A scalable, production-ready customer support system using Retrieval Augmented Generation (RAG) with vector databases.
The Intelligent Customer Support System is a production-ready solution that leverages Retrieval Augmented Generation (RAG) to provide accurate, context-aware responses to customer inquiries. By combining the power of large language models with vector search, the system can retrieve relevant information from company documentation and previous support tickets to generate helpful responses.
Customer support teams face several challenges:
This system addresses these challenges by:
The core of the system is implemented in langchain_rag_app.py, which provides a comprehensive RAG pipeline with the following features:
# Initialize the RAG pipeline
rag = CustomerSupportRAG(config)
# Ingest documents
rag.ingest_documents(documents)
# Answer customer questions
response = rag.answer_question("My X1000 is not connecting to the internet. What should I do?")
The CustomerSupportRAG class handles:
The infrastructure is defined in terraform_infrastructure.tf, which provisions all necessary AWS resources:
# ECS Service
resource "aws_ecs_service" "app" {
name = "${var.app_name}-service"
cluster = aws_ecs_cluster.main.id
task_definition = aws_ecs_task_definition.app.arn
desired_count = 2
launch_type = "FARGATE"
network_configuration {
subnets = aws_subnet.private[*].id
security_groups = [aws_security_group.ecs.id]
assign_public_ip = false
}
load_balancer {
target_group_arn = aws_lb_target_group.app.arn
container_name = "${var.app_name}-container"
container_port = 80
}
}
The infrastructure includes:
The system uses contextual compression to extract the most relevant information from retrieved documents, improving response accuracy.
Local caching with FAISS improves response time for frequently accessed documents, while Pinecone provides scalable primary storage.
The system includes source attribution, confidence scoring, and relevance checking to ensure high-quality, trustworthy responses.
Containerized deployment with ECS/EKS allows for horizontal scaling to handle varying loads, with auto-scaling based on demand.
Infrastructure as Code with Terraform and CI/CD pipelines with GitHub Actions enable automated testing and deployment.
CloudWatch metrics, logs, and alarms provide visibility into system performance and enable proactive issue resolution.
terraform init
terraform plan -out=plan.out
terraform apply plan.out
This project demonstrates expertise in building RAG applications with LangChain, including:
The project showcases experience with vector databases, including:
The project utilizes multiple AWS services, demonstrating cloud expertise:
The project incorporates modern DevOps practices:
Future enhancements to the Intelligent Customer Support System could include:
Extend the system to handle images and other media types, enabling support for product images, screenshots, and diagrams.
Implement LangChain agents for more complex problem-solving, including tool use and multi-step reasoning.
Add automated fine-tuning of embedding and LLM models based on user feedback and performance metrics.
Develop connectors for popular CRM and ticketing systems to seamlessly integrate with existing support workflows.