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:**  
   Land Use Image Classification Using k-NN and Embeddings in n8n with Qdrant and Voyage AI
   
   **Meta Description:**  
   Discover how to build an automated k-Nearest Neighbors (k-NN) classifier for land use image recognition using n8n, Voyage AI for multimodal embeddings, and Qdrant for vector similarity search. Achieve over 93% accuracy on satellite imagery without model fine-tuning.
   
   **Keywords:**  
   n8n, machine learning, k-NN classifier, Qdrant, Voyage AI, image classification, satellite imagery, automation, land use detection, vector embeddings, multimodal AI
   
   ---
   
   ## Land Use Image Classification with k-NN in n8n Using Qdrant and Voyage AI
   
   Advancements in low-code tools and AI services are transforming how developers and data scientists implement machine learning workflows. With this in mind, we explore a powerful and modular classifier built using n8n, which achieves over 93% accuracy in classifying satellite land use images—without any model training or fine-tuning.
   
   This workflow utilizes k-Nearest Neighbors (k-NN), a fundamental yet robust algorithm, in conjunction with state-of-the-art vector embeddings via Voyage AI and fast similarity search via Qdrant. The result: an image classification tool that can identify scenes such as forests, golf courses, harbors, and more, based purely on visual embeddings.
   
   ### Why This Workflow Matters
   
   Traditional ML pipelines often require substantial resources to preprocess data, train models, and deploy infrastructure. In contrast, this n8n-based approach:
   - Eliminates the need for training models from scratch
   - Leverages advanced embedding APIs for feature extraction
   - Uses Qdrant’s similarity search to perform k-NN in production-ready environments
   - Automates the entire process via visual workflows
   
   It represents the kind of democratization of ML tooling that accelerates prototyping and production deployments across industries.
   
   ---
   
   ## Overview of the Workflow Pipeline
   
   This workflow is the second part of a two-stage pipeline. While the first stage handles uploading and organizing image data into Qdrant collections, this part accepts a new image and returns a predicted class label.
   
   ### Step-by-Step Breakdown
   
   1. **Trigger & Image Input**  
      The workflow starts with an Execute Workflow Trigger that receives a JSON object containing the URL of the image to classify. This image is typically not part of the training or validation set, ensuring evaluation on unseen data.
   
   2. **Image Embedding via Voyage AI**  
      The image URL is sent to Voyage AI’s Multimodal Embeddings endpoint. This API returns a dense vector representing visual and contextual features of the image using the model `voyage-multimodal-3`.
   
   3. **Prepare Variables for k-NN Search**  
      Once the embedding is received, the workflow sets up the required parameters:
      - Image embedding
      - Qdrant cluster URL
      - Name of the targeted collection (in this case: land-use)
      - Initial neighbor limit (`limitKNN`, default = 10)
   
   4. **Query Qdrant for Neighbors**  
      The embedding is used in a similarity search against a Qdrant collection to retrieve the top k similar image vectors. These points are pre-labeled with their land use class, allowing the workflow to extract their labels.
   
   5. **Majority Vote Decision**  
      Using Python code embedded in n8n’s Code node, the workflow analyzes the predominant class label among the nearest neighbors. A simple Majority Vote identifies the top two most common labels.
   
   6. **Check for Ties**  
      If the two most common classes have the same frequency (e.g., 5 “forest,” 5 “beach”), and if fewer than 100 neighbors have been queried, the workflow triggers a loop:
      - Increases `limitKNN` by 5
      - Repeats the Qdrant query and Majority Vote
   
   7. **Final Classification Output**  
      Once a tie is resolved (or a maximum neighbor limit is reached), the resulting class is returned to the calling workflow as the predicted land use.
   
   ---
   
   ## Classification Categories
   
   The Qdrant collection used in this setup consists of 21 landscape categories from a satellite image dataset. Classes include:
   
   - Agricultural
   - Airplane
   - Baseball Diamond
   - Beach
   - Buildings
   - Chaparral
   - Dense Residential
   - Forest
   - Freeway
   - Golf Course
   - Harbor
   - Intersection
   - Medium Residential
   - Mobile Home Park
   - Overpass
   - Parking Lot
   - River
   - Runway
   - Sparse Residential
   - Storage Tanks
   - Tennis Court
   
   These are loaded into Qdrant with image vectors and labels, serving as ground truth for similarity searches.
   
   ---
   
   ## Performance: 93.24% Accuracy
   
   The workflow has been tested on a held-out test set not included in the Qdrant collection used for classification. Despite no fine-tuning, metric learning, or neural network training, the pipeline achieves impressive results:
   
   ✅ Classification Accuracy: **93.24%**
   
   This high performance is attributed to:
   - Quality visual embeddings from Voyage AI
   - Diverse and well-organized labeled images in Qdrant
   - Adaptive logic to resolve ambiguous votes via increasing k
   
   ---
   
   ## Reproducibility and Adaptability
   
   To recreate and adapt this workflow for your own use:
   1. Upload a labeled image dataset (e.g., from Kaggle) to Google Cloud Storage
   2. Use Voyage AI for generating visual embeddings of each image
   3. Upload those embeddings to a Qdrant collection
   4. Deploy this n8n workflow to classify new images based on their embeddings
   
   📌 Note: While this example is based on land use scenes, the entire pipeline is dataset-agnostic. With appropriate substitution of your image dataset, it becomes an out-of-the-box image classifier for any visual domain.
   
   ---
   
   ## Third-Party APIs Used
   
   Here’s a list of external services that power this workflow:
   
   1. 🚀 **Voyage AI - Multimodal Embeddings API**  
      Used to generate embeddings from image URLs  
      API: `https://api.voyageai.com/v1/multimodalembeddings`
   
   2. 🧠 **Qdrant Cloud - Vector Search Engine**  
      Used to store labeled embeddings and perform fast similarity search  
      API: `https://<your-cluster>.cloud.qdrant.io/collections/<collectionName>/points/query`
   
   ---
   
   ## Final Thoughts
   
   This powerful and flexible workflow demonstrates the strength of combining modern vector databases like Qdrant with AI-as-a-Service tools like Voyage AI—all orchestrated through the visual automation capabilities of n8n.
   
   With no training required, minimal infrastructure overhead, and strong accuracy metrics, this classifier can be extended to numerous use cases including agriculture monitoring, urban planning, and environmental protection.
   
   🎯 Whether you're a developer, data scientist, or simply curious about applied AI, tools like this make intelligent image recognition accessible—and productive.
   
   — Written with ❤️ by your AI assistant.

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.