Automatisation chatbot avec n8n : intégration d'OpenAI et ElevenLabs
- Ce workflow n8n permet de créer un chatbot intelligent en intégrant les technologies d'OpenAI et d'ElevenLabs. Dans un contexte où les entreprises cherchent à améliorer l'interaction avec leurs clients, ce type d'automatisation n8n est particulièrement utile pour les équipes de support client et de vente. Grâce à ce workflow, les utilisateurs peuvent interagir avec un agent virtuel capable de répondre à des questions en temps réel, tout en accédant à des données stockées dans un vecteur de Qdrant.
- Le processus commence par un déclencheur Webhook qui écoute les requêtes des utilisateurs. Ensuite, le workflow utilise des nœuds comme 'AI Agent' et 'OpenAI Chat Model' pour traiter les demandes et générer des réponses pertinentes. Les données sont chargées à partir de Google Drive via le nœud 'Get folder', et les fichiers sont téléchargés pour enrichir les réponses du chatbot. Les nœuds 'Embeddings OpenAI' et 'Vector Store Tool' sont utilisés pour gérer les données vectorisées, permettant une recherche efficace des informations.
- Les bénéfices de cette automatisation sont multiples : réduction des délais de réponse, amélioration de l'expérience utilisateur et optimisation des ressources humaines. En intégrant ce workflow, les entreprises peuvent offrir un service client 24/7, tout en réduisant les coûts opérationnels.
Workflow n8n OpenAI, chatbot, ElevenLabs : vue d'ensemble
Schéma des nœuds et connexions de ce workflow n8n, généré à partir du JSON n8n.
Workflow n8n OpenAI, chatbot, ElevenLabs : détail des nœuds
Inscris-toi pour voir l'intégralité du workflow
Inscription gratuite
S'inscrire gratuitementBesoin d'aide ?{
"id": "ibiHg6umCqvcTF4g",
"meta": {
"instanceId": "a4bfc93e975ca233ac45ed7c9227d84cf5a2329310525917adaf3312e10d5462",
"templateCredsSetupCompleted": true
},
"name": "Voice RAG Chatbot with ElevenLabs and OpenAI",
"tags": [],
"nodes": [
{
"id": "5898da57-38b0-4d29-af25-fe029cda7c4a",
"name": "AI Agent",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
-180,
800
],
"parameters": {
"text": "={{ $json.body.question }}",
"options": {},
"promptType": "define"
},
"typeVersion": 1.7
},
{
"id": "81bbedb6-5a07-4977-a68f-2bdc75b17aba",
"name": "Vector Store Tool",
"type": "@n8n/n8n-nodes-langchain.toolVectorStore",
"position": [
20,
1040
],
"parameters": {
"name": "company",
"description": "Risponde alle domande relative a ciò che ti viene chiesto"
},
"typeVersion": 1
},
{
"id": "fd021f6c-248d-41f4-a4f9-651e70692327",
"name": "Qdrant Vector Store",
"type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant",
"position": [
-140,
1300
],
"parameters": {
"options": {},
"qdrantCollection": {
"__rl": true,
"mode": "id",
"value": "=COLLECTION"
}
},
"credentials": {
"qdrantApi": {
"id": "iyQ6MQiVaF3VMBmt",
"name": "QdrantApi account"
}
},
"typeVersion": 1
},
{
"id": "84aca7bb-4812-498f-b319-88831e4ca412",
"name": "Embeddings OpenAI",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"position": [
-140,
1460
],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"id": "CDX6QM4gLYanh0P4",
"name": "OpenAi account"
}
},
"typeVersion": 1.1
},
{
"id": "82e430db-2ad7-427d-bcf9-6aa226253d18",
"name": "Sticky Note4",
"type": "n8n-nodes-base.stickyNote",
"position": [
-760,
520
],
"parameters": {
"color": 5,
"width": 1400,
"height": 240,
"content": "# STEP 4\n\n## RAG System\n\nClick on \"test workflow\" on n8n and \"Test AI agent\" on ElevenLabs. If everything is configured correctly, when you ask a question to the agent, the webhook on n8n is activated with the \"question\" field in the body filled with the question asked to the voice agent.\n\nThe AI Agent will extract the information from the vector database, send it to the model to create the response which will be sent via the response webhook to ElevenLabs which will transform it into voice"
},
"typeVersion": 1
},
{
"id": "6a19e9fa-50fa-4d51-ba41-d03c999e4649",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
-780,
-880
],
"parameters": {
"color": 3,
"width": 1420,
"height": 360,
"content": "# STEP 1\n\n## Create an Agent on ElevenLabs \n- Create an agent on ElevenLabs (eg. test_n8n)\n- Add \"First message\" (eg. Hi, Can I help you?)\n- Add the \"System Prompt\" message... eg:\n'You are the waiter of \"Pizzeria da Michele\" in Verona. If you are asked a question, use the tool \"test_chatbot_elevenlabs\". When you receive the answer from \"test_chatbot_elevenlabs\" answer the user clearly and precisely.'\n- In Tools add a Webhook called eg. \"test_chatbot_elevenlabs\" and add the following description:\n'You are the waiter. Answer the questions asked and store them in the question field.'\n- Add the n8n webhook URL (method POST)\n- Enable \"Body Parameters\" and insert in the description \"Ask the user the question to ask the place.\", then in the \"Properties\" add a data type string called \"question\", value type \"LLM Prompt\" and description \"user question\""
},
"typeVersion": 1
},
{
"id": "ec053ee7-3a4a-4697-a08c-5645810d23f0",
"name": "When clicking ‘Test workflow’",
"type": "n8n-nodes-base.manualTrigger",
"position": [
-740,
-200
],
"parameters": {},
"typeVersion": 1
},
{
"id": "3e71e40c-a5cc-40cf-a159-aeedc97c47d1",
"name": "Create collection",
"type": "n8n-nodes-base.httpRequest",
"position": [
-440,
-340
],
"parameters": {
"url": "https://QDRANTURL/collections/COLLECTION",
"method": "POST",
"options": {},
"jsonBody": "{\n \"filter\": {}\n}",
"sendBody": true,
"sendHeaders": true,
"specifyBody": "json",
"authentication": "genericCredentialType",
"genericAuthType": "httpHeaderAuth",
"headerParameters": {
"parameters": [
{
"name": "Content-Type",
"value": "application/json"
}
]
}
},
"credentials": {
"httpHeaderAuth": {
"id": "qhny6r5ql9wwotpn",
"name": "Qdrant API (Hetzner)"
}
},
"typeVersion": 4.2
},
{
"id": "240283fc-50ec-475c-bd24-e6d0a367c10c",
"name": "Refresh collection",
"type": "n8n-nodes-base.httpRequest",
"position": [
-440,
-80
],
"parameters": {
"url": "https://QDRANTURL/collections/COLLECTION/points/delete",
"method": "POST",
"options": {},
"jsonBody": "{\n \"filter\": {}\n}",
"sendBody": true,
"sendHeaders": true,
"specifyBody": "json",
"authentication": "genericCredentialType",
"genericAuthType": "httpHeaderAuth",
"headerParameters": {
"parameters": [
{
"name": "Content-Type",
"value": "application/json"
}
]
}
},
"credentials": {
"httpHeaderAuth": {
"id": "qhny6r5ql9wwotpn",
"name": "Qdrant API (Hetzner)"
}
},
"typeVersion": 4.2
},
{
"id": "7d10fda0-c6ab-4bf5-b73e-b93a84937eff",
"name": "Get folder",
"type": "n8n-nodes-base.googleDrive",
"position": [
-220,
-80
],
"parameters": {
"filter": {
"driveId": {
"__rl": true,
"mode": "list",
"value": "My Drive",
"cachedResultUrl": "https://drive.google.com/drive/my-drive",
"cachedResultName": "My Drive"
},
"folderId": {
"__rl": true,
"mode": "id",
"value": "=test-whatsapp"
}
},
"options": {},
"resource": "fileFolder"
},
"credentials": {
"googleDriveOAuth2Api": {
"id": "HEy5EuZkgPZVEa9w",
"name": "Google Drive account"
}
},
"typeVersion": 3
},
{
"id": "c5761ad2-e66f-4d65-b653-0e89ea017f17",
"name": "Download Files",
"type": "n8n-nodes-base.googleDrive",
"position": [
0,
-80
],
"parameters": {
"fileId": {
"__rl": true,
"mode": "id",
"value": "={{ $json.id }}"
},
"options": {
"googleFileConversion": {
"conversion": {
"docsToFormat": "text/plain"
}
}
},
"operation": "download"
},
"credentials": {
"googleDriveOAuth2Api": {
"id": "HEy5EuZkgPZVEa9w",
"name": "Google Drive account"
}
},
"typeVersion": 3
},
{
"id": "1f031a11-8ef3-4392-a7db-9bca00840b8f",
"name": "Default Data Loader",
"type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
"position": [
380,
120
],
"parameters": {
"options": {},
"dataType": "binary"
},
"typeVersion": 1
},
{
"id": "7f614392-7bc7-408c-8108-f289a81d5cf6",
"name": "Token Splitter",
"type": "@n8n/n8n-nodes-langchain.textSplitterTokenSplitter",
"position": [
360,
280
],
"parameters": {
"chunkSize": 300,
"chunkOverlap": 30
},
"typeVersion": 1
},
{
"id": "648c5b3d-37a8-4a89-b88c-38e1863f09dc",
"name": "Sticky Note3",
"type": "n8n-nodes-base.stickyNote",
"position": [
-240,
-400
],
"parameters": {
"color": 6,
"width": 880,
"height": 220,
"content": "# STEP 2\n\n## Create Qdrant Collection\nChange:\n- QDRANTURL\n- COLLECTION"
},
"typeVersion": 1
},
{
"id": "a6c50f3c-3c73-464e-9bdc-49de96401c1b",
"name": "Qdrant Vector Store1",
"type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant",
"position": [
240,
-80
],
"parameters": {
"mode": "insert",
"options": {},
"qdrantCollection": {
"__rl": true,
"mode": "id",
"value": "=COLLECTION"
}
},
"credentials": {
"qdrantApi": {
"id": "iyQ6MQiVaF3VMBmt",
"name": "QdrantApi account"
}
},
"typeVersion": 1
},
{
"id": "7e19ac49-4d90-4258-bd44-7ca4ffa0128a",
"name": "Embeddings OpenAI1",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"position": [
220,
120
],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"id": "CDX6QM4gLYanh0P4",
"name": "OpenAi account"
}
},
"typeVersion": 1.1
},
{
"id": "bfa104a2-1f9c-4200-ae7b-4659894c1e6f",
"name": "Sticky Note5",
"type": "n8n-nodes-base.stickyNote",
"position": [
-460,
-140
],
"parameters": {
"color": 4,
"width": 620,
"height": 400,
"content": "# STEP 3\n\n\n\n\n\n\n\n\n\n\n\n\n## Documents vectorization with Qdrant and Google Drive\nChange:\n- QDRANTURL\n- COLLECTION"
},
"typeVersion": 1
},
{
"id": "a148ffcf-335f-455d-8509-d98c711ed740",
"name": "Respond to ElevenLabs",
"type": "n8n-nodes-base.respondToWebhook",
"position": [
380,
800
],
"parameters": {
"options": {}
},
"typeVersion": 1.1
},
{
"id": "5d19f73a-b8e8-4e75-8f67-836180597572",
"name": "OpenAI",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
-300,
1040
],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"id": "CDX6QM4gLYanh0P4",
"name": "OpenAi account"
}
},
"typeVersion": 1
},
{
"id": "802b76e1-3f3e-490c-9e3b-65dc5b28d906",
"name": "Listen",
"type": "n8n-nodes-base.webhook",
"position": [
-700,
800
],
"webhookId": "e9f611eb-a8dd-4520-8d24-9f36deaca528",
"parameters": {
"path": "test_voice_message_elevenlabs",
"options": {},
"httpMethod": "POST",
"responseMode": "responseNode"
},
"typeVersion": 2
},
{
"id": "bdc55a38-1d4b-48fe-bbd8-29bf1afd954a",
"name": "Window Buffer Memory",
"type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
"position": [
-140,
1040
],
"parameters": {},
"typeVersion": 1.3
},
{
"id": "2d5dd8cb-81eb-41bc-af53-b894e69e530c",
"name": "OpenAI Chat Model",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
200,
1320
],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"id": "CDX6QM4gLYanh0P4",
"name": "OpenAi account"
}
},
"typeVersion": 1
},
{
"id": "92d04432-1dbb-4d79-9edc-42378aee1c53",
"name": "Sticky Note6",
"type": "n8n-nodes-base.stickyNote",
"position": [
-760,
1620
],
"parameters": {
"color": 7,
"width": 1400,
"height": 240,
"content": "# STEP 5\n\n## Add Widget\n\nAdd the widget to your business website by replacing AGENT_ID with the agent id you created on ElevenLabs\n\n<elevenlabs-convai agent-id=\"AGENT_ID\"></elevenlabs-convai><script src=\"https://elevenlabs.io/convai-widget/index.js\" async type=\"text/javascript\"></script>"
},
"typeVersion": 1
}
],
"active": false,
"pinData": {},
"settings": {
"executionOrder": "v1"
},
"versionId": "6738abfe-e626-488d-a00b-81021cb04aaf",
"connections": {
"Listen": {
"main": [
[
{
"node": "AI Agent",
"type": "main",
"index": 0
}
]
]
},
"OpenAI": {
"ai_languageModel": [
[
{
"node": "AI Agent",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"AI Agent": {
"main": [
[
{
"node": "Respond to ElevenLabs",
"type": "main",
"index": 0
}
]
]
},
"Get folder": {
"main": [
[
{
"node": "Download Files",
"type": "main",
"index": 0
}
]
]
},
"Download Files": {
"main": [
[
{
"node": "Qdrant Vector Store1",
"type": "main",
"index": 0
}
]
]
},
"Token Splitter": {
"ai_textSplitter": [
[
{
"node": "Default Data Loader",
"type": "ai_textSplitter",
"index": 0
}
]
]
},
"Embeddings OpenAI": {
"ai_embedding": [
[
{
"node": "Qdrant Vector Store",
"type": "ai_embedding",
"index": 0
}
]
]
},
"OpenAI Chat Model": {
"ai_languageModel": [
[
{
"node": "Vector Store Tool",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Vector Store Tool": {
"ai_tool": [
[
{
"node": "AI Agent",
"type": "ai_tool",
"index": 0
}
]
]
},
"Embeddings OpenAI1": {
"ai_embedding": [
[
{
"node": "Qdrant Vector Store1",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Refresh collection": {
"main": [
[
{
"node": "Get folder",
"type": "main",
"index": 0
}
]
]
},
"Default Data Loader": {
"ai_document": [
[
{
"node": "Qdrant Vector Store1",
"type": "ai_document",
"index": 0
}
]
]
},
"Qdrant Vector Store": {
"ai_vectorStore": [
[
{
"node": "Vector Store Tool",
"type": "ai_vectorStore",
"index": 0
}
]
]
},
"Window Buffer Memory": {
"ai_memory": [
[
{
"node": "AI Agent",
"type": "ai_memory",
"index": 0
}
]
]
},
"When clicking ‘Test workflow’": {
"main": [
[
{
"node": "Create collection",
"type": "main",
"index": 0
},
{
"node": "Refresh collection",
"type": "main",
"index": 0
}
]
]
}
}
}Workflow n8n OpenAI, chatbot, ElevenLabs : pour qui est ce workflow ?
Ce workflow s'adresse aux entreprises de taille moyenne à grande qui souhaitent automatiser leur service client. Il est particulièrement adapté aux équipes techniques et aux responsables de l'innovation, cherchant à intégrer des solutions d'intelligence artificielle dans leurs processus.
Workflow n8n OpenAI, chatbot, ElevenLabs : problème résolu
Ce workflow résout le problème de la lenteur des réponses aux requêtes des clients en automatisant les interactions via un chatbot intelligent. Il élimine les frustrations liées aux temps d'attente et réduit les risques d'erreurs humaines dans les réponses. Les utilisateurs bénéficient d'une assistance rapide et efficace, ce qui améliore leur satisfaction et fidélité.
Workflow n8n OpenAI, chatbot, ElevenLabs : étapes du workflow
Étape 1 : Le workflow est déclenché par un Webhook qui écoute les requêtes des utilisateurs.
- Étape 1 : Les données sont récupérées à partir de Google Drive via le nœud 'Get folder'.
- Étape 2 : Les fichiers sont téléchargés pour enrichir les réponses du chatbot.
- Étape 3 : L'agent AI traite les demandes en utilisant le nœud 'OpenAI Chat Model'.
- Étape 4 : Les réponses sont générées et envoyées à l'utilisateur via le nœud 'Respond to ElevenLabs'.
Workflow n8n OpenAI, chatbot, ElevenLabs : guide de personnalisation
Pour personnaliser ce workflow, vous pouvez modifier l'URL du Webhook pour l'adapter à votre application. Il est également possible de changer les paramètres des nœuds 'OpenAI' et 'AI Agent' pour ajuster le comportement du chatbot. Pensez à définir les collections dans le nœud 'Qdrant Vector Store' pour optimiser la recherche d'informations. Enfin, vous pouvez intégrer d'autres services ou API selon vos besoins spécifiques.