Automate Crop Image Vectorization and Qdrant Upload

This robust n8n workflow provides an automated pipeline for vectorizing and ingesting image datasets into a Qdrant vector database. Designed as a foundational component for AI applications, it begins by programmatically fetching crop images stored in Google Cloud Storage. Each image is then sent to Voyage AI's multimodal embedding service, converting visual data into high-dimensional vectors. Concurrently, relevant metadata like crop names and public image links are extracted. Finally, the workflow efficiently batches these embeddings and payloads, performing a bulk upload to a specified Qdrant collection. This streamlines data preparation, enabling rapid development of machine learning models for tasks such as anomaly detection, visual search, and K-Nearest Neighbors classification on agricultural images.