This workflow follows the Execute Workflow Trigger → HTTP Request recipe pattern — see all workflows that pair these two integrations.
The workflow JSON
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{
"id": "itzURpN5wbUNOXOw",
"name": "[2/2] KNN classifier (lands dataset)",
"tags": [
{
"id": "QN7etptCmdcGIpkS",
"name": "classifier",
"createdAt": "2024-12-08T22:08:15.968Z",
"updatedAt": "2024-12-09T19:25:04.113Z"
}
],
"nodes": [
{
"id": "33373ccb-164e-431c-8a9a-d68668fc70be",
"name": "Embed image",
"type": "n8n-nodes-base.httpRequest",
"position": [
-140,
-240
],
"parameters": {
"url": "https://api.voyageai.com/v1/multimodalembeddings",
"method": "POST",
"options": {},
"jsonBody": "={{\n{\n \"inputs\": [\n {\n \"content\": [\n {\n \"type\": \"image_url\",\n \"image_url\": $json.imageURL\n }\n ]\n }\n ],\n \"model\": \"voyage-multimodal-3\",\n \"input_type\": \"document\"\n}\n}}",
"sendBody": true,
"specifyBody": "json",
"authentication": "genericCredentialType",
"genericAuthType": "httpHeaderAuth"
},
"credentials": {
"httpHeaderAuth": {
"name": "<your credential>"
}
},
"typeVersion": 4.2
},
{
"id": "58adecfa-45c7-4928-b850-053ea6f3b1c5",
"name": "Query Qdrant",
"type": "n8n-nodes-base.httpRequest",
"position": [
440,
-240
],
"parameters": {
"url": "={{ $json.qdrantCloudURL }}/collections/{{ $json.collectionName }}/points/query",
"method": "POST",
"options": {},
"jsonBody": "={{\n{\n \"query\": $json.ImageEmbedding,\n \"using\": \"voyage\",\n \"limit\": $json.limitKNN,\n \"with_payload\": true\n}\n}}",
"sendBody": true,
"specifyBody": "json",
"authentication": "predefinedCredentialType",
"nodeCredentialType": "qdrantApi"
},
"credentials": {
"qdrantApi": {
"name": "<your credential>"
}
},
"typeVersion": 4.2
},
{
"id": "258026b7-2dda-4165-bfe1-c4163b9caf78",
"name": "Majority Vote",
"type": "n8n-nodes-base.code",
"position": [
840,
-240
],
"parameters": {
"language": "python",
"pythonCode": "from collections import Counter\n\ninput_json = _input.all()[0]\npoints = input_json['json']['result']['points']\nmajority_vote_two_most_common = Counter([point[\"payload\"][\"landscape_name\"] for point in points]).most_common(2)\n\nreturn [{\n \"json\": {\n \"result\": majority_vote_two_most_common \n }\n}]\n"
},
"typeVersion": 2
},
{
"id": "e83e7a0c-cb36-46d0-8908-86ee1bddf638",
"name": "Increase limitKNN",
"type": "n8n-nodes-base.set",
"position": [
1240,
-240
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "0b5d257b-1b27-48bc-bec2-78649bc844cc",
"name": "limitKNN",
"type": "number",
"value": "={{ $('Propagate loop variables').item.json.limitKNN + 5}}"
},
{
"id": "afee4bb3-f78b-4355-945d-3776e33337a4",
"name": "ImageEmbedding",
"type": "array",
"value": "={{ $('Qdrant variables + embedding + KNN neigbours').first().json.ImageEmbedding }}"
},
{
"id": "701ed7ba-d112-4699-a611-c0c134757a6c",
"name": "qdrantCloudURL",
"type": "string",
"value": "={{ $('Qdrant variables + embedding + KNN neigbours').first().json.qdrantCloudURL }}"
},
{
"id": "f5612f78-e7d8-4124-9c3a-27bd5870c9bf",
"name": "collectionName",
"type": "string",
"value": "={{ $('Qdrant variables + embedding + KNN neigbours').first().json.collectionName }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "8edbff53-cba6-4491-9d5e-bac7ad6db418",
"name": "Propagate loop variables",
"type": "n8n-nodes-base.set",
"position": [
640,
-240
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "880838bf-2be2-4f5f-9417-974b3cbee163",
"name": "=limitKNN",
"type": "number",
"value": "={{ $json.result.points.length}}"
},
{
"id": "5fff2bea-f644-4fd9-ad04-afbecd19a5bc",
"name": "result",
"type": "object",
"value": "={{ $json.result }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "6fad4cc0-f02c-429d-aa4e-0d69ebab9d65",
"name": "Image Test URL",
"type": "n8n-nodes-base.set",
"position": [
-320,
-240
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "46ceba40-fb25-450c-8550-d43d8b8aa94c",
"name": "imageURL",
"type": "string",
"value": "={{ $json.query.imageURL }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "f02e79e2-32c8-4af0-8bf9-281119b23cc0",
"name": "Return class",
"type": "n8n-nodes-base.set",
"position": [
1240,
0
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "bd8ca541-8758-4551-b667-1de373231364",
"name": "class",
"type": "string",
"value": "={{ $json.result[0][0] }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "83ca90fb-d5d5-45f4-8957-4363a4baf8ed",
"name": "Check tie",
"type": "n8n-nodes-base.if",
"position": [
1040,
-240
],
"parameters": {
"options": {},
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "980663f6-9d7d-4e88-87b9-02030882472c",
"operator": {
"type": "number",
"operation": "gt"
},
"leftValue": "={{ $json.result.length }}",
"rightValue": 1
},
{
"id": "9f46fdeb-0f89-4010-99af-624c1c429d6a",
"operator": {
"type": "number",
"operation": "equals"
},
"leftValue": "={{ $json.result[0][1] }}",
"rightValue": "={{ $json.result[1][1] }}"
},
{
"id": "c59bc4fe-6821-4639-8595-fdaf4194c1e1",
"operator": {
"type": "number",
"operation": "lte"
},
"leftValue": "={{ $('Propagate loop variables').item.json.limitKNN }}",
"rightValue": 100
}
]
}
},
"typeVersion": 2.2
},
{
"id": "847ced21-4cfd-45d8-98fa-b578adc054d6",
"name": "Qdrant variables + embedding + KNN neigbours",
"type": "n8n-nodes-base.set",
"position": [
120,
-240
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "de66070d-5e74-414e-8af7-d094cbc26f62",
"name": "ImageEmbedding",
"type": "array",
"value": "={{ $json.data[0].embedding }}"
},
{
"id": "58b7384d-fd0c-44aa-9f8e-0306a99be431",
"name": "qdrantCloudURL",
"type": "string",
"value": "=https://152bc6e2-832a-415c-a1aa-fb529f8baf8d.eu-central-1-0.aws.cloud.qdrant.io"
},
{
"id": "e34c4d88-b102-43cc-a09e-e0553f2da23a",
"name": "collectionName",
"type": "string",
"value": "=land-use"
},
{
"id": "db37e18d-340b-4624-84f6-df993af866d6",
"name": "limitKNN",
"type": "number",
"value": "=10"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "d1bc4edc-37d2-43ac-8d8b-560453e68d1f",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
-940,
-120
],
"parameters": {
"color": 6,
"width": 320,
"height": 540,
"content": "Here we're classifying existing types of satellite imagery of land types:\n- 'agricultural',\n- 'airplane',\n- 'baseballdiamond',\n- 'beach',\n- 'buildings',\n- 'chaparral',\n- 'denseresidential',\n- 'forest',\n- 'freeway',\n- 'golfcourse',\n- 'harbor',\n- 'intersection',\n- 'mediumresidential',\n- 'mobilehomepark',\n- 'overpass',\n- 'parkinglot',\n- 'river',\n- 'runway',\n- 'sparseresidential',\n- 'storagetanks',\n- 'tenniscourt'\n"
},
"typeVersion": 1
},
{
"id": "13560a31-3c72-43b8-9635-3f9ca11f23c9",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
-520,
-460
],
"parameters": {
"color": 6,
"content": "I tested this KNN classifier on a whole `test` set of a dataset (it's not a part of the collection, only `validation` + `train` parts). Accuracy of classification on `test` is **93.24%**, no fine-tuning, no metric learning."
},
"typeVersion": 1
},
{
"id": "8c9dcbcb-a1ad-430f-b7dd-e19b5645b0f6",
"name": "Execute Workflow Trigger",
"type": "n8n-nodes-base.executeWorkflowTrigger",
"position": [
-520,
-240
],
"parameters": {},
"typeVersion": 1
},
{
"id": "b36fb270-2101-45e9-bb5c-06c4e07b769c",
"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1080,
-520
],
"parameters": {
"width": 460,
"height": 380,
"content": "## KNN classification workflow-tool\n### This n8n template takes an image URL (as anomaly detection tool does), and as output, it returns a class of the object on the image (out of land types list)\n\n* An image URL is received via the Execute Workflow Trigger, which is then sent to the Voyage.ai Multimodal Embeddings API to fetch its embedding.\n* The image's embedding vector is then used to query Qdrant, returning a set of X similar images with pre-labeled classes.\n* Majority voting is done for classes of neighbouring images.\n* A loop is used to resolve scenarios where there is a tie in Majority Voting (for example, we have 5 \"forest\" and 5 \"beach\"), and we increase the number of neighbours to retrieve.\n* When the loop finally resolves, the identified class is returned to the calling workflow."
},
"typeVersion": 1
},
{
"id": "51ece7fc-fd85-4d20-ae26-4df2d3893251",
"name": "Sticky Note3",
"type": "n8n-nodes-base.stickyNote",
"position": [
120,
-40
],
"parameters": {
"height": 200,
"content": "Variables define another Qdrant's collection with landscapes (uploaded similarly as the crops collection, don't forget to switch it with your data) + amount of neighbours **limitKNN** in the database we'll use for an input image classification."
},
"typeVersion": 1
},
{
"id": "7aad5904-eb0b-4389-9d47-cc91780737ba",
"name": "Sticky Note4",
"type": "n8n-nodes-base.stickyNote",
"position": [
-180,
-60
],
"parameters": {
"height": 80,
"content": "Similarly to anomaly detection tool, we're embedding input image with the Voyage model"
},
"typeVersion": 1
},
{
"id": "d3702707-ee4a-481f-82ca-d9386f5b7c8a",
"name": "Sticky Note5",
"type": "n8n-nodes-base.stickyNote",
"position": [
440,
-500
],
"parameters": {
"width": 740,
"height": 200,
"content": "## Tie loop\nHere we're [querying](https://api.qdrant.tech/api-reference/search/query-points) Qdrant, getting **limitKNN** nearest neighbours to our image <*Query Qdrant node*>, parsing their classes from payloads (images were pre-labeled & uploaded with their labels to Qdrant) & calculating the most frequent class name <*Majority Vote node*>. If there is a tie <*check tie node*> in 2 most common classes, for example, we have 5 \"forest\" and 5 \"harbor\", we repeat the procedure with the number of neighbours increased by 5 <*propagate loop variables node* and *increase limitKNN node*>.\nIf there is no tie, or we have already checked 100 neighbours, we exit the loop <*check tie node*> and return the class-answer."
},
"typeVersion": 1
},
{
"id": "d26911bb-0442-4adc-8511-7cec2d232393",
"name": "Sticky Note6",
"type": "n8n-nodes-base.stickyNote",
"position": [
1240,
160
],
"parameters": {
"height": 80,
"content": "Here, we extract the name of the input image class decided by the Majority Vote\n"
},
"typeVersion": 1
},
{
"id": "84ffc859-1d5c-4063-9051-3587f30a0017",
"name": "Sticky Note10",
"type": "n8n-nodes-base.stickyNote",
"position": [
-520,
80
],
"parameters": {
"color": 4,
"width": 540,
"height": 260,
"content": "### KNN (k nearest neighbours) classification\n1. The first pipeline is uploading (lands) dataset to Qdrant's collection.\n2. **This is the KNN classifier tool, which takes any image as input and classifies it based on queries to the Qdrant (lands) collection.**\n\n### To recreate it\nYou'll have to upload [lands](https://www.kaggle.com/datasets/apollo2506/landuse-scene-classification) dataset from Kaggle to your own Google Storage bucket, and re-create APIs/connections to [Qdrant Cloud](https://qdrant.tech/documentation/quickstart-cloud/) (you can use **Free Tier** cluster), Voyage AI API & Google Cloud Storage\n\n**In general, pipelines are adaptable to any dataset of images**\n"
},
"typeVersion": 1
}
],
"active": false,
"settings": {
"executionOrder": "v1"
},
"versionId": "c8cfe732-fd78-4985-9540-ed8cb2de7ef3",
"connections": {
"Check tie": {
"main": [
[
{
"node": "Increase limitKNN",
"type": "main",
"index": 0
}
],
[
{
"node": "Return class",
"type": "main",
"index": 0
}
]
]
},
"Embed image": {
"main": [
[
{
"node": "Qdrant variables + embedding + KNN neigbours",
"type": "main",
"index": 0
}
]
]
},
"Query Qdrant": {
"main": [
[
{
"node": "Propagate loop variables",
"type": "main",
"index": 0
}
]
]
},
"Majority Vote": {
"main": [
[
{
"node": "Check tie",
"type": "main",
"index": 0
}
]
]
},
"Image Test URL": {
"main": [
[
{
"node": "Embed image",
"type": "main",
"index": 0
}
]
]
},
"Increase limitKNN": {
"main": [
[
{
"node": "Query Qdrant",
"type": "main",
"index": 0
}
]
]
},
"Execute Workflow Trigger": {
"main": [
[
{
"node": "Image Test URL",
"type": "main",
"index": 0
}
]
]
},
"Propagate loop variables": {
"main": [
[
{
"node": "Majority Vote",
"type": "main",
"index": 0
}
]
]
},
"Qdrant variables + embedding + KNN neigbours": {
"main": [
[
{
"node": "Query Qdrant",
"type": "main",
"index": 0
}
]
]
}
}
}
Credentials you'll need
Each integration node will prompt for credentials when you import. We strip credential IDs before publishing — you'll add your own.
httpHeaderAuthqdrantApi
Pro
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How this works
This workflow automates image classification using a k-nearest neighbours algorithm trained on a lands dataset, delivering accurate categorisation of uploaded images such as satellite or terrain photos without requiring manual intervention. It suits data analysts, environmental researchers, or remote sensing teams handling large volumes of visual data who need quick, reliable labelling to inform decisions like land use planning. The key step involves querying a Qdrant vector database via HTTP requests to find the nearest matching embeddings, followed by a majority vote in a code node to determine the final class, ensuring robust results even with noisy inputs.
Use this workflow for event-driven classification tasks where images arrive sporadically, such as monitoring deforestation via satellite feeds, integrating seamlessly with tools like Qdrant for scalable searches. Avoid it for real-time video processing or datasets far removed from lands imagery, as retraining would be necessary for optimal accuracy. Common variations include adjusting the k-value for broader searches or adding post-processing to handle ties in voting outcomes.
About this workflow
[2/2] KNN classifier (lands dataset). Uses httpRequest, stickyNote, executeWorkflowTrigger. Event-driven trigger; 18 nodes.
Source: https://github.com/Zie619/n8n-workflows — original creator credit. Request a take-down →
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