Movie recommendation generation using OpenAI and Qdrant

• Generate personalized movie recommendations based on user preferences.
• Integrate AI-driven chat capabilities for real-time user interaction.
• Utilize vector databases for efficient data retrieval and processing.

Trigger the workflow manually or via chat message. Fetch movie data from a GitHub-hosted CSV file. Extract relevant movie details and create embeddings using OpenAI. Store embeddings in Qdrant for efficient querying. Respond to user queries with tailored movie recommendations.

1. 1
   Import the workflow into n8n.
2. 2
   Configure GitHub credentials to access the movie data file.
3. 3
   Set up OpenAI and Qdrant credentials for embedding and data storage.
4. 4
   Test the workflow using the manual trigger or chat message.