Multilingual AI Customer Support System using AWS SageMaker and LORA

A scalable multilingual customer support system that demonstrates how to efficiently deploy and manage multiple language models using AWS SageMaker and LORA adapters. This system can handle customer queries in Spanish, French, and Russian while maintaining specialized support across technical, billing, and product domains.

Features

• Cost-efficient multilingual support using LORA adapters
• Dynamic adapter loading for optimal resource utilization
• Concurrent request handling with batching
• Language and domain detection
• Comprehensive logging and monitoring
• Automated cleanup and resource management

Architecture

The system uses:

• Base Model: Hosted on SageMaker using LMI container
• LORA Adapters: Language and domain-specific adapters
• G5 Instance: NVIDIA A10G GPU for efficient inference
• S3 Storage: For adapter management

Prerequisites

• AWS Account with SageMaker access
• Python 3.8+

1. Installation

1. Clone the repository:

git clone https://github.com/Lucky-akash321/Multilingual-Customer-Support-using-Sagemaker

2. Install dependencies:

pip install -r requirements.txt

3. Configuration

Update config.py with your settings:

AWS region Instance type Model configurations Adapter settings

4. Deployment

• Initialize SageMaker resources:

python sagemaker_setup.py

• Verify the setup:

python test_access.py

• Test the endpoint:

python test_endpoint.py

5. Usage

Example of processing a customer query:

from inference_handler import CustomerSupportInference

handler = CustomerSupportInference()
response = handler.process_query("Hola, necesito ayuda técnica")
print(response)

6. Resource Management

Clean up resources when done:

python cleanup.py

7. Cost Optimization

• Uses unmerged LORA inference to minimize GPU memory usage
• Dynamic adapter loading reduces resource requirements
• Batching for efficient request processing
• Automatic resource cleanup

8. Performance

• Response time: ~2-3 seconds per query
• Concurrent requests: Up to 4 per GPU
• Memory usage: ~24GB GPU memory
• Cost: ~70% lower than traditional deployment