Build an Intelligent Movie Recommendation System with Qdrant
Workflow Description
An automation that constructs a retrieval-augmented generation (RAG) system for movie recommendations, integrating Qdrant vector storage with an intelligent agent that extracts data from GitHub, processes it, and generates personalized suggestions via OpenAI.
How it works
- 1.Extract movie data from GitHub files and split content into manageable text chunks for processing
- 2.Convert text into numerical vectors using OpenAI embeddings and store them in Qdrant vector database
- 3.Receive user queries through chat interface and execute semantic search across the vector repository
- 4.Generate personalized movie recommendations based on retrieved results and conversation context
- 5.Publish recommendations to social media or deliver them to the user
Use cases
- Build an intelligent movie recommendation engine that understands user preferences and suggests relevant content
- Create a natural language movie query system that handles complex and loosely-defined questions about films
Requirements
- Valid OpenAI API key for embeddings generation and AI-powered recommendations
- Active Qdrant vector store instance with sufficient storage capacity for movie data embeddings
- Structured movie dataset files hosted in a GitHub repository
Service Value
Ideal as a smart automation service combining integrations and AI to produce ready-to-use results.
Apps Used
Details
How to Use
- 1.Click "Download Template"
- 2.Open your n8n dashboard
- 3.Go to Workflows > Import from File
- 4.Select downloaded file and configure credentials
Nodes Used (27)
When clicking ‘Test workflow’
Manual Trigger
GitHub
Github
Extract from File
Extract From File
Embeddings OpenAI
OpenAI
Default Data Loader
Document Default Data Loader
Token Splitter
Text Splitter Token Splitter
Qdrant Vector Store
Vector Store Qdrant
When chat message received
Chat Trigger
OpenAI Chat Model
OpenAI
Call n8n Workflow Tool
Tool Workflow
Window Buffer Memory
Memory Buffer Window
Execute Workflow Trigger
Execute Workflow Trigger
Merge
Merge
Split Out
Split Out
Split Out1
Split Out
Merge1
Merge
Aggregate
Aggregate
AI Agent
Agent
Embedding Recommendation Request with Open AI
HTTP Request
Embedding Anti-Recommendation Request with Open AI
HTTP Request
Extracting Embedding
Set
Extracting Embedding1
Set
Calling Qdrant Recommendation API
HTTP Request
Retrieving Recommended Movies Meta Data
HTTP Request
Selecting Fields Relevant for Agent
Set
Sticky Note
Sticky Note
Sticky Note1
Sticky Note