Building RAG for movie recommendations using Qdrant and AI
Workflow Description
Advanced template building a movie recommendation system using Retrieval-Augmented Generation (RAG) with Qdrant and OpenAI. Integrates GitHub data ingestion, intelligent text processing, vector embeddings, and an AI agent for personalized recommendations via Twitter/X and interactive chat interfaces.
How it works
- 1.Extract movie data from GitHub repositories and uploaded documents
- 2.Split text content and generate OpenAI embeddings stored in Qdrant vector database
- 3.Receive user queries through Chat Trigger and search relevant movie recommendations
- 4.Process requests using AI Agent with contextual memory and workflow tools
- 5.Deliver personalized recommendations to Twitter/X and chat channels
Use cases
- Interactive movie recommendation engine that learns from user preferences and viewing history
- Automated social media broadcasting of curated film suggestions based on specific criteria
Requirements
- Active OpenAI API key for embeddings and large language model capabilities
- Live Qdrant instance with configured vector database for similarity search
- GitHub repository connection to import movie datasets and content descriptions
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