Steps:

• 1Launch Model HQ locally
• 2Open the Fast Start, select Medium Chatbot
• 3
  Attach an Executive Employment Agreement document

  (e.g., 15-page PDF found at: C:\users\[username]\llmware_data\sample_files\agreements\)
• 4Model HQ auto-ingests, parses, and chunks the document (~1s)
• 5
  Ask natural language questions like:

  • "What's the effective date of the agreement?"
  • "How many vacation days is the executive entitled to?"
  • "What is the executive's annual base salary?"

Steps:

• 1Switch to Agent Mode from Home
• 2Select the built-in Contract Analyzer Agent
• 3
  Upload a document from the agreements folder found in:C:\users\[username]\llmware_data\sample_files\agreements\[list of 12 executive employment agreements]
• 4Agent will run 3 predefined questions (effective date, annual rate of base salary, number of vacation days)

Steps:

• 1Go to Agent → Load Existing, select ContractAnalyzer
• 2Choose Batch Run in the UI
• 3
  Upload multiple documents (e.g., first 5 from the sample agreements folder for this example)C:\users\[username]\llmware_data\sample_files\agreements\
• 4Agent will iterate through each document and apply consistent questions

Steps:

1. 1
   Launch Model HQ as a local backend server:

   • Option 1: Localhost
   • Option 2: Mini API server for LAN access
2. 2Access the API using the Model HQ Python client

Two Integration Modes:

from modelhq_client import ModelHQ

client = ModelHQ()
result = client.run_agent(
    file="mycontract.pdf", 
    agent="contract_analyzer"
)

questions = [
    "How much is the base salary?", 
    "What is the termination clause?"
]
client.batch_analyze(
    folder="agreements_folder", 
    questions=questions
)