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RAG 기반 LLM 채팅 로봇

고급

이것은AI RAG, Multimodal AI분야의자동화 워크플로우로, 17개의 노드를 포함합니다.주로 GoogleDrive, Agent, GoogleDriveTrigger, ChatTrigger, LmChatOpenAi 등의 노드를 사용하며. RAG, Pinecone 벡터 데이터베이스와 OpenAI로 회사 정책 채팅 로봇 생성

사전 요구사항
  • Google Drive API 인증 정보
  • OpenAI API Key
  • Pinecone API Key
워크플로우 미리보기
노드 연결 관계를 시각적으로 표시하며, 확대/축소 및 이동을 지원합니다
워크플로우 내보내기
다음 JSON 구성을 복사하여 n8n에 가져오면 이 워크플로우를 사용할 수 있습니다
{
  "id": "ijORqghBWmOcVaCd",
  "meta": {
    "instanceId": "87616dfce72ffd690a51f4d408ac3b27a946f12a1119e0e281f5ed271497219e",
    "templateCredsSetupCompleted": true
  },
  "name": "ChatBot For RAG Based LLMs",
  "tags": [],
  "nodes": [
    {
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      "name": "채팅 메시지 수신 시",
      "type": "@n8n/n8n-nodes-langchain.chatTrigger",
      "position": [
        -440,
        -300
      ],
      "webhookId": "ba6e0e80-df72-4993-a938-16604c978e0b",
      "parameters": {
        "public": true,
        "options": {}
      },
      "typeVersion": 1.1
    },
    {
      "id": "688546eb-decc-4d88-b991-e6851d00d3c3",
      "name": "AI 에이전트",
      "type": "@n8n/n8n-nodes-langchain.agent",
      "position": [
        -80,
        -300
      ],
      "parameters": {
        "options": {
          "systemMessage": "=You are an AI assistant specialized in analyzing user queries and retreive the data using pine cone vectore store via Vectore store QnA Tool. \n\nYour primary task is to answer questions accurately and precisely using the vector database, which contains relevant documents.\n\nOnly provide information that you retrieve from the documents (or verified expert knowledge). If something is not included in the dataset or is unclear, clearly state that you do not have sufficient information.\n\nStructure of your responses:\n• Concise and to the point\n• Specific numbers and facts, when available\n• Clearly indicate which quarterly tax deduction with the information comes from\n\nObjective:\nProvide users with reliable and quick insights to user questions without unnecessary details."
        }
      },
      "typeVersion": 2
    },
    {
      "id": "47d5f4e0-45e6-4a75-8250-76277a33addd",
      "name": "OpenAI 채팅 모델",
      "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
      "position": [
        -220,
        -60
      ],
      "parameters": {
        "model": {
          "__rl": true,
          "mode": "list",
          "value": "gpt-4o-mini"
        },
        "options": {}
      },
      "credentials": {
        "openAiApi": {
          "id": "qlhg6EzexBZ9bwXO",
          "name": "OpenAi account"
        }
      },
      "typeVersion": 1.2
    },
    {
      "id": "c5e00f0e-2494-443d-9ff5-ab476f174fda",
      "name": "심플 메모리",
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      "position": [
        20,
        80
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      "parameters": {},
      "typeVersion": 1.3
    },
    {
      "id": "5bdc6723-18e4-4637-b31e-2b7a722c2d30",
      "name": "Pinecone 벡터 저장소",
      "type": "@n8n/n8n-nodes-langchain.vectorStorePinecone",
      "position": [
        300,
        80
      ],
      "parameters": {
        "options": {
          "pineconeNamespace": "<yourNameSpace>"
        },
        "pineconeIndex": {
          "__rl": true,
          "mode": "list",
          "value": "n8ntest",
          "cachedResultName": "n8ntest"
        }
      },
      "credentials": {
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          "id": "PJJqeU0YnPNhhvLk",
          "name": "PineconeApi account"
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      },
      "typeVersion": 1.2
    },
    {
      "id": "c9325bbd-95ee-49b6-80e3-b8eb18a3044e",
      "name": "OpenAI 임베딩",
      "type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
      "position": [
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        240
      ],
      "parameters": {
        "options": {
          "batchSize": 512,
          "dimensions": 512
        }
      },
      "credentials": {
        "openAiApi": {
          "id": "qlhg6EzexBZ9bwXO",
          "name": "OpenAi account"
        }
      },
      "typeVersion": 1.2
    },
    {
      "id": "1752c787-78f3-4c03-a44a-f619358b002b",
      "name": "계산기",
      "type": "@n8n/n8n-nodes-langchain.toolCalculator",
      "position": [
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        -120
      ],
      "parameters": {},
      "typeVersion": 1
    },
    {
      "id": "c5130a15-b2fa-4c96-b890-6cda5a5d1e8a",
      "name": "Google 드라이브 트리거",
      "type": "n8n-nodes-base.googleDriveTrigger",
      "position": [
        -1960,
        -240
      ],
      "parameters": {
        "event": "fileCreated",
        "options": {},
        "pollTimes": {
          "item": [
            {
              "mode": "everyMinute"
            }
          ]
        },
        "triggerOn": "specificFolder",
        "folderToWatch": {
          "__rl": true,
          "mode": "list",
          "value": "18ElQ-fxK0zXX5Ahx1lk80OXnAJ9NwvHl",
          "cachedResultUrl": "https://drive.google.com/drive/folders/18ElQ-fxK0zXX5Ahx1lk80OXnAJ9NwvHl",
          "cachedResultName": "n8n"
        }
      },
      "credentials": {
        "googleDriveOAuth2Api": {
          "id": "xFTXDFHVEmRlrR5e",
          "name": "Google Drive account 2"
        }
      },
      "typeVersion": 1
    },
    {
      "id": "e918feee-188e-42c7-8012-09614b6d73a4",
      "name": "Google 드라이브",
      "type": "n8n-nodes-base.googleDrive",
      "position": [
        -1740,
        -240
      ],
      "parameters": {
        "fileId": {
          "__rl": true,
          "mode": "list",
          "value": "1AWznNmqjEyztSOFYG9PgKOYX_GfJvI-P",
          "cachedResultUrl": "https://drive.google.com/file/d/1AWznNmqjEyztSOFYG9PgKOYX_GfJvI-P/view?usp=drivesdk",
          "cachedResultName": "income-tax-bill-2025.pdf"
        },
        "options": {},
        "operation": "download"
      },
      "credentials": {
        "googleDriveOAuth2Api": {
          "id": "xFTXDFHVEmRlrR5e",
          "name": "Google Drive account 2"
        }
      },
      "typeVersion": 3
    },
    {
      "id": "f2da487d-cbda-478d-8a38-b4c2a920dbaf",
      "name": "기본 데이터 로더",
      "type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
      "position": [
        -1140,
        40
      ],
      "parameters": {
        "loader": "pdfLoader",
        "options": {},
        "dataType": "binary"
      },
      "typeVersion": 1
    },
    {
      "id": "0ce84124-e903-4b41-a9dc-2b701efbeeaf",
      "name": "재귀적 문자 텍스트 분할기",
      "type": "@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter",
      "position": [
        -1120,
        220
      ],
      "parameters": {
        "options": {
          "splitCode": "markdown"
        },
        "chunkOverlap": 50
      },
      "typeVersion": 1
    },
    {
      "id": "c6fff7f0-d215-4d25-8e4e-bcd5cc200330",
      "name": "스티키 노트",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -2020,
        -600
      ],
      "parameters": {
        "width": 940,
        "height": 280,
        "content": "## Data Loader\n- #### GDrive - Trigger \n  This node will trigger for every minute and retrieve details for any change to the folder.\n- #### GDrive - Trigger\n  This node will download the latest changes from GDrive and pass them to pin cone vector store\n- #### PineCone Vector Store\n  Here, we are actually storing the files by splitting them by leveraging the capabilities of default data loader. \n\n\n"
      },
      "typeVersion": 1
    },
    {
      "id": "abab349f-8cb4-489e-8a94-d369f89db0e0",
      "name": "PineconeVectorStore",
      "type": "@n8n/n8n-nodes-langchain.vectorStorePinecone",
      "position": [
        -1480,
        -240
      ],
      "parameters": {
        "mode": "insert",
        "options": {
          "pineconeNamespace": "[YouNameSpace]"
        },
        "pineconeIndex": {
          "__rl": true,
          "mode": "list",
          "value": "n8ntest",
          "cachedResultName": "n8ntest"
        }
      },
      "credentials": {
        "pineconeApi": {
          "id": "PJJqeU0YnPNhhvLk",
          "name": "PineconeApi account"
        }
      },
      "typeVersion": 1.2
    },
    {
      "id": "9cee9812-3cc7-4d3f-b7be-6d02b8f7ca3e",
      "name": "OpenAI 임베딩",
      "type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
      "position": [
        -1480,
        0
      ],
      "parameters": {
        "options": {
          "batchSize": 512,
          "dimensions": 512
        }
      },
      "credentials": {
        "openAiApi": {
          "id": "qlhg6EzexBZ9bwXO",
          "name": "OpenAi account"
        }
      },
      "typeVersion": 1.2
    },
    {
      "id": "401070d0-b61c-4fd4-9139-f82624e4d7fc",
      "name": "스티키 노트1",
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      "position": [
        -500,
        -500
      ],
      "parameters": {
        "color": 4,
        "width": 940,
        "height": 180,
        "content": "## Data Retrieval\nWe have an AI Agent that connects to Vector Store and Retrieve the required information to the user questions. \n\nThis connects to Vector Store QnA tool which then further connects to Vector Store to fetch the information and share to Ai Agent for further processing. "
      },
      "typeVersion": 1
    },
    {
      "id": "82fe4b78-b7d9-4ee1-a319-eb6e44d013dd",
      "name": "벡터 저장소 QnA",
      "type": "@n8n/n8n-nodes-langchain.toolVectorStore",
      "position": [
        380,
        -100
      ],
      "parameters": {
        "description": "Returns documents related to company polices and everything important from those policy documents"
      },
      "typeVersion": 1.1
    },
    {
      "id": "84e007f0-7bfe-4788-900b-9a73376a1318",
      "name": "OpenAI 채팅 모델2",
      "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
      "position": [
        780,
        60
      ],
      "parameters": {
        "model": {
          "__rl": true,
          "mode": "list",
          "value": "gpt-4o-mini"
        },
        "options": {}
      },
      "credentials": {
        "openAiApi": {
          "id": "qlhg6EzexBZ9bwXO",
          "name": "OpenAi account"
        }
      },
      "typeVersion": 1.2
    }
  ],
  "active": false,
  "pinData": {},
  "settings": {
    "executionOrder": "v1"
  },
  "versionId": "74b8ed97-4177-4d50-ae41-856da7e16a43",
  "connections": {
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}
자주 묻는 질문

이 워크플로우를 어떻게 사용하나요?

위의 JSON 구성 코드를 복사하여 n8n 인스턴스에서 새 워크플로우를 생성하고 "JSON에서 가져오기"를 선택한 후, 구성을 붙여넣고 필요에 따라 인증 설정을 수정하세요.

이 워크플로우는 어떤 시나리오에 적합한가요?

고급 - AI RAG, 멀티모달 AI

유료인가요?

이 워크플로우는 완전히 무료이며 직접 가져와 사용할 수 있습니다. 다만, 워크플로우에서 사용하는 타사 서비스(예: OpenAI API)는 사용자 직접 비용을 지불해야 할 수 있습니다.

워크플로우 정보
난이도
고급
노드 수17
카테고리2
노드 유형13
난이도 설명

고급 사용자를 위한 16+개 노드의 복잡한 워크플로우

저자
Pramod Kumar Rathoure

Pramod Kumar Rathoure

@prathoure

Design and build custom n8n workflows that:\n ⚡ Eliminate repetitive work 📊 Integrate tools like Google Workspace, CRMs, and APIs ✅ Deliver faster, error-free results I create solutions that save time, boost accuracy, and let teams focus on high-value work. For me, automation isn’t just about technology—it’s about freeing people from busywork so they can do what matters most. let’s talk—I can help you turn them into smooth, automated workflows.

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카테고리: 34