Classification automatique des dépenses Revolut

• •URL et Clé API Supabase
• •Informations d'identification Google Drive API
• •Clé API OpenAI

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  "name": "Revolut Expenses Manual",
  "tags": [
    {
      "id": "ea4Bb1csGvuESWWb",
      "name": "Revolut",
      "createdAt": "2025-08-11T13:32:47.326Z",
      "updatedAt": "2025-08-11T13:32:47.326Z"
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        "content": "## Revolut Extracts Analyzer\n\n### This n8n template processes Revolut statements, normalizes transactions, and uses AI to categorize expenses automatically.\n### Use cases include detecting subscriptions, separating internal transfers, and building dashboards to track spending.\n---\n\n## How it works\n* **Get Categories from Supabase**\n* **Download & Transform**\n* **Loop Over Items**\n* **LLM Categorizer**  \n* **Insert into Supabase**\n\n---\n\n## How to use\n* Start with the **manual trigger node** or replace it with a schedule/webhook.  \n* Connect **Google Drive** to provide Revolut CSV files.  \n* Ensure **Supabase** has tables for `transactions` and `categories`.  \n* Extend with notifications, reports, or BI tools.  \n\n---\n\n## Requirements\n* Google Drive for CSV files  \n* Supabase tables for categories & transactions  \n* LLM provider (OpenAI/Gemini)"
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              "message": "=You are a financial transaction classifier and merchant extractor.\nYou will receive a raw financial transaction in JSON format.\nYou must return a single valid JSON object following exactly the required schema.\n\nAllowed categories:\n{{ $('Aggregate').item.json.normalized_name }}\n\nInstructions:\n1. Read the raw transaction.\n2. Extract a merchant_name: a clean, standardized version of the business or entity involved.\n   - If the description clearly contains a brand or business name, return it cleaned (no codes, extra spaces, or special characters).\n   - If there is no clear merchant, return \"Unknown\".\n3. Choose exactly ONE category from the allowed list based on the description, merchant, type, and amount.\n4. Set is_internal=true if the movement is between the user’s own accounts (top up, vault, transfer to self).\n5. Set is_subscription=true if the transaction appears to be a recurring service charge (streaming, memberships, etc.).\n6. Set confidence between 0.0 and 1.0 based on how certain you are about the classification.\n7. Set rule_reason as a short, clear explanation of why you chose that category and merchant.\n8. Respond only with JSON. No extra text, no explanations outside the JSON.\n\nExample output:\n{\n  \"merchant_name\": \"Spotify\",\n  \"category\": \"Subscriptions\",\n  \"is_subscription\": true,\n  \"is_internal\": false,\n  \"confidence\": 0.98,\n  \"rule_reason\": \"The description contains 'Spotify', which is a well-known recurring music subscription service.\"\n}\n"
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        "content": "## Get Categories from Supabase\nRetrieve categories from Supabase to feed into later LLM categorization phases."
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        "content": "## Download and Transform extraction\nDownload the file from Google Drive, extract and parse the CSV data, normalize and standardize its content, then generate a unique hash to ensure consistency and prevent duplicates."
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        "content": "## LLM Categorizer\nIterate over all transactions, send each to the LLM together with the previously retrieved category list, and extract the assigned category plus flags for whether it is a subscription, an internal transfer, or other relevant markers."
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        "content": "## Insert into supabase\nThen insert into supabase avoiding duplicates"
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