This workflow follows the Google Sheets → HTTP Request recipe pattern — see all workflows that pair these two integrations.
The workflow JSON
Copy or download the full n8n JSON below. Paste it into a new n8n workflow, add your credentials, activate. Full import guide →
{
"name": "QFO Rank Tracking - EmbeddingGemma Semantic Dedup & Daily Tracking",
"nodes": [
{
"parameters": {
"rule": {
"interval": [
{
"field": "hours",
"hoursInterval": 24
}
]
}
},
"id": "daily-trigger",
"name": "Daily at 2AM UTC",
"type": "n8n-nodes-base.scheduleTrigger",
"typeVersion": 1.2,
"position": [
0,
0
]
},
{
"parameters": {
"operation": "read",
"documentId": {
"__rl": true,
"mode": "url",
"value": "={{ $json.sheetUrl }}"
},
"sheetName": {
"__rl": true,
"mode": "byName",
"value": "QFO Queries"
},
"options": {}
},
"id": "get-qfo-queries",
"name": "Get QFO Queries from Sheet",
"type": "n8n-nodes-base.googleSheets",
"typeVersion": 4.5,
"position": [
220,
0
],
"credentials": {
"googleSheetsOAuth2Api": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"jsCode": "// Prepare queries and batch for EmbeddingGemma\n// Gemini batchEmbedContents supports up to 100 requests per call\nconst items = $input.all();\nconst queries = items.map((item, idx) => ({\n text: (item.json.query || item.json.Query || item.json.keyword || item.json.Keyword || '').trim(),\n original_index: idx,\n topic: item.json.topic || item.json.Topic || item.json.parent_query || '',\n seed_query: item.json.seed_query || item.json.SeedQuery || ''\n})).filter(q => q.text.length > 0);\n\n// Batch into groups of 100 (Gemini batch limit)\nconst batchSize = 100;\nconst batches = [];\nfor (let i = 0; i < queries.length; i += batchSize) {\n batches.push({\n batch_id: Math.floor(i / batchSize),\n queries: queries.slice(i, i + batchSize)\n });\n}\n\nconsole.log(`Total queries: ${queries.length}, Batches: ${batches.length}`);\nreturn batches.map(b => ({ json: b }));"
},
"id": "prepare-embedding-batches",
"name": "Prepare Embedding Batches (100/batch)",
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
440,
0
]
},
{
"parameters": {
"batchSize": 1,
"options": {
"reset": false
}
},
"id": "loop-embed-batches",
"name": "Loop Over Embedding Batches",
"type": "n8n-nodes-base.splitInBatches",
"typeVersion": 3,
"position": [
660,
0
]
},
{
"parameters": {
"amount": 500,
"unit": "milliseconds"
},
"id": "embed-rate-limit",
"name": "Rate Limit (500ms)",
"type": "n8n-nodes-base.wait",
"typeVersion": 1.1,
"position": [
880,
0
]
},
{
"parameters": {
"method": "POST",
"url": "=https://generativelanguage.googleapis.com/v1beta/models/gemma-embedding-exp-03-07:batchEmbedContents?key={{ $env.GOOGLE_AI_API_KEY }}",
"sendHeaders": true,
"headerParameters": {
"parameters": [
{
"name": "Content-Type",
"value": "application/json"
}
]
},
"sendBody": true,
"specifyBody": "json",
"jsonBody": "={{ JSON.stringify({ requests: $json.queries.map(q => ({ model: 'models/gemma-embedding-exp-03-07', content: { parts: [{ text: q.text }] }, taskType: 'SEMANTIC_SIMILARITY' })) }) }}",
"options": {
"timeout": 60000
}
},
"id": "embed-gemma",
"name": "Embed with EmbeddingGemma (batchEmbedContents)",
"type": "n8n-nodes-base.httpRequest",
"typeVersion": 4.2,
"position": [
1100,
0
],
"continueOnFail": true
},
{
"parameters": {
"jsCode": "// Attach embeddings back to query objects for this batch\nconst item = $input.first();\nconst embeddingsResponse = item.json.embeddings || [];\nconst batchQueries = $('Loop Over Embedding Batches').first().json.queries;\n\nconst enriched = batchQueries.map((q, idx) => ({\n text: q.text,\n original_index: q.original_index,\n topic: q.topic,\n seed_query: q.seed_query,\n embedding: embeddingsResponse[idx]?.values || []\n}));\n\nreturn [{ json: { enriched_queries: enriched } }];"
},
"id": "attach-embeddings",
"name": "Attach Embeddings to Queries",
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
1320,
0
]
},
{
"parameters": {
"jsCode": "// Collect all embedded queries from all batches\n// This node aggregates across the loop\nconst items = $input.all();\n\nlet allEmbedded = [];\nfor (const item of items) {\n const batch = item.json.enriched_queries || [];\n allEmbedded = allEmbedded.concat(batch);\n}\n\n// Store total for reduction ratio\nconst totalInput = allEmbedded.length;\n\n// =============================================\n// SEMANTIC DEDUPLICATION via greedy cosine clustering\n// Threshold: 0.92 \u2014 queries above this similarity\n// are considered semantically equivalent\n// =============================================\nfunction cosineSim(a, b) {\n if (!a || !b || a.length === 0 || b.length === 0 || a.length !== b.length) return 0;\n let dot = 0, nA = 0, nB = 0;\n for (let i = 0; i < a.length; i++) {\n dot += a[i] * b[i];\n nA += a[i] * a[i];\n nB += b[i] * b[i];\n }\n return dot / (Math.sqrt(nA) * Math.sqrt(nB));\n}\n\nconst THRESHOLD = 0.92;\nconst clusters = [];\nconst assigned = new Set();\n\nfor (let i = 0; i < allEmbedded.length; i++) {\n if (assigned.has(i)) continue;\n if (allEmbedded[i].embedding.length === 0) continue;\n\n const cluster = {\n centroid_idx: i,\n members: [allEmbedded[i]]\n };\n assigned.add(i);\n\n for (let j = i + 1; j < allEmbedded.length; j++) {\n if (assigned.has(j)) continue;\n if (allEmbedded[j].embedding.length === 0) continue;\n\n const sim = cosineSim(allEmbedded[i].embedding, allEmbedded[j].embedding);\n if (sim >= THRESHOLD) {\n cluster.members.push(allEmbedded[j]);\n assigned.add(j);\n }\n }\n\n // Representative selection strategy:\n // Pick the shortest query (most general/head-like)\n // unless a member is clearly a seed query\n let representative = cluster.members.reduce((best, curr) => {\n // Prefer seed queries\n if (curr.seed_query && curr.text === curr.seed_query) return curr;\n if (best.seed_query && best.text === best.seed_query) return best;\n // Otherwise pick shortest\n return curr.text.length <= best.text.length ? curr : best;\n });\n\n clusters.push({\n representative,\n members: cluster.members\n });\n}\n\n// Also capture any queries that had empty embeddings (API failures)\nconst unembedded = allEmbedded.filter((q, idx) => q.embedding.length === 0 && !assigned.has(idx));\nfor (const q of unembedded) {\n clusters.push({\n representative: q,\n members: [q]\n });\n}\n\nconst reductionPct = totalInput > 0 ? Math.round((1 - clusters.length / totalInput) * 100) : 0;\n\nconsole.log(`Semantic dedup: ${totalInput} \u2192 ${clusters.length} (${reductionPct}% reduction)`);\n\nreturn clusters.map(c => ({\n json: {\n representative_query: c.representative.text,\n topic: c.representative.topic,\n seed_query: c.representative.seed_query,\n cluster_size: c.members.length,\n cluster_members: c.members.map(m => m.text).join(' | '),\n total_input_queries: totalInput,\n total_representative_queries: clusters.length,\n reduction_pct: reductionPct\n }\n}));"
},
"id": "semantic-dedup",
"name": "Semantic Dedup (Cosine 0.92 Threshold)",
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
1540,
0
]
},
{
"parameters": {
"operation": "append",
"documentId": {
"__rl": true,
"mode": "url",
"value": "={{ $json.sheetUrl }}"
},
"sheetName": {
"__rl": true,
"mode": "byName",
"value": "Reduction Log"
},
"columns": {
"mappingMode": "defineBelow",
"values": {
"date": "={{ new Date().toISOString().split('T')[0] }}",
"total_input": "={{ $json.total_input_queries }}",
"total_representative": "={{ $json.total_representative_queries }}",
"reduction_pct": "={{ $json.reduction_pct }}%",
"model": "gemma-embedding-exp-03-07",
"threshold": "0.92"
}
},
"options": {}
},
"id": "log-reduction-sheet",
"name": "Log Reduction Stats to Sheet",
"type": "n8n-nodes-base.googleSheets",
"typeVersion": 4.5,
"position": [
1540,
220
],
"credentials": {
"googleSheetsOAuth2Api": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"batchSize": 1,
"options": {
"reset": false
}
},
"id": "loop-serp-queries",
"name": "Loop Over Representative Queries",
"type": "n8n-nodes-base.splitInBatches",
"typeVersion": 3,
"position": [
1760,
0
]
},
{
"parameters": {
"amount": 2,
"unit": "seconds"
},
"id": "serp-delay",
"name": "Polite Delay (2s)",
"type": "n8n-nodes-base.wait",
"typeVersion": 1.1,
"position": [
1980,
0
]
},
{
"parameters": {
"url": "https://serpapi.com/search",
"sendQuery": true,
"queryParameters": {
"parameters": [
{
"name": "q",
"value": "={{ $json.representative_query }}"
},
{
"name": "api_key",
"value": "={{ $env.SERPAPI_KEY }}"
},
{
"name": "engine",
"value": "google"
},
{
"name": "gl",
"value": "us"
},
{
"name": "hl",
"value": "en"
},
{
"name": "num",
"value": "20"
}
]
},
"options": {
"timeout": 30000
}
},
"id": "pull-serp",
"name": "Pull SERP via SerpAPI",
"type": "n8n-nodes-base.httpRequest",
"typeVersion": 4.2,
"position": [
2200,
0
],
"continueOnFail": true
},
{
"parameters": {
"jsCode": "const serpData = $input.first().json;\nconst queryData = $('Loop Over Representative Queries').first().json;\nconst query = queryData.representative_query;\nconst topic = queryData.topic;\nconst seed_query = queryData.seed_query;\nconst cluster_size = queryData.cluster_size;\nconst cluster_members = queryData.cluster_members;\nconst timestamp = new Date().toISOString();\nconst date = timestamp.split('T')[0];\n\nconst results = [];\n\n// ---- ORGANIC RESULTS ----\nconst organic = serpData.organic_results || [];\nfor (const r of organic) {\n results.push({\n date,\n timestamp,\n query,\n topic,\n seed_query,\n cluster_size,\n position: r.position || null,\n url: r.link || '',\n title: (r.title || '').substring(0, 200),\n snippet: (r.snippet || '').substring(0, 500),\n domain: r.displayed_link || r.source || '',\n result_type: 'organic',\n serp_feature: null\n });\n}\n\n// ---- AI OVERVIEW ----\nif (serpData.ai_overview) {\n const aio = serpData.ai_overview;\n const sources = aio.references || aio.sources || aio.cited_sources || [];\n results.push({\n date, timestamp, query, topic, seed_query, cluster_size,\n position: 0,\n url: '',\n title: 'AI Overview',\n snippet: (typeof aio === 'string' ? aio : (aio.text || aio.snippet || JSON.stringify(aio))).substring(0, 500),\n domain: '',\n result_type: 'ai_overview',\n serp_feature: 'ai_overview',\n aio_source_count: sources.length,\n aio_sources_json: JSON.stringify(sources.slice(0, 10)).substring(0, 2000)\n });\n // Also log individual AIO sources\n for (let si = 0; si < sources.length && si < 10; si++) {\n const src = sources[si];\n results.push({\n date, timestamp, query, topic, seed_query, cluster_size,\n position: si + 1,\n url: src.link || src.url || '',\n title: (src.title || src.name || '').substring(0, 200),\n snippet: (src.snippet || '').substring(0, 500),\n domain: src.displayed_link || src.source || '',\n result_type: 'ai_overview_source',\n serp_feature: 'ai_overview'\n });\n }\n}\n\n// ---- FEATURED SNIPPET ----\nconst fs = serpData.answer_box || serpData.featured_snippet;\nif (fs) {\n results.push({\n date, timestamp, query, topic, seed_query, cluster_size,\n position: 0,\n url: fs.link || '',\n title: (fs.title || 'Featured Snippet').substring(0, 200),\n snippet: (fs.snippet || fs.answer || fs.contents?.map(c => c.text || '').join(' ') || '').substring(0, 500),\n domain: fs.displayed_link || '',\n result_type: 'featured_snippet',\n serp_feature: 'featured_snippet'\n });\n}\n\n// ---- PEOPLE ALSO ASK ----\nconst paa = serpData.related_questions || [];\nfor (const q of paa.slice(0, 8)) {\n results.push({\n date, timestamp, query, topic, seed_query, cluster_size,\n position: null,\n url: q.link || '',\n title: (q.question || '').substring(0, 200),\n snippet: (q.snippet || '').substring(0, 500),\n domain: q.displayed_link || q.source || '',\n result_type: 'people_also_ask',\n serp_feature: 'paa'\n });\n}\n\n// ---- KNOWLEDGE PANEL ----\nif (serpData.knowledge_graph) {\n const kg = serpData.knowledge_graph;\n results.push({\n date, timestamp, query, topic, seed_query, cluster_size,\n position: null,\n url: kg.website || kg.source?.link || '',\n title: (kg.title || '').substring(0, 200),\n snippet: (kg.description || '').substring(0, 500),\n domain: kg.source?.name || '',\n result_type: 'knowledge_graph',\n serp_feature: 'knowledge_graph'\n });\n}\n\n// ---- LOCAL PACK ----\nconst local = serpData.local_results?.places || [];\nfor (const l of local.slice(0, 3)) {\n results.push({\n date, timestamp, query, topic, seed_query, cluster_size,\n position: l.position || null,\n url: l.link || '',\n title: (l.title || '').substring(0, 200),\n snippet: (l.address || '').substring(0, 500),\n domain: '',\n result_type: 'local_pack',\n serp_feature: 'local_pack'\n });\n}\n\n// ---- VIDEO RESULTS ----\nconst videos = serpData.inline_videos || [];\nfor (const v of videos.slice(0, 3)) {\n results.push({\n date, timestamp, query, topic, seed_query, cluster_size,\n position: null,\n url: v.link || '',\n title: (v.title || '').substring(0, 200),\n snippet: (v.source || '').substring(0, 500),\n domain: v.platform || v.source || '',\n result_type: 'video',\n serp_feature: 'video_carousel'\n });\n}\n\n// ---- RELATED SEARCHES ----\nconst related = serpData.related_searches || [];\nfor (const r of related.slice(0, 8)) {\n results.push({\n date, timestamp, query, topic, seed_query, cluster_size,\n position: null,\n url: '',\n title: (r.query || '').substring(0, 200),\n snippet: '',\n domain: '',\n result_type: 'related_search',\n serp_feature: 'related_searches'\n });\n}\n\n// Fallback if nothing came back\nif (results.length === 0) {\n results.push({\n date, timestamp, query, topic, seed_query, cluster_size,\n position: null, url: '', title: '', snippet: 'No SERP data returned',\n domain: '', result_type: 'error', serp_feature: null\n });\n}\n\nreturn results.map(r => ({ json: r }));"
},
"id": "parse-serp-features",
"name": "Parse All SERP Features",
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
2420,
0
]
},
{
"parameters": {
"projectId": {
"__rl": true,
"mode": "list",
"value": "YOUR_GCP_PROJECT_ID"
},
"datasetId": {
"__rl": true,
"mode": "list",
"value": "seo_monitoring"
},
"tableId": "qfo_rank_tracking",
"options": {
"createTableIfNotExists": true,
"createDisposition": "CREATE_IF_NEEDED",
"writeDisposition": "WRITE_APPEND"
}
},
"id": "insert-rankings-bq",
"name": "Insert to BigQuery",
"type": "n8n-nodes-base.googleBigQuery",
"typeVersion": 2,
"position": [
2640,
0
],
"credentials": {
"googleBigQueryOAuth2Api": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"jsCode": "// Filter for only AIO source rows to create a dedicated AIO tracking view\nconst items = $input.all();\nconst aioItems = items.filter(i => \n i.json.result_type === 'ai_overview' || \n i.json.result_type === 'ai_overview_source'\n);\nif (aioItems.length === 0) return [];\nreturn aioItems;"
},
"id": "filter-aio",
"name": "Filter AIO Results",
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
2640,
220
]
},
{
"parameters": {
"projectId": {
"__rl": true,
"mode": "list",
"value": "YOUR_GCP_PROJECT_ID"
},
"datasetId": {
"__rl": true,
"mode": "list",
"value": "seo_monitoring"
},
"tableId": "qfo_aio_tracking",
"options": {
"createTableIfNotExists": true,
"createDisposition": "CREATE_IF_NEEDED",
"writeDisposition": "WRITE_APPEND"
}
},
"id": "insert-aio-bq",
"name": "Insert AIO Data to BigQuery",
"type": "n8n-nodes-base.googleBigQuery",
"typeVersion": 2,
"position": [
2860,
220
],
"credentials": {
"googleBigQueryOAuth2Api": {
"name": "<your credential>"
}
}
}
],
"connections": {
"Daily at 2AM UTC": {
"main": [
[
{
"node": "Get QFO Queries from Sheet",
"type": "main",
"index": 0
}
]
]
},
"Get QFO Queries from Sheet": {
"main": [
[
{
"node": "Prepare Embedding Batches (100/batch)",
"type": "main",
"index": 0
}
]
]
},
"Prepare Embedding Batches (100/batch)": {
"main": [
[
{
"node": "Loop Over Embedding Batches",
"type": "main",
"index": 0
}
]
]
},
"Loop Over Embedding Batches": {
"main": [
[
{
"node": "Rate Limit (500ms)",
"type": "main",
"index": 0
}
]
]
},
"Rate Limit (500ms)": {
"main": [
[
{
"node": "Embed with EmbeddingGemma (batchEmbedContents)",
"type": "main",
"index": 0
}
]
]
},
"Embed with EmbeddingGemma (batchEmbedContents)": {
"main": [
[
{
"node": "Attach Embeddings to Queries",
"type": "main",
"index": 0
}
]
]
},
"Attach Embeddings to Queries": {
"main": [
[
{
"node": "Semantic Dedup (Cosine 0.92 Threshold)",
"type": "main",
"index": 0
}
]
]
},
"Semantic Dedup (Cosine 0.92 Threshold)": {
"main": [
[
{
"node": "Loop Over Representative Queries",
"type": "main",
"index": 0
},
{
"node": "Log Reduction Stats to Sheet",
"type": "main",
"index": 0
}
]
]
},
"Loop Over Representative Queries": {
"main": [
[
{
"node": "Polite Delay (2s)",
"type": "main",
"index": 0
}
]
]
},
"Polite Delay (2s)": {
"main": [
[
{
"node": "Pull SERP via SerpAPI",
"type": "main",
"index": 0
}
]
]
},
"Pull SERP via SerpAPI": {
"main": [
[
{
"node": "Parse All SERP Features",
"type": "main",
"index": 0
}
]
]
},
"Parse All SERP Features": {
"main": [
[
{
"node": "Insert to BigQuery",
"type": "main",
"index": 0
},
{
"node": "Filter AIO Results",
"type": "main",
"index": 0
}
]
]
},
"Filter AIO Results": {
"main": [
[
{
"node": "Insert AIO Data to BigQuery",
"type": "main",
"index": 0
}
]
]
}
},
"settings": {
"executionOrder": "v1"
}
}
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.
googleBigQueryOAuth2ApigoogleSheetsOAuth2Api
Pro
For the full experience including quality scoring and batch install features for each workflow upgrade to Pro
About this workflow
QFO Rank Tracking - EmbeddingGemma Semantic Dedup & Daily Tracking. Uses googleSheets, httpRequest, googleBigQuery. Scheduled trigger; 16 nodes.
Source: https://github.com/ipullrank/r19g-workflows/blob/0df550090c17d82937b4f09220a0ba5bf158c9d9/n8n/qfo-rank-tracking.json — original creator credit. Request a take-down →
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