{
  "name": "Generate LinkedIn posts using Google Gemini, MongoDB Atlas, Google Drive and Sheets",
  "nodes": [
    {
      "id": "c3704437-9960-470b-be03-69b769e0b450",
      "name": "Sticky Note: Ingestion",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -2368,
        448
      ],
      "parameters": {
        "width": 400,
        "height": 260,
        "content": "### 1. DATA INGESTION PHASE\nThis section watches a Google Drive folder for new post examples (CSV). When a file is updated, it automatically embeds the text and stores it in MongoDB Atlas for long-ter"
      }
    },
    {
      "id": "1e843974-b012-42ca-9051-5f5218b72c98",
      "name": "Sticky Note: Vector",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
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      ],
      "parameters": {
        "width": 380,
        "height": 260,
        "content": "### 2. METHOD A: VECTOR SEARCH\nThis agent uses Semantic Search. It finds posts that are *thematically* similar to your prompt, even if they don't share the same keywords. Best for matching 'Vibe' and "
      }
    },
    {
      "id": "0a23b11e-dd30-488e-92cf-242ed7ecdfab",
      "name": "Sticky Note: Sheets",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
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      ],
      "parameters": {
        "width": 380,
        "height": 260,
        "content": "### 3. METHOD B: DIRECT SHEET TOOL\nThis agent has a direct line to your Google Sheet. It can pull specific rows or data points, ensuring 100% accuracy from your spreadsheet source."
      }
    },
    {
      "id": "3578ec95-a96f-45b0-a300-8bf548779637",
      "name": "MongoDB Vector Store Inserter",
      "type": "@n8n/n8n-nodes-langchain.vectorStoreMongoDBAtlas",
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    },
    {
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      "name": "MongoDB Vector Search",
      "type": "@n8n/n8n-nodes-langchain.vectorStoreMongoDBAtlas",
      "position": [
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      ]
    },
    {
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      "name": "Knowledge Base Agent",
      "type": "@n8n/n8n-nodes-langchain.agent",
      "position": [
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      ]
    },
    {
      "id": "5282a1f2-3899-4124-82f5-fbb0f2f1f6cc",
      "name": "Embeddings Google Gemini",
      "type": "@n8n/n8n-nodes-langchain.embeddingsGoogleGemini",
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      ]
    },
    {
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      "name": "Google Gemini Chat Model",
      "type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
      "position": [
        -1360,
        864
      ]
    },
    {
      "id": "613b72b6-ad68-4b89-af1d-6a37353d3919",
      "name": "Default Data Loader",
      "type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
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      ]
    },
    {
      "id": "c7ac7c3e-878a-49cd-b134-88ef7938f04b",
      "name": "Download file",
      "type": "n8n-nodes-base.googleDrive",
      "position": [
        -2096,
        656
      ]
    },
    {
      "id": "00878485-4242-4b67-92ef-21a9c0c92740",
      "name": "Google Drive Trigger",
      "type": "n8n-nodes-base.googleDriveTrigger",
      "position": [
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        656
      ]
    },
    {
      "id": "b3988fa5-66bb-423a-a667-4346a2b23356",
      "name": "When chat message received",
      "type": "@n8n/n8n-nodes-langchain.chatTrigger",
      "position": [
        -656,
        624
      ]
    },
    {
      "id": "c643af0e-5aa8-40be-971b-c819d6c458e9",
      "name": "Embeddings Google Gemini1",
      "type": "@n8n/n8n-nodes-langchain.embeddingsGoogleGemini",
      "position": [
        -1088,
        976
      ]
    },
    {
      "id": "47ec6512-7b51-4dfe-8b60-242f66c01a69",
      "name": "Simple Memory",
      "type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
      "position": [
        -1184,
        832
      ]
    },
    {
      "id": "4cb99463-37aa-4d61-a5b8-0c0bed6f2b8e",
      "name": "Knowledge Base Agent1",
      "type": "@n8n/n8n-nodes-langchain.agent",
      "position": [
        -256,
        672
      ]
    },
    {
      "id": "1cc62e89-8cce-4463-85b5-ca797ae0826f",
      "name": "Google Gemini Chat Model1",
      "type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
      "position": [
        -320,
        848
      ]
    },
    {
      "id": "13205b10-c807-400a-bd87-43b075680f53",
      "name": "Simple Memory1",
      "type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
      "position": [
        -192,
        896
      ]
    },
    {
      "id": "98927dac-609f-44b5-9dba-931b089d75d7",
      "name": "Google Sheets Tool",
      "type": "n8n-nodes-base.googleSheetsTool",
      "position": [
        0,
        880
      ]
    }
  ],
  "connections": {
    "Download file": {
      "main": [
        [
          {
            "node": "MongoDB Vector Store Inserter",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Simple Memory": {
      "ai_memory": [
        [
          {
            "node": "Knowledge Base Agent",
            "type": "ai_memory",
            "index": 0
          }
        ]
      ]
    },
    "Simple Memory1": {
      "ai_memory": [
        [
          {
            "node": "Knowledge Base Agent1",
            "type": "ai_memory",
            "index": 0
          }
        ]
      ]
    },
    "Google Sheets Tool": {
      "ai_tool": [
        [
          {
            "node": "Knowledge Base Agent1",
            "type": "ai_tool",
            "index": 0
          }
        ]
      ]
    },
    "Default Data Loader": {
      "ai_document": [
        [
          {
            "node": "MongoDB Vector Store Inserter",
            "type": "ai_document",
            "index": 0
          }
        ]
      ]
    },
    "Google Drive Trigger": {
      "main": [
        [
          {
            "node": "Download file",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "MongoDB Vector Search": {
      "ai_tool": [
        [
          {
            "node": "Knowledge Base Agent",
            "type": "ai_tool",
            "index": 0
          }
        ]
      ]
    },
    "Embeddings Google Gemini": {
      "ai_embedding": [
        [
          {
            "node": "MongoDB Vector Store Inserter",
            "type": "ai_embedding",
            "index": 0
          }
        ]
      ]
    },
    "Google Gemini Chat Model": {
      "ai_languageModel": [
        [
          {
            "node": "Knowledge Base Agent",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "Embeddings Google Gemini1": {
      "ai_embedding": [
        [
          {
            "node": "MongoDB Vector Search",
            "type": "ai_embedding",
            "index": 0
          }
        ]
      ]
    },
    "Google Gemini Chat Model1": {
      "ai_languageModel": [
        [
          {
            "node": "Knowledge Base Agent1",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "When chat message received": {
      "main": [
        [
          {
            "node": "Knowledge Base Agent1",
            "type": "main",
            "index": 0
          }
        ]
      ]
    }
  }
}