This workflow follows the Agent → Chat Trigger recipe pattern — see all workflows that pair these two integrations.
The workflow JSON
Copy or download the full n8n JSON below. Paste it into a new n8n workflow, add your credentials, activate. Full import guide →
{
"id": "a58HZKwcOy7lmz56",
"meta": {
"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 \u2018Test workflow\u2019",
"type": "n8n-nodes-base.manualTrigger",
"position": [
4980,
740
],
"parameters": {},
"typeVersion": 1
},
{
"id": "9213003d-433f-41ab-838b-be93860261b2",
"name": "GitHub",
"type": "n8n-nodes-base.github",
"position": [
5200,
740
],
"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": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "9850d1a9-3a6f-44c0-9f9d-4d20fda0b602",
"name": "Extract from File",
"type": "n8n-nodes-base.extractFromFile",
"position": [
5360,
740
],
"parameters": {
"options": {}
},
"typeVersion": 1
},
{
"id": "7704f993-b1c9-477a-8b5a-77dc2cb68161",
"name": "Embeddings OpenAI",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"position": [
5560,
940
],
"parameters": {
"model": "text-embedding-3-small",
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "bc6dd8e5-0186-4bf9-9c60-2eab6d9b6520",
"name": "Default Data Loader",
"type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
"position": [
5700,
960
],
"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": [
5700,
1140
],
"parameters": {},
"typeVersion": 1
},
{
"id": "d8d28cec-c8e8-4350-9e98-cdbc6da54988",
"name": "Qdrant Vector Store",
"type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant",
"position": [
5600,
740
],
"parameters": {
"mode": "insert",
"options": {},
"qdrantCollection": {
"__rl": true,
"mode": "id",
"value": "imdb"
}
},
"credentials": {
"qdrantApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "f86e03dc-12ea-4929-9035-4ec3cf46e300",
"name": "When chat message received",
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"position": [
4920,
1140
],
"parameters": {
"options": {}
},
"typeVersion": 1.1
},
{
"id": "ead23ef6-2b6b-428d-b412-b3394bff8248",
"name": "OpenAI Chat Model",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
5040,
1340
],
"parameters": {
"model": "gpt-4o-mini",
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "7ab936e1-aac8-43bc-a497-f2d02c2c19e5",
"name": "Call n8n Workflow Tool",
"type": "@n8n/n8n-nodes-langchain.toolWorkflow",
"position": [
5320,
1340
],
"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": [
5160,
1340
],
"parameters": {},
"typeVersion": 1.2
},
{
"id": "41c1ee11-3117-4765-98fc-e56cc6fc8fb2",
"name": "Execute Workflow Trigger",
"type": "n8n-nodes-base.executeWorkflowTrigger",
"position": [
5640,
1600
],
"parameters": {},
"typeVersion": 1
},
{
"id": "db8d6ab6-8cd2-4a8c-993d-f1b7d7fdcffd",
"name": "Merge",
"type": "n8n-nodes-base.merge",
"position": [
6540,
1500
],
"parameters": {
"mode": "combine",
"options": {},
"combineBy": "combineAll"
},
"typeVersion": 3
},
{
"id": "c7bc5e04-22b1-40db-ba74-1ab234e51375",
"name": "Split Out",
"type": "n8n-nodes-base.splitOut",
"position": [
7260,
1480
],
"parameters": {
"options": {},
"fieldToSplitOut": "result"
},
"typeVersion": 1
},
{
"id": "a2002d2e-362a-49eb-a42d-7b665ddd67a0",
"name": "Split Out1",
"type": "n8n-nodes-base.splitOut",
"position": [
7140,
1260
],
"parameters": {
"options": {},
"fieldToSplitOut": "result.points"
},
"typeVersion": 1
},
{
"id": "f69a87f1-bfb9-4337-9350-28d2416c1580",
"name": "Merge1",
"type": "n8n-nodes-base.merge",
"position": [
7520,
1400
],
"parameters": {
"mode": "combine",
"options": {},
"fieldsToMatchString": "id"
},
"typeVersion": 3
},
{
"id": "b2f2529e-e260-4d72-88ef-09b804226004",
"name": "Aggregate",
"type": "n8n-nodes-base.aggregate",
"position": [
7960,
1400
],
"parameters": {
"options": {},
"aggregate": "aggregateAllItemData",
"destinationFieldName": "response"
},
"typeVersion": 1
},
{
"id": "bedea10f-b4de-4f0e-9d60-cc8117a2b328",
"name": "AI Agent",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
5140,
1140
],
"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": [
5900,
1460
],
"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": {
"name": "<your credential>"
}
},
"typeVersion": 4.2
},
{
"id": "68e99f06-82f5-432c-8b31-8a1ae34981a6",
"name": "Embedding Anti-Recommendation Request with Open AI",
"type": "n8n-nodes-base.httpRequest",
"position": [
5920,
1660
],
"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": {
"name": "<your credential>"
}
},
"typeVersion": 4.2
},
{
"id": "ecb1d7e1-b389-48e8-a34a-176bfc923641",
"name": "Extracting Embedding",
"type": "n8n-nodes-base.set",
"position": [
6180,
1460
],
"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": [
6180,
1660
],
"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,
1500
],
"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": {
"name": "<your credential>"
}
},
"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": {
"name": "<your credential>"
}
},
"typeVersion": 4.2
},
{
"id": "28cdcad5-3dca-48a1-b626-19eef657114c",
"name": "Selecting Fields Relevant for Agent",
"type": "n8n-nodes-base.set",
"position": [
7740,
1400
],
"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,
1780
],
"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,
720
],
"parameters": {
"content": "Uploading data (movies and their descriptions) to Qdrant Vector Store\n"
},
"typeVersion": 1
}
],
"active": false,
"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 \u2018Test workflow\u2019": {
"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
}
]
]
}
}
}
Credentials you'll need
Each integration node will prompt for credentials when you import. We strip credential IDs before publishing — you'll add your own.
githubApiopenAiApiqdrantApi
Pro
For the full experience including quality scoring and batch install features for each workflow upgrade to Pro
How this works
This workflow enables you to create a personalised movie recommendation chatbot by leveraging your own film data, delivering tailored suggestions based on user queries without relying on generic databases. It suits film enthusiasts, bloggers, or developers keen to build an interactive recommendation tool that feels customised and responsive. The core step involves extracting and embedding movie details from a GitHub repository into Qdrant for vector search, then using OpenAI to generate intelligent responses via a chat interface, ensuring quick and relevant matches to queries like 'suggest a thriller like Inception'.
Use this when you have a structured movie dataset on GitHub and want an event-driven chatbot for real-time recommendations, such as on a website or app. Avoid it for massive, unstructured data sources needing heavy preprocessing, or if you prefer pre-built services without setup. Common variations include swapping Qdrant for another vector store or integrating additional triggers like webhooks for broader deployment.
About this workflow
Building RAG Chatbot for Movie Recommendations with Qdrant and Open AI. Uses manualTrigger, github, extractFromFile, embeddingsOpenAi. Event-driven trigger; 27 nodes.
Source: https://github.com/Zie619/n8n-workflows — original creator credit. Request a take-down →
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