{ "name": "Air Pollution Analysis \u2014 Local AI", "tags": [], "nodes": [ { "id": "5ea3a848-296f-4358-a655-c0d87bf70efb", "name": "Sticky Note", "type": "n8n-nodes-base.stickyNote", "position": [ -848, -272 ], "parameters": { "width": 480, "height": 768, "content": "## Air Pollution Analysis \u2014 Local AI\n\n### How it works\n\nThis workflow exposes a webhook that triggers an air-pollution analysis request. It queries a large MotherDuck-backed dataset through the MCP endpoint, sends the retrieved context to a local Ollama model for analysis, formats the result, and returns it as the webhook response.\n\n### Setup steps\n\n- Configure the Webhook node with the desired path, HTTP method, and response mode suitable for your caller.\n- Set up the MotherDuck MCP HTTP request with the correct endpoint, authentication headers or tokens, and query payload for the air-pollution data.\n- Ensure Ollama is reachable at http://ollama:11434 from the n8n environment and that the requested model is installed and available.\n- Verify the Set node maps the Ollama response into the field expected by the Respond to Webhook node.\n\n### Customization\n\nAdjust the MotherDuck query to target different pollutants, time ranges, or locations, and update the Ollama prompt/model to change the style or depth of the analysis." }, "typeVersion": 1 }, { "id": "8ec3aea0-5a68-46e5-842b-9e1c6b714e17", "name": "Sticky Note1", "type": "n8n-nodes-base.stickyNote", "position": [ -288, -272 ], "parameters": { "color": 7, "width": 400, "height": 336, "content": "## Receive and query data\n\nStarts from the webhook trigger and sends the incoming request into the MotherDuck MCP API query that retrieves the air-pollution dataset/results." }, "typeVersion": 1 }, { "id": "7306d643-73dc-4712-810f-da740c6820a2", "name": "Sticky Note2", "type": "n8n-nodes-base.stickyNote", "position": [ 144, -272 ], "parameters": { "color": 7, "width": 240, "height": 336, "content": "## Run local AI analysis\n\nPasses the queried data to the local Ollama generate endpoint for analysis and interpretation." }, "typeVersion": 1 }, { "id": "ec5e89e5-bdc5-4b1a-9ddf-b9d66dc62153", "name": "Sticky Note3", "type": "n8n-nodes-base.stickyNote", "position": [ 416, -256 ], "parameters": { "color": 7, "width": 368, "height": 320, "content": "## Format and return response\n\nShapes the Ollama output into a response field, then sends the final result back to the original webhook caller." }, "typeVersion": 1 }, { "id": "15366acb-4e47-471d-8945-73fcb90ef810", "name": "Post to Ollama API", "type": "n8n-nodes-base.httpRequest", "position": [ 192, -96 ], "parameters": { "url": "http://ollama:11434/api/generate", "method": "POST", "options": {}, "jsonBody": "={\n \"model\": \"qwen2.5:3b\",\n \"prompt\": \"You are an air quality data analyst. Here is PM10 pollution data from Seoul monitoring stations retrieved from a database of 3.8 million IoT sensor readings. Analyse this data and in 4 sentences identify the key findings, which stations are most concerning, and whether any readings suggest dangerous levels above WHO guidelines of 50 micrograms per cubic metre: Gangseo-gu avg=54.55 max=3586, Seocho-gu avg=53.84 max=1985, Guro-gu avg=51.5 max=1985, Yeongdeungpo-gu avg=50.55 max=985, Seongdong-gu avg=49.85 max=985, Mapo-gu avg=46.79 max=985, Songpa-gu avg=45.88 max=985, Seongbuk-gu avg=45.84 max=3403, Gwangjin-gu avg=45.78 max=1661, Gangdong-gu avg=45.32 max=622.\",\n \"stream\": false\n}", "sendBody": true, "specifyBody": "json" }, "typeVersion": 4.4 }, { "id": "1fb6fb75-e130-4cd1-a95c-52b1adac9c50", "name": "Post to Motherduck API", "type": "n8n-nodes-base.httpRequest", "position": [ -32, -96 ], "parameters": { "url": "https://api.motherduck.com/mcp", "method": "POST", "options": {}, "jsonBody": "{\n \"jsonrpc\": \"2.0\",\n \"id\": 1,\n \"method\": \"tools/call\",\n \"params\": {\n \"name\": \"query\",\n \"arguments\": {\n \"database\": \"air_pollution\",\n \"sql\": \"SELECT s.\\\"Station name(district)\\\", ROUND(AVG(m.\\\"Average value\\\"),2) as avg_pm10, ROUND(MAX(m.\\\"Average value\\\"),2) as max_pm10 FROM measurement_info m JOIN measurement_station_info s ON m.\\\"Station code\\\" = s.\\\"Station code\\\" WHERE m.\\\"Item code\\\" = 8 GROUP BY s.\\\"Station name(district)\\\" ORDER BY avg_pm10 DESC LIMIT 10\"\n }\n }\n}", "sendBody": true, "sendHeaders": true, "specifyBody": "json", "authentication": "genericCredentialType", "genericAuthType": "httpBearerAuth", "headerParameters": { "parameters": [ { "name": "Content-Type", "value": "application/json" }, { "name": "Accept", "value": "application/json, text/event-stream" }, { "name": "MCP-Protocol-Version", "value": "2024-11-05" } ] } }, "typeVersion": 4.4 }, { "id": "63ee4181-5bfd-42e5-a427-3527564b4c4a", "name": "When Air Quality Requested", "type": "n8n-nodes-base.webhook", "position": [ -240, -96 ], "parameters": { "path": "air-quality", "options": {}, "responseMode": "responseNode" }, "typeVersion": 2.1 }, { "id": "9a81ccbb-51b8-4e6a-ba7f-f1fb2267377d", "name": "Send Webhook Response", "type": "n8n-nodes-base.respondToWebhook", "position": [ 640, -96 ], "parameters": { "options": { "responseCode": "={{ 200 }}", "responseHeaders": { "entries": [ { "name": "Content-Type", "value": "text/html" } ] } }, "respondWith": "text", "responseBody": "=\n\n\n

AI-Enabled Air Quality Analysis

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Seoul IoT Sensor Network \u2014 3,885,066 rows queried from MotherDuck

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Top 10 Most Polluted Stations (PM10)

\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
StationAvg PM10Max PM10Status
Gangseo-gu54.553,586\u26a0 DANGEROUS
Seocho-gu53.841,985\u26a0 HIGH
Guro-gu51.501,985\u26a0 HIGH
Yeongdeungpo-gu50.55985\u25cf ELEVATED
Seongdong-gu49.85985\u25cf ELEVATED
Mapo-gu46.79985\u2713 OK
Songpa-gu45.88985\u2713 OK
Seongbuk-gu45.843,403\u26a0 HIGH PEAK
Gwangjin-gu45.781,661\u2713 OK
Gangdong-gu45.32622\u2713 OK
\n
\n

\ud83e\udd16 Local AI Analysis \u2014 Ollama qwen2.5:3b

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{{ $('Post to Ollama API').item.json.response }}

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\u2713 Local CPU only  |  \u2713 No data sent to cloud  |  \u2713 Ollama qwen2.5:3b  |  \u2713 MotherDuck 3,885,066 rows

\n\n" }, "typeVersion": 1.5 }, { "id": "be071f74-f38b-4530-8adb-ae6a8c0601ed", "name": "Set Response Data", "type": "n8n-nodes-base.set", "position": [ 464, -96 ], "parameters": { "options": {}, "assignments": { "assignments": [ { "id": "a3f1e445-a59f-4ac8-80de-d91d190ecdb8", "name": "response", "type": "string", "value": "={{ $json.response }}" } ] } }, "typeVersion": 3.4 } ], "active": false, "settings": { "binaryMode": "separate", "availableInMCP": false, "executionOrder": "v1" }, "versionId": "fdab63fe-3706-4ba1-9e55-b6eb92ee676c", "nodeGroups": [], "connections": { "Set Response Data": { "main": [ [ { "node": "Send Webhook Response", "type": "main", "index": 0 } ] ] }, "Post to Ollama API": { "main": [ [ { "node": "Set Response Data", "type": "main", "index": 0 } ] ] }, "Post to Motherduck API": { "main": [ [ { "node": "Post to Ollama API", "type": "main", "index": 0 } ] ] }, "When Air Quality Requested": { "main": [ [ { "node": "Post to Motherduck API", "type": "main", "index": 0 } ] ] } } }