DBBot-telegram/Создание базы (TG Text) (2).json

{
  "name": "Создание базы (TG Text)",
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      "parameters": {
        "mode": "insert",
        "qdrantCollection": {
          "__rl": true,
          "value": "=11111",
          "mode": "id"
        },
        "options": {}
      },
      "type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant",
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      "name": "Qdrant Vector Store",
      "credentials": {
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          "id": "gS1LJOMgnR7VJFRO",
          "name": "QdrantApi account 2"
        }
      }
    },
    {
      "parameters": {
        "model": "qwen3-embedding:8b"
      },
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      "name": "Embeddings Ollama",
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        "ollamaApi": {
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    },
    {
      "parameters": {
        "chunkSize": 500,
        "chunkOverlap": 50
      },
      "type": "@n8n/n8n-nodes-langchain.textSplitterCharacterTextSplitter",
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      "name": "Character Text Splitter"
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    {
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        "httpMethod": "POST",
        "path": "tg-inbox",
        "options": {}
      },
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      "notesInFlow": false
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        "options": {}
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      "name": "Default Data Loader"
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    {
      "parameters": {
        "jsCode": "// В n8n данные из POST запроса всегда лежат в объекте body\nconst msg = $input.item.json.body;\n\n// 1. Берем текст\nconst rawText = msg.text || \"\"; \n\n// 2. Достаем метаданные из объекта metadata, который прислал Python\nconst meta = msg.metadata || {};\n\n// 3. Извлекаем имя и ID из объекта metadata\nconst senderName = meta.sender_name || \"Unknown\";\nconst senderId = meta.sender_id || \"0\";\n\n// 4. Формируем результат для Qdrant\nreturn {\n  json: {\n    content: `Пользователь ${senderName} (ID: ${senderId}) написал: ${rawText}`,\n    metadata: {\n      ...meta, // Копируем всю метадату (date, message_id, group_id и т.д.)\n      sender_name: senderName,\n      sender_id: senderId,\n      original_text: rawText\n    }\n  }\n};\n"
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      ],
      "id": "0ff56c0f-9208-4ae9-920c-4332c09f6df4",
      "name": "Code in JavaScript"
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  "pinData": {},
  "connections": {
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          {
            "node": "Code in JavaScript",
            "type": "main",
            "index": 0
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    },
    "Embeddings Ollama": {
      "ai_embedding": [
        [
          {
            "node": "Qdrant Vector Store",
            "type": "ai_embedding",
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        ]
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    "Default Data Loader": {
      "ai_document": [
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            "node": "Qdrant Vector Store",
            "type": "ai_document",
            "index": 0
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        ]
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    },
    "Code in JavaScript": {
      "main": [
        [
          {
            "node": "Qdrant Vector Store",
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            "index": 0
          }
        ]
      ]
    },
    "Character Text Splitter": {
      "ai_textSplitter": [
        [
          {
            "node": "Default Data Loader",
            "type": "ai_textSplitter",
            "index": 0
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        ]
      ]
    }
  },
  "active": false,
  "settings": {
    "executionOrder": "v1",
    "binaryMode": "separate",
    "availableInMCP": false
  },
  "versionId": "f5079d8e-590c-4db5-988a-3fe709346a4a",
  "meta": {
    "templateCredsSetupCompleted": true,
    "instanceId": "96706479c2e398d2a4e75bb05002310bb6030e8268be45294cab77c6640a0fe6"
  },
  "id": "QpXms3TNMSqlQD2ULX3WO",
  "tags": []
}