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
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 →