Extract personal data with a self-hosted LLM Mistral NeMo
Extract sensitive personal data from unstructured text using a self-hosted Mistral NeMo Large Language Model (LLM) with this n8n workflow. This automation connects an AI chat trigger, an Ollama Chat Model, and various AI output parsers to identify and structure personal information from incoming chat messages. Businesses can leverage this for privacy-preserving data extraction, such as automatically redacting personally identifiable information (PII) from customer support transcripts before storage, or securely processing inbound inquiries for specific data points without relying on external, cloud-based LLM providers. This workflow significantly reduces the manual effort and potential security risks associated with handling sensitive data, ensuring compliance and improving data governance by keeping data processing in-house.
Workflow JSON
{"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": "When chat message received", "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": "", "name": "[Your ollamaApi]"}}, "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 Chain", "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": "Sticky Note", "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": "Sticky Note1", "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": "Sticky Note2", "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": "Sticky Note3", "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": "Sticky Note6", "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": "Sticky Note7", "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": "Extract JSON Output", "type": "main", "index": 0}], [{"node": "On Error", "type": "main", "index": 0}]]}, "Ollama Chat Model": {"ai_languageModel": [[{"node": "Auto-fixing Output Parser", "type": "ai_languageModel", "index": 0}, {"node": "Basic LLM Chain", "type": "ai_languageModel", "index": 0}]]}, "Structured Output Parser": {"ai_outputParser": [[{"node": "Auto-fixing Output Parser", "type": "ai_outputParser", "index": 0}]]}, "Auto-fixing Output Parser": {"ai_outputParser": [[{"node": "Basic LLM Chain", "type": "ai_outputParser", "index": 0}]]}, "When chat message received": {"main": [[{"node": "Basic LLM Chain", "type": "main", "index": 0}]]}}}How to Import This Workflow
- 1Copy the workflow JSON above using the Copy Workflow JSON button.
- 2Open your n8n instance and go to Workflows.
- 3Click Import from JSON and paste the copied workflow.
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