Étiquetage et analyse automatiques des retours clients dans Google Sheets avec OpenAI

• •Informations d'identification Google Sheets API
• •Clé API OpenAI

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            {
              "role": "system",
              "content": "=You are a tagging, sentiment, and emotion analysis assistant.  \n\nInput format:\n- You will receive multiple feedbacks in an array under `feedbacks`.\n- Each feedback item has: `row_number` and `text`.\n- Feedback text may be multilingual (e.g., Persian/Farsi). Analyze natively.\n- You will also receive an `allowed_tags` array (may be empty).\n\nGeneral rules:\n- Analyze each feedback independently.\n- Detect and describe emotional tone naturally.\n- Identify one **primary emotion** that captures the dominant feeling.\n- Optionally include up to two **secondary emotions** that reflect additional nuances.\n- Output must be a **valid JSON array** only, with **no extra text** before or after.\n- Maintain the original input order.\n\n⚠️ CRITICAL TAGGING RULES (2-tier system with STRONG preference for allowed_tags):\n\n**TIER 1: ALWAYS TRY ALLOWED TAGS FIRST (Your Primary Responsibility)**\n- If `allowed_tags` is provided, you MUST thoroughly examine it for matches.\n- **Be creative and flexible in matching:**\n  - Look for direct matches (e.g., \"Bug\" for bug reports)\n  - Look for semantic matches (e.g., \"Payment\" tag for \"billing issue\" feedback)\n  - Look for category matches (e.g., \"Performance\" for \"slow\", \"lag\", \"loading\")\n  - Look for broader themes (e.g., \"User Experience\" for usability complaints)\n- **Think broadly:** A tag doesn't need to be a perfect word-for-word match.\n- Consider the **intent and topic** of the feedback, not just exact keywords.\n- Return up to **3** most relevant tags from `allowed_tags` (sorted by relevance).\n- **Only proceed to Tier 2 if you genuinely cannot find ANY relevant match** after careful analysis.\n\n**TIER 2: AI-Generated Tags (ONLY as a last resort)**\n- Use this ONLY when:\n  - `allowed_tags` is completely empty, OR\n  - After thorough analysis, absolutely NO tag in `allowed_tags` relates to the feedback topic\n- Generate up to **2 custom, descriptive tags** (short labels like \"UI Bug\", \"Pricing Question\").\n- Put these in `ai_tag_1` and `ai_tag_2`.\n- Use \"\" for empty AI tag fields.\n\n**Decision Flow:**\n1. Read the feedback carefully\n2. Review ALL allowed_tags thoroughly\n3. Can you match any tag semantically or thematically? → YES = Use allowed tags, set ai_tag_1=\"\", ai_tag_2=\"\"\n4. Still no match after careful review? → Use AI tags as fallback\n\nSentiment scale (choose one):\n[\"Very Negative\", \"Negative\", \"Neutral\", \"Positive\", \"Very Positive\"].\n\nEmotions:\n- primary_emotion: one clear emotional label (e.g., \"anger\", \"hope\", \"disappointment\", \"relief\", \"satisfaction\", \"gratitude\").\n- secondary_emotion: optional supporting emotional state (e.g., \"frustration\", \"trust\", \"curiosity\", \"anxiety\"). Use \"\" if none applies.\n\nResponse JSON schema (use exactly these keys):\n[\n  {\n    \"row_number\": <row_number from input>,\n    \"tags\": [\"tag_from_allowed_list_1\", \"tag_from_allowed_list_2\"],\n    \"ai_tag_1\": \"\",\n    \"ai_tag_2\": \"\",\n    \"sentiment\": \"Positive\",\n    \"primary_emotion\": \"Gratitude\",\n    \"secondary_emotion\": \"Relief\"\n  }\n]\n\nExamples of GOOD matching behavior:\n✅ Feedback: \"The app crashes constantly\" + allowed_tags: [\"Bug\", \"Stability\", \"Feature\"] → tags: [\"Bug\", \"Stability\"]\n✅ Feedback: \"Can't complete checkout\" + allowed_tags: [\"Payment\", \"Technical Issue\", \"UI\"] → tags: [\"Payment\", \"Technical Issue\"]\n✅ Feedback: \"Love the new design!\" + allowed_tags: [\"Design\", \"Positive Feedback\", \"UI\"] → tags: [\"Design\", \"Positive Feedback\"]\n✅ Feedback: \"Too expensive for what it offers\" + allowed_tags: [\"Pricing\", \"Value\", \"Feature Request\"] → tags: [\"Pricing\", \"Value\"]\n\nExamples of when to use AI tags:\n❌ Feedback: \"Bug in login\" + allowed_tags: [] → tags: [], ai_tag_1: \"Authentication Bug\", ai_tag_2: \"Login\"\n❌ Feedback: \"Need dark mode\" + allowed_tags: [\"Pricing\", \"Support\", \"Payment\"] → tags: [], ai_tag_1: \"Feature Request\", ai_tag_2: \"UI Customization\"\n\nConstraints:\n- **STRONGLY PREFER allowed_tags** - exhaust all matching possibilities first\n- Do **not** invent tags in the `tags` array — only use exact names from `allowed_tags`\n- Generate creative tags for `ai_tag_1` and `ai_tag_2` ONLY when truly necessary\n- Use ASCII keys exactly as shown. Values must be strings or arrays of strings\n- Do not include reasoning, markdown, or comments — **JSON array only**"
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            "Tag 2": "={{ $json.tags[1] || ''}}",
            "Tag 3": "={{ $json.tags[2] || ''}}",
            "Status": "={{ $json.tags && $json.tags.length > 0 ? 'Updated' : 'Needs Review' }}",
            "AI Tag 1": "={{ $json.ai_tag_1 }}",
            "AI Tag 2": "={{ $json.ai_tag_2 }}",
            "Sentiment": "={{ $json.sentiment }}",
            "row_number": "={{ $json.row_number }}",
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            "Updated Date (N8N)": "={{ new Date().toISOString().split('T')[0] }}"
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      "name": "Traiter les retours par lots",
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        "batchSize": "=10"
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    {
      "id": "12824491-a5d8-42ba-b602-5ad577d0108c",
      "name": "Agréger les éléments du lot",
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      "parameters": {
        "jsCode": "// Aggregate all items in the current batch into a single item\nconst feedbackBatch = $input.all().map((item, index) => ({\n  id: index,\n  row_number: item.json.row_number,\n  text: item.json.Feedbacks\n}));\n\nconst tags = $input.first().json.tags;\n\nreturn {\n  json: {\n    feedbackBatch,\n    tags\n  }\n};"
      },
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      "id": "54ebf56d-27ec-411d-9973-10a717921e47",
      "name": "Diviser les résultats du lot",
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        "jsCode": "// Parse the OpenAI response and split it back into individual items\nconst response = JSON.parse($input.first().json.message.content);\n\n// Handle both array format and object with results property\nconst results = Array.isArray(response) ? response : response.results || [];\n\nreturn results.map(result => ({\n  json: {\n    row_number: result.row_number,\n    tags: result.tags || [],\n    sentiment: result.sentiment || \"\",\n    ai_tag_1: result.ai_tag_1 || \"\",\n    ai_tag_2: result.ai_tag_2 || \"\",\n    primary_emotion: result.primary_emotion || \"\",\n    secondary_emotion: result.secondary_emotion || \"\"\n  }\n}));"
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        "content": "## 💬 **Auto-tag customer feedback in Google Sheets with OpenAI sentiment analysis**\n\nThis workflow automatically tags user feedback stored in Google Sheets using OpenAI with sentiment and emotion analysis.  \nIt reads new feedback, analyzes them in **batches** with a single AI call, and returns relevant tags with detailed insights.\n\n---\n\n### 🧠 What It Does\n- Fetches **allowed tags** from a Tags Sheet  \n- Reads **new feedback** (Status = empty) from a Feedbacks Sheet  \n- **Sends entire batches (e.g., 10 items) to OpenAI as a single request** 🚀\n- Returns up to **3 most relevant tags** from allowed list (or AI-generated tags as fallback)\n- Analyzes **sentiment** (Very Negative to Very Positive)\n- Detects **emotions** (primary & secondary feelings)\n- Writes all results back to Google Sheets (tags, sentiment, emotions, status, timestamp)\n\n---\n\n### 🚀 How to Set Up  \n1. Duplicate the provided [Google Sheet template](https://docs.google.com/spreadsheets/d/1y7B3u5vgQLDidf-NdfPgAiBuQxP9Qa7RvdgsTPG14Fs/edit?usp=sharing).\n2. Connect your **Google Sheets** and **OpenAI** credentials in n8n.  \n3. Update the Sheet URLs in the **Fetch Allowed Tags**, **Fetch New Feedbacks**, and **Update Google Sheet** nodes.  \n4. Run manually with the **Manual Trigger** or enable the **Schedule Trigger** (runs every 60 minutes).  \n\n---\n\n### 🎨 How to Customize  \n- Adjust the **System Prompt** in the \"Tag Feedbacks with AI\" node to change tagging rules.  \n- Edit the **batch size** (default: 10) in the \"Process Feedbacks in Batches\" node for performance optimization.  \n- Change the **schedule interval** in the \"Schedule Trigger\" node (default: 60 minutes).  \n- Extend the workflow to send results to Notion, Slack, or Airtable for real-time reporting.\n\n---\n\n#### 💡 Example Use Case\nIdeal for product and research teams who collect customer feedback and want to categorize it automatically into clear, actionable topics with sentiment and emotional insights."
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        "content": "### 🏷️ **Fetch Allowed Tags**\n\nReads the list of available tag names from your **Tags Sheet**.  \nEach tag is stored in an array and later passed to OpenAI as `allowed_tags`.\n\n✅ Make sure your Tags sheet has a single column named `Tags`."
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        "content": "## 💬 **Fetch New Feedbacks**\n\nReads feedback entries from **Feedbacks Sheet** where `Status` is empty.\n\n### Expected Columns\n- **Feedbacks:** The feedback text\n- **Status:** Empty = new | \"Updated\" = tagged | \"Needs Review\" = no tags found\n- **Tag 1, Tag 2, Tag 3:** Up to 3 tags from allowed list\n- **AI Tag 1, AI Tag 2:** Fallback tags when no allowed tags match\n- **Sentiment:** Very Negative to Very Positive\n- **Primary Emotion:** Dominant feeling (e.g., frustration, gratitude)\n- **Secondary Emotion:** Optional secondary feeling\n- **Updated Date (N8N):** Processing timestamp\n\n✅ All columns must exist with exact names."
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        "content": "## 🤖 **Tag Feedbacks with OpenAI**\n\nSends entire batch (10 items) as ONE OpenAI request. 🚀\n\n**Output per feedback:**\n- Up to 3 tags from allowed_tags (preferred)\n- Up to 2 AI-generated tags (fallback only)\n- Sentiment: Very Negative to Very Positive\n- Primary & secondary emotions\n- Multilingual support (including Persian/Farsi)\n\n✅ Returns valid JSON array."
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        "content": "## 📊 **Update Google Sheet (Tagged)**\n\nUpdates **Feedbacks Sheet** with:\n- `Tag 1-3` (from allowed list)\n- `AI Tag 1-2` (fallback tags)\n- `Sentiment`\n- `Primary Emotion` & `Secondary Emotion`\n- `Status` (\"Updated\" or \"Needs Review\")\n- `Updated Date (N8N)`\n\n✅ Ensure columns exist with exact names."
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        "content": "## 🔁 **Split In Batches**\n\nBreaks large feedback lists into smaller batches to avoid API rate limits.  \nAfter each batch is processed, the loop continues automatically until all rows are tagged.\n\n💡 Default batch size = 10 (you can adjust in config sheet)."
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        "content": "## 🔗 **Merge Tags & Feedbacks**\n\nThis node combines two input streams:\n- **Input 1:** Feedback items fetched from the Feedbacks Sheet  \n- **Input 2:** Allowed tag list fetched from the Tags Sheet  \n\nMode: `Choose Branch`  \nOutput: Keeps Feedback data (Input 1) while waiting for Tags (Input 2) to arrive.  \nEnsures each feedback item can access the full tag list before analysis."
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        "content": "## 🧩 **Attach Tags Array**\n\nAdds the list of tags from the Tags Sheet to each feedback item.  \nThe result includes a field named `tags`, used by OpenAI for tagging."
      },
      "typeVersion": 1
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        "content": "## 🖱️ **Manual Trigger**\n\nRuns the workflow manually when you click **\"Execute Workflow\"** inside n8n.  \nUse this for quick testing or when setting up new credentials and connections.\n\n💡 Best for testing small batches before enabling the automatic schedule."
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      "typeVersion": 1
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