Build Your Own AI Movie Recommender with RAG, Qdrant, and OpenAI
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This n8n workflow empowers you to create a sophisticated RAG (Retrieval-Augmented Generation) chatbot for personalized movie recommendations. Leveraging Qdrant for vector storage and OpenAI for AI capabilities, it intelligently suggests films based on your preferences.
About This Workflow
Dive into the future of personalized entertainment with this n8n workflow that builds a powerful RAG chatbot for movie recommendations. It ingeniously integrates Qdrant, a high-performance vector database, and OpenAI's cutting-edge language models to create a truly intelligent recommendation engine. The workflow first ingests and indexes a dataset of movies, generating embeddings for each. When a user query is received, the system retrieves relevant movie information from Qdrant and then uses OpenAI to generate a tailored movie recommendation based on the retrieved context.
This solution showcases how to combine powerful AI tools within a no-code/low-code environment, enabling anyone to build advanced applications. Whether you're a movie buff looking for your next watch or a developer exploring AI integration, this workflow provides a solid foundation for building intelligent recommendation systems.
Key Features
- Intelligent RAG Architecture: Utilizes Retrieval-Augmented Generation for contextually relevant and accurate movie suggestions.
- Vector Database Integration: Leverages Qdrant for efficient storage and retrieval of movie embeddings.
- OpenAI Powered Responses: Employs advanced OpenAI models for natural language understanding and response generation.
- Customizable Movie Data: Easily adapt the workflow to your preferred movie dataset.
- No-Code Workflow Automation: Build and deploy your recommendation chatbot without extensive coding.
How To Use
- Set up Credentials: Configure your OpenAI API key and Qdrant API credentials within n8n.
- Import Workflow: Load this n8n workflow JSON into your n8n instance.
- Configure GitHub Node: Point the GitHub node to your repository containing the
Top_1000_IMDB_movies.csvfile and specify the correct owner and repository name. - Run Initial Setup: Execute the workflow once using the 'Test workflow' trigger to ingest and index the movie data into your Qdrant collection named 'imdb'.
- Activate Chat Trigger: Ensure the 'When chat message received' node is active to listen for incoming user queries.
- Interact with Chatbot: Send a message to your chatbot (which will be exposed via the webhook) describing the type of movie you're looking for, and receive recommendations.
Apps Used
Workflow JSON
{
"id": "ddb443a8-4788-4bc6-bb75-4c3ad1e05f0d",
"name": "Build Your Own AI Movie Recommender with RAG, Qdrant, and OpenAI",
"nodes": 17,
"category": "Marketing",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
Get This Workflow
ID: ddb443a8-4788...
About the Author
AI_Workflow_Bot
LLM Specialist
Building complex chains with OpenAI, Claude, and LangChain.
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