{"workflow":{"id":14051,"name":"Query expenses on Telegram with GPT-4.1 and Google Sheets","views":50,"recentViews":1,"totalViews":50,"createdAt":"2026-03-14T16:43:36.648Z","description":"# π¬ Chat with Your Finances on Telegram\n\nAsk questions like *βHow much did I spend on food last month?β* and get instant answers from your financial data β directly in Telegram.\n\n\nThis workflow connects your **Google Sheets expense log** to an **AI-powered query engine** that understands natural language, resolves ambiguous categories and person names, and sends back a **clean formatted summary** in Telegram.\n\nNo spreadsheets. No dashboards. \nJust **chat with your financial data**.\n\n---\n\n## β‘ How it works\n\nSimply send a message like:\n\n- *How much did we spend on groceries last month?*\n- *Show me a breakdown by category for this week.*\n\nThe workflow automatically:\n\n1. **Parses your intent** using GPT-4.1-nano \n2. **Resolves categories and person names** via mapping tables \n3. **Filters and aggregates expense data** from Google Sheets \n4. **Returns a formatted summary** directly in Telegram\n\nIf a category or person is unknown, the workflow uses AI to **suggest the closest match** and asks the user to confirm via **Telegram inline buttons**.\n\nConfirmed aliases are saved automatically, making the system **self-learning over time**.\n\n---\n\n## β¨ Key Features\n\n- **Natural language queries** in English and German \n- **AI intent parsing** with GPT-4.1-nano (time range, person, category, filters) \n- **Self-learning entity resolution** for categories and names \n- **Interactive disambiguation** via Telegram inline buttons \n- **Relative date support** (`this week`, `last month`, `this year`) \n- **Group-by breakdowns** by category or person \n- **Shared expense filtering** via `common_only` flag \n- **Multi-user support** via Chat ID allowlist\n\n---\n\n## π Requirements\n\nTo run this workflow you need:\n\n- **Telegram Bot** (created via `@BotFather`)\n- **OpenAI API key**\n- **Four Google Sheets**\n\nRequired sheets:\n\n- `expenses`\n- `expense_categories`\n- `categories_mapping`\n- `person_mapping`\n\n---\n\n## β± Setup Time\n\nApprox. **15 minutes**\n\nAll required configuration steps are documented directly inside the workflow using **\"Action Required\" notes** in each workflow layer.\n\n---\n\n## Examples\n\n\n\n","workflow":{"id":"","meta":{"templateCredsSetupCompleted":false},"name":"Telegram Expense Tracker β Query Bot (Template)","tags":[],"nodes":[{"id":"58535881-40c5-4d75-82c6-f0faa9443e1a","name":"MSG | Telegram Inbound","type":"n8n-nodes-base.telegramTrigger","position":[2144,1088],"webhookId":"","parameters":{"updates":["message","callback_query"],"additionalFields":{}},"credentials":{"telegramApi":{"id":"","name":"YOUR_TELEGRAM_CREDENTIAL"}},"typeVersion":1.1},{"id":"69f61c14-bd15-4c5a-b0b8-ada463bff839","name":"LLM | Parse Intent","type":"@n8n/n8n-nodes-langchain.openAi","position":[2880,1632],"parameters":{"modelId":{"__rl":true,"mode":"list","value":"gpt-4.1-nano","cachedResultName":"GPT-4.1-NANO"},"options":{},"messages":{"values":[{"role":"system","content":"=You are a financial intent parser.\nYour ONLY task is to convert a natural language finance question into structured JSON.\nYou must:\n- Return ONLY valid JSON.\n- Do NOT include explanations.\n- Do NOT include markdown.\n- Do NOT answer the question.\n- Do NOT guess missing information.\n- Do NOT add extra fields.\n\nThe user's first name is: {{ $('MSG | Telegram Inbound').item.json.message.from.first_name }}\nThe current year is: {{ new Date().getFullYear() }}\nThe current date is: {{ new Date().toISOString().split('T')[0] }}\n\nAlways return the following JSON structure:\n{\n \"intent\": \"spending_query\",\n \"time_reference\": \"last_week, this_week, last_month, this_month, last_year, this_year, or null\",\n \"explicit_start_date\": \"YYYY-MM-DD or null\",\n \"explicit_end_date\": \"YYYY-MM-DD or null\",\n \"person\": \"string or null\",\n \"category\": [\"string\"] or null,\n \"common_only\": true, false, or null,\n \"comparison\": \"previous_period, previous_month, previous_year, or null\",\n \"group_by\": \"category, person, or null\"\n}\n\nInterpretation Rules:\n1. Date Handling (IMPORTANT):\n- If the user mentions a specific month without a year (e.g. \"february\", \"januar\"),\n assume the current year and set explicit_start_date and explicit_end_date accordingly.\n Example: \"february\" β explicit_start_date: \"2026-02-01\", explicit_end_date: \"2026-02-28\"\n- If the user refers to relative time (e.g., last week, this month, last month, this year, etc.), \n set the correct value in \"time_reference\".\n- DO NOT calculate actual dates for relative time references.\n- Leave explicit_start_date and explicit_end_date as null for relative time references.\n- If the user provides an explicit date range (e.g., 01.01.2026 - 31.01.2026),\n set explicit_start_date and explicit_end_date accordingly,\n and set time_reference to null.\n- If no time reference is mentioned β set all three fields to null.\n\n2. Person Handling:\n- If user says \"I\", \"me\", \"ich\", \"mir\" β use \"{{ $('MSG | Telegram Inbound').item.json.message.from.first_name }}\"\n- If user says \"we\", \"wir\", \"uns\", \"beide\" β null (refers to both persons, no filter needed).\n- If a specific name or nickname is mentioned that refers to another person β use that exact name as written.\n- If no person is mentioned β null.\n- NEVER invent or guess a person name.\n- The value for \"person\" must always be either the mentioned name/nickname, \"{{ $('MSG | Telegram Inbound').item.json.message.from.first_name }}\", or null.\n\n3. category:\n- If one category is mentioned, return it as a single-element array.\n- If multiple categories are mentioned, return all of them as an array.\n- Use lowercase strings.\n- If none mentioned β null.\n\n4. Common Expense Handling (VERY IMPORTANT):\nSet \"common_only\" to true if the user explicitly refers to shared expenses.\nThis includes words or phrases such as:\n- shared\n- common\n- joint\n- together\n- gemeinschaftlich\n- gemeinsam\n- gemeinsame ausgaben\n- geteilte ausgaben\n- fΓΌr uns\n- zusammen\nIf the user explicitly says:\n- \"fΓΌr gemeinsame Ausgaben\"\n- \"fΓΌr gemeinsame Kosten\"\nyou MUST set \"common_only\" to true.\nSet \"common_only\" to false if the user explicitly refers to private expenses.\nThis includes:\n- private\n- persΓΆnlich\n- eigene ausgaben\n- nur fΓΌr sich\n- privat\nIf shared/private is NOT explicitly mentioned β set \"common_only\" to null.\nNever assume.\n\n5. Comparison:\n- If user compares with previous month β \"previous_month\"\n- If compares with previous period β \"previous_period\"\n- If compares with previous year β \"previous_year\"\n- Otherwise β null\n\n6. Grouping:\n- If user asks for breakdown by category β \"category\"\n- If breakdown by person β \"person\"\n- Otherwise β null\n\nReturn ONLY JSON."},{"content":"={{ $json.message.text }}"}]}},"credentials":{"openAiApi":{"id":"","name":"YOUR_OPENAI_CREDENTIAL"}},"typeVersion":1.4},{"id":"1f1984d4-cc31-4730-8354-69c6ec425bbe","name":"JS | Extract Intent JSON","type":"n8n-nodes-base.code","position":[3232,1632],"parameters":{"jsCode":"const rawContent = $input.first().json.message?.content || $input.first().json.choices?.[0]?.message?.content || '{}';\nlet parsed = {};\ntry {\n const cleaned = rawContent.replace(/```json|```/g, '').trim();\n parsed = JSON.parse(cleaned);\n} catch(e) {\n parsed = { intent: 'spending_query', start_date: null, end_date: null, person: null, category: null, common_only: null, comparison: null, group_by: null };\n}\nreturn [{ json: parsed }];"},"typeVersion":2},{"id":"4e830963-ede6-4d56-b328-7f3119040143","name":"GS | Load Expenses","type":"n8n-nodes-base.googleSheets","position":[9824,1440],"parameters":{"options":{},"sheetName":{"__rl":true,"mode":"list","value":"gid=0","cachedResultUrl":"https://docs.google.com/spreadsheets/d/YOUR_SPREADSHEET_ID","cachedResultName":"Sheet1"},"documentId":{"__rl":true,"mode":"list","value":"YOUR_SPREADSHEET_ID","cachedResultUrl":"https://docs.google.com/spreadsheets/d/YOUR_SPREADSHEET_ID","cachedResultName":"expenses"}},"credentials":{"googleSheetsOAuth2Api":{"id":"","name":"YOUR_GOOGLE_SHEETS_CREDENTIAL"}},"typeVersion":4.4},{"id":"4271bf57-4b8d-4f68-8896-2d55f162161e","name":"GS | Load Categories","type":"n8n-nodes-base.googleSheets","position":[9824,1152],"parameters":{"options":{},"sheetName":{"__rl":true,"mode":"list","value":"gid=0","cachedResultUrl":"https://docs.google.com/spreadsheets/d/YOUR_SPREADSHEET_ID","cachedResultName":"Sheet1"},"documentId":{"__rl":true,"mode":"list","value":"YOUR_SPREADSHEET_ID","cachedResultUrl":"https://docs.google.com/spreadsheets/d/YOUR_SPREADSHEET_ID","cachedResultName":"expense_categories"}},"credentials":{"googleSheetsOAuth2Api":{"id":"","name":"YOUR_GOOGLE_SHEETS_CREDENTIAL"}},"typeVersion":4.4},{"id":"21d72a8f-e9cd-4fac-b832-f0804777d270","name":"JS | Filter & Aggregate","type":"n8n-nodes-base.code","position":[10576,1264],"parameters":{"jsCode":"// =============================\n// Date Engine\n// =============================\n\nconst now = new Date();\n\nfunction normalizeDate(date) {\n const d = new Date(date);\n if (isNaN(d)) return null;\n d.setHours(0,0,0,0);\n return d;\n}\n\nfunction getDateRange(timeReference) {\n\n switch (timeReference) {\n\n case \"this_month\":\n return {\n start: normalizeDate(new Date(now.getFullYear(), now.getMonth(), 1)),\n end: normalizeDate(now)\n };\n\n case \"last_month\":\n return {\n start: normalizeDate(new Date(now.getFullYear(), now.getMonth() - 1, 1)),\n end: normalizeDate(new Date(now.getFullYear(), now.getMonth(), 0))\n };\n\n case \"this_year\":\n return {\n start: normalizeDate(new Date(now.getFullYear(), 0, 1)),\n end: normalizeDate(now)\n };\n\n case \"last_year\":\n return {\n start: normalizeDate(new Date(now.getFullYear() - 1, 0, 1)),\n end: normalizeDate(new Date(now.getFullYear() - 1, 11, 31))\n };\n\n case \"last_week\":\n const day = now.getDay();\n const diffToMonday = (day + 6) % 7;\n\n const lastMonday = new Date(now);\n lastMonday.setDate(now.getDate() - diffToMonday - 7);\n\n const lastSunday = new Date(lastMonday);\n lastSunday.setDate(lastMonday.getDate() + 6);\n\n return {\n start: normalizeDate(lastMonday),\n end: normalizeDate(lastSunday)\n };\n\n case \"this_week\":\n const currentDay = now.getDay();\n const diff = (currentDay + 6) % 7;\n\n const monday = new Date(now);\n monday.setDate(now.getDate() - diff);\n\n return {\n start: normalizeDate(monday),\n end: normalizeDate(now)\n };\n\n default:\n return { start: null, end: null };\n }\n}\n\nfunction formatDate(date) {\n if (!date) return null;\n return date.toISOString().split('T')[0];\n}\n\n// =============================\n// Retrieve Intent\n// =============================\n\nconst intent = $('JS | Extract Intent JSON').first().json;\nconst edit3 = $('SET | Assemble Resolved Intent').first().json;\n\n// IMPORTANT: overwrite values if edited\nintent.person = edit3.person || intent.person;\nintent.category = edit3.category || intent.category;\n\n// =============================\n// Load & Normalize Data\n// =============================\n\nconst expenseItems = $('GS | Load Expenses').all().map(i => ({\n date: i.json.date,\n amount: parseFloat(i.json.amount) || 0,\n category: i.json.category?.toLowerCase() || null,\n description: i.json.description,\n common_expense: i.json.common_expense,\n person: i.json.Person?.toLowerCase() || null\n}));\n\n// =============================\n// Date Handling\n// =============================\n\nlet startDate = null;\nlet endDate = null;\n\nif (intent.time_reference) {\n const range = getDateRange(intent.time_reference);\n startDate = range.start;\n endDate = range.end;\n}\n\nif (intent.explicit_start_date) {\n startDate = normalizeDate(intent.explicit_start_date);\n}\n\nif (intent.explicit_end_date) {\n endDate = normalizeDate(intent.explicit_end_date);\n}\n\nconst formattedStart = formatDate(startDate);\nconst formattedEnd = formatDate(endDate);\n\n// =============================\n// Other Filters\n// =============================\n\nconst filterPerson = intent.person ? intent.person.toLowerCase() : null;\n\nconst filterCategories = (() => {\n const c = intent.category;\n if (!c) return [];\n if (Array.isArray(c)) {\n return c.map(x => String(x).trim().toLowerCase()).filter(Boolean);\n }\n return [String(c).trim().toLowerCase()].filter(Boolean);\n})();\n\nconst filterCommon = intent.common_only;\nconst groupBy = intent.group_by;\n\n// =============================\n// Apply Filters\n// =============================\n\nconst filtered = expenseItems.filter(row => {\n\n if (!row.date) return false;\n\n const rowDate = normalizeDate(row.date);\n if (!rowDate) return false;\n\n if (startDate && rowDate < startDate) return false;\n if (endDate && rowDate > endDate) return false;\n\n if (filterPerson && row.person !== filterPerson) return false;\n\n if (filterCategories.length > 0) {\n if (!row.category) return false;\n if (!filterCategories.includes(row.category)) return false;\n }\n\n if (filterCommon !== null && filterCommon !== undefined) {\n\n const isCommon =\n String(row.common_expense).toLowerCase() === 'true' ||\n row.common_expense === true ||\n row.common_expense === 1;\n\n if (filterCommon && !isCommon) return false;\n if (!filterCommon && isCommon) return false;\n }\n\n return true;\n\n});\n\n// =============================\n// Aggregations\n// =============================\n\nconst total = filtered.reduce((sum, row) => sum + row.amount, 0);\n\n// Category breakdown\nlet categoryBreakdown = null;\n\nif (groupBy === \"category\") {\n\n const breakdown = {};\n\n filtered.forEach(row => {\n\n const cat = row.category || \"uncategorized\";\n\n breakdown[cat] = (breakdown[cat] || 0) + row.amount;\n\n });\n\n categoryBreakdown = breakdown;\n}\n\n// Person breakdown\nlet personBreakdown = null;\n\nif (groupBy === \"person\") {\n\n const breakdown = {};\n\n filtered.forEach(row => {\n\n const person = row.person || \"unknown\";\n\n breakdown[person] = (breakdown[person] || 0) + row.amount;\n\n });\n\n personBreakdown = breakdown;\n}\n\n// =============================\n// Return Result\n// =============================\n\nreturn [{\n json: {\n intent,\n total: Math.round(total * 100) / 100,\n count: filtered.length,\n categoryBreakdown,\n personBreakdown,\n period: {\n start: formattedStart,\n end: formattedEnd\n },\n filters: {\n person: intent.person,\n category: intent.category,\n common_only: intent.common_only\n }\n }\n}];"},"typeVersion":2},{"id":"b24a37e4-d82f-43e6-b343-e91f7f838d19","name":"JS | Format Response Message","type":"n8n-nodes-base.code","position":[11056,1264],"parameters":{"jsCode":"const data = $input.first().json;\nconst filters = data.filters || {};\nconst period = data.period || {};\n\nlet lines = [];\n\n// =============================\n// Header\n// =============================\n\nlines.push('π *Expense Summary*');\nlines.push('');\n\n// =============================\n// Period Display (NEW)\n// =============================\n\nif (period.start || period.end) {\n const from = period.start || 'Beginning';\n const to = period.end || 'Today';\n lines.push(`π
Period: ${from} β ${to}`);\n} else {\n lines.push(`π
Period: All time`);\n}\n\n// =============================\n// Applied filters\n// =============================\n\nif (filters.person) lines.push(`π€ Person: ${filters.person}`);\nif (filters.category) lines.push(`π·οΈ Category: ${filters.category}`);\n\nif (filters.common_only !== null && filters.common_only !== undefined) {\n lines.push(`π Common expenses only: ${filters.common_only ? 'Yes' : 'No'}`);\n}\n\nlines.push('');\nlines.push(`π° *Total: β¬${data.total.toFixed(2)}*`);\nlines.push(`π Transactions: ${data.count}`);\n\n// =============================\n// Category breakdown\n// =============================\n\nif (data.categoryBreakdown) {\n lines.push('');\n lines.push('*By Category:*');\n\n const sorted = Object.entries(data.categoryBreakdown)\n .sort((a, b) => b[1] - a[1]);\n\n sorted.forEach(([cat, amt]) => {\n const pct = data.total > 0\n ? ((amt / data.total) * 100).toFixed(1)\n : 0;\n\n lines.push(` β’ ${cat}: $${amt.toFixed(2)} (${pct}%)`);\n });\n}\n\n// =============================\n// Person breakdown\n// =============================\n\nif (data.personBreakdown) {\n lines.push('');\n lines.push('*By Person:*');\n\n const sorted = Object.entries(data.personBreakdown)\n .sort((a, b) => b[1] - a[1]);\n\n sorted.forEach(([person, amt]) => {\n const pct = data.total > 0\n ? ((amt / data.total) * 100).toFixed(1)\n : 0;\n\n lines.push(` β’ ${person}: $${amt.toFixed(2)} (${pct}%)`);\n });\n}\n\n// =============================\n// Comparison placeholder\n// =============================\n\nif (data.comparison) {\n lines.push('');\n lines.push(`π Comparison: [Feature coming soon]`);\n}\n\n// =============================\n// Empty state\n// =============================\n\nif (data.count === 0) {\n lines.push('');\n lines.push('_No expenses found for the given filters._');\n}\n\nreturn [{ json: { message: lines.join('\\n') } }];"},"typeVersion":2},{"id":"c7dc9c2c-8bc0-4b0a-a988-27a966b3f814","name":"TG | Send Reply","type":"n8n-nodes-base.telegram","position":[11312,1264],"webhookId":"","parameters":{"text":"={{ $json.message }}","chatId":"={{ $('MSG | Telegram Inbound').first().json.message.chat.id }}","additionalFields":{"parse_mode":"Markdown","appendAttribution":false,"reply_to_message_id":"={{ $('MSG | Telegram Inbound').first().json.message.message_id }}"}},"credentials":{"telegramApi":{"id":"","name":"YOUR_TELEGRAM_CREDENTIAL"}},"typeVersion":1.2},{"id":"4876799b-af52-4218-80aa-7603258ff084","name":"MERGE | Combine Expenses + Categories","type":"n8n-nodes-base.merge","position":[10144,1264],"parameters":{"mode":"chooseBranch"},"typeVersion":3.2},{"id":"db13a254-5fde-42e4-9e82-2060a04451f0","name":"IF | User Authorized?","type":"n8n-nodes-base.if","position":[2528,1072],"parameters":{"options":{},"conditions":{"options":{"version":2,"leftValue":"","caseSensitive":true,"typeValidation":"strict"},"combinator":"or","conditions":[{"id":"0f85f7d5-8b7e-4bcd-ae10-d7a1785192c4","operator":{"type":"number","operation":"equals"},"leftValue":"={{ $json.message.chat.id }}","rightValue":1000000000},{"id":"2d18f8cf-2910-4e20-b2f3-16a9ef04e63e","operator":{"type":"number","operation":"equals"},"leftValue":"={{ $json.message.chat.id }}","rightValue":1000000001}]}},"typeVersion":2.2},{"id":"7a76b7d6-f533-4785-b822-8d7ac464d883","name":"SPLIT | Split Categories","type":"n8n-nodes-base.splitOut","position":[4016,768],"parameters":{"options":{},"fieldToSplitOut":"category"},"typeVersion":1},{"id":"8e963faf-3079-447d-bfd9-89eff8da207e","name":"GS | Read Category Mapping","type":"n8n-nodes-base.googleSheets","position":[4016,1072],"parameters":{"options":{},"sheetName":{"__rl":true,"mode":"list","value":"gid=0","cachedResultUrl":"https://docs.google.com/spreadsheets/d/YOUR_SPREADSHEET_ID","cachedResultName":"Sheet1"},"documentId":{"__rl":true,"mode":"list","value":"YOUR_SPREADSHEET_ID","cachedResultUrl":"https://docs.google.com/spreadsheets/d/YOUR_SPREADSHEET_ID","cachedResultName":"categories_mapping"}},"credentials":{"googleSheetsOAuth2Api":{"id":"","name":"YOUR_GOOGLE_SHEETS_CREDENTIAL"}},"typeVersion":4.7},{"id":"e992cd5e-ba4e-4ab5-b885-14c55546d0a3","name":"MERGE | Join Categories with Mapping","type":"n8n-nodes-base.merge","position":[4272,768],"parameters":{"mode":"combineBySql","query":"SELECT * FROM input1 LEFT JOIN input2 ON input1.category = input2.find","options":{}},"typeVersion":3.2},{"id":"58c889ef-e918-43d4-a7a3-1a47bf890ffc","name":"TG | Confirm Category Suggestion","type":"n8n-nodes-base.telegram","position":[6224,368],"webhookId":"","parameters":{"chatId":"={{ $('MSG | Telegram Inbound').item.json.message.chat.id }}","message":"=I couldnβt find the category \"{{ $('IF | Category Known?').item.json.category_new }}\".\n\nDid you mean \"{{ $json.content }}\"?\n\nβ
Yes \nβ No","options":{"appendAttribution":false},"operation":"sendAndWait","approvalOptions":{"values":{"approvalType":"double","approveLabel":"β
Yes","disapproveLabel":"β No"}}},"credentials":{"telegramApi":{"id":"","name":"YOUR_TELEGRAM_CREDENTIAL"}},"typeVersion":1.2},{"id":"fd9c3d1c-223a-44d8-afbf-4773a11d09bd","name":"GS | Read Allowed Categories","type":"n8n-nodes-base.googleSheets","position":[4464,1072],"parameters":{"options":{},"sheetName":{"__rl":true,"mode":"list","value":"gid=0","cachedResultUrl":"https://docs.google.com/spreadsheets/d/YOUR_SPREADSHEET_ID","cachedResultName":"Sheet1"},"documentId":{"__rl":true,"mode":"list","value":"YOUR_SPREADSHEET_ID","cachedResultUrl":"https://docs.google.com/spreadsheets/d/YOUR_SPREADSHEET_ID","cachedResultName":"expense_categories"}},"credentials":{"googleSheetsOAuth2Api":{"id":"","name":"YOUR_GOOGLE_SHEETS_CREDENTIAL"}},"typeVersion":4.7},{"id":"2f1f1de8-cae3-4792-920c-ba490986019e","name":"SET | Normalize Category","type":"n8n-nodes-base.set","position":[4464,768],"parameters":{"options":{},"assignments":{"assignments":[{"id":"8f458013-1006-4634-a1c8-aedff820a48f","name":"category_new","type":"string","value":"={{ $json.replace ?? $json.category }}"}]}},"typeVersion":3.4},{"id":"7a819214-50e5-460a-bd4d-a546eb68dc20","name":"MERGE | Check Category against Allowed","type":"n8n-nodes-base.merge","position":[4720,768],"parameters":{"mode":"combineBySql","query":"SELECT * FROM input1\n \nLEFT JOIN input2 ON input1.category_new = input2.category","options":{}},"typeVersion":3.2,"alwaysOutputData":false},{"id":"ed0de8c5-8c55-49f2-96a0-973b49b95d6a","name":"IF | Category Known?","type":"n8n-nodes-base.if","position":[4960,768],"parameters":{"options":{},"conditions":{"options":{"version":3,"leftValue":"","caseSensitive":true,"typeValidation":"strict"},"combinator":"and","conditions":[{"id":"e329be08-8c46-4898-9480-5946735ad54d","operator":{"type":"string","operation":"notExists","singleValue":true},"leftValue":"={{ $json.description }}","rightValue":""}]}},"typeVersion":2.3,"alwaysOutputData":false},{"id":"9ecd305a-5e42-43f1-91fa-21fbb0d89e53","name":"LLM | Classify Category","type":"@n8n/n8n-nodes-langchain.openAi","position":[5712,368],"parameters":{"modelId":{"__rl":true,"mode":"list","value":"gpt-4.1-nano","cachedResultName":"GPT-4.1-NANO"},"options":{},"messages":{"values":[{"role":"system","content":"=You are a strict category classification system.\n\nYour task:\nMap the given input category to exactly ONE category from the allowed category list.\n\nRules:\n- You MUST select one category from the allowed list.\n- Do NOT invent new categories.\n- Do NOT return explanations.\n- Do NOT return multiple categories.\n- Output ONLY the exact category name from the allowed list.\n- Matching must be case-insensitive.\n- Choose the closest semantic match if there is no exact match.\n\nInput category:\n{{ $('IF | Category Known?').item.json.category_new }}\n\nAllowed categories:\n{{ $json.category }}"}]}},"credentials":{"openAiApi":{"id":"","name":"YOUR_OPENAI_CREDENTIAL"}},"typeVersion":1.4},{"id":"73085ca0-da81-4f8f-a232-5680c7d88b88","name":"MERGE | Combine Categories with List","type":"n8n-nodes-base.merge","position":[5216,672],"parameters":{"mode":"combine","options":{},"combineBy":"combineAll"},"typeVersion":3.2},{"id":"19fe45dc-435b-4e70-9b7a-cd7c0bedd41e","name":"SET | Extract LLM Category","type":"n8n-nodes-base.set","position":[5968,368],"parameters":{"mode":"raw","options":{},"jsonOutput":"={{ $json.message }}"},"typeVersion":3.4},{"id":"20b22d0e-35bf-476f-9aba-e2abb14f0377","name":"IF | Category Suggestion Accepted?","type":"n8n-nodes-base.if","position":[6464,368],"parameters":{"options":{},"conditions":{"options":{"version":3,"leftValue":"","caseSensitive":true,"typeValidation":"strict"},"combinator":"and","conditions":[{"id":"4fb343c7-9eb2-4491-b017-3637fb543564","operator":{"type":"boolean","operation":"equals"},"leftValue":"={{ $json.data.approved }}","rightValue":true}]}},"typeVersion":2.3},{"id":"1d692432-6601-47f1-9bdd-cafd50aeef5c","name":"GS | Save Category Mapping","type":"n8n-nodes-base.googleSheets","position":[7968,368],"parameters":{"columns":{"value":{"find":"={{ $json.old }}","replace":"={{ $json.new 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Entries","type":"n8n-nodes-base.merge","position":[7920,1904],"parameters":{},"typeVersion":3.2},{"id":"eaee6930-3da6-40a9-ba0f-84ab8543b451","name":"MERGE | Loop Entry (Person)","type":"n8n-nodes-base.merge","position":[5696,2256],"parameters":{},"typeVersion":3.2},{"id":"e2241bb1-eae6-4311-909c-a67bb2bd52fe","name":"SET | Set Resolved Person","type":"n8n-nodes-base.set","position":[6208,2256],"parameters":{"options":{},"assignments":{"assignments":[{"id":"47a00b45-f580-4568-9913-b2359e1c1d8c","name":"person_new","type":"string","value":"={{ $json.replace ?? $('IF | Person Known?').item.json.person_new}}"}]}},"typeVersion":3.4},{"id":"9a37179d-2d3c-48e3-9ce1-1624b626b6fd","name":"MERGE | Combine Person Mapping","type":"n8n-nodes-base.merge","position":[6448,2048],"parameters":{},"typeVersion":3.2},{"id":"183d2e80-d18b-403e-a3f0-86d41fd0c0b1","name":"IF | Category or Person Callback?","type":"n8n-nodes-base.if","position":[2816,576],"parameters":{"options":{},"conditions":{"options":{"version":3,"leftValue":"","caseSensitive":true,"typeValidation":"strict"},"combinator":"and","conditions":[{"id":"a6650d78-63a0-4bc4-ac46-e076eba14def","operator":{"type":"string","operation":"contains"},"leftValue":"{{json.message.text}}","rightValue":"Kategorie"}]}},"typeVersion":2.3},{"id":"0ac9e7b3-a794-4a42-b48b-1d42ba317bbc","name":"HTTP | Forward Person Selection","type":"n8n-nodes-base.httpRequest","position":[3312,688],"parameters":{"url":"={{ $json.resumeUrl }}","method":"POST","options":{},"jsonBody":"={\n \"person\": \"{{ $json.person }}\"\n}","sendBody":true,"specifyBody":"json"},"typeVersion":4.3},{"id":"36db9da8-b32c-4b48-b7d4-6871eed49cb0","name":"TG | Confirm Person Selection","type":"n8n-nodes-base.telegram","position":[3504,688],"webhookId":"","parameters":{"text":"=Du hast \" {{ $('MSG | Telegram Inbound').item.json.callback_query.data }}\" ausgewΓ€hlt\"","chatId":"={{ $('MSG | Telegram Inbound').item.json.callback_query.message.chat.id }}","additionalFields":{"appendAttribution":false}},"credentials":{"telegramApi":{"id":"","name":"YOUR_TELEGRAM_CREDENTIAL"}},"typeVersion":1.2},{"id":"17fceb64-ac93-42b7-af34-fc5f1d0cb3d2","name":"JS | Read Person Callback","type":"n8n-nodes-base.code","position":[3104,688],"parameters":{"jsCode":"const callbackData = $json.callback_query.data;\nconst text = $json.callback_query.message.text;\nconst entities = $json.callback_query.message.entities;\n\n// URL aus den message entities auslesen\nconst urlEntity = entities.find(e => e.type === 'text_link');\nconst resumeUrl = urlEntity.url;\n\nreturn [{\n json: {\n person: callbackData,\n resumeUrl: resumeUrl\n }\n}];"},"typeVersion":2},{"id":"a742d127-062b-4f7d-8c4d-0b089439508e","name":"JS | Merge Intent Fields","type":"n8n-nodes-base.code","position":[9168,1264],"parameters":{"jsCode":"const items = $input.all();\n\nconst result = {};\n\nfor (const item of items) {\n for (const [key, value] of Object.entries(item.json)) {\n if (\n value !== undefined &&\n value !== null &&\n value !== \"\" &&\n value !== \"undefined\"\n ) {\n result[key] = value;\n }\n }\n}\n\nreturn [{\n json: result,\n pairedItem: { item: 0 }\n}];"},"typeVersion":2},{"id":"f4353b36-c5f3-4e13-baf8-a257e81fbf43","name":"StickyNote_Layer1","type":"n8n-nodes-base.stickyNote","position":[1968,0],"parameters":{"color":5,"width":1740,"height":1288,"content":"## π’ LAYER 1 β INPUT\nReceives incoming Telegram messages & callbacks.\nAuthorizes users and routes by message type.\n\n---\n\nβοΈ **ACTION REQUIRED**\n\n**1. Connect Telegram Credential**\nOpen the `MSG | Telegram Inbound` node and connect your Telegram Bot credential.\nDo the same for all other `TG |` nodes in this layer.\nβ *Nodes: MSG | Telegram Inbound, TG | Confirm Category Selection, TG | Confirm Person Selection*\n\n**2. Add authorized Chat IDs**\nOpen `IF | User Authorized?` and replace the placeholder values (`1000000000`, `1000000001`) with the real Telegram Chat IDs of all users who should have access.\nEach person can find their Chat ID by messaging @userinfobot on Telegram.\n"},"typeVersion":1},{"id":"b6684ba8-b5ab-41fe-8c1c-9ec20255983d","name":"StickyNote_Layer2","type":"n8n-nodes-base.stickyNote","position":[1968,1312],"parameters":{"width":1730,"height":1284,"content":"## π΅ LAYER 2 β INTENT PARSING\n\n---\n\nβοΈ **ACTION REQUIRED**\n\n**1. Connect OpenAI Credential**\nOpen the `LLM | Parse Intent` node and connect your OpenAI API credential."},"typeVersion":1},{"id":"c6ddda36-1b63-428b-932d-2d9c857aa12d","name":"StickyNote_Layer3","type":"n8n-nodes-base.stickyNote","position":[3712,0],"parameters":{"color":3,"width":4970,"height":1296,"content":"## π‘ LAYER 3a β ENTITY RESOLUTION: CATEGORIES\nResolves raw category strings to canonical names via mapping sheets.\nUnknown entities β LLM classification β user confirmation β alias saved.\n\n---\n\nβοΈ **ACTION REQUIRED**\n\n**1. Connect your `categories_mapping` Google Sheet**\nOpen `GS | Read Category Mapping` and `GS | Save Category Mapping`.\nReplace `YOUR_SPREADSHEET_ID` with the ID of your `categories_mapping` sheet.\nRequired columns: `find` Β· `replace`\n\n**2. Connect your `expense_categories` Google Sheet**\nOpen `GS | Read Allowed Categories`.\nReplace `YOUR_SPREADSHEET_ID` with the ID of your `expense_categories` sheet.\nRequired columns: `category` Β· `description` Β· `examples`\n\n**3. Connect OpenAI Credential**\nOpen `LLM | Classify Category` and connect your OpenAI API credential.\n\n**4. Connect Telegram Credential**\nOpen `TG | Confirm Category Suggestion` and connect your Telegram Bot credential.\n\n**5. Set your Bot Token in HTTP nodes**\nOpen `HTTP | Send Category Selection Message`.\nReplace `{{YOUR_BOT_TOKEN}}` in the URL with your actual Telegram Bot Token."},"typeVersion":1},{"id":"90172522-bfdb-4fa7-8ef3-48c2ee914994","name":"StickyNote_Layer4","type":"n8n-nodes-base.stickyNote","position":[8688,0],"parameters":{"color":4,"width":1712,"height":2592,"content":"## π LAYER 4 β QUERY ENGINE\nLoads expense data from Google Sheets.\nMerges resolved intent with raw data for filtering.\n\n---\n\nβοΈ **ACTION REQUIRED**\n\n**1. Connect your `expenses` Google Sheet**\nOpen `GS | Load Expenses` and replace `YOUR_SPREADSHEET_ID` with your expenses sheet ID.\nRequired columns: `date` Β· `amount` Β· `category` Β· `description` Β· `common_expense` Β· `Person`\n\n**2. Connect your `expense_categories` Google Sheet**\nOpen `GS | Load Categories` and replace `YOUR_SPREADSHEET_ID` with your categories sheet ID.\nRequired columns: `category` Β· `description` Β· `examples`"},"typeVersion":1},{"id":"4e94aa93-bca3-4087-b52c-de55b3f7be23","name":"StickyNote_Layer5","type":"n8n-nodes-base.stickyNote","position":[10400,0],"parameters":{"color":2,"width":564,"height":2592,"content":"## π΄ LAYER 5 β ANALYTICS & AGGREGATION\nApplies date, person, category and common_only filters.\nComputes totals, category breakdown and person breakdown.\n\n---\n\nβοΈ **ACTION REQUIRED**\n\nNo setup required for this layer.\nThe `JS | Filter & Aggregate` node works automatically based on the resolved intent from the previous layers.\nIf you add custom columns to your expenses sheet, update the filter logic in this node accordingly."},"typeVersion":1},{"id":"a566ae23-972c-4c15-aa4d-b2ea0e14c8f4","name":"StickyNote_Layer6","type":"n8n-nodes-base.stickyNote","position":[10976,0],"parameters":{"color":6,"width":560,"height":2592,"content":"## π£ LAYER 6 β RESPONSE\nFormats the final message and sends it back via Telegram.\n\n---\n\nβοΈ **ACTION REQUIRED**\n\n**1. Connect Telegram Credential**\nOpen `TG | Send Reply` and connect your Telegram Bot credential.\n\n**2. Adjust the message format (optional)**\nOpen `JS | Format Response Message` to customise the output format, currency symbol, or language of the reply message."},"typeVersion":1},{"id":"ed4de5df-0c7f-49e6-9580-9bcec3a29756","name":"StickyNote_Layer","type":"n8n-nodes-base.stickyNote","position":[3712,1296],"parameters":{"color":6,"width":4970,"height":1296,"content":"## π‘ LAYER 3b β ENTITY RESOLUTION: PERSONS\n\n\n---\n\nβοΈ **ACTION REQUIRED**\n\n**1. Connect your `person_mapping` Google Sheet**\nOpen `GS | Read Person Mapping` and `GS | Save Person Mapping`.\nReplace `YOUR_SPREADSHEET_ID` with the ID of your `person_mapping` sheet.\nRequired columns: `find` Β· `replace`\n\n**2. Connect your `list_persons` sheet**\nOpen `GS | Read Allowed Persons`.\nThis reads from the `list_persons` tab of the same `person_mapping` spreadsheet.\nRequired column: `person`\n\n**3. Connect OpenAI Credential**\nOpen `LLM | Classify Person` and connect your OpenAI API credential.\n\n**4. Connect Telegram Credential**\nOpen `TG | Confirm Person Suggestion` and connect your Telegram Bot credential.\n\n**5. Set your Bot Token in HTTP nodes**\nOpen `HTTP | Send Person Selection Message`.\nReplace `{{YOUR_BOT_TOKEN}}` in the URL with your actual Telegram Bot Token."},"typeVersion":1},{"id":"453a9f82-a297-4fb6-adc1-08f2e5619986","name":"DOC | Layer 1 β Input","type":"n8n-nodes-base.stickyNote","position":[1968,464],"parameters":{"color":5,"width":540,"height":500,"content":"\n\n**Purpose:** Entry point of the workflow. Receives all incoming Telegram events and routes them to the correct processing path.\n\n**What happens here:**\n- `MSG | Telegram Inbound` β Listens for incoming Telegram messages and callback queries (inline button taps).\n- `IF | Message or Callback?` β Splits the flow: regular text messages go to intent parsing, callback queries (button responses) are handled separately.\n- `IF | User Authorized?` β Checks whether the sender's chat ID is on the allowlist. Unauthorized users are silently dropped.\n- `IF | Category or Person Callback?` β Determines whether an incoming callback is a category or person selection and routes accordingly.\n- `JS | Read Category Callback` / `JS | Read Person Callback` β Extracts the selected value and the resume URL from the callback payload.\n- `HTTP | Forward Category/Person Selection` β Resumes the waiting workflow branch by POSTing the selected value to the internal webhook.\n- `TG | Confirm Category/Person Selection` β Sends a short confirmation message to the user acknowledging their selection."},"typeVersion":1},{"id":"a1283c4a-bad8-4116-a97c-0d2bbaa0f5ef","name":"DOC | Layer 2 β Intent Parsing","type":"n8n-nodes-base.stickyNote","position":[1984,1664],"parameters":{"width":412,"height":392,"content":"\n**Purpose:** Converts the user's free-text message into a structured JSON object that all downstream layers can work with.\n\n**What happens here:**\n- `LLM | Parse Intent` β Sends the raw message to GPT-4.1-nano with a strict system prompt. Returns a JSON object containing: intent, time_reference, explicit dates, person, category, common_only flag, comparison type and group_by dimension.\n- `JS | Extract Intent JSON` β Cleans the raw LLM response (strips markdown fences if present) and parses it into a proper JSON node output. Falls back to a safe empty-intent object if parsing fails."},"typeVersion":1},{"id":"f593b6d4-1dce-475b-928d-7c9850bda508","name":"DOC | Layer 3a β Entity Resolution: Categories","type":"n8n-nodes-base.stickyNote","position":[4384,128],"parameters":{"color":3,"width":540,"height":644,"content":"\n\n**Purpose:** Resolves raw category strings from the intent into valid canonical category names as defined in the categories master sheet.\n\n**What happens here:**\n- `IF | Category Present?` β Skips the entire branch if no category was mentioned.\n- `SPLIT | Split Categories` β Splits multi-category intents into individual items for per-item processing.\n- `GS | Read Category Mapping` β Loads the aliasβcanonical mapping sheet (e.g. \"essen\" β \"food\").\n- `MERGE | Join Categories with Mapping` + `SET | Normalize Category` β Applies alias replacement where available, keeps original otherwise.\n- `GS | Read Allowed Categories` + `MERGE | Check Category against Allowed` β Validates the normalised category against the master list.\n- `IF | Category Known?` β Known categories pass through directly; unknown ones enter the LLM resolution loop.\n- `LOOP | Iterate Categories` β Processes unresolved categories one by one.\n- `LLM | Classify Category` β GPT maps the unknown input to the closest canonical category from the allowed list.\n- `TG | Confirm Category Suggestion` β Sends the LLM suggestion to the user as a Yes/No confirmation.\n- `IF | Category Suggestion Accepted?` β On confirmation the mapping is saved; otherwise an inline-button picker is shown (`JS | Build Category Inline Buttons` β `HTTP | Send Category Selection Message` β `WAIT | Wait for Category Selection`).\n- `GS | Save Category Mapping` β Persists the newly learned alias for future queries."},"typeVersion":1},{"id":"1b9d5cb8-c8b9-4afd-919f-ef51ffbd2109","name":"DOC | Layer 3b β Entity Resolution: Persons","type":"n8n-nodes-base.stickyNote","position":[4448,1376],"parameters":{"color":6,"width":572,"height":556,"content":"**Purpose:** Mirrors Layer 3a but for person names β resolves nicknames and unknown names to canonical person records.\n\n**What happens here:**\n- `IF | Person Present?` β Skips the branch entirely when no person was specified.\n- `SPLIT | Split Persons` β Splits the person field into individual items.\n- `GS | Read Person Mapping` β Loads the aliasβcanonical person mapping (e.g. \"nickname\" β \"Full Name\").\n- `MERGE | Join Persons with Mapping` + `SET | Normalize Person` β Applies alias replacement where available.\n- `GS | Read Allowed Persons` + `MERGE | Check Person against Allowed` β Validates the resolved name against the known persons list.\n- `IF | Person Known?` β Known persons pass through; unknown ones enter the LLM resolution loop.\n- `LOOP | Iterate Persons` β Processes unresolved person names one at a time.\n- `LLM | Classify Person` β GPT maps the unknown name to the nearest known person from the allowed list.\n- `TG | Confirm Person Suggestion` β Asks the user to confirm the suggestion via Yes/No button.\n- `IF | Person Suggestion Accepted?` β On confirmation the mapping is saved; otherwise a button picker is shown (`JS | Build Person Inline Buttons` β `HTTP | Send Person Selection Message` β `WAIT | Wait for Person Selection`).\n- `GS | Save Person Mapping` β Stores the new alias for future use."},"typeVersion":1},{"id":"c7f0bb66-e951-4962-ac01-999e805c887c","name":"DOC | Layer 4 β Query Engine","type":"n8n-nodes-base.stickyNote","position":[8704,416],"parameters":{"color":4,"width":524,"height":428,"content":"\n**Purpose:** Assembles the fully resolved intent and loads raw expense data from Google Sheets, ready for filtering.\n\n**What happens here:**\n- `SET | Assemble Resolved Intent` β Merges canonical category and person values back into the original intent object, producing one clean query descriptor.\n- `GS | Load Expenses` β Reads all rows from the expenses Google Sheet (date, amount, category, person, common_expense flag).\n- `GS | Load Categories` β Loads the categories reference sheet in parallel (used for validation during filtering).\n- `MERGE | Combine Expenses + Categories` β Combines both data sources into a single output so the next layer has everything it needs."},"typeVersion":1},{"id":"4f17b844-0e95-469e-a6b3-b33edf5b96f7","name":"DOC | Layer 5 β Analytics & Aggregation","type":"n8n-nodes-base.stickyNote","position":[10448,480],"parameters":{"color":2,"width":348,"height":340,"content":"\n**Purpose:** Applies all filters from the resolved intent to the expense data and computes the requested aggregations.\n\n**What happens here:**\n- `JS | Filter & Aggregate` β Single code node that does everything: resolves relative date references (this_week, last_month, β¦) into concrete ranges, applies person / category / common_only filters row by row, then computes total amount, transaction count, and optional breakdowns by category or person (driven by the group_by field in the intent)."},"typeVersion":1},{"id":"6d8d1b8c-93db-4374-9240-b01174d354d5","name":"DOC | Layer 6 β Response","type":"n8n-nodes-base.stickyNote","position":[11008,128],"parameters":{"color":6,"width":460,"height":308,"content":"**Purpose:** Formats the aggregation result into a human-readable Telegram message and delivers it back to the user.\n\n**What happens here:**\n- `JS | Format Response Message` β Builds the final message string: period header, applied filters, total amount, transaction count, and β if requested β a sorted category or person breakdown with percentage shares. Handles the empty-result case gracefully.\n- `TG | Send Reply` β Sends the formatted message as a Markdown reply to the original chat, referencing the triggering message ID so it appears as a thread reply."},"typeVersion":1},{"id":"9c55a9fc-45aa-4ac4-a94f-7736abee4e73","name":"Sticky Note","type":"n8n-nodes-base.stickyNote","position":[1008,0],"parameters":{"color":7,"width":912,"height":2576,"content":"# π WORKFLOW OVERVIEW\n\n## π© Input & Security (Layer 1)\n\nEvery incoming Telegram message or button tap is received by the trigger.\n\nThe workflow checks whether the sender's **Chat ID** is on the approved list β unauthorized users are silently ignored.\n\nCallback responses (inline button taps for category or person disambiguation) are also handled here and routed back into the correct waiting branch.\n\n---\n\n## π¦ Intent Parsing (Layer 2)\n\nThe raw message is sent to **GPT-4.1-nano** with a strict system prompt.\n\nThe model returns a structured JSON object containing:\n\n- `intent`\n- `time_reference`\n- `explicit_date_range`\n- `person`\n- `category`\n- `common_only`\n- `comparison_type`\n- `group_by`\n\nA fallback **Code Node** cleans the response and handles parsing errors gracefully.\n\n---\n\n## π¨ Entity Resolution\n\n### Layer 3a β Categories \n### Layer 3b β Persons\n\nRaw category strings and person names from the intent are resolved to canonical values using **Google Sheets mapping tables**.\n\nIf a value is unknown:\n\n1. GPT classifies it against the allowed list \n2. The user is asked to confirm via a **Yes/No Telegram button**\n\nIf the user rejects the suggestion, an **inline button picker** is shown instead.\n\nEvery confirmed match is saved back to the mapping sheet so future queries resolve automatically.\n\n**The system learns over time.**\n\n---\n\n## π§ Query Engine (Layer 4)\n\nThe fully resolved intent is assembled into one clean **query descriptor**.\n\nAll expense rows and the category reference list are loaded from **Google Sheets in parallel** and merged into a single data object.\n\n---\n\n## π₯ Analytics & Aggregation (Layer 5)\n\nA single **Code Node** applies all filters:\n\n- date range \n- relative time references (last month, this week, etc.) \n- person \n- category \n- `common_only` flag \n\nIt then computes:\n\n- **total amount**\n- **transaction count**\n- optional **breakdowns**\n\nBreakdowns are calculated depending on the `group_by` field:\n\n- by **category**\n- by **person**\n\n---\n\n## πͺ Response (Layer 6)\n\nThe result is formatted into a clean **Markdown message** including:\n\n- period \n- applied filters \n- total \n- count \n- optional breakdowns with percentage shares \n\nThe reply is sent directly back to the user's **Telegram chat** as a thread reply to the original message."},"typeVersion":1},{"id":"14b52ff0-e0db-42be-8f44-066315ba7642","name":"Sticky Note1","type":"n8n-nodes-base.stickyNote","position":[-16,0],"parameters":{"color":7,"width":1024,"height":2576,"content":"# HOW IT WORKS β AI TELEGRAM EXPENSE TRACKER (QUERY MODE)\n\nThis workflow answers natural language expense questions from Telegram using AI.\n\nInstead of opening spreadsheets, simply send a message like:\n\n- *How much did I spend on food last month?*\n- *What did we spend together this week?*\n- *Show me a breakdown by category for January.*\n\nThe workflow understands your question, resolves ambiguous names or categories, queries your expense data, and replies with a formatted summary β automatically.\n\n\n\n# π WHAT YOU NEED\n\nFour **Google Sheets** are required before using this workflow.\n\n## π WHAT YOU NEED\n\nFive Google Sheets are required before using this workflow.\n\n---\n\n**expenses**\n\nColumns:\n- date (date of expense)\n- amount (amount of expense)\n- category (expense category - e.g. supermarket)\n- description (description of the expense)\n- common_expense (true if it was an expense for all people - e.g. in a household)\n- Person - (Who made the expense)\n\nPurpose:\nMain expense table containing all recorded transactions.\n\n---\n\n**expense_categories**\n\nColumns:\n- category\n- description\n- examples\n\nPurpose:\nReference list of allowed expense categories used for classification.\n\n---\n\n**categories_mapping**\n\nColumns:\n- find\n- replace\n\nPurpose:\nMaps user aliases to canonical categories.\n\nExample:\n- sushi β food\n\n---\n\n**person_mapping**\n\nColumns:\n- find\n- replace\n\nPurpose:\nMaps nicknames or aliases to canonical person names.\n\nExample:\n- Johnny -> John\n\n---\n\n**list_persons**\n\nColumns:\n- person\n- description\n\nPurpose:\nReference list of all persons that can appear in the expense data.\n\n---\n\n# π‘ KEY FEATURES\n\n- Natural language queries in **German & English**\n- AI-powered intent parsing with **GPT-4.1-nano**\n- **Self-learning category & person resolution system**\n- Interactive disambiguation via **Telegram inline buttons**\n- Relative date support: this week, last month, this year\n- Group-by breakdowns: by category or by person\n- `common_only` filter for shared expense queries\n\n---\n\n# β MULTI-USER SETUP\n\nThis workflow supports **multiple users simultaneously**.\n\nAdd all approved **Chat IDs** in the node:\n\nIF | User Authorized?\n\nEach expense query is automatically scoped to the sender unless:\n\n- a specific **person** is mentioned\n- or **\"we\"** is used.\n\nShared expenses are identified by the column:\n\ncommon_expense (boolean true / false)\n\nQueries like:\n\n*\"what did we spend together?\"*\n\nautomatically filter for **shared entries only**."},"typeVersion":1}],"active":false,"pinData":{},"settings":{"executionOrder":"v1"},"versionId":"","connections":{"GS | Load Expenses":{"main":[[{"node":"MERGE | Combine Expenses + Categories","type":"main","index":0}]]},"IF | Person Known?":{"main":[[{"node":"MERGE | Combine Persons with List","type":"main","index":0}],[{"node":"MERGE | Loop Entry (Person)","type":"main","index":1}]]},"LLM | Parse Intent":{"main":[[{"node":"JS | Extract Intent JSON","type":"main","index":0}]]},"GS | Load Categories":{"main":[[{"node":"MERGE | Combine Expenses + Categories","type":"main","index":1}]]},"IF | Category Known?":{"main":[[{"node":"MERGE | Combine Categories with List","type":"main","index":0}],[{"node":"MERGE | Loop Entry (Category)","type":"main","index":1}]]},"IF | Person Present?":{"main":[[{"node":"MERGE | Combine Person Mapping","type":"main","index":0}],[{"node":"SPLIT | Split Persons","type":"main","index":0},{"node":"GS | Read Person Mapping","type":"main","index":0},{"node":"GS | Read Allowed Persons","type":"main","index":0}]]},"IF | User Authorized?":{"main":[[{"node":"LLM | Parse Intent","type":"main","index":0}]]},"LLM | Classify Person":{"main":[[{"node":"SET | Extract LLM Person","type":"main","index":0}]]},"SPLIT | Split Persons":{"main":[[{"node":"MERGE | Join Persons with Mapping","type":"main","index":0}]]},"IF | Category Present?":{"main":[[{"node":"MERGE | Combine Category + Person","type":"main","index":0}],[{"node":"SPLIT | Split Categories","type":"main","index":0},{"node":"GS | Read Category Mapping","type":"main","index":0},{"node":"GS | Read Allowed Categories","type":"main","index":0}]]},"LOOP | Iterate Persons":{"main":[[{"node":"MERGE | Loop Entry (Person)","type":"main","index":0}],[{"node":"LLM | Classify Person","type":"main","index":0}]]},"MSG | Telegram Inbound":{"main":[[{"node":"IF | Message or 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