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Extract personal data with a self-hosted LLM Mistral NeMo

13 nodes · chat, lmChatOllama, outputParserAutofixing, outputParserStructured, chainLlm

Engine
n8n
Nodes
13
Trigger
scheduled/triggered
Source file
1486_Noop_Stickynote_Automation_Triggered.json

Workflow flow

IntegrationsChatLmChatOllamaOutputParserAutofixingOutputParserStructuredChainLlm@n8n/n8n-nodes-langchain.chat@n8n/n8n-nodes-langchain.lmChatOllama@n8n/n8n-nodes-langchain.outputParserAutofixing@n8n/n8n-nodes-langchain.outputParserStructured@n8n/n8n-nodes-langchain.chainLlmNoOpSet
100%
TRIGGER

When chat message received

@n8n/n8n-nodes-langchain.chat

Ollama Chat Model

@n8n/n8n-nodes-langchain.lmChatOllama

Auto-fixing Output Parser

@n8n/n8n-nodes-langchain.outputParserAutofixing

Structured Output Parser

@n8n/n8n-nodes-langchain.outputParserStructured

Basic LLM Chain

@n8n/n8n-nodes-langchain.chainLlm

On Error

NoOp

Extract JSON Output

Set

7 steps · flows top to bottom, branches left to right

Browse by integration

chatlmChatOllamaoutputParserAutofixingoutputParserStructuredchainLlm
Browse all integrations →
Download n8n JSONDownload sanitized JSONDownload flow graph

Import into n8n

  1. Download the n8n JSON file (full or sanitized).
  2. In n8n: Workflows → Import from file.
  3. Open each node and reconnect credentials to your accounts.
  4. Test with manual execution before enabling triggers.

Sanitized JSON removes credential IDs — safer for sharing. Flow graph JSON is OpenRuna's lightweight format for diagrams and other tools, not n8n import.

View source corpus ↗

Overview

Free n8n workflow: Extract personal data with a self-hosted LLM Mistral NeMo. 13 nodes across chat, lmChatOllama, outputParserAutofixing, outputParserStructured, chainLlm, with a downloadable JSON and a visual flow diagram on OpenRuna.

What this workflow does

"Extract personal data with a self-hosted LLM Mistral NeMo" is an n8n automation built from 13 nodes spanning chat, lmChatOllama, outputParserAutofixing, outputParserStructured, chainLlm. 13 nodes · chat, lmChatOllama, outputParserAutofixing, outputParserStructured, chainLlm It starts from a scheduled/triggered trigger and routes data through every node in order. OpenRuna renders the node graph and offers the JSON export so you can import and adapt it in minutes. Use it to learn integration patterns, fork it for your own use case, or deploy it quickly after reconnecting credentials.

Use cases

  • Benchmark it against your current chat integration to spot missing steps or redundant nodes.
  • Fork it to add error handling, retries, or a notification step without designing the 13-node flow from scratch.
  • Use it as a starting template when wiring chat, lmChatOllama, outputParserAutofixing, outputParserStructured, chainLlm — adjust node parameters to your accounts and record IDs.
  • Import "Extract personal data with a self-hosted LLM Mistral NeMo" into n8n, reconnect credentials for chat, lmChatOllama, outputParserAutofixing, outputParserStructured, chainLlm, and run the same automation in your own workspace.

Example output

Imported into n8n, "Extract personal data with a self-hosted LLM Mistral NeMo" exposes 13 configured nodes. Trigger type: scheduled/triggered. Integrations: chat, lmChatOllama, outputParserAutofixing, outputParserStructured, chainLlm. Set your credentials on the chat node, then verify each downstream node picks them up. Run it once in test mode to verify any branch logic (Switch/IF nodes) before enabling production triggers.

Tips by platform

Claude

Paste the exported JSON for "Extract personal data with a self-hosted LLM Mistral NeMo" into Claude and ask it to explain each of the 13 nodes. Claude is good at suggesting credential mappings for chat, lmChatOllama, outputParserAutofixing, outputParserStructured, chainLlm, plus error-handling and idempotency improvements before you run it in production.

ChatGPT

Have ChatGPT generate a checklist for adapting "Extract personal data with a self-hosted LLM Mistral NeMo" to your accounts — which chat, lmChatOllama, outputParserAutofixing, outputParserStructured, chainLlm credentials to create and which node parameters to change first.

Cursor

Open the downloaded JSON in Cursor and use Agent mode to refactor hard-coded URLs into env vars, or to generate tests for any Code nodes. Keep the OpenRuna flow diagram open so you do not break connection order across the 13 nodes.

Frequently asked questions

How do I import "Extract personal data with a self-hosted LLM Mistral NeMo" into n8n?
Click Download n8n JSON on this page, then in n8n choose Workflows → Import from file and select the downloaded file. Reconnect credentials on each node — the export carries credential IDs from the original author's instance, not working secrets.
Which integrations does "Extract personal data with a self-hosted LLM Mistral NeMo" use?
It uses: chat, lmChatOllama, outputParserAutofixing, outputParserStructured, chainLlm. The flow diagram above shows how each service connects across the 13 nodes. Filter OpenRuna by integration to find related workflows and tools.
Can I import a sanitized export without credentials?
Yes. Use "Download sanitized JSON" to strip credential references before sharing internally. You will still add your own chat and lmChatOllama credentials in n8n after import.
What trigger type does "Extract personal data with a self-hosted LLM Mistral NeMo" use?
Trigger type: scheduled/triggered. After import, confirm the trigger node (webhook, schedule, or app event) matches your environment, then activate the workflow when you are ready to run it.
Is "Extract personal data with a self-hosted LLM Mistral NeMo" free to use?
OpenRuna catalogs community n8n workflows under open licenses. Check the source corpus link for the original license; commercial use may require verifying the third-party API terms for chat, lmChatOllama, outputParserAutofixing, outputParserStructured, chainLlm.

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