This n8n workflow creates an AI-powered movie recommendation system integrated with MongoDB and OpenAI. It allows users to interact via chat messages, receive movie suggestions based on a database query, and save favorite movies.
The process begins with a webhook trigger that receives chat messages from users. These messages are processed by an AI language model (OpenAI) that leverages a memory buffer to maintain context across exchanges.
The core logic involves querying MongoDB to retrieve movies with a rating of 5 using an aggregation pipeline dynamically generated via AI. The AI agent then uses this data to provide tailored movie suggestions to the user.
When a user confirms a favorite movie, the workflow inserts this movie into the MongoDB favorites collection, enabling personalized recommendations and curated lists.
This setup is practical for building interactive movie or content recommendation engines, personalized user engagement platforms, or chatbots that combine AI with database insights to deliver contextually relevant suggestions.
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