Agente de conocimiento (con Google Drive, Telegram)

• •Bot Token de Telegram
• •Credenciales de API de Google Drive
• •Clave de API de OpenAI

{
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  "meta": {
    "instanceId": "0e8fdabaa466e62faf8e9a5c8aa5dd452ffac3f7ec047a63d5c8b1d769a5fcf7",
    "templateId": "knowledge_store_agent_with_google_drive",
    "templateCredsSetupCompleted": true
  },
  "name": "Knowledge agent (with Google Drive, Telegram)",
  "tags": [],
  "nodes": [
    {
      "id": "708d9e4e-6566-4b18-86b8-8301bf0c72dd",
      "name": "Default Data Loader",
      "type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
      "position": [
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      "parameters": {
        "options": {},
        "dataType": "binary"
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    {
      "id": "604a4e5e-5ccc-4db1-94dc-673711bea6af",
      "name": "Descargar archivo",
      "type": "n8n-nodes-base.googleDrive",
      "position": [
        -192,
        -240
      ],
      "parameters": {
        "fileId": {
          "__rl": true,
          "mode": "id",
          "value": "={{ $json.id }}"
        },
        "options": {},
        "operation": "download"
      },
      "credentials": {
        "googleDriveOAuth2Api": {
          "id": "LQ155GD7daIPvHOZ",
          "name": "Google Drive account"
        }
      },
      "typeVersion": 3
    },
    {
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      "name": "Embedding model",
      "type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
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      "parameters": {
        "options": {}
      },
      "credentials": {
        "openAiApi": {
          "id": "EwkNMKtlWGUUxIVL",
          "name": "OpenAi account"
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      "typeVersion": 1.2
    },
    {
      "id": "99f9315f-4f3d-48d9-a6f4-8c519b249eba",
      "name": "Agente IA",
      "type": "@n8n/n8n-nodes-langchain.agent",
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      "parameters": {
        "text": "={{ $json.message.text }}",
        "options": {
          "systemMessage": "# Knowledge Store Agent System Prompt\n\nYou are a data analysis agent that retrieves and analyzes information from a vector store to answer user questions.\n\n## Your Task\n\n1. **Search the vector store** - Use similarity search to find relevant documents and data\n2. **Analyze the results** - Understand what the retrieved data tells you\n3. **Provide clear answers** - Give helpful responses based on the data you found\n\n## How to Work with Vector Data\n\n### Search Process\n- Use the user's question to search for similar content\n- Retrieve multiple relevant chunks of data\n- Look for patterns and connections across the results\n- Consider both exact matches and conceptually similar information\n\n### Analysis Guidelines\n- Read through all retrieved documents carefully\n- Identify key information that answers the user's question\n- Note any conflicting or incomplete information\n- Look for trends, patterns, or insights in the data\n\n### Response Format\n- Start with a direct answer to the user's question\n- Support your answer with specific information from the data\n- Cite which documents or sources your information comes from\n- Be clear about what you found and what you didn't find\n\n## Response Guidelines\n\n### When You Find Good Data\n- Give a confident, detailed answer\n- Include relevant quotes or data points\n- Explain how the information relates to their question\n- Offer additional insights if available\n\n### When Data is Limited\n- Be honest about what information is available\n- Share what you did find, even if partial\n- Suggest related questions you could help with\n- Don't make up information not in the data\n\n### When No Relevant Data is Found\n- Clearly state that you couldn't find relevant information\n- Suggest alternative ways to phrase the question\n- Offer to search for related topics\n\n## Key Principles\n\n- Always base answers on the retrieved data\n- Be transparent about your sources\n- Admit when information is unclear or missing\n- Help users understand what the data shows\n- Ask clarifying questions if the user's request is vague\n\nRemember: Your strength is finding and explaining information that already exists in the vector store. Focus on being accurate and helpful with the data you can retrieve."
        },
        "promptType": "define"
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      "typeVersion": 2.2
    },
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      "name": "Model",
      "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
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      "parameters": {
        "model": {
          "__rl": true,
          "mode": "list",
          "value": "gpt-4.1-mini",
          "cachedResultName": "gpt-4.1-mini"
        },
        "options": {}
      },
      "credentials": {
        "openAiApi": {
          "id": "EwkNMKtlWGUUxIVL",
          "name": "OpenAi account"
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      "typeVersion": 1.2
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    {
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      "name": "Simple Memoria",
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      "parameters": {
        "sessionKey": "={{ $json.message.chat.id }}",
        "sessionIdType": "customKey"
      },
      "typeVersion": 1.3
    },
    {
      "id": "0cddc21e-a75f-48df-b168-8e288be12469",
      "name": "Archivo cargado",
      "type": "n8n-nodes-base.googleDriveTrigger",
      "position": [
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      ],
      "parameters": {
        "event": "fileCreated",
        "options": {},
        "pollTimes": {
          "item": [
            {
              "mode": "everyMinute"
            }
          ]
        },
        "triggerOn": "specificFolder",
        "folderToWatch": {
          "__rl": true,
          "mode": "list",
          "value": "1Nc_T_wHj8eF6LLed8D8hZX-q1YUeZT5j",
          "cachedResultUrl": "https://drive.google.com/drive/folders/1Nc_T_wHj8eF6LLed8D8hZX-q1YUeZT5j",
          "cachedResultName": "Rag-Folder"
        }
      },
      "credentials": {
        "googleDriveOAuth2Api": {
          "id": "LQ155GD7daIPvHOZ",
          "name": "Google Drive account"
        }
      },
      "typeVersion": 1
    },
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      "name": "Nota adhesiva",
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      "position": [
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      "parameters": {
        "color": 5,
        "width": 304,
        "height": 512,
        "content": "### Knowledge store agent\nA chat-based AI agent to retrieve, analyze, and answer questions using documents uploaded to Google Drive and stored in a vector database.\n\n#### Set up\n- Configure credentials in the **Google Drive** and **Open AI** nodes\n- Create a folder in Google Drive to store your documents, then select it in the \"File uploaded\" trigger node\n- Upload a file to that folder, return to n8n and click \"Execute workflow\"\n- Once **Insert documents** has been completed you can Open chat and ask the agent questions about your files.\n\n#### Next steps\nTry connecting other data sources to your knowledge base, using other triggers before the **Insert documents** node.\n"
      },
      "typeVersion": 1
    },
    {
      "id": "2fa9684d-f20a-4262-9b8b-81babb2fecc5",
      "name": "Nota adhesiva 3",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
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      "parameters": {
        "color": 4,
        "width": 320,
        "height": 240,
        "content": "### Embeddings\n\nThe Insert and Retrieve operation use the same embedding node.\n\nThis is to ensure that they are using the **exact same embeddings and settings**.\n\nDifferent embeddings might not work at all, or have unintended consequences.\n"
      },
      "typeVersion": 1
    },
    {
      "id": "64d17551-1637-4a53-a3e0-db45894c12a6",
      "name": "Insertar documentos",
      "type": "@n8n/n8n-nodes-langchain.vectorStoreInMemory",
      "position": [
        240,
        -240
      ],
      "parameters": {
        "mode": "insert",
        "memoryKey": {
          "__rl": true,
          "mode": "list",
          "value": "vector_store_key",
          "cachedResultName": "vector_store_key"
        }
      },
      "typeVersion": 1.3
    },
    {
      "id": "4f8a4a45-28a4-4684-b838-66011cc6a4a5",
      "name": "Recuperar documentos",
      "type": "@n8n/n8n-nodes-langchain.vectorStoreInMemory",
      "position": [
        -288,
        0
      ],
      "parameters": {
        "mode": "retrieve-as-tool",
        "topK": 10,
        "memoryKey": {
          "__rl": true,
          "mode": "list",
          "value": "vector_store_key",
          "cachedResultName": "vector_store_key"
        },
        "toolDescription": "Use this tool to retrieve any information required."
      },
      "typeVersion": 1.3
    },
    {
      "id": "a5249962-686f-40fe-bee4-1b7d3bea5e79",
      "name": "Escuchar eventos entrantes",
      "type": "n8n-nodes-base.telegramTrigger",
      "position": [
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      ],
      "webhookId": "322dce18-f93e-4f86-b9b1-3305519b7834",
      "parameters": {
        "updates": [
          "message"
        ],
        "additionalFields": {}
      },
      "credentials": {
        "telegramApi": {
          "id": "23MJQt59L7oOwA07",
          "name": "Telegram account"
        }
      },
      "typeVersion": 1
    },
    {
      "id": "36f64ae1-cd45-4c4d-a130-434240fae577",
      "name": "Telegram",
      "type": "n8n-nodes-base.telegram",
      "onError": "continueErrorOutput",
      "position": [
        32,
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      ],
      "webhookId": "b0c8b1dc-6607-4681-84d7-7e600348cb56",
      "parameters": {
        "text": "={{ $json.output }}",
        "chatId": "={{ $('Listen for incoming events').first().json.message.from.id }}",
        "additionalFields": {
          "parse_mode": "Markdown",
          "appendAttribution": false
        }
      },
      "credentials": {
        "telegramApi": {
          "id": "23MJQt59L7oOwA07",
          "name": "Telegram account"
        }
      },
      "typeVersion": 1.1
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    "executionOrder": "v1"
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