Building RAG Chatbot for Movie Recommendations with Qdrant and Open AI

Ready-to-use n8n workflow template for ai. This automation connects Qdrant, OpenAI, GitHub with 27 nodes. Import directly into your n8n instance and customize for your needs.

27 nodesmanual trigger49 views0 copiesAI
QdrantOpenAIGitHub

Workflow JSON

{"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 \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": {"id": "", "name": "[Your githubApi]"}}, "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": {"id": "", "name": "[Your openAiApi]"}}, "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": {"id": "", "name": "[Your qdrantApi]"}}, "typeVersion": 1}, {"id": "f86e03dc-12ea-4929-9035-4ec3cf46e300", "name": "When chat message received", "type": "@n8n/n8n-nodes-langchain.chatTrigger", "position": [4920, 1140], "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": [5040, 1340], "parameters": {"model": "gpt-4o-mini", "options": {}}, "credentials": {"openAiApi": {"id": "", "name": "[Your openAiApi]"}}, "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": {"id": "", "name": "[Your openAiApi]"}}, "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": {"id": "", "name": "[Your openAiApi]"}}, "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": {"id": "", "name": "[Your qdrantApi]"}}, "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": "", "name": "[Your qdrantApi]"}}, "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, "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 \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}]]}}}

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  2. 2Open your n8n instance and go to Workflows.
  3. 3Click Import from JSON and paste the copied workflow.

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