将邮件相关提问转换为SQL查询并执行
高级
这是一个Building Blocks、AI、IT Ops领域的自动化工作流，包含 26 个节点。主要使用 If、Set、Merge、Postgres、ConvertToFile 等节点，结合人工智能技术实现智能自动化。 邮件助手：使用Phi4-mini和PostgreSQL将自然语言转换为SQL查询
前置要求
- •PostgreSQL 数据库连接信息
使用的节点 (26 个)
工作流预览
可视化展示节点连接关系，支持缩放和平移
导出工作流
复制以下 JSON 配置到 n8n 导入，即可使用此工作流
{
"id": "AC4paL1SXMFURgmc",
"meta": {
"instanceId": "8a3ba313628b26e4e4cf0504ff23322f235d6b433d92e59bcf8762764730ed80",
"templateCredsSetupCompleted": true
},
"name": "将关于邮件的提问转换为 SQL 查询并执行",
"tags": [],
"nodes": [
{
"id": "dd63600a-6bee-43cd-a1d2-87aae2089ed4",
"name": "在输出中添加表名",
"type": "n8n-nodes-base.set",
"position": [
840,
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": "将数据转换为二进制",
"type": "n8n-nodes-base.convertToFile",
"position": [
1040,
160
],
"parameters": {
"options": {},
"operation": "toJson"
},
"typeVersion": 1.1
},
{
"id": "cf930fa2-03bd-46fa-af4d-df282262f965",
"name": "本地保存文件",
"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": "从文件中提取数据",
"type": "n8n-nodes-base.extractFromFile",
"position": [
920,
620
],
"parameters": {
"options": {},
"operation": "fromJson"
},
"typeVersion": 1
},
{
"id": "0d6a0a55-a7cb-4471-ba80-a336324d2939",
"name": "聊天触发器",
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"position": [
260,
520
],
"webhookId": "c308dec7-655c-4b79-832e-991bd8ea891f",
"parameters": {
"options": {}
},
"typeVersion": 1.1
},
{
"id": "8f39276c-4ce7-4b27-b022-231607a9cfb3",
"name": "便签",
"type": "n8n-nodes-base.stickyNote",
"position": [
160,
-60
],
"parameters": {
"color": 3,
"width": 1505,
"height": 486,
"content": "## 可手动运行"
},
"typeVersion": 1
},
{
"id": "4fb5174f-a3ed-413f-98f7-41b0b46b62ae",
"name": "点击\"测试工作流\"时",
"type": "n8n-nodes-base.manualTrigger",
"position": [
260,
160
],
"parameters": {},
"typeVersion": 1
},
{
"id": "cf6e9426-18ca-4d6e-bff2-d517ae7b4c1e",
"name": "合并架构数据和聊天输入",
"type": "n8n-nodes-base.set",
"position": [
1140,
620
],
"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": "从本地文件加载架构",
"type": "n8n-nodes-base.readWriteFile",
"onError": "continueRegularOutput",
"maxTries": 2,
"position": [
480,
620
],
"parameters": {
"options": {},
"fileSelector": "=/files/pgsql-{{ $workflow.id }}.json"
},
"retryOnFail": false,
"typeVersion": 1,
"alwaysOutputData": true
},
{
"id": "0bad6e46-e8ed-4ba6-a7d9-2d69fd11227b",
"name": "提取 SQL 查询",
"type": "n8n-nodes-base.set",
"position": [
1740,
620
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "ebbe194a-4b8b-44c9-ac19-03cf69d353bf",
"name": "query",
"type": "string",
"value": "={{ ($json.output.match(/SELECT[^;]*/i) || [])[0] || \"\" }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "2aa91c40-8648-4fba-899d-5599866122e3",
"name": "检查查询是否存在",
"type": "n8n-nodes-base.if",
"position": [
2400,
620
],
"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": "={{ $json.query }}",
"rightValue": ""
}
]
}
},
"typeVersion": 2.2
},
{
"id": "24b59747-7f9b-473c-9d31-660e17867986",
"name": "格式化查询结果",
"type": "n8n-nodes-base.set",
"position": [
2840,
460
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "f944d21f-6aac-4842-8926-4108d6cad4bf",
"name": "sqloutput",
"type": "string",
"value": "={{ Object.keys($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": "合并查询结果和聊天回答",
"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": "列出表中所有列",
"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": "列出数据库中所有表",
"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 聊天模型",
"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": "添加尾部分号",
"type": "n8n-nodes-base.set",
"position": [
2180,
540
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "15622b82-a226-4f54-9c0e-3f30b2c0cf4b",
"name": "query",
"type": "string",
"value": "={{ $json.query }};"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "7725f9c3-9c5d-41d6-b4d1-fc444122ae2f",
"name": "检查尾部分号",
"type": "n8n-nodes-base.if",
"position": [
1960,
620
],
"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": "工作流触发器",
"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": "如果手动运行",
"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": "如果文件存在或已重试生成",
"type": "n8n-nodes-base.if",
"position": [
700,
620
],
"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": "便签1",
"type": "n8n-nodes-base.stickyNote",
"position": [
160,
440
],
"parameters": {
"width": 720,
"height": 540,
"content": "## 由聊天触发或作为子工作流运行"
},
"typeVersion": 1
},
{
"id": "05dce292-4d93-4b0d-87e1-09e8b1dab70a",
"name": "AI 代理",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
1360,
620
],
"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": "格式化空输出",
"type": "n8n-nodes-base.set",
"position": [
2620,
760
],
"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": "便签2",
"type": "n8n-nodes-base.stickyNote",
"position": [
1320,
440
],
"parameters": {
"width": 340,
"height": 540,
"content": "## 相当复杂的提示词 😅"
},
"typeVersion": 1
}
],
"active": false,
"pinData": {},
"settings": {
"executionOrder": "v1"
},
"versionId": "c4e0962f-2c7f-4d14-af37-df491db2ebd0",
"connections": {
"AI Agent": {
"main": [
[
{
"node": "Extract SQL query",
"type": "main",
"index": 0
}
]
]
},
"Postgres": {
"main": [
[
{
"node": "Format query results",
"type": "main",
"index": 0
}
]
]
},
"Chat Trigger": {
"main": [
[
{
"node": "Load the schema from the local file",
"type": "main",
"index": 0
}
]
]
},
"If ran manually": {
"main": [
[],
[
{
"node": "Load the schema from the local file",
"type": "main",
"index": 0
}
]
]
},
"WorkflowTrigger": {
"main": [
[
{
"node": "Load the schema from the local file",
"type": "main",
"index": 0
}
]
]
},
"Extract SQL query": {
"main": [
[
{
"node": "Check for trailing semicolon",
"type": "main",
"index": 0
}
]
]
},
"Ollama Chat Model": {
"ai_languageModel": [
[
{
"node": "AI Agent",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Save file locally": {
"main": [
[
{
"node": "If ran manually",
"type": "main",
"index": 0
}
]
]
},
"Format query results": {
"main": [
[
{
"node": "Combine query result and chat answer",
"type": "main",
"index": 0
}
]
]
},
"Check if query exists": {
"main": [
[
{
"node": "Combine query result and chat answer",
"type": "main",
"index": 1
},
{
"node": "Postgres",
"type": "main",
"index": 0
}
],
[
{
"node": "Format empty output",
"type": "main",
"index": 0
}
]
]
},
"Add trailing semicolon": {
"main": [
[
{
"node": "Check if query exists",
"type": "main",
"index": 0
}
]
]
},
"Convert data to binary": {
"main": [
[
{
"node": "Save file locally",
"type": "main",
"index": 0
}
]
]
},
"Extract data from file": {
"main": [
[
{
"node": "Combine schema data and chat input",
"type": "main",
"index": 0
}
]
]
},
"Add table name to output": {
"main": [
[
{
"node": "Convert data to binary",
"type": "main",
"index": 0
}
]
]
},
"List all columns in a table": {
"main": [
[
{
"node": "Add table name to output",
"type": "main",
"index": 0
}
]
]
},
"Check for trailing semicolon": {
"main": [
[
{
"node": "Add trailing semicolon",
"type": "main",
"index": 0
}
],
[
{
"node": "Check if query exists",
"type": "main",
"index": 0
}
]
]
},
"List all tables in a database": {
"main": [
[
{
"node": "List all columns in a table",
"type": "main",
"index": 0
}
]
]
},
"When clicking \"Test workflow\"": {
"main": [
[
{
"node": "List all tables in a database",
"type": "main",
"index": 0
}
]
]
},
"Combine schema data and chat input": {
"main": [
[
{
"node": "AI Agent",
"type": "main",
"index": 0
}
]
]
},
"Load the schema from the local file": {
"main": [
[
{
"node": "If file exists or already retried generating it",
"type": "main",
"index": 0
}
],
[]
]
},
"Combine query result and chat answer": {
"main": [
[]
]
},
"If file exists or already retried generating it": {
"main": [
[
{
"node": "Extract data from file",
"type": "main",
"index": 0
}
],
[
{
"node": "List all tables in a database",
"type": "main",
"index": 0
}
]
]
}
}
}常见问题
如何使用这个工作流？
复制上方的 JSON 配置代码，在您的 n8n 实例中创建新工作流并选择「从 JSON 导入」，粘贴配置后根据需要修改凭证设置即可。
这个工作流适合什么场景？
这是一个高级难度的工作流，适用于Building Blocks、AI、IT Ops等场景。适合高级用户，包含 16+ 个节点的复杂工作流
需要付费吗？
本工作流完全免费，您可以直接导入使用。但请注意，工作流中使用的第三方服务（如 OpenAI API）可能需要您自行付费。
相关工作流推荐
与数据对话：将文本转换为 SQL 查询和可视化曲线
与数据对话：将文本转换为 SQL 查询和可视化曲线
If
Set
Merge
+12
36 节点hippolyte-hu
Engineering
使用正则表达式和 AI 发现隐藏的网站 API 端点
使用正则表达式和人工智能发现隐藏的网站API端点
If
Set
Html
+19
58 节点Yulia
Engineering
仅从数据库架构生成 SQL 查询 - AI 驱动
仅从数据库架构生成 SQL 查询 - AI 驱动
If
Set
Merge
+11
29 节点Yulia
Engineering
🤖 WhatsApp AI个人助手：GPT-4o、记忆和日程安排功能
AI个人助手：集成GPT-4o、RAG和语音功能，使用Supabase的WhatsApp助手
If
Set
Wait
+22
76 节点Amanda Benks
AI
使用 Gemini AI 和 Chunkr.ai 按目录分段 PDF
使用 Gemini AI 和 Chunkr.ai 按目录分段 PDF
Set
Code
Html
+16
36 节点Lukas Kunhardt
AI
基于AI的MIS代理
基于AI的管理信息系统代理
If
Set
Code
+29
129 节点Kumar Shivam
Support
