Automatisez le Résumé de Vos Emails avec A.I. et Messagerie
Ce workflow n8n vous permet d'automatiser la gestion de vos emails en utilisant l'intelligence artificielle pour résume...
Documentation Complète
📋 Optimisation de la Récupération de Fichiers avec OpenAI et n8n
💡 Description
Ce workflow utilise n8n pour intégrer un assistant OpenAI permettant une récupération optimisée des fichiers. Grâce à l'utilisation d'un magasin vectoriel, ce workflow assure que les textes générés incluent correctement les citations et les sources. Les utilisateurs peuvent ainsi formater les résultats en balisage Markdown ou HTML, améliorant la clarté et la précision des informations présentées. Ce processus est essentiel pour éviter les erreurs de caractères étranges générés par l'assistant, tout en offrant une flexibilité dans le formatage du contenu final.
📈 Impact & ROI: Améliore la précision des documents générés par l'automatisation et réduit le temps passé sur le formatage manuel.
🚀 Fonctionnalités Clés
- ✅ Intégration fluide avec OpenAI pour récupérer des fichiers
- ✅ Formatage flexible avec support Markdown et HTML
- ✅ Gestion automatisée des citations pour plus de précision
- ✅ Personnalisation facile pour s'adapter aux besoins spécifiques
📊 Architecture Technique
19
Nodes
Nodes
11
Connexions
Connexions
3
Services
Services
🔌 Services Intégrés
OpenAIn8n-nodes-base.httpRequest@n8n/n8n-nodes-langchain.openAi
🔧 Composition du Workflow
| Node | Type | Description |
|---|---|---|
| Aggregate | aggregate | Traitement des données |
| Window Buffer Memory | @n8n/n8n-nodes-langchain.memoryBufferWindow | Traitement des données |
| Sticky Note4 | stickyNote | Traitement des données |
| Sticky Note | stickyNote | Traitement des données |
| Create a simple Trigger to have the Chat button within N8N | @n8n/n8n-nodes-langchain.chatTrigger | Traitement des données |
| OpenAI Assistant with Vector Store | @n8n/n8n-nodes-langchain.openAi | Traitement des données |
| Sticky Note1 | stickyNote | Traitement des données |
| Get ALL Thread Content | httpRequest | Requête HTTP vers une API externe |
| Sticky Note2 | stickyNote | Traitement des données |
| Split all message iterations from a thread | splitOut | Division des données en plusieurs branches |
| Split all content from a single message | splitOut | Division des données en plusieurs branches |
| Split all citations from a single message | splitOut | Division des données en plusieurs branches |
| Retrieve file name from a file ID | httpRequest | Requête HTTP vers une API externe |
| Regularize output | set | Traitement des données |
| Sticky Note3 | stickyNote | Traitement des données |
| Sticky Note5 | stickyNote | Traitement des données |
| Sticky Note6 | stickyNote | Traitement des données |
| Optional Markdown to HTML | markdown | Traitement des données |
| Finnaly format the output | code | Traitement des données |
📖 Guide d'Implémentation
- Import du workflow: Téléchargez le fichier JSON et importez-le dans votre instance n8n
- Configuration des credentials: Configurez les accès pour chaque service utilisé
- Personnalisation: Adaptez les paramètres selon vos besoins spécifiques
- Test: Exécutez le workflow en mode test pour vérifier le bon fonctionnement
- Activation: Activez le workflow pour une exécution automatique
🏷️ Tags
Structure JSON
Voir le code JSON complet
{
"id": "5NAbfX550LJsfz6f",
"meta": {
"instanceId": "00493e38fecfc163cb182114bc2fab90114038eb9aad665a7a752d076920d3d5",
"templateCredsSetupCompleted": true
},
"name": "Make OpenAI Citation for File Retrieval RAG",
"tags": [
{
"id": "urxRtGxxLObZWPvX",
"name": "sample",
"createdAt": "2024-09-13T02:43:13.014Z",
"updatedAt": "2024-09-13T02:43:13.014Z"
},
{
"id": "nMXS3c9l1WqDwWF5",
"name": "assist",
"createdAt": "2024-12-23T16:09:38.737Z",
"updatedAt": "2024-12-23T16:09:38.737Z"
}
],
"nodes": [
{
"id": "b9033511-3421-467a-9bfa-73af01b99c4f",
"name": "Aggregate",
"type": "n8n-nodes-base.aggregate",
"position": [
740,
120
],
"parameters": {
"options": [],
"aggregate": "aggregateAllItemData"
},
"typeVersion": 1,
"alwaysOutputData": true
},
{
"id": "a61dd9d3-4faa-4878-a6f3-ba8277279002",
"name": "Window Buffer Memory",
"type": "@n8n\/n8n-nodes-langchain.memoryBufferWindow",
"position": [
980,
-320
],
"parameters": [],
"typeVersion": 1.3
},
{
"id": "2daabca5-37ec-4cad-9157-29926367e1a7",
"name": "Sticky Note4",
"type": "n8n-nodes-base.stickyNote",
"position": [
220,
320
],
"parameters": {
"color": 3,
"width": 840,
"height": 80,
"content": "## Within N8N, there will be a chat button to test"
},
"typeVersion": 1
},
{
"id": "bf4485b1-cd94-41c8-a183-bf1b785f2761",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
-440,
-520
],
"parameters": {
"color": 5,
"width": 500,
"height": 720,
"content": "## Make OpenAI Citation for File Retrieval RAG\n\n## Use case\n\nIn this example, we will ensure that all texts from the OpenAI assistant search for citations and sources in the vector store files. We can also format the output for Markdown or HTML tags.\n\nThis is necessary because the assistant sometimes generates strange characters, and we can also use dynamic references such as citations 1, 2, 3, for example.\n\n## What this workflow does\n\nIn this workflow, we will use an OpenAI assistant created within their interface, equipped with a vector store containing some files for file retrieval.\n\nThe assistant will perform the file search within the OpenAI infrastructure and will return the content with citations.\n\n- We will make an HTTP request to retrieve all the details we need to format the text output.\n\n## Setup\n\nInsert an OpenAI Key\n\n## How to adjust it to your needs\n\nAt the end of the workflow, we have a block of code that will format the output, and there we can add Markdown tags to create links. Optionally, we can transform the Markdown formatting into HTML.\n\n\nby Davi Saranszky Mesquita\nhttps:\/\/www.linkedin.com\/in\/mesquitadavi\/"
},
"typeVersion": 1
},
{
"id": "539a4e40-9745-4a26-aba8-2cc2b0dd6364",
"name": "Create a simple Trigger to have the Chat button within N8N",
"type": "@n8n\/n8n-nodes-langchain.chatTrigger",
"notes": "https:\/\/www.npmjs.com\/package\/@n8n\/chat",
"position": [
260,
-520
],
"webhookId": "8ccaa299-6f99-427b-9356-e783893a3d0c",
"parameters": {
"options": []
},
"notesInFlow": true,
"typeVersion": 1.1
},
{
"id": "aa5b2951-df32-43ac-9939-83b02d818e73",
"name": "OpenAI Assistant with Vector Store",
"type": "@n8n\/n8n-nodes-langchain.openAi",
"position": [
580,
-520
],
"parameters": {
"options": {
"preserveOriginalTools": false
},
"resource": "assistant",
"assistantId": {
"__rl": true,
"mode": "list",
"value": "asst_QAfdobVCVCMJz8LmaEC7nlId",
"cachedResultName": "Teste"
}
},
"credentials": {
"openAiApi": {
"id": "UfNrqPCRlD8FD9mk",
"name": "OpenAi Lourival"
}
},
"typeVersion": 1.7
},
{
"id": "1817b673-6cb3-49aa-9f38-a5876eb0e6fa",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
560,
-680
],
"parameters": {
"width": 300,
"content": "## Setup\n\n- Configure OpenAI Key\n\n### In this step, we will use an assistant created within the OpenAI platform that contains a vector store a.k.a file retrieval"
},
"typeVersion": 1
},
{
"id": "16429226-e850-4698-b419-fd9805a03fb7",
"name": "Get ALL Thread Content",
"type": "n8n-nodes-base.httpRequest",
"position": [
1260,
-520
],
"parameters": {
"url": "=https:\/\/api.openai.com\/v1\/threads\/{{ $json.threadId }}\/messages",
"options": [],
"sendHeaders": true,
"authentication": "predefinedCredentialType",
"headerParameters": {
"parameters": [
{
"name": "OpenAI-Beta",
"value": "assistants=v2"
}
]
},
"nodeCredentialType": "openAiApi"
},
"credentials": {
"openAiApi": {
"id": "UfNrqPCRlD8FD9mk",
"name": "OpenAi Lourival"
}
},
"typeVersion": 4.2,
"alwaysOutputData": true
},
{
"id": "e8c88b08-5be2-4f7e-8b17-8cf804b3fe9f",
"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
"position": [
1160,
-620
],
"parameters": {
"content": "### Retrieving all thread content is necessary because the OpenAI tool does not retrieve all citations upon request."
},
"typeVersion": 1
},
{
"id": "0f51e09f-2782-4e2d-b797-d4d58fcabdaf",
"name": "Split all message iterations from a thread",
"type": "n8n-nodes-base.splitOut",
"position": [
220,
-300
],
"parameters": {
"options": [],
"fieldToSplitOut": "data"
},
"typeVersion": 1,
"alwaysOutputData": true
},
{
"id": "4d569993-1ce3-4b32-beaf-382feac25da9",
"name": "Split all content from a single message",
"type": "n8n-nodes-base.splitOut",
"position": [
460,
-300
],
"parameters": {
"options": [],
"fieldToSplitOut": "content"
},
"typeVersion": 1,
"alwaysOutputData": true
},
{
"id": "999e1c2b-1927-4483-aac1-6e8903f7ed25",
"name": "Split all citations from a single message",
"type": "n8n-nodes-base.splitOut",
"position": [
700,
-300
],
"parameters": {
"options": [],
"fieldToSplitOut": "text.annotations"
},
"typeVersion": 1,
"alwaysOutputData": true
},
{
"id": "98af62f5-adb0-4e07-a146-fc2f13b851ce",
"name": "Retrieve file name from a file ID",
"type": "n8n-nodes-base.httpRequest",
"onError": "continueRegularOutput",
"position": [
220,
120
],
"parameters": {
"url": "=https:\/\/api.openai.com\/v1\/files\/{{ $json.file_citation.file_id }}",
"options": [],
"sendQuery": true,
"authentication": "predefinedCredentialType",
"queryParameters": {
"parameters": [
{
"name": "limit",
"value": "1"
}
]
},
"nodeCredentialType": "openAiApi"
},
"credentials": {
"openAiApi": {
"id": "UfNrqPCRlD8FD9mk",
"name": "OpenAi Lourival"
}
},
"typeVersion": 4.2,
"alwaysOutputData": true
},
{
"id": "b11f0d3d-bdc4-4845-b14b-d0b0de214f01",
"name": "Regularize output",
"type": "n8n-nodes-base.set",
"position": [
480,
120
],
"parameters": {
"options": [],
"assignments": {
"assignments": [
{
"id": "2dcaafee-5037-4a97-942a-bcdd02bc2ad9",
"name": "id",
"type": "string",
"value": "={{ $json.id }}"
},
{
"id": "b63f967d-ceea-4aa8-98b9-91f5ab21bfe8",
"name": "filename",
"type": "string",
"value": "={{ $json.filename }}"
},
{
"id": "f611e749-054a-441d-8610-df8ba42de2e1",
"name": "text",
"type": "string",
"value": "={{ $('Split all citations from a single message').item.json.text }}"
}
]
}
},
"typeVersion": 3.4,
"alwaysOutputData": true
},
{
"id": "0e999a0e-76ed-4897-989b-228f075e9bfb",
"name": "Sticky Note3",
"type": "n8n-nodes-base.stickyNote",
"position": [
440,
-60
],
"parameters": {
"width": 200,
"height": 220,
"content": "### A file retrieval request contains a lot of information, and we want only the text that will be substituted and the file name.\n\n- id\n- filename\n- text\n"
},
"typeVersion": 1
},
{
"id": "53c79a6c-7543-435f-b40e-966dff0904d4",
"name": "Sticky Note5",
"type": "n8n-nodes-base.stickyNote",
"position": [
700,
-60
],
"parameters": {
"width": 200,
"height": 220,
"content": "### With the last three splits, we may have many citations and texts to substitute. By doing an aggregation, it will be possible to handle everything as a single request."
},
"typeVersion": 1
},
{
"id": "381fb6d6-64fc-4668-9d3c-98aaa43a45ca",
"name": "Sticky Note6",
"type": "n8n-nodes-base.stickyNote",
"position": [
960,
-60
],
"parameters": {
"height": 220,
"content": "### This simple code will take all the previous files and citations and alter the original text, formatting the output. In this way, we can use Markdown tags to create links, or if you prefer, we can add an HTML transformation node."
},
"typeVersion": 1
},
{
"id": "d0cbb943-57ab-4850-8370-1625610a852a",
"name": "Optional Markdown to HTML",
"type": "n8n-nodes-base.markdown",
"disabled": true,
"position": [
1220,
120
],
"parameters": {
"html": "={{ $json.output }}",
"options": [],
"destinationKey": "output"
},
"typeVersion": 1
},
{
"id": "589e2418-5dec-47d0-ba08-420d84f09da7",
"name": "Finnaly format the output",
"type": "n8n-nodes-base.code",
"position": [
980,
120
],
"parameters": {
"mode": "runOnceForEachItem",
"jsCode": "let saida = $('OpenAI Assistant with Vector Store').item.json.output;\n\nfor (let i of $input.item.json.data) {\n saida = saida.replaceAll(i.text, \" _(\"+ i.filename+\")_ \");\n}\n\n$input.item.json.output = saida;\nreturn $input.item;"
},
"typeVersion": 2
}
],
"active": false,
"pinData": [],
"settings": {
"executionOrder": "v1"
},
"versionId": "0e621a5a-d99d-4db3-9ae4-ea98c31467e9",
"connections": {
"Aggregate": {
"main": [
[
{
"node": "Finnaly format the output",
"type": "main",
"index": 0
}
]
]
},
"Regularize output": {
"main": [
[
{
"node": "Aggregate",
"type": "main",
"index": 0
}
]
]
},
"Window Buffer Memory": {
"ai_memory": [
[
{
"node": "OpenAI Assistant with Vector Store",
"type": "ai_memory",
"index": 0
}
]
]
},
"Get ALL Thread Content": {
"main": [
[
{
"node": "Split all message iterations from a thread",
"type": "main",
"index": 0
}
]
]
},
"Finnaly format the output": {
"main": [
[
{
"node": "Optional Markdown to HTML",
"type": "main",
"index": 0
}
]
]
},
"Retrieve file name from a file ID": {
"main": [
[
{
"node": "Regularize output",
"type": "main",
"index": 0
}
]
]
},
"OpenAI Assistant with Vector Store": {
"main": [
[
{
"node": "Get ALL Thread Content",
"type": "main",
"index": 0
}
]
]
},
"Split all content from a single message": {
"main": [
[
{
"node": "Split all citations from a single message",
"type": "main",
"index": 0
}
]
]
},
"Split all citations from a single message": {
"main": [
[
{
"node": "Retrieve file name from a file ID",
"type": "main",
"index": 0
}
]
]
},
"Split all message iterations from a thread": {
"main": [
[
{
"node": "Split all content from a single message",
"type": "main",
"index": 0
}
]
]
},
"Create a simple Trigger to have the Chat button within N8N": {
"main": [
[
{
"node": "OpenAI Assistant with Vector Store",
"type": "main",
"index": 0
}
]
]
}
}
}
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