Automated Crop Anomaly Detection with Qdrant and VoyageAI
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Streamline agricultural anomaly detection by automatically uploading crop datasets to Qdrant. This workflow leverages advanced embeddings from VoyageAI for powerful image analysis and classification.
About This Workflow
This n8n workflow automates the process of preparing and uploading crop image datasets for anomaly detection. It begins by fetching images from Google Cloud Storage, extracts relevant fields such as public links and crop names, and then generates embeddings using VoyageAI's multimodal model. These embeddings, along with payload data, are batched and uploaded to a Qdrant vector database for efficient storage and retrieval. The workflow includes steps for checking and creating Qdrant collections, ensuring a robust setup for your AI-driven agricultural insights.
Key Features
- Automated Data Ingestion: Seamlessly pull crop images from Google Cloud Storage.
- Advanced Image Embedding: Utilizes VoyageAI for powerful multimodal embeddings.
- Vector Database Integration: Efficiently stores and manages embeddings in Qdrant.
- Anomaly Detection Preparation: Organizes data for subsequent classification and anomaly identification.
- Batch Processing: Handles data in batches for optimized performance.
How To Use
- Configure Trigger: Set up the 'When clicking ‘Test workflow’' node to initiate the process.
- Google Cloud Storage Node: Connect your Google Cloud Storage account and specify the bucket ('n8n-qdrant-demo') and prefix ('agricultural-crops') for your dataset.
- Set Qdrant Variables: Configure the 'Qdrant cluster variables' node with your Qdrant cloud URL, desired collection name, embedding dimension, and batch size.
- Check/Create Qdrant Collection: The 'Check Qdrant Collection Existence' and 'Create Qdrant Collection' nodes will manage your Qdrant collection. Ensure your Qdrant API credentials are set.
- Get Fields for Qdrant: This node transforms the GCS object data into usable formats, creating public links and extracting crop names.
- Batch Data Formatting: The 'Batches in the API's format' node prepares data for both VoyageAI embedding and Qdrant upload, including creating image content for embeddings and structuring payloads.
- Embed Crop Image: Configure the 'Embed crop image' node with your VoyageAI API credentials and ensure the JSON body correctly references the batch for embedding.
- Batch Upload to Qdrant: Finally, the 'Batch Upload to Qdrant' node takes the generated embeddings and formatted payloads to upload them into your Qdrant collection.
Apps Used
Workflow JSON
{
"id": "655ed70f-cba7-435c-a967-55750266724d",
"name": "Automated Crop Anomaly Detection with Qdrant and VoyageAI",
"nodes": 20,
"category": "Operations",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
Get This Workflow
ID: 655ed70f-cba7...
About the Author
Crypto_Watcher
Web3 Developer
Automated trading bots and blockchain monitoring workflows.
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