Data extraction and movie recommendation generation from GitHub to Qdrant

• Automate movie data extraction from GitHub repositories.
• Generate personalized movie recommendations based on user preferences.
• Utilize AI to enhance user engagement through tailored content.

Trigger the workflow manually to start the process. Fetch movie data from a GitHub repository. Extract relevant information from the CSV file. Create embeddings using OpenAI's language models. Store embeddings in Qdrant for efficient querying. Receive user input for positive and negative movie examples. Query the Qdrant vector store for personalized recommendations. Return recommended movies along with their metadata.

1. 1
   Import the workflow template into your n8n instance.
2. 2
   Set up GitHub credentials to access the movie data repository.
3. 3
   Configure OpenAI credentials for embedding generation.
4. 4
   Establish Qdrant credentials for vector storage access.
5. 5
   Run the workflow by clicking 'Test workflow' to initiate data extraction.