{
  "name": "Hardware store product assistant with PostgreSQL & Google Gemini AI",
  "nodes": [
    {
      "id": "fbc411f2-3338-4a02-b72d-ab543374fbfc",
      "name": "Database Tools Trigger",
      "type": "@n8n/n8n-nodes-langchain.mcpTrigger",
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    {
      "id": "01e12dad-723e-4363-94e4-6e44b0850ba1",
      "name": "Query Product by ID",
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      "name": "Query Product by Name",
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    {
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      "name": "Chat Trigger",
      "type": "@n8n/n8n-nodes-langchain.chatTrigger",
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      "id": "fdaba65d-ebd9-40cf-8041-cb3b59de0a0f",
      "name": "AI Agent",
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      "name": "Language Model (Google Gemini)",
      "type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
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    },
    {
      "id": "b93eaac2-2de5-425a-800c-07dfcca60afb",
      "name": "Chat Memory",
      "type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
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    },
    {
      "id": "c247fa37-e815-4fbb-a954-98bf247527d5",
      "name": "DB Tools Client",
      "type": "@n8n/n8n-nodes-langchain.mcpClientTool",
      "position": [
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    },
    {
      "id": "8f1238d5-d0a0-4981-a236-fd5a58a83d68",
      "name": "Sticky Note",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
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      ],
      "parameters": {
        "width": 352,
        "height": 736,
        "content": "## 📝 Descripción del Flujo / Workflow Description\n\nEste agente de IA conversacional responde preguntas sobre productos de ferretería. Utiliza un modelo de lenguaje de Google (Gemini) y se conecta a un"
      }
    },
    {
      "id": "93ca2a57-7d7b-43ff-ac50-cfa3514217bc",
      "name": "Sticky Note1",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        608,
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      ],
      "parameters": {
        "width": 352,
        "height": 768,
        "content": "## ⚙️ Configuración Requerida / Required Configuration\n\nPara que este flujo funcione, asegúrate de configurar lo siguiente:\n\n1.  **Credenciales de PostgreSQL:**\n    - Deben estar configuradas y selecc"
      }
    },
    {
      "id": "06499bb5-76aa-40c6-b6ee-44b0e1aa3d1c",
      "name": "Sticky Note2",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -672,
        -112
      ],
      "parameters": {
        "width": 1248,
        "height": 176,
        "content": "## AI Agent with PostgreSQL & MCP for Hardware Store Chatbot\n\nA conversational AI agent workflow in n8n powered by Google Gemini. It connects to a PostgreSQL database via the MCP Client tool to handle"
      }
    }
  ],
  "connections": {
    "Chat Memory": {
      "ai_memory": [
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          {
            "node": "AI Agent",
            "type": "ai_memory",
            "index": 0
          }
        ]
      ]
    },
    "Chat Trigger": {
      "main": [
        [
          {
            "node": "AI Agent",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "DB Tools Client": {
      "ai_tool": [
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          {
            "node": "AI Agent",
            "type": "ai_tool",
            "index": 0
          }
        ]
      ]
    },
    "Query Product by ID": {
      "ai_tool": [
        [
          {
            "node": "Database Tools Trigger",
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          }
        ]
      ]
    },
    "Query Product by Name": {
      "ai_tool": [
        [
          {
            "node": "Database Tools Trigger",
            "type": "ai_tool",
            "index": 0
          }
        ]
      ]
    },
    "Query Product by Note": {
      "ai_tool": [
        [
          {
            "node": "Database Tools Trigger",
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      ]
    },
    "Query Product by Category": {
      "ai_tool": [
        [
          {
            "node": "Database Tools Trigger",
            "type": "ai_tool",
            "index": 0
          }
        ]
      ]
    },
    "Query Product by Description": {
      "ai_tool": [
        [
          {
            "node": "Database Tools Trigger",
            "type": "ai_tool",
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    },
    "Query Product by Subcategory": {
      "ai_tool": [
        [
          {
            "node": "Database Tools Trigger",
            "type": "ai_tool",
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      ]
    },
    "Language Model (Google Gemini)": {
      "ai_languageModel": [
        [
          {
            "node": "AI Agent",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    }
  }
}