GPT-4OとSupabaseの対話メモリを使った自動返信メール
上級
これはSupport Chatbot, AI Chatbot分野の自動化ワークフローで、32個のノードを含みます。主にIf, Code, Postgres, Supabase, Aggregateなどのノードを使用。 GPT-4OとSupabaseの会話メモリを使用した自動返信メール
前提条件
- •PostgreSQLデータベース接続情報
- •Supabase URL と API Key
- •ターゲットAPIの認証情報が必要な場合あり
- •OpenAI API Key
使用ノード (32)
カテゴリー
ワークフロープレビュー
ノード接続関係を可視化、ズームとパンをサポート
ワークフローをエクスポート
以下のJSON設定をn8nにインポートして、このワークフローを使用できます
{
"meta": {
"instanceId": "7d7ddc233aab4d8c51542670cf7f945eb6d373593fbd55505f36a0a5efbbf885"
},
"nodes": [
{
"id": "2ea256d3-ba6f-4150-8f2b-e157b531967e",
"name": "Embeddings OpenAI1",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"position": [
3184,
528
],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"id": "xKlH7tyFCN8T1zQi",
"name": "OpenAi account"
}
},
"typeVersion": 1.2
},
{
"id": "8aa40c2d-d7db-4c09-9d57-ead456da3a19",
"name": "Embeddings OpenAI2",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"position": [
2880,
528
],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"id": "xKlH7tyFCN8T1zQi",
"name": "OpenAi account"
}
},
"typeVersion": 1.2
},
{
"id": "ddbda963-adae-4555-a2df-a37e96a45de2",
"name": "Microsoft Outlookトリガー",
"type": "n8n-nodes-base.microsoftOutlookTrigger",
"position": [
1024,
224
],
"parameters": {
"output": "raw",
"filters": {
"readStatus": "unread",
"hasAttachments": false,
"foldersToInclude": [
"AQMkADAwATMwMAItNDc5YS02YzcyLTAwAi0wMAoALgAAAyr74A1aBA5FmGCW-N3seyYBAK7gOo5dtNlAihU21SrvhjMAAAIBDAAAAA=="
]
},
"options": {
"attachmentsPrefix": "attachment",
"downloadAttachments": false
},
"pollTimes": {
"item": [
{
"mode": "everyMinute"
}
]
}
},
"credentials": {
"microsoftOutlookOAuth2Api": {
"id": "rsOPp75XXqgvG8j6",
"name": "Microsoft Outlook account"
}
},
"typeVersion": 1
},
{
"id": "bf25d741-540a-4c47-a656-477c150dfa0f",
"name": "HTMLクリーニング",
"type": "n8n-nodes-base.code",
"position": [
1520,
224
],
"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(/ /g, ' ')\n .replace(/"/g, '\"')\n .replace(/&/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
},
{
"id": "0154c66d-cedc-4021-b895-ff00e91fc524",
"name": "カテゴライズ",
"type": "@n8n/n8n-nodes-langchain.openAi",
"position": [
2112,
224
],
"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"
}
]
},
"jsonOutput": true
},
"credentials": {
"openAiApi": {
"id": "xKlH7tyFCN8T1zQi",
"name": "OpenAi account"
}
},
"typeVersion": 1.8
},
{
"id": "4e661d6c-b4f5-43f2-bc74-58ea5dc8b349",
"name": "JSON",
"type": "@n8n/n8n-nodes-langchain.outputParserStructured",
"position": [
3152,
144
],
"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": [
2976,
144
],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4o",
"cachedResultName": "gpt-4o"
},
"options": {}
},
"credentials": {
"openAiApi": {
"id": "xKlH7tyFCN8T1zQi",
"name": "OpenAi account"
}
},
"typeVersion": 1.2
},
{
"id": "7beff76f-0000-4abf-b676-e9989d1def42",
"name": "会話検索",
"type": "n8n-nodes-base.postgres",
"position": [
2640,
224
],
"parameters": {
"query": "SELECT subject, category, content, reply, date\nFROM emailreplies\nWHERE conversation_id = '{{ $('Clean HTML').item.json.conversationId }}';\n",
"options": {},
"operation": "executeQuery"
},
"credentials": {
"postgres": {
"id": "lYn12YZBR5aezI4R",
"name": "Lukmanabdh21"
}
},
"executeOnce": false,
"typeVersion": 2.6,
"alwaysOutputData": true
},
{
"id": "63ac0d0b-5167-466d-8814-6cc4933b1390",
"name": "アイテムループ処理1",
"type": "n8n-nodes-base.splitInBatches",
"position": [
1280,
224
],
"parameters": {
"options": {}
},
"typeVersion": 3
},
{
"id": "77ac6486-243d-41b4-9822-81d79a219f82",
"name": "集約1",
"type": "n8n-nodes-base.aggregate",
"position": [
1904,
224
],
"parameters": {
"options": {},
"fieldsToAggregate": {
"fieldToAggregate": [
{
"fieldToAggregate": "category"
}
]
}
},
"typeVersion": 1
},
{
"id": "17e15174-3f73-4e93-9610-82891b90ae0a",
"name": "スパムフィルター",
"type": "n8n-nodes-base.if",
"position": [
2432,
224
],
"parameters": {
"options": {
"ignoreCase": true
},
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": false,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "99049b97-ec32-4af1-8f46-7362f431ce0d",
"operator": {
"type": "string",
"operation": "notEquals"
},
"leftValue": "={{ $json.message.content.category }}",
"rightValue": "spam"
}
]
}
},
"typeVersion": 2.2
},
{
"id": "0fba875c-61ae-4779-861c-bf1d0c76a820",
"name": "メールマネージャー",
"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."
},
"promptType": "define",
"hasOutputParser": true
},
"typeVersion": 2.2
},
{
"id": "5e2a8030-3705-44fe-9530-f2c79cccde0f",
"name": "フォーマット",
"type": "n8n-nodes-base.code",
"position": [
2800,
224
],
"parameters": {
"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": "メールテンプレート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
},
{
"id": "ddfbc5a8-9449-4bde-87ea-9a807d843e21",
"name": "FAQ DB",
"type": "@n8n/n8n-nodes-langchain.vectorStoreSupabase",
"position": [
2880,
448
],
"parameters": {
"mode": "retrieve-as-tool",
"options": {
"queryName": "match_faq"
},
"tableName": {
"__rl": true,
"mode": "list",
"value": "faq",
"cachedResultName": "faq"
},
"toolDescription": "Use this to find the most relevant FAQ by vector similarity in the FAQ table."
},
"credentials": {
"supabaseApi": {
"id": "c0kq8tDZCHRcBrV1",
"name": "Lukmanabdh21"
}
},
"typeVersion": 1.3
},
{
"id": "8f31665f-f493-4ad6-b14e-9b6bb6e49cdd",
"name": "返信",
"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": {
"microsoftOutlookOAuth2Api": {
"id": "rsOPp75XXqgvG8j6",
"name": "Microsoft Outlook account"
}
},
"typeVersion": 2
},
{
"id": "9dacab9f-27f1-4fe8-a826-3957d75207d7",
"name": "返信1",
"type": "n8n-nodes-base.microsoftOutlook",
"position": [
3584,
128
],
"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
},
{
"id": "e0ce9f54-37ef-42b4-b807-60b8f6de35a8",
"name": "転送確認",
"type": "n8n-nodes-base.if",
"position": [
3312,
224
],
"parameters": {
"options": {},
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "0abfde8c-38e2-48fe-918a-f10f962a2d63",
"operator": {
"type": "boolean",
"operation": "true",
"singleValue": true
},
"leftValue": "={{ $json.output.forward }}",
"rightValue": ""
}
]
}
},
"typeVersion": 2.2
},
{
"id": "1a6ce559-bd71-4dd0-b1cb-9dd40efdb3e0",
"name": "カテゴリ取得",
"type": "n8n-nodes-base.postgres",
"position": [
1712,
224
],
"parameters": {
"query": "SELECT DISTINCT category\nFROM emailreplies;",
"options": {},
"operation": "executeQuery"
},
"credentials": {
"postgres": {
"id": "lYn12YZBR5aezI4R",
"name": "Lukmanabdh21"
}
},
"executeOnce": true,
"typeVersion": 2.6
},
{
"id": "cbd88406-110c-4dd8-8d77-2a48a1424e18",
"name": "ワークフロー実行",
"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転送",
"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": "付箋",
"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": "他ワークフロー実行時",
"type": "n8n-nodes-base.executeWorkflowTrigger",
"position": [
1040,
960
],
"parameters": {
"inputSource": "jsonExample",
"jsonExample": "{\n \"subject\": \"\",\n \"body\": \"\",\n \"category\": \"\",\n \"reply\": \"\",\n \"date\": \"\",\n \"conversationID\": \"\"\n}"
},
"typeVersion": 1.1
},
{
"id": "e3de77e5-f79f-4904-b43c-2f8ba4a55f45",
"name": "Supabase Vector Store4",
"type": "@n8n/n8n-nodes-langchain.vectorStoreSupabase",
"position": [
1296,
960
],
"parameters": {
"mode": "insert",
"options": {
"queryName": "match_emailreplies"
},
"tableName": {
"__rl": true,
"mode": "list",
"value": "emailreplies",
"cachedResultName": "emailreplies"
}
},
"credentials": {
"supabaseApi": {
"id": "c0kq8tDZCHRcBrV1",
"name": "Lukmanabdh21"
}
},
"typeVersion": 1.3
},
{
"id": "49825c43-d994-45a9-92e7-4f8b0ef903b6",
"name": "Embeddings OpenAI6",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"position": [
1296,
1056
],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
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},
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},
{
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"name": "デフォルトデータローダー3",
"type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
"position": [
1392,
1104
],
"parameters": {
"options": {},
"jsonData": "=\"subject\": \"{{ $json.subject }}\",\n\"body\": \"{{ $json.body }}\",\n\"category\": \"{{ $json.category }}\"",
"jsonMode": "expressionData",
"textSplittingMode": "custom"
},
"typeVersion": 1.1
},
{
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"name": "再帰的文字列分割器3",
"type": "@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter",
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"parameters": {
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},
{
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"name": "行更新2",
"type": "n8n-nodes-base.supabase",
"position": [
1648,
960
],
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"filters": {
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{
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}
]
},
"tableId": "emailreplies",
"fieldsUi": {
"fieldValues": [
{
"fieldId": "category",
"fieldValue": "={{ $('When Executed by Another Workflow').item.json.category }}"
},
{
"fieldId": "flag",
"fieldValue": "FALSE"
},
{
"fieldId": "subject",
"fieldValue": "={{ $('When Executed by Another Workflow').item.json.subject }}"
},
{
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"fieldValue": "={{ $('When Executed by Another Workflow').item.json.conversationID }}"
},
{
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"fieldValue": "={{ $('When Executed by Another Workflow').item.json.date }}"
},
{
"fieldId": "reply",
"fieldValue": "={{ $('When Executed by Another Workflow').item.json.reply }}"
}
]
},
"matchType": "allFilters",
"operation": "update"
},
"credentials": {
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"name": "Lukmanabdh21"
}
},
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},
{
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"name": "付箋1",
"type": "n8n-nodes-base.stickyNote",
"position": [
1488,
16
],
"parameters": {
"width": 1072,
"height": 432,
"content": "# Phase 1\n- Every email gets stripped of HTML\n- Categorized via LLM\n- Filter for spam"
},
"typeVersion": 1
},
{
"id": "4c30cd33-812b-43a7-9403-ef5d0a9d35d0",
"name": "付箋2",
"type": "n8n-nodes-base.stickyNote",
"position": [
2560,
16
],
"parameters": {
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"width": 864,
"height": 704,
"content": "# Phase 2\n- Conversation history retrieved to provide AI agent context\n- AI agent has access to FAQ and Email Reply Template database stored in Supabase and will make one of two decisions: Flag for human review or reply using answer found in FAQ"
},
"typeVersion": 1
},
{
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"type": "n8n-nodes-base.stickyNote",
"position": [
3424,
16
],
"parameters": {
"color": 5,
"width": 864,
"height": 496,
"content": "# Phase 3\n- Route determined by AI agent's ability to answer\n- Email + response sent to a subworkflow to ingest into Supabase to be used as training data and retain conversation history"
},
"typeVersion": 1
},
{
"id": "2524c1d1-9364-41fd-9cfd-64ec3f7ddbc8",
"name": "付箋4",
"type": "n8n-nodes-base.stickyNote",
"position": [
992,
752
],
"parameters": {
"color": 7,
"width": 864,
"height": 576,
"content": "# Phase 4\n- Subworkflow to ingest email + response into Supabase via vector embedding"
},
"typeVersion": 1
}
],
"pinData": {},
"connections": {
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}よくある質問
このワークフローの使い方は?
上記のJSON設定コードをコピーし、n8nインスタンスで新しいワークフローを作成して「JSONからインポート」を選択、設定を貼り付けて認証情報を必要に応じて変更してください。
このワークフローはどんな場面に適していますか?
上級 - サポートチャットボット, AIチャットボット
有料ですか?
このワークフローは完全無料です。ただし、ワークフローで使用するサードパーティサービス(OpenAI APIなど)は別途料金が発生する場合があります。
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難易度
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ノード数32
カテゴリー2
ノードタイプ20
作成者
Lukman
@lukmanabdhFirm believer that the best automation doesn't replace humans, but compliments their workflow.
外部リンク
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