[3/3] アノーマリ検知ツール（作物データセット）
上級
これはAI, SecOps分野の自動化ワークフローで、17個のノードを含みます。主にSet, Code, HttpRequest, ExecuteWorkflowTriggerなどのノードを使用、AI技術を活用したスマート自動化を実現。 異常検出ツール [3/3 - 異常]
前提条件
- •ターゲットAPIの認証情報が必要な場合あり
ワークフロープレビュー
ノード接続関係を可視化、ズームとパンをサポート
ワークフローをエクスポート
以下のJSON設定をn8nにインポートして、このワークフローを使用できます
{
"id": "G8jRDBvwsMkkMiLN",
"meta": {
"instanceId": "205b3bc06c96f2dc835b4f00e1cbf9a937a74eeb3b47c99d0c30b0586dbf85aa"
},
"name": "[3/3] Anomaly detection tool (crops dataset)",
"tags": [
{
"id": "spMntyrlE9ydvWFA",
"name": "anomaly-detection",
"createdAt": "2024-12-08T22:05:15.945Z",
"updatedAt": "2024-12-09T12:50:19.287Z"
}
],
"nodes": [
{
"id": "e01bafec-eb24-44c7-b3c4-a60f91666350",
"name": "付箋1",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1200,
180
],
"parameters": {
"color": 6,
"width": 400,
"height": 740,
"content": "We are working here with crops dataset: \nExisting (so not anomalies) crops images in dataset are:\n- 'pearl_millet(bajra)',\n- 'tobacco-plant',\n- 'cherry',\n- 'cotton',\n- 'banana',\n- 'cucumber',\n- 'maize',\n- 'wheat',\n- 'clove',\n- 'jowar',\n- 'olive-tree',\n- 'soyabean',\n- 'coffee-plant',\n- 'rice',\n- 'lemon',\n- 'mustard-oil',\n- 'vigna-radiati(mung)',\n- 'coconut',\n- 'gram',\n- 'pineapple',\n- 'sugarcane',\n- 'sunflower',\n- 'chilli',\n- 'fox_nut(makhana)',\n- 'jute',\n- 'papaya',\n- 'tea',\n- 'cardamom',\n- 'almond'\n"
},
"typeVersion": 1
},
{
"id": "b9943781-de1f-4129-9b81-ed836e9ebb11",
"name": "画像の埋め込み",
"type": "n8n-nodes-base.httpRequest",
"position": [
680,
60
],
"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\": $('Image URL hardcode').first().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": {
"id": "Vb0RNVDnIHmgnZOP",
"name": "Voyage API"
}
},
"typeVersion": 4.2
},
{
"id": "47b72bc2-4817-48c6-b517-c1328e402468",
"name": "Medoidの類似度取得",
"type": "n8n-nodes-base.httpRequest",
"position": [
940,
60
],
"parameters": {
"url": "={{ $('Variables for medoids').first().json.qdrantCloudURL }}/collections/{{ $('Variables for medoids').first().json.collectionName }}/points/query",
"method": "POST",
"options": {},
"jsonBody": "={{\n{\n \"query\": $json.data[0].embedding,\n \"using\": \"voyage\",\n \"limit\": $('Info About Crop Labeled Clusters').first().json.cropsNumber,\n \"with_payload\": true,\n \"filter\": {\n \"must\": [\n { \n \"key\": $('Variables for medoids').first().json.clusterCenterType,\n \"match\": {\n \"value\": true\n }\n }\n ]\n }\n}\n}}",
"sendBody": true,
"specifyBody": "json",
"authentication": "predefinedCredentialType",
"nodeCredentialType": "qdrantApi"
},
"credentials": {
"qdrantApi": {
"id": "it3j3hP9FICqhgX6",
"name": "QdrantApi account"
}
},
"typeVersion": 4.2
},
{
"id": "42d7eb27-ec38-4406-b5c4-27eb45358e93",
"name": "スコア比較",
"type": "n8n-nodes-base.code",
"position": [
1140,
60
],
"parameters": {
"language": "python",
"pythonCode": "points = _input.first()['json']['result']['points']\nthreshold_type = _('Variables for medoids').first()['json']['clusterThresholdCenterType']\n\nmax_score = -1\ncrop_with_max_score = None\nundefined = True\n\nfor center in points:\n if center['score'] >= center['payload'][threshold_type]:\n undefined = False\n if center['score'] > max_score:\n max_score = center['score']\n crop_with_max_score = center['payload']['crop_name']\n\nif undefined:\n result_message = \"ALERT, we might have a new undefined crop!\"\nelse:\n result_message = f\"Looks similar to {crop_with_max_score}\"\n\nreturn [{\n \"json\": {\n \"result\": result_message\n }\n}]\n"
},
"typeVersion": 2
},
{
"id": "23aa604a-ff0b-4948-bcd5-af39300198c0",
"name": "付箋4",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1200,
-220
],
"parameters": {
"width": 400,
"height": 380,
"content": "## Crop Anomaly Detection Tool\n### This is the tool that can be used directly for anomalous crops detection. \nIt takes as input (any) **image URL** and returns a **text message** telling if whatever this image depicts is anomalous to the crop dataset stored in Qdrant. \n\n* An Image URL is received via the Execute Workflow Trigger which is used to generate embedding vectors via the Voyage.ai Embeddings API.\n* The returned vectors are used to query the Qdrant collection to determine if the given crop is known by comparing it to **threshold scores** of each image class (crop type).\n* If the image scores lower than all thresholds, then the image is considered an anomaly for the dataset."
},
"typeVersion": 1
},
{
"id": "3a79eca2-44f9-4aee-8a0d-9c7ca2f9149d",
"name": "Medoid用変数",
"type": "n8n-nodes-base.set",
"position": [
-200,
60
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "dbbc1e7b-c63e-4ff1-9524-8ef3e9f6cd48",
"name": "clusterCenterType",
"type": "string",
"value": "is_medoid"
},
{
"id": "a994ce37-2530-4030-acfb-ec777a7ddb05",
"name": "qdrantCloudURL",
"type": "string",
"value": "https://152bc6e2-832a-415c-a1aa-fb529f8baf8d.eu-central-1-0.aws.cloud.qdrant.io"
},
{
"id": "12f0a9e6-686d-416e-a61b-72d034ec21ba",
"name": "collectionName",
"type": "string",
"value": "=agricultural-crops"
},
{
"id": "4c88a617-d44f-4776-b457-8a1dffb1d03c",
"name": "clusterThresholdCenterType",
"type": "string",
"value": "is_medoid_cluster_threshold"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "13b25434-bd66-4293-93f1-26c67b9ec7dd",
"name": "付箋3",
"type": "n8n-nodes-base.stickyNote",
"position": [
-340,
260
],
"parameters": {
"color": 6,
"width": 360,
"height": 200,
"content": "**clusterCenterType** - either\n* \"is_text_anchor_medoid\" or\n* \"is_medoid\"\n\n\n**clusterThresholdCenterType** - either\n* \"is_text_anchor_medoid_cluster_threshold\" or\n* \"is_medoid_cluster_threshold\""
},
"typeVersion": 1
},
{
"id": "869b0962-6cae-487d-8230-539a0cc4c14c",
"name": "ラベル付きクラスタの情報（農作物）",
"type": "n8n-nodes-base.set",
"position": [
440,
60
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "5327b254-b703-4a34-a398-f82edb1d6d6b",
"name": "=cropsNumber",
"type": "number",
"value": "={{ $json.result.hits.length }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "5d3956f8-f43b-439e-b176-a594a21d8011",
"name": "コレクション内の総ポイント数",
"type": "n8n-nodes-base.httpRequest",
"position": [
40,
60
],
"parameters": {
"url": "={{ $json.qdrantCloudURL }}/collections/{{ $json.collectionName }}/points/count",
"method": "POST",
"options": {},
"jsonBody": "={\n \"exact\": true\n}",
"sendBody": true,
"specifyBody": "json",
"authentication": "predefinedCredentialType",
"nodeCredentialType": "qdrantApi"
},
"credentials": {
"qdrantApi": {
"id": "it3j3hP9FICqhgX6",
"name": "QdrantApi account"
}
},
"typeVersion": 4.2
},
{
"id": "14ba3db9-3965-4b20-b333-145616d45c3a",
"name": "各農作物のカウント",
"type": "n8n-nodes-base.httpRequest",
"position": [
240,
60
],
"parameters": {
"url": "={{ $('Variables for medoids').first().json.qdrantCloudURL }}/collections/{{ $('Variables for medoids').first().json.collectionName }}/facet",
"method": "POST",
"options": {},
"jsonBody": "={{\n{\n \"key\": \"crop_name\",\n \"limit\": $json.result.count,\n \"exact\": true\n}\n}}",
"sendBody": true,
"specifyBody": "json",
"authentication": "predefinedCredentialType",
"nodeCredentialType": "qdrantApi"
},
"credentials": {
"qdrantApi": {
"id": "it3j3hP9FICqhgX6",
"name": "QdrantApi account"
}
},
"typeVersion": 4.2
},
{
"id": "e37c6758-0556-4a56-ab14-d4df663cb53a",
"name": "画像URL（ハードコード）",
"type": "n8n-nodes-base.set",
"position": [
-480,
60
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "46ceba40-fb25-450c-8550-d43d8b8aa94c",
"name": "imageURL",
"type": "string",
"value": "={{ $json.query.imageURL }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "b24ad1a7-0cf8-4acc-9c18-6fe9d58b10f2",
"name": "ワークフロー実行トリガー",
"type": "n8n-nodes-base.executeWorkflowTrigger",
"position": [
-720,
60
],
"parameters": {},
"typeVersion": 1
},
{
"id": "50424f2b-6831-41bf-8de4-81f69d901ce1",
"name": "付箋2",
"type": "n8n-nodes-base.stickyNote",
"position": [
-240,
-80
],
"parameters": {
"width": 180,
"height": 120,
"content": "Variables to access Qdrant's collection we uploaded & prepared for anomaly detection in 2 previous pipelines\n"
},
"typeVersion": 1
},
{
"id": "2e8ed3ca-1bba-4214-b34b-376a237842ff",
"name": "付箋5",
"type": "n8n-nodes-base.stickyNote",
"position": [
40,
-120
],
"parameters": {
"width": 560,
"height": 140,
"content": "These three nodes are needed just to figure out how many different classes (crops) we have in our Qdrant collection: **cropsNumber** (needed in *\"Get similarity of medoids\"* node. \n[Note] *\"Total Points in Collection\"* -> *\"Each Crop Counts\"* were used&explained already in *\"[2/4] Set up medoids (2 types) for anomaly detection (crops dataset)\"* pipeline.\n"
},
"typeVersion": 1
},
{
"id": "e2fa5763-6e97-4ff5-8919-1cb85a3c6968",
"name": "付箋6",
"type": "n8n-nodes-base.stickyNote",
"position": [
620,
240
],
"parameters": {
"height": 120,
"content": "Here, we're embedding the image passed to this workflow tool with the Voyage embedding model to compare the image to all crop images in the database."
},
"typeVersion": 1
},
{
"id": "cdb6b8d3-f7f4-4d66-850f-ce16c8ed98b9",
"name": "付箋7",
"type": "n8n-nodes-base.stickyNote",
"position": [
920,
220
],
"parameters": {
"width": 400,
"height": 180,
"content": "Checking how similar the image is to all the centres of clusters (crops).\nIf it's more similar to the thresholds we set up and stored in centres in the previous workflow, the image probably belongs to this crop class; otherwise, it's anomalous to the class. \nIf image is anomalous to all the classes, it's an anomaly."
},
"typeVersion": 1
},
{
"id": "03b4699f-ba43-4f5f-ad69-6f81deea2641",
"name": "付箋22",
"type": "n8n-nodes-base.stickyNote",
"position": [
-620,
580
],
"parameters": {
"color": 4,
"width": 540,
"height": 300,
"content": "### For anomaly detection\n1. The first pipeline is uploading (crops) dataset to Qdrant's collection.\n2. The second pipeline sets up cluster (class) centres in this Qdrant collection & cluster (class) threshold scores.\n3. **This is the anomaly detection tool, which takes any image as input and uses all preparatory work done with Qdrant (crops) collection.**\n\n### To recreate it\nYou'll have to upload [crops](https://www.kaggle.com/datasets/mdwaquarazam/agricultural-crops-image-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/agricultural-crops%2Fcotton%2Fimage%20(36).jpg"
}
}
}
]
},
"settings": {
"executionOrder": "v1"
},
"versionId": "f67b764b-9e1a-4db0-b9f2-490077a62f74",
"connections": {
"b9943781-de1f-4129-9b81-ed836e9ebb11": {
"main": [
[
{
"node": "47b72bc2-4817-48c6-b517-c1328e402468",
"type": "main",
"index": 0
}
]
]
},
"14ba3db9-3965-4b20-b333-145616d45c3a": {
"main": [
[
{
"node": "869b0962-6cae-487d-8230-539a0cc4c14c",
"type": "main",
"index": 0
}
]
]
},
"e37c6758-0556-4a56-ab14-d4df663cb53a": {
"main": [
[
{
"node": "3a79eca2-44f9-4aee-8a0d-9c7ca2f9149d",
"type": "main",
"index": 0
}
]
]
},
"3a79eca2-44f9-4aee-8a0d-9c7ca2f9149d": {
"main": [
[
{
"node": "5d3956f8-f43b-439e-b176-a594a21d8011",
"type": "main",
"index": 0
}
]
]
},
"b24ad1a7-0cf8-4acc-9c18-6fe9d58b10f2": {
"main": [
[
{
"node": "e37c6758-0556-4a56-ab14-d4df663cb53a",
"type": "main",
"index": 0
}
]
]
},
"47b72bc2-4817-48c6-b517-c1328e402468": {
"main": [
[
{
"node": "42d7eb27-ec38-4406-b5c4-27eb45358e93",
"type": "main",
"index": 0
}
]
]
},
"5d3956f8-f43b-439e-b176-a594a21d8011": {
"main": [
[
{
"node": "14ba3db9-3965-4b20-b333-145616d45c3a",
"type": "main",
"index": 0
}
]
]
},
"869b0962-6cae-487d-8230-539a0cc4c14c": {
"main": [
[
{
"node": "b9943781-de1f-4129-9b81-ed836e9ebb11",
"type": "main",
"index": 0
}
]
]
}
}
}よくある質問
このワークフローの使い方は？
上記のJSON設定コードをコピーし、n8nインスタンスで新しいワークフローを作成して「JSONからインポート」を選択、設定を貼り付けて認証情報を必要に応じて変更してください。
このワークフローはどんな場面に適していますか？
上級 - 人工知能, セキュリティ運用
有料ですか？
このワークフローは完全無料です。ただし、ワークフローで使用するサードパーティサービス（OpenAI APIなど）は別途料金が発生する場合があります。
関連ワークフロー
[2/2] KNN分類器（土地データセット）
KNN（画像）分類ツール [2/2 KNN]
If
Set
Code
+
If
Set
Code
18 ノードJenny
人工知能
ChatGPTを使って suspicious メールコンテンツを分析し分類する
可疑メール本文をChatGPTで分析して分類する
If
Set
Code
+
If
Set
Code
25 ノードAngel Menendez
人工知能
ChatGPT Visionを使用したスパムメールコンテンツの分析
ChatGPT Vision を使用して疑わしいメールコンテンツを分析する
Set
Code
Jira
+
Set
Code
Jira
18 ノードAngel Menendez
人工知能
AIエージェント：LinkedIn人脈を数秒で高精度でマッチング
AIエージェント：LinkedInビジネスネットワークを数秒で正確にマッチング
Set
Code
Split Out
+
Set
Code
Split Out
15 ノードBadr
人工知能
自動ニュース要約AIエージェントv13
AIニュース制作チーム：24/7リポート自動化＋Perplexity引用
Set
Code
Gmail
+
Set
Code
Gmail
37 ノードDerek Cheung
プロダクト
AI研究エージェント：Mistral最適OCRによるPDF自動分析
AI研究エージェント：Mistral最適OCRによるPDF自動分析
Set
Code
Gmail
+
Set
Code
Gmail
30 ノードDerek Cheung
人工知能
