メールに関連する質問をSQLクエリに変換して実行
上級
これはBuilding Blocks, AI, IT Ops分野の自動化ワークフローで、26個のノードを含みます。主にIf, Set, Merge, Postgres, ConvertToFileなどのノードを使用、AI技術を活用したスマート自動化を実現。 メールアシスタント：自然言語をSQLクエリに変換（Phi4-miniとPostgreSQL使用）
前提条件
- •PostgreSQLデータベース接続情報
使用ノード (26)
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
{
"id": "AC4paL1SXMFURgmc",
"meta": {
"instanceId": "8a3ba313628b26e4e4cf0504ff23322f235d6b433d92e59bcf8762764730ed80",
"templateCredsSetupCompleted": true
},
"name": "Translate questions about e-mails into SQL queries and run them",
"tags": [],
"nodes": [
{
"id": "dd63600a-6bee-43cd-a1d2-87aae2089ed4",
"name": "出力にテーブル名を追加",
"type": "n8n-nodes-base.set",
"position": [
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160
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "764176d6-3c89-404d-9c71-301e8a406a68",
"name": "table",
"type": "string",
"value": "={{ $('List all tables in a database').item.json.table_name ?? 'emails_metadata'}}"
}
]
},
"includeOtherFields": true
},
"typeVersion": 3.4
},
{
"id": "1bf02b6d-e8e4-4b1b-8ee2-c91a8c390a21",
"name": "データをバイナリに変換",
"type": "n8n-nodes-base.convertToFile",
"position": [
1040,
160
],
"parameters": {
"options": {},
"operation": "toJson"
},
"typeVersion": 1.1
},
{
"id": "cf930fa2-03bd-46fa-af4d-df282262f965",
"name": "ローカルにファイルを保存",
"type": "n8n-nodes-base.readWriteFile",
"position": [
1220,
160
],
"parameters": {
"options": {},
"fileName": "=/files/pgsql-{{ $workflow.id }}.json",
"operation": "write"
},
"typeVersion": 1
},
{
"id": "48bc8812-7e1b-4d08-8610-884e00069f3c",
"name": "ファイルからデータを抽出",
"type": "n8n-nodes-base.extractFromFile",
"position": [
920,
620
],
"parameters": {
"options": {},
"operation": "fromJson"
},
"typeVersion": 1
},
{
"id": "0d6a0a55-a7cb-4471-ba80-a336324d2939",
"name": "チャットトリガー",
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"position": [
260,
520
],
"webhookId": "c308dec7-655c-4b79-832e-991bd8ea891f",
"parameters": {
"options": {}
},
"typeVersion": 1.1
},
{
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"name": "付箋ノート",
"type": "n8n-nodes-base.stickyNote",
"position": [
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],
"parameters": {
"color": 3,
"width": 1505,
"height": 486,
"content": "## This can run manually\nThis section:\n* loads a list of all tables from the database\n* extracts the database schema for each table and adds the table name\n* converts the schema into a binary JSON format\n* saves the schema file locally"
},
"typeVersion": 1
},
{
"id": "4fb5174f-a3ed-413f-98f7-41b0b46b62ae",
"name": "\"Test workflow\"クリック時",
"type": "n8n-nodes-base.manualTrigger",
"position": [
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],
"parameters": {},
"typeVersion": 1
},
{
"id": "cf6e9426-18ca-4d6e-bff2-d517ae7b4c1e",
"name": "スキーマデータとチャット入力を結合",
"type": "n8n-nodes-base.set",
"position": [
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],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "42abd24e-419a-47d6-bc8b-7146dd0b8314",
"name": "sessionId",
"type": "string",
"value": "={{ $('Chat Trigger').isExecuted && $('Chat Trigger').first().json.sessionId }}"
},
{
"id": "39244192-a1a6-42fe-bc75-a6fba1f264df",
"name": "action",
"type": "string",
"value": "={{ $('Chat Trigger').isExecuted && $('Chat Trigger').first().json.action }}"
},
{
"id": "f78c57d9-df13-43c7-89a7-5387e528107e",
"name": "chatinput",
"type": "string",
"value": "={{ $('WorkflowTrigger').isExecuted ? $('WorkflowTrigger').first().json.natural_language_query: $('Chat Trigger').first().json.chatInput }}"
},
{
"id": "e42b39eb-dfbd-48d9-94ed-d658bdd41454",
"name": "schema",
"type": "string",
"value": "={{ $json.data }}"
}
]
}
},
"executeOnce": true,
"typeVersion": 3.4
},
{
"id": "6a960e03-ea13-4090-8ef8-9b294963fa63",
"name": "ローカルファイルからスキーマを読み込み",
"type": "n8n-nodes-base.readWriteFile",
"onError": "continueRegularOutput",
"maxTries": 2,
"position": [
480,
620
],
"parameters": {
"options": {},
"fileSelector": "=/files/pgsql-{{ $workflow.id }}.json"
},
"retryOnFail": false,
"typeVersion": 1,
"alwaysOutputData": true
},
{
"id": "0bad6e46-e8ed-4ba6-a7d9-2d69fd11227b",
"name": "SQLクエリを抽出",
"type": "n8n-nodes-base.set",
"position": [
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],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "ebbe194a-4b8b-44c9-ac19-03cf69d353bf",
"name": "query",
"type": "string",
"value": "={{ ($json.output.match(/SELECT[^;]*/i) || [])[0] || \"\" }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "2aa91c40-8648-4fba-899d-5599866122e3",
"name": "クエリの存在を確認",
"type": "n8n-nodes-base.if",
"position": [
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],
"parameters": {
"options": {},
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "2963d04d-9d79-49f9-b52a-dc8732aca781",
"operator": {
"type": "string",
"operation": "notEmpty",
"singleValue": true
},
"leftValue": "={{ $json.query }}",
"rightValue": ""
}
]
}
},
"typeVersion": 2.2
},
{
"id": "24b59747-7f9b-473c-9d31-660e17867986",
"name": "クエリ結果を整形",
"type": "n8n-nodes-base.set",
"position": [
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],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "f944d21f-6aac-4842-8926-4108d6cad4bf",
"name": "sqloutput",
"type": "string",
"value": "={{ Object.keys($jmespath($input.all(),'[].json')[0]).join(' | ') }} \n{{ ($jmespath($input.all(),'[].json')).map(obj => Object.values(obj).join(' | ')).join('\\n') }}"
}
]
}
},
"executeOnce": true,
"typeVersion": 3.4
},
{
"id": "a25acba2-74c5-4af6-a1e4-46cfd1364b44",
"name": "クエリ結果とチャット回答を結合",
"type": "n8n-nodes-base.merge",
"position": [
3060,
540
],
"parameters": {
"mode": "combine",
"options": {
"includeUnpaired": true
},
"combineBy": "combineByPosition"
},
"typeVersion": 3
},
{
"id": "a1cde4a1-7b47-4aa2-bd2c-a7090bfb0bb2",
"name": "テーブルの全カラムを一覧表示",
"type": "n8n-nodes-base.postgres",
"position": [
640,
160
],
"parameters": {
"query": "SELECT\n column_name, \n udt_name as data_type, \n CASE WHEN data_type = 'ARRAY' THEN TRUE ELSE FALSE END AS is_array,\n is_nullable \nFROM INFORMATION_SCHEMA.COLUMNS where table_name = '{{ $json.table_name }}'",
"options": {},
"operation": "executeQuery"
},
"credentials": {},
"typeVersion": 2.6
},
{
"id": "cf167b64-007d-469a-bb3e-1144fe435a17",
"name": "データベースの全テーブルを一覧表示",
"type": "n8n-nodes-base.postgres",
"position": [
460,
160
],
"parameters": {
"query": "SELECT table_name FROM INFORMATION_SCHEMA.TABLES WHERE table_schema='public'",
"options": {},
"operation": "executeQuery"
},
"credentials": {},
"typeVersion": 2.6
},
{
"id": "6f6fd892-d779-41d4-ac19-1d5630674f67",
"name": "Ollamaチャットモデル",
"type": "@n8n/n8n-nodes-langchain.lmChatOllama",
"position": [
1440,
840
],
"parameters": {
"model": "phi4-mini:latest",
"options": {}
},
"credentials": {},
"typeVersion": 1
},
{
"id": "6cb76f04-3183-4bce-aa15-0724205d0ab3",
"name": "Postgres",
"type": "n8n-nodes-base.postgres",
"onError": "continueRegularOutput",
"position": [
2620,
460
],
"parameters": {
"query": "{{ $json.query }}",
"options": {},
"operation": "executeQuery"
},
"credentials": {},
"typeVersion": 2.6,
"alwaysOutputData": true
},
{
"id": "9c2a4d74-c2e6-4fac-a00d-2a84a5150027",
"name": "末尾にセミコロンを追加",
"type": "n8n-nodes-base.set",
"position": [
2180,
540
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
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"name": "query",
"type": "string",
"value": "={{ $json.query }};"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "7725f9c3-9c5d-41d6-b4d1-fc444122ae2f",
"name": "末尾のセミコロンを確認",
"type": "n8n-nodes-base.if",
"position": [
1960,
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],
"parameters": {
"options": {},
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "94bd2686-21e7-44aa-b6a8-be5a17bd0242",
"operator": {
"type": "string",
"operation": "notEmpty",
"singleValue": true
},
"leftValue": "={{ $json.query }}",
"rightValue": ""
},
{
"id": "f22c8914-62f3-4f15-be6f-dd23de5a099a",
"operator": {
"type": "string",
"operation": "notEndsWith"
},
"leftValue": "={{ $json.query }}",
"rightValue": ";"
}
]
}
},
"typeVersion": 2.2
},
{
"id": "c7dd1e14-a8f6-4222-a12a-802928b10f56",
"name": "ワークフロートリガー",
"type": "n8n-nodes-base.executeWorkflowTrigger",
"position": [
260,
720
],
"parameters": {
"workflowInputs": {
"values": [
{
"name": "natural_language_query"
}
]
}
},
"typeVersion": 1.1
},
{
"id": "f658fbba-54e3-40f5-9217-a0c8730b1ff4",
"name": "手動実行時",
"type": "n8n-nodes-base.if",
"position": [
1420,
160
],
"parameters": {
"options": {},
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "or",
"conditions": [
{
"id": "c761a475-43ac-483b-827c-0eb69dfebc9a",
"operator": {
"type": "boolean",
"operation": "true",
"singleValue": true
},
"leftValue": "={{ $('When clicking \"Test workflow\"').isExecuted }}",
"rightValue": ""
}
]
}
},
"typeVersion": 2.2
},
{
"id": "67810482-afb7-47b0-ba0d-8b79a140e890",
"name": "ファイルが存在するか再生成済みの場合",
"type": "n8n-nodes-base.if",
"position": [
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],
"parameters": {
"options": {},
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "or",
"conditions": [
{
"id": "28000886-13f4-4628-b1c0-afaaf596ec56",
"operator": {
"type": "object",
"operation": "exists",
"singleValue": true
},
"leftValue": "={{ $input.item.binary }}",
"rightValue": ""
},
{
"id": "ddcd8702-8774-4075-a2d0-6d99cf0cb2c2",
"operator": {
"type": "boolean",
"operation": "true",
"singleValue": true
},
"leftValue": "={{ $('If ran manually').isExecuted }}",
"rightValue": ""
}
]
}
},
"typeVersion": 2.2
},
{
"id": "38121ff4-b0d2-4274-92bf-be346b71c1e9",
"name": "付箋ノート1",
"type": "n8n-nodes-base.stickyNote",
"position": [
160,
440
],
"parameters": {
"width": 720,
"height": 540,
"content": "## This is triggered by chat or as a sub-workflow\nNatural language requests can be asked, and a SQL query as well as its results will be returned."
},
"typeVersion": 1
},
{
"id": "05dce292-4d93-4b0d-87e1-09e8b1dab70a",
"name": "AIエージェント",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
1360,
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],
"parameters": {
"text": "=You have access to a database containing all my personal email and documents.\n\nToday's date is {{ $now.toLocaleString() }}\n\nThe database schema is:\n```\n{{ $json.schema }}\n```\n\nGenerate a SQL query that will:\n```\n{{ $json.chatinput }}\n```\n\nIMPORTANT: \n1. ONLY use column names that exist in the schema above\n2. NEVER invent columns or assume JSON fields that aren't listed\n3. The only metadata fields are emails_metadata.id and emails_metadata.thread_id\n4. Use operators appropriate for each data type:\n - Text fields → ILIKE '%term%'\n - Date fields → Date comparisons (>,<,BETWEEN)\n - Array fields → @>, ANY(), IS NOT NULL\n5. Output ONLY the raw SQL statement ending with a semicolon\n6. The database cannot contain emails from the future",
"options": {
"systemMessage": "=You are an expert SQL query generator that creates precise PostgreSQL queries based on natural language requests. You must strictly adhere to the provided database schema and NEVER invent columns that don't exist.\n\nCRITICAL SCHEMA ADHERENCE RULES:\n\n1. ONLY use columns explicitly listed in the schema\n2. The metadata fields are strictly limited to:\n - emails_metadata.id\n - emails_metadata.thread_id\n3. NEVER invent fields like \"priority\", \"category\", or any metadata attributes not in the schema\n4. NEVER use JSON operators (->>, @>) unless the schema shows JSONB columns\n\nDATA TYPE HANDLING:\n\n1. TEXT/VARCHAR FIELDS:\n - Use ILIKE '%term%' for case-insensitive pattern matching\n - Example: WHERE email_subject ILIKE '%meeting%'\n\n2. TIMESTAMP/DATE FIELDS:\n - NEVER use LIKE/ILIKE on date fields\n - \"yesterday\" → date > CURRENT_DATE - INTERVAL '1 day' AND date < CURRENT_DATE\n - \"last week\" → date > CURRENT_DATE - INTERVAL '7 days'\n - Example: WHERE date > CURRENT_DATE - INTERVAL '3 days'\n\n3. ARRAY FIELDS:\n - Use @> for checking if array contains elements\n - Example: WHERE attachments IS NOT NULL\n\n4. BOOLEAN LOGIC:\n - Always use parentheses to clarify operator precedence\n - Example: WHERE (email_subject ILIKE '%report%' OR email_text ILIKE '%report%') AND date > '2023-01-01'\n\nQUERY CONSTRUCTION GUIDELINES:\n- Start with \"SELECT * FROM\" unless specific fields are requested\n- Use ORDER BY date DESC for recency when appropriate\n- Apply LIMIT only when specifically requested or implied by quantity terms\n- End all statements with semicolons\n- Output only the raw SQL without explanations or code blocks\n- Mind the difference between emails _about_ future dates references, and emails _received_ in specific date references. The database cannot contain emails from the future.\n\nEXAMPLE QUERIES:\n1. \"recent emails about projects from Sarah with attachments\"\n SELECT * FROM emails_metadata \n WHERE (email_subject ILIKE '%project%' OR email_text ILIKE '%project%')\n AND email_from ILIKE '%sarah%' \n AND attachments IS NOT NULL\n ORDER BY date DESC;\n\n2. \"emails received yesterday\"\n SELECT * FROM emails_metadata \n WHERE date > CURRENT_DATE - INTERVAL '1 day' AND date < CURRENT_DATE;\n\n3. \"one email about budget\"\n SELECT * FROM emails_metadata \n WHERE (email_subject ILIKE '%budget%' OR email_text ILIKE '%budget%')\n LIMIT 1;\n\n4. \"Find emails about interviews scheduled from April 28 to May 4\"\n SELECT * FROM emails_metadata\n WHERE (email_subject ILIKE '%interview%' OR email_text ILIKE '%interview%');\n\n5. \"Find emails from April about interviews\"\n SELECT * FROM emails_metadata \n WHERE (email_subject ILIKE '%interview%' OR email_text ILIKE '%interview%') AND date BETWEEN '2025-04-01' AND '2025-04-30';\n\n6. \"emails in thread 123\"\n SELECT * FROM emails_metadata \n WHERE thread_id = '123';\n\n7. \"what's my latest email?\"\n SELECT * FROM emails_metadata\n ORDER BY date DESC LIMIT 1;\n"
},
"promptType": "define"
},
"typeVersion": 1.8
},
{
"id": "6961fed9-4dcf-4a7f-97eb-bbf9e66dff3e",
"name": "空の出力を整形",
"type": "n8n-nodes-base.set",
"position": [
2620,
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],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "aa55e186-1535-4923-aee4-e088ca69575b",
"name": "query",
"type": "string",
"value": "={{ $json.query ?? '' }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "8138aed4-e38d-4c3c-9850-a200bd4d762e",
"name": "付箋ノート2",
"type": "n8n-nodes-base.stickyNote",
"position": [
1320,
440
],
"parameters": {
"width": 340,
"height": 540,
"content": "## Quite the prompt 😅\nSome refined prompt engineering work here.\n\nIt may or may not been done aided by Kagi's Assistant and Claude 3.7 Sonnet 👀"
},
"typeVersion": 1
}
],
"active": false,
"pinData": {},
"settings": {
"executionOrder": "v1"
},
"versionId": "c4e0962f-2c7f-4d14-af37-df491db2ebd0",
"connections": {
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"1bf02b6d-e8e4-4b1b-8ee2-c91a8c390a21": {
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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 - ビルディングブロック, 人工知能, IT運用
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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