{"workflow":{"id":13568,"name":"Analyze, classify, and summarize Gmail with OpenAI RAG and Google Sheets","views":64,"recentViews":0,"totalViews":64,"createdAt":"2026-02-21T08:23:41.405Z","description":"

πŸ“§ Analyze, classify, and summarize emails using RAG (automatic taxonomy learning)

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\nThis workflow automatically reads incoming Gmail messages, analyzes them using AI with a retrieval-augmented\nclassification system (RAG), organizes emails into structured categories, stores results in Google Sheets,\nand even generates an audio summary sent directly to Telegram.\n

\n\n
\n\n

βš™οΈ How It Works

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\nThe workflow creates an intelligent email processing pipeline combining Gmail, OpenAI, vector embeddings,\nand Google Sheets to continuously learn and improve email categorization accuracy.\n

\n\n
    \n
  1. \nEmail Trigger
    \nThe workflow starts automatically whenever a new email arrives in Gmail using the Gmail Trigger node.\n
    2. \n\n
  2. \nEmail Extraction & Cleaning
    \nThe full email content, sender information, subject, and timestamp are retrieved and normalized.\nHTML formatting is removed so the AI receives clean text data.\n
    3. \n\n
  3. \nKnowledge Base Loading (RAG)
    \nEmail tagging examples stored in a Google Sheets document are loaded and converted into embeddings.\nThese examples act as the official taxonomy for categories and subcategories.\n
    4. \n\n
  4. \nVector Store Creation
    \nThe workflow builds an in-memory vector database containing historical tagging samples.\nThis allows semantic similarity search during classification.\n
    5. \n\n
  5. \nAI Analysis Agent
    \nAn OpenAI-powered AI Agent performs multiple tasks in a single step:\n\n\nThe agent strictly prioritizes retrieved taxonomy examples to maintain consistent labeling.\n
    6. \n\n
  6. \nStructured Data Storage
    \nProcessed email data β€” including summary, category, keywords, and metadata β€” is appended to a Google Sheets log,\ncreating a searchable email intelligence database.\n
    7. \n\n
  7. \nSelf-Learning Taxonomy
    \nIf the AI determines a new category or subcategory is required:\n\nThis allows the system to evolve over time.\n
    8. \n\n
  8. \nAudio Summary Generation
    \nThe summarized email text is converted into speech using OpenAI audio generation.\n
    9. \n\n
  9. \nTelegram Notification
    \nThe generated audio summary is automatically delivered to Telegram, allowing quick email review without reading.\n
    10. \n
\n\n
\n\n

🧩 Features

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πŸš€ Setup Steps

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1. Required Accounts

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2. Configure Credentials in n8n

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Create the following credentials inside n8n:

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3. Prepare Google Sheets

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Sheet 1 β€” Email Log

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Sheet 2 β€” Tagging Samples (Knowledge Base)

\n\n\n

\nThis sheet acts as the classification training reference used by the vector store.\n

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4. Configure Telegram

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5. Connect OpenAI

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6. Activate the Workflow

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πŸ’‘ Recommended Usage

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\n\n

🧠 Architecture Notes

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\nThis workflow uses a Retrieval-Augmented Generation (RAG) pattern inside n8n by combining\nGoogle Sheets as a lightweight knowledge base with an in-memory vector store.\nInstead of relying purely on model reasoning, classification decisions are grounded\nin previously approved examples, producing stable and explainable categorization.\n

\n\n

\nOver time, the workflow becomes smarter as new categories are automatically added,\neffectively turning your inbox into a continuously learning AI system.\n

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Provide a confidence score between 0 and 1 indicating classification certainty.\n\n--------------------------------------------------\nCLASSIFICATION RULES (STRICT)\n--------------------------------------------------\n\n- ALWAYS prefer categories and subcategories found in retrieved examples.\n- DO NOT invent new categories or subcategories unless no reasonable semantic match exists.\n- Retrieved examples have higher authority than general model knowledge.\n- If similarity is weak, still choose the closest existing category and LOWER the confidence score.\n- Only create a new category or subcategory when NO retrieved example reasonably matches the email topic.\n\nDecision priority order:\n1) Retrieved vector store examples (highest priority)\n2) Semantic meaning of the email\n3) General reasoning (last resort)\n\n--------------------------------------------------\nTAXONOMY CONSISTENCY CHECK (MANDATORY)\n--------------------------------------------------\n\nAfter selecting a category and subcategory:\n\n1. Compare the selected category against ALL categories present in the retrieved examples from \"Simple Vector Store1\".\n\n2. If the selected category DOES NOT appear in retrieved examples from knowledge_base and you generated new one:\n set new_category = true\n otherwise set new_category = false.\n\n3. If the category exists but the selected subcategory DOES NOT appear in retrieved examples from knowledge_base and you generated new one:\n set new_subcategory = true\n otherwise set new_subcategory = false.\n\n4. This decision MUST be based ONLY on retrieved examples, not on general knowledge.\n\n--------------------------------------------------\nCONFIDENCE SCORING\n--------------------------------------------------\n\n0.90–1.00 β†’ strong semantic match with retrieved examples\n0.70–0.89 β†’ clear topic with minor ambiguity\n0.50–0.69 β†’ weak similarity but reasonable classification\n0.00–0.49 β†’ uncertain classification\n\nLower confidence when retrieved similarity is weak or ambiguous.\n\n--------------------------------------------------\nOUTPUT REQUIREMENTS (CRITICAL)\n--------------------------------------------------\n\n- Output MUST be valid JSON ONLY.\n- DO NOT include markdown, explanations, comments, or extra text.\n- DO NOT remove, rename, or modify any existing input fields.\n- Preserve all original fields exactly as received.\n- Append ONLY the following fields:\n\n{\n \"summary\": \"short paragraph summary\",\n \"short_message\": \"1 long line summary\",\n \"category\": \"main 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When a new email arrives, the workflow extracts and normalizes its content, then sends it to an AI agent powered by an OpenAI model.\n\nThe agent retrieves examples from a vector database built from a Google Sheets taxonomy dataset. These examples act as the source of truth for email categories and subcategories. Using this context, the AI summarizes the email, assigns a category and subcategory, extracts keywords, and calculates a confidence score.\n\nProcessed emails are stored in a Google Sheet for tracking and analytics. If the AI detects a previously unseen category or subcategory, the workflow automatically expands the taxonomy dataset and notifies the administrator. Optionally, an audio version of the email summary is generated and sent via Telegram.\n\n## Setup steps\n\n1. Connect Gmail credentials for the trigger node.\n2. Connect OpenAI credentials for chat, embeddings, and audio generation.\n3. Connect Google Sheets credentials and duplicate the provided sheets.\n4. Replace Telegram chat IDs with your own.\n5. Run the workflow once manually to populate the vector store before activating it.\n\nAfter setup, the workflow continuously learns and improves email classification over time."},"typeVersion":1}],"pinData":{},"connections":{"Gmail Trigger":{"main":[[{"node":"Fetch full Gmail message","type":"main","index":0}]]},"OpenAI Chat Model":{"ai_languageModel":[[{"node":"Analyze email with RAG agent","type":"ai_languageModel","index":0}]]},"Get row(s) in sheet":{"main":[[{"node":"Vector store β€” taxonomy index","type":"main","index":0}]]},"Get next taxonomy ID":{"main":[[{"node":"Append new taxonomy entry","type":"main","index":0}]]},"Load taxonomy dataset":{"main":[[{"node":"Get next taxonomy ID","type":"main","index":0}]]},"Store processed email":{"main":[[{"node":"Check for new taxonomy items","type":"main","index":0}]]},"Generate audio summary":{"main":[[{"node":"Send audio summary to Telegram","type":"main","index":0}]]},"Normalize email fields":{"main":[[{"node":"Analyze email with RAG 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