Who is this workflow for? This workflow demonstrates how to leverage vector databases for large-scale data analysis in AI agents. By utilizing n8n, Qdrant, and other integrated tools, you can build robust pipelines for tasks such as anomaly detection and image classification..

What does this workflow do?

Anomaly Detection Pipeline

  • Upload Image Dataset to Qdrant: Initiate the first pipeline to transfer your image datasets to the Qdrant vector database.
  • Set Up Cluster Centers and Thresholds: Configure cluster centers and threshold scores using either the distance matrix approach or multimodal embedding models to establish parameters for anomaly detection.
  • Anomaly Detection Tool: Input any image to determine if it deviates from the established dataset based on the predefined thresholds.

K-Nearest Neighbors (KNN) Classification Pipeline

  • Upload Image Dataset to Qdrant: Transfer your image datasets to Qdrant to prepare for classification.
  • KNN Classifier Tool: Input an image to classify it against the uploaded dataset using the KNN algorithm, determining its category based on proximity to existing data points.

Recreating the Workflows

To set up both pipelines:

  • Upload the crops and lands datasets from Kaggle to your Google Storage bucket.
  • Configure APIs and connections to Qdrant Cloud using the Free Tier cluster, Voyage AI API, and Google Cloud Storage.

🤖 Why Use This Automation Workflow?

  • Scalability: Efficiently handle and analyze large image datasets.
  • Flexibility: Adaptable to various datasets and production environments.
  • Automation: Streamline the process of data uploading, clustering, and classification without manual intervention.
  • Integration: Seamlessly connects with services like Qdrant Cloud, Google Cloud Storage, and Voyage AI API.

👨‍💻 Who is This Workflow For?

This workflow is ideal for data scientists, AI developers, and machine learning engineers who need to implement scalable big data analysis tools. It’s also suitable for organizations looking to integrate anomaly detection and classification capabilities into their AI systems.

🎯 Use Cases

  1. Anomaly Detection: Identify unusual patterns or outliers in image datasets for quality control or security applications.
  2. Image Classification: Automatically categorize images into predefined classes using K-Nearest Neighbors (KNN) algorithms.
  3. Data Preparation: Streamline the uploading and organizing of large image datasets into vector databases for further analysis.

TL;DR

This n8n workflow template provides a comprehensive solution for building scalable big data analysis tools using vector databases. By automating data upload, clustering, and classification processes, it enables the creation of production-ready AI agents capable of anomaly detection and image classification.

Get started with n8n
Need help with n8n?

Related

Backup n8n Workflows to GitHub Using Subfolders

Automate saving workflows to GitHub, benefit from organized subfolders, and ensure data safety with this efficient n8n template. 

  • 576
  • 11

Automate AI-Powered Blog Creation in Your Brand Voice

Streamline blog creation using AI in your brand voice. Leverage automation, maintain consistency, and enhance productivity with key features. 

  • 486
  • 11

ChatGPT-Driven Code Review Automation for GitLab Merges

Streamline code reviews with AI in GitLab, utilizing automated analysis and feedback features for efficient merge request evaluations. 

  • 547
  • 18

Help us find the best n8n templates

About

A curated directory of the best n8n templates for workflow automations.

Navigation

Submit