构建基于Supabase和GPT-5的高级多查询RAG系统
高级
这是一个AI RAG, Multimodal AI领域的自动化工作流，包含 22 个节点。主要使用 If, Set, Filter, SplitOut, Aggregate 等节点。 构建基于Supabase和GPT-5的高级多查询RAG系统
前置要求
- •OpenAI API Key
- •Supabase URL 和 API Key
使用的节点 (22)
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
{
"nodes": [
{
"id": "14e54443-1722-476a-9f7a-44be7bd2b2bf",
"name": "AI Agent",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
208,
-704
],
"parameters": {
"options": {
"systemMessage": "=You are a helpful assistant that answers based on a biology course.\n\nFor that, you always start by calling the tool \"Query knowledge base\" to send an array of 1 to 5 questions that are relevant to ask to the RAG knowledge base that contains all the content of the course and get as an output all chunks that seem to help to craft the final answer. The more the user query is complex, the more you will break it down into sub-queries (up to 5).\n\nFrom there, use the Think tool to critically analyse the initial user query and the content you've retrieved from the knowledge retrieval tool and reason to prepare the best answer possible, challenge the content to be sure that you actually have the right information to be able to respond.\n\nOnly answer based on the course content that you get from using the tool, if you receive any question outside that scope, redirect the conversation, if you don't have the right information to answer, be transparent and say so - don't try to reply anyway with general knowledge.",
"enableStreaming": false
}
},
"typeVersion": 2.2
},
{
"id": "4df46be3-c8b7-4f88-9af2-a644ca1bab2d",
"name": "当收到聊天消息时",
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"position": [
-256,
-704
],
"webhookId": "19fb162f-87ff-454f-96b2-cce0aaa6e22b",
"parameters": {
"public": true,
"options": {
"responseMode": "lastNode"
}
},
"typeVersion": 1.3
},
{
"id": "5f07d924-7727-478a-abf6-eaf11543e19b",
"name": "OpenAI 聊天模型",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
48,
-480
],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-5-mini",
"cachedResultName": "gpt-5-mini"
},
"options": {}
},
"credentials": {
"openAiApi": {
"id": "dMiSy27YCK6c6rra",
"name": "Duv's OpenAI"
}
},
"typeVersion": 1.2
},
{
"id": "dfc7c805-79cc-4326-8edb-f53a88af285d",
"name": "简单记忆",
"type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
"position": [
240,
-480
],
"parameters": {
"contextWindowLength": 8
},
"typeVersion": 1.3
},
{
"id": "7ade6fc1-84cc-48b2-bb20-672f0c5b4c27",
"name": "拆分输出",
"type": "n8n-nodes-base.splitOut",
"position": [
-160,
96
],
"parameters": {
"options": {},
"fieldToSplitOut": "queries"
},
"typeVersion": 1
},
{
"id": "f4c92e45-e037-4477-ac50-1d6096fd902e",
"name": "聚合块",
"type": "n8n-nodes-base.aggregate",
"position": [
1312,
0
],
"parameters": {
"options": {},
"aggregate": "aggregateAllItemData",
"destinationFieldName": "All chunks for this question"
},
"typeVersion": 1
},
{
"id": "cb5d42fe-9e27-4117-8a1c-9a78da8e770f",
"name": "聚合项目",
"type": "n8n-nodes-base.aggregate",
"position": [
352,
-208
],
"parameters": {
"options": {},
"aggregate": "aggregateAllItemData",
"destinationFieldName": "Knowledge base retrieval"
},
"typeVersion": 1
},
{
"id": "4e7f3e28-c316-4e21-b505-a211c1b23841",
"name": "有任何块吗？",
"type": "n8n-nodes-base.if",
"position": [
1088,
96
],
"parameters": {
"options": {},
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "66402fe0-918e-4268-8928-f4e83cbb3c4f",
"operator": {
"type": "string",
"operation": "exists",
"singleValue": true
},
"leftValue": "={{ $json['Chunk content'] }}",
"rightValue": ""
}
]
}
},
"typeVersion": 2.2
},
{
"id": "26d04029-da7f-4292-802a-4c233caef219",
"name": "清理RAG输出",
"type": "n8n-nodes-base.set",
"position": [
640,
96
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "1eddb72f-9c99-465b-8f94-0ff0f686b542",
"name": "Chunk content",
"type": "string",
"value": "={{ $json.document.pageContent }}"
},
{
"id": "09fe6c91-2cce-40ff-9f8c-86a6857f0772",
"name": "=Chunk metadata",
"type": "object",
"value": "={\n \"Resource chapter name\": \"{{ $json.document.metadata['Chapter name'] }}\",\n \"Retrieval relevance score\": {{ $json.score.round(2) }}\n}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "545514d9-107e-4af9-b407-7cdfc3770e3f",
"name": "遍历项目 1",
"type": "n8n-nodes-base.splitInBatches",
"position": [
64,
96
],
"parameters": {
"options": {}
},
"typeVersion": 3
},
{
"id": "ebdbaea5-405f-4a58-b0b4-198154344329",
"name": "RAG子工作流",
"type": "n8n-nodes-base.executeWorkflowTrigger",
"position": [
-384,
96
],
"parameters": {
"workflowInputs": {
"values": [
{
"name": "queries",
"type": "array"
}
]
}
},
"typeVersion": 1.1
},
{
"id": "d2362d6f-a6a0-4651-9f2b-827b8f7eb1c1",
"name": "查询知识库",
"type": "@n8n/n8n-nodes-langchain.toolWorkflow",
"position": [
432,
-480
],
"parameters": {
"workflowId": {
"__rl": true,
"mode": "list",
"value": "c9FlK6mLuWAwqLsP",
"cachedResultName": "TEMPLATE RAG with Supabase and GPT5"
},
"description": "在构建最终用户答案之前调用此工具以获取有关生物学课程的内容。向知识库发送查询数组。",
"workflowInputs": {
"value": {
"queries": "={{ $fromAI('queries', `The array of queries (between 1 and 5) that you've planned to ask to the RAG knowledge base of the course. \nUse an Array format even if there's only one question - this is necessary to not break the workflow format!\n\nExample array output: \n\n[\n {\n \"query\": \"What is Lorem ipsum sir amet?\"\n },\n {\n \"query\": \"How to lorem ipsum dolor sir lorem when lorem ipsum?'?\"\n },\n {\n \"query\": \"Lorem ipsum lorem ipsum dolor sir lorem when lorem ipsum??\"\n }\n]\n`, 'json') }}"
},
"schema": [
{
"id": "queries",
"type": "array",
"display": true,
"removed": false,
"required": false,
"displayName": "queries",
"defaultMatch": false,
"canBeUsedToMatch": true
}
],
"mappingMode": "defineBelow",
"matchingColumns": [
"queries"
],
"attemptToConvertTypes": false,
"convertFieldsToString": false
}
},
"typeVersion": 2.2
},
{
"id": "db958756-f1a2-4162-afcf-2b6a0f936200",
"name": "Supabase 向量存储1",
"type": "@n8n/n8n-nodes-langchain.vectorStoreSupabase",
"position": [
288,
96
],
"parameters": {
"mode": "load",
"prompt": "={{ $json.query }}",
"options": {
"queryName": "match_documents"
},
"tableName": {
"__rl": true,
"mode": "list",
"value": "documents",
"cachedResultName": "documents"
}
},
"credentials": {
"supabaseApi": {
"id": "WuxmgfzPKmocqt0M",
"name": "Supabase account 2"
}
},
"typeVersion": 1.3
},
{
"id": "478c2c07-ec28-427e-b33a-85a0f72c576f",
"name": "Embeddings OpenAI1",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"position": [
368,
320
],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"id": "G6pwE0s12sGlHRe3",
"name": "1 - Plan A's OpenAI"
}
},
"typeVersion": 1.2
},
{
"id": "da138097-8c28-4662-b916-8de388894330",
"name": "便签1",
"type": "n8n-nodes-base.stickyNote",
"position": [
-480,
-832
],
"parameters": {
"color": 5,
"width": 1472,
"height": 528,
"content": "# AI代理"
},
"typeVersion": 1
},
{
"id": "93a8e212-2a8f-4e9f-8956-b1cca02da212",
"name": "便签2",
"type": "n8n-nodes-base.stickyNote",
"position": [
-480,
-272
],
"parameters": {
"color": 4,
"width": 2320,
"height": 768,
"content": "# 子工作流，代理工具"
},
"typeVersion": 1
},
{
"id": "21ade708-3f0e-4419-9edb-bc57fb543963",
"name": "便签3",
"type": "n8n-nodes-base.stickyNote",
"position": [
816,
-80
],
"parameters": {
"color": 7,
"width": 688,
"height": 432,
"content": "## 过滤系统"
},
"typeVersion": 1
},
{
"id": "ce4ce8ce-0f12-4dc6-ab24-585a81d71ca5",
"name": "思考",
"type": "@n8n/n8n-nodes-langchain.toolThink",
"position": [
608,
-480
],
"parameters": {
"description": "在您获得知识检索工具的输出后使用此工具，批判性地分析初始用户查询和您从知识检索工具检索到的内容，并进行推理以准备最佳答案，质疑内容以确保您确实拥有正确的信息来做出回应。"
},
"typeVersion": 1.1
},
{
"id": "f1d619f3-42fb-4f48-83b3-3c0d1c43d574",
"name": "便签",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1024,
-832
],
"parameters": {
"width": 512,
"height": 784,
"content": "# 高级多查询RAG代理"
},
"typeVersion": 1
},
{
"id": "b26b291d-9f95-4012-b830-cd07a9b8015f",
"name": "保持分数超过0.4",
"type": "n8n-nodes-base.filter",
"position": [
864,
96
],
"parameters": {
"options": {},
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "9a3f844e-7d19-4631-9876-140118e61b6b",
"operator": {
"type": "number",
"operation": "gt"
},
"leftValue": "={{ $json['Chunk metadata']['Retrieval relevance score'] }}",
"rightValue": 0.4
}
]
}
},
"typeVersion": 2.2,
"alwaysOutputData": true
},
{
"id": "14d3efaf-dc35-491f-91df-f085829812ee",
"name": "告知无匹配块",
"type": "n8n-nodes-base.set",
"position": [
1312,
192
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "245fe8f8-b217-4626-bc4d-84f53e47fbbf",
"name": "Retrieval output",
"type": "string",
"value": "=No chunks reached the relevance threshold, the knowledge base was unable to provide information that would be helpful to answer this question."
}
]
}
},
"typeVersion": 3.4
},
{
"id": "e9eb2328-e9e2-4138-9d9e-468359a5e49d",
"name": "准备循环输出",
"type": "n8n-nodes-base.set",
"position": [
1568,
192
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "838f21a4-f7bc-414e-83da-99fbaca4fcca",
"name": "Query to the knowledge base",
"type": "string",
"value": "={{ $('Loop Over Items1').first().json.query }}"
},
{
"id": "10a89085-1937-459f-9721-8715cd51ad39",
"name": "Chunks returned",
"type": "string",
"value": "={{ JSON.stringify($json, null, 2) }}"
}
]
}
},
"typeVersion": 3.4
}
],
"connections": {
"Think": {
"ai_tool": [
[
{
"node": "AI Agent",
"type": "ai_tool",
"index": 0
}
]
]
},
"AI Agent": {
"main": [
[]
]
},
"Split Out": {
"main": [
[
{
"node": "Loop Over Items1",
"type": "main",
"index": 0
}
]
]
},
"Any chunk?": {
"main": [
[
{
"node": "Aggregate chunks",
"type": "main",
"index": 0
}
],
[
{
"node": "Say no chunk match",
"type": "main",
"index": 0
}
]
]
},
"Simple Memory": {
"ai_memory": [
[
{
"node": "AI Agent",
"type": "ai_memory",
"index": 0
}
]
]
},
"Aggregate chunks": {
"main": [
[
{
"node": "Prepare loop output",
"type": "main",
"index": 0
}
]
]
},
"Clean RAG output": {
"main": [
[
{
"node": "Keep score over 0.4",
"type": "main",
"index": 0
}
]
]
},
"Loop Over Items1": {
"main": [
[
{
"node": "Aggregate items",
"type": "main",
"index": 0
}
],
[
{
"node": "Supabase Vector Store1",
"type": "main",
"index": 0
}
]
]
},
"RAG sub-workflow": {
"main": [
[
{
"node": "Split Out",
"type": "main",
"index": 0
}
]
]
},
"OpenAI Chat Model": {
"ai_languageModel": [
[
{
"node": "AI Agent",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Embeddings OpenAI1": {
"ai_embedding": [
[
{
"node": "Supabase Vector Store1",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Say no chunk match": {
"main": [
[
{
"node": "Prepare loop output",
"type": "main",
"index": 0
}
]
]
},
"Keep score over 0.4": {
"main": [
[
{
"node": "Any chunk?",
"type": "main",
"index": 0
}
]
]
},
"Prepare loop output": {
"main": [
[
{
"node": "Loop Over Items1",
"type": "main",
"index": 0
}
]
]
},
"Query knowledge base": {
"ai_tool": [
[
{
"node": "AI Agent",
"type": "ai_tool",
"index": 0
}
]
]
},
"Supabase Vector Store1": {
"main": [
[
{
"node": "Clean RAG output",
"type": "main",
"index": 0
}
]
]
},
"When chat message received": {
"main": [
[
{
"node": "AI Agent",
"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？
高级 - AI RAG 检索增强, 多模态 AI
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。
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