Automatisation chatbot avec n8n : intégration ElevenLabs et OpenAI
Ce workflow n8n a pour objectif de créer un chatbot intelligent en intégrant les technologies d'ElevenLabs et d'OpenAI. Il s'adresse aux entreprises souhaitant améliorer leur service client ou automatiser des interactions via des canaux numériques. En utilisant ce workflow, les utilisateurs peuvent facilement configurer un chatbot capable de répondre à des questions en temps réel, d'analyser des données et d'interagir de manière fluide avec les utilisateurs. Le processus commence par un déclencheur Webhook qui écoute les requêtes entrantes. Ensuite, le workflow utilise des nœuds tels que 'AI Agent' pour gérer les requêtes et 'OpenAI' pour générer des réponses basées sur les entrées des utilisateurs. Les données sont ensuite traitées à l'aide de 'Token Splitter' et 'Embeddings OpenAI' pour assurer une compréhension contextuelle. Les réponses sont stockées dans un 'Vector Store' pour une récupération rapide. Enfin, le nœud 'Respond to ElevenLabs' envoie les réponses générées aux utilisateurs. Grâce à cette automatisation n8n, les entreprises peuvent réduire le temps de réponse, améliorer l'expérience utilisateur et optimiser leurs opérations de service client.
Workflow n8n chatbot, OpenAI, service client : vue d'ensemble
Schéma des nœuds et connexions de ce workflow n8n, généré à partir du JSON n8n.
Workflow n8n chatbot, OpenAI, service client : détail des nœuds
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"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 chatbot, OpenAI, service client : pour qui est ce workflow ?
Ce workflow s'adresse aux entreprises de toutes tailles souhaitant intégrer des solutions d'IA dans leur service client. Il est particulièrement adapté aux équipes techniques et aux responsables de l'innovation qui cherchent à automatiser les interactions avec les clients.
Workflow n8n chatbot, OpenAI, service client : problème résolu
Ce workflow résout le problème de la lenteur et de l'inefficacité des réponses aux demandes clients. En automatisant les interactions, il réduit le temps d'attente pour les utilisateurs et diminue la charge de travail des équipes de support. Les entreprises peuvent ainsi offrir un service client réactif et personnalisé, tout en optimisant leurs ressources.
Workflow n8n chatbot, OpenAI, service client : étapes du workflow
Étape 1 : Le workflow commence par le nœud 'Listen' qui capte les requêtes via un Webhook.
- Étape 1 : Les données reçues sont traitées par le nœud 'AI Agent' qui les analyse.
- Étape 2 : Les réponses sont générées à l'aide du nœud 'OpenAI' qui utilise des modèles de langage avancés.
- Étape 3 : Les données sont ensuite segmentées par le 'Token Splitter' pour une meilleure compréhension.
- Étape 4 : Les réponses sont stockées dans le 'Vector Store Tool' pour un accès rapide.
- Étape 5 : Enfin, le nœud 'Respond to ElevenLabs' envoie les réponses aux utilisateurs.
Workflow n8n chatbot, OpenAI, service client : guide de personnalisation
Pour personnaliser ce workflow, vous pouvez modifier les paramètres du nœud 'Listen' pour adapter le chemin du Webhook. Il est également possible de configurer les options des nœuds 'OpenAI' et 'AI Agent' pour ajuster le ton et le style des réponses. Si vous souhaitez intégrer d'autres services, vous pouvez ajouter des nœuds supplémentaires pour enrichir les fonctionnalités du chatbot. Assurez-vous de sécuriser le Webhook avec des authentifications appropriées pour protéger vos données.