Upload Crops Dataset to Qdrant for Anomaly & KNN Classification

Manual Trigger

Complex Trigger

This n8n workflow is designed for the batch ingestion of a 'crops dataset' into the Qdrant vector database. Triggered manually, it likely fetches raw data, potentially from Google Cloud Storage, and then performs a series of data preparation and filtering steps. The workflow's structure, indicated by '[1/3 - anomaly detection]' and '[1/2 - KNN classification]', signifies that the data is being specifically pre-processed and formatted for subsequent machine learning tasks. This includes preparing the dataset for use in anomaly detection models and K-Nearest Neighbors (KNN) classification algorithms. The ultimate goal is to efficiently populate Qdrant with vector embeddings or structured data, enabling high-performance similarity searches and real-time inference for these AI applications.