Automatisierte E-Mail-Antwort mit GPT-4O und Supabase Konversationsgedächtnis

• •PostgreSQL-Datenbankverbindungsdaten
• •Supabase URL und API Key
• •Möglicherweise sind Ziel-API-Anmeldedaten erforderlich
• •OpenAI API Key

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      "parameters": {
        "options": {}
      },
      "credentials": {
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          "name": "OpenAi account"
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      "parameters": {
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      "credentials": {
        "openAiApi": {
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    {
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      "name": "Microsoft Outlook Trigger",
      "type": "n8n-nodes-base.microsoftOutlookTrigger",
      "position": [
        1024,
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      ],
      "parameters": {
        "output": "raw",
        "filters": {
          "readStatus": "unread",
          "hasAttachments": false,
          "foldersToInclude": [
            "AQMkADAwATMwMAItNDc5YS02YzcyLTAwAi0wMAoALgAAAyr74A1aBA5FmGCW-N3seyYBAK7gOo5dtNlAihU21SrvhjMAAAIBDAAAAA=="
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        "options": {
          "attachmentsPrefix": "attachment",
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        "pollTimes": {
          "item": [
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              "mode": "everyMinute"
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      },
      "credentials": {
        "microsoftOutlookOAuth2Api": {
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    {
      "id": "bf25d741-540a-4c47-a656-477c150dfa0f",
      "name": "HTML bereinigen",
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      "position": [
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      "parameters": {
        "jsCode": "const items = $input.all();\n\nreturn items.map(item => {\n  let html = item.json.body.content;\n  \n  // FIRST: Remove quoted blocks while HTML is still structured\n  html = html\n    .replace(/<div class=\"gmail_quote\">[\\s\\S]*?<\\/div>/gi, '')\n    .replace(/<blockquote[\\s\\S]*?<\\/blockquote>/gi, '')\n    .replace(/<div[^>]*id=\"divRplyFwdMsg\"[\\s\\S]*?<\\/div>/gi, '')\n    .replace(/<hr[^>]*>[\\s\\S]*$/gi, '');\n  \n  // THEN: Strip all HTML\n  let text = html\n    .replace(/<style[^>]*>.*?<\\/style>/gis, '')\n    .replace(/<script[^>]*>.*?<\\/script>/gis, '')\n    .replace(/<[^>]+>/g, '')\n    .replace(/&nbsp;/g, ' ')\n    .replace(/&quot;/g, '\"')\n    .replace(/&amp;/g, '&')\n    .replace(/&[a-z]+;/gi, ' ')\n    .replace(/\\s+/g, ' ')\n    .trim();\n  \n  // FINALLY: Regex fallback for plain text quotes\n  const quotePatterns = [\n    /On\\s+.+?wrote:/i,\n    /From:\\s*.+?Sent:/is,\n    /_{5,}/,\n    /-{5,}\\s*Original Message\\s*-{5,}/i\n  ];\n  \n  let splitIndex = text.length;\n  for (const pattern of quotePatterns) {\n    const match = text.search(pattern);\n    if (match !== -1 && match < splitIndex) {\n      splitIndex = match;\n    }\n  }\n  \n  text = text.substring(0, splitIndex).trim();\n  \n  return {\n    json: {\n      ...item.json,\n      cleanBody: text\n    }\n  };\n});"
      },
      "typeVersion": 2
    },
    {
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      "name": "Kategorisieren",
      "type": "@n8n/n8n-nodes-langchain.openAi",
      "position": [
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      ],
      "parameters": {
        "modelId": {
          "__rl": true,
          "mode": "list",
          "value": "gpt-4o-mini",
          "cachedResultName": "GPT-4O-MINI"
        },
        "options": {},
        "messages": {
          "values": [
            {
              "content": "=Here are the email details:\nFrom Email: {{ $('Clean HTML').item.json.from.emailAddress.address }}\nFrom Name: {{ $('Clean HTML').item.json.from.emailAddress.name }}\nSubject: {{ $('Clean HTML').item.json.subject }}\nBody: {{ $('Clean HTML').item.json.cleanBody }}\n\n"
            },
            {
              "role": "system",
              "content": "=You are an email classifier for a [COMPANY].\n\n# Task:\n1. Categorize the incoming email based on the provided category list\n2. If the email strongly fits an existing category, use it\n3. If the email would be \"Other\" but represents a meaningful, recurring business pattern, create a new specific category\n4. Output in structured JSON\n\n# Current Categories:\n{{ $json.category }}\n\n# Spam Detection (Always check first):\n- Generic greetings with urgent money requests\n- Cryptocurrency, loans, prizes, inheritance scams\n- Suspicious links or poor grammar with urgency\n- Unsolicited financial offers\n\nIf spam detected, immediately output: {\"category\": \"SPAM\"}\n\n# Categorization Logic:\n1. Check if email clearly matches an existing category\n2. If yes: Use that category\n3. If no strong match: Evaluate if a NEW category would be beneficial\n\n# New Category Criteria (All must be true):\n- Represents a distinct, recurring business function for a construction company\n- Will likely receive multiple similar emails per month\n- Requires different handling than existing categories\n- Category name is specific and action-oriented\n- Not already covered by existing categories\n\n# Invalid New Category Examples:\n- \"Important\"\n- \"Urgent\"\n- \"Miscellaneous\"\n- \"Random Emails\"\n- \"Needs Review\"\n- \"Follow Up\"\n\n# Legitimate Business Patterns:\n- Specific project references\n- Construction/renovation terminology\n- Professional supplier/customer correspondence\n- Job applications with CV\n\n# Output Format (JSON only):\n{\n  \"category\": \"string\"\n}\n\n# Rules:\n- SPAM always uses existing \"SPAM\" category\n- Only create new categories for legitimate, recurring business needs\n- If uncertain, use the closest existing category\n- New category names: 1-3 words, title case, construction-relevant\n- Only respond in valid JSON format"
            }
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        },
        "jsonOutput": true
      },
      "credentials": {
        "openAiApi": {
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          "name": "OpenAi account"
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      "name": "JSON",
      "type": "@n8n/n8n-nodes-langchain.outputParserStructured",
      "position": [
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      ],
      "parameters": {
        "jsonSchemaExample": "{\n  \"body\": \"full email body with <br> tags for line breaks\",\n  \"forward\": true\n}"
      },
      "typeVersion": 1.3
    },
    {
      "id": "3ea7fe84-95e1-40ae-b992-11c637b85b55",
      "name": "4o",
      "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
      "position": [
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      ],
      "parameters": {
        "model": {
          "__rl": true,
          "mode": "list",
          "value": "gpt-4o",
          "cachedResultName": "gpt-4o"
        },
        "options": {}
      },
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        "openAiApi": {
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      "type": "n8n-nodes-base.postgres",
      "position": [
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      ],
      "parameters": {
        "query": "SELECT subject, category, content, reply, date\nFROM emailreplies\nWHERE conversation_id = '{{ $('Clean HTML').item.json.conversationId }}';\n",
        "options": {},
        "operation": "executeQuery"
      },
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        "postgres": {
          "id": "lYn12YZBR5aezI4R",
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      "alwaysOutputData": true
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        "fieldsToAggregate": {
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            }
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    {
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      "position": [
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      ],
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        "options": {
          "ignoreCase": true
        },
        "conditions": {
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            "version": 2,
            "leftValue": "",
            "caseSensitive": false,
            "typeValidation": "strict"
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          "conditions": [
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              "operator": {
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                "operation": "notEquals"
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              "leftValue": "={{ $json.message.content.category }}",
              "rightValue": "spam"
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    {
      "id": "0fba875c-61ae-4779-861c-bf1d0c76a820",
      "name": "E-Mail-Manager",
      "type": "@n8n/n8n-nodes-langchain.agent",
      "position": [
        2992,
        224
      ],
      "parameters": {
        "text": "=From: {{ $('Clean HTML').first().json.from.emailAddress.address }}\nName: {{ $('Clean HTML').first().json.from.emailAddress.name }}\nEmail Content: {{ $('Clean HTML').first().json.cleanBody }}\nCategory: {{ $('Categorize').item.json.message.content.category }}\nHas attachment: {{ $('Clean HTML').item.json.hasAttachments }}\nConversation History: {{ $json.conversationHistory }}\n",
        "options": {
          "systemMessage": "=You are an email assistant for a [COMPANY] drafting professional email replies.\n\n# Context Provided\n- From/Name: Sender details\n- Email Content: The message\n- Category: Email category \n- Has attachment: true/false\n- Conversation History: Previous exchanges (if any)\n\n# Process\n1. Check if email has attachment:\n   - IF Has attachment = true: Skip to step 4 (draft acknowledgment + set forward=true)\n\n2. Use 'FAQ DB' tool to search for relevant answers\n   - Evaluate results: Do they adequately answer the sender's question?\n   - Are the results on-topic and helpful?\n\n3. Use 'Email Template DB' tool to find appropriate reply format\n   - First search: \"Category: {{ $('Categorize').item.json.message.content.category }}. [relevant search terms]\"\n   - If results seem off-topic or unhelpful: Retry without category prefix\n\n4. Determine action:\n   - Can answer with confidence: Draft full reply, set forward=false\n   - FAQ results are off-topic/incomplete: Draft placeholder, set forward=true\n   - Email is a complaint/urgent/complex: Draft placeholder, set forward=true\n   - Has attachment: Draft acknowledgment, set forward=true\n\n# Reply Templates\n- Full answer: Use FAQ information + Email Template style, personalized with sender's name\n- Placeholder (when forwarding):\n\"Dear [Name],<br><br>Thank you for your email. Our team will review your [request/question/attachment] and respond within 24 hours.<br><br>Best regards\"\n\n# Output Format (strict JSON only)\n{\n  \"body\": \"full email body with <br> tags for line breaks\",\n  \"forward\": true or false\n}\n\n# Rules\n- ALWAYS use both tools unless attachment is present\n- ALWAYS address sender by name in email body\n- Evaluate tool results based on relevance to the question, not scores\n- Set forward=true if: attachment present, FAQ unhelpful, or email requires human attention\n- Output valid JSON only\n- Use <br> tags for line breaks."
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        "jsCode": "// Get all input items\nconst items = $input.all();\n\n// Sort by date (earliest first)\nconst sortedItems = items.sort((a, b) => {\n  const dateA = new Date(a.json.date);\n  const dateB = new Date(b.json.date);\n  return dateA - dateB;\n});\n\n// Build the formatted string\nlet output = '';\n\n// Add date, body and reply for each item\nsortedItems.forEach((item, index) => {\n  const data = item.json;\n  output += `Date ${index + 1}: ${data.date || ''}\\n`;\n  output += `Body ${index + 1}: ${data.content || ''}\\n`;\n  output += `Reply ${index + 1}: ${data.reply || ''}\\n`;\n});\n\n// Return the formatted string\nreturn [{ json: { conversationHistory: output } }];"
      },
      "typeVersion": 2
    },
    {
      "id": "a23f1876-6ef1-4678-999b-1d39c969676c",
      "name": "E-Mail-Vorlagen-DB",
      "type": "@n8n/n8n-nodes-langchain.vectorStoreSupabase",
      "position": [
        3184,
        448
      ],
      "parameters": {
        "mode": "retrieve-as-tool",
        "options": {
          "queryName": "match_emailreplies"
        },
        "tableName": {
          "__rl": true,
          "mode": "list",
          "value": "emailreplies",
          "cachedResultName": "emailreplies"
        },
        "toolDescription": "Use this to find the most relevant email reply templates by vector similarity in the Email Reply Template table."
      },
      "credentials": {
        "supabaseApi": {
          "id": "c0kq8tDZCHRcBrV1",
          "name": "Lukmanabdh21"
        }
      },
      "typeVersion": 1.3
    },
    {
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      "type": "@n8n/n8n-nodes-langchain.vectorStoreSupabase",
      "position": [
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        "mode": "retrieve-as-tool",
        "options": {
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        "tableName": {
          "__rl": true,
          "mode": "list",
          "value": "faq",
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        },
        "toolDescription": "Use this to find the most relevant FAQ by vector similarity in the FAQ table."
      },
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        "supabaseApi": {
          "id": "c0kq8tDZCHRcBrV1",
          "name": "Lukmanabdh21"
        }
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      "name": "Antworten",
      "type": "n8n-nodes-base.microsoftOutlook",
      "position": [
        3584,
        304
      ],
      "webhookId": "0beecb97-10fb-4640-9056-74268311baa2",
      "parameters": {
        "message": "={{ $json.output.body }}",
        "options": {},
        "messageId": {
          "__rl": true,
          "mode": "id",
          "value": "={{ $('Clean HTML').item.json.id }}"
        },
        "operation": "reply"
      },
      "credentials": {
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          "name": "Microsoft Outlook account"
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      "name": "Antworten1",
      "type": "n8n-nodes-base.microsoftOutlook",
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      ],
      "webhookId": "0beecb97-10fb-4640-9056-74268311baa2",
      "parameters": {
        "message": "={{ $json.output.body }}",
        "options": {},
        "messageId": {
          "__rl": true,
          "mode": "id",
          "value": "={{ $('Clean HTML').item.json.id }}"
        },
        "operation": "reply"
      },
      "credentials": {
        "microsoftOutlookOAuth2Api": {
          "id": "rsOPp75XXqgvG8j6",
          "name": "Microsoft Outlook account"
        }
      },
      "typeVersion": 2
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    {
      "id": "e0ce9f54-37ef-42b4-b807-60b8f6de35a8",
      "name": "Weiterleiten?",
      "type": "n8n-nodes-base.if",
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        "conditions": {
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            "version": 2,
            "leftValue": "",
            "caseSensitive": true,
            "typeValidation": "strict"
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          "combinator": "and",
          "conditions": [
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              "id": "0abfde8c-38e2-48fe-918a-f10f962a2d63",
              "operator": {
                "type": "boolean",
                "operation": "true",
                "singleValue": true
              },
              "leftValue": "={{ $json.output.forward }}",
              "rightValue": ""
            }
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    },
    {
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      "name": "Kategorien abrufen",
      "type": "n8n-nodes-base.postgres",
      "position": [
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      ],
      "parameters": {
        "query": "SELECT DISTINCT category\nFROM emailreplies;",
        "options": {},
        "operation": "executeQuery"
      },
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        "postgres": {
          "id": "lYn12YZBR5aezI4R",
          "name": "Lukmanabdh21"
        }
      },
      "executeOnce": true,
      "typeVersion": 2.6
    },
    {
      "id": "cbd88406-110c-4dd8-8d77-2a48a1424e18",
      "name": "Workflow ausführen",
      "type": "n8n-nodes-base.executeWorkflow",
      "position": [
        4096,
        224
      ],
      "parameters": {
        "options": {},
        "workflowId": {
          "__rl": true,
          "mode": "id",
          "value": "l6VbNoViT2nEeTmN"
        },
        "workflowInputs": {
          "value": {
            "body": "={{ $('Clean HTML').item.json.cleanBody }}",
            "date": "={{ $('Clean HTML').item.json.receivedDateTime }}",
            "reply": "={{ $('Email Manager').item.json.output.body }}",
            "subject": "={{ $('Clean HTML').item.json.subject }}",
            "category": "={{ $('Categorize').item.json.message.content.category }}",
            "conversationID": "={{ $('Clean HTML').item.json.conversationId }}"
          },
          "schema": [
            {
              "id": "subject",
              "type": "string",
              "display": true,
              "removed": false,
              "required": false,
              "displayName": "subject",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            },
            {
              "id": "body",
              "type": "string",
              "display": true,
              "removed": false,
              "required": false,
              "displayName": "body",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            },
            {
              "id": "category",
              "type": "string",
              "display": true,
              "removed": false,
              "required": false,
              "displayName": "category",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            },
            {
              "id": "reply",
              "type": "string",
              "display": true,
              "removed": false,
              "required": false,
              "displayName": "reply",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            },
            {
              "id": "date",
              "type": "string",
              "display": true,
              "removed": false,
              "required": false,
              "displayName": "date",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            },
            {
              "id": "conversationID",
              "type": "string",
              "display": true,
              "removed": false,
              "required": false,
              "displayName": "conversationID",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            }
          ],
          "mappingMode": "defineBelow",
          "matchingColumns": [],
          "attemptToConvertTypes": false,
          "convertFieldsToString": true
        }
      },
      "typeVersion": 1.2
    },
    {
      "id": "d0c6e016-a059-4f12-bb17-5aeb685c4a2f",
      "name": "Outlook-Weiterleitung",
      "type": "n8n-nodes-base.httpRequest",
      "position": [
        3792,
        128
      ],
      "parameters": {
        "url": "=https://graph.microsoft.com/v1.0/me/messages/{{ $('Clean HTML').item.json.id }}/forward",
        "method": "POST",
        "options": {},
        "jsonBody": "={\n     \"toRecipients\": [\n       {\n         \"emailAddress\": {\n           \"address\": \"\"\n         }\n       }\n     ],\n     \"comment\": \"Please review the following email\"\n   }",
        "sendBody": true,
        "sendHeaders": true,
        "specifyBody": "json",
        "authentication": "predefinedCredentialType",
        "headerParameters": {
          "parameters": [
            {
              "name": "Content-Type",
              "value": "application/json"
            }
          ]
        },
        "nodeCredentialType": "microsoftOutlookOAuth2Api"
      },
      "credentials": {
        "microsoftOutlookOAuth2Api": {
          "id": "rsOPp75XXqgvG8j6",
          "name": "Microsoft Outlook account"
        }
      },
      "typeVersion": 4.2
    },
    {
      "id": "e7d9b7a8-ba35-4734-b212-6eb64d23f265",
      "name": "Haftnotiz",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        480,
        -16
      ],
      "parameters": {
        "width": 400,
        "height": 560,
        "content": "# Automation Overview\n\n1. Polls inbox for incoming emails\n2. Cleans HTML tags to reduce token bloating\n3. Identifies existing categories the emails could fall under\n4. LLM categorizes it and identifies spam\n5. Filters out spam emails\n6. Identifies whether or not an existing conversation exists\n7. AI Agent uses conversation history (if available) for context and uses FAQ documents ingested in Supabase to answer incoming questions\n8. If it can confidently answer, it will use an email template to structure it's reply\n9. If it can't answer using the FAQ documents, it will flag for human review.\n10. Every email gets ingested into Supabase to build a database of conversation history"
      },
      "typeVersion": 1
    },
    {
      "id": "67b4bb5a-0e42-4191-97d7-db66a033c7e7",
      "name": "Bei Ausführung durch anderen Workflow",
      "type": "n8n-nodes-base.executeWorkflowTrigger",
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        "tableId": "emailreplies",
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              "fieldValue": "FALSE"
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              "fieldId": "date",
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              "fieldId": "reply",
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      "position": [
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      "parameters": {
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        "height": 432,
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      },
      "typeVersion": 1
    },
    {
      "id": "4c30cd33-812b-43a7-9403-ef5d0a9d35d0",
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      "position": [
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      ],
      "parameters": {
        "color": 4,
        "width": 864,
        "height": 704,
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      },
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      },
      "typeVersion": 1
    },
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