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Vector Database System for AI-Driven KNN Classification
Streamline data analysis with AI-driven KNN classification. Leverage vector databases and automate processes for efficient results.
Who is this workflow for? This workflow leverages Qdrant and n8n to implement a K-Nearest Neighbors (KNN) classification tool. It processes image URLs, classifies objects based on a pre-uploaded dataset in Qdrant, and returns accurate classifications through an automated pipeline..
What does this workflow do?
- Image URL Input: The workflow begins by receiving an image URL via the Execute Workflow Trigger node.
- Embedding Retrieval: The image URL is sent to the Voyage AI Multimodal Embeddings API to obtain its embedding vector.
- Vector Query: The embedding vector is used to query the Qdrant database, retrieving a set of similar images with existing class labels.
- Majority Voting: The classes of the retrieved neighbors are subjected to majority voting to determine the most likely classification.
- Tie Resolution: If a tie occurs in the majority voting, the workflow enters a loop to retrieve additional neighbors, incrementally increasing the number until the tie is resolved.
- Final Classification: Once resolved, the identified class is returned to the calling workflow, completing the classification process.
🤖 Why Use This Automation Workflow?
- Efficient Classification: Automates the KNN classification process, saving time and reducing manual effort.
- Scalable Integration: Easily integrates with various AI models and data sources, enhancing flexibility.
- Reliable Anomaly Detection: Incorporates anomaly detection to ensure data integrity and enhance classification accuracy.
👨💻 Who is This Workflow For?
This workflow is designed for data scientists, AI developers, and businesses seeking to implement scalable image classification systems. It is ideal for those working with large image datasets and requiring robust, automated classification and anomaly detection.
🎯 Use Cases
- Agricultural Monitoring: Classify crop types from aerial images to monitor agricultural land.
- Land Use Analysis: Identify and categorize different land use patterns from satellite imagery.
- Quality Control: Detect and classify defects in manufacturing processes through image analysis.
TL;DR
This n8n workflow automates the KNN classification of images by integrating Qdrant for vector storage and Voyage AI for embedding generation. It provides a scalable and reliable solution for image classification tasks, facilitating efficient data analysis and decision-making for AI-driven applications.
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A curated directory of the best n8n templates for workflow automations.