Intelligent Image Classification with KNN and Voyage AI
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Automate sophisticated image classification by leveraging the power of Voyage AI's multimodal embeddings and Qdrant's vector database. This workflow intelligently identifies and categorizes images using a K-Nearest Neighbors (KNN) approach.
About This Workflow
This n8n workflow provides a robust solution for image classification, seamlessly integrating advanced AI models and vector search capabilities. It begins by embedding an input image using Voyage AI's voyage-multimodal-3 model, capturing its visual essence. This embedding is then used to query a Qdrant vector database, retrieving the most similar image embeddings based on a configurable KNN approach. A majority vote mechanism is employed on the landscape_name payload of the nearest neighbors to determine the final classification. The workflow intelligently handles potential ties by iteratively increasing the KNN search limit until a definitive classification is achieved, ensuring accuracy and reliability.
Key Features
- Multimodal Image Embedding: Utilizes Voyage AI for rich image representation.
- Vector Database Integration: Connects with Qdrant for efficient similarity search.
- K-Nearest Neighbors (KNN) Classification: Employs a proven machine learning technique for accurate categorization.
- Automated Tie Resolution: Dynamically adjusts search parameters to ensure a definitive classification.
- Configurable Search Parameters: Allows customization of the KNN limit for optimal performance.
How To Use
- Configure Voyage API Credentials: Set up your Voyage API key in n8n under
HTTP Header Authfor theEmbed imagenode. - Configure Qdrant Credentials: Set up your Qdrant API key and cloud URL in n8n under
Qdrant APIfor theQuery Qdrantnode. - Define Initial Parameters: In the
Image Test URLnode, provide theimageURLfor the image you want to classify. In theQdrant variables + embedding + KNN neigboursnode, define yourqdrantCloudURL,collectionName, and initiallimitKNN. - Connect Nodes: Ensure the nodes are connected in the following order:
Image Test URL->Embed image->Qdrant variables + embedding + KNN neigbours->Query Qdrant->Propagate loop variables->Majority Vote->Check tie. - Handle Tie Resolution: Connect the
Check tienode's 'True' output toIncrease limitKNN, and the 'False' output toReturn class. - Trigger Workflow: Execute the workflow with your desired image URL to receive the classified result.
Apps Used
Workflow JSON
{
"id": "39a762b0-2116-4ee1-b01e-7ab5a8bcfddb",
"name": "Intelligent Image Classification with KNN and Voyage AI",
"nodes": 24,
"category": "Marketing",
"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: 39a762b0-2116...
About the Author
AI_Workflow_Bot
LLM Specialist
Building complex chains with OpenAI, Claude, and LangChain.
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