RAG再ランキング
上級
これはInternal Wiki, AI RAG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Code, GoogleDrive, ManualTrigger, Agent, ExtractFromFileなどのノードを使用。 Supabase、OpenAI、Cohereリランカーを使ってドキュメントから質問に回答する
前提条件
- •Google Drive API認証情報
- •OpenAI API Key
- •Supabase URL と API Key
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": "p8bHqYEvjtOrvz3q",
"meta": {
"instanceId": "",
"templateCredsSetupCompleted": true
},
"name": "RAG Reranking",
"tags": [],
"nodes": [
{
"id": "d690d954-6291-4355-9b51-42fe9ab2791a",
"name": "ファイルをダウンロード",
"type": "n8n-nodes-base.googleDrive",
"position": [
-100,
-320
],
"parameters": {
"fileId": {
"__rl": true,
"mode": "list",
"value": "16ahWlNwBvd53xFHA4UUh6EbkFd8ogxBv",
"cachedResultUrl": "https://drive.google.com/file/d/16ahWlNwBvd53xFHA4UUh6EbkFd8ogxBv/view?usp=drivesdk",
"cachedResultName": "Rules_of_Golf_Simplified.pdf"
},
"options": {},
"operation": "download"
},
"credentials": {
"googleDriveOAuth2Api": {
"id": "V2ewjiHO0o6xhQ2R",
"name": "nateherk88@gmail.com"
}
},
"typeVersion": 3
},
{
"id": "ad9a4d3c-ace1-428c-8957-edb456bf864f",
"name": "デフォルトデータローダー",
"type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
"position": [
460,
-180
],
"parameters": {
"options": {
"metadata": {
"metadataValues": [
{
"name": "ruleNumber",
"value": "={{ $json.ruleNumber }}"
}
]
}
},
"jsonData": "={{ $('Code').item.json.fullText }}",
"jsonMode": "expressionData"
},
"typeVersion": 1.1
},
{
"id": "f6d44c38-8cb4-43ad-8130-7ab8cd142c9a",
"name": "ファイルから抽出",
"type": "n8n-nodes-base.extractFromFile",
"position": [
40,
-320
],
"parameters": {
"options": {},
"operation": "pdf"
},
"typeVersion": 1
},
{
"id": "dfc604ab-b7bd-4a08-b65d-f8fe2c3b5c13",
"name": "コード",
"type": "n8n-nodes-base.code",
"position": [
180,
-320
],
"parameters": {
"jsCode": "// n8n Code Node - Split Golf Rules\n// This code takes the input text and splits it into separate items for each rule\n\n// Get the input text from the first item\nconst inputText = $input.first().json.text;\n\n// Split the text by \"Rule\" pattern, keeping the \"Rule\" text with each section\nconst ruleSections = inputText.split(/(?=Rule \\d+)/);\n\n// Remove the first empty element (everything before the first \"Rule\")\nconst cleanedSections = ruleSections.filter(section => section.trim().startsWith('Rule'));\n\n// Create output items - one for each rule\nconst outputItems = cleanedSections.map((ruleText, index) => {\n // Extract rule number from the text\n const ruleMatch = ruleText.match(/Rule (\\d+)/);\n const ruleNumber = ruleMatch ? ruleMatch[1] : (index + 1).toString();\n \n // Extract rule title (everything between \"Rule X –\" and the first numbered item)\n const titleMatch = ruleText.match(/Rule \\d+ – (.+?)(?=\\n1\\.|\\n\\d+\\.)/);\n const ruleTitle = titleMatch ? titleMatch[1].trim() : 'Unknown Rule';\n \n return {\n json: {\n ruleNumber: ruleNumber,\n ruleTitle: ruleTitle,\n fullText: ruleText.trim(),\n originalIndex: index\n }\n };\n});\n\nreturn outputItems;"
},
"typeVersion": 2
},
{
"id": "cc659be4-709e-4d59-a386-d7cc60166293",
"name": "チャットメッセージ受信時",
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"position": [
-280,
-1180
],
"webhookId": "79772045-628b-4cf6-b2ec-cecceca9fe24",
"parameters": {
"options": {}
},
"typeVersion": 1.1
},
{
"id": "9f02235d-8c3f-4309-bd14-d4c6bcdfab11",
"name": "GPT 4.1-mini",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenRouter",
"position": [
-100,
-1040
],
"parameters": {
"options": {}
},
"credentials": {
"openRouterApi": {
"id": "fpo6OUh9TcHg29jk",
"name": "OpenRouter account"
}
},
"typeVersion": 1
},
{
"id": "dad869f9-4c1d-44a4-b523-31f007efccc7",
"name": "Cohereリランカー",
"type": "@n8n/n8n-nodes-langchain.rerankerCohere",
"position": [
520,
-1040
],
"parameters": {},
"credentials": {
"cohereApi": {
"id": "vCsqiDhFNdSGhDKu",
"name": "CohereApi account"
}
},
"typeVersion": 1
},
{
"id": "24cbdd3d-afee-46d2-83ef-888d432b4874",
"name": "Supabaseにアップロード",
"type": "@n8n/n8n-nodes-langchain.vectorStoreSupabase",
"position": [
320,
-320
],
"parameters": {
"mode": "insert",
"options": {
"queryName": "match_documents"
},
"tableName": {
"__rl": true,
"mode": "list",
"value": "documents",
"cachedResultName": "documents"
}
},
"credentials": {
"supabaseApi": {
"id": "r1eLu64ie9Tz6yOK",
"name": "Demo 2.22.25"
}
},
"typeVersion": 1.3
},
{
"id": "f80184cb-fc7e-40d7-bf2d-a723350c9f0f",
"name": "Supabaseベクトルストア",
"type": "@n8n/n8n-nodes-langchain.vectorStoreSupabase",
"position": [
360,
-1180
],
"parameters": {
"mode": "retrieve-as-tool",
"topK": 20,
"options": {},
"tableName": {
"__rl": true,
"mode": "list",
"value": "documents",
"cachedResultName": "documents"
},
"useReranker": true,
"toolDescription": "Use this tool to search the database"
},
"credentials": {
"supabaseApi": {
"id": "r1eLu64ie9Tz6yOK",
"name": "Demo 2.22.25"
}
},
"typeVersion": 1.3
},
{
"id": "de08fce1-3db6-4452-a30a-27294328bdb9",
"name": "GPT 4.1-mini1",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenRouter",
"position": [
220,
-600
],
"parameters": {
"options": {}
},
"credentials": {
"openRouterApi": {
"id": "fpo6OUh9TcHg29jk",
"name": "OpenRouter account"
}
},
"typeVersion": 1
},
{
"id": "2fbb1dbc-aeb3-4f5d-b1b1-f8947bec45e4",
"name": "Cohereリランカー1",
"type": "@n8n/n8n-nodes-langchain.rerankerCohere",
"position": [
780,
-620
],
"parameters": {},
"credentials": {
"cohereApi": {
"id": "vCsqiDhFNdSGhDKu",
"name": "CohereApi account"
}
},
"typeVersion": 1
},
{
"id": "64140fce-9e7c-4cd2-a5ba-2bfb4c8bdaad",
"name": "OpenAI2埋め込み",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"position": [
620,
-620
],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"id": "WnxUhaEPMn5hIsEp",
"name": "Demo 4/2"
}
},
"typeVersion": 1.2
},
{
"id": "fe882466-73db-4141-8c70-baff299b4e1c",
"name": "Supabaseベクトルストア1",
"type": "@n8n/n8n-nodes-langchain.vectorStoreSupabase",
"position": [
620,
-760
],
"parameters": {
"mode": "retrieve-as-tool",
"topK": 20,
"options": {
"metadata": {
"metadataValues": [
{
"name": "ruleNumber",
"value": "={{ $('Metadata Agent').item.json.output }}"
}
]
}
},
"tableName": {
"__rl": true,
"mode": "list",
"value": "documents",
"cachedResultName": "documents"
},
"useReranker": true,
"toolDescription": "Use this tool to search the database"
},
"credentials": {
"supabaseApi": {
"id": "r1eLu64ie9Tz6yOK",
"name": "Demo 2.22.25"
}
},
"typeVersion": 1.3
},
{
"id": "12e4fe9d-d97d-4252-a235-66017fadad66",
"name": "付箋",
"type": "n8n-nodes-base.stickyNote",
"position": [
-320,
-460
],
"parameters": {
"color": 2,
"width": 1000,
"height": 440,
"content": "# Vectorize Document w/ Metadata\n(this code node is set up for the golf rules PDF specifically)"
},
"typeVersion": 1
},
{
"id": "406521ff-0f01-4688-a352-62ae49d71ff6",
"name": "付箋1",
"type": "n8n-nodes-base.stickyNote",
"position": [
-320,
-1280
],
"parameters": {
"color": 4,
"width": 620,
"height": 380,
"content": "# RAG Agent\n"
},
"typeVersion": 1
},
{
"id": "11f6a7fd-b540-43d9-ad55-86c2874e8ddd",
"name": "付箋2",
"type": "n8n-nodes-base.stickyNote",
"position": [
300,
-1280
],
"parameters": {
"color": 5,
"width": 380,
"height": 380,
"content": "## Vector Store w/ Reranker\n"
},
"typeVersion": 1
},
{
"id": "d295d851-b64b-41c9-9289-f7c5c640b704",
"name": "OpenAI1埋め込み",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"position": [
300,
-180
],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"id": "WnxUhaEPMn5hIsEp",
"name": "Demo 4/2"
}
},
"typeVersion": 1.2
},
{
"id": "5b11e4ea-c497-4d18-8dfe-3dcdcadde1e6",
"name": "OpenAI埋め込み",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"position": [
360,
-1040
],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"id": "WnxUhaEPMn5hIsEp",
"name": "Demo 4/2"
}
},
"typeVersion": 1.2
},
{
"id": "62282da2-0dc5-4758-8182-13a7bf1afff9",
"name": "メタデータエージェント",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
-220,
-760
],
"parameters": {
"options": {
"systemMessage": "=# Overview\nYour job is to understand the rule number that the human is requesting and output only the number.\n\n## Example\nInput - what's rule number 27?\nOutput - 27"
}
},
"typeVersion": 2
},
{
"id": "9fbd11cd-195d-4bbe-aa81-718c063d1133",
"name": "RAGエージェント",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
-80,
-1180
],
"parameters": {
"options": {
"systemMessage": "=# Overview\nYou are an AI agent who is an expert at the rules of golf. You will receive a question from the human, and you must use your tool called \"Supabase Vector Store\" in order to retrieve information from the database to make sure you are answering the question accurately. "
}
},
"typeVersion": 2
},
{
"id": "150a92c9-fdb4-45e0-a838-45364dd6140b",
"name": "RAGエージェント2",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
200,
-760
],
"parameters": {
"text": "={{ $('When chat message received').item.json.chatInput }}",
"options": {
"systemMessage": "=# Overview\nYou are an AI agent who is an expert at the rules of golf. You will receive a question from the human, and you must use your tool called \"Supabase Vector Store\" in order to retrieve information from the database to make sure you are answering the question accurately. "
},
"promptType": "define"
},
"typeVersion": 2
},
{
"id": "e149b963-2f39-472b-962a-12bdd270e63b",
"name": "付箋3",
"type": "n8n-nodes-base.stickyNote",
"position": [
120,
-880
],
"parameters": {
"color": 4,
"width": 440,
"height": 400,
"content": "# RAG Agent\n"
},
"typeVersion": 1
},
{
"id": "ede1b0d8-d402-4fa5-abe0-8ee4169be45b",
"name": "付箋4",
"type": "n8n-nodes-base.stickyNote",
"position": [
560,
-880
],
"parameters": {
"color": 5,
"width": 380,
"height": 400,
"content": "## Vector Store w/ Reranker & Metadata\n"
},
"typeVersion": 1
},
{
"id": "c56cce9d-2d8c-4942-94fa-a8d62e062842",
"name": "付箋5",
"type": "n8n-nodes-base.stickyNote",
"position": [
-320,
-880
],
"parameters": {
"color": 6,
"width": 440,
"height": 400,
"content": "# Metadata Agent\n"
},
"typeVersion": 1
},
{
"id": "7e6dd534-9f8a-42c2-bac0-0bb0e4fa99e6",
"name": "手動トリガー",
"type": "n8n-nodes-base.manualTrigger",
"position": [
-240,
-320
],
"parameters": {},
"typeVersion": 1
},
{
"id": "85ee82ce-f0b2-49f0-852e-9b888b9235a9",
"name": "付箋6",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1040,
-1280
],
"parameters": {
"width": 700,
"height": 800,
"content": "# 🛠️ Setup Guide \n**Author:** [Nate Herk](https://www.youtube.com/@nateherk)\n\nFollow the steps below to get your Retrieval-Augmented Generation (RAG) workflow up and running:\n\n### ✅ Step 1: Connect Your [Supabase](https://supabase.com/) Vector Store \nEnsure your Supabase instance is ready and accessible. This will store your embedded documents with metadata.\nHere is a [video tutorial](https://youtu.be/JjBofKJnYIU) on setting that up.\n\n### ✅ Step 2: Connect Your [OpenAI](https://platform.openai.com/account/api-keys) Embeddings \nUse the `text-embedding-3-small` or similar model for embedding your documents. Make sure your API key is active.\n\n### ✅ Step 3: Connect Your [OpenAI API Key](https://platform.openai.com/account/api-keys) \nThis powers your embedding generation model. Add it via the HTTP Request node or a credential.\n\n### ✅ Step 4: Add Your [OpenRouter](https://openrouter.ai/) API Key \nUse this for your main RAG agent—add your key via HTTP request or credential node.\n\n### ✅ Step 5: Connect a [Cohere](https://dashboard.cohere.com/api-keys) Re-Ranker \nThe re-ranker improves answer quality. Add your API key for better relevance ranking on retrieved documents.\n\n### ✅ Step 6: Vectorize Documents with Metadata \nEnsure your data ingestion process tags documents with meaningful metadata before vectorization. This helps with structured retrieval.\n\n### 💬 Final Step: Start Chatting \nPrompt your agent and test the RAG flow end-to-end—watch it pull context-rich answers from your vector store.\n"
},
"typeVersion": 1
}
],
"active": false,
"pinData": {},
"settings": {
"executionOrder": "v1"
},
"versionId": "80eccd78-53ac-4cca-aedd-63ddf77ff7af",
"connections": {
"dfc604ab-b7bd-4a08-b65d-f8fe2c3b5c13": {
"main": [
[
{
"node": "24cbdd3d-afee-46d2-83ef-888d432b4874",
"type": "main",
"index": 0
}
]
]
},
"9f02235d-8c3f-4309-bd14-d4c6bcdfab11": {
"ai_languageModel": [
[
{
"node": "9fbd11cd-195d-4bbe-aa81-718c063d1133",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"d690d954-6291-4355-9b51-42fe9ab2791a": {
"main": [
[
{
"node": "f6d44c38-8cb4-43ad-8130-7ab8cd142c9a",
"type": "main",
"index": 0
}
]
]
},
"de08fce1-3db6-4452-a30a-27294328bdb9": {
"ai_languageModel": [
[
{
"node": "150a92c9-fdb4-45e0-a838-45364dd6140b",
"type": "ai_languageModel",
"index": 0
},
{
"node": "62282da2-0dc5-4758-8182-13a7bf1afff9",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"7e6dd534-9f8a-42c2-bac0-0bb0e4fa99e6": {
"main": [
[
{
"node": "d690d954-6291-4355-9b51-42fe9ab2791a",
"type": "main",
"index": 0
}
]
]
},
"62282da2-0dc5-4758-8182-13a7bf1afff9": {
"main": [
[
{
"node": "150a92c9-fdb4-45e0-a838-45364dd6140b",
"type": "main",
"index": 0
}
]
]
},
"dad869f9-4c1d-44a4-b523-31f007efccc7": {
"ai_reranker": [
[
{
"node": "f80184cb-fc7e-40d7-bf2d-a723350c9f0f",
"type": "ai_reranker",
"index": 0
}
]
]
},
"2fbb1dbc-aeb3-4f5d-b1b1-f8947bec45e4": {
"ai_reranker": [
[
{
"node": "fe882466-73db-4141-8c70-baff299b4e1c",
"type": "ai_reranker",
"index": 0
}
]
]
},
"5b11e4ea-c497-4d18-8dfe-3dcdcadde1e6": {
"ai_embedding": [
[
{
"node": "f80184cb-fc7e-40d7-bf2d-a723350c9f0f",
"type": "ai_embedding",
"index": 0
}
]
]
},
"f6d44c38-8cb4-43ad-8130-7ab8cd142c9a": {
"main": [
[
{
"node": "dfc604ab-b7bd-4a08-b65d-f8fe2c3b5c13",
"type": "main",
"index": 0
}
]
]
},
"d295d851-b64b-41c9-9289-f7c5c640b704": {
"ai_embedding": [
[
{
"node": "24cbdd3d-afee-46d2-83ef-888d432b4874",
"type": "ai_embedding",
"index": 0
}
]
]
},
"64140fce-9e7c-4cd2-a5ba-2bfb4c8bdaad": {
"ai_embedding": [
[
{
"node": "fe882466-73db-4141-8c70-baff299b4e1c",
"type": "ai_embedding",
"index": 0
}
]
]
},
"ad9a4d3c-ace1-428c-8957-edb456bf864f": {
"ai_document": [
[
{
"node": "24cbdd3d-afee-46d2-83ef-888d432b4874",
"type": "ai_document",
"index": 0
}
]
]
},
"f80184cb-fc7e-40d7-bf2d-a723350c9f0f": {
"ai_tool": [
[
{
"node": "9fbd11cd-195d-4bbe-aa81-718c063d1133",
"type": "ai_tool",
"index": 0
}
]
]
},
"fe882466-73db-4141-8c70-baff299b4e1c": {
"ai_tool": [
[
{
"node": "150a92c9-fdb4-45e0-a838-45364dd6140b",
"type": "ai_tool",
"index": 0
}
]
]
},
"cc659be4-709e-4d59-a386-d7cc60166293": {
"main": [
[
{
"node": "9fbd11cd-195d-4bbe-aa81-718c063d1133",
"type": "main",
"index": 0
}
]
]
}
}
}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 - 内部Wiki, AI RAG検索拡張
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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If
Set
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76 ノードMichael Taleb
AI RAG検索拡張
