n8n ローカルAIエージェントRAGテンプレート
上級
これはInternal Wiki, AI RAG分野の自動化ワークフローで、41個のノードを含みます。主にSet, Switch, Webhook, Postgres, Aggregateなどのノードを使用。 Ollama AI、スマートRAGエージェント、PGVectorを使用したローカル文書Q&Aシステム
前提条件
- •HTTP Webhookエンドポイント（n8nが自動生成）
- •PostgreSQLデータベース接続情報
- •OpenAI API Key
使用ノード (41)
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": "dlA7uMt2f1hTW3xd",
"meta": {
"instanceId": "8cf060ebda3ec45b5ebb6a30779eaf0c03dfba83865feab3f32adb31b82caa08"
},
"name": "n8n Local AI Agentic RAG Template",
"tags": [],
"nodes": [
{
"id": "397d00eb-8034-49e5-a8f6-0a0fd9b97d5b",
"name": "デフォルトデータローダー",
"type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
"position": [
3312,
1280
],
"parameters": {
"options": {
"metadata": {
"metadataValues": [
{
"name": "=file_id",
"value": "={{ $('Set File ID').first().json.file_id }}"
},
{
"name": "file_title",
"value": "={{ $('Set File ID').first().json.file_title }}"
}
]
}
},
"jsonData": "={{ $json.data || $json.text || $json.concatenated_data }}",
"jsonMode": "expressionData"
},
"typeVersion": 1
},
{
"id": "e57065a2-9087-48e9-839e-d9c5c5fb477f",
"name": "付箋",
"type": "n8n-nodes-base.stickyNote",
"position": [
2304,
144
],
"parameters": {
"color": 4,
"width": 583.4552380860637,
"height": 528.85546469693,
"content": "## Agent Tools for RAG"
},
"typeVersion": 1
},
{
"id": "f7efaf27-78fb-4429-beba-74ffcc700342",
"name": "付箋1",
"type": "n8n-nodes-base.stickyNote",
"position": [
560,
688
],
"parameters": {
"color": 5,
"width": 3073,
"height": 867,
"content": "## Tool to Add a Google Drive File to Vector DB"
},
"typeVersion": 1
},
{
"id": "a137d00b-fb01-408c-9963-645e2beb44d9",
"name": "文書テキスト抽出",
"type": "n8n-nodes-base.extractFromFile",
"position": [
2512,
1280
],
"parameters": {
"options": {},
"operation": "text"
},
"typeVersion": 1,
"alwaysOutputData": true
},
{
"id": "1aec304d-7264-4e65-8654-cb9294c96c82",
"name": "Postgresチャットメモリ",
"type": "@n8n/n8n-nodes-langchain.memoryPostgresChat",
"position": [
1712,
512
],
"parameters": {},
"notesInFlow": false,
"typeVersion": 1
},
{
"id": "9c407f2b-4f6a-46d6-a607-225c1c628ae5",
"name": "ファイルID設定",
"type": "n8n-nodes-base.set",
"position": [
992,
960
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "10646eae-ae46-4327-a4dc-9987c2d76173",
"name": "file_id",
"type": "string",
"value": "={{ $json.path }}"
},
{
"id": "f4536df5-d0b1-4392-bf17-b8137fb31a44",
"name": "file_type",
"type": "string",
"value": "={{ $json.path.split(/[\\\\/]/).pop().split('.').pop(); }}"
},
{
"id": "77d782de-169d-4a46-8a8e-a3831c04d90f",
"name": "file_title",
"type": "string",
"value": "={{ $json.path.split(/[\\\\/]/).pop().split('.').slice(0, -1).join('.'); }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "bc93aa94-10ec-4670-99f4-3bcec36be1ce",
"name": "付箋2",
"type": "n8n-nodes-base.stickyNote",
"position": [
1264,
208
],
"parameters": {
"width": 1035.6381264595484,
"height": 464.8027193303974,
"content": "## RAG AI Agent with Chat Interface"
},
"typeVersion": 1
},
{
"id": "8ccc451e-2fac-49b0-8700-085476add599",
"name": "Webhook への応答",
"type": "n8n-nodes-base.respondToWebhook",
"position": [
2128,
288
],
"parameters": {
"options": {}
},
"typeVersion": 1.1
},
{
"id": "55abb8ac-7988-430a-ae41-5155471228a2",
"name": "フィールド編集",
"type": "n8n-nodes-base.set",
"position": [
1568,
288
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "9a9a245e-f1a1-4282-bb02-a81ffe629f0f",
"name": "chatInput",
"type": "string",
"value": "={{ $json?.chatInput || $json.body.chatInput }}"
},
{
"id": "b80831d8-c653-4203-8706-adedfdb98f77",
"name": "sessionId",
"type": "string",
"value": "={{ $json?.sessionId || $json.body.sessionId}}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "78b3fd17-23e9-4693-b782-918a5a8e5aed",
"name": "チャットメッセージ受信時",
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"position": [
1312,
288
],
"webhookId": "e104e40e-6134-4825-a6f0-8a646d882662",
"parameters": {
"public": true,
"options": {}
},
"typeVersion": 1.1
},
{
"id": "06e362d1-d20c-407a-a75a-ed175c07439d",
"name": "Webhook",
"type": "n8n-nodes-base.webhook",
"position": [
1312,
480
],
"webhookId": "bf4dd093-bb02-472c-9454-7ab9af97bd1d",
"parameters": {
"path": "bf4dd093-bb02-472c-9454-7ab9af97bd1d",
"options": {},
"httpMethod": "POST",
"responseMode": "responseNode"
},
"typeVersion": 2
},
{
"id": "e8ba5c17-3426-4d76-b69b-ff91dff7958f",
"name": "PDFテキスト抽出",
"type": "n8n-nodes-base.extractFromFile",
"position": [
2512,
720
],
"parameters": {
"options": {},
"operation": "pdf"
},
"typeVersion": 1
},
{
"id": "b40eb123-d7fc-4799-b248-4b9516aee49e",
"name": "集約",
"type": "n8n-nodes-base.aggregate",
"position": [
2544,
912
],
"parameters": {
"options": {},
"aggregate": "aggregateAllItemData"
},
"typeVersion": 1
},
{
"id": "0e3755e8-9532-447f-9137-f65d542c247e",
"name": "要約",
"type": "n8n-nodes-base.summarize",
"position": [
2752,
992
],
"parameters": {
"options": {},
"fieldsToSummarize": {
"values": [
{
"field": "data",
"aggregation": "concatenate"
}
]
}
},
"typeVersion": 1
},
{
"id": "b185f2be-06bf-4a14-8d58-4b411a709f18",
"name": "RAG AIエージェント",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
1792,
288
],
"parameters": {
"text": "={{ $json.chatInput }}",
"options": {
"systemMessage": "You are a personal assistant who helps answer questions from a corpus of documents. The documents are either text based (Txt, docs, extracted PDFs, etc.) or tabular data (CSVs or Excel documents).\n\nYou are given tools to perform RAG in the 'documents' table, look up the documents available in your knowledge base in the 'document_metadata' table, extract all the text from a given document, and query the tabular files with SQL in the 'document_rows' table.\n\nAlways start by performing RAG unless the users asks you to check a document or the question requires a SQL query for tabular data (fetching a sum, finding a max, something a RAG lookup would be unreliable for). If RAG doesn't help, then look at the documents that are available to you, find a few that you think would contain the answer, and then analyze those.\n\nAlways tell the user if you didn't find the answer. Don't make something up just to please them."
},
"promptType": "define"
},
"typeVersion": 1.6
},
{
"id": "2ee45951-3553-49b7-9f79-3cef3d065e8a",
"name": "スイッチ",
"type": "n8n-nodes-base.switch",
"position": [
1840,
944
],
"parameters": {
"rules": {
"values": [
{
"conditions": {
"options": {
"version": 1,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"operator": {
"type": "string",
"operation": "equals"
},
"leftValue": "={{ $('Set File ID').item.json.file_type }}",
"rightValue": "pdf"
}
]
}
},
{
"conditions": {
"options": {
"version": 1,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "2ae7faa7-a936-4621-a680-60c512163034",
"operator": {
"name": "filter.operator.equals",
"type": "string",
"operation": "equals"
},
"leftValue": "={{ $('Set File ID').item.json.file_type }}",
"rightValue": "xlsx"
}
]
}
},
{
"conditions": {
"options": {
"version": 1,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "fc193b06-363b-4699-a97d-e5a850138b0e",
"operator": {
"name": "filter.operator.equals",
"type": "string",
"operation": "equals"
},
"leftValue": "={{ $('Set File ID').item.json.file_type }}",
"rightValue": "=csv"
}
]
}
},
{
"conditions": {
"options": {
"version": 1,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "b69f5605-0179-4b02-9a32-e34bb085f82d",
"operator": {
"name": "filter.operator.equals",
"type": "string",
"operation": "equals"
},
"leftValue": "={{ $('Set File ID').item.json.file_type }}",
"rightValue": "txt"
}
]
}
}
]
},
"options": {
"fallbackOutput": 3
}
},
"typeVersion": 3
},
{
"id": "20bf7dde-e073-4288-a9d6-34df3973b5c3",
"name": "Excelから抽出",
"type": "n8n-nodes-base.extractFromFile",
"position": [
2320,
912
],
"parameters": {
"options": {},
"operation": "xlsx"
},
"typeVersion": 1
},
{
"id": "f1840995-3f1c-4f4e-9d78-bc9225ecbe2b",
"name": "スキーマ設定",
"type": "n8n-nodes-base.set",
"position": [
3184,
848
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "f422e2e0-381c-46ea-8f38-3f58c501d8b9",
"name": "schema",
"type": "string",
"value": "={{ $('Extract from Excel').isExecuted ? $('Extract from Excel').first().json.keys().toJsonString() : $('Extract from CSV').first().json.keys().toJsonString() }}"
},
{
"id": "bb07c71e-5b60-4795-864c-cc3845b6bc46",
"name": "data",
"type": "string",
"value": "={{ $json.concatenated_data }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "b79ceb0b-f370-4ffb-9953-14b411acb5d9",
"name": "CSV から抽出",
"type": "n8n-nodes-base.extractFromFile",
"position": [
2320,
1088
],
"parameters": {
"options": {}
},
"typeVersion": 1
},
{
"id": "7067874e-4123-4a6c-a94d-89e4d1878309",
"name": "付箋3",
"type": "n8n-nodes-base.stickyNote",
"position": [
560,
368
],
"parameters": {
"color": 3,
"width": 680,
"height": 300,
"content": "## Run Each Node Once to Set Up Database Tables"
},
"typeVersion": 1
},
{
"id": "130c53e8-d507-4b6f-b1cf-f79dbc571c46",
"name": "文書メタデータテーブル作成",
"type": "n8n-nodes-base.postgres",
"position": [
688,
464
],
"parameters": {
"query": "CREATE TABLE document_metadata (\n id TEXT PRIMARY KEY,\n title TEXT,\n created_at TIMESTAMP DEFAULT NOW(),\n schema TEXT\n);",
"options": {},
"operation": "executeQuery"
},
"typeVersion": 2.5
},
{
"id": "421d2123-b68a-4c51-a482-db5bdffd3f76",
"name": "文書行テーブル作成（表形式データ用）",
"type": "n8n-nodes-base.postgres",
"position": [
992,
464
],
"parameters": {
"query": "CREATE TABLE document_rows (\n id SERIAL PRIMARY KEY,\n dataset_id TEXT REFERENCES document_metadata(id),\n row_data JSONB -- Store the actual row data\n);",
"options": {},
"operation": "executeQuery"
},
"typeVersion": 2.5
},
{
"id": "55ff6535-bedb-479f-b3da-eb45e1127e77",
"name": "文書一覧表示",
"type": "n8n-nodes-base.postgresTool",
"position": [
1840,
512
],
"parameters": {
"table": {
"__rl": true,
"mode": "list",
"value": "document_metadata",
"cachedResultName": "document_metadata"
},
"schema": {
"__rl": true,
"mode": "list",
"value": "public"
},
"options": {},
"operation": "select",
"returnAll": true,
"descriptionType": "manual",
"toolDescription": "Use this tool to fetch all available documents, including the table schema if the file is a CSV or Excel file."
},
"typeVersion": 2.5
},
{
"id": "ffcb630b-5119-4ff6-b85a-d77eeb8d5713",
"name": "ファイル内容取得",
"type": "n8n-nodes-base.postgresTool",
"position": [
1984,
512
],
"parameters": {
"query": "SELECT \n string_agg(text, ' ') as document_text\nFROM documents_pg\n WHERE metadata->>'file_id' = $1\nGROUP BY metadata->>'file_id';",
"options": {
"queryReplacement": "={{ $fromAI('file_id') }}"
},
"operation": "executeQuery",
"descriptionType": "manual",
"toolDescription": "Given a file ID, fetches the text from the document."
},
"typeVersion": 2.5
},
{
"id": "f504b2f4-ffb5-4ef7-ba93-753151b77d9e",
"name": "文書行クエリ",
"type": "n8n-nodes-base.postgresTool",
"position": [
2144,
512
],
"parameters": {
"query": "{{ $fromAI('sql_query') }}",
"options": {},
"operation": "executeQuery",
"descriptionType": "manual",
"toolDescription": "Run a SQL query - use this to query from the document_rows table once you know the file ID (which is the file path) you are querying. dataset_id is the file_id (file path) and you are always using the row_data for filtering, which is a jsonb field that has all the keys from the file schema given in the document_metadata table.\n\nExample query:\n\nSELECT AVG((row_data->>'revenue')::numeric)\nFROM document_rows\nWHERE dataset_id = '/data/shared/document.csv';\n\nExample query 2:\n\nSELECT \n row_data->>'category' as category,\n SUM((row_data->>'sales')::numeric) as total_sales\nFROM dataset_rows\nWHERE dataset_id = '/data/shared/document2.csv'\nGROUP BY row_data->>'category';"
},
"typeVersion": 2.5
},
{
"id": "4abe03ca-297c-4509-b0db-7bed4338a158",
"name": "アイテムループ処理",
"type": "n8n-nodes-base.splitInBatches",
"position": [
800,
800
],
"parameters": {
"options": {
"reset": false
}
},
"typeVersion": 3
},
{
"id": "e382d750-85ba-492d-9d3e-eb839af0bfc1",
"name": "文書メタデータ挿入",
"type": "n8n-nodes-base.postgres",
"position": [
1488,
832
],
"parameters": {
"table": {
"__rl": true,
"mode": "list",
"value": "document_metadata",
"cachedResultName": "document_metadata"
},
"schema": {
"__rl": true,
"mode": "list",
"value": "public"
},
"columns": {
"value": {
"id": "={{ $('Set File ID').item.json.file_id }}",
"title": "={{ $('Set File ID').item.json.file_title }}"
},
"schema": [
{
"id": "id",
"type": "string",
"display": true,
"removed": false,
"required": true,
"displayName": "id",
"defaultMatch": true,
"canBeUsedToMatch": true
},
{
"id": "title",
"type": "string",
"display": true,
"required": false,
"displayName": "title",
"defaultMatch": false,
"canBeUsedToMatch": false
},
{
"id": "url",
"type": "string",
"display": true,
"removed": true,
"required": false,
"displayName": "url",
"defaultMatch": false,
"canBeUsedToMatch": false
},
{
"id": "created_at",
"type": "dateTime",
"display": true,
"required": false,
"displayName": "created_at",
"defaultMatch": false,
"canBeUsedToMatch": false
},
{
"id": "schema",
"type": "string",
"display": true,
"removed": true,
"required": false,
"displayName": "schema",
"defaultMatch": false,
"canBeUsedToMatch": false
}
],
"mappingMode": "defineBelow",
"matchingColumns": [
"id"
],
"attemptToConvertTypes": false,
"convertFieldsToString": false
},
"options": {},
"operation": "upsert"
},
"executeOnce": true,
"typeVersion": 2.5
},
{
"id": "bbf6f704-b4a2-4ff2-ac09-27626526b35f",
"name": "テーブル行挿入",
"type": "n8n-nodes-base.postgres",
"position": [
2544,
1088
],
"parameters": {
"table": {
"__rl": true,
"mode": "list",
"value": "document_rows",
"cachedResultName": "document_rows"
},
"schema": {
"__rl": true,
"mode": "list",
"value": "public"
},
"columns": {
"value": {
"row_data": "={{ $json.toJsonString().replaceAll(/'/g, \"''\") }}",
"dataset_id": "={{ $('Set File ID').item.json.file_id }}"
},
"schema": [
{
"id": "id",
"type": "number",
"display": true,
"removed": true,
"required": false,
"displayName": "id",
"defaultMatch": true,
"canBeUsedToMatch": true
},
{
"id": "dataset_id",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "dataset_id",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "row_data",
"type": "object",
"display": true,
"removed": false,
"required": false,
"displayName": "row_data",
"defaultMatch": false,
"canBeUsedToMatch": true
}
],
"mappingMode": "defineBelow",
"matchingColumns": [
"id"
],
"attemptToConvertTypes": false,
"convertFieldsToString": false
},
"options": {}
},
"typeVersion": 2.5
},
{
"id": "3265a7df-dd40-421e-b1fb-53293a7460f8",
"name": "文書メタデータ用スキーマ更新",
"type": "n8n-nodes-base.postgres",
"position": [
3408,
848
],
"parameters": {
"table": {
"__rl": true,
"mode": "list",
"value": "document_metadata",
"cachedResultName": "document_metadata"
},
"schema": {
"__rl": true,
"mode": "list",
"value": "public"
},
"columns": {
"value": {
"id": "={{ $('Set File ID').item.json.file_id }}",
"schema": "={{ $json.schema }}"
},
"schema": [
{
"id": "id",
"type": "string",
"display": true,
"removed": false,
"required": true,
"displayName": "id",
"defaultMatch": true,
"canBeUsedToMatch": true
},
{
"id": "title",
"type": "string",
"display": true,
"removed": true,
"required": false,
"displayName": "title",
"defaultMatch": false,
"canBeUsedToMatch": false
},
{
"id": "url",
"type": "string",
"display": true,
"removed": true,
"required": false,
"displayName": "url",
"defaultMatch": false,
"canBeUsedToMatch": false
},
{
"id": "created_at",
"type": "dateTime",
"display": true,
"required": false,
"displayName": "created_at",
"defaultMatch": false,
"canBeUsedToMatch": false
},
{
"id": "schema",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "schema",
"defaultMatch": false,
"canBeUsedToMatch": false
}
],
"mappingMode": "defineBelow",
"matchingColumns": [
"id"
],
"attemptToConvertTypes": false,
"convertFieldsToString": false
},
"options": {},
"operation": "upsert"
},
"typeVersion": 2.5
},
{
"id": "53f9f045-bb08-4b22-a11e-dfd2c964b687",
"name": "付箋9",
"type": "n8n-nodes-base.stickyNote",
"position": [
0,
0
],
"parameters": {
"color": 6,
"width": 540,
"height": 1320,
"content": "## 🚀 n8n Local AI Agentic RAG Template\n\n**Author:** [Jadai kongolo](https://my.jadaikongolo.tech)\n\n## What is this?\nThis template provides an entirely local implementation of an **Agentic RAG (Retrieval Augmented Generation)** system in n8n that can be extended easily for your specific use case and knowledge base. Unlike standard RAG which only performs simple lookups, this agent can reason about your knowledge base, self-improve retrieval, and dynamically switch between different tools based on the specific question. \n\n## Why Agentic RAG?\nStandard RAG has significant limitations:\n- Poor analysis of numerical/tabular data\n- Missing context due to document chunking\n- Inability to connect information across documents\n- No dynamic tool selection based on question type\n\n## What makes this template powerful:\n- **Intelligent tool selection**: Switches between RAG lookups, SQL queries, or full document retrieval based on the question\n- **Complete document context**: Accesses entire documents when needed instead of just chunks\n- **Accurate numerical analysis**: Uses SQL for precise calculations on spreadsheet/tabular data\n- **Cross-document insights**: Connects information across your entire knowledge base\n- **Multi-file processing**: Handles multiple documents in a single workflow loop\n- **Efficient storage**: Uses JSONB in Supabase to store tabular data without creating new tables for each CSV\n\n## Getting Started\n1. Run the table creation nodes first to set up your database tables in Supabase\n2. Upload your documents to the folder on your computer that is mounted to /data/shared in the n8n container. This folder by default is the \"shared\" folder in the local AI package.\n3. The agent will process them automatically (chunking text, storing tabular data in Supabase)\n4. Start asking questions that leverage the agent's multiple reasoning approaches\n\n## Customization\nThis template provides a solid foundation that you can extend by:\n- Tuning the system prompt for your specific use case\n- Adding document metadata like summaries\n- Implementing more advanced RAG techniques\n- Optimizing for larger knowledge bases\n\n---\n\nThe non-local (\"cloud\") version of this Agentic RAG agent can be [found here](https://kongolo.gumroad.com/l/anxwv)."
},
"typeVersion": 1
},
{
"id": "cdee87fe-e154-47ab-9330-32dee5c213d3",
"name": "ローカルファイルトリガー",
"type": "n8n-nodes-base.localFileTrigger",
"position": [
608,
800
],
"parameters": {
"path": "/data/shared",
"events": [
"add",
"change"
],
"options": {
"usePolling": true,
"followSymlinks": true
},
"triggerOn": "folder"
},
"typeVersion": 1
},
{
"id": "67311475-7928-4ddc-957a-79817c98d26d",
"name": "ディスクからファイル読み書き",
"type": "n8n-nodes-base.readWriteFile",
"position": [
1648,
960
],
"parameters": {
"options": {
"dataPropertyName": "=data"
},
"fileSelector": "={{ $('Set File ID').item.json.file_id }}"
},
"typeVersion": 1
},
{
"id": "366e800a-9bd7-4822-a11c-f555800bbba6",
"name": "Embeddings Ollama",
"type": "@n8n/n8n-nodes-langchain.embeddingsOllama",
"position": [
3072,
1280
],
"parameters": {
"model": "nomic-embed-text:latest"
},
"typeVersion": 1
},
{
"id": "be37cfb9-ea40-4244-87d7-b562be315573",
"name": "Embeddings Ollama1",
"type": "@n8n/n8n-nodes-langchain.embeddingsOllama",
"position": [
2560,
480
],
"parameters": {
"model": "nomic-embed-text:latest"
},
"typeVersion": 1
},
{
"id": "1306b972-2b24-4c62-846e-f1c5b3d0482c",
"name": "再帰的文字テキスト分割器",
"type": "@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter",
"position": [
3200,
1408
],
"parameters": {
"options": {},
"chunkSize": 400
},
"typeVersion": 1
},
{
"id": "677ad468-8118-4f8f-9a47-f5429cdc7582",
"name": "Ollama（ベースURL変更）",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
1568,
512
],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "qwen2.5:14b-8k",
"cachedResultName": "qwen2.5:14b-8k"
},
"options": {}
},
"typeVersion": 1.2
},
{
"id": "b3e23401-8868-4b3c-a3fe-37fda44419d5",
"name": "付箋4",
"type": "n8n-nodes-base.stickyNote",
"position": [
0,
1344
],
"parameters": {
"color": 6,
"width": 540,
"height": 200,
"content": "## NOTE\n\nThe Ollama chat model node doesn't work with the RAG nodes - known issue with n8n.\n\nSo for now, we are using the OpenAI chat model but changing the base URL to Ollama when creating the credentials (i.e. http://ollama:11434/v1). The API key can be set to whatever, it isn't used for local LLMs."
},
"typeVersion": 1
},
{
"id": "987a6081-cdfd-457e-a2e5-4fa93fa018f4",
"name": "旧文書レコード削除",
"type": "n8n-nodes-base.postgres",
"position": [
1168,
832
],
"parameters": {
"query": "DO $$\nBEGIN\n IF EXISTS (SELECT 1 FROM information_schema.tables WHERE table_name = 'documents_pg') THEN\n EXECUTE 'DELETE FROM documents_pg WHERE metadata->>''file_id'' LIKE ''%' || $1 || '%''';\n END IF;\nEND\n$$;",
"options": {
"queryReplacement": "={{ $json.file_id }}"
},
"operation": "executeQuery"
},
"typeVersion": 2.5
},
{
"id": "619a8a54-5fb8-4d8f-9cac-5a1c2a58f44b",
"name": "旧データレコード削除",
"type": "n8n-nodes-base.postgres",
"position": [
1328,
960
],
"parameters": {
"query": "DELETE FROM document_rows\nWHERE dataset_id LIKE '%' || $1 || '%';",
"options": {
"queryReplacement": "={{ $('Set File ID').item.json.file_id }}"
},
"operation": "executeQuery"
},
"typeVersion": 2.5
},
{
"id": "c975f943-3c05-45eb-9b11-4bd254845fbc",
"name": "Postgres PGVectorストア",
"type": "@n8n/n8n-nodes-langchain.vectorStorePGVector",
"position": [
3184,
1072
],
"parameters": {
"mode": "insert",
"options": {},
"tableName": "documents_pg"
},
"typeVersion": 1
},
{
"id": "9bba5830-ad14-454c-b653-48baf03844bb",
"name": "Postgres PGVectorストア1",
"type": "@n8n/n8n-nodes-langchain.vectorStorePGVector",
"position": [
2464,
288
],
"parameters": {
"mode": "retrieve-as-tool",
"options": {},
"toolName": "documents",
"tableName": "documents_pg",
"toolDescription": "Use RAG to look up information in the knowledgebase."
},
"typeVersion": 1
}
],
"active": false,
"pinData": {},
"settings": {
"executionOrder": "v1"
},
"versionId": "43f092c7-957d-42d3-8ea5-26108c4cd991",
"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 - 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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ノード数41
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作成者
Jadai kongolo
@jadai-ai-automationHi 👋 I'm Jadai kongolo. As an AI Automation Expert, I’m passionate about simplifying tech and empowering small businesses and young coders through AI automation. With my AI agency, Oki, I create efficient, n8n-powered workflows that save time, streamline operations, and boost growth for SMBs.
外部リンク
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