Extrahieren personenbezogener Daten mit selbstgehostetem LLM Mistral NeMo

Name: Extrahieren personenbezogener Daten mit selbstgehostetem LLM Mistral NeMo
Rating: 4.5 (10 reviews)
Author: Yulia

Fortgeschritten

Dies ist ein Building Blocks, AI-Bereich Automatisierungsworkflow mit 13 Nodes. Hauptsächlich werden Set, ChainLlm, ChatTrigger, LmChatOllama, OutputParserAutofixing und andere Nodes verwendet, kombiniert mit KI-Technologie für intelligente Automatisierung. Extrahieren Sie personenbezogene Daten mit selbst gehostetem LLM Mistral NeMo

Voraussetzungen

•KI-Service API Key (z.B. OpenAI, Anthropic)

Verwendete Nodes (13)

OutputParserAutofixing

OutputParserStructured

Kategorie

Bausteine

Künstliche Intelligenz

Workflow-Vorschau

Visualisierung der Node-Verbindungen, mit Zoom und Pan

Bei Empfang einer Chat-Nachricht

Ollama Chat Model

Auto-fixing Output Parser

Structured Output Parser

Basic LLM Kette

On Error

Extract JSON Output

React Flow

Workflow exportieren

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

{
  "id": "HMoUOg8J7RzEcslH",
  "meta": {
    "instanceId": "3f91626b10fcfa8a3d3ab8655534ff3e94151838fd2709ecd2dcb14afb3d061a",
    "templateCredsSetupCompleted": true
  },
  "name": "Extract personal data with a self-hosted LLM Mistral NeMo",
  "tags": [],
  "nodes": [
    {
      "id": "7e67ae65-88aa-4e48-aa63-2d3a4208cf4b",
      "name": "Bei Empfang einer Chat-Nachricht",
      "type": "@n8n/n8n-nodes-langchain.chatTrigger",
      "position": [
        -500,
        20
      ],
      "webhookId": "3a7b0ea1-47f3-4a94-8ff2-f5e1f3d9dc32",
      "parameters": {
        "options": {}
      },
      "typeVersion": 1.1
    },
    {
      "id": "e064921c-69e6-4cfe-a86e-4e3aa3a5314a",
      "name": "Ollama Chat Model",
      "type": "@n8n/n8n-nodes-langchain.lmChatOllama",
      "position": [
        -280,
        420
      ],
      "parameters": {
        "model": "mistral-nemo:latest",
        "options": {
          "useMLock": true,
          "keepAlive": "2h",
          "temperature": 0.1
        }
      },
      "credentials": {
        "ollamaApi": {
          "id": "vgKP7LGys9TXZ0KK",
          "name": "Ollama account"
        }
      },
      "typeVersion": 1
    },
    {
      "id": "fe1379da-a12e-4051-af91-9d67a7c9a76b",
      "name": "Auto-fixing Output Parser",
      "type": "@n8n/n8n-nodes-langchain.outputParserAutofixing",
      "position": [
        -200,
        220
      ],
      "parameters": {
        "options": {
          "prompt": "Instructions:\n--------------\n{instructions}\n--------------\nCompletion:\n--------------\n{completion}\n--------------\n\nAbove, the Completion did not satisfy the constraints given in the Instructions.\nError:\n--------------\n{error}\n--------------\n\nPlease try again. Please only respond with an answer that satisfies the constraints laid out in the Instructions:"
        }
      },
      "typeVersion": 1
    },
    {
      "id": "b6633b00-6ebb-43ca-8e5c-664a53548c17",
      "name": "Structured Output Parser",
      "type": "@n8n/n8n-nodes-langchain.outputParserStructured",
      "position": [
        60,
        400
      ],
      "parameters": {
        "schemaType": "manual",
        "inputSchema": "{\n  \"type\": \"object\",\n  \"properties\": {\n    \"name\": {\n      \"type\": \"string\",\n      \"description\": \"Name of the user\"\n    },\n    \"surname\": {\n      \"type\": \"string\",\n      \"description\": \"Surname of the user\"\n    },\n    \"commtype\": {\n      \"type\": \"string\",\n      \"enum\": [\"email\", \"phone\", \"other\"],\n      \"description\": \"Method of communication\"\n    },\n    \"contacts\": {\n      \"type\": \"string\",\n      \"description\": \"Contact details. ONLY IF PROVIDED\"\n    },\n    \"timestamp\": {\n      \"type\": \"string\",\n      \"format\": \"date-time\",\n      \"description\": \"When the communication occurred\"\n    },\n    \"subject\": {\n      \"type\": \"string\",\n      \"description\": \"Brief description of the communication topic\"\n    }\n  },\n  \"required\": [\"name\", \"commtype\"]\n}"
      },
      "typeVersion": 1.2
    },
    {
      "id": "23681a6c-cf62-48cb-86ee-08d5ce39bc0a",
      "name": "Basic LLM Kette",
      "type": "@n8n/n8n-nodes-langchain.chainLlm",
      "onError": "continueErrorOutput",
      "position": [
        -240,
        20
      ],
      "parameters": {
        "messages": {
          "messageValues": [
            {
              "message": "=Please analyse the incoming user request. Extract information according to the JSON schema. Today is: \"{{ $now.toISO() }}\""
            }
          ]
        },
        "hasOutputParser": true
      },
      "typeVersion": 1.5
    },
    {
      "id": "8f4d1b4b-58c0-41ec-9636-ac555e440821",
      "name": "On Error",
      "type": "n8n-nodes-base.noOp",
      "position": [
        200,
        140
      ],
      "parameters": {},
      "typeVersion": 1
    },
    {
      "id": "f4d77736-4470-48b4-8f61-149e09b70e3e",
      "name": "Haftnotiz",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -560,
        -160
      ],
      "parameters": {
        "color": 2,
        "width": 960,
        "height": 500,
        "content": "## Update data source\nWhen you change the data source, remember to update the `Prompt Source (User Message)` setting in the **Basic LLM Chain node**."
      },
      "typeVersion": 1
    },
    {
      "id": "5fd273c8-e61d-452b-8eac-8ac4b7fff6c2",
      "name": "Haftnotiz1",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -560,
        340
      ],
      "parameters": {
        "color": 2,
        "width": 440,
        "height": 220,
        "content": "## Configure local LLM\nOllama offers additional settings \nto optimize model performance\nor memory usage."
      },
      "typeVersion": 1
    },
    {
      "id": "63cbf762-0134-48da-a6cd-0363e870decd",
      "name": "Haftnotiz2",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        0,
        340
      ],
      "parameters": {
        "color": 2,
        "width": 400,
        "height": 220,
        "content": "## Define JSON Schema"
      },
      "typeVersion": 1
    },
    {
      "id": "9625294f-3cb4-4465-9dae-9976e0cf5053",
      "name": "Extract JSON Output",
      "type": "n8n-nodes-base.set",
      "position": [
        200,
        -80
      ],
      "parameters": {
        "mode": "raw",
        "options": {},
        "jsonOutput": "={{ $json.output }}\n"
      },
      "typeVersion": 3.4
    },
    {
      "id": "2c6fba3b-0ffe-4112-b904-823f52cc220b",
      "name": "Haftnotiz3",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -560,
        200
      ],
      "parameters": {
        "width": 960,
        "height": 120,
        "content": "If the LLM response does not pass \nthe **Structured Output Parser** checks,\n**Auto-Fixer** will call the model again with a different \nprompt to correct the original response."
      },
      "typeVersion": 1
    },
    {
      "id": "c73ba1ca-d727-4904-a5fd-01dd921a4738",
      "name": "Haftnotiz6",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -560,
        460
      ],
      "parameters": {
        "height": 80,
        "content": "The same LLM connects to both **Basic LLM Chain** and to the **Auto-fixing Output Parser**. \n"
      },
      "typeVersion": 1
    },
    {
      "id": "193dd153-8511-4326-aaae-47b89d0cd049",
      "name": "Haftnotiz7",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        200,
        440
      ],
      "parameters": {
        "width": 200,
        "height": 100,
        "content": "When the LLM model responds, the output is checked in the **Structured Output Parser**"
      },
      "typeVersion": 1
    }
  ],
  "active": false,
  "pinData": {},
  "settings": {
    "executionOrder": "v1"
  },
  "versionId": "9f3721a8-f340-43d5-89e7-3175c29c2f3a",
  "connections": {
    "Basic LLM Chain": {
      "main": [
        [
          {
            "node": "9625294f-3cb4-4465-9dae-9976e0cf5053",
            "type": "main",
            "index": 0
          }
        ],
        [
          {
            "node": "8f4d1b4b-58c0-41ec-9636-ac555e440821",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "e064921c-69e6-4cfe-a86e-4e3aa3a5314a": {
      "ai_languageModel": [
        [
          {
            "node": "fe1379da-a12e-4051-af91-9d67a7c9a76b",
            "type": "ai_languageModel",
            "index": 0
          },
          {
            "node": "Basic LLM Chain",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "b6633b00-6ebb-43ca-8e5c-664a53548c17": {
      "ai_outputParser": [
        [
          {
            "node": "fe1379da-a12e-4051-af91-9d67a7c9a76b",
            "type": "ai_outputParser",
            "index": 0
          }
        ]
      ]
    },
    "fe1379da-a12e-4051-af91-9d67a7c9a76b": {
      "ai_outputParser": [
        [
          {
            "node": "Basic LLM Chain",
            "type": "ai_outputParser",
            "index": 0
          }
        ]
      ]
    },
    "7e67ae65-88aa-4e48-aa63-2d3a4208cf4b": {
      "main": [
        [
          {
            "node": "Basic LLM Chain",
            "type": "main",
            "index": 0
          }
        ]
      ]
    }
  }
}

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?

Fortgeschritten - Bausteine, Künstliche Intelligenz

Ist es kostenpflichtig?

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