Batch Upload Dataset to Qdrant for Anomaly Detection
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Uploads a dataset of crop images to Qdrant for KNN classification and anomaly detection.
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About This Workflow
Overview
This workflow automates the process of uploading a dataset of crop images to a Qdrant vector database. It leverages the Voyage AI API to generate embeddings for each image, creating a searchable index for anomaly detection and KNN classification.
Key Features
- Reads image data from Google Cloud Storage.
- Generates unique IDs and public links for each image.
- Creates batches of data for efficient processing.
- Utilizes Voyage AI for image embedding generation.
- Upserts data into a Qdrant collection, storing embeddings and metadata.
- Checks for existing Qdrant collections and creates them if necessary.
How To Use
- Configure Google Cloud Storage credentials.
- Set up Voyage AI API credentials.
- Configure Qdrant API credentials and base URL in environment variables.
- Ensure the
n8n-qdrant-demobucket in Google Cloud Storage contains the crop images. - Set
API_BASE_URLandWEBHOOK_URLenvironment variables. - Run the workflow to upload the dataset.
Apps Used
Workflow JSON
{
"id": "81c2e7b5-b3cd-4d24-90dd-9cf46bcfa843",
"name": "Batch Upload Dataset to Qdrant for Anomaly Detection",
"nodes": 0,
"category": "Data Ingestion",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
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ID: 81c2e7b5-b3cd...
About the Author
DevOps_Master_X
Infrastructure Expert
Specializing in CI/CD pipelines, Docker, and Kubernetes automations.
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