Deploy LangChain RAG on AWS EC2 with Docker & Docker Compose | Complete Production Deployment 2026

Deploy a complete LangChain RAG application on AWS EC2 using Docker and Docker Compose! 🚀 In this end-to-end 2026 tutorial, we take a real LangChain Retrieval-Augmented Generation (RAG) application from a local environment and deploy it on an AWS EC2 Linux server using Docker. You will learn how to containerize a Python LangChain RAG application using a Dockerfile, configure the application with Docker Compose, deploy it on EC2, securely configure environment variables/API keys, and test the live RAG application. Docker Compose is designed to define and run multi-container applications and can also be used for single-host deployments. Docker's production guidance recommends production-specific configuration such as restart policies and appropriate environment settings. 🔥 What You Will Build User ↓ LangChain RAG Application ↓ Document Loader ↓ Text Splitter ↓ Embeddings ↓ Vector Database ↓ Retriever ↓ LLM ↓ AI Answer Then: Local RAG Application ↓ Dockerfile ↓ Docker Image ↓ Docker Compose ↓ AWS EC2 ↓ Running Container ↓ Live RAG API 🎯 Topics Covered ✅ What is RAG and why deployment matters ✅ Build a real LangChain RAG application ✅ Python RAG project structure ✅ Dockerfile for LangChain RAG ✅ Docker image creation ✅ Docker container lifecycle ✅ Docker Compose configuration ✅ .env environment variables ✅ .dockerignore ✅ AWS EC2 setup ✅ Amazon Linux deployment ✅ Install Docker on EC2 ✅ Configure Docker service ✅ Copy project to EC2 ✅ Build RAG Docker image ✅ Run RAG using Docker Compose ✅ Port mapping and Security Groups ✅ Container logs ✅ Container troubleshooting ✅ Restart policies ✅ Persistent storage considerations ✅ API key/security best practices ✅ Production deployment concepts ✅ Rebuild and redeploy workflow After this deployment, the next step is to take the architecture toward production: EC2 ↓ Docker ↓ Docker Compose ↓ Nginx ↓ HTTPS ↓ CloudWatch ↓ CI/CD ↓ ECR ↓ ECS / EKS You can also extend the RAG application with: 🔥 Ragas evaluation 🔥 LangSmith tracing 🔥 Hybrid search 🔥 Reranking 🔥 Authentication 🔥 Prompt-injection protection 🔥 RAG security 🔥 Observability 🔥 CI/CD 🔥 AWS ECR 🔥 ECS/EKS deployment 🎓 Who Should Watch? ✔️ Freshers learning Generative AI ✔️ Python Developers ✔️ Cloud Engineers ✔️ DevOps Engineers ✔️ AWS Engineers ✔️ AI/ML Engineers ✔️ GenAI Engineers ✔️ LangChain learners ✔️ RAG developers ✔️ Students preparing for AI/Cloud interviews Watch until the end to see the complete journey from a local LangChain RAG application → Docker container → AWS EC2 → live AI application. 🔥 🔥 Hashtags #LangChain #RAG #GenerativeAI #AWS #AWSEC2 #Docker #DockerCompose #Dockerfile #GenAI #AI #ArtificialIntelligence #LLM #OpenAI #Python #LangChainRAG #RAGApplication #AWSCloud #DevOps #CloudComputing #AIEngineering #GenAIEngineer #AIDeveloper #MachineLearning #VectorDatabase #CloudEngineer #DevOpsEngineer #AgenticAI #MLOps #AWSDevOps #TechTutorial