Automatisation Chatbot avec n8n : support client en temps réel
Ce workflow n8n a pour objectif d'automatiser les interactions avec un chatbot de support client, permettant ainsi une réponse rapide et efficace aux demandes des utilisateurs. Dans un contexte où les entreprises cherchent à améliorer leur service client tout en réduisant les coûts, ce type d'automatisation n8n devient essentiel. Les cas d'usage incluent la gestion des questions fréquentes, la redirection vers des ressources pertinentes et l'amélioration de l'expérience utilisateur.
- Étape 1 : le workflow est déclenché par la réception d'un message dans le chat, grâce au nœud 'When chat message received'.
- Étape 2 : le message est ensuite traité par le modèle de langage OpenAI, qui génère une réponse appropriée.
- Étape 3 : les résultats sont extraits et vérifiés pour déterminer s'ils contiennent des informations pertinentes. Si des résultats sont trouvés, le workflow continue avec l'appel à l'API de recherche de support Acuity, sinon, une réponse vide est renvoyée.
- Étape 4 : les réponses pertinentes sont ensuite agrégées et présentées à l'utilisateur. Ce processus permet non seulement de réduire le temps de réponse, mais aussi d'améliorer la satisfaction client en fournissant des réponses précises et rapides. En intégrant ce workflow, les entreprises peuvent optimiser leurs ressources tout en offrant un service client de qualité.
Workflow n8n chatbot, support client, OpenAI : vue d'ensemble
Schéma des nœuds et connexions de ce workflow n8n, généré à partir du JSON n8n.
Workflow n8n chatbot, support client, OpenAI : détail des nœuds
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"meta": {
"instanceId": "408f9fb9940c3cb18ffdef0e0150fe342d6e655c3a9fac21f0f644e8bedabcd9",
"templateCredsSetupCompleted": true
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"nodes": [
{
"id": "8f203423-b063-4918-a6ec-dad3ac7d1a20",
"name": "When chat message received",
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"webhookId": "c82193c7-163c-4556-942f-81c80037e0ea",
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{
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"name": "OpenAI Chat Model",
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"parameters": {
"model": {
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"mode": "list",
"value": "gpt-4o-mini"
},
"options": {}
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"credentials": {
"openAiApi": {
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"name": "OpenAi account"
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{
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"name": "Simple Memory",
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"parameters": {},
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{
"id": "61ca5a4b-3661-4330-ac4c-e09e75dd764c",
"name": "Acuity Support Search API",
"type": "n8n-nodes-base.httpRequest",
"position": [
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],
"parameters": {
"url": "https://2al21hjwoz-dsn.algolia.net/1/indexes/*/queries?x-algolia-agent=Algolia%20for%20JavaScript%20(3.35.1)%3B%20Browser%20(lite)%3B%20instantsearch.js%201.12.1%3B%20Zendesk%20Integration%20(2.32.0)%3B%20JS%20Helper%20(2.28.1)&x-algolia-application-id=2AL21HJWOZ&x-algolia-api-key=c3c07dd7fb575008575163c085a62b92",
"method": "POST",
"options": {},
"jsonBody": "={{\n{\n \"requests\":[\n {\n \"indexName\":\"Zendesk 4-25\",\n \"params\": \"query=\" + $json.query + \"&hitsPerPage=5&page=0&facets=%5B%22locale.locale%22%2C%22label_names%22%2C%22category.title%22%5D&tagFilters=&facetFilters=%5B%22locale.locale%3Aen-us%22%5D\"\n }\n ]\n}\n}}",
"sendBody": true,
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{
"name": "Accept-Language",
"value": "en"
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{
"name": "Cache-Control",
"value": "no-cache"
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{
"name": "Connection",
"value": "keep-alive"
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{
"name": "Origin",
"value": "https://help.acuityscheduling.com"
},
{
"name": "Referer",
"value": "https://help.acuityscheduling.com/"
},
{
"name": "User-Agent",
"value": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/134.0.0.0 Safari/537.36"
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"name": "Extract Relevant Fields",
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"parameters": {
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"assignments": {
"assignments": [
{
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"name": "title",
"type": "string",
"value": "={{ $json.title }}"
},
{
"id": "88092adb-7f63-4daa-8c7a-cbd85750e180",
"name": "body",
"type": "string",
"value": "={{ $json.body_safe }}"
},
{
"id": "12718897-a73d-4c3a-bcfb-b17c890458ec",
"name": "url",
"type": "string",
"value": "=https://help.acuityscheduling.com/hc/en-us/articles/{{ $json.id }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "bf5855b2-8e73-4c29-b277-adee63e8bf59",
"name": "Results to Items",
"type": "n8n-nodes-base.splitOut",
"position": [
2360,
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],
"parameters": {
"options": {},
"fieldToSplitOut": "results[0].hits"
},
"typeVersion": 1
},
{
"id": "c9329816-bbe0-4de7-b6fb-fa87783f6a5c",
"name": "Has Results?",
"type": "n8n-nodes-base.if",
"position": [
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],
"parameters": {
"options": {},
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
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"combinator": "and",
"conditions": [
{
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"operator": {
"type": "array",
"operation": "lengthGt",
"rightType": "number"
},
"leftValue": "={{ $json.results[0]?.hits ?? [] }}",
"rightValue": 0
}
]
}
},
"typeVersion": 2.2
},
{
"id": "860a178a-d500-4291-acfc-9c9f4638d6c7",
"name": "Empty Response",
"type": "n8n-nodes-base.set",
"position": [
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],
"parameters": {
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"assignments": {
"assignments": [
{
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"name": "response",
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{
"id": "c9f2a08b-88c2-4287-994c-f7af58e98301",
"name": "Aggregate Response",
"type": "n8n-nodes-base.aggregate",
"position": [
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],
"parameters": {
"options": {},
"aggregate": "aggregateAllItemData",
"destinationFieldName": "response"
},
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},
{
"id": "5f1f8874-7022-4ea1-b0a7-de42c4f800a1",
"name": "Knowledgebase Tool",
"type": "@n8n/n8n-nodes-langchain.toolWorkflow",
"position": [
1320,
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],
"parameters": {
"name": "acuity_support_search",
"workflowId": {
"__rl": true,
"mode": "id",
"value": "={{ $workflow.id }}"
},
"description": "Call this tool to query AcuityScheduling's Support Center Search API.",
"workflowInputs": {
"value": {
"query": "={{ /*n8n-auto-generated-fromAI-override*/ $fromAI('query', ``, 'string') }}"
},
"schema": [
{
"id": "query",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "query",
"defaultMatch": false,
"canBeUsedToMatch": true
}
],
"mappingMode": "defineBelow",
"matchingColumns": [],
"attemptToConvertTypes": false,
"convertFieldsToString": false
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},
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{
"id": "3913ddaa-852e-4463-a072-fe8be22bc184",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
720,
-300
],
"parameters": {
"color": 7,
"width": 780,
"height": 580,
"content": "## 1. Simple Chatbot with Knowledgebase Tool\n[Learn more about AI agents](https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.agent)\n\nThe AI agent node is the simplest and recommended way to create user-friendly chatbots in n8n. Here, we'll define a support agent which can answer AcuityScheduling.com questions. To ensure the answers are accurate and up-to-date, we'll connect it to the support knowledgebase via a custom workflow tool."
},
"typeVersion": 1
},
{
"id": "e24d75f9-6d3c-4bca-b67f-33737ee969ee",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
1540,
-140
],
"parameters": {
"color": 7,
"width": 700,
"height": 440,
"content": "## 2. Use your Existing Help Portal Search\n[Read more about the HTTP request tool](https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.httprequest)\n\nThe concept of RAG need to be synonymous with vector stores! In truth, many companies with a decent enough support website are able to leverage this existing knowledgebase for support agents. This saves time, money and effort and additional avoids maintenance of a vector store where syncs and updates are common."
},
"typeVersion": 1
},
{
"id": "f5feebf1-fd6d-4558-a868-7ea4f852386c",
"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
"position": [
2260,
-140
],
"parameters": {
"color": 7,
"width": 720,
"height": 600,
"content": "## 3. Clean up the Results to Optimise Tokens\n[Read more about the aggregate node](https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.aggregate)\n\nOf course, the results are intended for the website format but by using the custom workflow tool, we can edit it down to suit our chat scenario and save LLM costs (in terms of tokens) whilst we're at it. "
},
"typeVersion": 1
},
{
"id": "8132de59-9b47-460a-9cb9-f2ec83123a3f",
"name": "AcuityScheduling Support Chatbot",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
1060,
-100
],
"parameters": {
"options": {
"systemMessage": "You are a support assistant for the SaaS company, AcuityScheduling.com. Your task is to openly help the user with any questions regarding the AcuityScheduling service however, you are restricted to only this service. If the user asks questions unrelated to AcuityScheduling, you may ask them for clarification, explain you are not able to help them out of scope or redirect them to support@acuityScheduling.com. Be factual in your answer, tap into the resources or tools available and do not rely on your training data (which might be out-of-date). When returning a response to the user, you are encouraged to share the URL of the knowledgebase page where the user can explore the documentation for themselves."
}
},
"typeVersion": 1.8
},
{
"id": "564bde38-25ea-4969-aa3f-bff66ec2782f",
"name": "Sticky Note3",
"type": "n8n-nodes-base.stickyNote",
"position": [
260,
-840
],
"parameters": {
"width": 440,
"height": 1120,
"content": "## Try it Out!\n### This n8n template demonstrates how you can leverage existing support site search to power your Support Chatbots and agents.\n\nBuilding a support chatbot need not be complicated! If building and indexing vector stores or duplicating data isn't necessarily your thing, an alternative implementation of the [RAG](https://www.databricks.com/glossary/retrieval-augmented-generation-rag) approach is to leverage existing knowledge-bases such as support portals.\n\n### How it works\n* A simple AI agent is connected with chat trigger to receive user queries.\n* The AI agent is instructed to fetch information from the knowledge-base via the attached custom workflow tool (aka \"knowledgebase tool\").\n* There is no step to replicate the entire support articles database into a vector store. You may choose not too because of time, cost and maintainence involved.\n* Instead, the tool leverages the existing support portal's search API to retrieve knowledge-base articles.\n* Finally, the search results are formatted before sending an aggregated response back to the agent.\n\n### How to use?\n* Customise the subworkflow to work with your own support portal API and format accordingly.\n* Try the following queries\n * How do I connect my icloud to acuityScheduling?\n * How do I download past invoices for my Acuity account?\n\n### Requirements\n* OpenAI for LLM.\n* If your organisation's APIs require authorisation, you may need to add custom credentials as necessary.\n\n### Customising this workflow\n* Add additional tools to reach other parts of your internal knowledgebase.\n* Not using OpenAI? Feel free to swap but ensure the LLM has tools/function calling support.\n\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": "a918718f-915d-4d5c-a7c2-a015b8a84bbb",
"name": "KnowledgeBase Tool Subworkflow",
"type": "n8n-nodes-base.executeWorkflowTrigger",
"position": [
1620,
80
],
"parameters": {
"workflowInputs": {
"values": [
{
"name": "query"
}
]
}
},
"typeVersion": 1.1
}
],
"pinData": {},
"connections": {
"Has Results?": {
"main": [
[
{
"node": "Results to Items",
"type": "main",
"index": 0
}
],
[
{
"node": "Empty Response",
"type": "main",
"index": 0
}
]
]
},
"Simple Memory": {
"ai_memory": [
[
{
"node": "AcuityScheduling Support Chatbot",
"type": "ai_memory",
"index": 0
}
]
]
},
"Results to Items": {
"main": [
[
{
"node": "Extract Relevant Fields",
"type": "main",
"index": 0
}
]
]
},
"OpenAI Chat Model": {
"ai_languageModel": [
[
{
"node": "AcuityScheduling Support Chatbot",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Knowledgebase Tool": {
"ai_tool": [
[
{
"node": "AcuityScheduling Support Chatbot",
"type": "ai_tool",
"index": 0
}
]
]
},
"Extract Relevant Fields": {
"main": [
[
{
"node": "Aggregate Response",
"type": "main",
"index": 0
}
]
]
},
"Acuity Support Search API": {
"main": [
[
{
"node": "Has Results?",
"type": "main",
"index": 0
}
]
]
},
"When chat message received": {
"main": [
[
{
"node": "AcuityScheduling Support Chatbot",
"type": "main",
"index": 0
}
]
]
},
"KnowledgeBase Tool Subworkflow": {
"main": [
[
{
"node": "Acuity Support Search API",
"type": "main",
"index": 0
}
]
]
}
}
}Workflow n8n chatbot, support client, OpenAI : pour qui est ce workflow ?
Ce workflow s'adresse aux entreprises de toutes tailles souhaitant améliorer leur service client via des solutions automatisées. Il est particulièrement pertinent pour les équipes de support technique et les départements de service client qui cherchent à réduire les délais de réponse et à augmenter l'efficacité opérationnelle. Un niveau technique intermédiaire est recommandé pour la mise en place.
Workflow n8n chatbot, support client, OpenAI : problème résolu
Ce workflow résout le problème de lenteur dans les réponses aux demandes de support client. En automatisant le processus de réponse, il élimine les frustrations des utilisateurs qui attendent des réponses, réduit le temps d'attente et minimise les erreurs humaines. À la suite de l'implémentation de ce workflow, les entreprises peuvent s'attendre à une augmentation de la satisfaction client et à une meilleure gestion des ressources humaines.
Workflow n8n chatbot, support client, OpenAI : étapes du workflow
Étape 1 : le workflow est déclenché par un message reçu dans le chat.
- Étape 1 : le message est envoyé au modèle de langage OpenAI pour générer une réponse.
- Étape 2 : les résultats sont extraits et vérifiés pour leur pertinence.
- Étape 3 : une requête est faite à l'API de support Acuity pour obtenir des informations supplémentaires.
- Étape 4 : si des résultats sont trouvés, ils sont agrégés et présentés à l'utilisateur. Sinon, une réponse vide est renvoyée.
Workflow n8n chatbot, support client, OpenAI : guide de personnalisation
Pour personnaliser ce workflow, vous pouvez modifier l'URL de l'API Acuity pour l'adapter à votre service. Il est également possible de changer le modèle utilisé dans le nœud OpenAI pour ajuster le ton et le style des réponses. Pensez à adapter les paramètres du nœud 'When chat message received' pour qu'il corresponde à votre plateforme de chat. Enfin, pour une meilleure sécurité, assurez-vous de mettre en place des contrôles d'accès appropriés pour l'API et de surveiller les performances du workflow.