This article provides a complete, practical walkthrough of the Http Executeworkflow Automation Webhook n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   **Title:**
   Building a Robust KNN Image Classifier Using n8n, Qdrant, and Voyage AI for Land Type Recognition
   
   **Meta Description:**
   Learn how to use n8n's visual automation platform to build a KNN-based image classifier for satellite land-type recognition using interactions with Qdrant vector database and Voyage AI's multimodal image embedding API.
   
   **Keywords:**
   KNN classifier, image classification, n8n workflow, Qdrant, Voyage AI, vector database, satellite imagery, land-use classification, machine learning, no-code AI, majority voting, image recognition automation
   
   **Article:**
   
   ---
   
   ## Building a Robust KNN Image Classifier Using n8n, Qdrant, and Voyage AI for Land Type Recognition
   
   With the advent of no-code platforms and powerful AI APIs, building complex machine learning pipelines no longer requires a PhD in data science or extensive programming experience. In this article, we explore a real-world application of such tools—developing a K-Nearest Neighbors (KNN) image classifier for recognizing types of land use from satellite imagery—using n8n, Qdrant Cloud, and Voyage AI.
   
   This low-code classification pipeline achieves over 93% accuracy on test data without any model training or fine-tuning. Instead, it uses vector similarity search, majority vote logic, and a smart tie-breaking loop to deliver robust results.
   
   ---
   
   ### What the Workflow Does
   
   The n8n workflow titled “[2/2] KNN classifier (lands dataset)” is designed to accept a URL of a satellite image and return a predicted land type label—such as forest, beach, or industrial zone—by leveraging the power of image embeddings and vector search.
   
   Here's a breakdown of how it works:
   
   1. **Receive an Image URL:**
      The workflow starts with an Execute Workflow Trigger node, which supplies an external image URL from a test dataset of satellite imagery.
   
   2. **Generate Multimodal Embedding using Voyage AI:**
      The image is sent to Voyage AI's `voyage-multimodal-3` model via API, which returns a high-dimensional embedding vector representing the semantic features of the image.
   
   3. **Query Vector Database (Qdrant):**
      This embedding is used to perform a similarity search against a pre-uploaded collection hosted in Qdrant Cloud. The returned results are the K (initially 10) closest vectors (images), each pre-labeled with a known land type (e.g., "forest", "airport").
   
   4. **Apply Majority Vote for Classification:**
      A Python Code node analyzes class labels from the neighbors and applies a simple majority vote to determine the most common class.
   
   5. **Tie Resolution Logic:**
      In case of a tie—say, 5 "beach" and 5 "forest"—the workflow automatically increases the K (limitKNN) by 5 and re-queries Qdrant until:
      - There is no tie, or
      - The limit reaches a ceiling of 100 neighbors.
   
   6. **Return Predicted Class:**
      Once a label reaches majority or the loop constraint is met, the workflow returns the most frequent class as the image classification result.
   
   ---
   
   ### Supported Land Types
   
   The classifier uses Qdrant preloaded with images from a land-use scene classification dataset, which includes the following 21 classes:
   
   - agricultural
   - airplane
   - baseballdiamond
   - beach
   - buildings
   - chaparral
   - denseresidential
   - forest
   - freeway
   - golfcourse
   - harbor
   - intersection
   - mediumresidential
   - mobilehomepark
   - overpass
   - parkinglot
   - river
   - runway
   - sparseresidential
   - storagetanks
   - tenniscourt
   
   These labeled images serve as the knowledge base for the classifier, allowing it to make accurate predictions by referencing similar entries.
   
   ---
   
   ### Performance
   
   Despite the simplicity of its design, the classifier achieved an impressive 93.24% accuracy on a test dataset not included in the Qdrant collection (i.e., unseen data), as noted in the included documentation. No additional metric learning, model training, or fine-tuning steps were required. The success hinges instead on high-quality image embeddings, a good variety of labeled samples in Qdrant, and a well-constructed similarity search loop.
   
   ---
   
   ### Why This Workflow Matters
   
   This no-code/low-code approach to machine learning classification demonstrates several key advantages:
   
   - 🔧 **Ease of Deployment:** Everything is built solely with configurable nodes in n8n—no complex backend code.
   - 🧠 **AI Capabilities Without Training:** Thanks to Voyage AI embeddings, there's no need to train a local model.
   - 🔄 **Dynamic Adjustment:** The workflow dynamically adapts KNN search depth to resolve ties.
   - ⚡ **Adaptable Design:** You can easily switch the dataset type (e.g., crops, urban infrastructure) by updating the Qdrant collection and labels.
   
   This makes it an ideal template for those in research, geospatial analysis, urban development, agriculture monitoring, and more.
   
   ---
   
   ### How to Recreate the Classifier
   
   To implement this workflow for your own project:
   
   1. Upload the “land use scene” dataset from Kaggle to your own cloud storage bucket (e.g., Google Cloud Storage).
   2. Connect Qdrant Cloud (free-tier works well) to store the pre-labeled vector embeddings.
   3. Set up a Voyage AI account and retrieve API keys for the image embedding endpoint.
   4. Import this workflow into your n8n instance and configure the appropriate credentials for Qdrant and Voyage AI.
   5. Send an image URL and receive an automated image classification in return!
   
   This makes for a cost-effective, rapid-deployment AI utility.
   
   ---
   
   ### List of Third-Party APIs and Services Used
   
   - 🔍 **Voyage AI Multimodal Embeddings API**  
     Used to convert image URLs into embedding vectors using the `voyage-multimodal-3` model.  
     API Reference: https://voyageai.dev/docs
   
   - 🗂 **Qdrant Cloud (Vector Search Engine)**  
     Used to store and query high-dimensional image embeddings labeled with land types.  
     API Reference: https://qdrant.tech/documentation/
   
   - ☁️ **Google Cloud Storage (Mentioned for dataset hosting)**  
     For image dataset hosting; enables easy image access via public URLs.
   
   ---
   
   ### Final Thoughts
   
   This workflow is a textbook example of practical, production-ready AI automation using no-code tools. By intelligently blending embedding generation, vector search, a dynamic KNN algorithm, and class resolution logic, the tool provides accurate and scalable image classification—all within a clear and maintainable n8n workflow.
   
   Whether you’re an AI enthusiast, a data scientist, or a domain expert in environmental or geospatial fields, this setup provides an elegant solution to image-based land classification—without writing extensive code or deploying models.
   
   Try it out, adapt it to your dataset, and experience firsthand how accessible intelligent automation can be.
   
   ---

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.