This workflow follows the Documentdefaultdataloader → OpenAI Embeddings recipe pattern — see all workflows that pair these two integrations.
The workflow JSON
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{
"nodes": [
{
"id": "5961a808-a873-497e-bc42-5b760ded1571",
"name": "When clicking \u2018Test workflow\u2019",
"type": "n8n-nodes-base.manualTrigger",
"position": [
380,
360
],
"parameters": {},
"typeVersion": 1
},
{
"id": "7fa03eaa-7865-46ce-9f58-7e19fc0ec89b",
"name": "Hacker News",
"type": "n8n-nodes-base.hackerNews",
"position": [
1200,
400
],
"parameters": {
"articleId": "={{ $('Set Variables').item.json.story_id }}",
"additionalFields": {
"includeComments": true
}
},
"typeVersion": 1
},
{
"id": "82675738-9df7-47a3-8363-264bb09255f4",
"name": "Split Out",
"type": "n8n-nodes-base.splitOut",
"position": [
1560,
400
],
"parameters": {
"options": {},
"fieldToSplitOut": "data"
},
"typeVersion": 1
},
{
"id": "6800be57-40da-4d80-ac35-304403423263",
"name": "Get Comments",
"type": "n8n-nodes-base.set",
"position": [
1380,
400
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "91110cf7-1932-43ca-b24e-9d4ed40447e6",
"name": "data",
"type": "array",
"value": "={{\n$json.children.flatMap(item => {\n return [\n { id: item.id, story_id: item.story_id, story_title: $json.title, author: item.author, text: item.text },\n ...item.children.flatMap(item1 => {\n return [\n { id: item1.id, story_id: item1.story_id, story_title: $json.title, author: item1.author, text: item1.text },\n ...item1.children.flatMap(item2 => {\n return [\n { id: item2.id, story_id: item2.story_id, story_title: $json.title, author: item2.author, text: item2.text },\n ...item2.children.flatMap(item3 => {\n return [\n { id: item3.id, story_id: item3.story_id, story_title: $json.title, author: item3.author, text: item3.text },\n ...item3.children.flatMap(item4 => {\n return { id: item4.id, story_id: item4.story_id, story_title: $json.title, author: item4.author, text: item4.text }\n })\n ]\n })\n ]\n })\n ]\n })\n ]\n})\n}}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "18e1b980-1d98-4a89-8cc6-f4793c004d9f",
"name": "Qdrant Vector Store",
"type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant",
"position": [
1960,
320
],
"parameters": {
"mode": "insert",
"options": {},
"qdrantCollection": {
"__rl": true,
"mode": "list",
"value": "hn_comments",
"cachedResultName": "hn_comments"
}
},
"credentials": {
"qdrantApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "c4ce1342-1460-4650-8338-055979339f46",
"name": "Embeddings OpenAI",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"position": [
1960,
480
],
"parameters": {
"model": "text-embedding-3-small",
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "00301fd6-8766-40f7-99eb-7f8af9a51b29",
"name": "Default Data Loader",
"type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
"position": [
2080,
480
],
"parameters": {
"options": {
"metadata": {
"metadataValues": [
{
"name": "item_id",
"value": "={{ $json.id }}"
},
{
"name": "item_author",
"value": "={{ $json.author }}"
},
{
"name": "story_id",
"value": "={{ $json.story_id }}"
},
{
"name": "story_title",
"value": "={{ $json.story_title }}"
}
]
}
},
"jsonData": "={{ $json.text }}",
"jsonMode": "expressionData"
},
"typeVersion": 1
},
{
"id": "c76d3aea-0906-4ed4-a828-47ad5775364c",
"name": "Recursive Character Text Splitter",
"type": "@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter",
"position": [
2080,
620
],
"parameters": {
"options": {},
"chunkSize": 4000
},
"typeVersion": 1
},
{
"id": "50735ca9-90eb-408a-9bca-97eea1a310d1",
"name": "Set Variables",
"type": "n8n-nodes-base.set",
"position": [
620,
360
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "5b77516d-acb5-41af-9346-a67acecd0419",
"name": "story_id",
"type": "string",
"value": "41123155"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "376a1a66-1d22-4969-af11-d1a9d474b67b",
"name": "Clear Existing Comments",
"type": "n8n-nodes-base.httpRequest",
"position": [
860,
360
],
"parameters": {
"url": "http://qdrant:6333/collections/hn_comments/points/delete",
"method": "POST",
"options": {},
"jsonBody": "={\n \"filter\": {\n \"must\": [\n {\n \"key\": \"metadata.story_id\",\n \"match\": {\n \"value\": \"{{ $('Set Variables').item.json.story_id }}\"\n }\n }\n ]\n }\n}",
"sendBody": true,
"specifyBody": "json",
"authentication": "predefinedCredentialType",
"nodeCredentialType": "qdrantApi"
},
"credentials": {
"qdrantApi": {
"name": "<your credential>"
}
},
"typeVersion": 4.2
},
{
"id": "e8bcf7d8-aa25-499e-a64f-4d20caf1d6d4",
"name": "Get Payload of Points",
"type": "n8n-nodes-base.httpRequest",
"position": [
1822,
1100
],
"parameters": {
"url": "=http://qdrant:6333/collections/hn_comments/points",
"method": "POST",
"options": {},
"jsonBody": "={{\n {\n \"ids\": $json.points,\n \"with_payload\": true\n }\n}}",
"sendBody": true,
"specifyBody": "json",
"authentication": "predefinedCredentialType",
"nodeCredentialType": "qdrantApi"
},
"credentials": {
"qdrantApi": {
"name": "<your credential>"
}
},
"typeVersion": 4.2
},
{
"id": "57cbc8e5-dd89-4c2a-9906-2bd0c2bbdede",
"name": "Clusters To List",
"type": "n8n-nodes-base.splitOut",
"position": [
1602,
1100
],
"parameters": {
"options": {},
"fieldToSplitOut": "output"
},
"typeVersion": 1
},
{
"id": "20b76291-f8fa-4aa7-8f1a-ff423ac3cb7f",
"name": "OpenAI Chat Model",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
2242,
1320
],
"parameters": {
"model": "gpt-4o-mini",
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "07fc19b3-33b4-42be-bda9-f1436d4e9e6f",
"name": "Only Clusters With 3+ points",
"type": "n8n-nodes-base.filter",
"position": [
1602,
1260
],
"parameters": {
"options": {},
"conditions": {
"options": {
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "328f806c-0792-4d90-9bee-a1e10049e78f",
"operator": {
"type": "array",
"operation": "lengthGt",
"rightType": "number"
},
"leftValue": "={{ $json.points }}",
"rightValue": 2
}
]
}
},
"typeVersion": 2
},
{
"id": "80583492-c454-4b9d-8df9-ded7d50930f2",
"name": "Set Variables1",
"type": "n8n-nodes-base.set",
"position": [
582,
1200
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "2e58a9fa-a14d-4a6c-8cc8-8ec947c791fb",
"name": "story_id",
"type": "string",
"value": "={{ $json.story_id || 41123155 }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "2cfb3a7a-01d2-4eee-b9f8-d19e81829882",
"name": "Prep Output For Export",
"type": "n8n-nodes-base.set",
"position": [
2842,
1200
],
"parameters": {
"mode": "raw",
"options": {},
"jsonOutput": "={{ {\n ...$json.output,\n \"Story ID\": $('Set Variables1').item.json.story_id,\n \"Story Title\": $('Get Payload of Points').item.json.result[0].payload.metadata.story_title,\n \"Number of Responses\": $('Get Payload of Points').item.json.result.length,\n \"Raw Responses\": $('Get Payload of Points').item.json.result.map(item =>\n [\n item.payload.metadata.item_id,\n item.payload.metadata.story_id,\n item.payload.metadata.story_title,\n item.payload.metadata.item_author,\n item.payload.content.replaceAll('\"', '\\\"').replaceAll('\\n', ' ').substring(0, 500)\n ]\n ).join('\\n')\n} }}\n"
},
"typeVersion": 3.4
},
{
"id": "ade302fd-93ad-4d96-9852-e4108ba435af",
"name": "Export To Sheets",
"type": "n8n-nodes-base.googleSheets",
"position": [
3062,
1200
],
"parameters": {
"columns": {
"value": {},
"schema": [
{
"id": "Story ID",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "Story ID",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Insight",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "Insight",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Sentiment",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "Sentiment",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Number of Responses",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "Number of Responses",
"defaultMatch": false,
"canBeUsedToMatch": true
},
{
"id": "Raw Responses",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "Raw Responses",
"defaultMatch": false,
"canBeUsedToMatch": true
}
],
"mappingMode": "autoMapInputData",
"matchingColumns": []
},
"options": {
"useAppend": true
},
"operation": "append",
"sheetName": {
"__rl": true,
"mode": "name",
"value": "Sheet1"
},
"documentId": {
"__rl": true,
"mode": "id",
"value": "=1CPA_SNpWr2OjZ2KMi49fZ6MA9yC9uik8PMOILan7qYE"
}
},
"credentials": {
"googleSheetsOAuth2Api": {
"name": "<your credential>"
}
},
"typeVersion": 4.4
},
{
"id": "22d54081-7a52-40f2-837c-0c8df05e1fe4",
"name": "Execute Workflow Trigger",
"type": "n8n-nodes-base.executeWorkflowTrigger",
"position": [
382,
1200
],
"parameters": {},
"typeVersion": 1
},
{
"id": "b1e6eb2b-4627-4c69-a2ce-6bb8451d6359",
"name": "Trigger Insights",
"type": "n8n-nodes-base.executeWorkflow",
"position": [
2780,
360
],
"parameters": {
"options": {},
"workflowId": "={{ $workflow.id }}"
},
"typeVersion": 1
},
{
"id": "f25e8b2a-5ce4-4e02-8e08-e3dd98072d0e",
"name": "Prep Values For Trigger",
"type": "n8n-nodes-base.set",
"position": [
2580,
360
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "24dd90ad-390f-444e-ba6c-8c06a41e836e",
"name": "story_id",
"type": "string",
"value": "={{ $('Set Variables').item.json.story_id }}"
}
]
}
},
"executeOnce": true,
"typeVersion": 3.4
},
{
"id": "d0270fa8-5ebc-4573-b070-05d19dd3302a",
"name": "Find Comments",
"type": "n8n-nodes-base.httpRequest",
"position": [
982,
1160
],
"parameters": {
"url": "=http://qdrant:6333/collections/hn_comments/points/scroll",
"method": "POST",
"options": {},
"jsonBody": "={\n \"limit\": 500,\n \"filter\":{\n \"must\": [\n {\n \"key\": \"metadata.story_id\",\n \"match\": { \"value\": {{ $('Set Variables1').item.json.story_id }} }\n }\n ]\n },\n \"with_vector\":true\n}",
"sendBody": true,
"specifyBody": "json",
"authentication": "predefinedCredentialType",
"nodeCredentialType": "qdrantApi"
},
"credentials": {
"qdrantApi": {
"name": "<your credential>"
}
},
"typeVersion": 4.2
},
{
"id": "ca3c040e-bfe1-4f4d-9c4e-154c2010f89b",
"name": "Sticky Note6",
"type": "n8n-nodes-base.stickyNote",
"position": [
2440,
160
],
"parameters": {
"color": 7,
"width": 595.5213902293318,
"height": 429.11782776909047,
"content": "## 4\ub2e8\uacc4. \ud1b5\ucc30\ub825 \uc11c\ube0c\uc6cc\ud06c\ud50c\ub85c\uc6b0 \ud2b8\ub9ac\uac70 \n[\uc6cc\ud06c\ud50c\ub85c\uc6b0 \ud2b8\ub9ac\uac70\uc5d0 \ub300\ud574 \ub354 \uc54c\uc544\ubcf4\uae30](https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.executeworkflow) \n\n\uc11c\ube0c\uc6cc\ud06c\ud50c\ub85c\uc6b0\ub294 \uc124\ubb38\uc870\uc0ac\uc758 \ubd84\uc11d\uc744 \ud2b8\ub9ac\uac70\ud558\ub294 \ub370 \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \uc774 \ubd84\ub9ac\ub294 \uc120\ud0dd\uc0ac\ud56d\uc774\uc9c0\ub9cc, \uc5ec\uae30\uc11c \ub450 \ubd80\ubd84 \ud504\ub85c\uc138\uc2a4\ub97c \ub354 \uc798 \uc2dc\uc5f0\ud558\uae30 \uc704\ud574 \uc0ac\uc6a9\ub429\ub2c8\ub2e4."
},
"typeVersion": 1
},
{
"id": "cdf04343-abfa-4705-9828-e246c96ffa2a",
"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
"position": [
1780,
60
],
"parameters": {
"color": 7,
"width": 638.5221986278162,
"height": 741.0186923170972,
"content": "## 3\ub2e8\uacc4. \ub313\uae00\uc744 Qdrant\uc5d0 \uc800\uc7a5\n\n[Qdrant Vector Store\uc5d0 \ub300\ud574 \ub354 \uc54c\uc544\ubcf4\uae30](https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.vectorstoreqdrant/)\n\n\ubca1\ud130 \ub370\uc774\ud130\ubca0\uc774\uc2a4\ub294 \ub370\uc774\ud130 \uc800\uc7a5\uc5d0 \ud6cc\ub96d\ud55c \ubc29\ubc95\uc785\ub2c8\ub2e4. \uc720\uc0ac\uc131 \uac80\uc0c9\uc5d0 \uad00\uc2ec\uc774 \uc788\uc73c\uc2dc\uba74, \uc5ec\uae30\uc5d0\uc11c\ub3c4 \uc801\uc6a9\ub429\ub2c8\ub2e4. \uc65c\ub0d0\ud558\uba74 \uc6b0\ub9ac\ub294 \ube44\uc2b7\ud55c \ub313\uae00\uc744 \uadf8\ub8f9\ud654\ud558\uc5ec \ud328\ud134\uc744 \ucc3e\uace0 \uc2f6\uae30 \ub54c\ubb38\uc785\ub2c8\ub2e4. Qdrant\ub294 \uac15\ub825\ud55c \ubca1\ud130 \ub370\uc774\ud130\ubca0\uc774\uc2a4\uc774\uba70, \uac15\ub825\ud55c API \uad6c\ud604\uacfc \uace0\uae09 \ud544\ud130\ub9c1 \uae30\ub2a5 \ub54c\ubb38\uc5d0 \uc120\ud0dd\ub41c \ub3c4\uad6c\uc785\ub2c8\ub2e4."
},
"typeVersion": 1
},
{
"id": "14f6872b-1c51-4359-a39f-cc6ba2ff29fb",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
1100,
200
],
"parameters": {
"color": 7,
"width": 656.0317138444963,
"height": 441.0753369736108,
"content": "## 2\ub2e8\uacc4. HN API\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub313\uae00 \uac00\uc838\uc624\uae30\n\n[HTTP Request Node\uc5d0 \ub300\ud574 \ub354 \uc77d\uae30](https://docs.n8n.io/integrations/builtin/app-nodes/n8n-nodes-base.hackernews)\n\nHN API \ub178\ub4dc\ub97c \uc0ac\uc6a9\ud558\uc5ec HN \uc2a4\ud1a0\ub9ac\uc758 \ubaa8\ub4e0 \ub313\uae00\uc744 \uc2a4\ud06c\ub798\ud551\ud569\ub2c8\ub2e4. \ucd94\uac00 \ub2e8\uacc4\ub85c \ub313\uae00 \ud2b8\ub9ac\ub97c \ud3c9\ud0c4\ud654\ud558\uc5ec \ub2f5\uae00\ub3c4 \ucd5c\uc0c1\uc704 \ub313\uae00\ub85c \uac04\uc8fc\ud569\ub2c8\ub2e4."
},
"typeVersion": 1
},
{
"id": "62935316-310a-4ce9-ac5f-8820666e2290",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
280,
180
],
"parameters": {
"color": 7,
"width": 787.3314861380661,
"height": 465.52420584035275,
"content": "## 1\ub2e8\uacc4. \uc0c8\ub85c \uc2dc\uc791\ud558\uae30 \n\uc774 \ub370\ubaa8\ub97c \uc704\ud574, \uc120\ud0dd\ub41c HN \uc2a4\ud1a0\ub9ac\uc5d0 \ub300\ud55c Qdrant \ubca1\ud130 \uc800\uc7a5\uc18c\uc5d0 \uc788\ub294 \uae30\uc874 \ub808\ucf54\ub4dc\ub97c \ubaa8\ub450 \uc9c0\uc6b8 \uac83\uc785\ub2c8\ub2e4. \uc774\ub97c Qdrant\uc758 delete points API\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc218\ud589\ud569\ub2c8\ub2e4."
},
"typeVersion": 1
},
{
"id": "a5e93a02-555c-48a3-afae-344a4884908b",
"name": "Sticky Note3",
"type": "n8n-nodes-base.stickyNote",
"position": [
269,
1005
],
"parameters": {
"color": 7,
"width": 551.2710561574413,
"height": 407.9295477646979,
"content": "## 5\ub2e8\uacc4. Insight \ud558\uc704\uc6cc\ud06c\ud50c\ub85c\n\n[\uc6cc\ud06c\ud50c\ub85c\uc6b0 \ud2b8\ub9ac\uac70\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc54c\uc544\ubcf4\uae30](https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.executeworkflowtrigger)\n\n\uc774 \ud558\uc704\uc6cc\ud06c\ud50c\ub85c\uc6b0\ub294 \uc2a4\ud1a0\ub9ac ID\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc6b0\ub9ac Qdrant \ubca1\ud130 \uc2a4\ud1a0\uc5b4\uc5d0\uc11c \uad00\ub828 \ub313\uae00 \ub808\ucf54\ub4dc\ub97c \ucc3e\uc2b5\ub2c8\ub2e4. \uc6b0\ub9ac\uc758 \ubaa9\ud45c\ub294 \ud2b9\uc815 HN \uc2a4\ud1a0\ub9ac\uc5d0 \ub300\ud55c \ucee4\ubba4\ub2c8\ud2f0\uc758 \ud569\uc758\ub97c \ud30c\uc545\ud558\ub294 \ud1b5\ucc30\uc744 \ucc3e\ub294 \uac83\uc785\ub2c8\ub2e4."
},
"typeVersion": 1
},
{
"id": "37217a2d-aca4-499b-9d6b-a1d4c6684194",
"name": "Sticky Note4",
"type": "n8n-nodes-base.stickyNote",
"position": [
840,
920
],
"parameters": {
"color": 7,
"width": 600.1809497875241,
"height": 482.99934349707576,
"content": "## 6\ub2e8\uacc4. \ub313\uae00\uc5d0 \ud074\ub7ec\uc2a4\ud130\ub9c1 \uc54c\uace0\ub9ac\uc998 \uc801\uc6a9 \n[ n8n\uc5d0\uc11c Python \uc0ac\uc6a9\uc5d0 \ub300\ud574 \ub354 \uc54c\uc544\ubcf4\uae30 ](https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.code) \n\n\uc6b0\ub9ac\ub294 \uc6d0\ud558\ub294 HN \uc2a4\ud1a0\ub9ac \ub313\uae00\uc5d0 \ub300\ud55c \ubca1\ud130 \uc784\ubca0\ub529\uc744 \uac00\uc838\uc640\uc11c \uace0\uae09 \ud074\ub7ec\uc2a4\ud130\ub9c1 \uc54c\uace0\ub9ac\uc998\uc744 \uc218\ud589\ud560 \uac83\uc785\ub2c8\ub2e4. \uc774 \uac15\ub825\ud55c \uae30\uc220\uc740 \uc720\uc0ac\ud55c \uc784\ubca0\ub529\uc744 \ud074\ub7ec\uc2a4\ud130\ub85c \ube60\ub974\uac8c \uadf8\ub8f9\ud654\ud558\uc5ec \uc778\uae30 \ud53c\ub4dc\ubc31, \uc758\uacac \ubc0f \ubb38\uc81c\uc810\uc744 \ubc1c\uacac\ud558\ub294 \ub370 \uc0ac\uc6a9\ud560 \uc218 \uc788\uac8c \ud574\uc90d\ub2c8\ub2e4! \n\n\uc6b0\ub9ac\ub294 Python Code Node \ub355\ubd84\uc5d0 \uc774\ub97c \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."
},
"typeVersion": 1
},
{
"id": "fcccc9a8-ee9f-41b7-b7d6-e8fbbe19dfa3",
"name": "Sticky Note5",
"type": "n8n-nodes-base.stickyNote",
"position": [
1466,
880
],
"parameters": {
"color": 7,
"width": 598.5585287222906,
"height": 605.9905193915599,
"content": "## 7\ub2e8\uacc4. \ud074\ub7ec\uc2a4\ud130\ubcc4 \ub313\uae00 \ub0b4\uc6a9 \uac00\uc838\uc624\uae30 \n[\ucf54\ub4dc \ub178\ub4dc \uc0ac\uc6a9\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc54c\uc544\ubcf4\uae30](https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.code/) \n\n\uc6b0\ub9ac\uc758 \ucf54\ub4dc \ub178\ub4dc\uc5d0 \uc758\ud574 \uadf8\ub8f9\ud654\ub418\uace0 \ubc18\ud658\ub41c Qdrant \ud3ec\uc778\ud2b8 ID\uc640 \ud568\uaed8, \uac01 \ud56d\ubaa9\uc758 \ud398\uc774\ub85c\ub4dc\ub97c \uac00\uc838\uc624\ub294 \uc77c\ub9cc \ub0a8\uc558\uc2b5\ub2c8\ub2e4. \ud074\ub7ec\uc2a4\ud130\ub9c1 \uc54c\uace0\ub9ac\uc998\uc774 \uc644\ubcbd\ud558\uc9c0 \uc54a\ub2e4\ub294 \uc810\uc5d0 \uc720\uc758\ud558\uc138\uc694. \ub370\uc774\ud130\uc5d0 \ub530\ub77c \uc870\uc815\uc774 \ud544\uc694\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4."
},
"typeVersion": 1
},
{
"id": "78e9cd03-dea4-4b11-947f-a00d7bb5f8cf",
"name": "Sticky Note7",
"type": "n8n-nodes-base.stickyNote",
"position": [
2086,
929
],
"parameters": {
"color": 7,
"width": 587.6069484146701,
"height": 583.305275883189,
"content": "## 8\ub2e8\uacc4. \uadf8\ub8f9\ud654\ub41c \ub313\uae00\uc5d0\uc11c \ud1b5\ucc30 \uc5bb\uae30\n\n[\uc815\ubcf4 \ucd94\ucd9c\uae30 \ub178\ub4dc \uc0ac\uc6a9\uc5d0 \ub300\ud574 \ub354 \uc77d\uae30](https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.information-extractor)\n\n\ub2e4\uc74c\uc73c\ub85c, \uc6b0\ub9ac\ub294 \ucd5c\ucca8\ub2e8 LLM\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub313\uae00 \uadf8\ub8f9\uc5d0 \ub300\ud55c \ud1b5\ucc30\uc744 \uc0dd\uc131\ud560 \uac83\uc785\ub2c8\ub2e4. \uc774\ub807\uac8c \ud558\uba74, \uc6b0\ub9ac\ub294 HN \uc2a4\ud1a0\ub9ac\uc5d0\uc11c \ub17c\uc758\ub41c \ub9ce\uc740 \uc8fc\uc694 \uc8fc\uc81c\ub97c \ub2e4\ub8e8\ub294 \ub354 \uc138\ubd80\uc801\uc778 \uacb0\uacfc\ub97c \ub04c\uc5b4\ub0bc \uc218 \uc788\uc744 \uac83\uc785\ub2c8\ub2e4."
},
"typeVersion": 1
},
{
"id": "d5427741-6015-4af5-8e45-f6fc6f5c4133",
"name": "Sticky Note8",
"type": "n8n-nodes-base.stickyNote",
"position": [
2706,
940
],
"parameters": {
"color": 7,
"width": 572.5638733479158,
"height": 464.4019616956416,
"content": "## 9\ub2e8\uacc4. Insights Sheet\uc5d0 \uc791\uc131\n\n\ub9c8\uc9c0\ub9c9\uc73c\ub85c, \uc6b0\ub9ac \uc644\ub8cc\ub41c \uc778\uc0ac\uc774\ud2b8\uac00 \uc6cc\ud06c\ud50c\ub85c\uc5d0\uc11c \uc774\uc804\uc5d0 \ub9cc\ub4e0 Insights Sheet\uc5d0 \ucd94\uac00\ub429\ub2c8\ub2e4.\n\n\uc0d8\ud50c \uc2dc\ud2b8\ub97c \uc5ec\uae30\uc5d0\uc11c \ucc3e\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4: https://docs.google.com/spreadsheets/d/e/2PACX-1vQXaQU9XxsxnUIIeqmmf1PuYRuYtwviVXTv6Mz9Vo6_a4ty-XaJHSeZsptjWXS3wGGDG8Z4u16rvE7l/pubhtml"
},
"typeVersion": 1
},
{
"id": "a66b7e6d-0602-4f6b-a9f6-76a63d590956",
"name": "Sticky Note9",
"type": "n8n-nodes-base.stickyNote",
"position": [
560,
313.32160655630304
],
"parameters": {
"width": 226.36363118160727,
"height": 296.5755172289686,
"content": "### \ud83d\udea8 \uc5ec\uae30\uc5d0 \uc2a4\ud1a0\ub9ac ID\ub97c \uc124\uc815\ud558\uc138\uc694! \uc720\ud6a8\ud55c HN \uc2a4\ud1a0\ub9ac ID\uc5ec\uc57c \ud569\ub2c8\ub2e4."
},
"typeVersion": 1
},
{
"id": "42f93189-4bd8-4487-975a-f1c8f8365242",
"name": "Apply K-means Clustering Algorithm",
"type": "n8n-nodes-base.code",
"position": [
1202,
1160
],
"parameters": {
"language": "python",
"pythonCode": "import numpy as np\nfrom sklearn.cluster import KMeans\n\n# get vectors for all answers\npoint_ids = [item.id for item in _input.first().json.result.points]\nvectors = [item.vector.to_py() for item in _input.first().json.result.points]\nvectors_array = np.array(vectors)\n\n# apply k-means clustering where n_clusters = 5\n# this is a max and we'll discard some of these clusters later\nkmeans = KMeans(n_clusters=min(len(vectors), 5), random_state=42).fit(vectors_array)\nlabels = kmeans.labels_\nunique_labels = set(labels)\n\n# Extract and print points in each cluster\nclusters = {}\nfor label in set(labels):\n clusters[label] = vectors_array[labels == label]\n\n# return Qdrant point ids for each cluster\n# we'll use these ids to fetch the payloads from the vector store.\noutput = []\nfor cluster_id, cluster_points in clusters.items():\n points = [point_ids[i] for i in range(len(labels)) if labels[i] == cluster_id]\n output.append({\n \"id\": f\"Cluster {cluster_id}\",\n \"total\": len(cluster_points),\n \"points\": points\n })\n\nreturn {\"json\": {\"output\": output } }"
},
"typeVersion": 2
},
{
"id": "4ddeab09-e401-41ad-861f-560b9e92bf89",
"name": "Sticky Note10",
"type": "n8n-nodes-base.stickyNote",
"position": [
-180,
40
],
"parameters": {
"width": 400.381109509268,
"height": 612.855812336249,
"content": "## \uc9c1\uc811 \ud574\ubcf4\uc138\uc694!\n\n### \uc774 \uc6cc\ud06c\ud50c\ub85c\uc6b0\ub294 HN \uc2a4\ud1a0\ub9ac \ub313\uae00\uc5d0\uc11c \uace0\ub3c4\ub85c \uc0c1\uc138\ud55c \ucee4\ubba4\ub2c8\ud2f0 \uc778\uc0ac\uc774\ud2b8\ub97c \uc0dd\uc131\ud569\ub2c8\ub2e4. \ub9ce\uc740 \uc218\uc758 \ub313\uae00\uc744 \ub2e4\ub8f0 \ub54c \uac00\uc7a5 \uc798 \uc791\ub3d9\ud569\ub2c8\ub2e4.\n\n* HN \uc2a4\ud1a0\ub9ac \ub313\uae00\uc744 \uac00\uc838\uc640 Qdrant \ubca1\ud130 \uc2a4\ud1a0\uc5b4\uc5d0\uc11c \ubca1\ud130\ud654\ud569\ub2c8\ub2e4.\n* K-means \ud074\ub7ec\uc2a4\ud130\ub9c1 \uc54c\uace0\ub9ac\uc998\uc744 \uc0ac\uc6a9\ud558\uc5ec \ud1a0\ub860\uc5d0\uc11c \uc778\uae30 \uc788\ub294 \uc8fc\uc81c\uc758 \ud074\ub7ec\uc2a4\ud130\ub97c \uc2dd\ubcc4\ud569\ub2c8\ub2e4. \n* \uac01 \uc720\ud6a8\ud55c \ud074\ub7ec\uc2a4\ud130\ub97c LLM\uc73c\ub85c \ubd84\uc11d\ud558\uace0 \uc694\uc57d\ud569\ub2c8\ub2e4.\n* LLM \uc751\ub2f5\uacfc \ud074\ub7ec\uc2a4\ud130 \uacb0\uacfc\ub97c \uc2dc\ud2b8\ub85c \ub2e4\uc2dc \ub0b4\ubcf4\ub0c5\ub2c8\ub2e4.\n\n\uc5ec\uae30\uc5d0\uc11c \ucc38\uc870 Google \uc2dc\ud2b8\ub97c \ud655\uc778\ud558\uc138\uc694: https://docs.google.com/spreadsheets/d/e/2PACX-1vQXaQU9XxsxnUIIeqmmf1PuYRuYtwviVXTv6Mz9Vo6_a4ty-XaJHSeZsptjWXS3wGGDG8Z4u16rvE7l/pubhtml\n\n### \ub3c4\uc6c0\uc774 \ud544\uc694\ud558\uc2e0\uac00\uc694?\n[Discord](https://discord.com/invite/XPKeKXeB7d)\uc5d0 \uac00\uc785\ud558\uac70\ub098 [Forum](https://community.n8n.io/)\uc5d0\uc11c \ubb3c\uc5b4\ubcf4\uc138\uc694!\n\n\uc990\uac81\uac8c \ud574\ud0b9\ud558\uc138\uc694!"
},
"typeVersion": 1
},
{
"id": "eea1b301-f030-48a9-bcfc-63fe3e1aac0d",
"name": "Information Extractor",
"type": "@n8n/n8n-nodes-langchain.informationExtractor",
"position": [
2260,
1140
],
"parameters": {
"text": "=The {{ $json.result.length }} comments were:\n{{\n$json.result.map(item =>\n`* Commenter \"${item.payload.metadata.item_author}\" says the following: \"${item.payload.content.replaceAll('\"', '\\\"').replaceAll('\\n', ' ')}\"`\n).join('\\n')\n}}",
"options": {
"systemPromptTemplate": "=You help summarise a selection of forum comments for an article called \"{{ $json.result[0].payload.metadata.story_title }}\".\nThe {{ $json.result.length }} comments were selected because their contents were similar in context.\n\nYour task is to: \n* summarise the given comments into a short paragraph. Provide an insight from this summary and what we could learn from the comments.\n* determine if the overall sentiment of all the listed responses to be either strongly negative, negative, neutral, positive or strongly positive."
},
"schemaType": "fromJson",
"jsonSchemaExample": "{\n\t\"Insight\": \"\",\n \"Sentiment\": \"\",\n \"Suggested Improvements\": \"\"\n}"
},
"typeVersion": 1
},
{
"id": "bee4dd57-c907-418f-ad87-21c6ce4e6698",
"name": "Sticky Note12",
"type": "n8n-nodes-base.stickyNote",
"position": [
280,
660
],
"parameters": {
"color": 5,
"width": 323.2987132716669,
"height": 80,
"content": "\uc774\uac83\uc744 \ud55c \ubc88\ub9cc \uc2e4\ud589\ud558\uc138\uc694! \n\ub9cc\uc57d \uc5b4\ub5a4 \uc774\uc720\ub85c \uc5ec\ub7ec \ubc88 \uc2e4\ud589\ud574\uc57c \ud55c\ub2e4\uba74, \uae30\uc874 \ub370\uc774\ud130\ub97c \uba3c\uc800 \uc9c0\uc6b0\ub294 \uac83\uc744 \ud655\uc2e4\ud788 \ud558\uc138\uc694."
},
"typeVersion": 1
},
{
"id": "429e080d-5a94-442c-a2b0-6a12f03a8a98",
"name": "Sticky Note11",
"type": "n8n-nodes-base.stickyNote",
"position": [
840,
1440
],
"parameters": {
"color": 5,
"width": 323.2987132716669,
"height": 110.05160146874424,
"content": "\ucc98\uc74c \uc2e4\ud589 \uc911\uc785\ub2c8\uae4c? \n\ucc98\uc74c \uc2e4\ud589 \uc2dc \uc57d\uac04\uc758 \uc9c0\uc5f0\uc774 \ubc1c\uc0dd\ud569\ub2c8\ub2e4. \ucf54\ub4dc \ub178\ub4dc\uac00 \ud544\uc694\ud55c \ud328\ud0a4\uc9c0\ub97c \ub2e4\uc6b4\ub85c\ub4dc\ud574\uc57c \ud558\uae30 \ub54c\ubb38\uc785\ub2c8\ub2e4."
},
"typeVersion": 1
}
],
"connections": {
"Split Out": {
"main": [
[
{
"node": "Qdrant Vector Store",
"type": "main",
"index": 0
}
]
]
},
"Hacker News": {
"main": [
[
{
"node": "Get Comments",
"type": "main",
"index": 0
}
]
]
},
"Get Comments": {
"main": [
[
{
"node": "Split Out",
"type": "main",
"index": 0
}
]
]
},
"Find Comments": {
"main": [
[
{
"node": "Apply K-means Clustering Algorithm",
"type": "main",
"index": 0
}
]
]
},
"Set Variables": {
"main": [
[
{
"node": "Clear Existing Comments",
"type": "main",
"index": 0
}
]
]
},
"Set Variables1": {
"main": [
[
{
"node": "Find Comments",
"type": "main",
"index": 0
}
]
]
},
"Clusters To List": {
"main": [
[
{
"node": "Only Clusters With 3+ points",
"type": "main",
"index": 0
}
]
]
},
"Embeddings OpenAI": {
"ai_embedding": [
[
{
"node": "Qdrant Vector Store",
"type": "ai_embedding",
"index": 0
}
]
]
},
"OpenAI Chat Model": {
"ai_languageModel": [
[
{
"node": "Information Extractor",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Default Data Loader": {
"ai_document": [
[
{
"node": "Qdrant Vector Store",
"type": "ai_document",
"index": 0
}
]
]
},
"Qdrant Vector Store": {
"main": [
[
{
"node": "Prep Values For Trigger",
"type": "main",
"index": 0
}
]
]
},
"Get Payload of Points": {
"main": [
[
{
"node": "Information Extractor",
"type": "main",
"index": 0
}
]
]
},
"Information Extractor": {
"main": [
[
{
"node": "Prep Output For Export",
"type": "main",
"index": 0
}
]
]
},
"Prep Output For Export": {
"main": [
[
{
"node": "Export To Sheets",
"type": "main",
"index": 0
}
]
]
},
"Clear Existing Comments": {
"main": [
[
{
"node": "Hacker News",
"type": "main",
"index": 0
}
]
]
},
"Prep Values For Trigger": {
"main": [
[
{
"node": "Trigger Insights",
"type": "main",
"index": 0
}
]
]
},
"Execute Workflow Trigger": {
"main": [
[
{
"node": "Set Variables1",
"type": "main",
"index": 0
}
]
]
},
"Only Clusters With 3+ points": {
"main": [
[
{
"node": "Get Payload of Points",
"type": "main",
"index": 0
}
]
]
},
"Recursive Character Text Splitter": {
"ai_textSplitter": [
[
{
"node": "Default Data Loader",
"type": "ai_textSplitter",
"index": 0
}
]
]
},
"When clicking \u2018Test workflow\u2019": {
"main": [
[
{
"node": "Set Variables",
"type": "main",
"index": 0
}
]
]
},
"Apply K-means Clustering Algorithm": {
"main": [
[
{
"node": "Clusters To List",
"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.
googleSheetsOAuth2ApiopenAiApiqdrantApi
Pro
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About this workflow
2374. Uses hackerNews, vectorStoreQdrant, embeddingsOpenAi, documentDefaultDataLoader. Event-driven trigger; 36 nodes.
Source: https://github.com/n8nKOR/n8n-shared-workflow/blob/62a671327e906c22a40d290b339ff6d2373f8d75/workflows/n8nworkflows/ai/2374.json — original creator credit. Request a take-down →
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