Curated

(5/5)

Testing Mulitple Local LLM with LM Studio

🧠Text Analysis Readability Score Ranges: When testing model responses, readability scores can range across different levels. Here’s a breakdown: - 90–100: Very easy to read (5th grade or below) - 80–89: Easy to read (6th grade) - 70–79: Fairly easy to read (7th grade) - 60–69: Standard (8th to 9th grade) - 50–59: Fairly difficult (10th to 12th grade) - 30–49: Difficult (College) - 0–29: Very difficult (College graduate) - Below 0: Extremely difficult (Post-graduate level)

Integrations Used (5)

Workflow Structure (12 nodes)

100%

Langchain.chat Trigger

When chat message received

Http Request

Get Models

Split Out

Extract Model IDsto Run Separately

Date Time

Capture Start Time

Set

Add System Prompt

Langchain.chain Llm

LLM Response Analysis

Langchain.lm Chat Open Ai

Run Model with Dunamic Inputs

Date Time

Capture End Time

Date Time

Get timeDifference

Set

Prepare Data for Analysis

Code

Analyze LLM Response Metrics

Google Sheets

Save Results to Google Sheets

Node Types:

http

trigger

default

code

action

transform

logic

Requires credentials

Prerequisites

Required Credentials (2)

Google Sheets

Used by: Save Results to Google Sheets

Docs

OpenAI

Used by: Run Model with Dunamic Inputs

Docs

Complexity

Complex

Advanced features used. Requires n8n experience.

Setup time: ~35 min

Score: 53/100

Custom Code

When to Use This Workflow

AI-Powered Content Creation

Automate content generation, summarization, or analysis using AI capabilities.

Save hours of manual work while maintaining quality and consistency.

Automated Reporting

Automatically collect and organize data into spreadsheets for analysis.

Spend less time on data entry and more time on insights.

Real-time Response

React instantly when events happen - new orders, form submissions, or API calls.

Zero delay between trigger and action for time-sensitive workflows.

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