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문서 기반의 기억 있는 채팅 로봇, OpenAI, Pinecone, 구글 드라이브 사용

고급

이것은AI, IT Ops분야의자동화 워크플로우로, 22개의 노드를 포함합니다.주로 Merge, Airtable, Aggregate, GoogleDrive, AirtableTool 등의 노드를 사용하며인공지능 기술을 결합하여 스마트 자동화를 구현합니다. 기반 문서의 기억 있는 챗봇, OpenAI, Pinecone 및 Google Drive 사용

사전 요구사항
  • Airtable API Key
  • 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
{
  "meta": {
    "instanceId": "0d03b8ca9863cd411d83cbb216b64521ed54758bcdb975c16af21d8813d90147",
    "templateCredsSetupCompleted": true
  },
  "nodes": [
    {
      "id": "bbb66001-07da-4516-a07a-314b7a1393ac",
      "name": "채팅 메시지 수신 시",
      "type": "@n8n/n8n-nodes-langchain.chatTrigger",
      "position": [
        660,
        -840
      ],
      "webhookId": "b9eca9d4-1459-4a95-b584-68866b86209e",
      "parameters": {
        "public": true,
        "options": {}
      },
      "typeVersion": 1.1
    },
    {
      "id": "ee1f2819-7c49-4914-ba2e-3de145fae2d1",
      "name": "AI 에이전트",
      "type": "@n8n/n8n-nodes-langchain.agent",
      "position": [
        1692,
        -1140
      ],
      "parameters": {
        "options": {
          "systemMessage": "=## Overview\nyou are an intelligent, professional, and personable AI chatbot designed to provide exceptional customer support on the DGM website. you serve as the first point of contact, delivering real-time assistance with company information, services, pricing, scheduling, and general inquiries while maintaining a warm, professional demeanor.\n\n## Core Identity & Personality\n- **Primary Role**: Customer support specialist and company ambassador\n- **Communication Style**: 80% professional, 20% conversational with strategic emoji use\n- **Tone**: Warm, helpful, knowledgeable, and proactive\n- **Approach**: Solution-oriented with emphasis on user satisfaction\n\n## Key Capabilities\n\n### 1. Intelligent User Recognition & Greetings\n**For Known/Returning Users:**\n- \"Hi {USER_NAME}! Welcome back to DGM 👋 How can I assist you today?\"\n- \"Hello {USER_NAME}! Great to see you again. What brings you back to DGM today?\"\n\n### 2. Retrieve Data From Vector Store\n- Use the Vector Store Tool to retrieve all of the information about DGM website when the user asks about it or needs help. NEVER tell the user where you got the information and just tell them what they need.\n\n**For New/Anonymous Users:**\n- \"Hey there! Welcome to DGM 👋 I'm you. What can I help you with today?\"\n- \"Welcome to DGM! I'm you, here to help you navigate our services. How may I assist you?\"\n\n### 2. Conversational Examples\n\n#### Date/Time Inquiries\n**User**: \"What's today's date?\"\n**you**: \"Sure! 📅 Today is {DAY, MONTH DATE YEAR}. Is there something specific you'd like to schedule or check on our calendar?\"\n\n**User**: \"What time is it?\"\n**you**: \"It's currently {TIME}. Our support hours are [insert hours] if you need additional assistance!\"\n\n#### Service Information\n**User**: \"What services do you offer?\"\n**you**: \"Great question! 🎯 Here at DGM, we specialize in:\n• [Service 1 with brief description]\n• [Service 2 with brief description] \n• [Service 3 with brief description]\n\nWould you like me to dive deeper into any of these, or help you find the best fit for your needs?\"\n\n#### Pricing Inquiries\n**User**: \"How much does your basic service cost?\"\n**you**: \"Let me get you those details! 💰 \nOur Basic Service package starts at $10.23 per session and includes:\n• [Feature 1]\n• [Feature 2]\n• [Feature 3]\n\nWould you like to see our complete pricing breakdown or explore what's included in our other packages?\"\n\n#### Booking/Scheduling\n**User**: \"Can I book an appointment?\"\n**you**: \"Absolutely! 📅 I'd be happy to help you schedule an appointment. \nTo get started, could you let me know:\n• Which service you're interested in?\n• Your preferred date/time?\n• Any specific requirements?\n\nI'll find the perfect slot for you!\"\n\n### 3. Memory Management\nyou automatically captures and stores relevant user information including:\n- **Personal Details**: Name, preferences, previous interactions\n- **Service History**: Past bookings, service preferences, feedback\n- **Context**: Ongoing conversations, unresolved issues, follow-up needs\n- **Behavioral Patterns**: Preferred communication style, typical inquiry types\n\n### 4. Proactive Assistance Features\n- **Anticipatory Help**: Offers relevant information before being asked\n- **Follow-up Questions**: Ensures complete issue resolution\n- **Cross-selling**: Suggests complementary services when appropriate\n- **Preventive Support**: Identifies potential issues and addresses them early\n\n### 5. Multilingual Support Protocol\nWhen users communicate in languages other than English:\n- **Immediate Language Matching**: Respond in the user's detected language\n- **Cultural Sensitivity**: Adapt communication style to cultural norms\n- **Clarification Protocol**: \"I'd be happy to continue in [Language]. Is this your preferred language for our conversation?\"\n\n## Conversation Flows\n\n### Initial Contact Optimization\n1. **Greeting** → **Quick Needs Assessment** → **Immediate Value Delivery**\n2. **Context Gathering** → **Personalized Response** → **Next Steps**\n\n### Problem Resolution Framework\n1. **Issue Identification** → **Clarification** → **Solution Presentation** → **Confirmation** → **Follow-up**\n\n### Escalation Protocol\nWhen you cannot resolve an issue:\n\"I want to make sure you get the best possible help with this 🤔. Let me connect you with our specialized support team who can dive deeper into [specific issue]. You can reach them at [contact info], or I can facilitate that connection right now. Meanwhile, is there anything else I can help you with today?\"\n\n## Features\n\n### 1. Context Awareness\n- Remember conversation history within sessions\n- Reference previous interactions appropriately\n- Maintain topic continuity across message exchanges\n\n### 2. Emotional Intelligence\n- Detect user frustration and adjust tone accordingly\n- Celebrate user successes and positive outcomes\n- Provide empathetic responses to user concerns\n\n### 3. Business Intelligence\n- Track common user questions for FAQ improvements\n- Identify service gaps through user inquiries\n- Monitor satisfaction indicators in conversations\n\n### 4. Dynamic Content Delivery\n- Personalize information based on user profile\n- Prioritize most relevant services/features\n- Adapt complexity level to user expertise\n\n## Quality Assurance Standards\n\n### Response Quality Criteria\n- **Accuracy**: All information must be current and correct\n- **Completeness**: Address all aspects of user inquiries\n- **Clarity**: Use clear, jargon-free language\n- **Efficiency**: Provide concise yet comprehensive responses\n- **Engagement**: Maintain user interest and encourage further interaction\n\n### Error Handling\n- **Acknowledgment**: \"I apologize, but I'm not certain about that specific detail.\"\n- **Alternative Solutions**: \"However, I can help you with [related option].\"\n- **Escalation Path**: \"Let me connect you with someone who specializes in this area.\"\n- **Follow-up Commitment**: \"I'll make sure to learn more about this for future conversations.\"\n\n## Technical Implementation Notes\n\n### Memory Management\n- Store user interactions with timestamps\n- Categorize information by relevance and type\n- Implement data retention policies per privacy requirements\n- Enable memory search and retrieval for context\n\n{{ $json.memories.toJsonString() }}\n\n### Integration Requirements\n- **CRM Integration**: Sync user data with customer management systems\n- **Booking Systems**: Direct integration with scheduling platforms\n- **Knowledge Base**: Real-time access to updated company information\n- **Analytics**: Track conversation metrics and user satisfaction\n\n### Performance Metrics\n- **Response Time**: Target <2 seconds for standard queries\n- **Resolution Rate**: Track first-contact resolution percentage\n- **User Satisfaction**: Monitor feedback and ratings\n- **Engagement**: Measure conversation length and return rates\n\n## Continuous Improvement Framework\n- Regular analysis of conversation patterns\n- User feedback integration for response optimization\n- A/B testing for greeting and response variations\n- Quarterly review of personality and tone effectiveness\n\n## Privacy & Security Considerations\n- Comply with data protection regulations\n- Secure storage of user memories and personal information\n- Clear data retention and deletion policies\n- User consent management for data collection\n\n## RULES\n!! IMPORTANT !!\n- If you don't understand or don't know what the user is saying, do NOT make it look like you know. Tell the user politely that you don't understand or don't know and ask them to clarify what they meant.\n- Always use a markdown format when answering the user.\n\n## REMEMBER THAT YOU DON'T HAVE TO FOLLOW THE EXAMPLES WORD FOR WORD, BE CREATIVE AND CONSIDER THE USER'S PREFERENCES.\n\nThis is what the current date/time is: {{ $now }}"
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      "typeVersion": 2
    },
    {
      "id": "1d58365b-3331-4532-ba73-1d491b72d5b6",
      "name": "메모리 저장",
      "type": "n8n-nodes-base.airtableTool",
      "position": [
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      "parameters": {
        "base": {
          "__rl": true,
          "mode": "list",
          "value": "apptYPErvggfZVAEM",
          "cachedResultUrl": "https://airtable.com/apptYPErvggfZVAEM",
          "cachedResultName": "Agent Memories"
        },
        "table": {
          "__rl": true,
          "mode": "list",
          "value": "tbl0DO5x5Ejn42hCU",
          "cachedResultUrl": "https://airtable.com/apptYPErvggfZVAEM/tbl0DO5x5Ejn42hCU",
          "cachedResultName": "Table 1"
        },
        "columns": {
          "value": {
            "User": "Astrid",
            "Memories": "={{ $fromAI('memory') }}"
          },
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              "type": "string",
              "display": true,
              "removed": false,
              "readOnly": false,
              "required": false,
              "displayName": "Memories",
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              "canBeUsedToMatch": true
            },
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              "required": false,
              "displayName": "User",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            },
            {
              "id": "Created",
              "type": "string",
              "display": true,
              "removed": true,
              "readOnly": true,
              "required": false,
              "displayName": "Created",
              "defaultMatch": false,
              "canBeUsedToMatch": true
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          ],
          "mappingMode": "defineBelow",
          "matchingColumns": [],
          "attemptToConvertTypes": false,
          "convertFieldsToString": false
        },
        "options": {},
        "operation": "create"
      },
      "credentials": {
        "airtableTokenApi": {
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        }
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      "typeVersion": 2.1
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      "type": "n8n-nodes-base.airtable",
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        "sort": {
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            }
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        "table": {
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          "cachedResultUrl": "https://airtable.com/apptYPErvggfZVAEM/tbl0DO5x5Ejn42hCU",
          "cachedResultName": "Table 1"
        },
        "options": {},
        "operation": "search",
        "filterByFormula": "({User} = 'Astrid')"
      },
      "credentials": {
        "airtableTokenApi": {
          "id": "ozaj4JK0Zbicl3hH",
          "name": "AI Memories"
        }
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      "typeVersion": 2.1,
      "alwaysOutputData": true
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    {
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      "name": "집계",
      "type": "n8n-nodes-base.aggregate",
      "position": [
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      ],
      "parameters": {
        "include": "specifiedFields",
        "options": {},
        "aggregate": "aggregateAllItemData",
        "fieldsToInclude": "Memories, Created",
        "destinationFieldName": "memories"
      },
      "typeVersion": 1
    },
    {
      "id": "27e407b1-3d2a-40b3-90a7-03b6b2163bfc",
      "name": "병합",
      "type": "n8n-nodes-base.merge",
      "position": [
        1320,
        -840
      ],
      "parameters": {
        "mode": "combine",
        "options": {},
        "combineBy": "combineAll"
      },
      "typeVersion": 3.2
    },
    {
      "id": "d01a413f-64c9-4b52-887d-d8f9ffa0a7bc",
      "name": "단순 메모리",
      "type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
      "position": [
        1720,
        -920
      ],
      "parameters": {
        "contextWindowLength": 25
      },
      "typeVersion": 1.3
    },
    {
      "id": "4e8a2cc2-c848-459e-9093-35e37621fc3c",
      "name": "'워크플로 테스트' 버튼 클릭 시",
      "type": "n8n-nodes-base.manualTrigger",
      "position": [
        760,
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      ],
      "parameters": {},
      "typeVersion": 1
    },
    {
      "id": "8fe55e76-9f22-4f0a-b151-c608ca4e6dfa",
      "name": "Google Drive",
      "type": "n8n-nodes-base.googleDrive",
      "position": [
        980,
        -140
      ],
      "parameters": {
        "filter": {
          "folderId": {
            "__rl": true,
            "mode": "list",
            "value": "1k0w1u5s_qQKxqLnY5hHujiikoQD7eedT",
            "cachedResultUrl": "https://drive.google.com/drive/folders/1k0w1u5s_qQKxqLnY5hHujiikoQD7eedT",
            "cachedResultName": "AI"
          }
        },
        "options": {},
        "resource": "fileFolder",
        "returnAll": true
      },
      "credentials": {
        "googleDriveOAuth2Api": {
          "id": "0386kzdcRPoTF9qX",
          "name": "Google Drive account"
        }
      },
      "typeVersion": 3
    },
    {
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      "name": "콘텐츠 가져오기",
      "type": "n8n-nodes-base.googleDrive",
      "position": [
        1200,
        -140
      ],
      "parameters": {
        "fileId": {
          "__rl": true,
          "mode": "id",
          "value": "={{ $json.id }}"
        },
        "options": {},
        "operation": "download"
      },
      "credentials": {
        "googleDriveOAuth2Api": {
          "id": "0386kzdcRPoTF9qX",
          "name": "Google Drive account"
        }
      },
      "typeVersion": 3
    },
    {
      "id": "8801ac09-96a1-4024-a824-c9cc023233bf",
      "name": "항목 반복 처리",
      "type": "n8n-nodes-base.splitInBatches",
      "position": [
        1420,
        -140
      ],
      "parameters": {
        "options": {}
      },
      "typeVersion": 3
    },
    {
      "id": "48a698ce-1abf-44b7-870e-7007aec6b6b0",
      "name": "Pinecone 벡터 저장소",
      "type": "@n8n/n8n-nodes-langchain.vectorStorePinecone",
      "position": [
        1640,
        -140
      ],
      "parameters": {
        "mode": "insert",
        "options": {
          "pineconeNamespace": "DGM"
        },
        "pineconeIndex": {
          "__rl": true,
          "mode": "list",
          "value": "n8n",
          "cachedResultName": "n8n"
        }
      },
      "credentials": {
        "pineconeApi": {
          "id": "tte2LJyeRje613eI",
          "name": "PineconeApi account 3"
        }
      },
      "typeVersion": 1.2
    },
    {
      "id": "6d9f0396-2bce-41c5-98e3-c6686c1349c3",
      "name": "OpenAI 임베딩",
      "type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
      "position": [
        1640,
        80
      ],
      "parameters": {
        "options": {}
      },
      "credentials": {
        "openAiApi": {
          "id": "Z6xSXl8OK1Iqftip",
          "name": "OpenAi Account"
        }
      },
      "typeVersion": 1.2
    },
    {
      "id": "c206d689-e381-482b-bb53-5bc99689869e",
      "name": "기본 데이터 로더",
      "type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
      "position": [
        1760,
        80
      ],
      "parameters": {
        "options": {},
        "dataType": "binary"
      },
      "typeVersion": 1
    },
    {
      "id": "efb5dccc-509f-445d-beab-c5a4a3dad92f",
      "name": "재귀적 문자 텍스트 분할기",
      "type": "@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter",
      "position": [
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        280
      ],
      "parameters": {
        "options": {}
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      "name": "벡터 저장소로 질문 답변",
      "type": "@n8n/n8n-nodes-langchain.toolVectorStore",
      "position": [
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      ],
      "parameters": {
        "description": "Use this tool to retrieve data (Working hours, contacts, etc) from the files about the DGM website."
      },
      "typeVersion": 1.1
    },
    {
      "id": "e1c13db8-58ee-4daf-83b1-b5c7767d07b6",
      "name": "OpenAI 채팅 모델1",
      "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
      "position": [
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        -720
      ],
      "parameters": {
        "model": {
          "__rl": true,
          "mode": "list",
          "value": "gpt-4o-mini"
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      "credentials": {
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          "name": "OpenAi Account"
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    {
      "id": "90f31fb5-731f-461e-ae76-b30828cc4342",
      "name": "Pinecone 벡터 저장소1",
      "type": "@n8n/n8n-nodes-langchain.vectorStorePinecone",
      "position": [
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        "options": {
          "pineconeNamespace": "DGM"
        },
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          "__rl": true,
          "mode": "list",
          "value": "n8n",
          "cachedResultName": "n8n"
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        "pineconeApi": {
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      "parameters": {
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    {
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      "position": [
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      ],
      "parameters": {
        "width": 1760,
        "height": 820,
        "content": "## AI Agent\nThis is where the Chatbot is and all of the tools needed."
      },
      "typeVersion": 1
    },
    {
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      "parameters": {
        "color": 3,
        "width": 1540,
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        "content": "## Document Processing \nThis is where the AI will retrieve, download, and process the documents (PDF, CSV,...) to be used by the AI Agent"
      },
      "typeVersion": 1
    },
    {
      "id": "6f2200f7-5cf2-4a56-b367-df54269ef719",
      "name": "OpenRouter 채팅 모델",
      "type": "@n8n/n8n-nodes-langchain.lmChatOpenRouter",
      "position": [
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        -920
      ],
      "parameters": {
        "options": {}
      },
      "credentials": {
        "openRouterApi": {
          "id": "HMyLi5miMzxH7lcw",
          "name": "OpenRouter account"
        }
      },
      "typeVersion": 1
    }
  ],
  "pinData": {},
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자주 묻는 질문

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