Intelligent movie recommendation engine with Qdrant and AI
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Workflow Description
An augmented retrieval system that extracts film data from GitHub repositories, chunks content, generates vector embeddings via OpenAI, stores them in Qdrant, and delivers personalized recommendations through a smart agent that interacts with users in real time.
How it works
- 1.Extract movie metadata from GitHub repositories and load document files
- 2.Split content into chunks and generate vector embeddings using OpenAI
- 3.Store embeddings in Qdrant and enable semantic search queries
- 4.Deploy an AI agent that receives user inquiries and produces recommendations
- 5.Share results through chat interface and distribute updates to Twitter/X
Use cases
- Build a movie platform that understands user preferences and suggests relevant titles
- Enhance internal search engines by capturing semantic meaning and context
- Create an intelligent assistant that answers film questions based on knowledge base
Requirements
- GitHub repository with organized film data and metadata
- OpenAI API key for embeddings generation and conversational AI
- Active Qdrant instance configured for vector storage and retrieval
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 (25)
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
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