Wissensbasis-Chatbot mit OpenAI, RAG und MongoDB-Vektoreinbettungen bauen

• •OpenAI API Key
• •MongoDB-Verbindungsstring

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    "instanceId": "074f90e2bb5206c5f405a8aac6551497c72005283a5405fb08207b1b3a78c2b8",
    "templateCredsSetupCompleted": true
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  "nodes": [
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      "name": "Wissensdatenbank-Agent",
      "type": "@n8n/n8n-nodes-langchain.agent",
      "position": [
        220,
        0
      ],
      "parameters": {
        "text": "={{ $json.chatInput }}",
        "options": {
          "systemMessage": "You are the AI assistant for an internal support team at a technology company specializing in advanced software solutions. Your task is to assist internal users by consulting the official product documentation stored in the company’s knowledge base.\n\nAvailable references:\n\nproductDocs: Step-by-step guides, technical configurations, and official manuals extracted from the product’s documentation.\n\nBehavior rules for answering questions:\nAlways consult the official product documentation first using the productDocs tool.\n\nRespond clearly and directly, explaining how to do what is requested.\n\nDo not filter by category unless explicitly asked by the user.\n\nDetect the language of each incoming message individually and respond in that language. Do not use prior conversation language or history to decide the response language.\n\nNever provide links, even if requested. If a user asks for a link, reply:\n“I cannot provide links. If you need specific information, please let me know and I will help with the details.”\n\nUse a professional, direct, and human tone.\n\nKeep answers between 2 and 4 lines unless the user requests more detail.\n\nDo not invent information that is not in the knowledge base.\n\nIf you give numbered steps or lists, number them sequentially (1, 2, 3...) without skipping or repeating numbers, even if the source content uses different numbering."
        },
        "promptType": "define"
      },
      "typeVersion": 1.9
    },
    {
      "id": "56e6fb75-6a97-4466-9e7f-70710c2740d7",
      "name": "OpenAI Chat-Modell",
      "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
      "position": [
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        240
      ],
      "parameters": {
        "model": {
          "__rl": true,
          "mode": "list",
          "value": "gpt-4o-mini"
        },
        "options": {}
      },
      "credentials": {
        "openAiApi": {
          "id": "cJRah9hGPQ7eX4jd",
          "name": "OpenAi account"
        }
      },
      "typeVersion": 1.2
    },
    {
      "id": "e352c32e-7108-4a0d-b081-b2532d96d092",
      "name": "Embeddings OpenAI",
      "type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
      "position": [
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      "parameters": {
        "options": {}
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      "credentials": {
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          "id": "cJRah9hGPQ7eX4jd",
          "name": "OpenAi account"
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      },
      "typeVersion": 1.2
    },
    {
      "id": "74bbfb00-1a00-4131-a291-bce5b79628b4",
      "name": "Bei Klick auf \"Workflow ausführen\"",
      "type": "n8n-nodes-base.manualTrigger",
      "position": [
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        -420
      ],
      "parameters": {},
      "typeVersion": 1
    },
    {
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      "name": "Einfacher Speicher",
      "type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
      "position": [
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        220
      ],
      "parameters": {},
      "typeVersion": 1.3
    },
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      "id": "94561d61-4a01-48b6-b114-dc4d47546ff3",
      "name": "MongoDB Vektorsuche",
      "type": "@n8n/n8n-nodes-langchain.vectorStoreMongoDBAtlas",
      "position": [
        560,
        220
      ],
      "parameters": {
        "mode": "retrieve-as-tool",
        "options": {},
        "toolName": "productDocs",
        "mongoCollection": {
          "__rl": true,
          "mode": "list",
          "value": "n8n-template",
          "cachedResultName": "n8n-template"
        },
        "toolDescription": "retreive documentation",
        "vectorIndexName": "data_index"
      },
      "credentials": {
        "mongoDb": {
          "id": "7riubYENUDZsmjyK",
          "name": "MongoDB account 2"
        }
      },
      "typeVersion": 1.1
    },
    {
      "id": "c473c33d-5681-4f3a-ac36-0d3012e7251f",
      "name": "Dokumentenabschnitt-Lader",
      "type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
      "position": [
        740,
        -260
      ],
      "parameters": {
        "options": {
          "metadata": {
            "metadataValues": [
              {
                "name": "doc_id",
                "value": "={{ $json.documentId }}"
              }
            ]
          }
        },
        "jsonData": "={{ $json.content }}",
        "jsonMode": "expressionData"
      },
      "typeVersion": 1
    },
    {
      "id": "321222cb-1daf-4be2-a6ca-1a03d24f670f",
      "name": "Dokumenten-Chunker",
      "type": "@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter",
      "position": [
        860,
        -100
      ],
      "parameters": {
        "options": {
          "splitCode": "markdown"
        },
        "chunkSize": 3000,
        "chunkOverlap": 200
      },
      "typeVersion": 1
    },
    {
      "id": "716519f5-cec1-4bfe-afbe-614fc23e74b5",
      "name": "MongoDB Vektorspeicher-Einfüger",
      "type": "@n8n/n8n-nodes-langchain.vectorStoreMongoDBAtlas",
      "position": [
        540,
        -420
      ],
      "parameters": {
        "mode": "insert",
        "options": {},
        "mongoCollection": {
          "__rl": true,
          "mode": "list",
          "value": "n8n-template",
          "cachedResultName": "n8n-template"
        },
        "vectorIndexName": "data_index"
      },
      "credentials": {
        "mongoDb": {
          "id": "7riubYENUDZsmjyK",
          "name": "MongoDB account 2"
        }
      },
      "typeVersion": 1.1
    },
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      "id": "a49c19fc-f5f5-4381-b6ba-1bfc12b96135",
      "name": "OpenAI Embeddings-Generator",
      "type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
      "position": [
        480,
        -180
      ],
      "parameters": {
        "options": {}
      },
      "credentials": {
        "openAiApi": {
          "id": "cJRah9hGPQ7eX4jd",
          "name": "OpenAi account"
        }
      },
      "typeVersion": 1.2
    },
    {
      "id": "6de724d5-2941-4e72-af8b-302ca2cf2ca0",
      "name": "Google Docs-Importer",
      "type": "n8n-nodes-base.googleDocs",
      "position": [
        200,
        -420
      ],
      "parameters": {
        "operation": "get",
        "documentURL": "https://docs.google.com/document/d/1gvgp71e9edob8WLqFIYCdzC7kUq3pLO37VKb-a-vVW4/edit?tab=t.0"
      },
      "credentials": {
        "googleDocsOAuth2Api": {
          "id": "FNXMwqMf7vl1WUFj",
          "name": "Google Docs account"
        }
      },
      "typeVersion": 2
    },
    {
      "id": "4f30bb21-72f0-4d13-b610-2ec218ad31b1",
      "name": "Haftnotiz",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -420,
        -440
      ],
      "parameters": {
        "color": 5,
        "content": "Run this workflow manually to import and index Google Docs product documentation into MongoDB with vector embeddings for fast search."
      },
      "typeVersion": 1
    },
    {
      "id": "25fd33d5-041b-4f01-a46b-1bacabd88376",
      "name": "Bei Empfang einer Chat-Nachricht",
      "type": "@n8n/n8n-nodes-langchain.chatTrigger",
      "position": [
        40,
        0
      ],
      "webhookId": "427ead97-647d-49c7-82d7-e76b40664fd1",
      "parameters": {
        "options": {}
      },
      "typeVersion": 1.1
    },
    {
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      "name": "Haftnotiz1",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -420,
        40
      ],
      "parameters": {
        "color": 4,
        "content": "This workflow uses retrieval-augmented generation (RAG) to answer user questions by searching the MongoDB vector store and generating AI responses with context."
      },
      "typeVersion": 1
    },
    {
      "id": "39eee95c-b332-4ae4-bde9-aaf0fe5e0546",
      "name": "Haftnotiz2",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        1060,
        -380
      ],
      "parameters": {
        "height": 520,
        "content": "Search Index Example \n\n{\n  \"mappings\": {\n    \"dynamic\": false,\n    \"fields\": {\n      \"_id\": {\n        \"type\": \"string\"\n      },\n      \"text\": {\n        \"type\": \"string\"\n      },\n      \"embedding\": {\n        \"type\": \"knnVector\",\n        \"dimensions\": 1536,\n        \"similarity\": \"cosine\"\n      },\n      \"source\": {\n        \"type\": \"string\"\n      },\n      \"doc_id\": {\n        \"type\": \"string\"\n      }\n    }\n  }\n}\n"
      },
      "typeVersion": 1
    }
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