Build an AI-Powered Movie Recommendation Chatbot with RAG
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Create an intelligent RAG chatbot for personalized movie recommendations. This workflow ingests movie data, transforms it into vector embeddings with OpenAI, stores it in Qdrant, and then leverages a GPT-powered chat interface to provide highly relevant suggestions.
About This Workflow
This comprehensive n8n workflow empowers you to build a sophisticated AI-powered movie recommendation chatbot using a Retrieval Augmented Generation (RAG) architecture. It begins by ingesting a dataset of 1000 IMDB movies from GitHub, processing their descriptions into vector embeddings via OpenAI's text-embedding-3-small model. These embeddings, along with rich movie metadata, are then efficiently stored in a Qdrant vector database. The chatbot front-end, driven by OpenAI's gpt-4o-mini and equipped with conversational memory, intelligently utilizes a custom "movie_recommender" tool to query Qdrant for highly relevant movie suggestions based on a user's explicit positive and negative preferences.
Key Features
- Automated Data Ingestion: Seamlessly pulls movie data from a GitHub CSV repository, simplifying your data pipeline.
- Advanced Vector Embeddings: Utilizes OpenAI's powerful embedding models (
text-embedding-3-small) to convert movie descriptions into semantically searchable vectors. - Scalable Vector Database: Integrates with Qdrant for efficient, high-performance storage and retrieval of movie embeddings and metadata.
- Intelligent RAG Chatbot: Employs OpenAI's chat models (
gpt-4o-mini) and conversational memory to understand user preferences and deliver contextual recommendations. - Custom Tooling for AI Agents: Features a bespoke n8n workflow tool that allows the LLM to dynamically query the vector database for tailored recommendations.
How To Use
- Configure GitHub Credentials: Ensure your GitHub account is connected to access the
Top_1000_IMDB_movies.csvfile from the specified repository. - Set Up OpenAI Credentials: Provide your OpenAI API key for both embedding generation (
text-embedding-3-small) and the chat model (gpt-4o-mini). - Connect Qdrant: Establish a connection to your Qdrant instance, specifying the
imdbcollection for vector storage. - Ingest Movie Data: Execute the workflow manually via the "When clicking ‘Test workflow’" trigger to populate your Qdrant database with movie embeddings.
- Activate Chat Trigger: Enable the "When chat message received" webhook to start interacting with your RAG movie recommendation chatbot.
Apps Used
Workflow JSON
{
"id": "99a3c49c-884d-492f-8c4f-8985a13e9370",
"name": "Build an AI-Powered Movie Recommendation Chatbot with RAG",
"nodes": 19,
"category": "Operations",
"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: 99a3c49c-884d...
About the Author
Crypto_Watcher
Web3 Developer
Automated trading bots and blockchain monitoring workflows.
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