Workflow n8n
Automatisation Notion avec n8n : gestion des données en temps réel
Ce workflow n8n a pour objectif d'automatiser la gestion des données dans Notion en intégrant des fonctionnalités avancées d'OpenAI. Dans un contexte où les entreprises doivent gérer des informations en constante évolution, ce workflow permet de récupérer, traiter et stocker des données de manière efficace. Parfait pour les équipes de gestion de projet ou de développement de contenu, il facilite la synchronisation des informations entre Notion et un système de stockage vectoriel, tout en offrant des capacités de question-réponse grâce à l'IA. Étape 1 : le déclencheur Notion active le workflow lorsqu'une mise à jour est détectée dans la base de données. Étape 2 : les données sont ensuite traitées par le nœud 'Token Splitter' pour les segmenter en morceaux plus petits. Étape 3 : les 'Embeddings OpenAI' transforment ces morceaux en vecteurs exploitables. Étape 4 : les données sont stockées dans Supabase via le nœud 'Supabase Vector Store', permettant un accès rapide et une recherche efficace. Étape 5 : le nœud 'OpenAI Chat Model' permet d'interagir avec les données en temps réel, offrant des réponses précises aux utilisateurs. Ce workflow apporte une valeur ajoutée significative en réduisant le temps passé à gérer manuellement les données, tout en augmentant la précision et la rapidité d'accès aux informations. Tags clés : automatisation, Notion, OpenAI.
Catégorie: Notion Trigger · Tags: automatisation, Notion, OpenAI, data management, n8n❤ 0
Vue d'ensemble du workflow n8n
Schéma des nœuds et connexions de ce workflow n8n, généré à partir du JSON n8n.
Détail des nœuds du workflow n8n
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"id": "JxFP8FJ2W7e4Kmqn",
"meta": {
"instanceId": "fb8bc2e315f7f03c97140b30aa454a27bc7883a19000fa1da6e6b571bf56ad6d",
"templateCredsSetupCompleted": true
},
"name": "RAG on living data",
"tags": [],
"nodes": [
{
"id": "49086cdf-a38c-4cb8-9be9-d3e6ea5bdde5",
"name": "Embeddings OpenAI",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"position": [
1740,
1040
],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"id": "X7Jf0zECd3IkQdSw",
"name": "OpenAi (octionicsolutions)"
}
},
"typeVersion": 1
},
{
"id": "f0670721-92f4-422a-99c9-f9c2aa6fe21f",
"name": "Token Splitter",
"type": "@n8n/n8n-nodes-langchain.textSplitterTokenSplitter",
"position": [
2380,
540
],
"parameters": {
"chunkSize": 500
},
"typeVersion": 1
},
{
"id": "fe80ecac-4f79-4b07-ad8e-60ab5f980cba",
"name": "Loop Over Items",
"type": "n8n-nodes-base.splitInBatches",
"position": [
1180,
-200
],
"parameters": {
"options": {}
},
"typeVersion": 3
},
{
"id": "81b79248-08e8-4214-872b-1796e51ad0a4",
"name": "Question and Answer Chain",
"type": "@n8n/n8n-nodes-langchain.chainRetrievalQa",
"position": [
744,
495
],
"parameters": {
"options": {}
},
"typeVersion": 1.3
},
{
"id": "e78f7b63-baef-4834-8f1b-aecfa9102d6c",
"name": "Vector Store Retriever",
"type": "@n8n/n8n-nodes-langchain.retrieverVectorStore",
"position": [
844,
715
],
"parameters": {},
"typeVersion": 1
},
{
"id": "1d5ffbd0-b2cf-4660-a291-581d18608ecd",
"name": "OpenAI Chat Model",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
704,
715
],
"parameters": {
"model": "gpt-4o",
"options": {}
},
"credentials": {
"openAiApi": {
"id": "X7Jf0zECd3IkQdSw",
"name": "OpenAi (octionicsolutions)"
}
},
"typeVersion": 1
},
{
"id": "37a3063f-aa21-4347-a72f-6dd316c58366",
"name": "When chat message received",
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"position": [
524,
495
],
"webhookId": "74479a54-418f-4de2-b70d-cfb3e3fdd5a7",
"parameters": {
"public": true,
"options": {}
},
"typeVersion": 1.1
},
{
"id": "5924bc01-1694-4b5c-8a06-7c46ee4c6425",
"name": "Schedule Trigger",
"type": "n8n-nodes-base.scheduleTrigger",
"position": [
520,
-200
],
"parameters": {
"rule": {
"interval": [
{
"field": "minutes",
"minutesInterval": 1
}
]
}
},
"typeVersion": 1.2
},
{
"id": "5067eda6-8bbe-407a-a6af-93e81be53661",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
620,
0
],
"parameters": {
"width": 329.16412916774584,
"height": 312.52803480051045,
"content": "## Switch trigger (optional)\nIf you are on the cloud plan, consider switching to the Notion Trigger Node instead, to save on executions."
},
"typeVersion": 1
},
{
"id": "33458828-484d-426b-a3d1-974a81c6162e",
"name": "Limit",
"type": "n8n-nodes-base.limit",
"position": [
1620,
-60
],
"parameters": {},
"typeVersion": 1
},
{
"id": "4d39503a-378e-4942-a5d4-8c62785aac44",
"name": "Limit1",
"type": "n8n-nodes-base.limit",
"position": [
2660,
-60
],
"parameters": {},
"typeVersion": 1
},
{
"id": "0e0b1391-3fe5-4d80-a2eb-a2483b79d9a6",
"name": "Delete old embeddings if exist",
"type": "n8n-nodes-base.supabase",
"position": [
1400,
-60
],
"parameters": {
"tableId": "documents",
"operation": "delete",
"filterType": "string",
"filterString": "=metadata->>id=eq.{{ $('Input Reference').item.json.id }}"
},
"credentials": {
"supabaseApi": {
"id": "DjIb4HMTYXhTU8Uc",
"name": "Supabase (VectorStore)"
}
},
"typeVersion": 1,
"alwaysOutputData": true
},
{
"id": "4a8614e4-0a53-4731-bc68-57505d7d0a09",
"name": "Get page blocks",
"type": "n8n-nodes-base.notion",
"position": [
1840,
-60
],
"parameters": {
"blockId": {
"__rl": true,
"mode": "id",
"value": "={{ $('Input Reference').item.json.id }}"
},
"resource": "block",
"operation": "getAll",
"returnAll": true,
"fetchNestedBlocks": true
},
"credentials": {
"notionApi": {
"id": "ObmaBA0dJss3JJPv",
"name": "Notion (octionicsolutions / Test)"
}
},
"executeOnce": true,
"typeVersion": 2.2
},
{
"id": "8c922895-49d6-4778-8356-6f6cf49e5420",
"name": "Default Data Loader",
"type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
"position": [
2300,
260
],
"parameters": {
"options": {
"metadata": {
"metadataValues": [
{
"name": "id",
"value": "={{ $('Input Reference').item.json.id }}"
},
{
"name": "name",
"value": "={{ $('Input Reference').item.json.name }}"
}
]
}
}
},
"typeVersion": 1
},
{
"id": "8ad7ff2e-4bc2-4821-ae03-bab2dc11d947",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
2220,
400
],
"parameters": {
"width": 376.2098538932132,
"height": 264.37628764336097,
"content": "## Adjust chunk size and overlap\nFor more accurate search results, increase the overlap. For the *text-embedding-ada-002* model the chunk size plus overlap must not exceed 8191"
},
"typeVersion": 1
},
{
"id": "8078d59a-f45f-4e96-a8ec-6c2f1c328e84",
"name": "Input Reference",
"type": "n8n-nodes-base.noOp",
"position": [
960,
-200
],
"parameters": {},
"typeVersion": 1
},
{
"id": "aae6c517-a316-40e3-aee9-1cc4b448689f",
"name": "Notion Trigger",
"type": "n8n-nodes-base.notionTrigger",
"disabled": true,
"position": [
740,
120
],
"parameters": {
"event": "pagedUpdatedInDatabase",
"pollTimes": {
"item": [
{
"mode": "everyMinute"
}
]
},
"databaseId": {
"__rl": true,
"mode": "list",
"value": "ec6dc7b4-9ce0-47f7-8025-ef09295999fd",
"cachedResultUrl": "https://www.notion.so/ec6dc7b49ce047f78025ef09295999fd",
"cachedResultName": "Knowledge Base"
}
},
"credentials": {
"notionApi": {
"id": "ObmaBA0dJss3JJPv",
"name": "Notion (octionicsolutions / Test)"
}
},
"typeVersion": 1
},
{
"id": "3a43d66d-d4e3-4ca1-aee9-85ac65160e45",
"name": "Get updated pages",
"type": "n8n-nodes-base.notion",
"position": [
740,
-200
],
"parameters": {
"filters": {
"conditions": [
{
"key": "Last edited time|last_edited_time",
"condition": "equals",
"lastEditedTime": "={{ $now.minus(1, 'minutes').toISO() }}"
}
]
},
"options": {},
"resource": "databasePage",
"operation": "getAll",
"databaseId": {
"__rl": true,
"mode": "list",
"value": "ec6dc7b4-9ce0-47f7-8025-ef09295999fd",
"cachedResultUrl": "https://www.notion.so/ec6dc7b49ce047f78025ef09295999fd",
"cachedResultName": "Knowledge Base"
},
"filterType": "manual"
},
"credentials": {
"notionApi": {
"id": "ObmaBA0dJss3JJPv",
"name": "Notion (octionicsolutions / Test)"
}
},
"typeVersion": 2.2
},
{
"id": "bbf1296f-4e2b-4a38-bdf3-ae2b63cc7774",
"name": "Sticky Note23",
"type": "n8n-nodes-base.stickyNote",
"position": [
900,
-300
],
"parameters": {
"color": 7,
"width": 216.47293010628914,
"height": 275.841854198618,
"content": "This placeholder serves as a reference point so it is easier to swap the data source with a different service"
},
"typeVersion": 1
},
{
"id": "631e1e10-0b52-4a17-89a4-769ac563321f",
"name": "Sticky Note24",
"type": "n8n-nodes-base.stickyNote",
"position": [
1340,
-160
],
"parameters": {
"color": 7,
"width": 216.47293010628914,
"height": 275.841854198618,
"content": "All chunks of a previous version of the document are being deleted by filtering the meta data by the given ID"
},
"typeVersion": 1
},
{
"id": "6c830c83-4b70-4719-8e2a-26846e60085c",
"name": "Sticky Note25",
"type": "n8n-nodes-base.stickyNote",
"position": [
1560,
-160
],
"parameters": {
"color": 7,
"width": 216.47293010628914,
"height": 275.841854198618,
"content": "Reduce the active streams/items to just 1 to prevent the following nodes from double-processing"
},
"typeVersion": 1
},
{
"id": "46c8e4e4-0a5e-4ede-947b-5773710d4e55",
"name": "Sticky Note26",
"type": "n8n-nodes-base.stickyNote",
"position": [
1780,
-160
],
"parameters": {
"color": 7,
"width": 216.47293010628914,
"height": 275.841854198618,
"content": "Retrieve all page contents/blocks"
},
"typeVersion": 1
},
{
"id": "0369e610-d074-4812-9d04-8615b42965a5",
"name": "Sticky Note27",
"type": "n8n-nodes-base.stickyNote",
"position": [
2600,
-160
],
"parameters": {
"color": 7,
"width": 216.47293010628914,
"height": 275.841854198618,
"content": "Reduce the active streams/items to just 1 to prevent the following nodes from double-processing"
},
"typeVersion": 1
},
{
"id": "4f3bce54-1650-45fa-abb0-c881358c7e8d",
"name": "Sticky Note28",
"type": "n8n-nodes-base.stickyNote",
"position": [
2220,
-160
],
"parameters": {
"color": 7,
"width": 375.9283286479995,
"height": 275.841854198618,
"content": "Embed item and store in Vector Store. Depending on the length the content is being split up into multiple chunks/embeds"
},
"typeVersion": 1
},
{
"id": "44125921-e068-4a5d-a56b-b0e63c103556",
"name": "Supabase Vector Store1",
"type": "@n8n/n8n-nodes-langchain.vectorStoreSupabase",
"position": [
924,
935
],
"parameters": {
"options": {},
"tableName": {
"__rl": true,
"mode": "list",
"value": "documents",
"cachedResultName": "documents"
}
},
"credentials": {
"supabaseApi": {
"id": "DjIb4HMTYXhTU8Uc",
"name": "Supabase (VectorStore)"
}
},
"typeVersion": 1
},
{
"id": "467322a9-949d-4569-aac6-92196da46ba5",
"name": "Sticky Note30",
"type": "n8n-nodes-base.stickyNote",
"position": [
460,
400
],
"parameters": {
"color": 7,
"width": 730.7522093855692,
"height": 668.724737081502,
"content": "Simple chat bot to ask specific questions while having access to the context of the Notion Knowledge Base which was stored in the Vector Store"
},
"typeVersion": 1
},
{
"id": "27f078cf-b309-4dd1-a8ce-b4fc504d6e29",
"name": "Sticky Note31",
"type": "n8n-nodes-base.stickyNote",
"position": [
1660,
900
],
"parameters": {
"color": 7,
"width": 219.31927574471658,
"height": 275.841854198618,
"content": "Model used for both creating and reading embeddings"
},
"typeVersion": 1
},
{
"id": "2f59cba1-4318-47e7-bf0b-b908d4186b86",
"name": "Supabase Vector Store",
"type": "@n8n/n8n-nodes-langchain.vectorStoreSupabase",
"position": [
2280,
-60
],
"parameters": {
"mode": "insert",
"options": {},
"tableName": {
"__rl": true,
"mode": "list",
"value": "documents",
"cachedResultName": "documents"
}
},
"credentials": {
"supabaseApi": {
"id": "DjIb4HMTYXhTU8Uc",
"name": "Supabase (VectorStore)"
}
},
"typeVersion": 1
},
{
"id": "729849e7-0eff-40c2-ae00-ae660c1eec69",
"name": "Sticky Note32",
"type": "n8n-nodes-base.stickyNote",
"position": [
1120,
-300
],
"parameters": {
"color": 7,
"width": 216.47293010628914,
"height": 275.841854198618,
"content": "Process each page/document separately."
},
"typeVersion": 1
},
{
"id": "3f632a24-ca0a-45c4-801d-041aa3f887a7",
"name": "Sticky Note29",
"type": "n8n-nodes-base.stickyNote",
"position": [
2220,
120
],
"parameters": {
"color": 7,
"width": 376.0759088111347,
"height": 275.841854198618,
"content": "Store additional meta data with each embed, especially the Notion ID, which can be later used to find all belonging entries of one page, even if they got split into multiple embeds."
},
"typeVersion": 1
},
{
"id": "ffaf3861-5287-4f57-8372-09216a18cb4d",
"name": "Sticky Note33",
"type": "n8n-nodes-base.stickyNote",
"position": [
460,
-300
],
"parameters": {
"color": 7,
"width": 216.47293010628914,
"height": 275.841854198618,
"content": "Using a manual approach for polling data from Notion for more accuracy."
},
"typeVersion": 1
},
{
"id": "cbbedfc0-4d64-42a6-8f55-21e04887305f",
"name": "Sticky Note34",
"type": "n8n-nodes-base.stickyNote",
"position": [
680,
-300
],
"parameters": {
"width": 216.47293010628914,
"height": 275.841854198618,
"content": "## Select Database\nChoose the database which represents your Knowledge Base"
},
"typeVersion": 1
},
{
"id": "8b6767f2-1bc9-42fb-b319-f39f6734b9f2",
"name": "Sticky Note35",
"type": "n8n-nodes-base.stickyNote",
"position": [
2000,
-160
],
"parameters": {
"color": 7,
"width": 216.47293010628914,
"height": 275.841854198618,
"content": "Combine all contents to a single text formatted into one line which can be easily stored as an embed"
},
"typeVersion": 1
},
{
"id": "cdff1756-77d7-421e-8672-25c9862840b0",
"name": "Concatenate to single string",
"type": "n8n-nodes-base.summarize",
"position": [
2060,
-60
],
"parameters": {
"options": {},
"fieldsToSummarize": {
"values": [
{
"field": "content",
"separateBy": "\n",
"aggregation": "concatenate"
}
]
}
},
"typeVersion": 1
}
],
"active": false,
"pinData": {},
"settings": {
"executionOrder": "v1"
},
"versionId": "51075175-868a-4a3a-9580-5ad55e25ac71",
"connections": {
"Limit": {
"main": [
[
{
"node": "Get page blocks",
"type": "main",
"index": 0
}
]
]
},
"Limit1": {
"main": [
[
{
"node": "Loop Over Items",
"type": "main",
"index": 0
}
]
]
},
"Notion Trigger": {
"main": [
[
{
"node": "Input Reference",
"type": "main",
"index": 0
}
]
]
},
"Token Splitter": {
"ai_textSplitter": [
[
{
"node": "Default Data Loader",
"type": "ai_textSplitter",
"index": 0
}
]
]
},
"Get page blocks": {
"main": [
[
{
"node": "Concatenate to single string",
"type": "main",
"index": 0
}
]
]
},
"Input Reference": {
"main": [
[
{
"node": "Loop Over Items",
"type": "main",
"index": 0
}
]
]
},
"Loop Over Items": {
"main": [
[],
[
{
"node": "Delete old embeddings if exist",
"type": "main",
"index": 0
}
]
]
},
"Schedule Trigger": {
"main": [
[
{
"node": "Get updated pages",
"type": "main",
"index": 0
}
]
]
},
"Embeddings OpenAI": {
"ai_embedding": [
[
{
"node": "Supabase Vector Store",
"type": "ai_embedding",
"index": 0
},
{
"node": "Supabase Vector Store1",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Get updated pages": {
"main": [
[
{
"node": "Input Reference",
"type": "main",
"index": 0
}
]
]
},
"OpenAI Chat Model": {
"ai_languageModel": [
[
{
"node": "Question and Answer Chain",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Default Data Loader": {
"ai_document": [
[
{
"node": "Supabase Vector Store",
"type": "ai_document",
"index": 0
}
]
]
},
"Supabase Vector Store": {
"main": [
[
{
"node": "Limit1",
"type": "main",
"index": 0
}
]
]
},
"Supabase Vector Store1": {
"ai_vectorStore": [
[
{
"node": "Vector Store Retriever",
"type": "ai_vectorStore",
"index": 0
}
]
]
},
"Vector Store Retriever": {
"ai_retriever": [
[
{
"node": "Question and Answer Chain",
"type": "ai_retriever",
"index": 0
}
]
]
},
"When chat message received": {
"main": [
[
{
"node": "Question and Answer Chain",
"type": "main",
"index": 0
}
]
]
},
"Concatenate to single string": {
"main": [
[
{
"node": "Supabase Vector Store",
"type": "main",
"index": 0
}
]
]
},
"Delete old embeddings if exist": {
"main": [
[
{
"node": "Limit",
"type": "main",
"index": 0
}
]
]
}
}
}Pour qui est ce workflow ?
Ce workflow s'adresse aux entreprises utilisant Notion pour la gestion de projets, aux équipes de développement de contenu et aux professionnels cherchant à automatiser la gestion de données. Un niveau technique intermédiaire est recommandé pour une personnalisation efficace.
Problème résolu
Ce workflow résout le problème de la gestion manuelle des données dans Notion, qui peut être chronophage et sujet à des erreurs. En automatisant la récupération et le traitement des informations, il permet aux utilisateurs de se concentrer sur des tâches à plus forte valeur ajoutée. Les entreprises bénéficient d'une meilleure organisation des données et d'une réactivité accrue face aux changements.
Étapes du workflow
Étape 1 : le workflow est déclenché par un événement dans Notion. Étape 2 : les données sont segmentées grâce au nœud 'Token Splitter'. Étape 3 : les morceaux sont transformés en vecteurs par 'Embeddings OpenAI'. Étape 4 : les données sont stockées dans Supabase via 'Supabase Vector Store'. Étape 5 : le modèle de chat OpenAI permet d'interagir avec ces données. Étape 6 : les anciennes embeddings sont supprimées si nécessaire pour éviter les doublons.
Guide de personnalisation du workflow n8n
Pour personnaliser ce workflow, vous pouvez modifier les paramètres du nœud 'Notion Trigger' pour cibler une base de données spécifique ou ajuster la fréquence des mises à jour. Il est également possible de changer les options dans le nœud 'Embeddings OpenAI' pour adapter le modèle à vos besoins. Pensez à sécuriser votre accès à Supabase en configurant les bonnes permissions. Enfin, vous pouvez ajouter d'autres nœuds pour intégrer des services supplémentaires ou modifier les conditions de traitement des données.