🤖 Chatbot IA piloté par l'IA pour votre document + Google Drive + Gemini + Qdrant

• •Point de terminaison HTTP Webhook (généré automatiquement par n8n)
• •Token Bot Telegram
• •Informations d'identification Google Drive API
• •Clé API OpenAI
• •Informations de connexion au serveur Qdrant
• •Clé API 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": "Chargeur de données",
      "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": "Séparateur de tokens",
      "type": "@n8n/n8n-nodes-langchain.textSplitterTokenSplitter",
      "position": [
        720,
        860
      ],
      "parameters": {
        "chunkSize": 3000
      },
      "typeVersion": 1
    },
    {
      "id": "82aa7016-f3af-4de8-a410-b1c802041213",
      "name": "Magasin de vecteurs 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": "Boucle sur les éléments",
      "type": "n8n-nodes-base.splitInBatches",
      "onError": "continueRegularOutput",
      "position": [
        180,
        -140
      ],
      "parameters": {
        "options": {}
      },
      "typeVersion": 3
    },
    {
      "id": "b0f9b066-62de-41bc-afce-dcb4d20d100c",
      "name": "Attente",
      "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": "Lors du clic sur 'Tester le workflow'",
      "type": "n8n-nodes-base.manualTrigger",
      "position": [
        -1160,
        -280
      ],
      "parameters": {},
      "typeVersion": 1
    },
    {
      "id": "cfbb6b77-142f-4689-b051-436f40ababe6",
      "name": "Modèle 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": "Fusion",
      "type": "n8n-nodes-base.merge",
      "position": [
        820,
        200
      ],
      "parameters": {
        "mode": "combine",
        "options": {},
        "combineBy": "combineAll"
      },
      "typeVersion": 3
    },
    {
      "id": "ea143b9f-9bd8-4f3f-8979-5a0fd2691e9d",
      "name": "Extraire les métadonnées",
      "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": "Obtenir le contenu du fichier",
      "type": "n8n-nodes-base.extractFromFile",
      "position": [
        820,
        -180
      ],
      "parameters": {
        "options": {},
        "operation": "text"
      },
      "typeVersion": 1
    },
    {
      "id": "cdbb8bbc-967c-4f01-b844-4d31328153df",
      "name": "Télécharger le fichier depuis 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": "Trouver les IDs de fichiers dans le dossier 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 du dossier 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": "Supprimer les points Qdrant par ID de fichier",
      "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": "Nom de la collection 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": "Liste des IDs de fichiers",
      "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": "Fusion1",
      "type": "n8n-nodes-base.merge",
      "position": [
        -340,
        340
      ],
      "parameters": {
        "mode": "combine",
        "options": {},
        "combineBy": "combineByPosition"
      },
      "typeVersion": 3
    },
    {
      "id": "cfe399fb-8560-4ac4-98f7-4f539ae6a52c",
      "name": "Fusion2",
      "type": "n8n-nodes-base.merge",
      "position": [
        -80,
        -140
      ],
      "parameters": {},
      "typeVersion": 3
    },
    {
      "id": "1059da47-2abd-4884-b9c2-6b7d1623e31d",
      "name": "Note adhésive",
      "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": "Confirmer la suppression des points 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": "Note adhésive1",
      "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": "Envoyer un message de refus",
      "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": "Note adhésive2",
      "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": "Note adhésive3",
      "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": "Note adhésive4",
      "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": "Note adhésive5",
      "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": "Note adhésive6",
      "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": "Note adhésive7",
      "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": "Note adhésive8",
      "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": "Agent 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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      "name": "Modèle de chat Google Gemini1",
      "type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
      "position": [
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      "parameters": {
        "options": {
          "maxOutputTokens": 8192
        },
        "modelName": "models/gemini-2.0-flash-exp"
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      "credentials": {
        "googlePalmApi": {
          "id": "L9UNQHflYlyF9Ngd",
          "name": "Google Gemini(PaLM) Api account"
        }
      },
      "typeVersion": 1
    },
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      "name": "text-embeddings-3-large1",
      "type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
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      "credentials": {
        "openAiApi": {
          "id": "jEMSvKmtYfzAkhe6",
          "name": "OpenAi account"
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      },
      "typeVersion": 1
    },
    {
      "id": "80056067-5d2b-4ac7-8e69-e75bd66a1ad4",
      "name": "Note adhésive9",
      "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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      "name": "Note adhésive10",
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        "width": 340,
        "height": 320,
        "content": "## 🗣️ Chat with Your Documents"
      },
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    },
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      "name": "Note adhésive11",
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        "content": "# Step 1 - Save Documents to Qdrant Vector Store"
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        "width": 1780,
        "height": 880,
        "content": "# Step 2 - Chat with Your Documents from Qdrant Vector Store"
      },
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      "name": "Google Drive",
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        "name": "=Nostr Chatbot - Avatar - {{ $now }}",
        "content": "={{ $json.output }}",
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          "mode": "list",
          "value": "My Drive"
        },
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        },
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        "operation": "update",
        "documentURL": "1ej_qLolUFp1h4eZkrb99T3DWQ3JOwXVEMS3VUjWyVf0"
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      "name": "Outil Magasin de vecteurs Qdrant",
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        "toolDescription": "Retrieve information about Nostr or Damus users",
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      "webhookId": "382a3b43-b83f-47b1-a276-67c6b98a441a",
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        "additionalFields": {
          "appendAttribution": false
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      },
      "credentials": {
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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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