Extract personal data with a self-hosted LLM Mistral NeMo
Configured with Chain LLM, Chat Trigger, LM Chat Ollama, Output Parser Autofixing, and additional nodes. Review the connections and setup before adapting this template.
Node categories in this workflow
- Trigger
- AI & models
- Transform
- Routing
Icons describe general node roles, inferred from node types. Inspect the parameters for the configured action.
Drag to pan; use + and − to zoom. Reconstructed from the exported node positions; nothing runs here. Dashed lines represent non-main connections. Full node names and notes appear below.
How it works
The exported workflow contains the following configured nodes. Their connections are shown above; inspect the JSON for the complete logic, expressions, and external actions.
| Node | Category | Type | Operation |
|---|---|---|---|
| When chat message received | Trigger | Chat Trigger | See node parameters |
| Ollama Chat Model | AI & models | LM Chat Ollama | See node parameters |
| Auto-fixing Output Parser | Transform | Output Parser Autofixing | See node parameters |
| Structured Output Parser | Transform | Output Parser Structured | See node parameters |
| Basic LLM Chain | AI & models | Chain LLM | See node parameters |
| On Error | Routing | No Op | See node parameters |
| Extract JSON Output | Transform | Edit Fields | See node parameters |
Setup
- Download the JSON and import it into an isolated n8n instance. Leave the workflow inactive.
- Connect your own credentials through n8n’s credential store. Replace
REPLACE_WITH_*values with your resource identifiers. - Inspect API destinations, Code nodes, database queries, file paths, and outbound actions.
- Run with synthetic data and harmless destinations. Enable triggers only after checking the actual outputs.
Requirements
An n8n instance with the listed node types and compatible versions. Service-connected workflows may require accounts, credentials, or paid APIs.
Credential types declared in the source
ollamaApi
Workflow notes
Update data source
Update data source
When you change the data source, remember to update the Prompt Source (User Message) setting in the Basic LLM Chain node.
Configure local LLM
Configure local LLM
Ollama offers additional settings to optimize model performance or memory usage.
Sticky Note3
If the LLM response does not pass the Structured Output Parser checks, Auto-Fixer will call the model again with a different prompt to correct the original response.
Sticky Note6
The same LLM connects to both Basic LLM Chain and to the Auto-fixing Output Parser.
Sticky Note7
When the LLM model responds, the output is checked in the Structured Output Parser
Extract personal data with a self-hosted LLM Mistral NeMo
Extract personal data with a self-hosted LLM Mistral NeMo
Purpose and structure
This folder contains the Extract personal data with a self-hosted LLM Mistral NeMo workflow. Its executable nodes use chainLlm, chatTrigger, lmChatOllama, noOp, outputParserAutofixing, outputParserStructured, set. The table and connection map below are derived from the JSON, not an inferred guarantee of behavior.
Setup
Import inactive. Configure your own credentials and replace REPLACE_WITH_* values. Review external destinations and Code nodes before running.
Entry points
When chat message received
Verification
Sanitized source candidate; not runtime-tested. This overview is generated from node metadata. Read the folder README for the connection map, credential types, and license.
Attribution
https://github.com/Zie619/n8n-workflows/blob/94007c1445d9258a7da116646b79473e7c7c3282/workflows/Noop/1442_Noop_Stickynote_Automation_Triggered.json MIT; preserve source notices.
Canvas section labels
These name areas of the original canvas; they do not include explanatory instructions.
- Define JSON Schema