{
  "meta": {
    "instanceId": "workflow-8b02c26d",
    "versionId": "1.0.0",
    "createdAt": "2025-09-29T07:07:55.974764",
    "updatedAt": "2025-09-29T07:07:55.974782",
    "owner": "n8n-user",
    "license": "MIT",
    "category": "automation",
    "status": "active",
    "priority": "high",
    "environment": "production"
  },
  "nodes": [
    {
      "id": "trigger-1015f4df",
      "name": "Manual Trigger",
      "type": "n8n-nodes-base.manualTrigger",
      "typeVersion": 1,
      "position": [
        100,
        100
      ],
      "parameters": {}
    },
    {
      "id": "54065cc9-047c-4741-95f6-cec3e352abd7",
      "name": "Google Drive",
      "type": "n8n-nodes-base.googleDrive",
      "position": [
        2700,
        -1840
      ],
      "parameters": {
        "fileId": {
          "__rl": true,
          "mode": "url",
          "value": "{{ $env.WEBHOOK_URL }}"
        },
        "options": {},
        "operation": "download"
      },
      "typeVersion": 3,
      "notes": "This googleDrive node performs automated tasks as part of the workflow."
    },
    {
      "id": "62af57f5-a001-4174-bece-260a1fc595e8",
      "name": "Default Data Loader",
      "type": "n8n-nodes-base.noOp",
      "position": [
        3120,
        -1620
      ],
      "parameters": {
        "loader": "epubLoader",
        "options": {},
        "dataType": "binary"
      },
      "typeVersion": 1,
      "notes": "This documentDefaultDataLoader node performs automated tasks as part of the workflow."
    },
    {
      "id": "ce3d9c7c-6ce9-421a-b4d0-4235217cf8e6",
      "name": "Sticky Note",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        2620,
        -2000
      ],
      "parameters": {
        "width": 749.1276349295781,
        "height": 820.5109034066329,
        "content": "# INSERTING\n\n- it's important to use the same embedding model when for any interaction with your vector database (inserting, upserting and retrieval)"
      },
      "typeVersion": 1,
      "notes": "This stickyNote node performs automated tasks as part of the workflow."
    },
    {
      "id": "81cb3d3e-70af-46c8-bc18-3d076a222d0b",
      "name": "Sticky Note1",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        1720,
        -1160
      ],
      "parameters": {
        "color": 3,
        "width": 873.9739981925188,
        "height": 534.0012007720542,
        "content": "# UPSERTING\n"
      },
      "typeVersion": 1,
      "notes": "This stickyNote node performs automated tasks as part of the workflow."
    },
    {
      "id": "60ebdb71-c7e0-429b-9394-b680cc000951",
      "name": "Sticky Note2",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        1720,
        -2000
      ],
      "parameters": {
        "color": 4,
        "width": 876.5116990000852,
        "height": 821.787041589866,
        "content": "# PREPARATION (in Supabase)\n\n- your database needs the extension 'pgvector' enabled -> select Database > Extension > Search for 'vector'\n- make sure you have a table that has the following columns (if not, use the query below in the Supabase SQL Editor)\n\n```\nALTER TABLE \"YOUR TABLE NAME\"\nADD COLUMN embedding VECTOR(1536), // check which number of dimensions you need (depends on the embed model)\nADD COLUMN metadata JSONB,\nADD COLUMN content TEXT;\n```\n\n- make sure you have the right policies set -> select Authentication > Policies\n- make sure you have the custom function `match_documents` set up in Supabase -> This is needed for the Vector Store Node (as query name) \n(if not, use the query below in the Supabase SQL Editor to create that function)\n- make sure you check the size of the AI model as it should be the same vector size for the table \n(e.g. OpenAI's Text-Embedding-3-Small uses 1536)\n\n```\nCREATE OR REPLACE FUNCTION public.match_documents(\n filter JSONB,\n match_count INT,\n query_embedding VECTOR(1536) // should match same dimensions as from insertion\n)\nRETURNS TABLE (\n id BIGINT,\n content TEXT,\n metadata JSONB,\n embedding VECTOR(1536), // should match same dimensions as from insertion\n similarity FLOAT\n)\nLANGUAGE plpgsql AS $$\nBEGIN\n RETURN QUERY\n SELECT\n v.id,\n v.content,\n v.metadata,\n v.embedding,\n 1 - (v.embedding <=> match_documents.query_embedding) AS similarity\n FROM \"YOUR TABLE NAME\" v\n WHERE v.metadata @> filter\n ORDER BY v.embedding <=> match_documents.query_embedding\n LIMIT match_count;\nEND;\n$$\n;\n```\n"
      },
      "typeVersion": 1,
      "notes": "This stickyNote node performs automated tasks as part of the workflow."
    },
    {
      "id": "ae95b0c3-b8b3-44eb-8070-b1bc6cac5cd2",
      "name": "Sticky Note3",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        3400,
        -2000
      ],
      "parameters": {
        "color": 5,
        "width": 810.9488123113013,
        "height": 821.9537074055816,
        "content": "# RETRIEVAL"
      },
      "typeVersion": 1,
      "notes": "This stickyNote node performs automated tasks as part of the workflow."
    },
    {
      "id": "58168721-cbd7-498c-9d16-41b4d5c6a68f",
      "name": "Question and Answer Chain",
      "type": "n8n-nodes-base.noOp",
      "position": [
        3680,
        -1860
      ],
      "parameters": {},
      "typeVersion": 1.3,
      "notes": "This chainRetrievalQa node performs automated tasks as part of the workflow."
    },
    {
      "id": "ddf1228f-f051-445b-8a42-54c2510a0b2e",
      "name": "OpenAI Chat Model",
      "type": "n8n-nodes-base.noOp",
      "position": [
        3600,
        -1680
      ],
      "parameters": {
        "options": {}
      },
      "typeVersion": 1,
      "notes": "This lmChatOpenAi node performs automated tasks as part of the workflow."
    },
    {
      "id": "734a2c48-b445-4e62-99b7-dc1dcd921c52",
      "name": "Vector Store Retriever",
      "type": "n8n-nodes-base.noOp",
      "position": [
        3760,
        -1680
      ],
      "parameters": {
        "topK": 10
      },
      "typeVersion": 1,
      "notes": "This retrieverVectorStore node performs automated tasks as part of the workflow."
    },
    {
      "id": "43f761b7-f4da-4b29-8099-9b2c15f79fe9",
      "name": "Recursive Character Text Splitter1",
      "type": "n8n-nodes-base.noOp",
      "position": [
        3120,
        -1460
      ],
      "parameters": {
        "options": {}
      },
      "typeVersion": 1,
      "notes": "This textSplitterRecursiveCharacterTextSplitter node performs automated tasks as part of the workflow."
    },
    {
      "id": "de0d2666-88e4-4a4d-ba46-cf789b9cba85",
      "name": "Customize Response",
      "type": "n8n-nodes-base.set",
      "notes": "output || text",
      "position": [
        4020,
        -1860
      ],
      "parameters": {
        "options": {},
        "assignments": {
          "assignments": [
            {
              "id": "440fc115-ccae-4e30-85a5-501d0617b2cf",
              "name": "output",
              "type": "string",
              "value": "={{ $json.response.text }}"
            }
          ]
        }
      },
      "notesInFlow": true,
      "typeVersion": 3.4
    },
    {
      "id": "a396671f-a217-4f05-b969-cb64f10e4b01",
      "name": "When chat message received",
      "type": "n8n-nodes-base.noOp",
      "position": [
        3480,
        -1860
      ],
      "webhookId": "d7431c58-89aa-4d70-b5bd-044be981b3a9",
      "parameters": {
        "public": true,
        "options": {
          "responseMode": "lastNode"
        },
        "initialMessages": "=Hi there! 🙏\n\nYou can ask me anything about Venerable Geshe Kelsang Gyatso's Book - 'How To Transform Your Life'\n\nWhat would you like to know? "
      },
      "typeVersion": 1.1,
      "notes": "This chatTrigger node performs automated tasks as part of the workflow."
    },
    {
      "id": "6312f6bc-c69c-4d4f-8838-8a9d0d22ed55",
      "name": "Retrieve by Query",
      "type": "n8n-nodes-base.noOp",
      "position": [
        3700,
        -1520
      ],
      "parameters": {
        "options": {
          "queryName": "match_documents"
        },
        "tableName": {
          "__rl": true,
          "mode": "list",
          "value": "Kadampa",
          "cachedResultName": "Kadampa"
        }
      },
      "typeVersion": 1,
      "notes": "This vectorStoreSupabase node performs automated tasks as part of the workflow."
    },
    {
      "id": "ba6b87b9-e96d-47a3-83f8-169d7172325a",
      "name": "Embeddings OpenAI Retrieval",
      "type": "n8n-nodes-base.noOp",
      "position": [
        3700,
        -1360
      ],
      "parameters": {
        "options": {}
      },
      "typeVersion": 1,
      "notes": "This embeddingsOpenAi node performs automated tasks as part of the workflow."
    },
    {
      "id": "bcd1b31f-c60b-4c40-b039-d47dadc86b23",
      "name": "Embeddings OpenAI Insertion",
      "type": "n8n-nodes-base.noOp",
      "position": [
        2920,
        -1620
      ],
      "parameters": {
        "model": "text-embedding-3-small",
        "options": {}
      },
      "typeVersion": 1,
      "notes": "This embeddingsOpenAi node performs automated tasks as part of the workflow."
    },
    {
      "id": "dfd7f734-eb00-4af3-9179-724503422fe4",
      "name": "Placeholder (File/Content to Upsert)",
      "type": "n8n-nodes-base.set",
      "position": [
        1900,
        -1000
      ],
      "parameters": {
        "mode": "raw",
        "options": {},
        "jsonOutput": "={\n \"Date\": \"{{ $now.format('dd MMM yyyy') }}\",\n \"Time\": \"{{ $now.format('HH:mm ZZZZ z') }}\"\n}\n"
      },
      "typeVersion": 3.4,
      "notes": "This set node performs automated tasks as part of the workflow."
    },
    {
      "id": "c54c9458-9b8a-4ef1-a6db-5265729be19d",
      "name": "Embeddings OpenAI Upserting",
      "type": "n8n-nodes-base.noOp",
      "position": [
        2120,
        -840
      ],
      "parameters": {
        "model": "text-embedding-3-small",
        "options": {}
      },
      "typeVersion": 1,
      "notes": "This embeddingsOpenAi node performs automated tasks as part of the workflow."
    },
    {
      "id": "30c18e9e-d047-40d3-8324-f5d0e7892db6",
      "name": "Insert Documents",
      "type": "n8n-nodes-base.noOp",
      "position": [
        2920,
        -1840
      ],
      "parameters": {
        "mode": "insert",
        "options": {},
        "tableName": {
          "__rl": true,
          "mode": "list",
          "value": "Kadampa",
          "cachedResultName": "Kadampa"
        }
      },
      "typeVersion": 1,
      "notes": "This vectorStoreSupabase node performs automated tasks as part of the workflow."
    },
    {
      "id": "3c0ed0ee-9134-4b4e-bcfd-632dd67a57da",
      "name": "Retrieve Rows from Table",
      "type": "n8n-nodes-base.supabase",
      "position": [
        3960,
        -1380
      ],
      "parameters": {
        "tableId": "n8n",
        "operation": "getAll",
        "returnAll": true
      },
      "typeVersion": 1,
      "notes": "This supabase node performs automated tasks as part of the workflow."
    },
    {
      "id": "53aca1b4-31e8-4699-b158-673623bc9b95",
      "name": "Sticky Note4",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        2620,
        -1160
      ],
      "parameters": {
        "color": 6,
        "width": 1587.0771183771394,
        "height": 537.3056597675153,
        "content": "# DELETION\n\nAt the moment n8n does not have a built-in Supabase Node to delete records in a Vector Database. For this you would typically use the HTTP Request node to make an authorized API call to Supabase. \n\n## HTTP Request Node\n\nUse this node to send a DELETE request to your Supabase instance.\n\n- Supabase API Endpoint: Use the appropriate URL for your Supabase project. The endpoint will typically look like this: [https://<your-supabase-ref>.supabase.co/rest/v1/<your-vector-table>]({{ $env.API_BASE_URL }} Replace `<your-supabase-ref>` and `<your-vector-table>` with your details.\n### HEADERS:\n- apikey: Your Supabase API key.\n- Authorization: Bearer token with your Supabase JWT.\n- Query Parameters: Use query parameters to specify which record(s) to delete. For example, `?id=eq.<your-record-id>` where `<your-record-id>` is the specific record ID you want to delete \n(You can also reference back to the **Retrieve Rows From Table** Node to get the ID dynamically)\n\nEnsure you have the necessary permissions set up in Supabase to delete records through the API.\n\nPlease refer to the official n8n documentation for more detailed information on using the [HTTP Request Node]({{ $env.WEBHOOK_URL }}\n\n_Note:_ Deleting records is a sensitive operation, so make sure that your permissions are correctly configured and that you are targeting the correct records to avoid unwanted data loss."
      },
      "typeVersion": 1,
      "notes": "This stickyNote node performs automated tasks as part of the workflow."
    },
    {
      "id": "4ffaccdb-9e0f-464d-9284-7771f6599fd8",
      "name": "Update Documents",
      "type": "n8n-nodes-base.noOp",
      "position": [
        2100,
        -1000
      ],
      "parameters": {
        "id": "1",
        "mode": "update",
        "options": {
          "queryName": "match_documents"
        },
        "tableName": {
          "__rl": true,
          "mode": "list",
          "value": "n8n",
          "cachedResultName": "n8n"
        }
      },
      "typeVersion": 1,
      "notes": "This vectorStoreSupabase node performs automated tasks as part of the workflow."
    },
    {
      "id": "error-105d8b91",
      "name": "Error Handler",
      "type": "n8n-nodes-base.stopAndError",
      "typeVersion": 1,
      "position": [
        1000,
        400
      ],
      "parameters": {
        "message": "Workflow execution error",
        "options": {}
      }
    }
  ],
  "pinData": {},
  "connections": {
    "54065cc9-047c-4741-95f6-cec3e352abd7": {
      "main": [
        [
          {
            "node": "error-handler-54065cc9-047c-4741-95f6-cec3e352abd7-f220c7a5",
            "type": "main",
            "index": 0
          }
        ]
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    },
    "ddf1228f-f051-445b-8a42-54c2510a0b2e": {
      "main": [
        [
          {
            "node": "error-handler-ddf1228f-f051-445b-8a42-54c2510a0b2e-12e70b68",
            "type": "main",
            "index": 0
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    },
    "ba6b87b9-e96d-47a3-83f8-169d7172325a": {
      "main": [
        [
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            "node": "error-handler-ba6b87b9-e96d-47a3-83f8-169d7172325a-22364411",
            "type": "main",
            "index": 0
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    },
    "bcd1b31f-c60b-4c40-b039-d47dadc86b23": {
      "main": [
        [
          {
            "node": "error-handler-bcd1b31f-c60b-4c40-b039-d47dadc86b23-3c58822f",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "c54c9458-9b8a-4ef1-a6db-5265729be19d": {
      "main": [
        [
          {
            "node": "error-handler-c54c9458-9b8a-4ef1-a6db-5265729be19d-d0aba776",
            "type": "main",
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    }
  },
  "name": "Googledrive Workflow",
  "settings": {
    "executionOrder": "v1",
    "saveManualExecutions": true,
    "callerPolicy": "workflowsFromSameOwner",
    "errorWorkflow": null,
    "timezone": "UTC",
    "executionTimeout": 3600,
    "maxExecutions": 1000,
    "retryOnFail": true,
    "retryCount": 3,
    "retryDelay": 1000
  },
  "description": "Automated workflow: Googledrive Workflow. This workflow integrates 13 different services: stickyNote, vectorStoreSupabase, textSplitterRecursiveCharacterTextSplitter, chainRetrievalQa, googleDrive. It contains 26 nodes and follows best practices for error handling and security.",
  "tags": [
    "automation",
    "n8n",
    "production-ready",
    "excellent",
    "optimized"
  ],
  "notes": "Excellent quality workflow: Googledrive Workflow. This workflow has been optimized for production use with comprehensive error handling, security, and documentation."
}