LENOHA-Kundensupport

Name: LENOHA-Kundensupport
Rating: 4.5 (10 reviews)
Author: Maxim Osipovs

Experte

Dies ist ein Automatisierungsworkflow mit 20 Nodes. Hauptsächlich werden If, Set, Merge, GoogleSheets, ManualTrigger und andere Nodes verwendet. Ein zweigleisiges Kunden-Support-System mit Google Sheets, Vektoren und Gemini LLM erstellen

Voraussetzungen

•Google Sheets API-Anmeldedaten

Verwendete Nodes (20)

DocumentDefaultDataLoader

EmbeddingsHuggingFaceInference

Kategorie

Workflow-Vorschau

Visualisierung der Node-Verbindungen, mit Zoom und Pan

Bei Klick auf 'Workflow ausführen'

Embeddings HuggingFace Inference

Standard-Datenlader

Bei Empfang einer Chat-Nachricht

Auf Chat antworten

Embeddings HuggingFace Inference2

Auf Chat antworten1

Wissensdatenbank

Fragen extrahieren

Embeddings generieren & speichern

Embeddings abrufen & bewerten

Fragentyp bestimmen

Entsprechende Antworten abrufen

Chat-Nachricht weiterleiten

Chat-Modell

React Flow

Workflow exportieren

Kopieren Sie die folgende JSON-Konfiguration und importieren Sie sie in n8n

{
  "id": "FqCqWFe2l39CkWJJ",
  "meta": {
    "instanceId": "1587aac4fefe327cb562f0b60579d4f70119b083ab9ab93deb05f9d7431f8788",
    "templateCredsSetupCompleted": true
  },
  "name": "LENOHA-Customer-Support",
  "tags": [],
  "nodes": [
    {
      "id": "a1572a1d-1d1f-4abc-8f1d-364e58381368",
      "name": "Bei Klick auf 'Workflow ausführen'",
      "type": "n8n-nodes-base.manualTrigger",
      "position": [
        -64,
        -432
      ],
      "parameters": {},
      "typeVersion": 1
    },
    {
      "id": "31098d41-56e5-4859-8218-41b79b6878c1",
      "name": "Embeddings HuggingFace Inference",
      "type": "@n8n/n8n-nodes-langchain.embeddingsHuggingFaceInference",
      "position": [
        480,
        -208
      ],
      "parameters": {
        "options": {}
      },
      "credentials": {
        "huggingFaceApi": {
          "id": "pTwemQjQAyHf8rnL",
          "name": "HuggingFaceApi account"
        }
      },
      "typeVersion": 1
    },
    {
      "id": "41ff1a9f-9b1a-4ae4-a93b-f55c63ad430b",
      "name": "Standard-Datenlader",
      "type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
      "position": [
        688,
        -208
      ],
      "parameters": {
        "options": {}
      },
      "typeVersion": 1.1
    },
    {
      "id": "d22d074b-35f8-4a65-9b2d-1e9d697076f5",
      "name": "Bei Empfang einer Chat-Nachricht",
      "type": "@n8n/n8n-nodes-langchain.chatTrigger",
      "position": [
        -128,
        496
      ],
      "webhookId": "fb5ca44e-a73c-44dd-834b-2df1ca9b49d6",
      "parameters": {
        "options": {
          "responseMode": "responseNodes"
        }
      },
      "typeVersion": 1.3
    },
    {
      "id": "8209ba33-0036-45e7-993b-d3a1ee26c4db",
      "name": "Auf Chat antworten",
      "type": "@n8n/n8n-nodes-langchain.chat",
      "position": [
        1104,
        192
      ],
      "parameters": {
        "message": "={{ $json.Answer }}",
        "options": {}
      },
      "typeVersion": 1
    },
    {
      "id": "9142cb58-be78-476c-a545-83f6f75c1986",
      "name": "Kurznotiz1",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -128,
        -560
      ],
      "parameters": {
        "width": 1104,
        "height": 512,
        "content": "## 0. Create Embeddings\n- Get common questions from your own knowledge base\n- Use persistent vector store for embeddings"
      },
      "typeVersion": 1
    },
    {
      "id": "c36b56dc-375f-417f-ae07-38433f800cca",
      "name": "Kurznotiz3",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -256,
        384
      ],
      "parameters": {
        "color": 2,
        "width": 368,
        "height": 272,
        "content": "## 1. Receive a user message"
      },
      "typeVersion": 1
    },
    {
      "id": "5f7477ea-3529-427e-a565-8653e4ba5210",
      "name": "Kurznotiz",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        272,
        32
      ],
      "parameters": {
        "color": 3,
        "width": 528,
        "height": 480,
        "content": "## 2. Evaluate user message\n- Generate embeddings for the user message\n- Check against your vector store for relevant questions\n- IMPORTANT: You need to fine-tune your similarity score threshold within the If/Else-Node"
      },
      "typeVersion": 1
    },
    {
      "id": "b5a86a31-cc26-43ec-bebc-18c3e6077777",
      "name": "Kurznotiz2",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        832,
        32
      ],
      "parameters": {
        "color": 4,
        "width": 512,
        "height": 352,
        "content": "## 3.1 Reply from FAQ\n- If the user query is similar to a question from your knowledge base, the pre-defined, static answer is returned"
      },
      "typeVersion": 1
    },
    {
      "id": "361a09fd-2384-478b-b05e-3dc07fcc6c40",
      "name": "Kurznotiz4",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        816,
        432
      ],
      "parameters": {
        "color": 4,
        "width": 784,
        "height": 368,
        "content": "## 3.2 Reply via LLM\n- If the user query is not found within the knowledge base, a smaller LLM can answer"
      },
      "typeVersion": 1
    },
    {
      "id": "ea069bda-92ef-441b-ab7c-79aa85602d0a",
      "name": "Embeddings HuggingFace Inference2",
      "type": "@n8n/n8n-nodes-langchain.embeddingsHuggingFaceInference",
      "position": [
        432,
        368
      ],
      "parameters": {
        "options": {}
      },
      "credentials": {
        "huggingFaceApi": {
          "id": "pTwemQjQAyHf8rnL",
          "name": "HuggingFaceApi account"
        }
      },
      "typeVersion": 1
    },
    {
      "id": "f04bce14-f24f-4ef2-968a-5d9c529ee9de",
      "name": "Auf Chat antworten1",
      "type": "@n8n/n8n-nodes-langchain.chat",
      "position": [
        1392,
        544
      ],
      "parameters": {
        "message": "={{ $json.content.parts[0].text }}",
        "options": {}
      },
      "typeVersion": 1
    },
    {
      "id": "4a302856-38e1-413f-8810-d140a99a0187",
      "name": "Wissensdatenbank",
      "type": "n8n-nodes-base.googleSheets",
      "position": [
        144,
        -432
      ],
      "parameters": {
        "options": {
          "returnFirstMatch": false
        },
        "sheetName": {
          "__rl": true,
          "mode": "list",
          "value": 1748220104,
          "cachedResultUrl": "https://docs.google.com/spreadsheets/d/1tSEu_-7KAupdkFJGzoPBc-zqBBAmPgGoc89JyvqhY90/edit#gid=1748220104",
          "cachedResultName": "Tabellenblatt2"
        },
        "documentId": {
          "__rl": true,
          "mode": "list",
          "value": "1tSEu_-7KAupdkFJGzoPBc-zqBBAmPgGoc89JyvqhY90",
          "cachedResultUrl": "https://docs.google.com/spreadsheets/d/1tSEu_-7KAupdkFJGzoPBc-zqBBAmPgGoc89JyvqhY90/edit?usp=drivesdk",
          "cachedResultName": "Stripe_Dummy_FAQ"
        }
      },
      "credentials": {
        "googleSheetsOAuth2Api": {
          "id": "DOSJUMfKHxvCgJTm",
          "name": "Google Sheets account"
        }
      },
      "typeVersion": 4.7,
      "alwaysOutputData": false
    },
    {
      "id": "d9c346d2-4b07-460d-8154-1a0749e4d921",
      "name": "Fragen extrahieren",
      "type": "n8n-nodes-base.set",
      "position": [
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      "parameters": {
        "options": {},
        "assignments": {
          "assignments": [
            {
              "id": "48e49d98-11b7-478a-91fb-ecf198fdd9e7",
              "name": "row_number",
              "type": "number",
              "value": "={{ $json.row_number }}"
            },
            {
              "id": "9b40b5a1-a73b-4af3-a96c-bc52226cc4bc",
              "name": "Question",
              "type": "string",
              "value": "={{ $json.Question }}"
            }
          ]
        }
      },
      "typeVersion": 3.4
    },
    {
      "id": "dc2510c2-0cc2-436c-92eb-854aae56efd8",
      "name": "Embeddings generieren & speichern",
      "type": "@n8n/n8n-nodes-langchain.vectorStoreInMemory",
      "position": [
        560,
        -432
      ],
      "parameters": {
        "mode": "insert",
        "memoryKey": {
          "__rl": true,
          "mode": "list",
          "value": "vector_store_key"
        },
        "embeddingBatchSize": 1
      },
      "typeVersion": 1.3
    },
    {
      "id": "672b7a01-d236-412b-acef-09b1e7674ae9",
      "name": "Embeddings abrufen & bewerten",
      "type": "@n8n/n8n-nodes-langchain.vectorStoreInMemory",
      "position": [
        336,
        208
      ],
      "parameters": {
        "mode": "load",
        "topK": 2,
        "prompt": "={{ $json.chatInput }}",
        "memoryKey": {
          "__rl": true,
          "mode": "list",
          "value": "vector_store_key"
        }
      },
      "typeVersion": 1.2
    },
    {
      "id": "b5d2db8d-b14c-4162-8fd1-c3163efd2b72",
      "name": "Fragentyp bestimmen",
      "type": "n8n-nodes-base.if",
      "position": [
        624,
        208
      ],
      "parameters": {
        "options": {},
        "conditions": {
          "options": {
            "version": 2,
            "leftValue": "",
            "caseSensitive": true,
            "typeValidation": "strict"
          },
          "combinator": "and",
          "conditions": [
            {
              "id": "f6f2c4ab-cebf-4dea-baec-5ec3e50e88ab",
              "operator": {
                "type": "number",
                "operation": "gte"
              },
              "leftValue": "={{ $json.score }}",
              "rightValue": 0.8
            }
          ]
        }
      },
      "typeVersion": 2.2
    },
    {
      "id": "d182bbc1-1822-424f-8c05-b3503b53b6c1",
      "name": "Entsprechende Antworten abrufen",
      "type": "n8n-nodes-base.googleSheets",
      "position": [
        880,
        192
      ],
      "parameters": {
        "options": {},
        "filtersUI": {
          "values": [
            {
              "lookupValue": "={{ $json.document.pageContent }}",
              "lookupColumn": "Question"
            }
          ]
        },
        "sheetName": {
          "__rl": true,
          "mode": "list",
          "value": 1748220104,
          "cachedResultUrl": "https://docs.google.com/spreadsheets/d/1tSEu_-7KAupdkFJGzoPBc-zqBBAmPgGoc89JyvqhY90/edit#gid=1748220104",
          "cachedResultName": "Tabellenblatt2"
        },
        "documentId": {
          "__rl": true,
          "mode": "list",
          "value": "1tSEu_-7KAupdkFJGzoPBc-zqBBAmPgGoc89JyvqhY90",
          "cachedResultUrl": "https://docs.google.com/spreadsheets/d/1tSEu_-7KAupdkFJGzoPBc-zqBBAmPgGoc89JyvqhY90/edit?usp=drivesdk",
          "cachedResultName": "Stripe_Dummy_FAQ"
        }
      },
      "credentials": {
        "googleSheetsOAuth2Api": {
          "id": "DOSJUMfKHxvCgJTm",
          "name": "Google Sheets account"
        }
      },
      "typeVersion": 4.7
    },
    {
      "id": "2f3bf992-7f08-40f4-86ab-16242abc79d5",
      "name": "Chat-Nachricht weiterleiten",
      "type": "n8n-nodes-base.merge",
      "position": [
        880,
        544
      ],
      "parameters": {
        "mode": "chooseBranch",
        "useDataOfInput": 2
      },
      "typeVersion": 3.2
    },
    {
      "id": "69646d36-32f5-4f04-bf15-4463398d969d",
      "name": "Chat-Modell",
      "type": "@n8n/n8n-nodes-langchain.googleGemini",
      "position": [
        1072,
        544
      ],
      "parameters": {
        "modelId": {
          "__rl": true,
          "mode": "list",
          "value": "models/gemini-2.5-flash-lite",
          "cachedResultName": "models/gemini-2.5-flash-lite"
        },
        "options": {
          "systemMessage": "# Role:\nYou are a conversational AI assistant. Your primary function is to handle casual conversation and general inquiries that do not pertain to specific company policies or user account details.\n\n# Task:\nEngage the user in a friendly, helpful, and brief manner. You are the fallback agent for queries that could not be answered by our primary FAQ knowledge base.\n\n# CRITICAL RULES:\nScope Limitation: You MUST NOT attempt to answer any specific questions related to policies, pricing, fees, disputes, payout schedules, security, or personal account information. Your knowledge base does not contain this information.\nNo Speculation: If you are unsure about a user's query, NEVER guess the answer. It is safer to state your limitation.\nBrevity: Keep your responses concise and to the point (1-2 sentences).\n\n# Response Strategy & Disclaimer:\nIf a user's question seems to be asking for specific details, even if it appears simple, you must use the following disclaimer. Do not attempt to answer the question first.\n\n# Disclaimer: \"I am not equipped to handle specific policy or account-level questions to ensure your information is kept safe and accurate. For detailed assistance, I recommend checking our official Help Center or contacting a human support agent.\""
        },
        "messages": {
          "values": [
            {
              "content": "={{ $json.chatInput }}"
            }
          ]
        }
      },
      "credentials": {
        "googlePalmApi": {
          "id": "GcENObs5mnIvIGOc",
          "name": "Google Gemini(PaLM) Api account"
        }
      },
      "typeVersion": 1
    }
  ],
  "active": false,
  "pinData": {},
  "settings": {
    "executionOrder": "v1"
  },
  "versionId": "650ce0df-104b-417a-903d-160f095dc823",
  "connections": {
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    "d9c346d2-4b07-460d-8154-1a0749e4d921": {
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    "41ff1a9f-9b1a-4ae4-a93b-f55c63ad430b": {
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    "31098d41-56e5-4859-8218-41b79b6878c1": {
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}

Häufig gestellte Fragen

Wie verwende ich diesen Workflow?

Kopieren Sie den obigen JSON-Code, erstellen Sie einen neuen Workflow in Ihrer n8n-Instanz und wählen Sie "Aus JSON importieren". Fügen Sie die Konfiguration ein und passen Sie die Anmeldedaten nach Bedarf an.

Für welche Szenarien ist dieser Workflow geeignet?

Experte

Ist es kostenpflichtig?

Dieser Workflow ist völlig kostenlos. Beachten Sie jedoch, dass Drittanbieterdienste (wie OpenAI API), die im Workflow verwendet werden, möglicherweise kostenpflichtig sind.