결과: “문서에서 찾을 수 없습니다” (있는데도!)

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3. 불완전한 답변

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문제: 검색은 잘 됐는데 답변이 부정확
원인: LLM이 문서를 잘못 해석하거나 환각(Hallucination)
결과: 신뢰할 수 없는 답변

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4. 컨텍스트 부족

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문제: 여러 문서를 조합해야 하는 질문
원인: Top-K 검색으로 중요한 문서 누락
결과: 불완전한 답변

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Self-Correcting RAG의 해결책

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✅ 자동 검증 및 재시도

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각 단계마다 품질 검증
실패 시 자동으로 재시도
최적의 결과 도출

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✅ 적응형 검색

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검색 실패 시 쿼리 재작성
다양한 검색 전략 시도
관련성 점수 기반 필터링

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✅ 답변 검증

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생성된 답변의 정확성 평가
문서와의 일치도 확인
환각 방지

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2. 데이터 준비

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벡터 스토어 로드

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# JSON 파일에서 데이터 로드\nwith open('company_info.json', 'r', encoding='utf-8') as f:\n    company_data = json.load(f)\n\n# Document 객체로 변환\ndocuments = []\nfor item in company_data:\n    doc = Document(\n        page_content=f\"주제: {item['topic']}\\n내용: {item['content']}\",\n        metadata={\"id\": item[\"id\"], \"topic\": item[\"topic\"]}\n    )\n    documents.append(doc)\n\n# 임베딩 및 벡터 스토어\nembeddings = OpenAIEmbeddings(model=\"text-embedding-3-small\")\n\nvectorstore = Chroma(\n    persist_directory=\"./chroma_db\",\n    embedding_function=embeddings,\n    collection_name=\"company_info\"\n)\n\nretriever = vectorstore.as_retriever(search_type=\"similarity\", search_kwargs={\"k\": 3})\n\n# LLM 초기화\nllm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0.7)\n\nprint(f\"✅ 준비 완료! 문서 수: {vectorstore._collection.count()}\")

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3. State 정의

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Self-Correcting RAG의 모든 상태를 관리하는 State를 정의합니다.

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class SelfCorrectingRAGState(TypedDict):\n    \"\"\"Self-Correcting RAG 워크플로우 상태\"\"\"\n    # 입력\n    original_question: str  # 원본 질문\n    \n    # 질문 처리\n    current_question: str  # 현재 처리 중인 질문 (재작성될 수 있음)\n    question_quality: str  # \"good\" or \"bad\"\n    question_rewrite_count: int  # 질문 재작성 횟수\n    \n    # 검색 처리\n    search_query: str  # 검색 쿼리\n    retrieved_docs: list  # 검색된 문서들\n    retrieval_quality: str  # \"relevant\" or \"irrelevant\"\n    search_retry_count: int  # 검색 재시도 횟수\n    \n    # 답변 처리\n    answer: str  # 생성된 답변\n    answer_quality: str  # \"good\" or \"bad\"\n    problem_diagnosis: str  # 문제 진단 결과\n    \n    # 제어\n    max_retries: int  # 최대 재시도 횟수

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4. 노드 함수 구현

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Step 1: 질문 평가 노드

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def evaluate_question(state: SelfCorrectingRAGState) -> SelfCorrectingRAGState:\n    \"\"\"질문이 명확하고 구체적인지 평가합니다.\"\"\"\n    question = state[\"current_question\"]\n    \n    evaluation_prompt = f\"\"\"\n다음 질문이 명확하고 구체적인지 평가해주세요.\n\n질문: \"{question}\"\n\n평가 기준:\n1. 질문이 무엇을 묻는지 명확한가?\n2. 문맥 없이도 이해 가능한가?\n3. 회사 정보에 대한 질문인가?\n\n\"good\" 또는 \"bad\" 중 하나로만 답변하세요.\n\"\"\".strip()\n    \n    response = llm.invoke(evaluation_prompt)\n    quality = response.content.strip().lower()\n    \n    if \"good\" in quality:\n        quality = \"good\"\n    else:\n        quality = \"bad\"\n    \n    print(f\"🔍 [질문 평가] {question}\")\n    print(f\"   결과: {quality}\")\n    \n    return {\n        **state,\n        \"question_quality\": quality\n    }

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Step 2: 질문 재작성 노드

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def rewrite_question(state: SelfCorrectingRAGState) -> SelfCorrectingRAGState:\n    \"\"\"질문을 더 명확하고 구체적으로 재작성합니다.\"\"\"\n    question = state[\"current_question\"]\n    \n    rewrite_prompt = f\"\"\"다음 질문을 더 명확하고 구체적으로 재작성해주세요.\n\n원본 질문: \"{question}\"\n\n재작성 시 고려사항:\n1. 회사 정보를 묻는 것이 명확하게 드러나도록\n2. 애매한 표현 제거\n3. 구체적인 정보 요청으로 변환\n\n재작성된 질문만 답변하세요.\n\"\"\"\n    \n    response = llm.invoke(rewrite_prompt)\n    rewritten = response.content.strip()\n    \n    print(f\"✏️ [질문 재작성]\")\n    print(f\"   원본: {question}\")\n    print(f\"   재작성: {rewritten}\")\n    \n    return {\n        **state,\n        \"current_question\": rewritten,\n        \"question_rewrite_count\": state.get(\"question_rewrite_count\", 0) + 1\n    }

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Step 3: 문서 검색 노드

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def retrieve_documents(state: SelfCorrectingRAGState) -> SelfCorrectingRAGState:\n    \"\"\"벡터 DB에서 관련 문서를 검색합니다.\"\"\"\n    query = state.get(\"search_query\") or state[\"current_question\"]\n    \n    docs = retriever.invoke(query)\n    \n    print(f\"📚 [문서 검색] 쿼리: {query}\")\n    print(f\"   검색된 문서 수: {len(docs)}\")\n    for i, doc in enumerate(docs, 1):\n        print(f\"   {i}. {doc.metadata.get('topic', 'N/A')}\")\n    \n    return {\n        **state,\n        \"retrieved_docs\": docs,\n        \"search_query\": query\n    }

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Step 4: 검색 결과 평가 노드

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def evaluate_retrieval(state: SelfCorrectingRAGState) -> SelfCorrectingRAGState:\n    \"\"\"검색된 문서가 질문과 관련있는지 평가합니다.\"\"\"\n    question = state[\"current_question\"]\n    docs = state[\"retrieved_docs\"]\n    \n    docs_content = \"\\n\\n\".join([f\"문서 {i+1}: {doc.page_content[:100]}...\" \n                                 for i, doc in enumerate(docs)])\n    \n    evaluation_prompt = f\"\"\"다음 질문에 대해 검색된 문서들이 관련성이 있는지 평가해주세요.\n\n질문: \"{question}\"\n\n검색된 문서들:\n{docs_content}\n\n평가 기준:\n1. 문서에 질문에 답할 수 있는 정보가 포함되어 있는가?\n2. 문서와 질문의 주제가 일치하는가?\n\n\"relevant\" 또는 \"irrelevant\" 중 하나로만 답변하세요.\n\"\"\"\n    \n    response = llm.invoke(evaluation_prompt)\n    quality = response.content.strip().lower()\n    \n    if \"relevant\" in quality:\n        quality = \"relevant\"\n    else:\n        quality = \"irrelevant\"\n    \n    print(f\"✅ [검색 평가] 결과: {quality}\")\n    \n    return {\n        **state,\n        \"retrieval_quality\": quality\n    }

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Step 5: 검색 쿼리 재작성 노드

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def rewrite_search_query(state: SelfCorrectingRAGState) -> SelfCorrectingRAGState:\n    \"\"\"더 나은 검색 결과를 위해 쿼리를 재작성합니다.\"\"\"\n    question = state[\"current_question\"]\n    previous_query = state.get(\"search_query\", question)\n    \n    rewrite_prompt = f\"\"\"검색 결과가 부적절했습니다. 더 나은 검색 결과를 위해 쿼리를 재작성해주세요.\n\n원본 질문: \"{question}\"\n이전 검색 쿼리: \"{previous_query}\"\n\n재작성 시 고려사항:\n1. 핵심 키워드 강조\n2. 동의어나 관련 용어 포함\n3. 더 구체적인 표현 사용\n\n재작성된 검색 쿼리만 답변하세요.\n\"\"\"\n    \n    response = llm.invoke(rewrite_prompt)\n    new_query = response.content.strip()\n    \n    print(f\"🔄 [쿼리 재작성]\")\n    print(f\"   이전: {previous_query}\")\n    print(f\"   재작성: {new_query}\")\n    \n    return {\n        **state,\n        \"search_query\": new_query,\n        \"search_retry_count\": state.get(\"search_retry_count\", 0) + 1\n    }

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Step 6: 답변 생성 노드

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def generate_answer(state: SelfCorrectingRAGState) -> SelfCorrectingRAGState:\n    \"\"\"검색된 문서를 바탕으로 답변을 생성합니다.\"\"\"\n    question = state[\"current_question\"]\n    docs = state[\"retrieved_docs\"]\n    \n    context = \"\\n\\n\".join([doc.page_content for doc in docs])\n    \n    answer_prompt = f\"\"\"당신은 회사 정보를 안내하는 전문 AI 어시스턴트입니다.\n아래 컨텍스트를 바탕으로 질문에 정확하고 친절하게 답변해주세요.\n\n컨텍스트:\n{context}\n\n질문: {question}\n\n답변:\"\"\"\n    \n    response = llm.invoke(answer_prompt)\n    answer = response.content\n    \n    print(f\"💬 [답변 생성] 완료\")\n    \n    return {\n        **state,\n        \"answer\": answer\n    }

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Step 7: 답변 평가 노드

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def evaluate_answer(state: SelfCorrectingRAGState) -> SelfCorrectingRAGState:\n    \"\"\"생성된 답변의 품질을 평가합니다.\"\"\"\n    question = state[\"current_question\"]\n    answer = state[\"answer\"]\n    \n    evaluation_prompt = f\"\"\"다음 답변의 품질을 평가해주세요.\n\n질문: \"{question}\"\n답변: \"{answer}\"\n\n평가 기준:\n1. 질문에 직접적으로 답변하는가?\n2. 컨텍스트의 정보를 정확히 사용했는가?\n3. 답변이 구체적이고 유용한가?\n4. 환각(hallucination)이 없는가?\n\n\"good\" 또는 \"bad\" 중 하나로만 답변하세요.\n\"\"\"\n    \n    response = llm.invoke(evaluation_prompt)\n    quality = response.content.strip().lower()\n    \n    if \"good\" in quality:\n        quality = \"good\"\n    else:\n        quality = \"bad\"\n    \n    print(f\"⭐ [답변 평가] 결과: {quality}\")\n    \n    return {\n        **state,\n        \"answer_quality\": quality\n    }

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Step 8: 문제 진단 노드

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def diagnose_problem(state: SelfCorrectingRAGState) -> SelfCorrectingRAGState:\n    \"\"\"답변에 문제가 있을 때, 어디에서 문제가 발생했는지 진단합니다.\"\"\"\n    question = state[\"current_question\"]\n    docs = state[\"retrieved_docs\"]\n    answer = state[\"answer\"]\n    \n    docs_summary = \"\\n\".join([f\"- {doc.metadata.get('topic', 'N/A')}\" for doc in docs])\n    \n    diagnosis_prompt = f\"\"\"답변에 문제가 있습니다. 어디에서 문제가 발생했는지 진단해주세요.\n\n질문: \"{question}\"\n검색된 문서들:\n{docs_summary}\n생성된 답변: \"{answer}\"\n\n다음 중 하나를 선택하세요:\n1. \"question_issue\" - 질문 자체에 문제가 있음 (애매하거나 불명확)\n2. \"retrieval_issue\" - 검색된 문서가 부적절함\n3. \"generation_issue\" - 답변 생성 과정에서 문제 발생\n\n진단 결과만 답변하세요 (question_issue, retrieval_issue, generation_issue 중 하나).\n\"\"\"\n    \n    response = llm.invoke(diagnosis_prompt)\n    diagnosis = response.content.strip().lower()\n    \n    if \"question\" in diagnosis:\n        diagnosis = \"question_issue\"\n    elif \"retrieval\" in diagnosis:\n        diagnosis = \"retrieval_issue\"\n    else:\n        diagnosis = \"generation_issue\"\n    \n    print(f\"🔧 [문제 진단] {diagnosis}\")\n    \n    return {\n        **state,\n        \"problem_diagnosis\": diagnosis\n    }

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5. 라우팅 함수 정의

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각 평가 결과에 따라 어디로 갈지 결정하는 함수들입니다.

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def route_after_question_eval(state: SelfCorrectingRAGState) -> str:\n    \"\"\"질문 평가 후 라우팅\"\"\"\n    if state[\"question_quality\"] == \"good\":\n        return \"retrieve\"\n    elif state.get(\"question_rewrite_count\", 0) >= state.get(\"max_retries\", 2):\n        return \"retrieve\"  # 최대 재시도 도달, 그냥 진행\n    else:\n        return \"rewrite_question\"\n\n\ndef route_after_retrieval_eval(state: SelfCorrectingRAGState) -> str:\n    \"\"\"검색 평가 후 라우팅\"\"\"\n    if state[\"retrieval_quality\"] == \"relevant\":\n        return \"generate\"\n    elif state.get(\"search_retry_count\", 0) >= state.get(\"max_retries\", 2):\n        return \"generate\"  # 최대 재시도 도달, 그냥 진행\n    else:\n        return \"rewrite_query\"\n\n\ndef route_after_answer_eval(state: SelfCorrectingRAGState) -> str:\n    \"\"\"답변 평가 후 라우팅\"\"\"\n    if state[\"answer_quality\"] == \"good\":\n        return \"end\"\n    else:\n        return \"diagnose\"\n\n\ndef route_after_diagnosis(state: SelfCorrectingRAGState) -> str:\n    \"\"\"문제 진단 후 라우팅\"\"\"\n    diagnosis = state[\"problem_diagnosis\"]\n    \n    if diagnosis == \"question_issue\":\n        return \"rewrite_question\"\n    elif diagnosis == \"retrieval_issue\":\n        return \"rewrite_query\"\n    else:  # generation_issue\n        return \"generate\"  # 답변만 다시 생성

\n

\n

6. 그래프 구성

\n

모든 노드와 라우팅을 연결하여 완전한 Self-Correcting RAG 그래프를 만듭니다.

\n

from IPython.display import Image, display\n\n# 그래프 생성\nworkflow = StateGraph(SelfCorrectingRAGState)\n\n# 노드 추가\nworkflow.add_node(\"evaluate_question\", evaluate_question)\nworkflow.add_node(\"rewrite_question\", rewrite_question)\nworkflow.add_node(\"retrieve\", retrieve_documents)\nworkflow.add_node(\"evaluate_retrieval\", evaluate_retrieval)\nworkflow.add_node(\"rewrite_query\", rewrite_search_query)\nworkflow.add_node(\"generate\", generate_answer)\nworkflow.add_node(\"evaluate_answer\", evaluate_answer)\nworkflow.add_node(\"diagnose\", diagnose_problem)\n\n# 시작점 설정\nworkflow.set_entry_point(\"evaluate_question\")\n\n# 엣지 연결\n# 1. 질문 평가 → 조건부 분기\nworkflow.add_conditional_edges(\n    \"evaluate_question\",\n    route_after_question_eval,\n    {\n        \"retrieve\": \"retrieve\",\n        \"rewrite_question\": \"rewrite_question\"\n    }\n)\n\n# 2. 질문 재작성 → 다시 평가\nworkflow.add_edge(\"rewrite_question\", \"evaluate_question\")\n\n# 3. 검색 → 검색 평가\nworkflow.add_edge(\"retrieve\", \"evaluate_retrieval\")\n\n# 4. 검색 평가 → 조건부 분기\nworkflow.add_conditional_edges(\n    \"evaluate_retrieval\",\n    route_after_retrieval_eval,\n    {\n        \"generate\": \"generate\",\n        \"rewrite_query\": \"rewrite_query\"\n    }\n)\n\n# 5. 쿼리 재작성 → 다시 검색\nworkflow.add_edge(\"rewrite_query\", \"retrieve\")\n\n# 6. 답변 생성 → 답변 평가\nworkflow.add_edge(\"generate\", \"evaluate_answer\")\n\n# 7. 답변 평가 → 조건부 분기\nworkflow.add_conditional_edges(\n    \"evaluate_answer\",\n    route_after_answer_eval,\n    {\n        \"end\": END,\n        \"diagnose\": \"diagnose\"\n    }\n)\n\n# 8. 문제 진단 → 조건부 분기 (해당 단계로 복귀)\nworkflow.add_conditional_edges(\n    \"diagnose\",\n    route_after_diagnosis,\n    {\n        \"rewrite_question\": \"rewrite_question\",\n        \"rewrite_query\": \"rewrite_query\",\n        \"generate\": \"generate\"\n    }\n)\n\n# 컴파일\napp = workflow.compile()\n\nprint(\"✅ Self-Correcting RAG 그래프 구성 완료!\")\n\ndisplay(Image(app.get_graph().draw_mermaid_png()))

\n

워크플로우 구조:

\n

[시작]\n  ↓\n[질문 평가] → bad? → [질문 재작성] → [질문 평가]\n  ↓ good\n[문서 검색]\n  ↓\n[검색 평가] → irrelevant? → [쿼리 재작성] → [문서 검색]\n  ↓ relevant\n[답변 생성]\n  ↓\n[답변 평가] → bad? → [문제 진단] → question? → [질문 재작성]\n  ↓ good              ↓              retrieval? → [쿼리 재작성]\n[종료]                                generation? → [답변 생성]

\n

\n

7. 실행 및 테스트

\n

헬퍼 함수

\n

def ask_with_self_correction(question: str, max_retries: int = 2):\n    \"\"\"Self-Correcting RAG로 질문에 답변\"\"\"\n    print(\"\\n\" + \"=\"*80)\n    print(f\"💭 원본 질문: {question}\")\n    print(\"=\"*80 + \"\\n\")\n    \n    initial_state = {\n        \"original_question\": question,\n        \"current_question\": question,\n        \"question_quality\": \"\",\n        \"question_rewrite_count\": 0,\n        \"search_query\": \"\",\n        \"retrieved_docs\": [],\n        \"retrieval_quality\": \"\",\n        \"search_retry_count\": 0,\n        \"answer\": \"\",\n        \"answer_quality\": \"\",\n        \"problem_diagnosis\": \"\",\n        \"max_retries\": max_retries\n    }\n    \n    # 실행\n    result = app.invoke(initial_state)\n    \n    print(\"\\n\" + \"=\"*80)\n    print(f\"✨ 최종 답변:\")\n    print(f\"{result['answer']}\")\n    print(\"=\"*80)\n    print(f\"\\n📊 통계:\")\n    print(f\"  - 질문 재작성 횟수: {result['question_rewrite_count']}\")\n    print(f\"  - 검색 재시도 횟수: {result['search_retry_count']}\")\n    print(f\"  - 최종 질문: {result['current_question']}\")\n    \n    return result

\n

테스트 실행

\n

ask_with_self_correction(\"테크노바의 근무시간은 어떻게 돼?\")

\n

\n

주요 특징

\n

1. 다층 검증 시스템

\n

질문 품질 검증
검색 결과 검증
답변 품질 검증

\n

2. 자동 복구 메커니즘

\n

문제 발생 시 자동 재시도
적절한 단계로 자동 복귀
최대 재시도 횟수 제한

\n

3. 지능형 문제 진단

\n

어느 단계에서 문제가 발생했는지 판단
문제에 맞는 해결책 적용
불필요한 재시도 방지

\n

\n

장단점

\n

장점

\n

✅ 높은 답변 품질
\n✅ 자동 오류 수정
\n✅ 투명한 프로세스
\n✅ 확장 가능한 구조

\n

단점

\n

❌ 응답 시간 증가
\n❌ 높은 토큰 사용량
\n❌ 복잡한 로직
\n❌ LLM 호출 빈도 증가

\n

\n

실전 활용 팁

\n

1. 최대 재시도 횟수 조정

\n

품질 우선: max_retries=3
속도 우선: max_retries=1

\n

2. 평가 기준 커스터마이징

\n

도메인별 평가 기준 추가
품질 임계값 설정

\n

3. 캐싱 활용

\n

동일 질문 결과 저장
검색 결과 재사용

\n

4. 모니터링 추가

\n

각 단계별 소요 시간 측정
재시도 빈도 분석
실패 원인 로깅

\n

\n

결론

\n

Self-Correcting RAG는 일반 RAG의 한계를 극복하는 강력한 시스템입니다. 자동 검증과 수정 메커니즘을 통해 높은 품질의 답변을 제공하며, LangGraph의 유연한 구조로 쉽게 확장할 수 있습니다.

\n

핵심 포인트:

\n

각 단계마다 품질을 검증하고 필요시 자동으로 수정
문제 발생 지점을 정확히 진단하여 효율적으로 대응
최대 재시도 횟수 설정으로 무한 루프 방지
투명한 프로세스로 디버깅과 개선이 용이

\n

이 시스템을 기반으로 도메인 특화 평가 기준을 추가하거나, 더 정교한 검색 전략을 구현할 수 있습니다!

","htmlAst":{"type":"root","children":[{"type":"element","tagName":"h1","properties":{},"children":[{"type":"text","value":"LangGraph로 구현하는 Self-Correcting RAG 시스템"}]},{"type":"text","value":"\n"},{"type":"element","tagName":"p","properties":{},"children":[{"type":"text","value":"이번 실습에서는 LangGraph를 활용하여 스스로 품질을 검증하고 개선하는 고급 RAG 시스템을 만듭니다."},{"type":"element","tagName":"br","properties":{},"children":[]},{"type":"text","value":"\n각 단계마다 품질을 체크하고, 문제가 있으면 자동으로 수정하는 시스템입니다!"}]},{"type":"text","value":"\n"},{"type":"element","tagName":"h2","properties":{},"children":[{"type":"text","value":"목차"}]},{"type":"text","value":"\n"},{"type":"element","tagName":"ul","properties":{},"children":[{"type":"text","value":"\n"},{"type":"element","tagName":"li","properties":{},"children":[{"type":"element","tagName":"a","properties":{"href":"https://claude.ai/chat/b18974d7-558c-4ce8-a784-a7e50f7b6ba6#0-%ED%99%98%EA%B2%BD-%EC%84%A4%EC%A0%95"},"children":[{"type":"text","value":"0. 환경 설정"}]}]},{"type":"text","value":"\n"},{"type":"element","tagName":"li","properties":{},"children":[{"type":"element","tagName":"a","properties":{"href":"https://claude.ai/chat/b18974d7-558c-4ce8-a784-a7e50f7b6ba6#1-self-correcting-rag-%EA%B0%9C%EC%9A%94"},"children":[{"type":"text","value":"1. Self-Correcting RAG 개요"}]}]},{"type":"text","value":"\n"},{"type":"element","tagName":"li","properties":{},"children":[{"type":"element","tagName":"a","properties":{"href":"https://claude.ai/chat/b18974d7-558c-4ce8-a784-a7e50f7b6ba6#2-%EB%8D%B0%EC%9D%B4%ED%84%B0-%EC%A4%80%EB%B9%84"},"children":[{"type":"text","value":"2. 데이터 준비"}]}]},{"type":"text","value":"\n"},{"type":"element","tagName":"li","properties":{},"children":[{"type":"element","tagName":"a","properties":{"href":"https://claude.ai/chat/b18974d7-558c-4ce8-a784-a7e50f7b6ba6#3-state-%EC%A0%95%EC%9D%98"},"children":[{"type":"text","value":"3. State 정의"}]}]},{"type":"text","value":"\n"},{"type":"element","tagName":"li","properties":{},"children":[{"type":"element","tagName":"a","properties":{"href":"https://claude.ai/chat/b18974d7-558c-4ce8-a784-a7e50f7b6ba6#4-%EB%85%B8%EB%93%9C-%ED%95%A8%EC%88%98-%EA%B5%AC%ED%98%84"},"children":[{"type":"text","value":"4. 노드 함수 구현"}]}]},{"type":"text","value":"\n"},{"type":"element","tagName":"li","properties":{},"children":[{"type":"element","tagName":"a","properties":{"href":"https://claude.ai/chat/b18974d7-558c-4ce8-a784-a7e50f7b6ba6#5-%EB%9D%BC%EC%9A%B0%ED%8C%85-%ED%95%A8%EC%88%98-%EC%A0%95%EC%9D%98"},"children":[{"type":"text","value":"5. 라우팅 함수 정의"}]}]},{"type":"text","value":"\n"},{"type":"element","tagName":"li","properties":{},"children":[{"type":"element","tagName":"a","properties":{"href":"https://claude.ai/chat/b18974d7-558c-4ce8-a784-a7e50f7b6ba6#6-%EA%B7%B8%EB%9E%98%ED%94%84-%EA%B5%AC%EC%84%B1"},"children":[{"type":"text","value":"6. 그래프 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설립된 AI 기반 솔루션을 제공하는 스타트업입니다. 서울 강남구에 본사를 두고 있으며, 현재 직원 수는 약 50명입니다.\""}]},{"type":"text","value":"\n "},{"type":"element","tagName":"span","properties":{"className":["token","punctuation"]},"children":[{"type":"text","value":"}"}]},{"type":"element","tagName":"span","properties":{"className":["token","punctuation"]},"children":[{"type":"text","value":","}]},{"type":"text","value":"\n "},{"type":"element","tagName":"span","properties":{"className":["token","punctuation"]},"children":[{"type":"text","value":"{"}]},{"type":"text","value":"\n "},{"type":"element","tagName":"span","properties":{"className":["token","property"]},"children":[{"type":"text","value":"\"id\""}]},{"type":"element","tagName":"span","properties":{"className":["token","operator"]},"children":[{"type":"text","value":":"}]},{"type":"text","value":" 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로깅"}]},{"type":"text","value":"\n"}]},{"type":"text","value":"\n"},{"type":"element","tagName":"hr","properties":{},"children":[]},{"type":"text","value":"\n"},{"type":"element","tagName":"h2","properties":{},"children":[{"type":"text","value":"결론"}]},{"type":"text","value":"\n"},{"type":"element","tagName":"p","properties":{},"children":[{"type":"text","value":"Self-Correcting RAG는 일반 RAG의 한계를 극복하는 강력한 시스템입니다. 자동 검증과 수정 메커니즘을 통해 높은 품질의 답변을 제공하며, LangGraph의 유연한 구조로 쉽게 확장할 수 있습니다."}]},{"type":"text","value":"\n"},{"type":"element","tagName":"p","properties":{},"children":[{"type":"element","tagName":"strong","properties":{},"children":[{"type":"text","value":"핵심 포인트:"}]}]},{"type":"text","value":"\n"},{"type":"element","tagName":"ul","properties":{},"children":[{"type":"text","value":"\n"},{"type":"element","tagName":"li","properties":{},"children":[{"type":"text","value":"각 단계마다 품질을 검증하고 필요시 자동으로 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