Workflow n8n
Automatisation de fichiers avec n8n : gestion de données en temps réel
- Ce workflow n8n a pour objectif d'automatiser la gestion de fichiers en temps réel, permettant ainsi aux entreprises de traiter efficacement des données sans intervention manuelle. Dans un contexte où la rapidité et la précision des informations sont essentielles, ce workflow s'avère particulièrement utile pour les équipes de data management et d'analyse. Il peut être utilisé pour des cas d'usage variés, tels que la lecture de fichiers locaux, la création de notes adhésives pour des rappels, et l'interaction avec des modèles de langage pour des réponses automatisées.
- Le déroulé du workflow commence par un déclencheur de type 'Local File Trigger', qui surveille les événements de fichiers dans un répertoire spécifique. Ensuite, un nœud 'Set Variables' permet de définir des variables nécessaires pour le traitement ultérieur. Le workflow utilise également des nœuds pour lire le contenu des fichiers et préparer des documents d'embedding, facilitant ainsi l'intégration avec des modèles de langage comme 'Mistral Cloud Chat Model'. Des nœuds conditionnels, tels que 'Has Existing Point?', permettent de vérifier l'existence de données avant de procéder à des actions comme la suppression ou la mise à jour des points existants.
- Les bénéfices de cette automatisation n8n sont multiples : elle réduit le temps de traitement des données, minimise les erreurs humaines et améliore la réactivité des équipes face aux nouvelles informations. En intégrant ce workflow, les entreprises peuvent optimiser leur gestion des données et se concentrer sur des tâches à plus forte valeur ajoutée.
Tags clés :automatisationn8ngestion de donnéesworkflowintelligence artificielle
Catégorie: Local File Trigger · Tags: automatisation, n8n, gestion de données, workflow, intelligence artificielle❤ 0
Workflow n8n gestion de données, intelligence artificielle : vue d'ensemble
Schéma des nœuds et connexions de ce workflow n8n, généré à partir du JSON n8n.
Workflow n8n gestion de données, intelligence artificielle : détail des nœuds
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"meta": {
"instanceId": "26ba763460b97c249b82942b23b6384876dfeb9327513332e743c5f6219c2b8e"
},
"nodes": [
{
"id": "c5525f47-4d91-4b98-87bb-566b90da64a1",
"name": "Local File Trigger",
"type": "n8n-nodes-base.localFileTrigger",
"position": [
660,
700
],
"parameters": {
"path": "/home/node/host_mount/local_file_search",
"events": [
"add",
"change",
"unlink"
],
"options": {
"awaitWriteFinish": true
},
"triggerOn": "folder"
},
"typeVersion": 1
},
{
"id": "804334d6-e34d-40d1-9555-b331ffe66f6f",
"name": "When clicking \"Test workflow\"",
"type": "n8n-nodes-base.manualTrigger",
"position": [
664.5766613599001,
881.8474780113352
],
"parameters": {},
"typeVersion": 1
},
{
"id": "7ab0e284-b667-4d1f-8ceb-fb05e4081a06",
"name": "Set Variables",
"type": "n8n-nodes-base.set",
"position": [
840,
700
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "35ea70c4-8669-4975-a68d-bbaa094713c0",
"name": "directory",
"type": "string",
"value": "/home/node/BankStatements"
},
{
"id": "1d081d19-ff4e-462a-9cbe-7af2244bf87f",
"name": "file_added",
"type": "string",
"value": "={{ $json.event === 'add' && $json.path || ''}}"
},
{
"id": "18f8dc03-51ca-48c7-947f-87ce8e1979bf",
"name": "file_changed",
"type": "string",
"value": "={{ $json.event === 'change' && $json.path || '' }}"
},
{
"id": "65074ff7-037b-4b3b-b2c3-8a61755ab43b",
"name": "file_deleted",
"type": "string",
"value": "={{ $json.event === 'unlink' && $json.path || '' }}"
},
{
"id": "9a1902e7-f94d-4d1f-9006-91c67354d3e8",
"name": "qdrant_collection",
"type": "string",
"value": "local_file_search"
}
]
}
},
"typeVersion": 3.3
},
{
"id": "76173972-ceca-43a4-b85f-00b41f774304",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
580,
460
],
"parameters": {
"color": 7,
"width": 665.0909497859384,
"height": 596.8351502261468,
"content": "## Step 1. Select the target folder\n[Read more about local file trigger](https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.localfiletrigger)\n\nIn this workflow, we'll monitor a specific folder on disk that n8n has access to. Since we're using docker, we can either use the n8n volume or mount a folder from the host machine.\n\nThe local file trigger is useful to execute the workflow whenever changes are made to our target folder."
},
"typeVersion": 1
},
{
"id": "eda839f7-dde4-4d1f-9fe6-692df4ac7282",
"name": "Sticky Note4",
"type": "n8n-nodes-base.stickyNote",
"position": [
184.57666135990007,
461.84747801133517
],
"parameters": {
"width": 372.51107341403605,
"height": 356.540665091993,
"content": "## Try It Out!\n### This workflow does the following:\n* Monitors a target folder for changes using the local file trigger\n* Synchronises files in the target folder with their vectors in Qdrant\n* Mistral AI is used to create a Q&A AI agent on all files in the target folder\n\n### Need Help?\nJoin the [Discord](https://discord.com/invite/XPKeKXeB7d) or ask in the [Forum](https://community.n8n.io/)!\n\nHappy Hacking!"
},
"typeVersion": 1
},
{
"id": "f82f6de0-af8f-4fdf-a733-f59ba4fed02f",
"name": "Read File",
"type": "n8n-nodes-base.readWriteFile",
"position": [
1340,
1120
],
"parameters": {
"options": {},
"fileSelector": "={{ $json.file_added }}"
},
"typeVersion": 1
},
{
"id": "7354a080-051b-479f-97b1-49cc0c14c9d8",
"name": "Embeddings Mistral Cloud",
"type": "@n8n/n8n-nodes-langchain.embeddingsMistralCloud",
"position": [
1720,
1280
],
"parameters": {
"options": {}
},
"credentials": {
"mistralCloudApi": {
"id": "EIl2QxhXAS9Hkg37",
"name": "Mistral Cloud account"
}
},
"typeVersion": 1
},
{
"id": "a1ad45ff-a882-4aed-82e2-cad2483cf4e8",
"name": "Default Data Loader",
"type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
"position": [
1820,
1280
],
"parameters": {
"options": {
"metadata": {
"metadataValues": [
{
"name": "filter_by_filename",
"value": "={{ $json.file_location }}"
},
{
"name": "filter_by_created_month",
"value": "={{ $now.year + '-' + $now.monthShort }}"
},
{
"name": "filter_by_created_week",
"value": "={{ $now.year + '-' + $now.monthShort + '-W' + $now.weekNumber }}"
}
]
}
},
"jsonData": "={{ $json.data }}",
"jsonMode": "expressionData"
},
"typeVersion": 1
},
{
"id": "0b0e29b9-8873-4074-94dc-9f0364c28835",
"name": "Recursive Character Text Splitter",
"type": "@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter",
"position": [
1840,
1400
],
"parameters": {
"options": {}
},
"typeVersion": 1
},
{
"id": "c0555ba6-a1bd-4aa9-a340-a9c617f8e6db",
"name": "Prepare Embedding Document",
"type": "n8n-nodes-base.set",
"position": [
1520,
1120
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "41a1d4ca-e5a5-4fb9-b249-8796ae759b33",
"name": "data",
"type": "string",
"value": "=## file location\n{{ [$json.directory, $json.fileName].join('/') }}\n## file created\n{{ $now.toISO() }}\n## file contents\n{{ $input.item.binary.data.data.base64Decode() }}"
},
{
"id": "c091704d-b81c-448b-8c90-156ef568b871",
"name": "file_location",
"type": "string",
"value": "={{ [$json.directory, $json.fileName].join('/') }}"
}
]
}
},
"typeVersion": 3.3
},
{
"id": "ffe8c363-0809-4d21-aa8f-34b0fc2dc57f",
"name": "Chat Trigger",
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"position": [
2280,
680
],
"webhookId": "37587fe0-b8db-4012-90a7-1f65b9bfd0df",
"parameters": {},
"typeVersion": 1
},
{
"id": "8d958669-60be-4bb2-80fc-2a6c7c7bfae6",
"name": "Question and Answer Chain",
"type": "@n8n/n8n-nodes-langchain.chainRetrievalQa",
"position": [
2500,
680
],
"parameters": {},
"typeVersion": 1.3
},
{
"id": "f143e438-8176-4923-a866-3f9a2a16793d",
"name": "Mistral Cloud Chat Model",
"type": "@n8n/n8n-nodes-langchain.lmChatMistralCloud",
"position": [
2500,
840
],
"parameters": {
"model": "mistral-small-2402",
"options": {}
},
"credentials": {
"mistralCloudApi": {
"id": "EIl2QxhXAS9Hkg37",
"name": "Mistral Cloud account"
}
},
"typeVersion": 1
},
{
"id": "06dd8f4c-3b66-43e0-85c8-ec222e275f87",
"name": "Vector Store Retriever",
"type": "@n8n/n8n-nodes-langchain.retrieverVectorStore",
"position": [
2620,
840
],
"parameters": {},
"typeVersion": 1
},
{
"id": "2fdabcb5-a7a7-4e02-8c1b-9190e2e52385",
"name": "Embeddings Mistral Cloud1",
"type": "@n8n/n8n-nodes-langchain.embeddingsMistralCloud",
"position": [
2620,
1080
],
"parameters": {
"options": {}
},
"credentials": {
"mistralCloudApi": {
"id": "EIl2QxhXAS9Hkg37",
"name": "Mistral Cloud account"
}
},
"typeVersion": 1
},
{
"id": "e5664534-de07-481f-87dd-68d7d0715baa",
"name": "Remap for File_Added Flow",
"type": "n8n-nodes-base.set",
"position": [
1920,
700
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "840219e1-ed47-4b00-83fd-6b3c0bd71650",
"name": "file_added",
"type": "string",
"value": "={{ $('Set Variables').item.json.file_changed }}"
}
]
}
},
"typeVersion": 3.3
},
{
"id": "1fd14832-aafe-4d72-b4f2-7afc72df97dc",
"name": "Search For Existing Point",
"type": "n8n-nodes-base.httpRequest",
"position": [
1340,
280
],
"parameters": {
"url": "=http://qdrant:6333/collections/{{ $('Set Variables').item.json.qdrant_collection }}/points/scroll",
"method": "POST",
"options": {},
"jsonBody": "={\n \"filter\": {\n \"must\": [\n {\n \"key\": \"metadata.filter_by_filename\",\n \"match\": {\n \"value\": \"{{ $json.file_changed }}\"\n }\n }\n ]\n },\n \"limit\": 1,\n \"with_payload\": false,\n \"with_vector\": false\n}",
"sendBody": true,
"specifyBody": "json",
"authentication": "predefinedCredentialType",
"nodeCredentialType": "qdrantApi"
},
"credentials": {
"qdrantApi": {
"id": "NyinAS3Pgfik66w5",
"name": "QdrantApi account"
}
},
"typeVersion": 4.2
},
{
"id": "b5fa817f-82d6-41dd-9817-4c1dd9137b76",
"name": "Has Existing Point?",
"type": "n8n-nodes-base.if",
"position": [
1520,
280
],
"parameters": {
"options": {},
"conditions": {
"options": {
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "0392bac0-8fb5-406b-b59f-575edf5ab30d",
"operator": {
"type": "array",
"operation": "notEmpty",
"singleValue": true
},
"leftValue": "={{ $json.result.points }}",
"rightValue": ""
}
]
}
},
"typeVersion": 2
},
{
"id": "b0fa4fa4-5d1b-4a12-b8ba-a10d71f31f94",
"name": "Delete Existing Point",
"type": "n8n-nodes-base.httpRequest",
"position": [
1720,
700
],
"parameters": {
"url": "=http://qdrant:6333/collections/{{ $('Set Variables').item.json.qdrant_collection }}/points/delete",
"method": "POST",
"options": {},
"sendBody": true,
"authentication": "predefinedCredentialType",
"bodyParameters": {
"parameters": [
{
"name": "points",
"value": "={{ $json.result.points.map(point => point.id) }}"
}
]
},
"nodeCredentialType": "qdrantApi"
},
"credentials": {
"qdrantApi": {
"id": "NyinAS3Pgfik66w5",
"name": "QdrantApi account"
}
},
"typeVersion": 4.2
},
{
"id": "5408adfe-4d6b-407c-aac7-e87c9b1a1592",
"name": "Search For Existing Point1",
"type": "n8n-nodes-base.httpRequest",
"position": [
1340,
700
],
"parameters": {
"url": "=http://qdrant:6333/collections/{{ $('Set Variables').item.json.qdrant_collection }}/points/scroll",
"method": "POST",
"options": {},
"jsonBody": "={\n \"filter\": {\n \"must\": [\n {\n \"key\": \"metadata.filter_by_filename\",\n \"match\": {\n \"value\": \"{{ $json.file_changed }}\"\n }\n }\n ]\n },\n \"limit\": 1,\n \"with_payload\": false,\n \"with_vector\": false\n}",
"sendBody": true,
"specifyBody": "json",
"authentication": "predefinedCredentialType",
"nodeCredentialType": "qdrantApi"
},
"credentials": {
"qdrantApi": {
"id": "NyinAS3Pgfik66w5",
"name": "QdrantApi account"
}
},
"typeVersion": 4.2
},
{
"id": "fac43587-0d24-4d6e-a0d5-8cc8f9615967",
"name": "Has Existing Point?1",
"type": "n8n-nodes-base.if",
"position": [
1520,
700
],
"parameters": {
"options": {},
"conditions": {
"options": {
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "0392bac0-8fb5-406b-b59f-575edf5ab30d",
"operator": {
"type": "array",
"operation": "notEmpty",
"singleValue": true
},
"leftValue": "={{ $json.result.points }}",
"rightValue": ""
}
]
}
},
"typeVersion": 2
},
{
"id": "010baacd-fac1-4cc1-86bf-9d6ef11916fe",
"name": "Delete Existing Point1",
"type": "n8n-nodes-base.httpRequest",
"position": [
1700,
280
],
"parameters": {
"url": "=http://qdrant:6333/collections/{{ $('Set Variables').item.json.qdrant_collection }}/points/delete",
"method": "POST",
"options": {},
"sendBody": true,
"authentication": "predefinedCredentialType",
"bodyParameters": {
"parameters": [
{
"name": "points",
"value": "={{ $json.result.points.map(point => point.id) }}"
}
]
},
"nodeCredentialType": "qdrantApi"
},
"credentials": {
"qdrantApi": {
"id": "NyinAS3Pgfik66w5",
"name": "QdrantApi account"
}
},
"typeVersion": 4.2
},
{
"id": "2d6fb29c-2fac-41de-9ad0-cc781b246378",
"name": "Handle File Event",
"type": "n8n-nodes-base.switch",
"position": [
1000,
700
],
"parameters": {
"rules": {
"values": [
{
"outputKey": "file_deleted",
"conditions": {
"options": {
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "a1f6d86a-9805-4d0e-ac70-90c9cf0ad339",
"operator": {
"type": "string",
"operation": "notEmpty",
"singleValue": true
},
"leftValue": "={{ $json.file_deleted }}",
"rightValue": ""
}
]
},
"renameOutput": true
},
{
"outputKey": "file_changed",
"conditions": {
"options": {
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "d15cde67-b5b0-4676-b4fb-ead749147392",
"operator": {
"type": "string",
"operation": "notEmpty",
"singleValue": true
},
"leftValue": "={{ $json.file_changed }}",
"rightValue": ""
}
]
},
"renameOutput": true
},
{
"outputKey": "file_added",
"conditions": {
"options": {
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"operator": {
"type": "string",
"operation": "notEmpty",
"singleValue": true
},
"leftValue": "={{ $json.file_added }}",
"rightValue": ""
}
]
},
"renameOutput": true
}
]
},
"options": {}
},
"typeVersion": 3
},
{
"id": "da91b2aa-613c-4e3e-af83-fbd3bb7e922e",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
1280,
123.92779403575491
],
"parameters": {
"color": 7,
"width": 847.032584995578,
"height": 335.8400964393443,
"content": "## Step 2. When files are removed, the vector point is cleared.\n[Learn how to delete points using the Qdrant API](https://qdrant.tech/documentation/concepts/points/#delete-points)\n\nTo keep our vectorstore relevant, we'll implement a simple synchronisation system whereby documents deleted from the local file folder are also purged from Qdrant. This can be simply achieved using Qdrant APIs."
},
"typeVersion": 1
},
{
"id": "2f9f5b2b-6504-4b27-a0c4-f3373df352df",
"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
"position": [
1280,
480
],
"parameters": {
"color": 7,
"width": 855.9952607674757,
"height": 433.01782147687817,
"content": "## Step 3. When files are updated, the vector point is updated.\n[Learn how to delete points using the Qdrant API](https://qdrant.tech/documentation/concepts/points/#delete-points)\n\nSimilarly to the files deleted branch, when we encounter a change in a file we'll update the matching vector point in Qdrant to ensure our vector store stays relevant. Here, we can achieve this my deleting the existing vector point and creating it anew with the updated bank statement."
},
"typeVersion": 1
},
{
"id": "38128b7f-d0f2-405c-a7de-662df812c344",
"name": "Sticky Note3",
"type": "n8n-nodes-base.stickyNote",
"position": [
1280,
940
],
"parameters": {
"color": 7,
"width": 846.8204626627492,
"height": 629.9714759033081,
"content": "## Step 4. When new files are added, add them to Qdrant Vectorstore.\n[Read more about the Qdrant node](https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.vectorstoreqdrant)\n\nUsing Qdrant, we'll able to create a simple yet powerful RAG based application for our bank statements. One of Qdrant's most powerful features is its filtering system, we'll use it to manage the synchronisation of our local file system and Qdrant."
},
"typeVersion": 1
},
{
"id": "e85e2a30-e775-42fe-a12a-ac5de4eb4673",
"name": "Sticky Note5",
"type": "n8n-nodes-base.stickyNote",
"position": [
2180,
491.43199269284935
],
"parameters": {
"color": 7,
"width": 744.4578330639196,
"height": 759.7908149448928,
"content": "## Step 5. Create AI Agent expert on historic bank statements \n[Read more about the Question & Answer Chain](https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.chainretrievalqa)\n\nFinally, let's use a Question & Answer AI node to combine the Mistral AI model and Qdrant as the vector store retriever to create a local expert for all our bank statements questions. "
},
"typeVersion": 1
},
{
"id": "7b29b0b9-ffee-4456-b036-9b39400d2b31",
"name": "Qdrant Vector Store",
"type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant",
"position": [
1700,
1120
],
"parameters": {
"mode": "insert",
"options": {},
"qdrantCollection": {
"__rl": true,
"mode": "id",
"value": "={{ $('Set Variables').item.json.qdrant_collection }}"
}
},
"credentials": {
"qdrantApi": {
"id": "NyinAS3Pgfik66w5",
"name": "QdrantApi account"
}
},
"typeVersion": 1
},
{
"id": "1857bebb-b492-415e-96c8-235329bfd28a",
"name": "Qdrant Vector Store1",
"type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant",
"position": [
2620,
960
],
"parameters": {
"qdrantCollection": {
"__rl": true,
"mode": "id",
"value": "BankStatements"
}
},
"credentials": {
"qdrantApi": {
"id": "NyinAS3Pgfik66w5",
"name": "QdrantApi account"
}
},
"typeVersion": 1
}
],
"pinData": {},
"connections": {
"Read File": {
"main": [
[
{
"node": "Prepare Embedding Document",
"type": "main",
"index": 0
}
]
]
},
"Chat Trigger": {
"main": [
[
{
"node": "Question and Answer Chain",
"type": "main",
"index": 0
}
]
]
},
"Set Variables": {
"main": [
[
{
"node": "Handle File Event",
"type": "main",
"index": 0
}
]
]
},
"Handle File Event": {
"main": [
[
{
"node": "Search For Existing Point",
"type": "main",
"index": 0
}
],
[
{
"node": "Search For Existing Point1",
"type": "main",
"index": 0
}
],
[
{
"node": "Read File",
"type": "main",
"index": 0
}
]
]
},
"Local File Trigger": {
"main": [
[
{
"node": "Set Variables",
"type": "main",
"index": 0
}
]
]
},
"Default Data Loader": {
"ai_document": [
[
{
"node": "Qdrant Vector Store",
"type": "ai_document",
"index": 0
}
]
]
},
"Has Existing Point?": {
"main": [
[
{
"node": "Delete Existing Point1",
"type": "main",
"index": 0
}
]
]
},
"Has Existing Point?1": {
"main": [
[
{
"node": "Delete Existing Point",
"type": "main",
"index": 0
}
]
]
},
"Qdrant Vector Store1": {
"ai_vectorStore": [
[
{
"node": "Vector Store Retriever",
"type": "ai_vectorStore",
"index": 0
}
]
]
},
"Delete Existing Point": {
"main": [
[
{
"node": "Remap for File_Added Flow",
"type": "main",
"index": 0
}
]
]
},
"Vector Store Retriever": {
"ai_retriever": [
[
{
"node": "Question and Answer Chain",
"type": "ai_retriever",
"index": 0
}
]
]
},
"Embeddings Mistral Cloud": {
"ai_embedding": [
[
{
"node": "Qdrant Vector Store",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Mistral Cloud Chat Model": {
"ai_languageModel": [
[
{
"node": "Question and Answer Chain",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Embeddings Mistral Cloud1": {
"ai_embedding": [
[
{
"node": "Qdrant Vector Store1",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Remap for File_Added Flow": {
"main": [
[
{
"node": "Read File",
"type": "main",
"index": 0
}
]
]
},
"Search For Existing Point": {
"main": [
[
{
"node": "Has Existing Point?",
"type": "main",
"index": 0
}
]
]
},
"Prepare Embedding Document": {
"main": [
[
{
"node": "Qdrant Vector Store",
"type": "main",
"index": 0
}
]
]
},
"Search For Existing Point1": {
"main": [
[
{
"node": "Has Existing Point?1",
"type": "main",
"index": 0
}
]
]
},
"When clicking \"Test workflow\"": {
"main": [
[
{
"node": "Set Variables",
"type": "main",
"index": 0
}
]
]
},
"Recursive Character Text Splitter": {
"ai_textSplitter": [
[
{
"node": "Default Data Loader",
"type": "ai_textSplitter",
"index": 0
}
]
]
}
}
}Workflow n8n gestion de données, intelligence artificielle : pour qui est ce workflow ?
Ce workflow s'adresse aux entreprises de taille moyenne à grande, notamment celles qui gèrent des volumes importants de données. Il est particulièrement pertinent pour les équipes de data management, d'analyse et de développement, qui recherchent des solutions d'automatisation pour améliorer leur efficacité opérationnelle. Un niveau technique intermédiaire est recommandé pour une mise en œuvre optimale.
Workflow n8n gestion de données, intelligence artificielle : problème résolu
Ce workflow résout le problème de la gestion manuelle des fichiers, qui peut être chronophage et sujet à des erreurs. En automatisant la lecture et le traitement des fichiers, il permet de gagner du temps et d'assurer une plus grande précision dans la manipulation des données. Les utilisateurs bénéficient d'une solution qui simplifie le flux de travail, réduit les risques d'erreurs et améliore la productivité des équipes.
Workflow n8n gestion de données, intelligence artificielle : étapes du workflow
Étape 1 : Le workflow est déclenché par un événement de fichier local via le nœud 'Local File Trigger'.
- Étape 1 : Les variables nécessaires sont définies grâce au nœud 'Set Variables'.
- Étape 2 : Le contenu du fichier est lu à l'aide du nœud 'Read File'.
- Étape 3 : Les données sont préparées pour l'embedding avec le nœud 'Prepare Embedding Document'.
- Étape 4 : Le workflow interagit avec le modèle de langage via le nœud 'Mistral Cloud Chat Model' pour générer des réponses.
- Étape 5 : Des vérifications sont effectuées avec les nœuds 'Has Existing Point?' pour gérer les données existantes.
- Étape 6 : Les notes adhésives sont créées pour des rappels visuels à l'aide des nœuds 'Sticky Note'.
Workflow n8n gestion de données, intelligence artificielle : guide de personnalisation
Pour personnaliser ce workflow, vous pouvez modifier le chemin d'accès dans le nœud 'Local File Trigger' pour surveiller un répertoire spécifique. Les paramètres dans les nœuds 'Set Variables' et 'Prepare Embedding Document' peuvent être ajustés pour répondre à vos besoins spécifiques en matière de données. Vous pouvez également intégrer d'autres outils ou services en ajoutant des nœuds supplémentaires, comme des API externes. Enfin, assurez-vous de sécuriser le flux en configurant les authentifications nécessaires dans les nœuds HTTP.