About this recipe
A recipe for Vector Database As A Big Data Analysis Tool For Ai Agents [2 2 Knn]
Recipe Template
{
"id": "itzURpN5wbUNOXOw",
"meta": {
"instanceId": "205b3bc06c96f2dc835b4f00e1cbf9a937a74eeb3b47c99d0c30b0586dbf85aa"
},
"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",
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"httpHeaderAuth": {
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"typeVersion": 4.2
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{
"id": "58adecfa-45c7-4928-b850-053ea6f3b1c5",
"name": "Query Qdrant",
"type": "n8n-nodes-base.httpRequest",
"position": [
440,
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],
"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"
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"credentials": {
"qdrantApi": {
"id": "it3j3hP9FICqhgX6",
"name": "QdrantApi account"
}
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{
"id": "258026b7-2dda-4165-bfe1-c4163b9caf78",
"name": "Majority Vote",
"type": "n8n-nodes-base.code",
"position": [
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"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
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{
"id": "e83e7a0c-cb36-46d0-8908-86ee1bddf638",
"name": "Increase limitKNN",
"type": "n8n-nodes-base.set",
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"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
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{
"id": "8edbff53-cba6-4491-9d5e-bac7ad6db418",
"name": "Propagate loop variables",
"type": "n8n-nodes-base.set",
"position": [
640,
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"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
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{
"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
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{
"id": "f02e79e2-32c8-4af0-8bf9-281119b23cc0",
"name": "Return class",
"type": "n8n-nodes-base.set",
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"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
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{
"id": "847ced21-4cfd-45d8-98fa-b578adc054d6",
"name": "Qdrant variables + embedding + KNN neigbours",
"type": "n8n-nodes-base.set",
"position": [
120,
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],
"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,
"pinData": {
"Execute Workflow Trigger": [
{
"json": {
"query": {
"imageURL": "https:\/\/storage.googleapis.com\/n8n-qdrant-demo\/land-use\/images_train_test_val\/test\/buildings\/buildings_000323.png"
}
}
}
]
},
"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
}
]
]
}
}
}How to Use an n8n Template
1
Create a New Workflow
Click "New Workflow" in your n8n dashboard to get started.
2
Copy & Paste Template
First, copy this template:
Click here to copy the JSON
.
Then, in n8n, click the three dots (···) → "Import from file" and paste the JSON code.
3
Customize the Nodes
Go through each node in the workflow to update inputs like spreadsheet IDs, email addresses, or message content. Adjust field mappings to match your data.
4
Grant Access
For nodes that connect to external apps (like Google Sheets or Slack), you'll need to grant access. Connect your accounts using OAuth or an API key and save the credentials in the node.
5
Test It
Run the workflow by clicking "Execute Node" for each step or "Run Once" for the whole thing. Check the right sidebar to inspect data and debug any errors (they'll show up in red).
6
Activate Workflow
Once everything works as expected, click the "Activate" toggle to turn your workflow on. You're all set!