Build an intelligent movie recommendation system using Qdrant and AI
Workflow Description
An automation that retrieves movie data from GitHub repositories, transforms it into intelligent vectors via OpenAI, stores them in Qdrant, and delivers personalized recommendations through an AI agent capable of interacting via web and email interfaces.
How it works
- 1.Fetch movie data files from GitHub repositories
- 2.Split and convert text to vectors using OpenAI embedding models
- 3.Store vectors in Qdrant database for fast semantic search
- 4.Receive user queries through a chat interface or trigger
- 5.Search the vector database and retrieve relevant movies
- 6.Deliver personalized recommendations using an intelligent agent with context memory
Use cases
- Streaming platforms delivering accurate movie recommendations based on user preferences
- Digital movie stores improving search and discovery experiences
- Entertainment apps that learn from user interactions and refine recommendations
- Customer support systems recommending suitable movies to users
Requirements
- OpenAI API key for text-to-vector conversion
- Access to a GitHub repository containing movie data files
- Running and configured Qdrant server for vector storage
- HTTP connection or chat interface for user interaction
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