Workflow n8n
Automatisation Notion avec n8n : gestion de données en temps réel
Ce workflow n8n a pour objectif de gérer efficacement des données en temps réel à partir de Notion et d'OpenAI. Il s'adresse aux équipes qui souhaitent automatiser la récupération et le traitement de données, notamment dans les secteurs de la gestion de projet et de la documentation. Grâce à ce workflow, les utilisateurs peuvent facilement interroger des données stockées dans Notion, générer des réponses intelligentes via OpenAI et maintenir une base de données à jour sans intervention manuelle. Étape 1 : le déclencheur Notion active le workflow lorsqu'une page est mise à jour. Étape 2 : les données sont récupérées et traitées à l'aide du nœud 'Get updated pages', qui permet de filtrer les informations pertinentes. Étape 3 : les données sont ensuite divisées en morceaux gérables grâce au 'Token Splitter', facilitant ainsi leur traitement. Étape 4 : les embeddings sont générés avec le nœud 'Embeddings OpenAI', permettant de créer des représentations vectorielles des données. Étape 5 : le modèle de chat OpenAI est utilisé pour répondre aux questions basées sur les données traitées. Ce workflow offre une automatisation n8n qui réduit considérablement le temps de gestion des données et améliore la réactivité des équipes face aux changements d'information. Tags clés : automatisation, Notion, OpenAI.
Catégorie: Notion Trigger · Tags: automatisation, Notion, OpenAI, workflow, 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 équipes de gestion de projet, aux développeurs et aux professionnels du marketing qui utilisent Notion pour organiser leurs 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 le processus de mise à jour et de traitement des données, les utilisateurs gagnent en efficacité et en précision. Les équipes peuvent ainsi se concentrer sur des tâches à plus forte valeur ajoutée, tout en ayant accès à des informations à jour et pertinentes.
Étapes du workflow
Étape 1 : le workflow est déclenché par un événement dans Notion. Étape 2 : les pages mises à jour sont récupérées via le nœud 'Get updated pages'. Étape 3 : les données sont divisées en morceaux grâce au 'Token Splitter'. Étape 4 : des embeddings sont créés avec 'Embeddings OpenAI'. Étape 5 : le modèle de chat d'OpenAI génère des réponses basées sur les données traitées. Étape 6 : les résultats sont stockés dans une base de données pour un accès futur.
Guide de personnalisation du workflow n8n
Pour personnaliser ce workflow, vous pouvez modifier les paramètres du nœud 'Notion Trigger' pour spécifier les événements qui déclenchent le flux. Vous pouvez également ajuster les options du nœud 'Embeddings OpenAI' pour changer le modèle utilisé ou les paramètres d'embedding. Si vous souhaitez intégrer d'autres outils, envisagez d'ajouter des nœuds supplémentaires pour des appels API ou des notifications. Assurez-vous de sécuriser le flux en utilisant des clés d'API appropriées et en surveillant les performances via les outils de suivi n8n.