🤖 asistente de chat RAG impulsado por IA para documentos + Google Drive + Gemini + Qdrant

• •Punto final de HTTP Webhook (n8n generará automáticamente)
• •Bot Token de Telegram
• •Credenciales de API de Google Drive
• •Clave de API de OpenAI
• •Información de conexión del servidor Qdrant
• •Clave de API de Google Gemini

{
  "id": "8tusZTTtcyaiznEG",
  "meta": {
    "instanceId": "31e69f7f4a77bf465b805824e303232f0227212ae922d12133a0f96ffeab4fef",
    "templateCredsSetupCompleted": true
  },
  "name": "🤖 AI Powered RAG Chatbot for Your Docs + Google Drive + Gemini + Qdrant",
  "tags": [],
  "nodes": [
    {
      "id": "7ad5796b-d1a0-4cc1-bed6-105ff499beeb",
      "name": "Cargador de Datos",
      "type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
      "position": [
        620,
        720
      ],
      "parameters": {
        "options": {
          "metadata": {
            "metadataValues": [
              {
                "name": "file_id",
                "value": "={{ $json.id }}"
              },
              {
                "name": "pubkey",
                "value": "={{ $json.name }}"
              },
              {
                "name": "=overarching_theme",
                "value": "={{ $('Extract Meta Data').item.json.output.overarching_theme }}"
              },
              {
                "name": "recurring_topics",
                "value": "={{ $('Extract Meta Data').item.json.output.recurring_topics }}"
              },
              {
                "name": "pain_points",
                "value": "={{ $('Extract Meta Data').item.json.output.pain_points }}"
              },
              {
                "name": "analytical_insights",
                "value": "={{ $('Extract Meta Data').item.json.output.analytical_insights }}"
              },
              {
                "name": "conclusion",
                "value": "={{ $('Extract Meta Data').item.json.output.conclusion }}"
              },
              {
                "name": "keywords",
                "value": "={{ $('Extract Meta Data').item.json.output.keywords }}"
              }
            ]
          }
        },
        "dataType": "binary",
        "binaryMode": "specificField"
      },
      "typeVersion": 1
    },
    {
      "id": "84986ee2-4d79-49e8-8778-b8a955cf2174",
      "name": "Separador de Tokens",
      "type": "@n8n/n8n-nodes-langchain.textSplitterTokenSplitter",
      "position": [
        720,
        860
      ],
      "parameters": {
        "chunkSize": 3000
      },
      "typeVersion": 1
    },
    {
      "id": "82aa7016-f3af-4de8-a410-b1c802041213",
      "name": "Almacén Vectorial Qdrant",
      "type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant",
      "onError": "continueRegularOutput",
      "position": [
        540,
        520
      ],
      "parameters": {
        "mode": "insert",
        "options": {},
        "qdrantCollection": {
          "__rl": true,
          "mode": "id",
          "value": "=nostr-damus-user-profiles"
        }
      },
      "credentials": {
        "qdrantApi": {
          "id": "DJQ4hVAVdWZytjr2",
          "name": "QdrantApi account"
        }
      },
      "executeOnce": false,
      "retryOnFail": true,
      "typeVersion": 1
    },
    {
      "id": "07f4d14a-1864-406b-946b-09c776e3038b",
      "name": "Bucle sobre Elementos",
      "type": "n8n-nodes-base.splitInBatches",
      "onError": "continueRegularOutput",
      "position": [
        180,
        -140
      ],
      "parameters": {
        "options": {}
      },
      "typeVersion": 3
    },
    {
      "id": "b0f9b066-62de-41bc-afce-dcb4d20d100c",
      "name": "Esperar",
      "type": "n8n-nodes-base.wait",
      "position": [
        1020,
        840
      ],
      "webhookId": "237d7f8a-aead-479a-b813-f407d1f37fa5",
      "parameters": {},
      "typeVersion": 1.1
    },
    {
      "id": "2a883003-f5ec-409e-97e4-809360ca11ed",
      "name": "Al hacer clic en 'Probar flujo de trabajo'",
      "type": "n8n-nodes-base.manualTrigger",
      "position": [
        -1160,
        -280
      ],
      "parameters": {},
      "typeVersion": 1
    },
    {
      "id": "cfbb6b77-142f-4689-b051-436f40ababe6",
      "name": "Modelo de Chat Google Gemini",
      "type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
      "position": [
        1120,
        -20
      ],
      "parameters": {
        "options": {
          "temperature": 0.4
        },
        "modelName": "models/gemini-2.0-flash-exp"
      },
      "credentials": {
        "googlePalmApi": {
          "id": "L9UNQHflYlyF9Ngd",
          "name": "Google Gemini(PaLM) Api account"
        }
      },
      "typeVersion": 1
    },
    {
      "id": "c9c50387-64a3-473f-9ea7-509a301836a3",
      "name": "Combinar",
      "type": "n8n-nodes-base.merge",
      "position": [
        820,
        200
      ],
      "parameters": {
        "mode": "combine",
        "options": {},
        "combineBy": "combineAll"
      },
      "typeVersion": 3
    },
    {
      "id": "ea143b9f-9bd8-4f3f-8979-5a0fd2691e9d",
      "name": "Extraer Metadatos",
      "type": "@n8n/n8n-nodes-langchain.informationExtractor",
      "position": [
        1020,
        -180
      ],
      "parameters": {
        "text": "={{ $json.data }}",
        "options": {
          "systemPromptTemplate": "You are an expert extraction algorithm.\nOnly extract relevant information from the text.\nIf you do not know the value of an attribute asked to extract, you may omit the attribute's value."
        },
        "attributes": {
          "attributes": [
            {
              "name": "overarching_theme",
              "description": "Summarize the main theme(s) discussed in the \"Overarching Theme\" section."
            },
            {
              "name": "recurring_topics",
              "description": "List the recurring topics mentioned in the \"Common Threads\" section as an array of strings."
            },
            {
              "name": "pain_points",
              "description": "Summarize the user's frustrations or challenges mentioned in the \"Pain Points\" section as an array of strings."
            },
            {
              "name": "analytical_insights",
              "description": "Extract a list of key analytical observations from the \"Analytical Insights\" section, including shifts in tone or behavior."
            },
            {
              "name": "conclusion",
              "description": "Summarize the conclusions drawn about the user’s threads and their overall focus."
            },
            {
              "name": "keywords",
              "description": "Generate a list of 10 keywords that capture the essence of the document (e.g., \"askNostr,\" \"decentralization,\" \"spam filtering\")."
            }
          ]
        }
      },
      "typeVersion": 1
    },
    {
      "id": "2ddd778f-6676-4a4b-b592-536401831bc8",
      "name": "Obtener Contenidos de Archivo",
      "type": "n8n-nodes-base.extractFromFile",
      "position": [
        820,
        -180
      ],
      "parameters": {
        "options": {},
        "operation": "text"
      },
      "typeVersion": 1
    },
    {
      "id": "cdbb8bbc-967c-4f01-b844-4d31328153df",
      "name": "Descargar Archivo desde Google Drive",
      "type": "n8n-nodes-base.googleDrive",
      "position": [
        500,
        -180
      ],
      "parameters": {
        "fileId": {
          "__rl": true,
          "mode": "id",
          "value": "={{ $json.id }}"
        },
        "options": {},
        "operation": "download"
      },
      "credentials": {
        "googleDriveOAuth2Api": {
          "id": "UhdXGYLTAJbsa0xX",
          "name": "Google Drive account"
        }
      },
      "typeVersion": 3
    },
    {
      "id": "399e196c-76ec-4077-aacc-f669173b0229",
      "name": "Buscar IDs de Archivo en Carpeta de Google Drive",
      "type": "n8n-nodes-base.googleDrive",
      "position": [
        -620,
        -160
      ],
      "parameters": {
        "filter": {
          "driveId": {
            "mode": "list",
            "value": "My Drive"
          },
          "folderId": {
            "__rl": true,
            "mode": "id",
            "value": "={{ $json.folder_id }}"
          }
        },
        "options": {},
        "resource": "fileFolder",
        "returnAll": true
      },
      "credentials": {
        "googleDriveOAuth2Api": {
          "id": "UhdXGYLTAJbsa0xX",
          "name": "Google Drive account"
        }
      },
      "typeVersion": 3
    },
    {
      "id": "3bc4f005-c6c8-4f73-816a-1573d6dfb62f",
      "name": "text-embeddings-3-large",
      "type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
      "position": [
        480,
        720
      ],
      "parameters": {
        "model": "text-embedding-3-large",
        "options": {}
      },
      "credentials": {
        "openAiApi": {
          "id": "jEMSvKmtYfzAkhe6",
          "name": "OpenAi account"
        }
      },
      "typeVersion": 1
    },
    {
      "id": "5d42b34a-7156-4163-a2c4-a9cb3c984c96",
      "name": "ID de Carpeta Google",
      "type": "n8n-nodes-base.set",
      "position": [
        -820,
        -160
      ],
      "parameters": {
        "options": {},
        "assignments": {
          "assignments": [
            {
              "id": "e6f6188f-c895-4c8c-b39a-0ef55b490fd6",
              "name": "folder_id",
              "type": "string",
              "value": "[Your-Google-Folder-ID]"
            }
          ]
        }
      },
      "typeVersion": 3.4
    },
    {
      "id": "7be310c2-d30f-42a2-8f57-33c7b184e429",
      "name": "gpt-4o-mini1",
      "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
      "position": [
        -400,
        940
      ],
      "parameters": {
        "options": {}
      },
      "credentials": {
        "openAiApi": {
          "id": "jEMSvKmtYfzAkhe6",
          "name": "OpenAi account"
        }
      },
      "typeVersion": 1
    },
    {
      "id": "18aacfd9-3f6e-4fba-9f68-d8cc95bc667b",
      "name": "Eliminar Puntos Qdrant por ID de Archivo",
      "type": "@n8n/n8n-nodes-langchain.code",
      "position": [
        -500,
        800
      ],
      "parameters": {
        "code": {
          "execute": {
            "code": "const { QdrantVectorStore } = require(\"@langchain/qdrant\");\nconst { OpenAIEmbeddings } = require(\"@langchain/openai\");\n\n// Qdrant connection details\nconst url = \"http://localhost:6333/\";\nconst apiKey = \"\";\n\n// OpenAI API configuration\nconst openAIApiKey = \"[Your-OpenAI-API-Key]\";\n\n// Get input data\nconst items = this.getInputData()[0];\n// console.log(items)\n\nconst collectionName =  items.json.qdrant_collection_name;\n// console.log(collectionName)\n\nasync function deleteDocumentsFromQdrant() {\n  try {\n    // Initialize OpenAI embeddings\n    const embeddings = new OpenAIEmbeddings({\n      model: \"text-embedding-3-large\",\n      openAIApiKey: openAIApiKey\n    });\n\n    // Connect to existing Qdrant collection\n    const vectorStore = await QdrantVectorStore.fromExistingCollection(embeddings, {\n      url: url,\n      apiKey: apiKey,\n      collectionName: collectionName,\n    });\n\n    const fileIds = items.json.appended_id.map(item => item);\n\n    console.log(fileIds)\n\n    // Delete points by fileId\n    const deletionResults = await Promise.all(fileIds.map(async (file_id) => {\n      const filter = {\n        must: [\n          {\n            key: \"metadata.file_id\",\n            match: { value: file_id }\n          }\n        ]\n      };\n\n      try {\n        // Access the underlying Qdrant client to perform the deletion\n        await vectorStore.client.delete(collectionName, { filter });\n        return { file_id, status: \"deleted\" };\n      } catch (error) {\n        console.error(`Error deleting documents for fileId ${file_id}:`, error);\n        return { file_id, status: \"error\", message: error.message };\n      }\n    }));\n\n    return deletionResults;\n  } catch (error) {\n    console.error(\"An error occurred during the deletion process:\", error);\n    return error.message;\n  }\n}\n\n// Execute the deletion process\ntry {\n  const result = await deleteDocumentsFromQdrant();\n  console.log(\"Deletion process completed:\", result);\n  return [];\n} catch (error) {\n  console.error(\"Failed to execute deletion process:\", error);\n  return [{ json: { error } }];\n}\n\n"
          }
        },
        "inputs": {
          "input": [
            {
              "type": "main",
              "required": true,
              "maxConnections": 1
            },
            {
              "type": "ai_languageModel",
              "required": true,
              "maxConnections": 1
            }
          ]
        },
        "outputs": {
          "output": [
            {
              "type": "main"
            }
          ]
        }
      },
      "typeVersion": 1
    },
    {
      "id": "329cec97-aac1-4714-97ce-add5915b1078",
      "name": "Nombre de Colección Qdrant",
      "type": "n8n-nodes-base.set",
      "position": [
        -700,
        100
      ],
      "parameters": {
        "options": {},
        "assignments": {
          "assignments": [
            {
              "id": "11fa71e9-6cbc-4183-9439-e3379b2b970e",
              "name": "qdrant_collection_name",
              "type": "string",
              "value": "nostr-damus-user-profiles"
            }
          ]
        }
      },
      "typeVersion": 3.4
    },
    {
      "id": "82e98da8-530d-4a3c-97e6-5e91ad644a46",
      "name": "Lista de IDs de Archivo",
      "type": "n8n-nodes-base.summarize",
      "position": [
        -700,
        280
      ],
      "parameters": {
        "options": {},
        "fieldsToSummarize": {
          "values": [
            {
              "field": "id",
              "aggregation": "append"
            }
          ]
        }
      },
      "typeVersion": 1.1
    },
    {
      "id": "f3def614-2709-4847-831d-d6a80c86ec1c",
      "name": "Combinar1",
      "type": "n8n-nodes-base.merge",
      "position": [
        -340,
        340
      ],
      "parameters": {
        "mode": "combine",
        "options": {},
        "combineBy": "combineByPosition"
      },
      "typeVersion": 3
    },
    {
      "id": "cfe399fb-8560-4ac4-98f7-4f539ae6a52c",
      "name": "Combinar2",
      "type": "n8n-nodes-base.merge",
      "position": [
        -80,
        -140
      ],
      "parameters": {},
      "typeVersion": 3
    },
    {
      "id": "1059da47-2abd-4884-b9c2-6b7d1623e31d",
      "name": "Nota Adhesiva",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -1280,
        560
      ],
      "parameters": {
        "color": 3,
        "width": 1180,
        "height": 760,
        "content": "## Prepare Qdrant Vector Store"
      },
      "typeVersion": 1
    },
    {
      "id": "2616e08f-070e-45b5-906f-40e832519aa2",
      "name": "Confirmar Eliminación de Puntos Qdrant",
      "type": "n8n-nodes-base.telegram",
      "position": [
        -1060,
        760
      ],
      "webhookId": "29aac1ac-9345-4e44-babf-ebcfae701d88",
      "parameters": {
        "chatId": "={{ $env.TELEGRAM_CHAT_ID }}",
        "message": "=WARNING - {{ $json.appended_id.length }} Records in the Qdrant vector store collection \"{{ $json.qdrant_collection_name }}\" will be deleted.  Are you sure you want to continue?  This action cannot be undone!",
        "options": {
          "limitWaitTime": {
            "values": {
              "resumeUnit": "minutes",
              "resumeAmount": 15
            }
          }
        },
        "operation": "sendAndWait",
        "approvalOptions": {
          "values": {
            "approvalType": "double"
          }
        }
      },
      "credentials": {
        "telegramApi": {
          "id": "pAIFhguJlkO3c7aQ",
          "name": "Telegram account"
        }
      },
      "typeVersion": 1.2
    },
    {
      "id": "9d167192-e87b-41be-87f2-d7ddf866bb8d",
      "name": "Si",
      "type": "n8n-nodes-base.if",
      "position": [
        -1060,
        1000
      ],
      "parameters": {
        "options": {},
        "conditions": {
          "options": {
            "version": 2,
            "leftValue": "",
            "caseSensitive": true,
            "typeValidation": "strict"
          },
          "combinator": "and",
          "conditions": [
            {
              "id": "20f530d6-fd55-420d-b8a9-70e5303f688e",
              "operator": {
                "type": "boolean",
                "operation": "true",
                "singleValue": true
              },
              "leftValue": "={{ $json.data.approved }}",
              "rightValue": ""
            }
          ]
        }
      },
      "typeVersion": 2.2
    },
    {
      "id": "1178f8cf-ece0-4b11-942d-18dd025da366",
      "name": "Nota Adhesiva1",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        320,
        420
      ],
      "parameters": {
        "color": 7,
        "width": 920,
        "height": 640,
        "content": "## Perform Qdrant Vector Store Operations"
      },
      "typeVersion": 1
    },
    {
      "id": "cbf17a08-2f4c-4605-8418-5bf93ade7cf4",
      "name": "Enviar Mensaje de Rechazo",
      "type": "n8n-nodes-base.telegram",
      "position": [
        -740,
        1120
      ],
      "webhookId": "382a3b43-b83f-47b1-a276-67c6b98a441a",
      "parameters": {
        "text": "Qdrant vector store upsert declined",
        "chatId": "={{ $env.TELEGRAM_CHAT_ID }}",
        "additionalFields": {
          "appendAttribution": false
        }
      },
      "credentials": {
        "telegramApi": {
          "id": "pAIFhguJlkO3c7aQ",
          "name": "Telegram account"
        }
      },
      "typeVersion": 1.2
    },
    {
      "id": "890f478a-e000-418d-8582-4fef946e44d8",
      "name": "Nota Adhesiva2",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -900,
        -380
      ],
      "parameters": {
        "width": 480,
        "height": 840,
        "content": "## 🌟Workflow Config\n\n- Google Drive Folder Id\n- Qdrant Collection Name\n- List of Google Drive File Id's"
      },
      "typeVersion": 1
    },
    {
      "id": "d2bc2fed-c134-4e54-bd9d-69996be966a3",
      "name": "Nota Adhesiva3",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        740,
        -280
      ],
      "parameters": {
        "color": 6,
        "width": 640,
        "height": 420,
        "content": "## Extract Metadata for Qdrant Hybrid Search"
      },
      "typeVersion": 1
    },
    {
      "id": "61f3c21e-bc2a-47bc-8ab1-678b1f425ee3",
      "name": "Nota Adhesiva4",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        400,
        -280
      ],
      "parameters": {
        "color": 2,
        "width": 300,
        "height": 320,
        "content": "## Google Drive"
      },
      "typeVersion": 1
    },
    {
      "id": "61e608e4-ac6a-4131-9ca8-c46a5134bff9",
      "name": "Nota Adhesiva5",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        80,
        -360
      ],
      "parameters": {
        "color": 5,
        "width": 1360,
        "height": 1480,
        "content": "## ✨ Save Documents to Qdrant Vector Store"
      },
      "typeVersion": 1
    },
    {
      "id": "410681cb-e757-42c0-89de-902edbe998e3",
      "name": "Nota Adhesiva6",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -580,
        680
      ],
      "parameters": {
        "color": 5,
        "width": 420,
        "height": 400,
        "content": "## Delete From Qdrant Vector Store\nThis operation can not be undone!!!"
      },
      "typeVersion": 1
    },
    {
      "id": "238469ce-3710-4681-b1b2-200c1b699d5e",
      "name": "Nota Adhesiva7",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -1200,
        640
      ],
      "parameters": {
        "color": 4,
        "width": 380,
        "height": 520,
        "content": "## Human In The Loop\nUser must verify deletion of points from Qdrant vector store"
      },
      "typeVersion": 1
    },
    {
      "id": "0b2f6ff4-dd02-4f0e-b2eb-60b1ce736cf5",
      "name": "Nota Adhesiva8",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -1280,
        -380
      ],
      "parameters": {
        "color": 4,
        "width": 340,
        "height": 460,
        "content": "## 👍Start Here!"
      },
      "typeVersion": 1
    },
    {
      "id": "abde545b-1a23-4b3e-8046-335b9d0fa445",
      "name": "Webhook",
      "type": "n8n-nodes-base.webhook",
      "disabled": true,
      "position": [
        -1160,
        -100
      ],
      "webhookId": "3a30557f-9264-4bc8-a253-a9356677c791",
      "parameters": {
        "path": "upsert",
        "options": {}
      },
      "typeVersion": 2
    },
    {
      "id": "897d29af-719d-4fe7-93a9-44c693cc6547",
      "name": "Agente de IA",
      "type": "@n8n/n8n-nodes-langchain.agent",
      "position": [
        -600,
        -1200
      ],
      "parameters": {
        "text": "={{ $json.chatInput }}",
        "options": {
          "systemMessage": "=You are an intelligent assistant specialized in answering user questions using Nostr user profiles. Your primary goal is to provide precise, contextually relevant, and concise answers based on the tools and resources available.\n\n### TOOL\nUse the \"nostr_damus_user_profiles\" tool to:\n- perform semantic similarity searches and retrieve information from Nostr user profiles relevant to the user's query.\n- access detailed information about Nostr and/or Damus users when additional context or specifics are required.\n\n### Key Instructions\n1. **Response Guidelines**:\n   - Clearly explain how the retrieved information addresses the user's query, if applicable.\n   - If no relevant information is found, respond with: \"I cannot find the answer in the available resources.\"\n\n2. **Focus and Relevance**:\n   - Ensure all responses are directly aligned with the user's question.\n   - Avoid including extraneous details or relying solely on internal knowledge.\n"
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      "webhookId": "5f1c0c82-0ff9-40c7-9e2e-b1a96ffe24cd",
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    },
    {
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      "name": "Modelo de Chat Google Gemini1",
      "type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
      "position": [
        -760,
        -1000
      ],
      "parameters": {
        "options": {
          "maxOutputTokens": 8192
        },
        "modelName": "models/gemini-2.0-flash-exp"
      },
      "credentials": {
        "googlePalmApi": {
          "id": "L9UNQHflYlyF9Ngd",
          "name": "Google Gemini(PaLM) Api account"
        }
      },
      "typeVersion": 1
    },
    {
      "id": "1c5818d4-6251-4d14-8b4f-d1f3ddfbed38",
      "name": "text-embeddings-3-large1",
      "type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
      "position": [
        -320,
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      ],
      "parameters": {
        "model": "text-embedding-3-large",
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      },
      "credentials": {
        "openAiApi": {
          "id": "jEMSvKmtYfzAkhe6",
          "name": "OpenAi account"
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      },
      "typeVersion": 1
    },
    {
      "id": "80056067-5d2b-4ac7-8e69-e75bd66a1ad4",
      "name": "Nota Adhesiva9",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -900,
        -1300
      ],
      "parameters": {
        "color": 5,
        "width": 860,
        "height": 680,
        "content": "## 🤖Retrieve Content from Qdrant Vector Store"
      },
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    },
    {
      "id": "b45b18e4-7773-4eec-837b-f43fc0eeb90c",
      "name": "Nota Adhesiva10",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
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        "color": 4,
        "width": 340,
        "height": 320,
        "content": "## 🗣️ Chat with Your Documents"
      },
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    },
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      "name": "Nota Adhesiva11",
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      "position": [
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        "width": 2880,
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        "content": "# Step 1 - Save Documents to Qdrant Vector Store"
      },
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      "name": "Nota Adhesiva12",
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        "width": 1780,
        "height": 880,
        "content": "# Step 2 - Chat with Your Documents from Qdrant Vector Store"
      },
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      "disabled": true,
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      ],
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        "content": "={{ $json.output }}",
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          "value": "My Drive"
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          "mode": "list",
          "value": "root",
          "cachedResultName": "/ (Root folder)"
        },
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        "operation": "update",
        "documentURL": "1ej_qLolUFp1h4eZkrb99T3DWQ3JOwXVEMS3VUjWyVf0"
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      "name": "Herramienta de Almacén Vectorial Qdrant",
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      "webhookId": "382a3b43-b83f-47b1-a276-67c6b98a441a",
      "parameters": {
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        "additionalFields": {
          "appendAttribution": false
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      },
      "credentials": {
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      "position": [
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        "content": "# 🤖 AI-Powered RAG Chatbot with Google Drive Integration\n\nThis workflow creates a powerful RAG (Retrieval-Augmented Generation) chatbot that can process, store, and interact with documents from Google Drive using Qdrant vector storage and Google's Gemini AI.\n\n## How It Works\n\n### Document Processing & Storage 📚\n- Retrieves documents from a specified Google Drive folder\n- Processes and splits documents into manageable chunks\n- Extracts metadata using AI for enhanced search capabilities\n- Stores document vectors in Qdrant for efficient retrieval\n\n### Intelligent Chat Interface 💬\n- Provides a conversational interface powered by Google Gemini\n- Uses RAG to retrieve relevant context from stored documents\n- Maintains chat history in Google Docs for reference\n- Delivers accurate, context-aware responses\n\n### Vector Store Management 🗄️\n- Features secure delete operations with human verification\n- Includes Telegram notifications for important operations\n- Maintains data integrity with proper version control\n- Supports batch processing of documents\n\n## Setup Steps\n\n1. Configure API Credentials:\n   - Set up Google Drive & Docs access\n   - Configure Gemini AI API\n   - Set up Qdrant vector store connection\n   - Add Telegram bot for notifications\n\n2. Configure Document Sources:\n   - Set Google Drive folder ID\n   - Define Qdrant collection name\n   - Set up document processing parameters\n\n3. Test and Deploy:\n   - Verify document processing\n   - Test chat functionality\n   - Confirm vector store operations\n   - Check notification system\n\n\nThis workflow is ideal for organizations needing to create intelligent chatbots that can access and understand large document repositories while maintaining context and providing accurate responses through RAG technology.\n"
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