Workflow n8n
Automatisation n8n : chatbot de recommandations de films
Ce workflow n8n a pour objectif de créer un chatbot capable de recommander des films en utilisant Qdrant et OpenAI. Dans un contexte où les utilisateurs cherchent des suggestions personnalisées, ce système permet d'améliorer l'expérience client en fournissant des recommandations pertinentes basées sur les préférences des utilisateurs. Les cas d'usage incluent des applications dans le secteur du divertissement, des plateformes de streaming ou des services de recommandation.
- Étape 1 : le workflow est déclenché manuellement par l'utilisateur.
- Étape 2 : les données sont extraites depuis un fichier via le nœud 'Extract from File'.
- Étape 3 : les embeddings sont générés à l'aide de 'Embeddings OpenAI', qui transforme les données en vecteurs exploitables.
- Étape 4 : le nœud 'Qdrant Vector Store' permet de stocker ces vecteurs pour une recherche rapide.
- Étape 5 : lorsqu'un message de chat est reçu, le nœud 'When chat message received' active le traitement.
- Étape 6 : le modèle de chat OpenAI est utilisé pour analyser les demandes des utilisateurs et fournir des réponses appropriées. Ce workflow offre des bénéfices significatifs pour les entreprises en automatisant le processus de recommandation, réduisant ainsi le temps de réponse et augmentant la satisfaction client.
Tags clés :automatisationOpenAIrecommandationsQdrantchatbot
Catégorie: Manual · Tags: automatisation, OpenAI, recommandations, Qdrant, chatbot❤ 0
Workflow n8n OpenAI, recommandations, Qdrant, chatbot : vue d'ensemble
Schéma des nœuds et connexions de ce workflow n8n, généré à partir du JSON n8n.
Workflow n8n OpenAI, recommandations, Qdrant, chatbot : détail des nœuds
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S'inscrire gratuitementBesoin d'aide ?{
"id": "a58HZKwcOy7lmz56",
"meta": {
"instanceId": "178ef8a5109fc76c716d40bcadb720c455319f7b7a3fd5a39e4f336a091f524a",
"templateCredsSetupCompleted": true
},
"name": "Building RAG Chatbot for Movie Recommendations with Qdrant and Open AI",
"tags": [],
"nodes": [
{
"id": "06a34e3b-519a-4b48-afd0-4f2b51d2105d",
"name": "When clicking ‘Test workflow’",
"type": "n8n-nodes-base.manualTrigger",
"position": [
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],
"parameters": {},
"typeVersion": 1
},
{
"id": "9213003d-433f-41ab-838b-be93860261b2",
"name": "GitHub",
"type": "n8n-nodes-base.github",
"position": [
5200,
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],
"parameters": {
"owner": {
"__rl": true,
"mode": "name",
"value": "mrscoopers"
},
"filePath": "Top_1000_IMDB_movies.csv",
"resource": "file",
"operation": "get",
"repository": {
"__rl": true,
"mode": "list",
"value": "n8n_demo",
"cachedResultUrl": "https://github.com/mrscoopers/n8n_demo",
"cachedResultName": "n8n_demo"
},
"additionalParameters": {}
},
"credentials": {
"githubApi": {
"id": "VbfC0mqEq24vPIwq",
"name": "GitHub n8n demo"
}
},
"typeVersion": 1
},
{
"id": "9850d1a9-3a6f-44c0-9f9d-4d20fda0b602",
"name": "Extract from File",
"type": "n8n-nodes-base.extractFromFile",
"position": [
5360,
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],
"parameters": {
"options": {}
},
"typeVersion": 1
},
{
"id": "7704f993-b1c9-477a-8b5a-77dc2cb68161",
"name": "Embeddings OpenAI",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"position": [
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],
"parameters": {
"model": "text-embedding-3-small",
"options": {}
},
"credentials": {
"openAiApi": {
"id": "deYJUwkgL1Euu613",
"name": "OpenAi account 2"
}
},
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},
{
"id": "bc6dd8e5-0186-4bf9-9c60-2eab6d9b6520",
"name": "Default Data Loader",
"type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
"position": [
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],
"parameters": {
"options": {
"metadata": {
"metadataValues": [
{
"name": "movie_name",
"value": "={{ $('Extract from File').item.json['Movie Name'] }}"
},
{
"name": "movie_release_date",
"value": "={{ $('Extract from File').item.json['Year of Release'] }}"
},
{
"name": "movie_description",
"value": "={{ $('Extract from File').item.json.Description }}"
}
]
}
},
"jsonData": "={{ $('Extract from File').item.json.Description }}",
"jsonMode": "expressionData"
},
"typeVersion": 1
},
{
"id": "f87ea014-fe79-444b-88ea-0c4773872b0a",
"name": "Token Splitter",
"type": "@n8n/n8n-nodes-langchain.textSplitterTokenSplitter",
"position": [
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],
"parameters": {},
"typeVersion": 1
},
{
"id": "d8d28cec-c8e8-4350-9e98-cdbc6da54988",
"name": "Qdrant Vector Store",
"type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant",
"position": [
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],
"parameters": {
"mode": "insert",
"options": {},
"qdrantCollection": {
"__rl": true,
"mode": "id",
"value": "imdb"
}
},
"credentials": {
"qdrantApi": {
"id": "Zin08PA0RdXVUKK7",
"name": "QdrantApi n8n demo"
}
},
"typeVersion": 1
},
{
"id": "f86e03dc-12ea-4929-9035-4ec3cf46e300",
"name": "When chat message received",
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"position": [
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],
"webhookId": "71bfe0f8-227e-466b-9d07-69fd9fe4a27b",
"parameters": {
"options": {}
},
"typeVersion": 1.1
},
{
"id": "ead23ef6-2b6b-428d-b412-b3394bff8248",
"name": "OpenAI Chat Model",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
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],
"parameters": {
"model": "gpt-4o-mini",
"options": {}
},
"credentials": {
"openAiApi": {
"id": "deYJUwkgL1Euu613",
"name": "OpenAi account 2"
}
},
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},
{
"id": "7ab936e1-aac8-43bc-a497-f2d02c2c19e5",
"name": "Call n8n Workflow Tool",
"type": "@n8n/n8n-nodes-langchain.toolWorkflow",
"position": [
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],
"parameters": {
"name": "movie_recommender",
"schemaType": "manual",
"workflowId": {
"__rl": true,
"mode": "id",
"value": "a58HZKwcOy7lmz56"
},
"description": "Call this tool to get a list of recommended movies from a vector database. ",
"inputSchema": "{\n\"type\": \"object\",\n\"properties\": {\n\t\"positive_example\": {\n \"type\": \"string\",\n \"description\": \"A string with a movie description matching the user's positive recommendation request\"\n },\n \"negative_example\": {\n \"type\": \"string\",\n \"description\": \"A string with a movie description matching the user's negative anti-recommendation reuqest\"\n }\n}\n}",
"specifyInputSchema": true
},
"typeVersion": 1.2
},
{
"id": "ce55f334-698b-45b1-9e12-0eaa473187d4",
"name": "Window Buffer Memory",
"type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
"position": [
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],
"parameters": {},
"typeVersion": 1.2
},
{
"id": "41c1ee11-3117-4765-98fc-e56cc6fc8fb2",
"name": "Execute Workflow Trigger",
"type": "n8n-nodes-base.executeWorkflowTrigger",
"position": [
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],
"parameters": {},
"typeVersion": 1
},
{
"id": "db8d6ab6-8cd2-4a8c-993d-f1b7d7fdcffd",
"name": "Merge",
"type": "n8n-nodes-base.merge",
"position": [
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],
"parameters": {
"mode": "combine",
"options": {},
"combineBy": "combineAll"
},
"typeVersion": 3
},
{
"id": "c7bc5e04-22b1-40db-ba74-1ab234e51375",
"name": "Split Out",
"type": "n8n-nodes-base.splitOut",
"position": [
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"parameters": {
"options": {},
"fieldToSplitOut": "result"
},
"typeVersion": 1
},
{
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"name": "Split Out1",
"type": "n8n-nodes-base.splitOut",
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"parameters": {
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},
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{
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"fieldsToMatchString": "id"
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{
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"type": "n8n-nodes-base.aggregate",
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"parameters": {
"options": {},
"aggregate": "aggregateAllItemData",
"destinationFieldName": "response"
},
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{
"id": "bedea10f-b4de-4f0e-9d60-cc8117a2b328",
"name": "AI Agent",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
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],
"parameters": {
"options": {
"systemMessage": "You are a Movie Recommender Tool using a Vector Database under the hood. Provide top-3 movie recommendations returned by the database, ordered by their recommendation score, but not showing the score to the user."
}
},
"typeVersion": 1.6
},
{
"id": "e04276b5-7d69-437b-bf4f-9717808cc8f6",
"name": "Embedding Recommendation Request with Open AI",
"type": "n8n-nodes-base.httpRequest",
"position": [
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],
"parameters": {
"url": "https://api.openai.com/v1/embeddings",
"method": "POST",
"options": {},
"sendBody": true,
"sendHeaders": true,
"authentication": "predefinedCredentialType",
"bodyParameters": {
"parameters": [
{
"name": "input",
"value": "={{ $json.query.positive_example }}"
},
{
"name": "model",
"value": "text-embedding-3-small"
}
]
},
"headerParameters": {
"parameters": [
{
"name": "Authorization",
"value": "Bearer $OPENAI_API_KEY"
}
]
},
"nodeCredentialType": "openAiApi"
},
"credentials": {
"openAiApi": {
"id": "deYJUwkgL1Euu613",
"name": "OpenAi account 2"
}
},
"typeVersion": 4.2
},
{
"id": "68e99f06-82f5-432c-8b31-8a1ae34981a6",
"name": "Embedding Anti-Recommendation Request with Open AI",
"type": "n8n-nodes-base.httpRequest",
"position": [
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],
"parameters": {
"url": "https://api.openai.com/v1/embeddings",
"method": "POST",
"options": {},
"sendBody": true,
"sendHeaders": true,
"authentication": "predefinedCredentialType",
"bodyParameters": {
"parameters": [
{
"name": "input",
"value": "={{ $json.query.negative_example }}"
},
{
"name": "model",
"value": "text-embedding-3-small"
}
]
},
"headerParameters": {
"parameters": [
{
"name": "Authorization",
"value": "Bearer $OPENAI_API_KEY"
}
]
},
"nodeCredentialType": "openAiApi"
},
"credentials": {
"openAiApi": {
"id": "deYJUwkgL1Euu613",
"name": "OpenAi account 2"
}
},
"typeVersion": 4.2
},
{
"id": "ecb1d7e1-b389-48e8-a34a-176bfc923641",
"name": "Extracting Embedding",
"type": "n8n-nodes-base.set",
"position": [
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],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "01a28c9d-aeb1-48bb-8a73-f8bddbd73460",
"name": "positive_example",
"type": "array",
"value": "={{ $json.data[0].embedding }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "4ed11142-a734-435f-9f7a-f59e2d423076",
"name": "Extracting Embedding1",
"type": "n8n-nodes-base.set",
"position": [
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],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "01a28c9d-aeb1-48bb-8a73-f8bddbd73460",
"name": "negative_example",
"type": "array",
"value": "={{ $json.data[0].embedding }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "ce3aa9bc-a5b1-4529-bff5-e0dba43b99f3",
"name": "Calling Qdrant Recommendation API",
"type": "n8n-nodes-base.httpRequest",
"position": [
6840,
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],
"parameters": {
"url": "https://edcc6735-2ffb-484f-b735-3467043828fe.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/imdb_1000_open_ai/points/query",
"method": "POST",
"options": {},
"jsonBody": "={\n \"query\": {\n \"recommend\": {\n \"positive\": [[{{ $json.positive_example }}]],\n \"negative\": [[{{ $json.negative_example }}]],\n \"strategy\": \"average_vector\"\n }\n },\n \"limit\":3\n}",
"sendBody": true,
"specifyBody": "json",
"authentication": "predefinedCredentialType",
"nodeCredentialType": "qdrantApi"
},
"credentials": {
"qdrantApi": {
"id": "Zin08PA0RdXVUKK7",
"name": "QdrantApi n8n demo"
}
},
"typeVersion": 4.2
},
{
"id": "9b8a6bdb-16fe-4edc-86d0-136fe059a777",
"name": "Retrieving Recommended Movies Meta Data",
"type": "n8n-nodes-base.httpRequest",
"position": [
7060,
1460
],
"parameters": {
"url": "https://edcc6735-2ffb-484f-b735-3467043828fe.europe-west3-0.gcp.cloud.qdrant.io:6333/collections/imdb_1000_open_ai/points",
"method": "POST",
"options": {},
"jsonBody": "={\n \"ids\": [\"{{ $json.result.points[0].id }}\", \"{{ $json.result.points[1].id }}\", \"{{ $json.result.points[2].id }}\"],\n \"with_payload\":true\n}",
"sendBody": true,
"specifyBody": "json",
"authentication": "predefinedCredentialType",
"nodeCredentialType": "qdrantApi"
},
"credentials": {
"qdrantApi": {
"id": "Zin08PA0RdXVUKK7",
"name": "QdrantApi n8n demo"
}
},
"typeVersion": 4.2
},
{
"id": "28cdcad5-3dca-48a1-b626-19eef657114c",
"name": "Selecting Fields Relevant for Agent",
"type": "n8n-nodes-base.set",
"position": [
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],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "b4b520a5-d0e2-4dcb-af9d-0b7748fd44d6",
"name": "movie_recommendation_score",
"type": "number",
"value": "={{ $json.score }}"
},
{
"id": "c9f0982e-bd4e-484b-9eab-7e69e333f706",
"name": "movie_description",
"type": "string",
"value": "={{ $json.payload.content }}"
},
{
"id": "7c7baf11-89cd-4695-9f37-13eca7e01163",
"name": "movie_name",
"type": "string",
"value": "={{ $json.payload.metadata.movie_name }}"
},
{
"id": "1d1d269e-43c7-47b0-859b-268adf2dbc21",
"name": "movie_release_year",
"type": "string",
"value": "={{ $json.payload.metadata.release_year }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "56e73f01-5557-460a-9a63-01357a1b456f",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
5560,
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],
"parameters": {
"content": "Tool, calling Qdrant's recommendation API based on user's request, transformed by AI agent"
},
"typeVersion": 1
},
{
"id": "cce5250e-0285-4fd0-857f-4b117151cd8b",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
4680,
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],
"parameters": {
"content": "Uploading data (movies and their descriptions) to Qdrant Vector Store\n"
},
"typeVersion": 1
}
],
"active": false,
"pinData": {
"Execute Workflow Trigger": [
{
"json": {
"query": {
"negative_example": "horror bloody movie",
"positive_example": "romantic comedy"
}
}
}
]
},
"settings": {
"executionOrder": "v1"
},
"versionId": "40d3669b-d333-435f-99fc-db623deda2cb",
"connections": {
"Merge": {
"main": [
[
{
"node": "Calling Qdrant Recommendation API",
"type": "main",
"index": 0
}
]
]
},
"GitHub": {
"main": [
[
{
"node": "Extract from File",
"type": "main",
"index": 0
}
]
]
},
"Merge1": {
"main": [
[
{
"node": "Selecting Fields Relevant for Agent",
"type": "main",
"index": 0
}
]
]
},
"Split Out": {
"main": [
[
{
"node": "Merge1",
"type": "main",
"index": 1
}
]
]
},
"Split Out1": {
"main": [
[
{
"node": "Merge1",
"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
}
]
]
},
"Extract from File": {
"main": [
[
{
"node": "Qdrant Vector Store",
"type": "main",
"index": 0
}
]
]
},
"OpenAI Chat Model": {
"ai_languageModel": [
[
{
"node": "AI Agent",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Default Data Loader": {
"ai_document": [
[
{
"node": "Qdrant Vector Store",
"type": "ai_document",
"index": 0
}
]
]
},
"Extracting Embedding": {
"main": [
[
{
"node": "Merge",
"type": "main",
"index": 0
}
]
]
},
"Window Buffer Memory": {
"ai_memory": [
[
{
"node": "AI Agent",
"type": "ai_memory",
"index": 0
}
]
]
},
"Extracting Embedding1": {
"main": [
[
{
"node": "Merge",
"type": "main",
"index": 1
}
]
]
},
"Call n8n Workflow Tool": {
"ai_tool": [
[
{
"node": "AI Agent",
"type": "ai_tool",
"index": 0
}
]
]
},
"Execute Workflow Trigger": {
"main": [
[
{
"node": "Embedding Recommendation Request with Open AI",
"type": "main",
"index": 0
},
{
"node": "Embedding Anti-Recommendation Request with Open AI",
"type": "main",
"index": 0
}
]
]
},
"When chat message received": {
"main": [
[
{
"node": "AI Agent",
"type": "main",
"index": 0
}
]
]
},
"Calling Qdrant Recommendation API": {
"main": [
[
{
"node": "Retrieving Recommended Movies Meta Data",
"type": "main",
"index": 0
},
{
"node": "Split Out1",
"type": "main",
"index": 0
}
]
]
},
"When clicking ‘Test workflow’": {
"main": [
[
{
"node": "GitHub",
"type": "main",
"index": 0
}
]
]
},
"Selecting Fields Relevant for Agent": {
"main": [
[
{
"node": "Aggregate",
"type": "main",
"index": 0
}
]
]
},
"Retrieving Recommended Movies Meta Data": {
"main": [
[
{
"node": "Split Out",
"type": "main",
"index": 0
}
]
]
},
"Embedding Recommendation Request with Open AI": {
"main": [
[
{
"node": "Extracting Embedding",
"type": "main",
"index": 0
}
]
]
},
"Embedding Anti-Recommendation Request with Open AI": {
"main": [
[
{
"node": "Extracting Embedding1",
"type": "main",
"index": 0
}
]
]
}
}
}Workflow n8n OpenAI, recommandations, Qdrant, chatbot : pour qui est ce workflow ?
Ce workflow s'adresse aux entreprises du secteur du divertissement, aux plateformes de streaming et aux développeurs cherchant à intégrer des systèmes de recommandation avancés. Un niveau technique intermédiaire est requis pour la mise en œuvre et la personnalisation.
Workflow n8n OpenAI, recommandations, Qdrant, chatbot : problème résolu
Ce workflow résout le problème de la recherche de recommandations de films pertinentes, souvent chronophage pour les utilisateurs. En automatisant ce processus, il réduit les frustrations liées à la recherche manuelle et améliore l'engagement des utilisateurs. Les entreprises bénéficient ainsi d'une expérience client enrichie et d'une augmentation de la fidélisation.
Workflow n8n OpenAI, recommandations, Qdrant, chatbot : étapes du workflow
Étape 1 : le workflow est déclenché manuellement par l'utilisateur.
- Étape 1 : les données sont extraites d'un fichier via le nœud 'Extract from File'.
- Étape 2 : les embeddings sont générés avec 'Embeddings OpenAI'.
- Étape 3 : les vecteurs sont stockés dans 'Qdrant Vector Store'.
- Étape 4 : lorsque le chatbot reçoit un message, le nœud 'When chat message received' est activé.
- Étape 5 : le modèle de chat OpenAI traite la demande et fournit des recommandations.
- Étape 6 : les résultats sont ensuite renvoyés à l'utilisateur, améliorant ainsi l'interaction.
Workflow n8n OpenAI, recommandations, Qdrant, chatbot : guide de personnalisation
Pour personnaliser ce workflow, vous pouvez modifier les paramètres du nœud 'Extract from File' pour changer la source des données. Les configurations des nœuds 'Embeddings OpenAI' et 'Qdrant Vector Store' peuvent être ajustées pour affiner les recommandations. Il est également possible d'ajouter des filtres ou de modifier les modèles de chat dans le nœud 'OpenAI Chat Model' pour mieux répondre aux besoins spécifiques des utilisateurs. Assurez-vous de sécuriser les connexions API et de monitorer les performances du workflow pour garantir une expérience utilisateur optimale.