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Building RAG Chatbot for Movie Recommendations with Qdrant and Open AI

Build a sophisticated RAG (Retrieval Augmented Generation) chatbot for movie recommendations by leveraging Qdrant for vector search and OpenAI for embeddings and chat generation. This comprehensive workflow automates the entire process from data ingestion to interactive chat, starting with ingesting movie data from GitHub via the GitHub node and then processing it with Extract from File. The movie data is then transformed into vector embeddings using Embeddings OpenAI and stored in Qdrant Vector Store after being prepared by Default Data Loader and Token Splitter. When a chat message is received through the When chat message received trigger, the AI Agent orchestrates a conversation, utilizing the OpenAI Chat Model for natural language understanding and generation, and the Window Buffer Memory to maintain conversational context. The workflow intelligently retrieves movie recommendations by making HTTP requests to Qdrant Recommendation API, which is informed by embedding recommendation and anti-recommendation requests generated by OpenAI. This allows for highly personalized movie suggestions, solving the problem of generic recommendations and enhancing user engagement. By automating data processing, vector storage, and conversational AI, this workflow significantly reduces the manual effort and development time typically required to deploy a powerful recommendation system, providing a dynamic and responsive user experience for movie enthusiasts.

27 nodesmanual trigger205 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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