On-Premise AWS Deployment Guide

This guide provides detailed instructions for deploying Inspeq AI platform in your AWS environment using our custom AMI. This deployment method ensures data privacy, security, and compliance with your organization's requirements.

Prerequisites

AWS Requirements

• Active AWS Account

• IAM user with EC2 permissions

• VPC with appropriate networking configuration

• Basic knowledge of AWS EC2 service

Deployment Process

1. Access Request Process

Required Information

Before requesting access to the Inspeq AMI, please prepare:

• AWS Account ID

• Preferred AWS Region(s)

• Expected instance type requirements

• Network requirements overview

• Technical team contact information

Requesting Access

1. Email [email protected] with subject "AMI Access Request"

2. Include:

   • AWS Account ID

   • Preferred region

   • Brief description of deployment needs

3. Our team will verify your request and provide:

   • AMI ID

   • Region-specific instructions

   • Support contact information

2. AWS Security Configuration

Security Group Setup

Create a new security group with the following configurations:

Basic Details

Field	Value	Description
Name	inspeq-service-sg	Security group name
Description	Security group for Inspeq service	For documentation
VPC	Your VPC	Example: vpc-0abfe6ff2aafa6ce0

Inbound Rules

Type	Protocol	Port Range	Source	Description
SSH	TCP	22	Your IP range	SSH access
HTTP	TCP	80	0.0.0.0/0	Web service access
HTTPS	TCP	443	0.0.0.0/0	Secure web service
Custom TCP	TCP	9000	0.0.0.0/0	Backend API access

Security Note: Consider restricting port 9000 access based on your security requirements. For enhanced security, use HTTPS (443), we can help you with that.

Outbound Rules

Type	Protocol	Port Range	Destination	Description
All traffic	All	All	0.0.0.0/0	Allow all outbound

Create Security Group

Security Group Reference screenshot

Sample Security Group

Security Group Reference screenshot

3. Instance Deployment

Instance Configuration

1. Navigate to EC2 in AWS Console

2. Navigate to AMIs section

3. Choose "My AMIs" > "Shared AMI"

4. Select the provided Inspeq AMI ID

5. Click on the AMI ID

6. Click on "Launch Instance"

Select AMI

Select ami

Recommended Specifications

1. Give the instance a name

2. Select inspeq recommended instance type

3. Create a key pair or use an existing key pair

4. Select the security group created in the previous step

5. Configure storage : minimum 150GB

6. Click on "Launch Instance"

Select instance type

Select instance type

Key pair creation

Key pair creation

Security group selection

Security group selection

Storage configuration

Storage configuration

Once you click on "Launch Instance" you will be redirected to the instance details page. And instance will be created in some time.

Instance details

Instance details

Get instance's public IP/URL

Instance details

4. Post-Deployment Verification

Accessing the Frontend services

1. Copy your Inspeq On-Prem instance URL and paste it into your web browser. This will take you to the Sign Up/Login page.

2. Sign up using your preferred email address and a secure password.

3. Once your account is created, log in with the same credentials.

4. After logging in, you will be automatically redirected to your personal dashboard.

Login page

Instance details

Welcome page

Instance details

Create project

Instance details

Copy project keys

Copy project keys

Check created project

Check created project

Integrating with the Inspeq SDK

1. Begin by following the official Inspeq SDK Quickstart guide to familiarize yourself with the SDK’s features and requirements

2. Configure the INSPEQ_API_URL environment variable to URL:9000

3. When calling inspeq_eval, ensure you pass the same INSPEQ_API_URL value as the inspeq_api_url parameter so your requests are routed correctly to your On-Prem deployment.

from inspeq.client import InspeqEval

# Initialize the client with all required parameters
INSPEQ_API_KEY = "your_inspeq_sdk_key"
INSPEQ_PROJECT_ID = "your_project_id"
INSPEQ_API_URL = "your_instance_url:9000"  # Updated for on-prem setup

# Create InspeqEval instance with the API URL parameter
inspeq_eval = InspeqEval(
    inspeq_api_key=INSPEQ_API_KEY,
    inspeq_project_id=INSPEQ_PROJECT_ID,
    inspeq_api_url=INSPEQ_API_URL  # Added API URL parameter
)

# Prepare input data
input_data = [{
    "prompt": "What is the capital of France?",
    "response": "Paris is the capital of France.",
    "context": "The user is asking about European capitals."
}]

# Define metrics to evaluate
metrics_list = ["RESPONSE_TONE", "FACTUAL_CONSISTENCY", "ANSWER_RELEVANCE"]

try:
    # Evaluate the LLM task
    results = inspeq_eval.evaluate_llm_task(
        metrics_list=metrics_list,
        input_data=input_data,
        task_name="capital_question"
    )
    print(results)
except Exception as e:
    print(f"An error occurred: {str(e)}")

Now you can use the setup to evaluate your LLM tasks similar to the quickstart guide for our managed service.

Ready to explore Inspeq AI's On-Prem deployment? Contact us at [email protected] to discuss your organization's specific needs and requirements.

Contact Information

• Technical Support: [email protected]

• Sales Inquiries: [email protected]

• Emergency Support: [Provided in welcome email]