Building a Production-Ready RAG Chatbot with AWS Bedrock, LangChain, and Terraform

In the rapidly evolving landscape of generative AI, the transition from a local prototype to a production-ready system is the most significant hurdle for developers. While simple API calls to models like Claude 3.5 Sonnet or OpenAI o3 are easy to implement, building a system that is scalable, maintainable, and context-aware requires a robust architectural foundation. This tutorial explores a sophisticated dual-mode chatbot system leveraging Retrieval-Augmented Generation (RAG), automated infrastructure, and intelligent query routing.

The Challenge: Beyond Generic Chatbots

Standard chatbots often struggle with domain-specific knowledge or hallucinate when asked about private enterprise data. RAG solves this by grounding the LLM's responses in a specific knowledge base. However, a production system needs more than just a vector store; it needs automated deployment, monitoring, and the flexibility to switch between models. For developers looking for the fastest path to testing these models across different providers, using an aggregator like n1n.ai can significantly reduce the friction of managing multiple API keys and endpoints.

High-Level Architecture

The system consists of a dual-mode interface: a General Chatbot for direct LLM interaction and a RAG Agent for document-based Q&A.

1. Frontend: Built with Streamlit for a responsive, Python-native UI.
2. Orchestration: LangChain manages the flow between user input, vector retrieval, and LLM prompting.
3. LLM Provider: AWS Bedrock (hosting models like Claude 3 and Cohere Command R+).
4. Vector Database: Amazon OpenSearch handles high-dimensional vector storage and similarity search.
5. Infrastructure: Terraform ensures the entire stack is reproducible and version-controlled.
6. Compute: AWS ECS Fargate provides a serverless container environment.

Implementing the RAG Logic with LangChain

The core of the RAG agent is its ability to automatically categorize queries. Instead of forcing users to select a document category, we use the LLM to classify the intent first.

def categorize_prompt(user_input: str, llm) -> str:
    CATEGORIES = ("Technical", "Healthcare", "Finance", "Legal")
    prompt = f"""Classify this question into ONE category from: {', '.join(CATEGORIES)}
    Question: {user_input}
    Return ONLY the category name."""
    response = llm.invoke(prompt)
    return response.content if response.content in CATEGORIES else "General"

Once categorized, the system retrieves relevant chunks from OpenSearch. If you are comparing different embedding models, n1n.ai offers a streamlined way to access various high-speed APIs to benchmark which embedding-to-LLM combination yields the highest accuracy for your specific dataset.

Infrastructure as Code: The Terraform Layer

To ensure production reliability, we avoid manual console configurations. Terraform allows us to define our ECS cluster and ECR repository as code.

resource "aws_ecs_task_definition" "chatbot_task" {
  family                   = "chatbot-task"
  network_mode             = "awsvpc"
  requires_compatibilities = ["FARGATE"]
  cpu                      = "1024"
  memory                   = "2048"

  container_definitions = jsonencode([{
    name  = "streamlit-app"
    image = "${aws_ecr_repository.app_repo.repository_url}:latest"
    portMappings = [{
      containerPort = 8501
      hostPort      = 8501
    }]
  }])
}

Key Features for Enterprise Readiness

1. Automatic Categorization: Reduces user friction by intelligently routing queries to the correct S3 prefix/knowledge base.
2. Conversation Memory: Uses ConversationBufferMemory to maintain context, allowing for natural follow-up questions.
3. Feedback Loop: Interactive 'Like/Dislike' buttons allow users to rate responses, providing data for future fine-tuning.
4. CI/CD Integration: A GitLab CI/CD pipeline automates the Docker build and Terraform apply stages, ensuring that every code push is validated and deployed.

Pro Tip: Optimizing for Latency and Cost

When running LLMs in production, latency is a critical metric. While AWS Bedrock provides excellent stability, developers often find that latency < 200ms is required for real-time applications. Utilizing n1n.ai allows you to tap into high-speed LLM API clusters that are optimized for throughput, ensuring your chatbot feels responsive even during peak traffic.

Comparing Foundation Models

Conclusion

Building a production-ready RAG system is a multi-disciplinary effort involving AI orchestration, cloud infrastructure, and DevOps. By combining AWS Bedrock's power with Terraform's stability and LangChain's flexibility, you create a system that can grow with your enterprise needs. For those starting their journey or looking to switch between the world's most powerful models with a single integration, n1n.ai provides the ultimate gateway.

Get a free API key at n1n.ai